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Speaker 1: The future may not be clear, but our commitment is

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Speaker 1: so when you sit with an advisor at Merrill Lynch,

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Speaker 1: we'll put your interests first. Visit mL dot com and

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Speaker 1: learn more about Merrill Lynch an affiliated Bank of America.

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Speaker 1: Merrill Lynch makes available products and services offered by Merrill Lynch.

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Speaker 1: Pierce Veneran Smith Incorporated or Register Broker Dealer. Remember s

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Speaker 1: I p c V is Masters in Business with Barry

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Speaker 1: Ridholts on Bloomberg Radio this week on the podcast this

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Speaker 1: was so much fun. I cannot begin to tell you

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Speaker 1: what a delightful time I had. Mark Andresen of Andres

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Speaker 1: and Horowitz, an absolute tour to force conversation about everything

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Speaker 1: from valuation to psychological impact of market crashes too, are

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Speaker 1: we or are we not in a bubble? The significance

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Speaker 1: of of UH founders, and and how are you better

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Speaker 1: off with somebody who has people skills um but isn't

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Speaker 1: a technologist or a technologist who doesn't necessary fairly have

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Speaker 1: the social skills? On and on. We covered so much

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Speaker 1: ground so quickly, absolutely fascinating. It was an incredible, incredible conversation.

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Speaker 1: It's one of those things where you just say, how

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Speaker 1: privileged am I to sit in a conference room, indeed

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Speaker 1: the conference room where Andreason Horowitz gets pitched from various

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Speaker 1: startups and have a ninety minute chat with someone with

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Speaker 1: his just amazing understanding of where tech came from, where

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Speaker 1: it is today, and where it's going. I could babble

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Speaker 1: about this endlessly, but rather than gush, why don't we

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Speaker 1: just listen to the conversation with no further ado. My

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Speaker 1: podcast conversation with Mark Andreson of andres In Horowitz. This

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Speaker 1: is Masters in Business with Barry Ridholtz on Bloomberg Radio.

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Speaker 1: My special guest today is Mark Andreason. He is the

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Speaker 1: co founder and general partner of venture capital firm Andreson Harowitz.

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Speaker 1: He comes with a storied background. He co created the

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Speaker 1: Mosaic Internet browser, co founded Netscape, sold to a O

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Speaker 1: L for a few billion dollars. Co founded loud Cloud,

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Speaker 1: also sold to HP for a few billion dollars. Crunch

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Speaker 1: Base notes that Andreson Harowitz has over five hundred investments

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Speaker 1: in three D one companies, a number of exits via

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Speaker 1: I p O s and acquisitions. I could go on

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Speaker 1: and on about his curricular vita, but really he speaks

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Speaker 1: for himself, so let's jump right into this, Mark Andreason,

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Speaker 1: welcome to Bloomberg Radio, and thank you for hosting us here.

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Speaker 1: In Andrews and Harrowitz. I'm thrilled to be on him

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Speaker 1: and it's great to have you here. The I love

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Speaker 1: your facilities. We we love all the artwork and everything here.

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Speaker 1: It seems to be like a very civilized place to

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Speaker 1: to work. It's like, I don't know said, being a

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Speaker 1: venture capitalist is like being an airline pilot, long stretches

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Speaker 1: of boardom followed by moments of sheer terror. We try

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Speaker 1: to have a calm atmosphere that makes a lot of sense.

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Speaker 1: Let's let's go back to the beginning. Your Your big

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Speaker 1: idea in was that people would want internet access in

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Speaker 1: their homes. Now that's a given today we want internet

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Speaker 1: access wherever we are seven But twenty five plus years

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Speaker 1: ago that wasn't very obvious. What led you to say

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Speaker 1: in I have an idea an Internet browser. Yes, I

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Speaker 1: would say it was even beyond not obvious. It was

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Speaker 1: considered ridiculous. So the idea that basically it was well

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Speaker 1: known in that the Internet was for academics and nerds,

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Speaker 1: and that there was there was no use case for

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Speaker 1: Mary people like it was just incomprehensible, like why would anybody,

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Speaker 1: how would anybody even have the technical capability to even

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Speaker 1: consider getting online, much less of anything that they could

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Speaker 1: do productively was their online. Um, I've since actually in

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Speaker 1: your sense that actually learned a principle that I have

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Speaker 1: generalized out that it explains what happened, which is what

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Speaker 1: William Gibson, the science famous science fiction author wrote Neuromancer.

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Speaker 1: It was a very influential book in our industry. He

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Speaker 1: he says, his his line is the future is already here,

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Speaker 1: it's just not evenly distributed yet. And there are many

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Speaker 1: applications to that line. It's so it's such a fabulous insight.

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Speaker 1: And so the new ideas, this is something I've really

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Speaker 1: come to believe, is the new ideas already exist. Uh,

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Speaker 1: there are a few ideas that actually materialized out of

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Speaker 1: thing air. They generally already exist somewhere, and they exist

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Speaker 1: somewhere in a lab, right if they exist somewhere in

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Speaker 1: a fringe group or in an underground movement or something

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Speaker 1: like that. And so what happened here was and this

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Speaker 1: this is the part that's just I think probably pure luck.

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Speaker 1: Um is I went to college at University of Illinois,

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Speaker 1: which at that time was one of four universities in

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Speaker 1: the country that had been funded by the government to

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Speaker 1: be the hubs for what at the time was called

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Speaker 1: the NSF net, which was what became the Internet. And

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Speaker 1: so then there's there's a whole program developed in the

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Speaker 1: eighties around supercomputers. And this is what you know when Elgar,

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Speaker 1: when Elgar claimed credit for creating the Internet, this is

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Speaker 1: what he was talking about, was the funding, UH for

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Speaker 1: for this program to wire these universities. And so we

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Speaker 1: had on campus in the level of broadband that people

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Speaker 1: have in their homes today. Um, and we had the

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Speaker 1: use We saw the use cases. You saw everybody using

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Speaker 1: all this stuff. Now it wasn't at the level of

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Speaker 1: sophistication is today, but you could you could see that,

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Speaker 1: you could see all the use cases. You can see

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Speaker 1: many people liked it. But the expectation was you had

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Speaker 1: in college and then you would graduate and you would

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Speaker 1: go off into life and you would just give it up.

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Speaker 1: And that was the part where I was like, uh

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Speaker 1: and s f NET was after darker NEET, but before

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Speaker 1: full on internet for the public. Is that a fair explanation. Yeah,

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Speaker 1: that's right, So our DARPA. ARPA was known as ARPA,

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Speaker 1: now known as DART, but funded a military network which

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Speaker 1: was originally conceived as a way to do nuclear command

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Speaker 1: and control in the event of bad things happening in

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Speaker 1: the world. Therefore the importance of packet switching UM had

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Speaker 1: to be able to respond even after you've been attacked

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Speaker 1: by nuclear You've got a network with you know, it's

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Speaker 1: a hundred nodes and then twenty of them get wiped

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Speaker 1: out by attack, and you still have to have the

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Speaker 1: network has to still keep running. In traditional networks don't

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Speaker 1: keep running, and at least in theory packets which networks

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Speaker 1: can keep running. So so that was the that Then

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Speaker 1: this is like fifty years ago they all figured this

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Speaker 1: up out and then in the eighties that the idea

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Speaker 1: got generalized out to the idea that civilian researchers should

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Speaker 1: be able to use this technology. UM. In particular, actually

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Speaker 1: the government was funding these national supercomputer centers that were

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Speaker 1: buying a super expensive supercomputers to put on four or

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Speaker 1: five college campuses, and then they wanted to provide access

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Speaker 1: to scientists from all over the country to be able

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Speaker 1: to access those and so they trans basically took the

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Speaker 1: template of our pendet, turned it into nsf net and

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Speaker 1: allowed anybody to love with permission to log into those

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Speaker 1: computers have temporary time. You would have a few minutes

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Speaker 1: or hours whatever it was. And and that's eventually where

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Speaker 1: led to uh the full on internet. But let's let's

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Speaker 1: talk about the other name and Andres and Harowitz Ben Harrowitz.

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Speaker 1: You guys have been working together for a long time.

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Speaker 1: Have you made that partnership work? And how did it

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Speaker 1: really begin? Because if if what I've read is to

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Speaker 1: be believed, some of it started out a little little rocky.

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Speaker 1: Ben Ben took great delight in his book uh In

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Speaker 1: in describing the origins of the relationships. So um uh

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Speaker 1: so at this point where an old married couple. Um,

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Speaker 1: and so you know, I guess the good news is

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Speaker 1: we can finish each other sentences. Bad news is we

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Speaker 1: usually try to finish each each other's sences. So um, so,

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Speaker 1: you know, we we've had a long running partnership and

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Speaker 1: friendship for you know, for a very long time, now

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Speaker 1: twenty two years. I guess, um the origin of it actually,

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Speaker 1: so he was he wasn't early he was one of

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Speaker 1: the early guys actually to jump int an escape early

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Speaker 1: on when it was when Escape was telerotical idea actually

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Speaker 1: came over from Lotus, which at the time was this

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Speaker 1: big important, you know, dominant software company eventually booked by IBM.

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Speaker 1: Actually fun story by M. After he left, IBM bought

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Speaker 1: Lotus for I think three and a half billion dollars

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Speaker 1: at the time, which was a blockbuster deal in retrospect,

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Speaker 1: I'd spent three and a half billion dollars. They could

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Speaker 1: have bought the entire Internet instead of Lotus. They could

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Speaker 1: have bought every Internet company and every I s P

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Speaker 1: and they could have owned the whole thing. So anyway,

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Speaker 1: he saw the future. Not the first time ib AM

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Speaker 1: missed a boat what is it and probably won't be

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Speaker 1: the last big companies. Yeah that we all we all

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Speaker 1: at some point tend to tend to do that. But

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Speaker 1: um uh, he saw, you know, he I think, to

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Speaker 1: his great he saw the future. He saw he saw

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Speaker 1: what was going to happen, I mean, even more clearly

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Speaker 1: than a lot of other people who were involved back down,

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Speaker 1: and so he jumped over um and then you know,

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Speaker 1: an Escape was a was a very short right, but

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Speaker 1: it was very intense. It was a pressure cooker, and

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Speaker 1: so it was very easy to see internally who was

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Speaker 1: high potential who wasn't UM, And so we had him

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Speaker 1: on a very fast promotion path basically from the day

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Speaker 1: he joined. And I think had we potential? Is that

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Speaker 1: that's the phrasey Yeah, high potential like UM, you know young?

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Speaker 1: You know basically you're always trying these organizations to find

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Speaker 1: the young managers who you think are going to be

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Speaker 1: able to scale and grow and eventually become the leaders

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Speaker 1: of the company. Right, And so I think that I

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Speaker 1: think had had an escape stayed and we sold a

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Speaker 1: company in had had we stayed independent, I think he

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Speaker 1: would have ended up as the CEO at some point.

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Speaker 1: He was just still very young at that point. But

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Speaker 1: it's just very clear how much but how smart he was,

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Speaker 1: and how good of a leader he was, and how

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Speaker 1: good his judgment was. UM. And so when we then

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Speaker 1: after we saw the company, we then had the opportunity

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Speaker 1: to start a new company, and we just we became

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Speaker 1: clear that we wanted to work together. So let's talk

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Speaker 1: about UM, Jim Clark, UM I became I always kind

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Speaker 1: of knew who Jim Clark was. But then Michael Lewis

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Speaker 1: is the new new thing is what really ted Jim

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Speaker 1: Clark for me? Um, what was that like, meaning, Jim Clark,

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Speaker 1: how did you guys start to work together? Yeah, so Jim,

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Speaker 1: that was again probably struck a luck in my career.

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Speaker 1: Jim called me one day out of blue. Also a

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Speaker 1: friend of his, an associate of his who he had

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Speaker 1: worked with. So he had been the founder of this

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Speaker 1: company called Silicon Graphics, which at the time had a

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Speaker 1: reputation similar to Google's reputation today. It was considered the

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Speaker 1: sort of the gold standard Silicon Valley company that all

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Speaker 1: the green engineers wanted to work for. It was very

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Speaker 1: much this is when after the movie Terminator two came

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Speaker 1: out and they were redefining this whole idea of three

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Speaker 1: D special effects and they were reading it was just

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Speaker 1: this kind of amazing moment for this company. But he

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Speaker 1: he left for a variety of reasons, um, and decided

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Speaker 1: he wanted to start a second company. Um. And so

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Speaker 1: this is a little bit little bit like Larry Page

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Speaker 1: leaving Google or Mark Zuckerberg leaving Facebook and saying I

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Speaker 1: need to start a new company. Um and then um,

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Speaker 1: but he had hired everybody he knew who was smart,

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Speaker 1: who he had ever worked with into Silicon Graphics, and

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Speaker 1: so they weren't available to start a new company with him,

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Speaker 1: and so he literally needed fresh people. So he got

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Speaker 1: together with a group of friends and basically made a

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Speaker 1: list of p bowl who were in the industry who

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Speaker 1: he had not yet worked with UM and he actually

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Speaker 1: I was culminated at a list of about a dozen

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Speaker 1: people who he approached at various stages and said, you

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Speaker 1: know what, do you want to consider doing something together UM,

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Speaker 1: which culminated in a big dinner in Palo Alto with

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Speaker 1: like the full dozen of us. And then at the end,

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Speaker 1: at the end of the whole process, I was the

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Speaker 1: only one left standing. Really, that's fascinating, by the way,

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Speaker 1: it wasn't because necessarily I think he knocked some of

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Speaker 1: the others out of the process, but also a lot

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Speaker 1: of people they were just like they had jobs, they

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Speaker 1: were committed, they weren't willing to take yet another leap.

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Speaker 1: And this was a pretty also people forget like ninety

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Speaker 1: four we started escaping ninety four. It was very similar

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Speaker 1: mood in the valley then to what happened ten years

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Speaker 1: later after the dot com crash. It was a very

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Speaker 1: dark time in the valley um. That valley had had

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Speaker 1: a severe economic crash in one and was still kind

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Speaker 1: of in the Dould rooms at niny four. So the

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Speaker 1: idea of a startup was considered kind of a radical

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Speaker 1: thing and and potentially a dangerous thing from a career standpoint,

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Speaker 1: And so it wasn't the most obvious thing in the

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Speaker 1: world for people to leap into the into the new

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Speaker 1: thing and so and then in contrast, I had just

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Speaker 1: arrived out of college. I had nothing. I didn't think

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Speaker 1: the lewis, you have no baggage, you were ready to go.

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Speaker 1: So I always have this vision of the reason Netscape

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Speaker 1: led to this explosion of new technologies. At the time,

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Speaker 1: Microsoft pretty much was the only game in town, and

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Speaker 1: very often I just envisioned young guys with great ideas

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Speaker 1: going to venture capitalists and and every pitch meeting someone

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Speaker 1: ends up saying, what prevents Microsoft from building this into

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Speaker 1: into their operating system? And if you didn't have an

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Speaker 1: answer to that, thanks for coming by, see you later.

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Speaker 1: And Netscape allowed everybody to say, hey, we could bypass

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Speaker 1: the operating system and do this online. Am I remotely

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Speaker 1: close to the that early nineties understanding of of what

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Speaker 1: who was then the dominant software player. Yeah, so strategically

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Speaker 1: I think that's right and that that was a big effect.

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Speaker 1: But I would say there was an equal kind of

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Speaker 1: effect at the same time that was at least as important,

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Speaker 1: which was unlocking the potential of the people in the valley.

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Speaker 1: So the most astonishing thing to me, um was I

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Speaker 1: mean because because the Internet, like I said, the Internet

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Speaker 1: stuff was not we started to escape like this was

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Speaker 1: not considered like, oh yeah, this is a great idea.

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Speaker 1: Most people like, Okay, this is nutty, Like everybody knows.

