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Speaker 1: This is Mesters in Business with very Renaults on Bluebird

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Speaker 1: Radio this weekend on the podcast, Boy Do I have

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Speaker 1: an extra special guest, venture capitalist Bill Gurley of Benchmark.

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Speaker 1: What a rock star. He's been just right at the

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Speaker 1: forefront of everything that's been going on UH in venture

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Speaker 1: capital over the past twenty years. Grub Hub, Open Tables, Zillo.

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Speaker 1: He was one of the very first investors into Uber,

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Speaker 1: was a board member there. Really just a fascinating career

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Speaker 1: from a design engineer a compact to becoming an Wall

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Speaker 1: Street analyst. Turns out he's the lead analyst on the

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Speaker 1: Amazon I p O to eventually becoming a VC at Benchmark,

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Speaker 1: where really he just has tremendous insight into entrepreneurs and

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Speaker 1: technology and to think about things like the network of

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Speaker 1: fact and what is this technology also UM parallel to

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Speaker 1: and where can we see UH scale and leveraging capital

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Speaker 1: all come together in a way that that is unique.

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Speaker 1: UM just really a fascinating, fascinating person, very forthcoming UM,

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Speaker 1: including about things that were really challenging periods of his

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Speaker 1: career that I would imagine wasn't a whole lot of fun.

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Speaker 1: If you read the book Super Pumped about Uber, UM

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Speaker 1: really he's the only guy that comes through that that

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Speaker 1: whole book with his reputation. Intact, he's really the only

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Speaker 1: good guy in the book. And the book is an amazing,

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Speaker 1: uh amazing story about Uber, which in of itself is

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Speaker 1: is just madness. Um. So, if you are at all

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Speaker 1: remotely interested in venture capital, investing, technology, direct listings replacing

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Speaker 1: I P O S, I think you're going to find

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Speaker 1: this conversation to be absolutely fascinating. So, with no further ado,

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Speaker 1: my conversation with Benchmarks Bill Gurley. This is mesters in

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Speaker 1: Business with very Results on Bloomberg Radio. My extra special

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Speaker 1: guest this week is Bill Gurley. He is a legendary

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Speaker 1: venture capital investor at Benchmark, where he's been since Some

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Speaker 1: of his better known investments have included grub Hub, next Door,

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Speaker 1: Open Table, Zillo, and of course Uber. He is a

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Speaker 1: member of the board of trustees of the Santa Fe Institute,

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Speaker 1: and he is considered one of the most influential dealmakers

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Speaker 1: in technology. In sixteen, he was named Tech Crunches VC

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Speaker 1: of the Year. Bill Gurley, Welcome to Bloomberg. Thanks for

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Speaker 1: having me. Yeah, this is overdue. We were supposed to

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Speaker 1: get together a couple of years ago and then events intervened.

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Speaker 1: Let's start with your early career. You began as a

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Speaker 1: design engineer at Impact. What does the design engineer do

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Speaker 1: and what sort of lessons did you take away from

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Speaker 1: that experience at Compact? You know that the job I

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Speaker 1: fell into a Compact which was super super interesting for

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Speaker 1: a person with a brain like mine. Um, we were

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Speaker 1: a bit of a like a fire squad that came

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Speaker 1: in when they were problems. So when they were bringing

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Speaker 1: new computers to market, they would pass them off to

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Speaker 1: the test group, and the test group would take all

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Speaker 1: the latest and greatest software. And back then it was

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Speaker 1: like banion vines and running three comic conneted actors, a

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Speaker 1: lot of stuff that doesn't existing or today. Um, But

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Speaker 1: inevitably they would break and and someone had to figure

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Speaker 1: out why, and the the people that are doing the

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Speaker 1: hardware design work didn't have enough understanding of how the

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Speaker 1: software worked to kind of reverse engineer the failure, and

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Speaker 1: so we kind of lived in between these two. What

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Speaker 1: was what was fun about it is that you know,

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Speaker 1: oftentimes they were holding up shipping until we could get

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Speaker 1: our job done. So it was this kind of they

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Speaker 1: put us in a room with a bunch of pizzas

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Speaker 1: and expect us to work like eighteen hour days until

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Speaker 1: we found the problem. But it was fun because it

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Speaker 1: was you know, shortened scope would usually be like one

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Speaker 1: day to a week before we figure it out. And

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Speaker 1: it felt important because everyone was kind of waiting on us,

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Speaker 1: um and it was fun and it was super interesting,

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Speaker 1: I think also for me just because of the problem

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Speaker 1: solving nature of the whole thing. I don't even know

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Speaker 1: if a group like that exists anymore. So when you're

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Speaker 1: involved in both hardware and software, we're talking thirty plus

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Speaker 1: years ago, how magical did the software look? Was it

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Speaker 1: still all potential? You're dealing with both hardware and software?

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Speaker 1: What what intrigued you? Well, Client server was starting to

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Speaker 1: happen already, and and and there were different way these

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Speaker 1: you know, I have no idea how they do this

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Speaker 1: today because the clock speeds are so dramatically faster. But

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Speaker 1: there were different types of analysis tools that you could

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Speaker 1: run either virtually in the background or you know, and

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Speaker 1: we plug this thing called an ice, which was a

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Speaker 1: Medusa head like thing on top of the processor where

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Speaker 1: you can measure every pin and you'd watch the signals

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Speaker 1: go by and you try and reverse the software failure

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Speaker 1: all the way down to where something was happening on

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Speaker 1: the on the motherboard that was causing a race condition

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Speaker 1: or this kind of thing. And there were always odd

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Speaker 1: things like you could, you know, make it really cold

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Speaker 1: and the failure would go away and then you knew

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Speaker 1: you had some type of analog rice time problem. But

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Speaker 1: but it was fun. I mean, you really had to

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Speaker 1: understand how the system worked all the way through or

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Speaker 1: you couldn't. You couldn't pull that that down. You know.

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Speaker 1: The funny thing is, you know when we were we

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Speaker 1: were already running into city stations where the equipment that

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Speaker 1: you would put on it, uh to measure it would

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Speaker 1: would mess with it. Um And back then, you know,

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Speaker 1: I think the last thing I worked on was a

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Speaker 1: forty six fifty And I can't imagine how they do

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Speaker 1: it today, Like I just I mean, I know they

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Speaker 1: do it, but I just can't imagine because the clock

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Speaker 1: speeds we would have never imagined back then the clock

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Speaker 1: speeds would be as fast as they are today. Yeah,

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Speaker 1: Moore's law just keeps on compounding. So so you um,

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Speaker 1: you moved to Wall Street as a research channelist in

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Speaker 1: the ninety nineties. What led you to cover tech stocks? Well,

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Speaker 1: Somewhere along the way, while I was had become an

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Speaker 1: engineer and was working at compact Um, I started trading stocks.

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Speaker 1: And I can't remember exactly how I fell into it. UM.

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Speaker 1: I'm sure I have a vivid memory of reading uh

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Speaker 1: Peter Lynch's book, and I also remember Prodigy. You remember

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Speaker 1: a Prodigy, which you're kind of a precursor to a

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Speaker 1: O L with a You could get a trading account

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Speaker 1: on Prodigy. And so I was buying stock and one

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Speaker 1: of the ones I bought. You know, if you read

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Speaker 1: read that Peter Lynch book, was it one up on

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Speaker 1: Wall Street? Was that the name um exactly? You know?

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Speaker 1: He said, by stocks of companies you love well. I

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Speaker 1: was doing a ton of programming in Borland, UH Borland,

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Speaker 1: Turbo Pascale, and I loved the Quadro spreadsheet. Liked it

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Speaker 1: way better than Lotus, and we're using those tools every day,

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Speaker 1: and so I bought I bought the Borland stock on

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Speaker 1: the I t o like the day of the I.

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Speaker 1: I don't think I got allocations be a good conversation

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Speaker 1: for direct listenings. I don't think I got allocation through

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Speaker 1: Prodigy but I bought it today of the offering. UM,

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Speaker 1: so see that kind of thing was already happening in

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Speaker 1: my brain. Um I ended up. I ended up leaving Compact,

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Speaker 1: going to business school before I transitioned into Wall Street.

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Speaker 1: And I think when I got to business school and

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Speaker 1: started really understanding, you know, different companies and reading about

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Speaker 1: companies and and that fun that I was having thinking

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Speaker 1: about stocks and stock prices. I when I when I

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Speaker 1: really started reading you know, magazines, Wall Street Journal, Fortune. Um,

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Speaker 1: I noticed that these analysts were quoted a lot, especially

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Speaker 1: Goldman had this killer team with Dan Benton, rich Sherlin,

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Speaker 1: you know, and uh they they owned it like every

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Speaker 1: like the top five tech Analystic Goldman, We're all all

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Speaker 1: considered to be the opts. And um, their names would

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Speaker 1: just come up and up and up. So, you know,

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Speaker 1: and I was getting a little more spirited, and you'd

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Speaker 1: reached store is about people knocking on doors. So I

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Speaker 1: spent a week in the summer of my in between

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Speaker 1: my two business school just begging for meetings on the

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Speaker 1: Wall Street, including with with Dan and Rick. And you

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Speaker 1: end up at CS First Boston, where it turns out

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Speaker 1: that you end up as the lead analyst for an

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Speaker 1: I p O for a little company called Amazon. Tell

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Speaker 1: us what that experience was like and did anybody have

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Speaker 1: any idea then what Amazon might become? Forget what it is?

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Speaker 1: What was it? Even? The skipped about four years so,

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Speaker 1: which is so it's kind of interesting because it involves

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Speaker 1: someone I know, I know, you think quite highly up.

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Speaker 1: So I um ended up getting a job. I ended

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Speaker 1: up getting turned down by every firm and set for

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Speaker 1: one which was Credit Pas First Boston and the research

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Speaker 1: director there was a guy named Al Jackson who I

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Speaker 1: still talked to the UM and you know, I went

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Speaker 1: to Texas. In my entering class, we went around a

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Speaker 1: room and stay wherever it was from you know, Wharton, Harvard,

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Speaker 1: like there was I was the only one that wasn't

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Speaker 1: like those five schools. And you know, I'll just felt

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Speaker 1: like taking a bet on me. And I don't know

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Speaker 1: where I'd be to day if he hadn't have done that.

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Speaker 1: I show up and um, they were going to assign

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Speaker 1: me passive components with some obscure tech category. UM. And

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Speaker 1: Charlie Wolf I don't know if you've ever heard of him. Oh,

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Speaker 1: Charlie was very well. He covered Apple when nobody liked

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Speaker 1: the apple. Charlie was the PC analyst, and two weeks

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Speaker 1: after I showed up, Charlie said he wanted to retire

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Speaker 1: and teach more Columbia. And I mean to just talk

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Speaker 1: about wild fortune. And so I went into my apartment

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Speaker 1: and spent all weekend writing this kind of assessment of

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Speaker 1: the PC industry and went in it begged alf for

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Speaker 1: for char his job, and he gave it to him.

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Speaker 1: And then Charlie, you know, stayed on the payroll part

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Speaker 1: time and became a mentor and helped me with all

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Speaker 1: my work and kind of was my shepherd and was incredible.

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Speaker 1: And then you know, the other part is just couldn't

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Speaker 1: be more fortunate. You know, my first weekend, I've become

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Speaker 1: friends with the food analysts you're talking about, Michael Mobison.

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Speaker 1: I assume, correct, correct. The sequence of fortunate events is

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Speaker 1: so like mind boggling. So I, um, I get to

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Speaker 1: know Mike. Mike hands me a book called Valuation that

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Speaker 1: Mackenzie had written in another one by Stern Stewart, all

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Speaker 1: about return on invested capital, which he's using in the

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Speaker 1: food space. Um, out of curiosity and because he's pushing me,

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Speaker 1: I run the numbers for all the PC manufacturers, and

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Speaker 1: it turns out Dell is crushing everyone on this metric

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Speaker 1: that no one's using in the in the analysis of

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Speaker 1: that market. And it also turns out that they've got

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Speaker 1: an option scandal problem and a laptop battery problem and

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Speaker 1: the stocks trading at six times earned. And so through

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Speaker 1: through using that methodology that Michael taught me and spending

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Speaker 1: a ton of time with Tom Meredith, who was the

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Speaker 1: CFO deal at the time, UM, you know, we went

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Speaker 1: to a strong by when everyone was was had a

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Speaker 1: cell rating, which was the only strong Bye I ever

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Speaker 1: did in my career. Um and I think the stock

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Speaker 1: went up a hundred acts in the public market from there.