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Speaker 1: It was common that everybody knows you can't make money

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Speaker 1: on the Internet. There's probably to do it. Commercial activity

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Speaker 1: on the Internet was actually illegal until en right, it

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Speaker 1: actually was not permitted, but because it was federally research

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Speaker 1: that this is before the days of dot com. It

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Speaker 1: was just dot edge you and yeah, but you could

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Speaker 1: have do comment, you couldn't do any commerce, you couldn't

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Speaker 1: be transactions. It was there was these called acceptable use

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Speaker 1: limits because it was federally funded and they didn't they

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Speaker 1: hadn't legalized it yet. So um, we started to asking

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Speaker 1: kind of right right as they switched it. So, but

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Speaker 1: it was widely views Number one it's illegal. Um. And

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Speaker 1: then number two, even if it were legal, people wouldn't

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Speaker 1: want to do it because you can't trust the Internet.

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Speaker 1: There's hackers and thieves and all this stuff and it's

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Speaker 1: just not something. And then ordinary people just won't do this,

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Speaker 1: and so so it was very contintuitive. Um. Within a year, though,

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Speaker 1: like with by when the sort of conventional wisdom, people

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Speaker 1: started to get exposed to it and they started to

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Speaker 1: have it in their lives and I started to say,

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Speaker 1: oh wow, I actually like this and this this starts

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Speaker 1: to making more sense. There was then this amazing flood

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Speaker 1: of talent. We're all these incredibly sharp people in Silicon

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Speaker 1: Valley who had worked on previous generation sub technology for

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Speaker 1: the previous twenty years. They turned out they were still here,

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Speaker 1: right and so, and they were either at the older

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Speaker 1: companies or they were just parked, you know, kind of

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Speaker 1: on the beach, and all of a sudden there was

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Speaker 1: a new thing and they just all showed up out

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Speaker 1: of nowhere um. And so it was like this materialization

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Speaker 1: of human capital and talent basically out of thin air.

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Speaker 1: And I was just astonished and say, these incredibly talented

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Speaker 1: people coming in, engineers, marketers, salespeople, and then all that

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Speaker 1: you know, all the other people you need to make

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Speaker 1: a company go hr people and lawyers and all these

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Speaker 1: people find its people. They just show up, and all

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Speaker 1: of a sudden, you've got this company that's like fully

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Speaker 1: up and running with all these people with all these

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Speaker 1: skills and experiences that you didn't even I didn't even

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Speaker 1: think was possible. Well did they. And this is the

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Speaker 1: magic of the valley. But I mean this is the

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Speaker 1: people's side of the magic of the valley. As the

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Speaker 1: human capital. Well that the people will flow into the

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Speaker 1: next opportunity much more aggressively than then you'll find, I think,

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Speaker 1: anywhere else in the world. So when I when you

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Speaker 1: say they just show up, in my mind's eye, you

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Speaker 1: have Stanford, you have Berkeley, you have all these great

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Speaker 1: universities nearby, cal Tech Um, you have a history of

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Speaker 1: of companies going back to Fairchild, Semi can Doctor and

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Speaker 1: what have you, plus a lot of defense and NASA

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Speaker 1: related stuff, So you would you would envision it's almost

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Speaker 1: like all the parts are are they're just waiting for

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Speaker 1: something to crystallize it and send everybody off. So I've

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Speaker 1: always looked at this as this is kind of a

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Speaker 1: magic one of the magic parts of the country, and

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Speaker 1: Boston is similar, and d C is similar, and and

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Speaker 1: now Seattle has it, and to a lesser degree, Portland's

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Speaker 1: has that same sort of Once you get a critical

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Speaker 1: mass of human capital, well all bets are off. Who

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Speaker 1: knows where this goes? Although you seemingly have known where

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Speaker 1: this was going to go before many other people in

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Speaker 1: some cases but not others. What was your So to

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Speaker 1: get back to the original question about Michael Lewis's New

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Speaker 1: New Thing, I assume you read the book. Did you

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Speaker 1: enjoy it? Did it ring true? Uh? Do you recognize

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Speaker 1: a lot of the characters and it even people you

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Speaker 1: may may not necessarily know. I would say, you're captured

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Speaker 1: a moment in time which is sort of the top

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Speaker 1: of the dot com bubble um. And then it captured

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Speaker 1: a part of Jim's personality, which is the ability to

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Speaker 1: kind of materialize new ideas out of thin air into existence.

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Speaker 1: I don't think it captured, in fairness to Jim, I

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Speaker 1: don't think it captured his depth as much as so

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Speaker 1: that's hard to do if you're hard to do so

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Speaker 1: just specifically Jim Jim, and the book tells a lot

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Speaker 1: of his life stories. He had very very interesting background

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Speaker 1: but basically culminated ultimately in the Navy and so forth,

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Speaker 1: but ultimately culminated in THEO had a PhD in computer

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Speaker 1: science from the University of Utah, which was actually the

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Speaker 1: creation origin point for a lot of than things in

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Speaker 1: our industry today. Actually, there was something special that happened

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Speaker 1: University of Utah in the sixties and seventies, um and uh,

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Speaker 1: basically all of computer graphics and video games and all

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Speaker 1: this stuff and all the stuff that led to s

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Speaker 1: g I. Ultimately it was all kind of conceived. The

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Speaker 1: ideas were conceived actually Utah at that time. Pixar it

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Speaker 1: all if you'd like trace Pixar back. It all flows

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Speaker 1: back to this moment in time at the University of Utah.

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Speaker 1: And so so anyways, say to PhD and CS and

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Speaker 1: then he went to Stanford. He's a professor at Stanford, right,

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Speaker 1: and then he just decided he was the life of

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Speaker 1: an academic was not. Academics don't have the largest yacht

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Speaker 1: in the world, and he was building one of the

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Speaker 1: largest sailing vessels. I think he wanted to play a

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Speaker 1: little more than he wanted to teach or at least

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Speaker 1: that's my take. Oh so this is part of the

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Speaker 1: thing also that yeah he's so that that that and

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Speaker 1: becoming a big part of the book. Um, But I

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Speaker 1: would then again, I would just say what was missed

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Speaker 1: was when I worked with Jim and other people over

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Speaker 1: to Jim will tell you like he goes just as

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Speaker 1: deep as he goes broad like he is he is

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Speaker 1: very deeply in the details of technology. He's not. He's not.

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Speaker 1: The book makes it, I think, come across like he

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Speaker 1: kind of glosses over the details and leads those to

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Speaker 1: the other to other people. Uh we I did not

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Speaker 1: find a big case like he's incredibly deep in technology.

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Speaker 1: He's an incredibly comprehensive thinker on all aspects of business.

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Speaker 1: Like he he goes really really deep. It's just that's

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Speaker 1: not quite as I don't know, not quite as sexy

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Speaker 1: for the book, I guess. Um. And so yeah, it

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Speaker 1: captured part of his personality, but it didn't capture the

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Speaker 1: depth and the seriousness and the application and his role

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Speaker 1: really as a primary inventor and inventor not just if ideas,

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Speaker 1: but inventor of actual technologies and then also by the way,

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Speaker 1: as a recruiter. Um. He's one of the great all

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Speaker 1: time people who can gather other people around them, and

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Speaker 1: he's he's really good at painting a picture. Um, and

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Speaker 1: he's because he's so deep the technology he carries tremendous.

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Speaker 1: There's a lot of people who are promotional, right, a

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Speaker 1: lot of people will show up and they'll say we're

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Speaker 1: gonna do X, y Z. And the engineers will be like, well,

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Speaker 1: you don't like what you know? Where did you go

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Speaker 1: to school? Like what's your professional credential to be able

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Speaker 1: to have this concept? And why do you think we're

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Speaker 1: just gonna do all this for you? Whereas a gym

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Speaker 1: he's like, he's like an actual technology legend, right, who

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Speaker 1: probably knows more about the topic than you do. Um.

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Speaker 1: And so he paints the picture and then he gets

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Speaker 1: buy in from people, you know, incredible amazing people. So

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Speaker 1: and so there's just he he has a level of

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Speaker 1: seriousness and capability that I think is just way beyond

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Speaker 1: what what what what? Michael is able to paint that book.

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Speaker 1: So the Netscape I p O is something. So we

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Speaker 1: went public. So we're in public eighteen months after founding.

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Speaker 1: I remember that because I was a junior trader. I

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Speaker 1: was the logo on the desk, and it was just

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Speaker 1: a day of mayhem, and that really be started a

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Speaker 1: run of technology and internet companies for the next few years.

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Speaker 1: What was that ride like? That had to be insane? Yeah,

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Speaker 1: I know, it was crazy, like being shot out of

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Speaker 1: a rocket, like and not just me, like the whole

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Speaker 1: the whole value had that had that had that experience,

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Speaker 1: and so it really was it was what you meant.

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Speaker 1: It was the establishment of a new platform. It was

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Speaker 1: the establishment of this kind of new front here. Um.

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Speaker 1: It was the you know, the idea that all of

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Speaker 1: a sudden, you there were these thousands of new businesses

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Speaker 1: could get created on top. It was this idea, all

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Speaker 1: of a sudden, that people are connected. It's actually really striking.

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Speaker 1: Um if you look. So the dot com crash right

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Speaker 1: hit in two thousand, um, and then all these ideas

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Speaker 1: that were viewed then as all the dot com ideas

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Speaker 1: that were viewed as genius and NTE nedty eight were

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Speaker 1: viewed as just complete lunacy and idiocy. And in two

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Speaker 1: thousand com doesn't make any sense being the classic example, right. Um,

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Speaker 1: So it's actually really striking all those ideas are working today. UM,

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Speaker 1: I can't think of a single idea from that era

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Speaker 1: that's not working today. And the kicker to the pest

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Speaker 1: dot com story is there's a company, Chewy that just

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Speaker 1: comes out for three billion dollars right out of Florida,

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Speaker 1: which is online pet food sales, like two weeks ago, right,

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Speaker 1: and so even online pet food sales turns out to

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Speaker 1: be a gigantic and very successful business. And so basically

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Speaker 1: the community, the community, the tech community, the Silicon Valley community,

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Speaker 1: the global community that got connected to the internet very

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Speaker 1: quickly between and call it actually came up with all

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Speaker 1: the ideas, like they all people are very creative, and

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Speaker 1: all of a sudden there was just like if you

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Speaker 1: read you remember you remember there was a magazine back

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Speaker 1: then called The Industry Standard, which became kind of the

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Speaker 1: defining kind of bible of the time. And if there

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Speaker 1: was a period where if you read the Industry Standard

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Speaker 1: after the crash, you're just like all these people were

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Speaker 1: just all high, like this is just this is just nuts.

432
00:19:17,320 --> 00:19:19,199
Speaker 1: If you read it today, it's like a direct forecast

433
00:19:19,280 --> 00:19:21,639
Speaker 1: of what's actually happening, right, Like, all this stuff actually

434
00:19:21,800 --> 00:19:24,080
Speaker 1: is happening, it's all working. So and so it was

435
00:19:24,119 --> 00:19:27,800
Speaker 1: an amazing unleashing of talent and creativity and ideas interrupted

436
00:19:28,080 --> 00:19:30,760
Speaker 1: by a very bad crash. Um, but it has sense.

437
00:19:31,000 --> 00:19:33,000
Speaker 1: All those ideas have sense, really come true. One of

438
00:19:33,040 --> 00:19:36,040
Speaker 1: the things I'm always hearing about is how well this is.

439
00:19:36,200 --> 00:19:38,399
Speaker 1: This is just like the dot com era. The valley

440
00:19:38,480 --> 00:19:43,120
Speaker 1: is booming. This all ends badly. But when we this week,

441
00:19:43,160 --> 00:19:47,280
Speaker 1: we've been in everywhere from Berkeley to Palo Alto to

442
00:19:47,359 --> 00:19:51,520
Speaker 1: pleasant In to Mountain View, this looks to me much

443
00:19:51,600 --> 00:19:55,360
Speaker 1: more like a robust economy. Then hey, this is completely

444
00:19:55,480 --> 00:19:58,639
Speaker 1: unhinged and it's a train wreck waiting to happen. You

445
00:19:58,800 --> 00:20:02,800
Speaker 1: live through the last version of the dot com bubble.

446
00:20:03,560 --> 00:20:08,679
Speaker 1: How is this time so different in nature? The obvious

447
00:20:08,760 --> 00:20:11,000
Speaker 1: thing is a lot of these companies are actually making money.

448
00:20:11,720 --> 00:20:13,639
Speaker 1: Some of them are making a lot of money. So um.

449
00:20:13,920 --> 00:20:15,920
Speaker 1: In fact, now the accusation is they're making too much money,

450
00:20:16,000 --> 00:20:18,840
Speaker 1: which is the criticism on the other side. So that

451
00:20:19,000 --> 00:20:21,679
Speaker 1: just means they're efficient and productive. It doesn't mean anything

452
00:20:21,840 --> 00:20:24,320
Speaker 1: is wrong exactly. So yes, from your from your lips

453
00:20:25,119 --> 00:20:28,280
Speaker 1: so um. So I guess I'd say, I guess I'd

454
00:20:28,320 --> 00:20:30,880
Speaker 1: say the economist, actually, there's a theory actually in economics

455
00:20:30,960 --> 00:20:33,440
Speaker 1: on this which they called the economists called the depression

456
00:20:33,560 --> 00:20:36,359
Speaker 1: baby effect, which is basically it's based on it's been

457
00:20:36,359 --> 00:20:38,680
Speaker 1: studies papers written about this. If your google, depression babies

458
00:20:38,720 --> 00:20:41,600
Speaker 1: will pop up. UM investors who were in the stock

459
00:20:41,640 --> 00:20:44,199
Speaker 1: market nine ever went back into the stock market. Um.

460
00:20:44,240 --> 00:20:45,600
Speaker 1: They stayed out of the stock market for the rest

461
00:20:45,640 --> 00:20:48,800
Speaker 1: of their lives because proved definitively right that stocks are

462
00:20:48,800 --> 00:20:51,359
Speaker 1: fundamentally unsafe. And if you look at the chart of it,

463
00:20:51,680 --> 00:20:54,040
Speaker 1: literally from ninety nine, you don't get back to break

464
00:20:54,080 --> 00:20:57,440
Speaker 1: even untill nineteen fifty four. It's twenty five years later.

465
00:20:57,920 --> 00:21:00,719
Speaker 1: It's a full generation has to be one, grow up,

466
00:21:01,080 --> 00:21:03,480
Speaker 1: get jobs and start investing in order for the market

467
00:21:03,560 --> 00:21:07,040
Speaker 1: to get above exactly with no memory and the investors

468
00:21:07,560 --> 00:21:09,600
Speaker 1: that's exactly that. And then by the twenty five years

469
00:21:09,600 --> 00:21:11,120
Speaker 1: exactly right. And then the other kicker is it takes

470
00:21:11,119 --> 00:21:13,359
Speaker 1: another fifteen years after that to get another actual bubble.

471
00:21:13,840 --> 00:21:16,280
Speaker 1: The next stock market bubble did not materialized until the

472
00:21:16,320 --> 00:21:20,840
Speaker 1: late sixties. The full forty years, the hits a thousand,

473
00:21:20,880 --> 00:21:24,080
Speaker 1: it's not over it for another sixteen years, quite quite amazing,

474
00:21:24,359 --> 00:21:26,040
Speaker 1: exactly so. So, anyway, the point is, if you if

475
00:21:26,080 --> 00:21:28,919
Speaker 1: you live through one of these scarring crashes, you get

476
00:21:29,000 --> 00:21:32,320
Speaker 1: psychologically marked, you get you get traumatized. And by the

477
00:21:32,359 --> 00:21:34,000
Speaker 1: way that you there is everybody, right, It's not just

478
00:21:34,080 --> 00:21:36,000
Speaker 1: the investors, it's also the people who follow the market.

479
00:21:36,040 --> 00:21:37,680
Speaker 1: It's also people who write about the market. It's also

480
00:21:37,680 --> 00:21:40,159
Speaker 1: people who work at the company's Everybody gets traumatized. And

481
00:21:40,200 --> 00:21:42,160
Speaker 1: so we had it. We we haven't entire We had

482
00:21:42,240 --> 00:21:44,800
Speaker 1: and have an entire generation of depression babies, including me

483
00:21:45,359 --> 00:21:48,240
Speaker 1: right um in Silicon Valley who went through two thousand UM.

484
00:21:48,440 --> 00:21:50,560
Speaker 1: That's significant for a couple of reasons. One is just

485
00:21:50,840 --> 00:21:53,160
Speaker 1: I would argue that right the narrative basically since two

486
00:21:53,160 --> 00:21:54,879
Speaker 1: thousand four has been its bubble two point oh. I

487
00:21:54,920 --> 00:21:56,680
Speaker 1: think exactly the opposite has been a it's been a

488
00:21:56,720 --> 00:21:59,680
Speaker 1: long term bust. I think text talks have been undervalued

489
00:21:59,720 --> 00:22:03,480
Speaker 1: sort consistently basically since two thousand two three, and even

490
00:22:03,520 --> 00:22:05,560
Speaker 1: to this day are probably undervalued relative to where they

491
00:22:05,560 --> 00:22:07,719
Speaker 1: should be. Void. We come back to that. But then

492
00:22:07,760 --> 00:22:09,680
Speaker 1: the other thing is happening is now finally enough time

493
00:22:09,760 --> 00:22:11,760
Speaker 1: is passing. It's now we're coming you know, seventeen years

494
00:22:11,800 --> 00:22:14,040
Speaker 1: since the crash, so now finally we're getting enough kids

495
00:22:14,119 --> 00:22:16,240
Speaker 1: coming to the valley who don't have a memory of

496
00:22:16,359 --> 00:22:20,159
Speaker 1: the crash. They don't have post crash traumatic They were

497
00:22:20,200 --> 00:22:22,320
Speaker 1: like in fourth grade, right, and so they just they

498
00:22:22,359 --> 00:22:24,119
Speaker 1: were like busy with legos, like they just it's not

499
00:22:24,280 --> 00:22:26,920
Speaker 1: a relevant concept. And so when and you get these

500
00:22:26,960 --> 00:22:29,360
Speaker 1: weird conversations we find are selling these conversations where you're

501
00:22:29,359 --> 00:22:33,439
Speaker 1: telling these cautionary tales right of what happened in nine

502
00:22:33,440 --> 00:22:36,200
Speaker 1: and they just look at you like Grandpa, like you know,

503
00:22:36,240 --> 00:22:38,439
Speaker 1: tell us about the model t like it's just an

504
00:22:38,480 --> 00:22:40,480
Speaker 1: absurd kind of thing. And by the way, it's not

505
00:22:40,520 --> 00:22:41,920
Speaker 1: that there's a good or a bad. It's just people

506
00:22:41,960 --> 00:22:43,800
Speaker 1: have different life experiences. But my point is like the

507
00:22:43,840 --> 00:22:46,440
Speaker 1: balance is shifting well, and so and you just have

508
00:22:46,560 --> 00:22:48,000
Speaker 1: you have this new generation of people in the valley

509
00:22:48,080 --> 00:22:50,440
Speaker 1: are just like let's let's just go, let's go build things,

510
00:22:50,480 --> 00:22:52,040
Speaker 1: like let's not be held back by the superstition of

511
00:22:52,080 --> 00:22:54,000
Speaker 1: what happened. Let's go build new things. And then to

512
00:22:54,080 --> 00:22:57,040
Speaker 1: your point, like these things have worked. When we were

513
00:22:57,080 --> 00:23:01,639
Speaker 1: building Netscape, the man maximum addressable market size for our

514
00:23:01,640 --> 00:23:04,800
Speaker 1: company was Internet users that were most run dial up

515
00:23:05,200 --> 00:23:08,280
Speaker 1: and worked to their desktop PCs, which were super slow

516
00:23:08,359 --> 00:23:10,000
Speaker 1: and crewed compared to today and now was you know,

517
00:23:10,080 --> 00:23:12,120
Speaker 1: you have three billion people with smartphones and mobile broad

518
00:23:12,119 --> 00:23:13,480
Speaker 1: down in the world on its way to six billion,

519
00:23:13,520 --> 00:23:15,560
Speaker 1: and so like this stuff is working like that, it

520
00:23:15,640 --> 00:23:19,000
Speaker 1: actually came true. The fascinating thing every time when the

521
00:23:19,119 --> 00:23:23,199
Speaker 1: NASDAC regains its regained its two thousand peak not too

522
00:23:23,280 --> 00:23:25,399
Speaker 1: long ago, people said, here we are, it's going to

523
00:23:25,480 --> 00:23:28,000
Speaker 1: crash again, and and you look. I always look at

524
00:23:28,040 --> 00:23:30,800
Speaker 1: those folks and say, well, wait a second, that was

525
00:23:31,000 --> 00:23:34,159
Speaker 1: an infinite pe. You now have companies actually not just

526
00:23:34,359 --> 00:23:38,120
Speaker 1: making money, but making boatloads of money. And it's fifteen

527
00:23:38,240 --> 00:23:41,520
Speaker 1: years later. So whatever the problems were, then, how do

528
00:23:41,600 --> 00:23:44,520
Speaker 1: you just assume at this price level it's the same thing.

529
00:23:44,960 --> 00:23:48,560
Speaker 1: Clearly something very different is going on today. So so

530
00:23:49,080 --> 00:23:52,400
Speaker 1: sticking with that that point, when when you hear people

531
00:23:52,480 --> 00:23:54,639
Speaker 1: saying Silicon Valley is in a bubble again, look at

532
00:23:54,640 --> 00:23:57,520
Speaker 1: all these unicorns. Here comes the train wreck. Do you

533
00:23:57,640 --> 00:24:00,040
Speaker 1: just kind of shrug to yourself? What's your reaction to that?

534
00:24:00,240 --> 00:24:02,640
Speaker 1: So those have been the headlines for excuse me every

535
00:24:02,720 --> 00:24:05,399
Speaker 1: year since two thousand four. So two thousand four, so two.

536
00:24:05,600 --> 00:24:07,399
Speaker 1: They'll get it right eventually. You just keep saying the

537
00:24:07,440 --> 00:24:09,119
Speaker 1: same thing over and over in the two thousand four,

538
00:24:10,040 --> 00:24:12,800
Speaker 1: two thousand four, granted two thousand one to two thousand

539
00:24:12,840 --> 00:24:15,920
Speaker 1: three were very bad, so fully granted two thousand four,

540
00:24:15,920 --> 00:24:18,320
Speaker 1: there were two M and A events, Uh, Yahoo bought

541
00:24:18,400 --> 00:24:22,320
Speaker 1: two companies million dollars each, Flicker and Delicious and those

542
00:24:22,480 --> 00:24:24,760
Speaker 1: that launched that created the bubble two point on narrative,

543
00:24:24,880 --> 00:24:26,960
Speaker 1: but in other words, like if anything at tech works

544
00:24:27,760 --> 00:24:32,280
Speaker 1: and if any company attack ever succeeds, it's therefore obviously evidence, right,

545
00:24:32,280 --> 00:24:34,199
Speaker 1: and so that that's been the narrative the entire time.

546
00:24:34,240 --> 00:24:35,680
Speaker 1: We and we've done we've we've done dex on this.

547
00:24:35,800 --> 00:24:37,200
Speaker 1: We have dex on our website, like we've done detailed

548
00:24:37,200 --> 00:24:39,359
Speaker 1: analytics on every aspect of the financial numbers and the

549
00:24:39,720 --> 00:24:41,040
Speaker 1: flows of money and the whole thing. And you go

550
00:24:41,119 --> 00:24:42,440
Speaker 1: through the whole thing, and when you when you do

551
00:24:42,520 --> 00:24:46,040
Speaker 1: the analysis, I think correctly, like just objectively correctly, it's

552
00:24:46,040 --> 00:24:48,200
Speaker 1: just crystal clear how out of control everything got really

553
00:24:48,200 --> 00:24:51,680
Speaker 1: between two thousand and how relatively plasmdate it's been. It's

554
00:24:51,680 --> 00:24:55,200
Speaker 1: been ever since. Um. But it just just it's people,

555
00:24:55,240 --> 00:24:56,399
Speaker 1: don't you know. It's the kind of thing people just

556
00:24:56,480 --> 00:24:58,560
Speaker 1: have a very strong emotional resp It's a depression maybe effect.

557
00:24:58,560 --> 00:25:00,879
Speaker 1: People have an emotional response right starting sponses I got

558
00:25:00,920 --> 00:25:03,040
Speaker 1: tricked once, I'm not gonna get tricked right, and so

559
00:25:03,200 --> 00:25:05,439
Speaker 1: it's just it's led to that's continuous, in my view,

560
00:25:05,520 --> 00:25:07,840
Speaker 1: underestimation of what's happening. Right, It's it's led to the

561
00:25:07,840 --> 00:25:09,919
Speaker 1: opposite problem, which is people are really deeply if they

562
00:25:09,960 --> 00:25:13,359
Speaker 1: were overestimating, overestimating what was happening in idea, they're underestimating

563
00:25:13,359 --> 00:25:15,480
Speaker 1: it and they're still underestimating it today, which makes sense.

564
00:25:16,400 --> 00:25:18,680
Speaker 1: Everything is probably still undervalua, which makes sense if you

565
00:25:18,840 --> 00:25:22,359
Speaker 1: come out after that experience and you are negatively tinged

566
00:25:22,480 --> 00:25:25,680
Speaker 1: with that. G I got burned less time because valuations

567
00:25:25,720 --> 00:25:29,240
Speaker 1: didn't make sense. Fast forward fifteen years. That doesn't leave

568
00:25:29,320 --> 00:25:32,639
Speaker 1: these folks, So that permanently colors and they're you know,

569
00:25:32,760 --> 00:25:37,000
Speaker 1: other related studies. People graduate college into a recession. It

570
00:25:37,119 --> 00:25:39,639
Speaker 1: not only affects their earnings power over the course of

571
00:25:39,720 --> 00:25:43,399
Speaker 1: their lifetime, it affects their philosophical outlook. They are actually

572
00:25:43,520 --> 00:25:46,600
Speaker 1: biased and expect recessions to occur more often than they

573
00:25:46,600 --> 00:25:49,320
Speaker 1: actually do. It's um our wet wear is kind of

574
00:25:49,560 --> 00:25:55,080
Speaker 1: deeply flawed. So you're a builder, um, you've you've created

575
00:25:55,119 --> 00:25:57,680
Speaker 1: a couple of a couple of companies, including this one.

576
00:25:58,240 --> 00:26:01,320
Speaker 1: In your mind's eye, Are you a founder, are you

577
00:26:01,480 --> 00:26:04,520
Speaker 1: a venture capitalist or a little bit of both. So

578
00:26:04,720 --> 00:26:07,320
Speaker 1: I think of myself as an engineer first, um, and

579
00:26:07,400 --> 00:26:09,600
Speaker 1: everything else after that. And so it's kind of the core.

580
00:26:10,320 --> 00:26:11,359
Speaker 1: And then you never know how much of this is

581
00:26:11,440 --> 00:26:14,040
Speaker 1: self delusion, of course, but um, I think my assumption

582
00:26:14,160 --> 00:26:17,080
Speaker 1: is we're all self del and you and occasionally if

583
00:26:17,119 --> 00:26:19,800
Speaker 1: you glimpse a little reality, fantastic and they either work

584
00:26:19,800 --> 00:26:21,880
Speaker 1: for you or they don't. So no, I was trained

585
00:26:21,920 --> 00:26:23,119
Speaker 1: as an engine I was always had sort of an

586
00:26:23,119 --> 00:26:25,280
Speaker 1: engineering mindset, and I was trained as an engineer, training

587
00:26:25,400 --> 00:26:27,000
Speaker 1: at a great engineering school, and so I just I

588
00:26:27,000 --> 00:26:28,679
Speaker 1: always few my I'm just trying to figure out how

589
00:26:28,720 --> 00:26:31,479
Speaker 1: things work and then try how to make them work better. Um.

590
00:26:31,600 --> 00:26:33,560
Speaker 1: And I'm far more interested in figuring that out than

591
00:26:33,560 --> 00:26:35,560
Speaker 1: in trying to draw value judgments on them, were trying

592
00:26:35,600 --> 00:26:37,320
Speaker 1: to have other you know, kinds of debates around them.

593
00:26:37,320 --> 00:26:40,119
Speaker 1: And so um uh that's just been you know, the

594
00:26:40,200 --> 00:26:41,520
Speaker 1: core of the thing. And so that to the extent

595
00:26:41,600 --> 00:26:43,480
Speaker 1: that my companies have worked, it's because they built products

596
00:26:43,480 --> 00:26:45,520
Speaker 1: that people wanted. Um. To the extent that this you

597
00:26:45,600 --> 00:26:47,399
Speaker 1: know that this firm works is because we're going we're

598
00:26:47,440 --> 00:26:49,320
Speaker 1: finding those products and the people who build those products

599
00:26:49,359 --> 00:26:51,440
Speaker 1: early um and betting on them. And so it's it's

600
00:26:51,440 --> 00:26:53,840
Speaker 1: sort of engineering as the core discipline. And then and

601
00:26:53,920 --> 00:26:56,120
Speaker 1: then we we we are lucky enough, in my view,

602
00:26:56,200 --> 00:26:57,920
Speaker 1: to live in a system in which you know that

603
00:26:58,040 --> 00:27:00,359
Speaker 1: that rewards that skill in the sense of people can

604
00:27:00,480 --> 00:27:03,040
Speaker 1: build things, right, people can build things, are encouraged to

605
00:27:03,040 --> 00:27:05,240
Speaker 1: build things, that culturally encouraged to build things, and those

606
00:27:05,280 --> 00:27:07,000
Speaker 1: things might be new products, but those things also might

607
00:27:07,000 --> 00:27:09,880
Speaker 1: be new companies. Back in two thousand eleven, you wrote

608
00:27:09,920 --> 00:27:14,800
Speaker 1: a fascinating screed called Software is Eating the World. Not

609
00:27:14,960 --> 00:27:17,600
Speaker 1: only did it end up becoming a meme that when

610
00:27:17,680 --> 00:27:21,359
Speaker 1: fairly viral, it turned out you were not just a

611
00:27:21,480 --> 00:27:24,520
Speaker 1: little bit right, you were very right. Tell us about

612
00:27:24,640 --> 00:27:29,200
Speaker 1: what motivated you to to put that those words on paper. Yes,

613
00:27:29,320 --> 00:27:30,840
Speaker 1: it was a two part at Towns. One was to

614
00:27:30,880 --> 00:27:33,320
Speaker 1: explain what was actually happening, and actually a lot of

615
00:27:33,400 --> 00:27:35,399
Speaker 1: it was actually against the bubble narrative. The bubble the

616
00:27:35,440 --> 00:27:37,200
Speaker 1: new bubble narrative was so strong at that point that

617
00:27:37,240 --> 00:27:39,040
Speaker 1: I wanted to articulate a view of what was happening

618
00:27:39,119 --> 00:27:40,399
Speaker 1: that was not just this is all a bunch of

619
00:27:40,520 --> 00:27:43,280
Speaker 1: a bunch of hot air, so partially explanatory but also

620
00:27:43,359 --> 00:27:47,000
Speaker 1: also an attempt to forecast UM. And specifically in the piece,

621
00:27:47,040 --> 00:27:48,840
Speaker 1: I sort of lay out a forecast for the future

622
00:27:49,040 --> 00:27:52,359
Speaker 1: UM in three parts UM, which I think is the

623
00:27:52,400 --> 00:27:55,040
Speaker 1: part that kind of remains the section that remains provocative today.