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Speaker 1: So now let's let's fast forward to the Amazon I

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Speaker 1: p O. You're the lead analyst, tell us about that experience. Yeah, So,

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Speaker 1: in my third year as an analyst at CSFP, I

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Speaker 1: was about to I was about to jump shift and

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Speaker 1: go to the Bye side. I've been spending a bunch

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Speaker 1: of time with Capital Group, actually who I think the

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Speaker 1: world of um uh you know, and still still keep

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Speaker 1: in touch with people there all the way until today.

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Speaker 1: And Frank Quatroum called me out of the Blue and

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Speaker 1: he said, I've heard a lot about you. We're leaving

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Speaker 1: Morgan Stanley. We're going to start this boutique in that

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Speaker 1: tech investment bank called d MG Technology Group. And when

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Speaker 1: you really want us to join? And I really wanted

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Speaker 1: you to join, and um I called Roger McNamee UM

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Speaker 1: because I did. I was looking for some advice and

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Speaker 1: Roger was a client of mine at TSFP and he said,

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Speaker 1: you got to meet with Frank. You have to meet

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Speaker 1: with Frank. And so I met with Frank. He he asked,

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Speaker 1: what do you want to do long term? And I

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Speaker 1: said I'd love to be a venture capitalist and he said,

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Speaker 1: I'll tell you what come work for me. I'll move

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Speaker 1: you to Silicon Valley. I'll introduce you to every DC

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Speaker 1: that I know. So that was a pretty good Uh,

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Speaker 1: that's a pretty good offer. Um. When when I got

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Speaker 1: to Frank, well, Frank and I then started. He said

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Speaker 1: he said what else? And I said, well, I don't

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Speaker 1: really want to cover PCs anymore because they're gonna they're

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Speaker 1: gonna merge with the Big Box Group and I'm gonna

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Speaker 1: have to cover HP and DECK and that's boring. He goes, well,

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Speaker 1: what do you want to cover and I said, this

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Speaker 1: Internet things popping up, let's do that. He said, fine, So, UM,

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Speaker 1: this is the priest Fitzer Wall so the bankers in

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Speaker 1: the research and now it's spent a lot more time together.

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Speaker 1: But UM I had already I believe UH reached out,

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Speaker 1: you know, the bezos one way or another, just paying

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Speaker 1: attention to what was happening with the internet. UM and

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Speaker 1: made a couple of trips up to Seattle UH to

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Speaker 1: speak with him. UM got to know him pretty well.

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Speaker 1: We're still close friends today and he's a large investor

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Speaker 1: in Benchmark and UM and you know it, luckily we

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Speaker 1: got to know him before it happened. But they ended

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Speaker 1: up being a bake off, and it was it was

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Speaker 1: you know, I think Morgan Stanley was in before us

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Speaker 1: in Goldman after the meetings were a Decliner building on

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Speaker 1: sand Hill and somehow we won the man do I

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Speaker 1: To this day it's one of my favorite trivia questions.

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Speaker 1: Who was lead left on the Amazon I p O

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Speaker 1: because not many people will be able to say Deutsch

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Speaker 1: to Morgan Gruntsfeld. But but there there is a photo

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Speaker 1: of it on the internet. If you do a search

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Speaker 1: to have a book somewhere right, don't you have a

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Speaker 1: I do. I have the prospectives and and I also

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Speaker 1: my my assistant at the time, JULIETT. Wilson, who was

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Speaker 1: it was really great. She had this idea that we

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Speaker 1: would find the pitch book, and she found a company

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Speaker 1: in San Francisco that was opened all night that would

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Speaker 1: bind a book for you. The bankers thought we were

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Speaker 1: nuts because they loved to put pages in at the

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Speaker 1: last minute, and these like three ring binders, but they

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Speaker 1: agreed to it. So like she drove to the city

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Speaker 1: at like two am and and we we used bound

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Speaker 1: pitch books for them. And I still have a copy

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Speaker 1: of that, which is kind of cool. That's quite amazing.

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Speaker 1: How did you value companies back then when there wasn't

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Speaker 1: a lot of revenue. There certainly wasn't any profit. There

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Speaker 1: were eyeballs, there were clicks. How do you put evaluation

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Speaker 1: on on a a relatively young startup? You know, I

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Speaker 1: um got that opportunity to jump to venture only thirteen

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Speaker 1: months after I joined Frank and I did, and so

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Speaker 1: I was I would I had left the equity research business.

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Speaker 1: By the times stuff started going really crazy um in

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Speaker 1: the in the in UM and so I started thinking

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Speaker 1: about it as a as an investor in less the

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Speaker 1: research analyst. But I didn't, I wasn't forced into that quandary. Um.

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Speaker 1: I wrote a blog post way back then though about

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Speaker 1: proxy valuations, and I noted that people like John Malone,

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Speaker 1: you know, had been able to convince Wall Street to

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Speaker 1: think about homes past instead of you know, a pe

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Speaker 1: multiple and that there's a value for home past if

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Speaker 1: we can grow homes path and there were so there

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Speaker 1: were other people at that time that had been successful

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Speaker 1: at it, kind of changing on how you think about things.

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Speaker 1: But of course in things just went super nutty. They

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Speaker 1: may be super nutty again. Um and uh and it.

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Speaker 1: It can cause a lot of anxiety for someone who

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Speaker 1: was brought up rooted in financial history and you know,

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Speaker 1: someone who's who felt like a study partner of Mike Mowison,

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Speaker 1: like it's it's hard when you see craziness. So you're

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Speaker 1: not a specially big fan of initial public offerings. What's

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Speaker 1: the problem with traditional I p O s. There's a

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Speaker 1: problem that's been inherit for a very long time, and

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Speaker 1: then there's a problem that's gotten way worse than the

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Speaker 1: past five years. The problem that's been around for a

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Speaker 1: very long time. You know. Bill Hamburg was pushing on

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Speaker 1: this over twenty years ago. You know, he said it's

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Speaker 1: an insider's game and it's rigged, and he was one

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Speaker 1: of the inside. You know, Um, and Pierre and Evey

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Speaker 1: got upset about it. Larry Sergey got upset about it.

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Speaker 1: You know. So I think it's and and net sweet

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Speaker 1: you know, Uh got upset about it. Uh, Allison was

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Speaker 1: upset and as a result of the team at that

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Speaker 1: sweet and So I think it's always important when this

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Speaker 1: topic comes up to highlight that there were plenty of

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Speaker 1: other people in Silicon Valley that we're ringing this bell

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Speaker 1: before I was. Um, I'm just the latest person to

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Speaker 1: pick up the baton. Um. There's two real problems, um

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Speaker 1: that have been exacerbated recently. One is they don't let

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Speaker 1: everyone been so you're there's a very restricted set of

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Speaker 1: people who get to decide if they get if they

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Speaker 1: want to buy stock in and office, and it's it's

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Speaker 1: not everybody. There's not even any oversight as to who

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Speaker 1: gets led into that circle. And then the second thing

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Speaker 1: is they don't use price, Uh just they don't use

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Speaker 1: supplying demand to set the price. And determine who gets

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Speaker 1: the shares. They literally have humans saying we think the

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Speaker 1: price should be this, then we're going to give it

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Speaker 1: to this many people. Um, it's no shock that that

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Speaker 1: has resulted in miss prices. And now there's forty years

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Speaker 1: of under pricing data. Professor j ritterd at uh Eurosity

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Speaker 1: of Florida has aggregated all this on his website. He

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Speaker 1: keeps it up to date, so I just get the

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Speaker 1: point of his stuff. Um, those numbers have gotten insane recently.

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Speaker 1: So the previous two years were six and seven billion

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Speaker 1: dollars one day underpricing, and then was thirty five billion.

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Speaker 1: So Bill, some people might say, on at least on

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Speaker 1: Wall Street, that that's not a bug, that's a feature.

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Speaker 1: Oh maybe for that client. You know. I do think

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Speaker 1: that all of it makes sense when you realize that

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Speaker 1: the key customer of the investment bank is the institutional

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Speaker 1: share the firms to the fidelities and t ros and

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Speaker 1: cathital groups. Um, that is the customer that's being served.

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Speaker 1: And I guess that's fine to a certain extent, except

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Speaker 1: the banker UM acts as if they're looking after your

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Speaker 1: best interests. Also, there's no high dollar transaction that you'll

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Speaker 1: do in your life very where you use the same

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Speaker 1: agent as the counterpart. It's the only one there. There

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Speaker 1: literally isn't. You wouldn't do that in M and A transaction,

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Speaker 1: you don't. You don't do it when you sell a house. UM,

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Speaker 1: you always get your own you know, agent to look

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Speaker 1: after your best interests. So it's bizarre how much trust

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Speaker 1: there is UM when people go into that process. And

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Speaker 1: I think that trust gets taken advantage of. When I

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Speaker 1: was on Wall Street, UM, there were these huge sales

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Speaker 1: forces in every region, and when you did an I

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Speaker 1: T O there was variable compensation for people that could

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Speaker 1: play shares, and even between the banks, there was this

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Speaker 1: thing called jump ball economics where your bank was encouraged

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Speaker 1: to outperform the other bank, so that they used to

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Speaker 1: use this word distribution. I'm looking to get this offering

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Speaker 1: out and you got to go sell it, right. The

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Speaker 1: sales force at the two largest investment banks today's one

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Speaker 1: tenth the size it was back then, so they don't

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Speaker 1: even have the people anymore. And what they've done is

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Speaker 1: they now focused the majority of the I P O process,

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Speaker 1: unlike the top twenty accounts UM, so there's no effort

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Speaker 1: anymore to push it out. It's just more of an

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Speaker 1: effort to see what these people want. And they've settled

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Speaker 1: in on an optimization function, which is truly remarkable that

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Speaker 1: they get that this actually passes muster. But they tell

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Speaker 1: each and every CEO and CFO and board that comes

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Speaker 1: to go public that their goal should be being thirty

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Speaker 1: to fifty x over subscripts. Amazing. Supplying demand was understood

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Speaker 1: three or four years ago. How you could sell the

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Speaker 1: most important asset in your firm, which is your stock,

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Speaker 1: um into a process where you're being told that x

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Speaker 1: oversubscribed is optimization. It's shocking. It's literally shocking to me.

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Speaker 1: So let's talk about an alternative like direct listings. How

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Speaker 1: do they work and what are their advantages versus a

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Speaker 1: traditional I P O. Yeah, so, like forty years ago,

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Speaker 1: people built algorithms into exchanges that allows you to match

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Speaker 1: supplying demand to determine price. And every single stock these days,

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Speaker 1: every day when it opens, goes through that process. Right

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Speaker 1: every morning, you know you gotta start trading a stock,

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Speaker 1: how do you determine which price? Where's the first trade happen? Um?

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Speaker 1: And it's all just a simple matter of matching supplying demand.

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Speaker 1: The most u highly used algorithms called priced time, which

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Speaker 1: is if if you put in a bit at the

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Speaker 1: price that they evinced the open apt or if you

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Speaker 1: put a bit of a pity above the price they

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Speaker 1: open at, you automatically get filled. If you're at that

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Speaker 1: price whoever had order in first. So that's the price

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Speaker 1: in the time. UM. That's exactly how every stocks opened

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Speaker 1: every day on both the NAT deck and the n

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Speaker 1: y C. And here's the best part. That same technique

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Speaker 1: is used the day after a hand allocated i p O.

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Speaker 1: So every company that does an i p O does

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Speaker 1: a direct listing that the very next morning, except the

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Speaker 1: only people allowed to sell, and I feel are the

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Speaker 1: ones that you gave the stock to the night before,

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Speaker 1: which is also perverse. Um. But the technique that I

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Speaker 1: think the most elegant thing about the direct listing is

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Speaker 1: it uses the systems that are already in place. And

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Speaker 1: so if you talk to the guys that Citadale that

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Speaker 1: have done all four of the high profile direct listing, um,

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Speaker 1: they they actually, you know, will tell you, yeah, this

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Speaker 1: is just like how we open every other stock, or

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Speaker 1: just like we opened a stock after an i p O.

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Speaker 1: The day before um, and now I had a front

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Speaker 1: row seat on a Sonic because we or an investor there.