624
00:27:55,760 --> 00:27:58,359
Speaker 1: The three parts maybe recap them quickly so so so

625
00:27:58,520 --> 00:28:02,119
Speaker 1: part one is UM. Every product or service UM that

626
00:28:02,240 --> 00:28:05,040
Speaker 1: can become software will become software UM. And there's a

627
00:28:05,080 --> 00:28:07,040
Speaker 1: long conversation we can have. That's that's the big theme

628
00:28:07,080 --> 00:28:08,680
Speaker 1: of what's happening in Silicon Valley right now, I think

629
00:28:08,760 --> 00:28:10,880
Speaker 1: is that we're we're we are now going after entire

630
00:28:10,920 --> 00:28:14,439
Speaker 1: industries that used to be dominated by by just like retail,

631
00:28:14,680 --> 00:28:19,200
Speaker 1: retail storefronts or numbers or so is this applification or

632
00:28:19,280 --> 00:28:22,000
Speaker 1: is this just the move to pure solf were Appification

633
00:28:22,040 --> 00:28:23,920
Speaker 1: is the consumer experience, right, which is there everything that

634
00:28:23,960 --> 00:28:25,439
Speaker 1: can be experienced as an app? If you can if

635
00:28:25,440 --> 00:28:26,719
Speaker 1: you want you know, you need, You want a mortgage,

636
00:28:26,720 --> 00:28:28,280
Speaker 1: it should be an app. Right, you want life insurance,

637
00:28:28,280 --> 00:28:29,760
Speaker 1: it should be an app. You want to order pizza,

638
00:28:29,760 --> 00:28:31,280
Speaker 1: it should be an app. Like every everything should just

639
00:28:31,359 --> 00:28:33,320
Speaker 1: happen through that, through that front end. But but it's

640
00:28:33,320 --> 00:28:35,280
Speaker 1: deeper than the app because it's the complete back it's

641
00:28:35,280 --> 00:28:36,919
Speaker 1: all the back end systems of software that may make

642
00:28:36,960 --> 00:28:40,080
Speaker 1: the economy ultimately work. So so so Part one, every

643
00:28:40,200 --> 00:28:42,520
Speaker 1: every every product or service that can become software will

644
00:28:42,840 --> 00:28:46,400
Speaker 1: um Part two therefore, every company that makes those products

645
00:28:46,480 --> 00:28:49,600
Speaker 1: or services has to and will become a software company. Right,

646
00:28:49,640 --> 00:28:52,320
Speaker 1: So companies will become more and more defined by building software,

647
00:28:52,600 --> 00:28:55,240
Speaker 1: just like solic valley companies already like that, selict companies

648
00:28:55,320 --> 00:28:57,840
Speaker 1: or software companies first that then get good at things

649
00:28:57,920 --> 00:28:59,960
Speaker 1: like sales and marketing. Right, Whereas in the rest of

650
00:29:00,000 --> 00:29:01,240
Speaker 1: the economy you have a lot of companies that are

651
00:29:01,240 --> 00:29:02,760
Speaker 1: good at sales and marketing that are trying to bolt

652
00:29:02,800 --> 00:29:05,760
Speaker 1: on software. But sitting here ten years from now, all

653
00:29:05,760 --> 00:29:07,920
Speaker 1: these companies are gonna be software companies first because they're

654
00:29:07,920 --> 00:29:11,200
Speaker 1: gonna have to be fascinating form of the product. And

655
00:29:11,280 --> 00:29:14,680
Speaker 1: then the third sort of most either provocative, arrogant you know,

656
00:29:14,880 --> 00:29:18,160
Speaker 1: or delusional. Uh principle is therefore, in any industry, in

657
00:29:18,200 --> 00:29:20,400
Speaker 1: the long run, uh, the winning company will be the

658
00:29:20,440 --> 00:29:23,600
Speaker 1: best software company. Right, So whoever is the best? Is

659
00:29:23,640 --> 00:29:26,680
Speaker 1: that really all? That? So the question the follow up

660
00:29:26,720 --> 00:29:29,360
Speaker 1: question is how much push pushed back did your original

661
00:29:30,040 --> 00:29:32,800
Speaker 1: writing get and are you still getting pushed back on that?

662
00:29:33,120 --> 00:29:35,040
Speaker 1: That third part of it, Well, the third one is

663
00:29:35,120 --> 00:29:37,360
Speaker 1: very third one is very provocative. Let me give an example,

664
00:29:37,480 --> 00:29:40,560
Speaker 1: but it's pretty it follows logically. It's if you believe

665
00:29:40,600 --> 00:29:43,479
Speaker 1: in deductive reasoning, it makes sense. Well, let's let's pick

666
00:29:43,560 --> 00:29:48,960
Speaker 1: up particularly provocative example. Cars. Right, Um, yeah, so car cars.

667
00:29:49,000 --> 00:29:51,040
Speaker 1: Obviously cars, there's a lot more software cars, and there

668
00:29:51,120 --> 00:29:53,280
Speaker 1: was ten twenty years ago. Everybody knows that. It's a

669
00:29:53,320 --> 00:29:55,240
Speaker 1: step further to say that in ten years, the winning

670
00:29:55,240 --> 00:29:56,680
Speaker 1: car company is going to be the car the car

671
00:29:56,720 --> 00:29:59,360
Speaker 1: company that makes the best software. Right. There's no existing

672
00:29:59,440 --> 00:30:01,520
Speaker 1: legacy car company in the world today that would say

673
00:30:01,520 --> 00:30:02,960
Speaker 1: that that's the case. Right. They would all say that

674
00:30:03,000 --> 00:30:05,120
Speaker 1: they are best at making cars, and that the software

675
00:30:05,160 --> 00:30:06,960
Speaker 1: is a component that goes in the cars. But the

676
00:30:07,000 --> 00:30:08,720
Speaker 1: whole point of being a car company to be great

677
00:30:08,720 --> 00:30:11,440
Speaker 1: at engineering the actual physical automobile to steal and the

678
00:30:11,520 --> 00:30:13,680
Speaker 1: rubber and this all the safety systems and then the

679
00:30:13,720 --> 00:30:16,400
Speaker 1: whole thing. Well, but if you believe self autonomous vehicles

680
00:30:16,440 --> 00:30:19,280
Speaker 1: are coming, that software exactly right, and so that that

681
00:30:19,360 --> 00:30:21,320
Speaker 1: would be our thesis is that no, it's actually going

682
00:30:21,360 --> 00:30:23,760
Speaker 1: to happen is the value will flow to the software layer.

683
00:30:23,880 --> 00:30:26,000
Speaker 1: The the entire experience of being in a car will

684
00:30:26,000 --> 00:30:29,000
Speaker 1: be defined by the software. And then at the limit,

685
00:30:29,080 --> 00:30:33,120
Speaker 1: whichever company that makes cars or makes the intellectual property

686
00:30:33,200 --> 00:30:36,040
Speaker 1: that goes into cars, whichever of those creates the best

687
00:30:36,080 --> 00:30:38,480
Speaker 1: software will will then be the winning company, and we'll

688
00:30:38,520 --> 00:30:41,479
Speaker 1: get the majority of the value, right and including ultimately majority.

689
00:30:41,520 --> 00:30:44,320
Speaker 1: So so what you really define describing is the iPhone.

690
00:30:44,440 --> 00:30:47,360
Speaker 1: This is manufactured in China. I believe they make about

691
00:30:47,400 --> 00:30:50,480
Speaker 1: five dollars pro phone building it together. And then this

692
00:30:50,600 --> 00:30:53,120
Speaker 1: little comple you may know them, they're up the street

693
00:30:53,160 --> 00:30:57,520
Speaker 1: and Cupertino Apple is the one that captures not only

694
00:30:57,560 --> 00:31:00,240
Speaker 1: all the value from the iPhone, but pretty much all

695
00:31:00,280 --> 00:31:03,600
Speaker 1: of the profits from the entire or the profits from

696
00:31:03,640 --> 00:31:08,320
Speaker 1: the entire mobile universe. Is that the same basic concept

697
00:31:08,520 --> 00:31:13,160
Speaker 1: is in an iPhone model for everything else, including automobiles.

698
00:31:13,400 --> 00:31:16,200
Speaker 1: So in general, yes, like so that that's that's the implication, right,

699
00:31:16,240 --> 00:31:18,040
Speaker 1: And that's the exact analogy by the way that the

700
00:31:18,080 --> 00:31:20,040
Speaker 1: car company, the car industry is grappling with right now,

701
00:31:20,120 --> 00:31:22,040
Speaker 1: which is you as you know and phones you used

702
00:31:22,040 --> 00:31:24,080
Speaker 1: to have these companies like Nokia and Rim and others

703
00:31:24,160 --> 00:31:26,239
Speaker 1: that made phones and feud software is just this kind

704
00:31:26,280 --> 00:31:27,760
Speaker 1: of add on the top of the hardware. But the

705
00:31:27,800 --> 00:31:31,760
Speaker 1: hardware I'm not that old. I don't really remember those um.

706
00:31:31,960 --> 00:31:34,200
Speaker 1: And so you know when when you look like car

707
00:31:34,280 --> 00:31:36,240
Speaker 1: CEOs now they're they're all thinking like they're working on this,

708
00:31:36,320 --> 00:31:38,680
Speaker 1: the new generation of car CEOs. They're they're working hard,

709
00:31:38,680 --> 00:31:40,240
Speaker 1: and they're spending a lot of time out here, and

710
00:31:40,440 --> 00:31:41,560
Speaker 1: they spend a lot of time with us, and they're

711
00:31:41,560 --> 00:31:43,280
Speaker 1: they're they're working hard on it. But literally the way

712
00:31:43,320 --> 00:31:46,800
Speaker 1: they frame that question is we we we existing car

713
00:31:46,840 --> 00:31:48,640
Speaker 1: company do not want to be the noive cars like

714
00:31:49,000 --> 00:31:51,440
Speaker 1: and so that that Nokia of cars, that analogy is

715
00:31:51,560 --> 00:31:55,200
Speaker 1: very front and center and in there thinking now at

716
00:31:55,280 --> 00:31:58,239
Speaker 1: one level of detail later or lower than that. Um.

717
00:31:58,320 --> 00:32:00,960
Speaker 1: You know, the iPhone is a particular success of vertical integration.

718
00:32:01,280 --> 00:32:03,400
Speaker 1: UM so Apple. If Apple does that, you know, they

719
00:32:03,440 --> 00:32:05,520
Speaker 1: don't manufacture every component in it, but they assemble the

720
00:32:05,560 --> 00:32:11,440
Speaker 1: whole thing hardware, software together. Sometimes the winner is vertically integrated.

721
00:32:11,520 --> 00:32:14,000
Speaker 1: There are other times in other industries and PCs were

722
00:32:14,040 --> 00:32:16,320
Speaker 1: like this. The other example, which it's is horizontal in

723
00:32:16,360 --> 00:32:18,400
Speaker 1: structure and so the the other outcome is you'll have,

724
00:32:18,560 --> 00:32:21,200
Speaker 1: for example, Microsoft of cars right where you'll have the

725
00:32:21,280 --> 00:32:24,120
Speaker 1: software layer that that actually ends up and they actually

726
00:32:24,120 --> 00:32:25,800
Speaker 1: in that model they instead of making the car, that

727
00:32:25,880 --> 00:32:27,800
Speaker 1: winning company might just sell it software to all the

728
00:32:27,840 --> 00:32:30,280
Speaker 1: car companies. But then it's like the relationship that Microsoft

729
00:32:30,400 --> 00:32:32,520
Speaker 1: end up having with all the PC companies right where

730
00:32:32,520 --> 00:32:34,320
Speaker 1: the PC companies did all the work of actually making

731
00:32:34,600 --> 00:32:36,560
Speaker 1: the PC and Microsoft got all the money. And so

732
00:32:37,000 --> 00:32:39,760
Speaker 1: the victory for the best software company could go either

733
00:32:39,840 --> 00:32:42,320
Speaker 1: vertical or horizontal, and that that varies a lot, but

734
00:32:42,480 --> 00:32:44,400
Speaker 1: the consist I believe the consistent theme will be the

735
00:32:44,440 --> 00:32:47,320
Speaker 1: company with the best software will exactly your point. They'll

736
00:32:47,360 --> 00:32:49,640
Speaker 1: end up getting most of the profits so and have

737
00:32:49,760 --> 00:32:52,080
Speaker 1: the strategic control, right, they'll they'll be able to dictate

738
00:32:52,120 --> 00:32:53,760
Speaker 1: the future of the industry, but by virtually the fact

739
00:32:53,760 --> 00:32:55,840
Speaker 1: that they control the software. So that naturally leads to

740
00:32:55,880 --> 00:32:59,760
Speaker 1: the question is Tesla an electric car manufacturer or is

741
00:33:00,040 --> 00:33:02,840
Speaker 1: SLA a software company that happens to make a pretty

742
00:33:02,840 --> 00:33:05,200
Speaker 1: good looking car as well? That's right, So this is

743
00:33:05,240 --> 00:33:06,840
Speaker 1: the thing if you squint with and by the way,

744
00:33:06,920 --> 00:33:08,920
Speaker 1: I mean, Ellen is deservedly a legend, and so nothing

745
00:33:08,960 --> 00:33:10,840
Speaker 1: I'm about to say should be interpreted as a criticism

746
00:33:10,880 --> 00:33:13,600
Speaker 1: of Ellen, but just from a historic sweep of history standpoint,

747
00:33:13,640 --> 00:33:16,000
Speaker 1: if you squint at Tesla one way, it looks like

748
00:33:16,080 --> 00:33:19,560
Speaker 1: Apple circa two thousand, you know, seven two eight, where

749
00:33:19,600 --> 00:33:21,840
Speaker 1: they release the iPhone. They just haven't sold very money yet,

750
00:33:21,880 --> 00:33:23,920
Speaker 1: but they're gonna sell a ton and they're going to

751
00:33:23,960 --> 00:33:26,560
Speaker 1: be the vertical winner alli Apple. Another way of looking

752
00:33:26,600 --> 00:33:29,920
Speaker 1: at Tesla is you know their Apple in two with McIntosh,

753
00:33:30,120 --> 00:33:32,479
Speaker 1: and yes they're integrated hardware. Software is good, but other

754
00:33:32,520 --> 00:33:34,200
Speaker 1: people are going to come up with software as well.

755
00:33:34,360 --> 00:33:36,040
Speaker 1: And then there are many other people who make cars,

756
00:33:36,080 --> 00:33:38,120
Speaker 1: and so you know, the whole thing may just you know,

757
00:33:38,160 --> 00:33:40,200
Speaker 1: they may set the model mode for what other people

758
00:33:40,280 --> 00:33:43,080
Speaker 1: end up doing much higher scale. And I think that's

759
00:33:43,120 --> 00:33:45,080
Speaker 1: the big question on Tesla is like, is which way

760
00:33:45,160 --> 00:33:47,600
Speaker 1: is that tip? Obviously Elon is highly determined to make

761
00:33:47,600 --> 00:33:49,720
Speaker 1: sure that he owns the whole stack, and to your point,

762
00:33:49,760 --> 00:33:52,280
Speaker 1: he's incredibly focused on software like he's he is like

763
00:33:52,440 --> 00:33:55,440
Speaker 1: Tesla is deeply deeply immersed in the details, not just

764
00:33:55,560 --> 00:33:57,440
Speaker 1: by the way of the software that obviously runs the car,

765
00:33:57,520 --> 00:34:00,600
Speaker 1: but for example, power management is a big at that's

766
00:34:00,600 --> 00:34:02,520
Speaker 1: just sort of a secret sort of advantage that Tesla

767
00:34:02,520 --> 00:34:04,720
Speaker 1: people and talk about that much is they buy the

768
00:34:04,760 --> 00:34:06,680
Speaker 1: batteries from somebody else, but they write the software that

769
00:34:06,680 --> 00:34:08,480
Speaker 1: manages the power, which is a big part of getting

770
00:34:08,480 --> 00:34:12,239
Speaker 1: the range and the ludicrous mode and the ludicrous mode

771
00:34:12,440 --> 00:34:14,719
Speaker 1: um and then um. And then they're also he's now

772
00:34:14,760 --> 00:34:17,200
Speaker 1: deeply involved in right building, making sure that Tesla. He's

773
00:34:17,200 --> 00:34:19,239
Speaker 1: gonna try to Tesla be the best self driving car,

774
00:34:19,800 --> 00:34:22,920
Speaker 1: which is entirely a software exercise. Um. And so he's

775
00:34:22,960 --> 00:34:25,279
Speaker 1: on it, like he's he's pushing very hard. He's and

776
00:34:25,320 --> 00:34:27,640
Speaker 1: then he's the other car companies are all in, you know,

777
00:34:27,680 --> 00:34:29,719
Speaker 1: more or less reactive mode to what he catches. He

778
00:34:29,800 --> 00:34:32,480
Speaker 1: definitely has the lead right now. But that said, the

779
00:34:32,520 --> 00:34:34,120
Speaker 1: other car companies a trying to figure this out. And