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Speaker 1: But um, they just start announcing a range. People tighten

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Speaker 1: up their their bids and their offers and eventually a

402
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Speaker 1: block trade and then you're often going. But it's completely blind,

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Speaker 1: so no one gets an allocation because of who they

404
00:25:15,400 --> 00:25:18,719
Speaker 1: are there, their brand, and it's all tied to that

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Speaker 1: algorithm the price and time and so on. Just to prove.

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Speaker 1: And here's another great thing, because it's in the network

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Speaker 1: already because they're using these systems at the exchanges. It's

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Speaker 1: connected to all brokerages. So for a direct listing, you

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Speaker 1: want to go on robin Hood, you want to go

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Speaker 1: on swab put in an order for one share of

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Speaker 1: fourteen buck. If that's above the opening price, you get filled.

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Speaker 1: Like and that's that's the the I said. There are

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Speaker 1: two parts, you know, using algorithms applying demand to set price,

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Speaker 1: but also being available to every shareholder on the exchange,

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Speaker 1: and you get both of those, which is way better.

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Speaker 1: It's just it's like it to me. The the the

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Speaker 1: onus on why we would do something should be on

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Speaker 1: why would we ever have someone sitting there? You know,

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Speaker 1: hand allocating in hand determining price makes no sense. We

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Speaker 1: hope we sell every bond in this country, you know,

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Speaker 1: with this type of system. UM, and and I hope

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Speaker 1: one day that that's how every one of these ideas were. So, Bill,

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Speaker 1: how does a direct listing compare to, say, the way

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Speaker 1: Google went public with that sort of reverse Dutch auction. Well,

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Speaker 1: I take three things about that. So so, first of all, um,

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Speaker 1: because they were trying to do it in a in

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Speaker 1: a innovative manner, it was bespoke and so uh, I

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Speaker 1: think Morgan Stanley hired like two hundred engineers to build

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Speaker 1: the system, and it was a one off. That's different

430
00:26:52,960 --> 00:26:55,640
Speaker 1: from what I just described with direct listings, where they're

431
00:26:55,720 --> 00:26:59,359
Speaker 1: using this process and systems that have been in place

432
00:26:59,440 --> 00:27:02,399
Speaker 1: for twenty or two years at the nassect in the NYC.

433
00:27:02,640 --> 00:27:06,119
Speaker 1: So obviously one of those is better than the other. Um.

434
00:27:06,320 --> 00:27:08,800
Speaker 1: For any retail investors that wanted to do Google, they

435
00:27:08,800 --> 00:27:11,920
Speaker 1: had to go open an account I think at Hamburg

436
00:27:11,920 --> 00:27:16,120
Speaker 1: con Quest or something like. It wasn't tied to the

437
00:27:16,160 --> 00:27:18,960
Speaker 1: Open Exchange the way this is, so you didn't have

438
00:27:19,600 --> 00:27:23,199
Speaker 1: all of that. And then and then lastly, you know

439
00:27:23,560 --> 00:27:27,440
Speaker 1: you you you because it was the first one, your

440
00:27:27,480 --> 00:27:31,440
Speaker 1: auction participants just didn't have a lot of experience. And

441
00:27:31,600 --> 00:27:35,960
Speaker 1: there's this great legendary story, uh that Bill Miller was

442
00:27:36,080 --> 00:27:39,080
Speaker 1: trying to figure out how much he should put in

443
00:27:39,119 --> 00:27:42,439
Speaker 1: on the Google auction and he he apparently hired a

444
00:27:42,440 --> 00:27:46,000
Speaker 1: bunch of auction specialists to come breathe his team on

445
00:27:46,119 --> 00:27:49,520
Speaker 1: how to think about this, and somewhere like the second day,

446
00:27:49,960 --> 00:27:53,399
Speaker 1: someone realized that all the auction theorists were starting by saying,

447
00:27:53,960 --> 00:27:59,159
Speaker 1: assuming you have participants who are experienced, like all the

448
00:27:59,200 --> 00:28:04,040
Speaker 1: auction theory based on people having experience. And he then realized,

449
00:28:04,240 --> 00:28:07,480
Speaker 1: everyone's going to be inexperienced here, They're going to be conservative,

450
00:28:07,480 --> 00:28:10,040
Speaker 1: they're gonna underbid, And he put in a huge order

451
00:28:11,400 --> 00:28:15,120
Speaker 1: and got failed. A great story he did, so he did,

452
00:28:15,240 --> 00:28:18,960
Speaker 1: he got failed. So there was no experience and and

453
00:28:18,800 --> 00:28:22,760
Speaker 1: a and a much a much less less uh efficient

454
00:28:22,800 --> 00:28:25,880
Speaker 1: tech tack relative to work we have today. So when

455
00:28:25,920 --> 00:28:29,080
Speaker 1: you do a direct listing, do you still have the

456
00:28:29,080 --> 00:28:31,760
Speaker 1: rest of the trappings, Is there still a road show?

457
00:28:31,920 --> 00:28:35,359
Speaker 1: Do you need to sort of show your wears to

458
00:28:35,520 --> 00:28:38,880
Speaker 1: bankers or do you just you know, listening go public.

459
00:28:39,400 --> 00:28:42,840
Speaker 1: So there's a misunderstanding on this point. And it was

460
00:28:42,920 --> 00:28:45,680
Speaker 1: partially driven by there was a there are a group

461
00:28:45,760 --> 00:28:47,760
Speaker 1: of naysayers. He said, you could only do a direct

462
00:28:47,800 --> 00:28:51,000
Speaker 1: listing if you're extremely well known brand. UM, which I

463
00:28:51,000 --> 00:28:54,240
Speaker 1: think was was was quite frankly bs from the beginning.

464
00:28:54,320 --> 00:28:58,080
Speaker 1: But UM, I think you know, with a Sauna having

465
00:28:58,080 --> 00:29:02,880
Speaker 1: a successful direct listing, it's not a not a household brand.

466
00:29:02,960 --> 00:29:05,480
Speaker 1: I think we've kind of blown that up. UM. But

467
00:29:05,560 --> 00:29:08,600
Speaker 1: the other thing was they thought that the in person

468
00:29:08,760 --> 00:29:12,480
Speaker 1: road show was was tremendously important and you can only

469
00:29:12,520 --> 00:29:15,080
Speaker 1: do that with an id O. Well, of course with

470
00:29:15,160 --> 00:29:19,240
Speaker 1: COVID that's been proven wrong in case every idea now

471
00:29:19,360 --> 00:29:23,120
Speaker 1: is virtual. But but the bigger point I would make

472
00:29:23,200 --> 00:29:26,400
Speaker 1: is the amount of information you can share is actually

473
00:29:26,440 --> 00:29:28,520
Speaker 1: a superset of an I p O so you you

474
00:29:28,600 --> 00:29:32,920
Speaker 1: have way more opportunity to educate. UM. All four direct

475
00:29:33,000 --> 00:29:36,280
Speaker 1: listings have used the equivalent of an investor day, so

476
00:29:36,360 --> 00:29:41,440
Speaker 1: like four to eight hours of information dissemination from different

477
00:29:41,520 --> 00:29:43,960
Speaker 1: parts of the company, which you would never get on

478
00:29:44,000 --> 00:29:49,080
Speaker 1: a traditional I p O, UM and and Slack and

479
00:29:49,120 --> 00:29:52,080
Speaker 1: then decided that in addition to the investor day, they

480
00:29:52,120 --> 00:29:55,320
Speaker 1: wanted to do the standard road show and that was

481
00:29:55,680 --> 00:29:59,480
Speaker 1: that was available and so they did vote, so they

482
00:29:59,480 --> 00:30:02,600
Speaker 1: were able to actually to do even more. Daniel act

483
00:30:02,720 --> 00:30:08,040
Speaker 1: interestingly at Spotify, felt strongly that he felt in this

484
00:30:08,240 --> 00:30:12,000
Speaker 1: tide to open access, he wanted every investor to have

485
00:30:12,160 --> 00:30:16,280
Speaker 1: the exact same information, and so he was uncomfortable doing

486
00:30:16,280 --> 00:30:19,200
Speaker 1: the road show because he wanted to retail investors to

487
00:30:19,720 --> 00:30:23,880
Speaker 1: not be at a disadvantage to the institutional investors. That

488
00:30:23,880 --> 00:30:29,240
Speaker 1: that's really interesting. Are there any legal or regulatory impediments

489
00:30:29,480 --> 00:30:33,600
Speaker 1: two direct listings becoming more popular or is it just

490
00:30:33,680 --> 00:30:37,800
Speaker 1: simply there's an entrenched I p O process and it's

491
00:30:37,800 --> 00:30:40,680
Speaker 1: what so many people are used to. Yeah. I think

492
00:30:40,720 --> 00:30:43,040
Speaker 1: there's a lot of that entrenched thing, you know, when

493
00:30:43,120 --> 00:30:47,920
Speaker 1: you take your company public. For many CEOs and founders

494
00:30:47,920 --> 00:30:50,400
Speaker 1: and CFOs, they may do it once in their lifetime,

495
00:30:50,960 --> 00:30:58,800
Speaker 1: and so it carries this kind of special uh like important.

496
00:30:59,160 --> 00:31:03,600
Speaker 1: And I think that weight of importance causes conservatism because

497
00:31:03,640 --> 00:31:06,320
Speaker 1: people are like, oh my god, I can't screw this up.

498
00:31:06,760 --> 00:31:10,000
Speaker 1: And I think bankers take advantage of that. I say,

499
00:31:10,160 --> 00:31:12,680
Speaker 1: often an I p O is like a grand Southern

500
00:31:12,720 --> 00:31:15,920
Speaker 1: wedding right where they tell you, you know, oh, no

501
00:31:16,120 --> 00:31:18,800
Speaker 1: cost is too much, right, you gotta have every bell

502
00:31:18,880 --> 00:31:22,280
Speaker 1: and whist here you want this to be spectacular. Um.

503
00:31:22,320 --> 00:31:26,040
Speaker 1: And that seduction I think plays a part of the

504
00:31:26,080 --> 00:31:30,120
Speaker 1: whole thing. Um. It's interesting to me when you look

505
00:31:30,160 --> 00:31:33,200
Speaker 1: at the thread that goes through the type of entrepreneur

506
00:31:33,680 --> 00:31:36,320
Speaker 1: who really stands up and says, you know, I need

507
00:31:36,320 --> 00:31:39,400
Speaker 1: to do this because this is what's right. And Larry

508
00:31:39,440 --> 00:31:43,080
Speaker 1: and Sergey did that, Daniel Act did that. You know,

509
00:31:43,200 --> 00:31:45,920
Speaker 1: Toby it's spot up at shopify and said of man,

510
00:31:46,000 --> 00:31:48,800
Speaker 1: if I ever had another chance, I would definitely do

511
00:31:48,840 --> 00:31:53,120
Speaker 1: a direct listing, and and and I have to believe

512
00:31:53,320 --> 00:31:55,880
Speaker 1: I would bet a lot, a lot, a lot of

513
00:31:55,880 --> 00:31:58,640
Speaker 1: money that Bezos would do a direct listing if we

514
00:31:58,800 --> 00:32:02,720
Speaker 1: were coming out today. Um. I just think, you know,

515
00:32:03,360 --> 00:32:05,800
Speaker 1: I think it takes a certain amount of hutspun, a

516
00:32:05,840 --> 00:32:09,560
Speaker 1: certain amount of conviction um to kind of go against

517
00:32:09,560 --> 00:32:12,320
Speaker 1: the grain. But the more of these that are under

518
00:32:12,360 --> 00:32:15,160
Speaker 1: our belt, and we've got roadblocks and coin based coming soon,

519
00:32:15,560 --> 00:32:18,040
Speaker 1: I think I think the more momentum will be. Obviously

520
00:32:18,400 --> 00:32:22,480
Speaker 1: there's one piece that that that is missing still today,

521
00:32:22,520 --> 00:32:26,120
Speaker 1: but there's a lot of progress on is adding primary

522
00:32:26,160 --> 00:32:29,520
Speaker 1: capital to it. Stacy Cunningham, who runs the n Y

523
00:32:29,640 --> 00:32:34,000
Speaker 1: sc Um, really really pushed hard on making this happen,

524
00:32:34,360 --> 00:32:38,560
Speaker 1: and Commissioner Clayton and his last act I think two

525
00:32:38,680 --> 00:32:41,920
Speaker 1: days before he stepped away as Chairman of the SEC

526
00:32:42,200 --> 00:32:45,880
Speaker 1: pushed that through and so it's now legal to do it,

527
00:32:46,200 --> 00:32:48,920
Speaker 1: and they're gonna you know, it'll take a few companies

528
00:32:48,960 --> 00:32:51,280
Speaker 1: to be the first one through the door. UM, I

529
00:32:51,320 --> 00:32:54,640
Speaker 1: don't think there's any technical issues with adding primary capital.