780
00:34:34,600 --> 00:34:38,200
Speaker 1: we now have an entire wave of new automotive software

781
00:34:38,200 --> 00:34:41,319
Speaker 1: companies in Silicon Valley. Right, there's I don't know, there's

782
00:34:42,200 --> 00:34:44,680
Speaker 1: dozens on its way to hundreds of highly capable founders

783
00:34:44,680 --> 00:34:47,880
Speaker 1: and engineers building new kinds of autonomous software for cars,

784
00:34:48,360 --> 00:34:50,480
Speaker 1: by the way, vehicles of all different side shape and description,

785
00:34:50,520 --> 00:34:53,680
Speaker 1: air vehicles, ground vehicles, water vehicles. Right, So there's there's

786
00:34:53,719 --> 00:34:55,799
Speaker 1: a revolution of foot of which Tesla is an important part,

787
00:34:55,800 --> 00:34:59,279
Speaker 1: but it's also become much broader now quite fascinating in

788
00:34:59,480 --> 00:35:02,560
Speaker 1: um in the yearly. I keep coming back to that

789
00:35:02,719 --> 00:35:07,440
Speaker 1: era when venture capitalists with funding startups, people had to

790
00:35:07,480 --> 00:35:09,600
Speaker 1: go out and buy their own servers. They have engineers

791
00:35:09,640 --> 00:35:11,680
Speaker 1: to manage that and build that, and there was a

792
00:35:11,840 --> 00:35:14,760
Speaker 1: giant infrastructure. It would take tens of millions of dollars

793
00:35:15,400 --> 00:35:18,880
Speaker 1: just to open a company. Today that stuff is, if

794
00:35:19,000 --> 00:35:22,880
Speaker 1: not free, will certainly very cheap with Amazon cloud services

795
00:35:22,920 --> 00:35:26,600
Speaker 1: and a whole huge infrastructure. So if you want to

796
00:35:26,680 --> 00:35:29,360
Speaker 1: launch a company today, it doesn't take ten million dollars

797
00:35:29,520 --> 00:35:32,439
Speaker 1: just to open the doors. You could really do things

798
00:35:32,520 --> 00:35:36,600
Speaker 1: on the cheap. How does that shift in infrastructure change

799
00:35:36,920 --> 00:35:41,120
Speaker 1: your job as a venture capitalist when you're looking at companies,

800
00:35:41,280 --> 00:35:43,279
Speaker 1: what do they really need? Do we really need to

801
00:35:43,320 --> 00:35:47,000
Speaker 1: give people million dollars at a bob when they could

802
00:35:47,080 --> 00:35:49,880
Speaker 1: start for a few thousand dollars. So that's exactly right,

803
00:35:49,880 --> 00:35:51,719
Speaker 1: that's exactly what's happened, and we think about that in

804
00:35:51,800 --> 00:35:54,759
Speaker 1: economic terms. It's a very rapid inflation of of input costs,

805
00:35:55,239 --> 00:35:58,480
Speaker 1: which is dramatic. It's like the inflation input the input

806
00:35:58,520 --> 00:36:00,680
Speaker 1: costs for a startup in economic term deflated by a

807
00:36:00,680 --> 00:36:03,920
Speaker 1: factor of like a thousand. That's amazing, which is amazing, Right,

808
00:36:03,920 --> 00:36:05,960
Speaker 1: It's it's hard to actually find a historical precedent for

809
00:36:06,040 --> 00:36:08,600
Speaker 1: that happening. We're used to cost ten million dollars just

810
00:36:08,800 --> 00:36:10,960
Speaker 1: to launch and now you could do it for a

811
00:36:11,080 --> 00:36:13,600
Speaker 1: few grand. Yeah, yeah, we yeah, we routinely get you know,

812
00:36:13,719 --> 00:36:15,359
Speaker 1: we'll get three or four kids in here. The entire

813
00:36:15,600 --> 00:36:18,359
Speaker 1: their entire CAPEX, the entire company is their laptops, right,

814
00:36:18,480 --> 00:36:21,719
Speaker 1: It's that's that's all they happen, and they're good to go.

815
00:36:21,880 --> 00:36:23,560
Speaker 1: That's it, They're good to go. Everything else is like yeah, no,

816
00:36:23,600 --> 00:36:25,279
Speaker 1: it's a hundred a hundred dollar bill of this month

817
00:36:25,320 --> 00:36:27,480
Speaker 1: on their m X for their for their Amazon Cloud

818
00:36:27,520 --> 00:36:29,040
Speaker 1: services to launch the thing and see if it works.

819
00:36:29,080 --> 00:36:32,359
Speaker 1: And so that's that's a dramatic step change. Like that's

820
00:36:32,440 --> 00:36:34,160
Speaker 1: that's huge. I mean, it's very hard to find any any,

821
00:36:34,200 --> 00:36:35,880
Speaker 1: any precedent for that, which is a big reason why

822
00:36:35,880 --> 00:36:37,120
Speaker 1: a lot of so much energy has come back to

823
00:36:37,160 --> 00:36:39,360
Speaker 1: the valley. So as a VC, what that results in

824
00:36:39,480 --> 00:36:42,360
Speaker 1: is that the Darwinian process of running experiments to surface

825
00:36:42,400 --> 00:36:44,440
Speaker 1: the good ideas and the good companies is now happening

826
00:36:44,520 --> 00:36:47,000
Speaker 1: at much higher scale and at a much faster pace, right,

827
00:36:47,040 --> 00:36:49,160
Speaker 1: and so it's a VC it's this is amazing. So

828
00:36:49,239 --> 00:36:52,600
Speaker 1: it's it's not just the pace of acceleration. The second

829
00:36:52,719 --> 00:36:55,279
Speaker 1: ary of ittive is increasing. Well, so it's it's accelerating

830
00:36:55,320 --> 00:36:57,400
Speaker 1: at a faster and faster rate. Yeah. Well, the number one,

831
00:36:57,440 --> 00:36:59,320
Speaker 1: it's just a sheer number of ideas that can explore.

832
00:36:59,320 --> 00:37:01,319
Speaker 1: It can be explored has gone way up, right, because

833
00:37:01,320 --> 00:37:03,600
Speaker 1: there's elasticity. The less it costs to test new idea,

834
00:37:03,640 --> 00:37:05,439
Speaker 1: the more new ideas will get short and more people

835
00:37:05,480 --> 00:37:07,279
Speaker 1: will try to test new ideas. And then right, the

836
00:37:07,320 --> 00:37:09,239
Speaker 1: second is it's just so much faster now, it's just

837
00:37:09,360 --> 00:37:10,920
Speaker 1: the speed to get to market is just so much.

838
00:37:10,960 --> 00:37:12,360
Speaker 1: How do you deal with that? How do you sift

839
00:37:12,440 --> 00:37:16,960
Speaker 1: through a near infinite amount of really fantastic and interesting ideas.

840
00:37:17,000 --> 00:37:19,080
Speaker 1: So that's that's the business. And so that the day

841
00:37:19,120 --> 00:37:21,799
Speaker 1: to day businesses we we see we see evaluate two

842
00:37:21,880 --> 00:37:26,320
Speaker 1: thousand referred qualified startups a year every year, So that

843
00:37:26,440 --> 00:37:28,000
Speaker 1: that's the day job here is to sort and sift

844
00:37:28,040 --> 00:37:29,719
Speaker 1: and filter through the two thousand, and of those of

845
00:37:29,800 --> 00:37:32,279
Speaker 1: two thousand, how many do you end up doing now

846
00:37:32,360 --> 00:37:35,520
Speaker 1: you also do something unusual. You do sort of angel

847
00:37:35,680 --> 00:37:39,160
Speaker 1: funding at a very modest level and then broader funding

848
00:37:39,640 --> 00:37:42,320
Speaker 1: for for more I guess we would say developed or

849
00:37:42,320 --> 00:37:45,840
Speaker 1: advanced companies. So out of those two thousand that get reviewed,

850
00:37:46,200 --> 00:37:48,080
Speaker 1: what does it break down to right now, it's about

851
00:37:48,080 --> 00:37:50,400
Speaker 1: the thirty a year, thirty year, thirty a year out

852
00:37:50,400 --> 00:37:52,200
Speaker 1: of two thousand. Yeah, and I could go through in detail,

853
00:37:52,239 --> 00:37:54,400
Speaker 1: but exactly where, but like that's basically the way. It's

854
00:37:54,400 --> 00:37:55,880
Speaker 1: a little bit more than one percent. By the way,

855
00:37:55,880 --> 00:37:58,200
Speaker 1: I should also say these are two thousand that are referred, right,

856
00:37:58,280 --> 00:38:00,600
Speaker 1: so these these are sent to us. There's some connectivity

857
00:38:00,600 --> 00:38:02,359
Speaker 1: to people we know. So I can give you an

858
00:38:02,360 --> 00:38:05,160
Speaker 1: elevator pitch. Now. Well, now, now now that you're here,

859
00:38:05,840 --> 00:38:08,320
Speaker 1: in fact, you're in, you're in the room, give me,

860
00:38:08,440 --> 00:38:10,520
Speaker 1: give me a few minutes. I'll think up of business models.

861
00:38:10,719 --> 00:38:12,239
Speaker 1: This is the this is the actual room for that. So,

862
00:38:12,440 --> 00:38:15,080
Speaker 1: um so two thousand a year, um and and and

863
00:38:15,239 --> 00:38:16,560
Speaker 1: and then the other side of it is it's not

864
00:38:16,719 --> 00:38:18,400
Speaker 1: just right that. The whole thing with this is it's

865
00:38:18,440 --> 00:38:19,759
Speaker 1: not just is it a good idea a good product,

866
00:38:19,840 --> 00:38:21,560
Speaker 1: it's also who are the people? Right, because these are

867
00:38:21,600 --> 00:38:23,400
Speaker 1: people at the end of the day, these are people businesses,

868
00:38:23,440 --> 00:38:26,080
Speaker 1: and so honestly, most of what we're doing is evaluating people.

869
00:38:26,800 --> 00:38:29,560
Speaker 1: So that was always a question that I find intriguing.

870
00:38:31,239 --> 00:38:33,960
Speaker 1: Very often these ideas come along, there's no patents on them.

871
00:38:35,120 --> 00:38:37,200
Speaker 1: There's almost something in the ether where a lot of

872
00:38:37,280 --> 00:38:40,279
Speaker 1: people get very similar ideas. How often do we see

873
00:38:40,440 --> 00:38:43,359
Speaker 1: four or five different companies in the same space. Hey,

874
00:38:43,400 --> 00:38:46,160
Speaker 1: there's Uber and Lift, and there's Pandora, and go down

875
00:38:46,239 --> 00:38:48,320
Speaker 1: the list, and every time you look at a concept,

876
00:38:48,680 --> 00:38:50,319
Speaker 1: it looks like a lot of people have said, hey,

877
00:38:50,360 --> 00:38:53,080
Speaker 1: you know it would be cool right about now. How

878
00:38:53,160 --> 00:38:57,280
Speaker 1: do you figure out who has not just the best idea,

879
00:38:57,440 --> 00:39:01,080
Speaker 1: but the best way to execute did When you're dealing

880
00:39:01,160 --> 00:39:06,400
Speaker 1: with some really young, inexperienced technologists who very often have

881
00:39:06,520 --> 00:39:10,719
Speaker 1: no business experience either just out of grad school, sometimes

882
00:39:10,800 --> 00:39:14,279
Speaker 1: dropping out of college, that sounds like a near impossible

883
00:39:14,400 --> 00:39:17,560
Speaker 1: decision making process. So we call this the volcano movie problem.

884
00:39:17,719 --> 00:39:20,759
Speaker 1: There there's never just one movie about volcanoes. There's two

885
00:39:20,880 --> 00:39:23,279
Speaker 1: or three or four or five there or two giants.

886
00:39:23,320 --> 00:39:26,759
Speaker 1: There's never just one. There's never two killer there's never

887
00:39:26,840 --> 00:39:28,560
Speaker 1: just one killer media movies, there's got to be too.

888
00:39:28,640 --> 00:39:31,080
Speaker 1: So yeah, so that's a that's the thing. And these ideas, yeah,

889
00:39:31,080 --> 00:39:33,520
Speaker 1: they come in waves, um, and so that's exactly what happened.

890
00:39:33,560 --> 00:39:36,360
Speaker 1: So the thing that we try to go I mean,

891
00:39:36,400 --> 00:39:38,040
Speaker 1: there's a lot of different lenses you apply to when

892
00:39:38,040 --> 00:39:39,879
Speaker 1: you're evaluating people, but one of the ones we really

893
00:39:39,920 --> 00:39:42,080
Speaker 1: dig into hard to try to pull kind of what

894
00:39:42,160 --> 00:39:43,719
Speaker 1: we call kind of the real founders away from the

895
00:39:43,719 --> 00:39:45,120
Speaker 1: people who just like think they have an idea and

896
00:39:45,160 --> 00:39:47,080
Speaker 1: how to try something. So the concept we call the

897
00:39:47,160 --> 00:39:50,399
Speaker 1: idea mazeh One of our partners, Crystallized, wrote a paper

898
00:39:50,440 --> 00:39:52,680
Speaker 1: on a biology front of us and called the idea maze.

899
00:39:52,680 --> 00:39:56,359
Speaker 1: And so the idea maze is it appears on the surface, look,

900
00:39:56,400 --> 00:39:57,880
Speaker 1: this is a cute idea, like and by the way,

901
00:39:57,920 --> 00:40:00,080
Speaker 1: lots of people have this idea. The real founder, the

902
00:40:00,200 --> 00:40:02,120
Speaker 1: really good ones, what you find is they have thought

903
00:40:02,160 --> 00:40:05,160
Speaker 1: about the problem so deeply and for so long that

904
00:40:05,440 --> 00:40:08,160
Speaker 1: they have worked their way through the logic maze of

905
00:40:08,520 --> 00:40:10,759
Speaker 1: how to try the idea and what they're going to

906
00:40:10,800 --> 00:40:12,760
Speaker 1: try next if it doesn't work, and what the implications

907
00:40:12,800 --> 00:40:14,920
Speaker 1: would be if they learn something, and who are the

908
00:40:14,960 --> 00:40:16,680
Speaker 1: people they need to bring into the company, and how

909
00:40:16,719 --> 00:40:18,320
Speaker 1: do they segment the market, and how do they go

910
00:40:18,400 --> 00:40:21,000
Speaker 1: acquire the customers and so all these details. It's it's

911
00:40:21,040 --> 00:40:23,080
Speaker 1: the convert it's all the topics and sort of that

912
00:40:23,120 --> 00:40:24,680
Speaker 1: you don't cover in the first hour of the meeting,

913
00:40:24,719 --> 00:40:26,560
Speaker 1: but you cover in the subsequent five hours when you

914
00:40:26,680 --> 00:40:29,399
Speaker 1: go really deep right with them. And the really good

915
00:40:29,440 --> 00:40:31,040
Speaker 1: founders tend to all have in common is they've been

916
00:40:31,080 --> 00:40:32,560
Speaker 1: all the way through the idea mays on their own

917
00:40:32,600 --> 00:40:34,239
Speaker 1: before they come in and talk to a VC. So

918
00:40:34,560 --> 00:40:37,160
Speaker 1: there is not ideally, in the best case, there's not

919
00:40:37,239 --> 00:40:39,000
Speaker 1: a question that we can ask them in the meeting

920
00:40:39,440 --> 00:40:41,479
Speaker 1: in the first six hours of discussion that they haven't

921
00:40:41,480 --> 00:40:43,040
Speaker 1: already not only have they already thought of the ques,

922
00:40:43,080 --> 00:40:46,080
Speaker 1: they've already figured out the answer for themselves. Right. In fact,

923
00:40:46,160 --> 00:40:47,520
Speaker 1: you actually get to the point with a really good

924
00:40:47,680 --> 00:40:49,839
Speaker 1: really sharpens to the point where they get frustrated because

925
00:40:49,880 --> 00:40:52,000
Speaker 1: they're like, why why is this not all obvious to you?

926
00:40:52,160 --> 00:40:54,480
Speaker 1: People like this, Why are you asking me all these

927
00:40:54,480 --> 00:40:56,480
Speaker 1: stupid questions? I figured this out two years ago, Like

928
00:40:56,520 --> 00:40:58,839
Speaker 1: I already know the like and it should be self

929
00:40:58,880 --> 00:41:01,759
Speaker 1: evidence yourself at everybody, because they figured the whole thing out.

930
00:41:02,040 --> 00:41:04,920
Speaker 1: As as contrasted by the way to the ones who

931
00:41:04,960 --> 00:41:06,680
Speaker 1: haven't done that. If if somebody's only put you know,

932
00:41:06,760 --> 00:41:08,480
Speaker 1: ten or twenty hours and are thinking through something and

933
00:41:08,520 --> 00:41:10,200
Speaker 1: they just decided to put together a pitch, it becomes

934
00:41:10,200 --> 00:41:12,479
Speaker 1: crystal clear because you get eight or ten or twelve

935
00:41:12,560 --> 00:41:14,239
Speaker 1: questions in and they stop being able to answer the questions.

936
00:41:14,320 --> 00:41:16,719
Speaker 1: Now you just they start making they start making up

937
00:41:16,760 --> 00:41:18,560
Speaker 1: the you know, start making up the answers. So you

938
00:41:18,680 --> 00:41:20,960
Speaker 1: just revealed this, so you would all concern that people

939
00:41:21,000 --> 00:41:23,120
Speaker 1: would come in and say, Okay, now I have all

940
00:41:23,200 --> 00:41:25,880
Speaker 1: my ducks lined up, and Mark isn't gonna throw me

941
00:41:26,000 --> 00:41:28,000
Speaker 1: off my pitch. Well, they try to trick us already,

942
00:41:28,040 --> 00:41:29,480
Speaker 1: So if they haven't been through the idea as they

943
00:41:29,520 --> 00:41:30,800
Speaker 1: try to convince this, they have been, but they just

944
00:41:30,840 --> 00:41:33,480
Speaker 1: start making up the answers, and you can you can

945
00:41:33,520 --> 00:41:35,279
Speaker 1: flash that out like you can figure that you can

946
00:41:35,360 --> 00:41:37,160
Speaker 1: if there's part about it and parts content like they

947
00:41:37,200 --> 00:41:39,520
Speaker 1: just can't back up their answers. It's like like this

948
00:41:39,640 --> 00:41:41,920
Speaker 1: is how it's a little bit like how police do interrogations.

949
00:41:42,239 --> 00:41:44,040
Speaker 1: So the way people believe if you're get ever getting

950
00:41:44,040 --> 00:41:45,719
Speaker 1: interrogateted by a company thinks he what he just does

951
00:41:45,800 --> 00:41:48,520
Speaker 1: is he just the questions get more and more detailed. Oh,

952
00:41:48,719 --> 00:41:50,160
Speaker 1: you were your alibi as you were out seeing a

953
00:41:50,160 --> 00:41:52,160
Speaker 1: movie that night. Okay, well what movie? You know? Well,

954
00:41:52,239 --> 00:41:54,120
Speaker 1: what was the opening scene? Right? Well, what you know,

955
00:41:54,200 --> 00:41:55,920
Speaker 1: what was the plot twist at the end? You know, well,

956
00:41:56,000 --> 00:41:57,920
Speaker 1: what kind of what snack? Did you have your popcorn?

957
00:41:58,000 --> 00:41:59,360
Speaker 1: Or did you have salt in your popcorn or not?

958
00:42:00,040 --> 00:42:01,959
Speaker 1: Just go deeper and deeper and deeper and deeper until

959
00:42:02,000 --> 00:42:03,960
Speaker 1: you finally freak out because you can't like make up

960
00:42:03,960 --> 00:42:06,000
Speaker 1: all these people. So it's it's it's the same kind

961
00:42:06,000 --> 00:42:07,400
Speaker 1: of thing. And so the reason why I say it

962
00:42:07,480 --> 00:42:09,200
Speaker 1: so openly is it would be good for us if

963
00:42:09,239 --> 00:42:11,759
Speaker 1: more founders actually went through the full idea mas like,

964
00:42:11,880 --> 00:42:14,040
Speaker 1: it would be great for us if more founders came

965
00:42:14,040 --> 00:42:15,920
Speaker 1: in here having thought much more deeply about what they're doing.

966
00:42:16,239 --> 00:42:17,760
Speaker 1: And this is part of the pop where the popular

967
00:42:17,840 --> 00:42:19,960
Speaker 1: narrative startups is probably a little bit problem at the

968
00:42:20,000 --> 00:42:22,360
Speaker 1: popular narrative startups is it's like the popular narrative and

969
00:42:22,440 --> 00:42:25,040
Speaker 1: invention is that these are somehow Eureka moments. It's this

970
00:42:25,120 --> 00:42:27,239
Speaker 1: sudden flash of brilliance and you know, you're just you're

971
00:42:27,320 --> 00:42:28,920
Speaker 1: just you know. And by the way, these companies all

972
00:42:28,960 --> 00:42:30,600
Speaker 1: these origin myths like oh, I was just a kid

973
00:42:30,680 --> 00:42:32,080
Speaker 1: doing this and this idea came to me and like

974
00:42:32,239 --> 00:42:35,359
Speaker 1: loan behold boom Facebook, right, And that's not at all

975
00:42:35,440 --> 00:42:37,600
Speaker 1: how it works, right, It's it's this incredibly deep and

976
00:42:37,680 --> 00:42:40,480
Speaker 1: elaborate process of thinking, uh and work that actually leads

977
00:42:40,480 --> 00:42:42,120
Speaker 1: to these things happening. And so I think for people

978
00:42:42,160 --> 00:42:44,560
Speaker 1: to be if more founders understand the concept of the

979
00:42:44,600 --> 00:42:47,000
Speaker 1: idea MAS and they set the bar higher for themselves

980
00:42:47,320 --> 00:42:48,800
Speaker 1: and the level of thought they'll put in their ideas,

981
00:42:49,040 --> 00:42:51,600
Speaker 1: then I think we benefit from that. What could your

982
00:42:51,640 --> 00:42:54,120
Speaker 1: future hold more than you think? Because at Merrill Lynch

983
00:42:54,239 --> 00:42:56,160
Speaker 1: we work with you to create a strategy built around

984
00:42:56,239 --> 00:42:58,919
Speaker 1: your priorities. Visit mL dot com and learn more about

985
00:42:58,960 --> 00:43:01,440
Speaker 1: Merrill Lynch than affiliate Bank of America. Merrill Lynch makes

986
00:43:01,440 --> 00:43:03,719
Speaker 1: available products and services offered by Merrill Lynch. Pierce, Feder

987
00:43:03,719 --> 00:43:05,600
Speaker 1: and Smith Incorporated, a register broth for dealer. Remember s

988
00:43:05,719 --> 00:43:09,480
Speaker 1: I PC. We talked about some of the unicorns before

989
00:43:10,320 --> 00:43:12,959
Speaker 1: we talked about this ends badly. I'm gonna I'm gonna

990
00:43:12,960 --> 00:43:16,480
Speaker 1: skip those questions. Let let me before I get to Facebook,

991
00:43:16,560 --> 00:43:20,279
Speaker 1: let me I have to ask a question. I love

992
00:43:20,360 --> 00:43:24,440
Speaker 1: the show Silicon Valley. I know that, uh, Mike Judge

993
00:43:24,480 --> 00:43:28,920
Speaker 1: and Alec Burg have described you as an unofficial writing

994
00:43:29,080 --> 00:43:33,400
Speaker 1: consultant to this, So tell us about your experience with

995
00:43:33,600 --> 00:43:38,359
Speaker 1: the show. And I have to I'm a fan of Portlandia,

996
00:43:38,840 --> 00:43:41,440
Speaker 1: and having been to Portland's several times, I came away

997
00:43:41,880 --> 00:43:44,279
Speaker 1: with the impression that, oh, this isn't a sitcom, it's

998
00:43:44,320 --> 00:43:48,160
Speaker 1: a documentary. How true is that for? For Silicon Fact's

999
00:43:48,160 --> 00:43:51,760
Speaker 1: my understanding, Portlandia is literally true? Right? It certainly feels

1000
00:43:51,800 --> 00:43:54,120
Speaker 1: that way to put a camera in for a normal

1001
00:43:54,200 --> 00:43:56,880
Speaker 1: nor Yes, exactly, Silicon Valley is exact same way. So

1002
00:43:57,600 --> 00:43:59,480
Speaker 1: so and all I have to confess I've still only

1003
00:43:59,520 --> 00:44:02,799
Speaker 1: seen the season get Out. I haven't seen yet. It's

1004
00:44:03,000 --> 00:44:05,279
Speaker 1: so hilarious. I know, I'm sure it is. I just

1005
00:44:05,400 --> 00:44:08,360
Speaker 1: the first season was so perfect, just gonna stop. I

1006
00:44:08,480 --> 00:44:09,960
Speaker 1: just I couldn't. I don't know. I've just had a

1007
00:44:10,000 --> 00:44:12,920
Speaker 1: mental block. Um, I haven't seen the one from the

1008
00:44:13,000 --> 00:44:15,759
Speaker 1: most recent one this Sunday, and people are telling me

1009
00:44:15,960 --> 00:44:20,000
Speaker 1: it's just so hilarious and over the time funnies and

1010
00:44:20,080 --> 00:44:24,680
Speaker 1: it just rings. Especially being out here, It's like, oh, okay,

1011
00:44:24,760 --> 00:44:27,480
Speaker 1: I recognize him. It rings really true. Do you have

1012
00:44:27,520 --> 00:44:29,719
Speaker 1: a get feedback from people saying, hey, what are you

1013
00:44:29,840 --> 00:44:35,040
Speaker 1: doing to us? Excuse me? Like Mark, you're kind of

1014
00:44:35,120 --> 00:44:38,760
Speaker 1: making us look silly. What are you doing from who? Exactly?

1015
00:44:39,320 --> 00:44:43,000
Speaker 1: Random people? People you know, other vcs, other founders, other

1016
00:44:43,239 --> 00:44:46,320
Speaker 1: other because there are very few people come out of

1017
00:44:46,360 --> 00:44:48,759
Speaker 1: the show with their reputation. And so that's the thing

1018
00:44:48,800 --> 00:44:51,160
Speaker 1: I can't call. I can't comment past I can't comment

1019
00:44:51,200 --> 00:44:53,479
Speaker 1: past the first season. I will say the first season

1020
00:44:53,600 --> 00:44:55,200
Speaker 1: was perfect, and I will tell you so that the

1021
00:44:55,400 --> 00:44:57,040
Speaker 1: secret of that show, at least the secret of the

1022
00:44:57,040 --> 00:44:59,880
Speaker 1: first season, which I consider a work of art. Ums.

1023
00:45:00,000 --> 00:45:02,840
Speaker 1: I Judge himself started his career as a chip engineering

1024
00:45:02,840 --> 00:45:05,879
Speaker 1: in Silicon Valley, UM and if you if you meet

1025
00:45:05,960 --> 00:45:07,800
Speaker 1: him at actually it's it's not what you'd expect. Like

1026
00:45:07,840 --> 00:45:09,320
Speaker 1: he's one of the lead, you know, the leading creative

1027
00:45:09,719 --> 00:45:12,359
Speaker 1: comedy geniuses of our era, the creator of all these

1028
00:45:12,400 --> 00:45:13,960
Speaker 1: you know, Pavis and Butthead and all these things, and

1029
00:45:14,440 --> 00:45:19,439
Speaker 1: which is fascinating because he started out so wildly overbroad idiocracy,

1030
00:45:19,560 --> 00:45:21,680
Speaker 1: and you work your way down the list, and as

1031
00:45:21,800 --> 00:45:25,160
Speaker 1: he seems to have gotten older and maybe his artistic

1032
00:45:25,280 --> 00:45:30,799
Speaker 1: vision matured, it went from insane to overbroad to slapstick too. Oh,

1033
00:45:31,480 --> 00:45:35,520
Speaker 1: this is mildly a parody and somewhat of cinema verity,

1034
00:45:35,719 --> 00:45:38,640
Speaker 1: although I think he'd Idiocracy was also a documentary. Um,

1035
00:45:38,800 --> 00:45:41,680
Speaker 1: I was a documentary, but five years ahead of his time, right,

1036
00:45:41,960 --> 00:45:45,320
Speaker 1: exactly right, and so um and so he just he

1037
00:45:45,440 --> 00:45:48,440
Speaker 1: has a very precise um uh. He has a very

1038
00:45:48,440 --> 00:45:50,960
Speaker 1: precise understanding actually how things work here, more so than

1039
00:45:51,000 --> 00:45:53,040
Speaker 1: I think people would think. Um, and so it comes

1040
00:45:53,080 --> 00:45:55,000
Speaker 1: across and then and then his you know, his partners

1041
00:45:55,040 --> 00:45:57,359
Speaker 1: are also geniuses, and so it comes across. So that's good.

1042
00:45:57,600 --> 00:45:59,279
Speaker 1: I would also say, for people who haven't seen it,

1043
00:45:59,360 --> 00:46:02,600
Speaker 1: Halt and Catch Fire, uh in a completely different, hundred

1044
00:46:02,640 --> 00:46:05,040
Speaker 1: eighty degree different movies. It's a dramatic. It's a drama.

1045
00:46:05,120 --> 00:46:07,160
Speaker 1: There's no Halt and Catch Fire fire. It's not funny

1046
00:46:07,160 --> 00:46:09,960
Speaker 1: at all. It's it's a it's a very serious show. Halton.

1047
00:46:10,000 --> 00:46:11,880
Speaker 1: Catch Fire is a show on AMC that's I think

1048
00:46:11,920 --> 00:46:15,000
Speaker 1: in its third season now. Um. It is a period piece.

1049
00:46:15,360 --> 00:46:18,399
Speaker 1: Unlike the Silic Value is HBO, Silicon Value is current

1050
00:46:18,480 --> 00:46:20,959
Speaker 1: Time Fires a period peace set in the early eighties

1051
00:46:21,000 --> 00:46:22,640
Speaker 1: at the birth of the PC. UM. But it's the

1052
00:46:22,680 --> 00:46:24,640
Speaker 1: story basically, it's the it's a fictionalized story of the

1053
00:46:24,640 --> 00:46:26,960
Speaker 1: creation of the company Compact and then the birth of

1054
00:46:27,000 --> 00:46:28,880
Speaker 1: the PC industry and the birth of the PC clone

1055
00:46:28,880 --> 00:46:30,800
Speaker 1: industry at a time when IBM was by far the

1056
00:46:30,880 --> 00:46:33,640
Speaker 1: dominant company in the industry right and and so it's

1057
00:46:33,680 --> 00:46:37,080
Speaker 1: a but it's a very um it's a spookily accurate

1058
00:46:37,400 --> 00:46:40,040
Speaker 1: portrayal of what a startup is actually like, and it's

1059
00:46:40,040 --> 00:46:42,520
Speaker 1: actually really funny. The critics of the show in the

1060
00:46:42,560 --> 00:46:45,000
Speaker 1: first season, it was getting criticized for being too melodramatic,

1061
00:46:45,560 --> 00:46:47,759
Speaker 1: like this is all too superheated and too emotional, and

1062
00:46:48,000 --> 00:46:49,800
Speaker 1: I'm thinking, nope, this is It's like they put a

1063
00:46:49,840 --> 00:46:52,839
Speaker 1: camera in a startup like this, this is what they're

1064
00:46:52,840 --> 00:46:54,960
Speaker 1: actually like. Everybody would like to And this is also

1065
00:46:55,000 --> 00:46:56,120
Speaker 1: part of the thing we see all the time is

1066
00:46:56,120 --> 00:46:57,960
Speaker 1: everybody wants to pretend that startups are fun, and it

1067
00:46:58,000 --> 00:46:59,360
Speaker 1: turns out start ups are no fun. No, it's a

1068
00:46:59,360 --> 00:47:01,239
Speaker 1: lot of work, a lot of work, and it is

1069
00:47:01,320 --> 00:47:04,359
Speaker 1: a stomach churning process um of being told no by

1070
00:47:04,440 --> 00:47:06,319
Speaker 1: everybody and being told your idea is stupid and it's

1071
00:47:06,400 --> 00:47:08,719
Speaker 1: just not stop. Just you just feel like as a

1072
00:47:08,719 --> 00:47:10,680
Speaker 1: start every started founding, they just feel like they're under assault.