530
00:32:55,320 --> 00:32:59,000
Speaker 1: Quite fascinating. So Bill, let's talk a little bit about Uber.

531
00:32:59,320 --> 00:33:02,840
Speaker 1: You are one of the earliest investors. You participated in

532
00:33:02,880 --> 00:33:06,120
Speaker 1: the A rounds. What did you see at that stage

533
00:33:06,360 --> 00:33:09,720
Speaker 1: that got you so excited? We we had made this

534
00:33:09,840 --> 00:33:15,120
Speaker 1: investment in Open Table, and the it felt like a

535
00:33:15,120 --> 00:33:17,120
Speaker 1: flyer at the time, I think it said, I don't

536
00:33:17,120 --> 00:33:20,640
Speaker 1: think it looks that way now. But we we made

537
00:33:20,720 --> 00:33:24,720
Speaker 1: this bet that if you could get this flywheel moving,

538
00:33:25,400 --> 00:33:30,960
Speaker 1: that connecting all of the world's restaurants would create a

539
00:33:31,040 --> 00:33:35,120
Speaker 1: very special um experience for the consumer. And you know,

540
00:33:35,200 --> 00:33:38,800
Speaker 1: pribor Open Table, you couldn't say, oh, I need a

541
00:33:38,880 --> 00:33:42,880
Speaker 1: table for six and would love Chinese in San Francisco

542
00:33:42,960 --> 00:33:46,200
Speaker 1: on Saturday and then immediately get a response like that

543
00:33:46,320 --> 00:33:50,760
Speaker 1: was an impossibility. And so my partnership and I started

544
00:33:50,800 --> 00:33:55,280
Speaker 1: asking what other industries, could you put this network layer

545
00:33:55,320 --> 00:33:59,440
Speaker 1: on and and have an AHA experience for the consumer.

546
00:34:00,080 --> 00:34:04,520
Speaker 1: And the one the one that has intrigued me just

547
00:34:04,600 --> 00:34:09,040
Speaker 1: in terms of like fundamental research and thinking was was cars.

548
00:34:09,239 --> 00:34:11,640
Speaker 1: I just thought, if you put a network on cars,

549
00:34:11,680 --> 00:34:16,799
Speaker 1: and there's so much inefficiency, especially around black cards, you know,

550
00:34:16,840 --> 00:34:19,560
Speaker 1: you would you spend as much on a business trip

551
00:34:19,800 --> 00:34:22,040
Speaker 1: you played Chicago for a day, You spent as much

552
00:34:22,080 --> 00:34:24,560
Speaker 1: on the black car that that you rode around in

553
00:34:24,600 --> 00:34:27,520
Speaker 1: all days you did the flight UM, and most of

554
00:34:27,520 --> 00:34:30,160
Speaker 1: the time they were just sitting there and then God forbid,

555
00:34:30,200 --> 00:34:31,879
Speaker 1: you come out and you can't find them, and you're

556
00:34:32,080 --> 00:34:37,040
Speaker 1: calling them like it's just all this waste and UM.

557
00:34:37,160 --> 00:34:40,640
Speaker 1: So I just started proactively meeting with companies and I

558
00:34:40,760 --> 00:34:44,280
Speaker 1: met with probably three or four companies that have built

559
00:34:44,320 --> 00:34:49,879
Speaker 1: this network layer on top of taxi UM and spent

560
00:34:49,960 --> 00:34:52,520
Speaker 1: a lot of time with those companies. It struck me

561
00:34:52,760 --> 00:34:56,919
Speaker 1: the more I learned about it that most taxis were

562
00:34:57,360 --> 00:35:03,239
Speaker 1: oligopolis and in cities, UM they were highly regulated so

563
00:35:03,280 --> 00:35:06,239
Speaker 1: you couldn't mess around with price, which isn't optimal for

564
00:35:06,400 --> 00:35:11,520
Speaker 1: a for a marketplace. UM. There were there was all

565
00:35:11,640 --> 00:35:15,680
Speaker 1: kind of of ugliness around people that have tried to

566
00:35:15,680 --> 00:35:20,919
Speaker 1: sell technology to them before they were installing uh devices

567
00:35:21,040 --> 00:35:24,040
Speaker 1: in the dash and stuff, and so there's just a

568
00:35:24,080 --> 00:35:27,680
Speaker 1: lot that that kind of didn't look great the more

569
00:35:27,719 --> 00:35:30,399
Speaker 1: I dug into that, and so I kind of came

570
00:35:30,440 --> 00:35:32,320
Speaker 1: back to the partnership and just said, Hey, if you

571
00:35:32,440 --> 00:35:35,560
Speaker 1: ever see anyone doing this around black cars instead of taxis,

572
00:35:36,080 --> 00:35:39,080
Speaker 1: let's let's talk to them. And and that's what happened.

573
00:35:39,080 --> 00:35:43,120
Speaker 1: Garrett and Travis and Brian Graves, you know, started doing

574
00:35:43,120 --> 00:35:46,319
Speaker 1: this thing right in our backyard. That was exactly what

575
00:35:46,400 --> 00:35:51,040
Speaker 1: we were talking about internally, and so we smothered We

576
00:35:51,080 --> 00:35:53,800
Speaker 1: actually met with him before the seed round. Um and

577
00:35:54,080 --> 00:35:57,520
Speaker 1: my partners couldn't quite get there. But six months later

578
00:35:57,560 --> 00:36:01,920
Speaker 1: we lead the a so and and no regrets to

579
00:36:01,960 --> 00:36:04,759
Speaker 1: say the least. So, so you are not only a

580
00:36:04,960 --> 00:36:10,240
Speaker 1: very engaged board member at Uber, you were Travis's Travis Kalis,

581
00:36:10,320 --> 00:36:14,959
Speaker 1: who's the CEO. You've been described as his consigliari. How

582
00:36:15,040 --> 00:36:18,800
Speaker 1: difficult of a role does that put you in where

583
00:36:18,800 --> 00:36:22,840
Speaker 1: you're both an investor into the company, but a cheerleader

584
00:36:22,880 --> 00:36:25,440
Speaker 1: into one of the founders in the CEO. You know,

585
00:36:25,600 --> 00:36:29,799
Speaker 1: I think I think that that is a role we

586
00:36:29,960 --> 00:36:35,200
Speaker 1: play as early stage venture capitalist that we always take

587
00:36:35,239 --> 00:36:36,920
Speaker 1: a board seat of bench mark. I think that's a

588
00:36:37,000 --> 00:36:40,200
Speaker 1: role we play in every company UM and I think

589
00:36:40,440 --> 00:36:44,520
Speaker 1: it is the most nuanced, difficult part of the job

590
00:36:45,400 --> 00:36:49,600
Speaker 1: to be simultaneously a friend and partner who's trying to

591
00:36:49,640 --> 00:36:54,040
Speaker 1: help develop a mentor, a founder and and shepherd demil

592
00:36:54,200 --> 00:36:58,920
Speaker 1: in their in their journey hopefully you know, evolving beyond

593
00:36:58,960 --> 00:37:03,240
Speaker 1: and past you know, yourself into into the public markets

594
00:37:03,800 --> 00:37:08,120
Speaker 1: UM and simultaneously have a fiduciary duty job as a

595
00:37:08,160 --> 00:37:11,640
Speaker 1: board member to look after the interest the shareholders. And

596
00:37:11,719 --> 00:37:15,719
Speaker 1: it's tough, um I. You know a lot of there's

597
00:37:15,800 --> 00:37:18,000
Speaker 1: lots of different stories with a lot of different factors,

598
00:37:18,000 --> 00:37:20,919
Speaker 1: a lot of different investors and that have been through

599
00:37:20,960 --> 00:37:24,359
Speaker 1: that UM. In this particular case, you know, it's well

600
00:37:24,400 --> 00:37:29,240
Speaker 1: known that became they got they got increasingly difficult UM

601
00:37:29,360 --> 00:37:32,080
Speaker 1: and And I would say, you know, that's not optimal,

602
00:37:32,200 --> 00:37:38,360
Speaker 1: like like in in in the in the perfect scenario,

603
00:37:38,560 --> 00:37:41,560
Speaker 1: you know, you have something like like Bezos at Amazon

604
00:37:41,719 --> 00:37:45,400
Speaker 1: where the founder is able to to rise and scale

605
00:37:45,440 --> 00:37:49,520
Speaker 1: and and do extremely well UM into the public markets

606
00:37:49,520 --> 00:37:52,680
Speaker 1: and beyond. But it doesn't always work out that way.

607
00:37:53,080 --> 00:37:56,479
Speaker 1: So I've been a user of Uber for god knows

608
00:37:56,480 --> 00:37:59,680
Speaker 1: how long. Um, and I've watched the Rise and Fall

609
00:37:59,719 --> 00:38:02,680
Speaker 1: and Eyes again and that led me to read the book.

610
00:38:03,080 --> 00:38:06,799
Speaker 1: Super pumped the battle for Uber. You're the only guy

611
00:38:06,840 --> 00:38:11,040
Speaker 1: in the whole book who comes out with your reputation intact.

612
00:38:11,080 --> 00:38:13,640
Speaker 1: Everybody else in the book is I don't know any

613
00:38:13,640 --> 00:38:16,920
Speaker 1: of these people, so I can't say how accurate it is.

614
00:38:17,320 --> 00:38:21,400
Speaker 1: But everybody comes across, as you know, either a jerk

615
00:38:21,520 --> 00:38:25,200
Speaker 1: or an egotist or just so sharp elbowed. You seem

616
00:38:25,239 --> 00:38:29,000
Speaker 1: to be the only mentioned, the only person of character

617
00:38:29,080 --> 00:38:32,120
Speaker 1: in the book. Have you read it and and if

618
00:38:32,120 --> 00:38:35,680
Speaker 1: you have, is it remotely accurate? I have not read

619
00:38:35,680 --> 00:38:38,600
Speaker 1: it very My wife read it and and echoed what

620
00:38:38,800 --> 00:38:42,640
Speaker 1: you just said. UM. I chose not to read it

621
00:38:42,680 --> 00:38:44,920
Speaker 1: just because I didn't necessarily want to live it again.

622
00:38:46,520 --> 00:38:48,680
Speaker 1: But that's probably not fair to the oscar I I

623
00:38:48,719 --> 00:38:51,080
Speaker 1: just probably showed at some point, So I don't know.

624
00:38:51,160 --> 00:38:53,520
Speaker 1: I can't speak to that because I haven't read it

625
00:38:53,520 --> 00:38:59,840
Speaker 1: in detail understandable. So ultimately Travis gets forced out and

626
00:39:00,200 --> 00:39:03,279
Speaker 1: the next day you resign from the board and you

627
00:39:03,360 --> 00:39:07,360
Speaker 1: show your support for him stay in. There will be

628
00:39:07,400 --> 00:39:11,240
Speaker 1: many pages in the history books devoted to at Travis

629
00:39:11,320 --> 00:39:15,360
Speaker 1: Kay that's his Twitter handle. Very few entrepreneurs have had

630
00:39:15,760 --> 00:39:18,719
Speaker 1: such a lasting impact on the world. That was a

631
00:39:18,719 --> 00:39:21,960
Speaker 1: good couple of years ago. With the passage of time,

632
00:39:22,600 --> 00:39:26,080
Speaker 1: Do you feel uh the same? How is your perspective

633
00:39:26,200 --> 00:39:31,000
Speaker 1: changed since since you stepped down? Yeah, I mean they're

634
00:39:31,120 --> 00:39:36,320
Speaker 1: they're Travis heads and probably still does. Has qualities that

635
00:39:36,520 --> 00:39:43,320
Speaker 1: are particularly unique. He is really really really bright. Um.

636
00:39:43,440 --> 00:39:48,240
Speaker 1: He is tireless, like like he will outwork you. Um.

637
00:39:48,360 --> 00:39:50,920
Speaker 1: I think at one point, Tom, you know, I said

638
00:39:50,640 --> 00:39:54,440
Speaker 1: I'd be afraid of having and money in and in

639
00:39:54,480 --> 00:39:58,280
Speaker 1: a company that he's competing with, just because I doubt

640
00:39:58,320 --> 00:40:00,040
Speaker 1: you're going to work as hard as he is a

641
00:40:00,239 --> 00:40:07,160
Speaker 1: combination of of intelligence and and remarkably hard worker. UM.

642
00:40:07,239 --> 00:40:11,480
Speaker 1: And I also think because and this is an interesting

643
00:40:11,520 --> 00:40:14,600
Speaker 1: thing to look out for his venture capitalists, because his

644
00:40:14,800 --> 00:40:20,319
Speaker 1: first two companies didn't have huge success. When he realized

645
00:40:20,400 --> 00:40:23,960
Speaker 1: he had product market fit, I think he leaned into

646
00:40:24,000 --> 00:40:27,160
Speaker 1: it with it, with it with an amount of of

647
00:40:29,000 --> 00:40:34,080
Speaker 1: recognition of how special that is, you know. I think,

648
00:40:34,280 --> 00:40:36,719
Speaker 1: you know, so for better for work, some people get

649
00:40:36,719 --> 00:40:40,880
Speaker 1: started on companies that just started as good as other companies. UM.

650
00:40:40,880 --> 00:40:44,080
Speaker 1: And you know the famous saying like Buffett, when a

651
00:40:44,120 --> 00:40:46,560
Speaker 1: bad business meets a good man's and team, it's the

652
00:40:46,640 --> 00:40:50,680
Speaker 1: business who comes out with its reputation intact um. And

653
00:40:50,760 --> 00:40:55,000
Speaker 1: I really think Travis knew that this was special, and

654
00:40:55,120 --> 00:40:58,879
Speaker 1: so he went after it a great recruiter, was able

655
00:40:58,920 --> 00:41:02,920
Speaker 1: to bring in a lot of great people, UM fearless,

656
00:41:03,480 --> 00:41:08,359
Speaker 1: like like he launched the business in China when every

657
00:41:08,360 --> 00:41:12,040
Speaker 1: every single founder in the in the US has been

658
00:41:12,080 --> 00:41:14,480
Speaker 1: told to give up on China, and was able to

659
00:41:14,520 --> 00:41:17,360
Speaker 1: parlay it into a you know, a pretty big ownership

660
00:41:17,400 --> 00:41:21,759
Speaker 1: position indeed, which Uber still has today. So a lot

661
00:41:21,800 --> 00:41:27,279
Speaker 1: of amazing quality. So the challenging question is where did

662
00:41:27,280 --> 00:41:31,640
Speaker 1: it go off the tracks? I mean, he clearly built

663
00:41:31,840 --> 00:41:36,640
Speaker 1: a behemoth um and arguably Uber is gonna be around

664
00:41:37,320 --> 00:41:41,799
Speaker 1: and going to influence transportation and food delivery and self

665
00:41:41,880 --> 00:41:44,760
Speaker 1: driving cars and go down the list for a long time.

666
00:41:45,440 --> 00:41:48,000
Speaker 1: Where did it go off the rails? You know? Like

667
00:41:48,080 --> 00:41:50,600
Speaker 1: I said earlier, I think I think if if a

668
00:41:50,680 --> 00:41:53,400
Speaker 1: venture capitalist is doing their best work, you know this

669
00:41:53,480 --> 00:41:57,719
Speaker 1: doesn't happen, right like you're able to help shepherd and

670
00:41:57,800 --> 00:42:01,880
Speaker 1: mentor um the founders that they can can rise to

671
00:42:01,920 --> 00:42:06,640
Speaker 1: the occasion and scale alongside the company. UM, A lot

672
00:42:06,719 --> 00:42:09,320
Speaker 1: of a lot of these has had a very special

673
00:42:09,760 --> 00:42:13,200
Speaker 1: weapon over the years. A gentleman named Bill Campbell that

674
00:42:13,360 --> 00:42:15,959
Speaker 1: you may have heard about from time to time, who

675
00:42:16,040 --> 00:42:19,440
Speaker 1: was known as the legendary coach of Silicon Valley. You

676
00:42:19,480 --> 00:42:23,120
Speaker 1: know when he you know, he spent time with Bezos

677
00:42:23,160 --> 00:42:26,720
Speaker 1: early on, not a lot, but a year or two. UM,

678
00:42:26,760 --> 00:42:30,560
Speaker 1: he spent an immense amount of time with with Steve Jobs,

679
00:42:30,600 --> 00:42:32,839
Speaker 1: and he spent an immense amount of time with Larry

680
00:42:32,880 --> 00:42:35,360
Speaker 1: and Sergey. Most people don't know this, but he was

681
00:42:35,400 --> 00:42:39,640
Speaker 1: still running the management team meeting at Google ten years

682
00:42:39,640 --> 00:42:45,319
Speaker 1: after they were public. UM. And so you know, he

683
00:42:45,440 --> 00:42:48,719
Speaker 1: unfortunately passed. I think I think if he also, you know,

684
00:42:48,760 --> 00:42:51,080
Speaker 1: I think if we had had the opportunity to introduce

685
00:42:51,200 --> 00:42:54,000
Speaker 1: him to Travis, he might have been super healthful in

686
00:42:54,040 --> 00:42:58,120
Speaker 1: that way. I don't know. UM, it's those kind of

687
00:42:58,120 --> 00:43:03,200
Speaker 1: things I think about in wreck respect. UM, everything got

688
00:43:03,280 --> 00:43:06,160
Speaker 1: so big so fast, and the money got so big

689
00:43:06,200 --> 00:43:12,759
Speaker 1: so fast that I think it can impact UM. I

690
00:43:12,760 --> 00:43:18,720
Speaker 1: think it came ipact perspective that people have UM and UH,

691
00:43:18,840 --> 00:43:20,879
Speaker 1: and I think it just got away from us. At

692
00:43:20,920 --> 00:43:24,640
Speaker 1: one point that year, you know, we were losing market

693
00:43:24,680 --> 00:43:29,480
Speaker 1: share pretty dramatically, and you know, the marketing department had

694
00:43:29,560 --> 00:43:32,319
Speaker 1: brand surveys, you know that we're in a free fall.

695
00:43:32,360 --> 00:43:38,080
Speaker 1: We had um five or six investigations that were underway.

696
00:43:38,520 --> 00:43:42,480
Speaker 1: I was on special committee conference calls you know, every week,

697
00:43:43,120 --> 00:43:47,520
Speaker 1: and it you know, it looked like the fate of

698
00:43:47,520 --> 00:43:50,160
Speaker 1: the company was at stake, and and and I you know,

699
00:43:50,880 --> 00:43:55,680
Speaker 1: and after that, some of some of the most touching moments.

700
00:43:55,760 --> 00:43:57,839
Speaker 1: You know, I've been out in a restaurant in San

701
00:43:57,840 --> 00:44:01,120
Speaker 1: Francisco where a random employee had come to me and say, hey,

702
00:44:01,280 --> 00:44:03,840
Speaker 1: thank you, thank you for what you guys did. You know,

703
00:44:03,920 --> 00:44:08,719
Speaker 1: you helped save the company. And so while I would

704
00:44:08,800 --> 00:44:12,279
Speaker 1: simultaneously say I wish I could have done a better

705
00:44:12,400 --> 00:44:16,920
Speaker 1: job so that we were never in that place, second

706
00:44:16,960 --> 00:44:19,839
Speaker 1: to that, I think the work that we did was

707
00:44:20,200 --> 00:44:24,200
Speaker 1: you know, well, in retrospect, I look back on as

708
00:44:24,239 --> 00:44:26,720
Speaker 1: one of the hardest things I've ever done, but something

709
00:44:26,800 --> 00:44:31,000
Speaker 1: I'm actually quite proud of because of the number of constituents,

710
00:44:31,239 --> 00:44:37,840
Speaker 1: you know, shareholders, employees, um drivers that were impacted by

711
00:44:37,960 --> 00:44:41,200
Speaker 1: by ensuring the company with successful that seems pretty reasonable.

712
00:44:41,719 --> 00:44:44,120
Speaker 1: Let's talk a little bit about the state of venture

713
00:44:44,200 --> 00:44:49,200
Speaker 1: investing today. The NASDAC was getting she lacked up until

714
00:44:49,880 --> 00:44:53,759
Speaker 1: Tuesday last week, where it popped five D points over four.

715
00:44:55,000 --> 00:44:58,919
Speaker 1: How do you look at the era we're in today

716
00:44:59,320 --> 00:45:02,200
Speaker 1: relative so when you were coming up in the nineties

717
00:45:02,320 --> 00:45:08,680
Speaker 1: and that entire dot com enclosion, Yeah, the dot com boom. Um.

718
00:45:08,719 --> 00:45:10,440
Speaker 1: You know, it's super interesting to have that at the

719
00:45:10,520 --> 00:45:15,200
Speaker 1: very beginning of my venture career because I saw both

720
00:45:15,360 --> 00:45:19,320
Speaker 1: the mania and then the correction. You know, really before

721
00:45:19,320 --> 00:45:21,279
Speaker 1: I ever got off the ground. You know, I was

722
00:45:21,360 --> 00:45:24,120
Speaker 1: doing early stays investing, so the companies I had backed

723
00:45:24,120 --> 00:45:27,400
Speaker 1: in that window were pretty small. Um. But I watched

724
00:45:27,440 --> 00:45:30,680
Speaker 1: it all and you know, as someone who's a student

725
00:45:30,719 --> 00:45:34,080
Speaker 1: of financial history, I've read all the Tulip books, you know,

726
00:45:34,239 --> 00:45:37,680
Speaker 1: I've read all about these types of things, and they're

727
00:45:37,719 --> 00:45:42,480
Speaker 1: known like their known when the world gets more speculative. Um,

728
00:45:42,680 --> 00:45:46,719
Speaker 1: that happens from time to time. In in the in

729
00:45:46,760 --> 00:45:48,680
Speaker 1: the dot com boom, we had a lot of companies

730
00:45:48,680 --> 00:45:52,800
Speaker 1: going public with like a million dollars of quarterly revenue. UM.

731
00:45:53,000 --> 00:45:56,879
Speaker 1: Show it was different. There was like any company could

732
00:45:56,880 --> 00:46:02,799
Speaker 1: go public, but they were all these little bitty tiny companies. Today, UM,

733
00:46:02,920 --> 00:46:07,160
Speaker 1: it's quite different in that Um, this this this market

734
00:46:07,239 --> 00:46:11,600
Speaker 1: has becomes speculative is has arisen on the back end

735
00:46:11,680 --> 00:46:14,080
Speaker 1: of what do you want you know, because this goes

736
00:46:14,120 --> 00:46:17,960
Speaker 1: back including like uber is like and the Vision Fund.

737
00:46:18,360 --> 00:46:21,200
Speaker 1: It's on the back of like six or years of

738
00:46:21,400 --> 00:46:27,120
Speaker 1: massive amounts of capital availability. And so it's not uncommon

739
00:46:27,680 --> 00:46:30,720
Speaker 1: for the amount of capital a company is raised before

740
00:46:30,760 --> 00:46:33,360
Speaker 1: they go public to be in the five million to

741
00:46:33,440 --> 00:46:37,200
Speaker 1: a billion range now across a wide variety of companies

742
00:46:37,719 --> 00:46:40,799
Speaker 1: and so, and the companies are coming public even though

743
00:46:40,840 --> 00:46:43,759
Speaker 1: they're not profitable, which we could talk about, most of

744
00:46:43,800 --> 00:46:47,680
Speaker 1: them have pretty significant revenue run rates pre spack Well, Bill,

745
00:46:47,760 --> 00:46:51,400
Speaker 1: what do you think is more important having a strong

746
00:46:51,520 --> 00:46:56,000
Speaker 1: revenue ramp for an early company or profitability? Yes, So

747
00:46:56,000 --> 00:46:58,440
Speaker 1: so look, I'm about to say something that I think

748
00:46:58,600 --> 00:47:02,040
Speaker 1: is pretty obvious to people that have been around investing

749
00:47:02,080 --> 00:47:05,040
Speaker 1: for a long time. So the most interesting thing about

750
00:47:05,160 --> 00:47:09,200
Speaker 1: this past five years has been the near zero interest

751
00:47:09,320 --> 00:47:13,319
Speaker 1: rate environment. That's probably a result of every country in

752
00:47:13,360 --> 00:47:18,839
Speaker 1: the around around the globe deciding it's okay to print money, uh,

753
00:47:19,040 --> 00:47:22,319
Speaker 1: you know, and so there's no where for money that

754
00:47:22,400 --> 00:47:25,520
Speaker 1: there's no interest rates anywhere. And you know, when you

755
00:47:25,600 --> 00:47:29,520
Speaker 1: talk to whether it's someone like Buffett or Druck and

756
00:47:29,600 --> 00:47:33,320
Speaker 1: Miller or or Howard Marks. These people that follow macro

757
00:47:33,480 --> 00:47:36,839
Speaker 1: way more than any venture capitalist would they know that

758
00:47:36,840 --> 00:47:41,520
Speaker 1: when interest rates go to near zero, speculation abounds. People

759
00:47:41,520 --> 00:47:43,520
Speaker 1: are looking for a home for their money. And I

760
00:47:43,600 --> 00:47:47,320
Speaker 1: even asked the question you just asked me of Mike Molbison.