1073
00:47:10,719 --> 00:47:12,719
Speaker 1: All that, it's all and catch fired as a very

1074
00:47:12,760 --> 00:47:15,719
Speaker 1: good job capturing the other side of that. What what about, um,

1075
00:47:15,960 --> 00:47:20,160
Speaker 1: the incubators like hy Combinator or back Monity, What about

1076
00:47:20,320 --> 00:47:24,320
Speaker 1: those where all right, this is a little more founder

1077
00:47:24,440 --> 00:47:27,080
Speaker 1: friendly environment with a little more support and a little

1078
00:47:27,160 --> 00:47:31,120
Speaker 1: less um of of that negative CASTI gade that you

1079
00:47:31,200 --> 00:47:33,919
Speaker 1: were referring to. Yeah, so they're they're fantastic, Like they're

1080
00:47:33,920 --> 00:47:36,600
Speaker 1: they're fantastic. They've been usually additive to the to the valley. UM.

1081
00:47:36,719 --> 00:47:39,040
Speaker 1: The big thing that why Combinator and other firms like

1082
00:47:39,160 --> 00:47:41,600
Speaker 1: it are doing is they're bringing people in earlier. UM.

1083
00:47:41,760 --> 00:47:44,000
Speaker 1: And so write the path once upon a time, I

1084
00:47:44,040 --> 00:47:45,279
Speaker 1: mean I was maybe an exception of this, but the

1085
00:47:45,320 --> 00:47:47,520
Speaker 1: path once upon a time was you go work, you know,

1086
00:47:47,600 --> 00:47:49,440
Speaker 1: in a real job for five or ten or fifteen

1087
00:47:49,480 --> 00:47:51,080
Speaker 1: years and your real experience before you try one of

1088
00:47:51,080 --> 00:47:54,280
Speaker 1: these things, because you need to build the skill set. UM. Basically,

1089
00:47:54,320 --> 00:47:56,160
Speaker 1: what why see basically viewed is almost like the fifth

1090
00:47:56,239 --> 00:47:58,000
Speaker 1: year of college or something where they have they have

1091
00:47:58,040 --> 00:48:00,239
Speaker 1: ability to pull in you know, kids. A lot cases

1092
00:48:00,280 --> 00:48:02,239
Speaker 1: are people who just haven't been exposed to startups and

1093
00:48:02,280 --> 00:48:04,279
Speaker 1: able to They basically try to spin them up much

1094
00:48:04,280 --> 00:48:06,840
Speaker 1: more quickly in their careers. They talk openly about this, right,

1095
00:48:06,920 --> 00:48:08,759
Speaker 1: it's like it's it's it's sort of the new career path,

1096
00:48:09,520 --> 00:48:11,759
Speaker 1: um so. And then it as part it's also part

1097
00:48:11,760 --> 00:48:13,640
Speaker 1: of this increased our winnie and kind of feeding frenzy

1098
00:48:13,640 --> 00:48:15,279
Speaker 1: that we're talking about. So as long as we throw

1099
00:48:15,440 --> 00:48:18,480
Speaker 1: enough competitors gladigators into the ring, will end up with

1100
00:48:18,600 --> 00:48:22,439
Speaker 1: that many more survivors that potentially can can be something

1101
00:48:22,520 --> 00:48:24,600
Speaker 1: one day. Yeah, and I suspect I think, you know,

1102
00:48:24,680 --> 00:48:26,200
Speaker 1: part of the impact that these things have are the

1103
00:48:26,239 --> 00:48:27,759
Speaker 1: companies that come out of them, you know, out of

1104
00:48:27,760 --> 00:48:30,680
Speaker 1: the programs. I suspect the long run effects positive effects

1105
00:48:30,680 --> 00:48:33,239
Speaker 1: will be even bigger sort of the second, third, fourth generation. Right.

1106
00:48:33,280 --> 00:48:35,680
Speaker 1: So it's it's you know, the the y C Finnders

1107
00:48:35,760 --> 00:48:38,000
Speaker 1: this year, right, the ice Finders got don't a hundred

1108
00:48:38,000 --> 00:48:39,399
Speaker 1: and fifty companies in the batch right now or something

1109
00:48:39,440 --> 00:48:41,759
Speaker 1: like that, Like, you know, some of them is gonna succeed,

1110
00:48:41,800 --> 00:48:43,440
Speaker 1: a lot of them are going to fail. But to me,

1111
00:48:43,560 --> 00:48:45,839
Speaker 1: that's almost beside the point because it's all the people

1112
00:48:45,920 --> 00:48:48,120
Speaker 1: in those companies. It's three people who are working on

1113
00:48:48,120 --> 00:48:50,120
Speaker 1: those companies, and then what those people are gonna do

1114
00:48:50,200 --> 00:48:51,840
Speaker 1: not just this year, but five years from now, ten

1115
00:48:51,920 --> 00:48:54,319
Speaker 1: years from now. And so these people are being set

1116
00:48:54,400 --> 00:48:56,440
Speaker 1: up on a career path um where they should be

1117
00:48:56,480 --> 00:48:58,680
Speaker 1: able to do amazing things years from now. One of

1118
00:48:58,719 --> 00:49:01,960
Speaker 1: the nice things about Silicon all his failure isn't considered

1119
00:49:02,000 --> 00:49:04,960
Speaker 1: a mark of shame. It's so this this startup blew up,

1120
00:49:05,000 --> 00:49:06,680
Speaker 1: and that startup didn't make it, and we killed this,

1121
00:49:06,800 --> 00:49:09,640
Speaker 1: but we pivoted this one. And you know, sometimes the

1122
00:49:09,719 --> 00:49:11,680
Speaker 1: fifth or six time is a draw. Yeah, that's right,

1123
00:49:11,719 --> 00:49:13,040
Speaker 1: and so you get you get the Yeah, the first

1124
00:49:13,080 --> 00:49:14,880
Speaker 1: startup doesn't work, you do you do get more swings

1125
00:49:14,920 --> 00:49:16,319
Speaker 1: at the bat, and you do get and by the way,

1126
00:49:16,440 --> 00:49:18,719
Speaker 1: you get different experiences like your if your first up

1127
00:49:18,760 --> 00:49:20,520
Speaker 1: doesn't work, it's very often it ends in it's called

1128
00:49:20,560 --> 00:49:22,520
Speaker 1: an aquahier where you end up getting sold a big company,

1129
00:49:22,600 --> 00:49:23,799
Speaker 1: or you just or some people just go and get

1130
00:49:23,800 --> 00:49:26,319
Speaker 1: a job just to recover from the trauma of going

1131
00:49:26,360 --> 00:49:28,960
Speaker 1: through a start up work. But then you get skills, right,

1132
00:49:29,040 --> 00:49:31,120
Speaker 1: so then you have you have your startup experience. You

1133
00:49:31,239 --> 00:49:33,440
Speaker 1: have now a experience maybe working at a great you know,

1134
00:49:33,520 --> 00:49:35,600
Speaker 1: growth company where you're learning how that stuff works. When

1135
00:49:35,600 --> 00:49:37,799
Speaker 1: it when it's actually running at scale, right, and then

1136
00:49:37,880 --> 00:49:39,600
Speaker 1: five tenures and you start to learn how to manage

1137
00:49:39,640 --> 00:49:41,480
Speaker 1: people and you sort of pick up these skills and

1138
00:49:41,520 --> 00:49:44,520
Speaker 1: then you go start another company, right, And so there's

1139
00:49:44,840 --> 00:49:46,480
Speaker 1: there is a sort of repeated swings at the bat

1140
00:49:46,800 --> 00:49:48,759
Speaker 1: um kind of effect that happens. And I think that

1141
00:49:49,120 --> 00:49:52,200
Speaker 1: these incubators just give people basically an early swing at

1142
00:49:52,200 --> 00:49:54,520
Speaker 1: the bat that maybe otherwise didn't exist. Coming up, we

1143
00:49:54,600 --> 00:49:59,080
Speaker 1: continue our conversation with Mark andreeson discussing the unique structure

1144
00:49:59,520 --> 00:50:02,640
Speaker 1: of Andrew Uson Horowitz. I'm Barry Ridhults. You're listening to

1145
00:50:02,800 --> 00:50:07,160
Speaker 1: Masters in Business on Bloomberg Radio. I'm Barry Ridholts. You're

1146
00:50:07,239 --> 00:50:11,000
Speaker 1: listening to Masters in Business on Bloomberg Radio. My extra

1147
00:50:11,080 --> 00:50:14,160
Speaker 1: special guest today is Mark Andreason. He is the co

1148
00:50:14,360 --> 00:50:18,000
Speaker 1: founder and general partner of the venture capital firm Andrews

1149
00:50:18,080 --> 00:50:21,719
Speaker 1: and Horowitz. He co created the Mosaic Internet browser, co

1150
00:50:21,920 --> 00:50:25,440
Speaker 1: founded Netscape, and has done more things over the past

1151
00:50:25,520 --> 00:50:27,680
Speaker 1: decade or two than most of us will do in

1152
00:50:27,760 --> 00:50:31,120
Speaker 1: our lifestyle entire lifespans. Let's talk a little bit about

1153
00:50:31,680 --> 00:50:35,040
Speaker 1: the firm we're sitting in, which is quite lovely. It

1154
00:50:35,239 --> 00:50:39,800
Speaker 1: is a very relaxing zen vibe heerre um with the

1155
00:50:40,400 --> 00:50:45,759
Speaker 1: blonde wood and the just generally soothing atmosphere. Not what

1156
00:50:46,080 --> 00:50:49,280
Speaker 1: you imagine when you think up think of the startup

1157
00:50:49,360 --> 00:50:53,520
Speaker 1: community and and Silophone Valley. Tell us how you structured

1158
00:50:53,600 --> 00:50:56,680
Speaker 1: the firm and what was the thinking behind it? Yeah,

1159
00:50:56,719 --> 00:50:59,920
Speaker 1: so the big thing. So we're trying to create, We're

1160
00:51:00,000 --> 00:51:02,080
Speaker 1: trying to work with founders to create something out of nothing. Right,

1161
00:51:02,080 --> 00:51:03,840
Speaker 1: and so at the time we invest in most companies,

1162
00:51:03,880 --> 00:51:06,200
Speaker 1: there's not a lot there. And then the path is

1163
00:51:06,239 --> 00:51:08,480
Speaker 1: going to be a five, ten, fifteen year path to

1164
00:51:08,560 --> 00:51:11,640
Speaker 1: get to you know, to get to something important and significant.

1165
00:51:11,680 --> 00:51:13,640
Speaker 1: So you're really playing a very long game. It's not

1166
00:51:13,760 --> 00:51:16,000
Speaker 1: two years. Hey, we've got to start looking for an exit. Yeah,

1167
00:51:16,000 --> 00:51:18,200
Speaker 1: it's it's my friend and friends at CNBC, right, have

1168
00:51:18,280 --> 00:51:20,160
Speaker 1: the show Fast Money, and I'm always pitching them. It's

1169
00:51:20,160 --> 00:51:22,120
Speaker 1: like you need the car market needs slow money, right,

1170
00:51:22,160 --> 00:51:23,920
Speaker 1: and the slow money should be on every day and

1171
00:51:24,080 --> 00:51:25,680
Speaker 1: every day it should just be right, you know, well,

1172
00:51:25,760 --> 00:51:29,440
Speaker 1: what's happened today that matters? And the answer is yeah, nothing, Um,

1173
00:51:29,760 --> 00:51:31,839
Speaker 1: you know, what's what's going on? You know? Not much? Okay,

1174
00:51:31,880 --> 00:51:34,000
Speaker 1: I'll see tomorrow, right and so and and and you

1175
00:51:34,040 --> 00:51:35,839
Speaker 1: know they start looking the start ups are working incredibly hard.

1176
00:51:35,840 --> 00:51:37,520
Speaker 1: Like the startups, you know, there's all kinds of activity

1177
00:51:37,520 --> 00:51:40,080
Speaker 1: at the startups. But the investors, say the vcs who

1178
00:51:40,160 --> 00:51:42,160
Speaker 1: freak out on a daily, weekly, monthly basis with these

1179
00:51:42,160 --> 00:51:44,760
Speaker 1: companies like burn themselves to a crisp, like because stuff

1180
00:51:45,040 --> 00:51:47,719
Speaker 1: these companies to cast. And so we go into it

1181
00:51:47,840 --> 00:51:50,040
Speaker 1: just knowing it's gonna be with every company is going

1182
00:51:50,080 --> 00:51:51,799
Speaker 1: to be this long running journey, and it just takes time.

1183
00:51:51,840 --> 00:51:53,759
Speaker 1: It takes at least five years to build something that

1184
00:51:54,080 --> 00:51:55,600
Speaker 1: has real value, and it takes at least ten years

1185
00:51:55,640 --> 00:51:57,680
Speaker 1: to build something that's going to endure for long, you know,

1186
00:51:57,760 --> 00:51:59,320
Speaker 1: for for for another ten years after that, and so

1187
00:51:59,400 --> 00:52:01,880
Speaker 1: it's it's just to be this this, this, this long thing.

1188
00:52:01,960 --> 00:52:04,200
Speaker 1: And then I mentioned earlier like so much of this

1189
00:52:04,400 --> 00:52:06,360
Speaker 1: people like we we like to I think, brag on

1190
00:52:06,480 --> 00:52:07,840
Speaker 1: like how good we are I thinking about the ideas

1191
00:52:07,880 --> 00:52:09,239
Speaker 1: and the technical and stuff, and then we do spend

1192
00:52:09,239 --> 00:52:10,400
Speaker 1: a lot of time on that stuff. But I think

1193
00:52:10,440 --> 00:52:13,200
Speaker 1: a lot of it is partnering with really high potential people.

1194
00:52:13,719 --> 00:52:15,239
Speaker 1: In a lot of cases, you know, to your point,

1195
00:52:15,320 --> 00:52:17,000
Speaker 1: like don't have that much experience at the time they

1196
00:52:17,000 --> 00:52:19,640
Speaker 1: start their companies UM or or even people with more

1197
00:52:19,680 --> 00:52:21,759
Speaker 1: career experience who haven't done a startup before, right where

1198
00:52:21,800 --> 00:52:23,200
Speaker 1: there's a whole new set of things they have to learn,

1199
00:52:23,200 --> 00:52:25,040
Speaker 1: and then we try to work and help them, you know,

1200
00:52:25,160 --> 00:52:27,560
Speaker 1: grow and become better. Uh and and and you know,

1201
00:52:27,640 --> 00:52:29,800
Speaker 1: develop the skills that they need to build important companies

1202
00:52:30,360 --> 00:52:32,320
Speaker 1: um and so the whole firm is kind of engineered

1203
00:52:32,360 --> 00:52:34,719
Speaker 1: around those ideas. Right. So, as an example, we will

1204
00:52:34,760 --> 00:52:36,759
Speaker 1: never back a startup if we think it's outcome is

1205
00:52:36,800 --> 00:52:38,520
Speaker 1: it's going to get bought. Like every once in a while.

1206
00:52:38,560 --> 00:52:40,200
Speaker 1: It's really like if there's a slide in the dock

1207
00:52:40,320 --> 00:52:42,279
Speaker 1: that says excess strategy, you don't get bought by Google

1208
00:52:42,320 --> 00:52:44,480
Speaker 1: in three years like for ten X, like we won't.

1209
00:52:44,760 --> 00:52:47,520
Speaker 1: We won't find the company spent a lot of time

1210
00:52:47,560 --> 00:52:49,440
Speaker 1: on that. But like we're we're know, you want to

1211
00:52:49,480 --> 00:52:52,719
Speaker 1: create a company that worst case scenario exists on its

1212
00:52:52,760 --> 00:52:56,160
Speaker 1: own for the foreseeable decades in the future, and if

1213
00:52:56,239 --> 00:52:59,200
Speaker 1: someone happens to acquire them, great, But that's not the target.