761
00:47:47,400 --> 00:47:50,120
Speaker 1: I said, if interest rates go to zero, based on

762
00:47:50,160 --> 00:47:54,080
Speaker 1: all your valuation work, what matters more growth or profitability?

763
00:47:54,160 --> 00:47:59,160
Speaker 1: He said, growth unquestioned, just because you're not. Yeah, and

764
00:47:59,200 --> 00:48:03,680
Speaker 1: that's what we're you know. Um, the these companies that

765
00:48:03,719 --> 00:48:08,240
Speaker 1: are coming public, almost no one cares whether there their

766
00:48:08,280 --> 00:48:15,480
Speaker 1: core profitability exists. But if they're growing north they love that. Um.

767
00:48:15,520 --> 00:48:18,120
Speaker 1: When you combine that with the money that's out there,

768
00:48:18,560 --> 00:48:24,880
Speaker 1: it leads to pretty manic discussions, you know, within a boardroom,

769
00:48:24,880 --> 00:48:29,120
Speaker 1: because the right answer, at least in the short run,

770
00:48:29,560 --> 00:48:33,879
Speaker 1: might be to increase that burn rate to really high levels, UM,

771
00:48:33,960 --> 00:48:38,680
Speaker 1: certainly unprecedented in the history of American business, and push

772
00:48:38,760 --> 00:48:43,840
Speaker 1: the gas and uh. It makes for a wild ride,

773
00:48:44,600 --> 00:48:48,239
Speaker 1: to say the least. So the froth sounds like it's

774
00:48:48,239 --> 00:48:52,360
Speaker 1: similar to the nineties, but the underlying conditions of zero

775
00:48:52,480 --> 00:48:56,960
Speaker 1: percent interest rate, lots of capital, but also lots of

776
00:48:57,080 --> 00:49:01,560
Speaker 1: companies with big revenue and taking market share or just

777
00:49:01,640 --> 00:49:06,080
Speaker 1: developing brand new markets. That seems a little more advanced,

778
00:49:06,080 --> 00:49:08,880
Speaker 1: a little more sophisticated than what was being thrown up

779
00:49:08,920 --> 00:49:12,000
Speaker 1: against the wall in the nineties probably. And then there's

780
00:49:12,000 --> 00:49:15,319
Speaker 1: this there's this other esoter corner of the whole thing

781
00:49:15,400 --> 00:49:20,239
Speaker 1: that's super interesting, which is, um, once people get comfortable

782
00:49:20,360 --> 00:49:23,239
Speaker 1: using capital as a weapon, all of a sudden, the

783
00:49:23,440 --> 00:49:28,960
Speaker 1: private companies are the ones that are being provocateurs with

784
00:49:29,000 --> 00:49:33,239
Speaker 1: more money against the public incumbents. And no one would

785
00:49:33,239 --> 00:49:37,160
Speaker 1: have ever imagined that twenty years ago. But I think

786
00:49:37,320 --> 00:49:41,080
Speaker 1: Amazon did it to Walmart, and everyone made fun of

787
00:49:41,120 --> 00:49:43,640
Speaker 1: him the whole way up. You can just you can

788
00:49:43,719 --> 00:49:47,400
Speaker 1: just imagine yourself being in the Walmart board room for

789
00:49:47,440 --> 00:49:52,160
Speaker 1: those twenty years, and how much dismissiveness there was probably

790
00:49:52,200 --> 00:49:58,120
Speaker 1: about Amazon's lack of profitability. Right, And because Bezos won

791
00:49:58,239 --> 00:50:02,000
Speaker 1: over Wall Street, he is given a green light to

792
00:50:02,200 --> 00:50:06,800
Speaker 1: spend and lose money while Walmart's being held to ape

793
00:50:07,120 --> 00:50:12,600
Speaker 1: or Hebdomalt, and that's got to be super frustrated. Um.

794
00:50:12,719 --> 00:50:16,880
Speaker 1: I think I think, uh, you know, Netflix did it also,

795
00:50:17,400 --> 00:50:20,160
Speaker 1: right where everyone's sitting there going but you don't make money,

796
00:50:20,160 --> 00:50:23,840
Speaker 1: but you don't make money and read got Wall Street

797
00:50:23,880 --> 00:50:25,920
Speaker 1: to believe in what he was doing, which I think

798
00:50:26,000 --> 00:50:29,680
Speaker 1: is a critical part of this UM and then was

799
00:50:29,760 --> 00:50:34,200
Speaker 1: allowed to lose way more money than any of the

800
00:50:34,239 --> 00:50:38,279
Speaker 1: production companies that he was ultimately competing with. UM it

801
00:50:38,400 --> 00:50:43,160
Speaker 1: took massive share. And so now that that that's Silicon

802
00:50:43,200 --> 00:50:46,440
Speaker 1: Valley entrepreneurs have been trained that that's a game you

803
00:50:46,480 --> 00:50:50,400
Speaker 1: can go play. It's being done on a massive scale.

804
00:50:50,560 --> 00:50:52,800
Speaker 1: I mean you look at something like what's the uh,

805
00:50:52,920 --> 00:50:57,279
Speaker 1: what's the India based hotel company Ohio? Like they're just

806
00:50:57,600 --> 00:51:05,839
Speaker 1: building hotels, Like they're literally building hotels adventure dollars um.

807
00:51:05,880 --> 00:51:08,840
Speaker 1: It's got to be immensely frustrating to someone that's competing

808
00:51:08,880 --> 00:51:12,040
Speaker 1: with them. Well, you know, when when you look at Amazon,

809
00:51:12,440 --> 00:51:16,640
Speaker 1: I remember the very first investor letter that Jeff Bezos

810
00:51:16,719 --> 00:51:19,319
Speaker 1: pens and in it he said, Hey, we're not going

811
00:51:19,360 --> 00:51:21,719
Speaker 1: to be profitable for twenty years. We're gonna build this

812
00:51:21,840 --> 00:51:25,720
Speaker 1: out and take market share, and we hope our investors

813
00:51:25,760 --> 00:51:29,120
Speaker 1: are patient with us. And he was pretty true to

814
00:51:29,160 --> 00:51:32,759
Speaker 1: his word. I mean, but for Amazon Web Services, he

815
00:51:32,800 --> 00:51:36,160
Speaker 1: could have gone a full twenty years without showing any profit. Yeah,

816
00:51:36,200 --> 00:51:39,319
Speaker 1: there's one thing about that letter I'd love to make

817
00:51:39,360 --> 00:51:43,879
Speaker 1: a point about just in case there's any entrepreneurs that's

818
00:51:43,880 --> 00:51:47,160
Speaker 1: ever going to write another one of those letters. Bezos

819
00:51:47,480 --> 00:51:52,600
Speaker 1: came from Wall Street. You know, he understood shareholders, and

820
00:51:52,680 --> 00:51:55,520
Speaker 1: I think that letter, I don't have an in front

821
00:51:55,520 --> 00:51:57,960
Speaker 1: of you, but I think he says to our share

822
00:51:58,000 --> 00:52:02,280
Speaker 1: owners or like, that's the inter u and he talks

823
00:52:02,440 --> 00:52:07,719
Speaker 1: to the shareholder in an immensely respectful way in their language,

824
00:52:08,239 --> 00:52:12,480
Speaker 1: and is telling them how they will have a synergistic relationship.

825
00:52:13,400 --> 00:52:20,480
Speaker 1: Ever since that Bezos letter, a ton of of spirited founders.

826
00:52:20,880 --> 00:52:23,799
Speaker 1: I think that what he was saying was I'm going

827
00:52:23,840 --> 00:52:26,960
Speaker 1: to do it my way, and there was an element

828
00:52:26,960 --> 00:52:30,800
Speaker 1: of that, but it was in this respectful tone of

829
00:52:30,800 --> 00:52:33,960
Speaker 1: of empathy to the shareholder. Since then, a lot of

830
00:52:34,000 --> 00:52:36,480
Speaker 1: these guys just right, I'm gonna do whatever I want.

831
00:52:36,560 --> 00:52:39,360
Speaker 1: If you don't like it, you can shove it, basically,

832
00:52:39,400 --> 00:52:42,279
Speaker 1: And that's not what he did. So I think that's

833
00:52:42,320 --> 00:52:46,480
Speaker 1: interesting how that's evolved, to say the very least. So

834
00:52:46,640 --> 00:52:49,640
Speaker 1: we were talking about on this capital as a weapon thing.

835
00:52:49,960 --> 00:52:52,759
Speaker 1: I think one really interesting thing to watch in the

836
00:52:52,800 --> 00:52:55,440
Speaker 1: next five years is going to be the consumer banking

837
00:52:55,480 --> 00:53:00,680
Speaker 1: industry in the US. So there are probably sick different

838
00:53:01,400 --> 00:53:07,920
Speaker 1: intensely um capitalized companies that are new age banks, you know,

839
00:53:08,040 --> 00:53:11,440
Speaker 1: from so Far to Wealth Front to you know, a

840
00:53:11,600 --> 00:53:16,480
Speaker 1: firm which went out um and coin Base and robin

841
00:53:16,520 --> 00:53:21,279
Speaker 1: Hood and and they're doing things that I don't think

842
00:53:21,320 --> 00:53:24,920
Speaker 1: there's one of them called Chime. So chime if you

843
00:53:25,280 --> 00:53:29,799
Speaker 1: take them your direct deposit with your check, you know,

844
00:53:29,800 --> 00:53:32,600
Speaker 1: you're earnings check from your work, they give you the

845
00:53:32,640 --> 00:53:36,640
Speaker 1: cast two days early. Do you think Wells Fargo Board

846
00:53:36,640 --> 00:53:39,040
Speaker 1: would ever tell their management team, Yeah, we should do

847
00:53:39,080 --> 00:53:43,480
Speaker 1: that too. I don't think they'll ever consider this hyper

848
00:53:43,920 --> 00:53:46,640
Speaker 1: you know, using capital as a weapon in a hyper

849
00:53:46,680 --> 00:53:52,280
Speaker 1: aggressive way is coming to to consumer banking. It's already here,

850
00:53:52,520 --> 00:53:54,720
Speaker 1: and I think it's gonna be super interesting to watch

851
00:53:55,239 --> 00:53:57,959
Speaker 1: the incumbents over the next three or four years. So

852
00:53:58,400 --> 00:54:02,320
Speaker 1: no doubt that finance is going to go through some changes.

853
00:54:02,800 --> 00:54:09,560
Speaker 1: You've written about the role tech could play in transforming healthcare.

854
00:54:09,640 --> 00:54:12,319
Speaker 1: Tell us a little bit about that sector and what

855
00:54:12,360 --> 00:54:15,719
Speaker 1: can technology do you know. I've been thinking about this

856
00:54:15,800 --> 00:54:19,800
Speaker 1: a lot lately, just because I've been running some numbers

857
00:54:19,840 --> 00:54:23,399
Speaker 1: on the PCR testing in in the US, which are

858
00:54:23,440 --> 00:54:28,040
Speaker 1: mind them me. Um, we have a massive problem in

859
00:54:28,080 --> 00:54:33,000
Speaker 1: the US healthcare system just massive that is almost entirely

860
00:54:33,800 --> 00:54:41,239
Speaker 1: um tied to regulatory capture. And there's another big, big problem,

861
00:54:41,320 --> 00:54:44,880
Speaker 1: which is the employer just shouldn't be involved at all. Um.

862
00:54:44,920 --> 00:54:47,520
Speaker 1: In all of the D twenty were the only country

863
00:54:47,640 --> 00:54:51,759
Speaker 1: where the employer pays for healthcare, and it it just

864
00:54:52,040 --> 00:54:56,840
Speaker 1: obviously skates everything. Um. There's also like like the person

865
00:54:56,920 --> 00:55:00,400
Speaker 1: that's paying isn't present at the time of the trans action.

866
00:55:00,840 --> 00:55:06,160
Speaker 1: No one knows the price until after the transactions already done, um,

867
00:55:06,280 --> 00:55:08,080
Speaker 1: and then they fight about it. I mean, it's just

868
00:55:08,160 --> 00:55:11,879
Speaker 1: it couldn't be more perverse. And I think we're going

869
00:55:11,960 --> 00:55:15,600
Speaker 1: from like seventeen to eighteen too towards twenty percent of

870
00:55:15,719 --> 00:55:18,960
Speaker 1: GDP and other countries aren't like that. I mean, there

871
00:55:18,960 --> 00:55:22,239
Speaker 1: are plenty of countries with very similar health profiles that

872
00:55:22,280 --> 00:55:27,040
Speaker 1: are half that or even less, and so I'd like

873
00:55:27,160 --> 00:55:29,759
Speaker 1: to believe technology can be a part of that. I

874
00:55:30,560 --> 00:55:32,640
Speaker 1: think what doug hersh is done a good our acts

875
00:55:32,719 --> 00:55:38,440
Speaker 1: is interesting, um, but it may require legislation also. Um.

876
00:55:38,520 --> 00:55:41,400
Speaker 1: Trump was pushing on some price transparency stuff that I

877
00:55:41,400 --> 00:55:44,680
Speaker 1: thought was interesting. I think most favorite nations with drug

878
00:55:44,760 --> 00:55:49,319
Speaker 1: manufacturs like you shouldn't. I think that's an okay, rule right,

879
00:55:49,440 --> 00:55:52,520
Speaker 1: you can't sell drug for for ten cents in Canada

880
00:55:52,560 --> 00:55:56,120
Speaker 1: and to fifty year like why not? That sounds realistic

881
00:55:56,160 --> 00:56:02,120
Speaker 1: to me. But but it's hard because as of the capture,

882
00:56:02,239 --> 00:56:06,680
Speaker 1: you know, there's so much money involved, and there's all

883
00:56:06,760 --> 00:56:10,960
Speaker 1: these rules. The rules are written by the incumbent. HIPPA

884
00:56:11,040 --> 00:56:15,560
Speaker 1: protects the incumbents, not not the consumer. And there and

885
00:56:15,600 --> 00:56:19,040
Speaker 1: there's there's there's twenty versions of HIPPA. I mean, it's

886
00:56:19,160 --> 00:56:21,920
Speaker 1: it's very very messy. You know, it's funny you you

887
00:56:22,000 --> 00:56:27,279
Speaker 1: mentioned why are the employers involved? When you look at

888
00:56:27,320 --> 00:56:32,120
Speaker 1: the First Cares Act. Rather than sending money directly to

889
00:56:32,239 --> 00:56:37,960
Speaker 1: small businesses or directly to employees, they put the companies

890
00:56:38,000 --> 00:56:41,000
Speaker 1: in the middle. They gave companies loans and said, hey,

891
00:56:41,040 --> 00:56:44,800
Speaker 1: we'll lend you money to cover salaries if these people

892
00:56:45,080 --> 00:56:48,480
Speaker 1: continue to work. You know, there's a history, there's a

893
00:56:48,480 --> 00:56:52,600
Speaker 1: long history of not wanting to appear socialists and send

894
00:56:52,680 --> 00:56:56,319
Speaker 1: checks to people. So we pretend by putting businesses in

895
00:56:56,320 --> 00:56:59,760
Speaker 1: the middle. But you know, ultimately money from the government

896
00:56:59,800 --> 00:57:05,080
Speaker 1: and is up being directed to people. And whether you

897
00:57:05,120 --> 00:57:07,560
Speaker 1: want to put a business in between or not, I

898
00:57:07,680 --> 00:57:11,720
Speaker 1: totally agree with you. There's just no reason why anybody

899
00:57:11,719 --> 00:57:14,440
Speaker 1: who's running a business has to also be running a

900
00:57:14,520 --> 00:57:17,400
Speaker 1: small medical practice on the side. It doesn't make any sense.

901
00:57:18,080 --> 00:57:21,600
Speaker 1: They hate it. They hated They're no good at it,

902
00:57:21,760 --> 00:57:27,040
Speaker 1: and they hate it. It's it's ridiculous. But but all

903
00:57:27,120 --> 00:57:30,720
Speaker 1: the obfuscation makes it easier for the people that are

904
00:57:30,720 --> 00:57:33,840
Speaker 1: in the system, and so no one, you know, no

905
00:57:33,880 --> 00:57:36,120
Speaker 1: one's going to complain about it at a carrier or

906
00:57:36,120 --> 00:57:40,840
Speaker 1: a healthcare provider. Um. I assume this was the motivation

907
00:57:41,480 --> 00:57:45,200
Speaker 1: that got Amazon and JP Morgan and who I forget

908
00:57:45,240 --> 00:57:48,400
Speaker 1: who else was in that thing um off the ground.

909
00:57:48,440 --> 00:57:51,840
Speaker 1: But looks like that. It looks like that lost momentum too, right.

910
00:57:52,040 --> 00:57:56,880
Speaker 1: It is a you know, it would if you consider

911
00:57:56,960 --> 00:58:01,160
Speaker 1: me chilting against direct listings for a couple of years

912
00:58:01,200 --> 00:58:05,600
Speaker 1: had any impact whatsoever. I imagine it's a harder push

913
00:58:06,640 --> 00:58:09,640
Speaker 1: still tick against this system. And I'm sure plenty of

914
00:58:09,680 --> 00:58:13,720
Speaker 1: people have but but it's hard, yeah, for sure. So

915
00:58:13,800 --> 00:58:17,400
Speaker 1: we were talking about the dot com implosion early in

916
00:58:17,440 --> 00:58:20,440
Speaker 1: your career. Fast forward a few years to the Great

917
00:58:20,480 --> 00:58:24,720
Speaker 1: Financial Crisis. You end up writing a very prescient letter

918
00:58:25,360 --> 00:58:29,720
Speaker 1: to the company's benchmark had invested in, basically how to

919
00:58:29,760 --> 00:58:35,200
Speaker 1: survive the great depression too, without firing everybody, basically saying

920
00:58:35,640 --> 00:58:38,560
Speaker 1: there are risks, but there are also lots of opportunities.

921
00:58:39,040 --> 00:58:41,280
Speaker 1: Tell us a little bit about your thinking when you

922
00:58:41,320 --> 00:58:45,920
Speaker 1: sent that email out to all of your companies. Yeah,

923
00:58:45,920 --> 00:58:48,520
Speaker 1: and I think at the exact same time Secoya had

924
00:58:48,720 --> 00:58:55,160
Speaker 1: circulated that deck. What was it? Good times whatever? After

925
00:58:55,200 --> 00:59:02,440
Speaker 1: your boy? Um, Look, I think because I've studied the

926
00:59:02,560 --> 00:59:05,800
Speaker 1: history of finance so much, I have a great deal

927
00:59:05,840 --> 00:59:14,080
Speaker 1: of respect for making hard decisions, for focus on profitability, UM,

928
00:59:14,120 --> 00:59:20,840
Speaker 1: for being good stewards of capital and emotionally ideal. Way

929
00:59:20,920 --> 00:59:25,040
Speaker 1: better with scenarios like that than when things are a

930
00:59:25,320 --> 00:59:30,400
Speaker 1: mania and popping, Because the conversations that you're having with

931
00:59:30,480 --> 00:59:35,680
Speaker 1: someone are quick. People have to make very smart decisions

932
00:59:35,800 --> 00:59:41,680
Speaker 1: very fast. Um, really good entrepreneurs, really thriving moments like that,

933
00:59:41,800 --> 00:59:46,120
Speaker 1: and you see people really rise to the occasion in

934
00:59:46,120 --> 00:59:49,560
Speaker 1: in a world, in a world that's probably you know,

935
00:59:49,840 --> 00:59:53,920
Speaker 1: the things that are rewarded are being more speculative, and

936
00:59:54,480 --> 00:59:59,280
Speaker 1: the very right answer maybe to take on excessive amounts

937
00:59:59,280 --> 01:00:03,400
Speaker 1: of risk. That is just not as intuitive to me.

938
01:00:04,120 --> 01:00:08,640
Speaker 1: And so UM, I have no trouble kind of delivering

939
01:00:08,680 --> 01:00:11,720
Speaker 1: that message at that moment in time and and I

940
01:00:12,360 --> 01:00:16,040
Speaker 1: take immense pride in seeing uh founders. There are a

941
01:00:16,120 --> 01:00:19,480
Speaker 1: companies survived, you know, a time period like that. Some

942
01:00:20,200 --> 01:00:23,360
Speaker 1: handful of our companies this past March, you know, had

943
01:00:23,400 --> 01:00:26,280
Speaker 1: to live through something like that because you know, if

944
01:00:26,320 --> 01:00:30,160
Speaker 1: you were focused on certain industries, COVID just wiped them out,

945
01:00:30,400 --> 01:00:33,920
Speaker 1: like your revenue went away. And how you scramble, you know,

946
01:00:34,080 --> 01:00:38,280
Speaker 1: is hard, it's but but it's super rewarding. So I

947
01:00:38,360 --> 01:00:41,720
Speaker 1: know you are stepping back a bit from benchmark. You're

948
01:00:41,720 --> 01:00:46,000
Speaker 1: not participating in the next fund they're raising. What are

949
01:00:46,040 --> 01:00:48,760
Speaker 1: you gonna do now? What are the plans looking forward?

950
01:00:49,040 --> 01:00:51,720
Speaker 1: You know, I don't know yet. Very I've been spending

951
01:00:51,720 --> 01:00:53,600
Speaker 1: a lot of time talking to a lot of people

952
01:00:53,600 --> 01:00:57,240
Speaker 1: who have kind of done the two of themselves. Um,

953
01:00:57,280 --> 01:00:59,720
Speaker 1: I'm fifty three, so I feel like I've got a

954
01:00:59,800 --> 01:01:04,200
Speaker 1: lot of energy left. UM. I love entrepreneurism, I love investing.

955
01:01:04,760 --> 01:01:09,320
Speaker 1: I have a great deal of respect for for it. UM.

956
01:01:09,360 --> 01:01:14,360
Speaker 1: I love talking to founders, executives, investors, and I'm trying

957
01:01:14,360 --> 01:01:16,800
Speaker 1: to figure that out. I still sit on tin boards

958
01:01:16,840 --> 01:01:18,720
Speaker 1: and I have a lot of of work to do.

959
01:01:18,760 --> 01:01:20,959
Speaker 1: A benchmark. I'm going to see all of those through.

960
01:01:21,760 --> 01:01:23,840
Speaker 1: But I'm I'm still in the I'm still in the

961
01:01:23,960 --> 01:01:27,640
Speaker 1: learning phase of what's next quite fascinating. If you have

962
01:01:27,680 --> 01:01:30,280
Speaker 1: any good ideas, let me know. Well, if you're looking

963
01:01:30,280 --> 01:01:32,280
Speaker 1: for a side gig in New York, there's always a

964
01:01:32,320 --> 01:01:35,080
Speaker 1: desk in my office for you. I don't know if

965
01:01:35,120 --> 01:01:36,840
Speaker 1: you want to give up the nice weather out there.

966
01:01:37,320 --> 01:01:41,120
Speaker 1: So so let's jump to our our favorite questions that

967
01:01:41,160 --> 01:01:44,000
Speaker 1: we ask all of our guests. Tell us what you're

968
01:01:44,040 --> 01:01:46,960
Speaker 1: streaming these days. Give us your favorite Netflix or Amazon

969
01:01:47,080 --> 01:01:50,400
Speaker 1: Prime or or podcast you're listening to. What's keeping you

970
01:01:50,560 --> 01:01:54,280
Speaker 1: entertained during lockdown? Yeah, you know. I I don't know

971
01:01:54,320 --> 01:01:57,920
Speaker 1: if I have anything particularly unique. I went through Queen's

972
01:01:57,920 --> 01:01:59,840
Speaker 1: Gammut in a couple of days, and The Loop and

973
01:02:00,000 --> 01:02:04,400
Speaker 1: things pretty good. Um, I'd probably if people are thinking

974
01:02:04,440 --> 01:02:07,000
Speaker 1: about this, I'd go back to an old one that

975
01:02:07,080 --> 01:02:10,200
Speaker 1: I just adore in case, because I don't think everyone's

976
01:02:10,240 --> 01:02:13,880
Speaker 1: watched this, but Justified as some of the best writing

977
01:02:14,640 --> 01:02:18,840
Speaker 1: that I've ever seen is just fantastic. Quite interesting. You

978
01:02:18,880 --> 01:02:21,440
Speaker 1: had hinted at some of your mentors, but let's give

979
01:02:21,480 --> 01:02:25,880
Speaker 1: everybody their due who helped shape your career both on

980
01:02:25,920 --> 01:02:29,160
Speaker 1: Wall Street and in Silicon Valley. Yeah, so I think

981
01:02:29,160 --> 01:02:31,720
Speaker 1: I mentioned them all, but Charlie Wolf and Mike Mobson

982
01:02:32,120 --> 01:02:37,280
Speaker 1: at CSFB unquestionably had a huge, massive impact on my

983
01:02:37,480 --> 01:02:41,320
Speaker 1: career direction. And then obviously Frank Quatrone bringing me out

984
01:02:41,360 --> 01:02:45,040
Speaker 1: to the valley and and getting to know Bezos early on.

985
01:02:45,280 --> 01:02:48,240
Speaker 1: I'd say all those things, uh, And then the entire

986
01:02:48,680 --> 01:02:51,640
Speaker 1: the entire founding team at Benchmark that brought me on

987
01:02:51,960 --> 01:02:54,920
Speaker 1: because I wasn't a founder UM and taught me everything

988
01:02:54,960 --> 01:02:58,200
Speaker 1: they knew. So super fortunate to have lots of different mentors.

989
01:02:58,560 --> 01:03:01,240
Speaker 1: Let's talk about books. What are some of your favorites

990
01:03:01,440 --> 01:03:04,520
Speaker 1: and what are you reading right now? My favorite book

991
01:03:04,520 --> 01:03:06,800
Speaker 1: of all time is a book called Complexity, about the

992
01:03:06,920 --> 01:03:09,920
Speaker 1: rise of the Santa Fe Institute, where I'm now fortunate

993
01:03:09,960 --> 01:03:13,120
Speaker 1: enough to serve on the board. UM. It's really an

994
01:03:13,160 --> 01:03:17,840
Speaker 1: analysis of multi variable non linear systems and how they

995
01:03:17,920 --> 01:03:20,960
Speaker 1: behave and that includes things like stock market or weather,

996
01:03:21,120 --> 01:03:25,920
Speaker 1: the pandemics UM, and a lot of the tools that

997
01:03:26,000 --> 01:03:31,760
Speaker 1: we think are scientific, like linear interpolation, actually don't work

998
01:03:31,880 --> 01:03:37,280
Speaker 1: very well in these chaotic systems. And I just it's

999
01:03:38,000 --> 01:03:41,920
Speaker 1: one of the early people there. UM was the first

1000
01:03:42,000 --> 01:03:44,560
Speaker 1: Brian actor, was the first to write about network effects,

1001
01:03:44,920 --> 01:03:47,400
Speaker 1: you know, back in the early nineties, and that had

1002
01:03:47,440 --> 01:03:50,640
Speaker 1: a big impact. So anyway, that's my favorite book, Complexity

1003
01:03:50,760 --> 01:03:55,240
Speaker 1: mental waldrope um right now, believe it or not. I

1004
01:03:55,520 --> 01:04:00,480
Speaker 1: finally during COVID decided to attempt the Caro book on

1005
01:04:00,560 --> 01:04:05,560
Speaker 1: Lyndon Johnson. Um, I am, I am three quarters to

1006
01:04:05,720 --> 01:04:09,040
Speaker 1: the first one. But if you know, if you know

1007
01:04:09,120 --> 01:04:13,000
Speaker 1: the books, they're like four thousand page books, the giant

1008
01:04:13,560 --> 01:04:16,440
Speaker 1: and there's a dozen of them, it seems, and and

1009
01:04:16,440 --> 01:04:19,680
Speaker 1: and all the journalists I've met my entire life, you know,

1010
01:04:19,760 --> 01:04:22,280
Speaker 1: so you have to read this thread this three So

1011
01:04:22,400 --> 01:04:26,520
Speaker 1: I finally I finally took it on. So if I

1012
01:04:26,560 --> 01:04:31,280
Speaker 1: haven't read Complexity, but if that floats your boat, I

1013
01:04:31,320 --> 01:04:36,360
Speaker 1: thought James Glicks Chaos did a really nice job explaining

1014
01:04:36,600 --> 01:04:39,840
Speaker 1: what's behind chaos theory. They came out at about the

1015
01:04:39,920 --> 01:04:45,920
Speaker 1: same time they were. Yeah, those two books are almost

1016
01:04:46,040 --> 01:04:51,240
Speaker 1: like years of one another. Um. I found the applicability

1017
01:04:51,600 --> 01:04:56,040
Speaker 1: of what was disgusting Complexity, you know, more realistic for

1018
01:04:56,120 --> 01:05:00,440
Speaker 1: me as an investment. Anyway, that book, you know, something

1019
01:05:00,480 --> 01:05:03,280
Speaker 1: that Mobison read at the time, and and and Bill

1020
01:05:03,320 --> 01:05:06,080
Speaker 1: Miller read at the time. Bill Miller is like the

1021
01:05:06,160 --> 01:05:09,040
Speaker 1: chair emeritus. There now and Mike's the chair at Santa

1022
01:05:09,040 --> 01:05:12,640
Speaker 1: FE and so it's been fun to get to know

1023
01:05:12,880 --> 01:05:16,040
Speaker 1: them and discuss those topics and then getting deeper at

1024
01:05:16,040 --> 01:05:19,640
Speaker 1: the program. Quite interesting. What sort of advice would you

1025
01:05:19,720 --> 01:05:22,760
Speaker 1: give to a recent graduate who is interested in the

1026
01:05:22,840 --> 01:05:29,040
Speaker 1: career in either entrepreneurship or technology or venture capital investment? Yeah,

1027
01:05:29,120 --> 01:05:34,480
Speaker 1: so it turns out, from my point of view, the

1028
01:05:34,720 --> 01:05:41,240
Speaker 1: venture capital is achievable for young people, even though most

1029
01:05:41,240 --> 01:05:44,640
Speaker 1: people would tell you it's impossible. Um, I think the

1030
01:05:44,680 --> 01:05:48,320
Speaker 1: odds are low of breaking in because the supply and

1031
01:05:48,360 --> 01:05:52,800
Speaker 1: demand just doesn't doesn't really yield itself. There's not there's

1032
01:05:52,800 --> 01:05:57,000
Speaker 1: not spaces, relatives and on people want end um, but

1033
01:05:57,040 --> 01:05:59,640
Speaker 1: I think it's an area that binds to youth and

1034
01:05:59,720 --> 01:06:07,360
Speaker 1: been to hustle. And so my my advice would be, um,

1035
01:06:07,680 --> 01:06:11,640
Speaker 1: take a bet on a very narrow sector. M I

1036
01:06:11,680 --> 01:06:16,760
Speaker 1: hope you're right, but then immerse yourself in it. And

1037
01:06:17,320 --> 01:06:21,200
Speaker 1: there's no way the generalists, your capitalist is going to

1038
01:06:21,280 --> 01:06:23,880
Speaker 1: be able to compete with you because you're going to

1039
01:06:24,000 --> 01:06:27,880
Speaker 1: know more about said subject matter. You know something popular

1040
01:06:27,920 --> 01:06:30,680
Speaker 1: today like the n f T S or or you

1041
01:06:30,720 --> 01:06:34,880
Speaker 1: know VR like you can easily be the smartest you

1042
01:06:34,880 --> 01:06:38,440
Speaker 1: could be smarter than every venture capitalist on VR. If

1043
01:06:38,440 --> 01:06:41,840
Speaker 1: you immerse yourself for six months, you just would Now

1044
01:06:41,840 --> 01:06:44,120
Speaker 1: you gotta get it right. I mean, I guess that's

1045
01:06:44,160 --> 01:06:48,800
Speaker 1: the downside, but but it yields to hustle and it

1046
01:06:48,880 --> 01:06:51,640
Speaker 1: yields to focus. And so those would be the two

1047
01:06:51,680 --> 01:06:56,760
Speaker 1: things I would would recommend. Really interesting. And our final question,

1048
01:06:57,280 --> 01:06:59,880
Speaker 1: what do you know about the world of venture capital

1049
01:07:00,080 --> 01:07:03,520
Speaker 1: and technology today that you wish you knew when you

1050
01:07:03,560 --> 01:07:08,960
Speaker 1: were starting out at benchmark back. This is a really

1051
01:07:09,000 --> 01:07:12,680
Speaker 1: hard question, um, And the reason it's really hard is

1052
01:07:12,960 --> 01:07:17,080
Speaker 1: because that all of my initial answers that I want

1053
01:07:17,120 --> 01:07:21,120
Speaker 1: to give you very really violently the premise of the question,

1054
01:07:21,200 --> 01:07:23,840
Speaker 1: because I could say, well, I should have invested in

1055
01:07:23,880 --> 01:07:27,360
Speaker 1: Google right or you know, hold Amazon forever. But but

1056
01:07:27,480 --> 01:07:33,520
Speaker 1: those things are just hindsight, They're not they're I was

1057
01:07:33,520 --> 01:07:35,880
Speaker 1: gonna say, don't don't make me call Mike Mobison on

1058
01:07:35,960 --> 01:07:41,680
Speaker 1: you exactly right like that. So I my answer, which

1059
01:07:41,840 --> 01:07:45,240
Speaker 1: which I guess will be confirming of probably every guest

1060
01:07:45,280 --> 01:07:48,640
Speaker 1: you've had on and and makes it not that original

1061
01:07:48,840 --> 01:07:52,080
Speaker 1: because I've heard many other great investors say this. It's

1062
01:07:52,200 --> 01:07:56,840
Speaker 1: just the power of compounding. For some of these platforms.

1063
01:07:57,600 --> 01:08:03,680
Speaker 1: UM is so so huge that you know, if you

1064
01:08:03,880 --> 01:08:07,640
Speaker 1: invest in an Amazon or whatever, like, the hardest thing

1065
01:08:07,720 --> 01:08:11,120
Speaker 1: to possibly do is just close your eyes and forget it.

1066
01:08:12,240 --> 01:08:17,120
Speaker 1: And the only thing analysis is going to cause you

1067
01:08:17,160 --> 01:08:22,960
Speaker 1: to do is sell the stop. You know you're not

1068
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Speaker 1: going to run analysis, and you know, for the fourteenth

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01:08:25,880 --> 01:08:29,960
Speaker 1: time ago I should keep holding like and that is

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01:08:30,080 --> 01:08:36,320
Speaker 1: so hard, so so hard um to implement. Easy to say,

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Speaker 1: very hard to implement. Yeah, that's a fascinating answer. You know.

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Speaker 1: The the problem with conceptualizing compounds is that it takes

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01:08:45,240 --> 01:08:49,280
Speaker 1: place over years and decades, and humans live in seconds

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Speaker 1: and minutes, and it's so challenging to conceptualize that exponential leap.

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Speaker 1: There's no doubt. Bill. Thank you for being so generous

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Speaker 1: with your time. I really appreciate this. We have been

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01:09:01,680 --> 01:09:06,800
Speaker 1: speaking with Bill Gurley. He is a venture capitalist at Benchmark.

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Speaker 1: If you enjoy this conversation, we'll be sure and check

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01:09:09,960 --> 01:09:13,599
Speaker 1: out any of our previous four hundred or so we've

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Speaker 1: those at iTunes, Spotify, wherever you feed your podcast fix.

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Speaker 1: my daily reading list at rid Halts dot com. Check

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Speaker 1: out my weekly column at Bloomberg dot com slash Opinion.

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Speaker 1: remiss if I did not thank the crack staff that

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Speaker 1: helps me put these conversations together each week. Reggie Brazil

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Speaker 1: head of research. I'm dairy. Re Helts Upend Sanatan Master's

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Speaker 1: in Business on Bloomberg Radio