1214
00:52:59,320 --> 00:53:01,359
Speaker 1: That's the kicker, as the companies that actually get bought

1215
00:53:01,360 --> 00:53:02,640
Speaker 1: are the ones that don't have to get bought, the

1216
00:53:03,160 --> 00:53:04,640
Speaker 1: ones that actually could have stood on their own because

1217
00:53:04,640 --> 00:53:07,680
Speaker 1: those are the ones that actually value that value, right exactly.

1218
00:53:07,719 --> 00:53:09,640
Speaker 1: So that's the twist on the whole thing. But so

1219
00:53:09,800 --> 00:53:12,439
Speaker 1: we're we're always backing these companies with and these people

1220
00:53:12,480 --> 00:53:15,120
Speaker 1: with an eye towards long run independence, developing something of

1221
00:53:15,239 --> 00:53:18,759
Speaker 1: of deepen enduring value. UM. So one is just the timeframe, Um,

1222
00:53:18,920 --> 00:53:21,839
Speaker 1: that's important part. A second part is how we coach

1223
00:53:21,920 --> 00:53:24,040
Speaker 1: and mentor and work with the founders and the team

1224
00:53:24,080 --> 00:53:25,680
Speaker 1: that the founders built around them, And that has to

1225
00:53:25,719 --> 00:53:27,640
Speaker 1: do with our how we how we s left general

1226
00:53:27,680 --> 00:53:29,640
Speaker 1: partners for our firm with the people who make the

1227
00:53:29,840 --> 00:53:32,200
Speaker 1: pull the trigger investments and go on the boards. Um.

1228
00:53:32,320 --> 00:53:34,120
Speaker 1: And there is all our our big claim to fame

1229
00:53:34,120 --> 00:53:36,280
Speaker 1: there is all of our general partners are former founders

1230
00:53:36,360 --> 00:53:38,880
Speaker 1: and our CEOs of tech startups. And so when we

1231
00:53:38,960 --> 00:53:41,600
Speaker 1: go on a board, it's always the case with us

1232
00:53:41,640 --> 00:53:43,840
Speaker 1: that you're sitting with somebody who has been through the

1233
00:53:43,880 --> 00:53:45,880
Speaker 1: same situation that you're in right now and has had

1234
00:53:45,960 --> 00:53:48,040
Speaker 1: to make the difficult decision and has had to live

1235
00:53:48,080 --> 00:53:50,759
Speaker 1: with the consequences of that decision, right. And so it's

1236
00:53:50,800 --> 00:53:53,080
Speaker 1: just it's a grounding in the in the advice and

1237
00:53:53,120 --> 00:53:54,799
Speaker 1: the guidance that you get that I think just goes

1238
00:53:54,880 --> 00:53:56,560
Speaker 1: deeper than you know. And there by the way, there

1239
00:53:56,600 --> 00:53:58,719
Speaker 1: are great vcs who have no operating experience or little

1240
00:53:58,719 --> 00:54:00,640
Speaker 1: operating experience, and some of them are actually very very

1241
00:54:00,640 --> 00:54:03,400
Speaker 1: good investors. But there's there's just so much in that

1242
00:54:03,480 --> 00:54:05,719
Speaker 1: intimate relationship with the founder and with the with the

1243
00:54:05,800 --> 00:54:08,279
Speaker 1: team that I think benefits from having the credibility from

1244
00:54:08,280 --> 00:54:10,680
Speaker 1: having actually been you guys. You guys also do something

1245
00:54:10,840 --> 00:54:14,759
Speaker 1: unique in that every partner is on every investment, as

1246
00:54:14,760 --> 00:54:18,000
Speaker 1: opposed to you do software, you do semi conductor. I'll

1247
00:54:18,040 --> 00:54:21,719
Speaker 1: do this. Every partner participates in every investment. Is that

1248
00:54:21,840 --> 00:54:24,479
Speaker 1: an accurate statement? So we do that. There is there's

1249
00:54:24,480 --> 00:54:26,520
Speaker 1: always a single GP on the board, so there's somebody

1250
00:54:26,520 --> 00:54:28,719
Speaker 1: who's focused on you. But then the way the firm

1251
00:54:28,760 --> 00:54:31,320
Speaker 1: ist structured is everybody shares equally in the in the

1252
00:54:31,400 --> 00:54:33,640
Speaker 1: results of that. So we so basically we've just there's

1253
00:54:33,640 --> 00:54:34,880
Speaker 1: a lot of things we've done in the firm to

1254
00:54:35,200 --> 00:54:37,560
Speaker 1: design the firm to be a zero politics environment, to

1255
00:54:37,640 --> 00:54:40,760
Speaker 1: be so what you discover what you do rowe politics

1256
00:54:40,800 --> 00:54:43,280
Speaker 1: and environe your politics environment with respect to how the entrepreneurs,

1257
00:54:43,520 --> 00:54:45,160
Speaker 1: I mean, I can Garantie. We've ruled out all politics

1258
00:54:45,239 --> 00:54:46,400
Speaker 1: in the firm. I'm just saying, as far as the

1259
00:54:46,440 --> 00:54:49,719
Speaker 1: founder's experience, that they should never run into for a second,

1260
00:54:49,719 --> 00:54:51,719
Speaker 1: it shouldn't be this guy in the firm likes this,

1261
00:54:51,880 --> 00:54:54,600
Speaker 1: but this guy is fighting against fighting again. So what

1262
00:54:54,719 --> 00:54:56,439
Speaker 1: you often find in a lot of entry capital firms

1263
00:54:56,440 --> 00:54:58,120
Speaker 1: since they present as united front and then there's a

1264
00:54:58,160 --> 00:55:00,560
Speaker 1: tremendous amount of in fighting this taking place. It's by

1265
00:55:00,560 --> 00:55:01,759
Speaker 1: the way, it's the same thing you see and like

1266
00:55:01,920 --> 00:55:03,520
Speaker 1: you'll see on on Wall Street, a lot like whis

1267
00:55:03,560 --> 00:55:05,439
Speaker 1: just for example, there's just a battle for the there's

1268
00:55:05,440 --> 00:55:06,840
Speaker 1: a battle for the profits, like there's a battle for

1269
00:55:06,880 --> 00:55:08,319
Speaker 1: the carry. And so for me to get more money,

1270
00:55:08,360 --> 00:55:10,439
Speaker 1: somebody else is to get less money. And that least

1271
00:55:10,480 --> 00:55:12,560
Speaker 1: to this weird adverse thing where I actually want the

1272
00:55:12,600 --> 00:55:14,560
Speaker 1: other guys deals to fail because then I'll have more

1273
00:55:14,600 --> 00:55:17,440
Speaker 1: negotiating leverage in my comp you know, discussion or there

1274
00:55:17,440 --> 00:55:19,359
Speaker 1: will be a battle between who wins the deal, right

1275
00:55:19,440 --> 00:55:21,759
Speaker 1: the maybe the entrepreneur starts the conversation with one GP,

1276
00:55:21,920 --> 00:55:23,919
Speaker 1: but maybe the entrepreneur really would rather have another GP

1277
00:55:24,040 --> 00:55:25,759
Speaker 1: with the more relevant skill set right in the same

1278
00:55:25,800 --> 00:55:27,520
Speaker 1: firm on their board, sure, and a lot of firms

1279
00:55:27,560 --> 00:55:30,040
Speaker 1: that that starts World War three. Internally, at our firm,

1280
00:55:30,640 --> 00:55:33,520
Speaker 1: everybody's fine with that. Another thing, a little twist that

1281
00:55:33,560 --> 00:55:35,160
Speaker 1: we have. We don't have a promotion path in the

1282
00:55:35,200 --> 00:55:37,719
Speaker 1: firm to general partner UM. So the way most other

1283
00:55:37,760 --> 00:55:39,320
Speaker 1: firms are structured as they have young people in the

1284
00:55:39,360 --> 00:55:41,800
Speaker 1: firm who get promoted up the rights over time. But

1285
00:55:41,920 --> 00:55:44,680
Speaker 1: it's a competitive process, right, So you'll have twenty junior

1286
00:55:44,719 --> 00:55:46,880
Speaker 1: people who are competing for you know, six sort of

1287
00:55:47,120 --> 00:55:49,400
Speaker 1: UM junior g P slots who are competing for two

1288
00:55:49,480 --> 00:55:52,440
Speaker 1: g two senior GP slots of course ten or fifteen years,

1289
00:55:52,719 --> 00:55:54,480
Speaker 1: and so they're in a constant knife fight with each other,

1290
00:55:54,719 --> 00:55:56,399
Speaker 1: right because they're it's just like had a law firm.

1291
00:55:56,640 --> 00:55:58,359
Speaker 1: I was gonna say, it sounds like the big big

1292
00:55:58,440 --> 00:56:00,759
Speaker 1: Wall Street or park Ev and you law firms where

1293
00:56:00,800 --> 00:56:03,200
Speaker 1: it's just a blood bath and when they make the

1294
00:56:03,280 --> 00:56:06,080
Speaker 1: announcements of partners, there's a handful of happy people and

1295
00:56:06,080 --> 00:56:08,480
Speaker 1: a lot of other people getting their resumes together and

1296
00:56:08,600 --> 00:56:11,040
Speaker 1: it's no fun for anyone. And so when that struck,

1297
00:56:11,120 --> 00:56:12,880
Speaker 1: that's exactly right. So when that structure works well, that

1298
00:56:13,000 --> 00:56:15,320
Speaker 1: leads to a very brutally Darwinian effective eat what you

1299
00:56:15,440 --> 00:56:18,120
Speaker 1: kill environment where you're you're breeding for you're basically breeding

1300
00:56:18,120 --> 00:56:21,000
Speaker 1: sharks for maximum infectiveness. The problem is, as a client

1301
00:56:21,080 --> 00:56:23,200
Speaker 1: of one of those firms, you get caught crosswise in

1302
00:56:23,239 --> 00:56:25,560
Speaker 1: the politics right, and you do not get you basically

1303
00:56:25,640 --> 00:56:27,200
Speaker 1: only get access to the people who have the direction

1304
00:56:27,280 --> 00:56:28,600
Speaker 1: sentive to help you in the firm. You don't get

1305
00:56:28,600 --> 00:56:30,160
Speaker 1: access to the rest of the firm. And that's the

1306
00:56:30,200 --> 00:56:32,480
Speaker 1: typical experience that founders have with vcs, which is they

1307
00:56:32,520 --> 00:56:34,320
Speaker 1: get promised the resources of the firm, they get what

1308
00:56:34,360 --> 00:56:37,560
Speaker 1: they get in practice is one person that seems really counterproductive.

1309
00:56:37,600 --> 00:56:39,239
Speaker 1: It's very kind of productive and and in our come

1310
00:56:39,280 --> 00:56:40,800
Speaker 1: by the way, a lot of our work with companies

1311
00:56:40,880 --> 00:56:42,480
Speaker 1: is working with our companies to help them resolve these

1312
00:56:42,560 --> 00:56:45,480
Speaker 1: kinds of issues with the other vcs. So because you're

1313
00:56:45,560 --> 00:56:48,600
Speaker 1: you guys are also co investors with other other ventures.

1314
00:56:48,719 --> 00:56:50,920
Speaker 1: So we just don't and again, like this may work

1315
00:56:50,920 --> 00:56:52,600
Speaker 1: for other firms, we just decided to take a different

1316
00:56:52,640 --> 00:56:54,279
Speaker 1: mentality where we don't have that kind of you ever

1317
00:56:54,280 --> 00:56:56,839
Speaker 1: get caught in the kind of crossbar because it doesn't exist. Um,

1318
00:56:56,920 --> 00:56:58,440
Speaker 1: so that's a big thing. And then the other big

1319
00:56:58,520 --> 00:57:00,680
Speaker 1: thing that we do is a firm um is, we

1320
00:57:00,719 --> 00:57:03,920
Speaker 1: do believe deeply in the idea that the founders not

1321
00:57:04,080 --> 00:57:05,839
Speaker 1: in every case, but in a lot of cases, really

1322
00:57:05,880 --> 00:57:07,680
Speaker 1: should be able to if they're if they want to,

1323
00:57:07,719 --> 00:57:08,960
Speaker 1: and if they're able to do it, they should they

1324
00:57:08,960 --> 00:57:10,719
Speaker 1: should run their own companies. And we could have a

1325
00:57:10,760 --> 00:57:13,600
Speaker 1: long conversation about that. Um. The other thing that we

1326
00:57:13,640 --> 00:57:15,680
Speaker 1: do is we really try to help arm up the

1327
00:57:15,719 --> 00:57:17,840
Speaker 1: founders who don't have a lot of deep business experience

1328
00:57:17,960 --> 00:57:21,680
Speaker 1: with all of the connectivity the network access that the

1329
00:57:21,760 --> 00:57:24,360
Speaker 1: professional CEOs will tend to have. So so the technical

1330
00:57:24,400 --> 00:57:26,600
Speaker 1: found technical founder starts a company right, and then you know,

1331
00:57:26,680 --> 00:57:27,919
Speaker 1: big part of it is are I think, just gonna

1332
00:57:27,920 --> 00:57:29,520
Speaker 1: be good at managing and leading, which is where the

1333
00:57:29,600 --> 00:57:33,040
Speaker 1: GP relationship is really critical mentoring. Do you find that

1334
00:57:33,240 --> 00:57:35,920
Speaker 1: very often engineers and people with that sort of technical

1335
00:57:36,040 --> 00:57:40,600
Speaker 1: skill set, um, the stereotype is they're socially awkward. They

1336
00:57:40,680 --> 00:57:44,640
Speaker 1: lack the usual skill set that a CEO typically has. Yeah,

1337
00:57:44,840 --> 00:57:47,200
Speaker 1: so that is often the case. But also the reverse

1338
00:57:47,320 --> 00:57:49,240
Speaker 1: is often the case, which is the highly social you know,

1339
00:57:49,360 --> 00:57:51,920
Speaker 1: the CEO is had of central casting have you know,

1340
00:57:52,040 --> 00:57:55,120
Speaker 1: the the great hair and the you know, brilliant smiles,

1341
00:57:55,200 --> 00:57:57,680
Speaker 1: and the great, you know, just the great in front

1342
00:57:57,680 --> 00:58:00,120
Speaker 1: of the crowd. They often not always, but they often

1343
00:58:00,200 --> 00:58:02,600
Speaker 1: lack the technical grounding to determine the direction of a

1344
00:58:02,640 --> 00:58:05,160
Speaker 1: technology company. And so and again like these are these

1345
00:58:05,200 --> 00:58:07,720
Speaker 1: are stereotypes, and they're overly broad, but they are patterns.

1346
00:58:07,960 --> 00:58:11,240
Speaker 1: There is some truth to it. Started in this case.

1347
00:58:11,320 --> 00:58:13,560
Speaker 1: And so so what we're now everything, I would say

1348
00:58:14,080 --> 00:58:16,880
Speaker 1: the the kicker everything adventure is on an exception basis, right,

1349
00:58:16,920 --> 00:58:18,560
Speaker 1: and so by definition we're not looking for the pattern,

1350
00:58:18,640 --> 00:58:20,640
Speaker 1: we're looking for the exception. And so we're looking for

1351
00:58:20,880 --> 00:58:23,880
Speaker 1: the to your point, the very particular technical founder, who,

1352
00:58:23,920 --> 00:58:25,400
Speaker 1: by the way, may start out without a lot of

1353
00:58:25,440 --> 00:58:27,760
Speaker 1: these social skills, and may start out without really knowing

1354
00:58:27,760 --> 00:58:29,280
Speaker 1: how to deal with customers, or may start out not

1355
00:58:29,320 --> 00:58:31,200
Speaker 1: really knowing how to manage people or hire people. And

1356
00:58:31,240 --> 00:58:34,160
Speaker 1: then we're trying and if they're willing and able to learn,

1357
00:58:34,400 --> 00:58:36,000
Speaker 1: we will then put a lot of effort into helping

1358
00:58:36,000 --> 00:58:38,280
Speaker 1: them become great at those things. By the way, it's

1359
00:58:38,320 --> 00:58:39,720
Speaker 1: the same astray on the other side when it comes

1360
00:58:39,800 --> 00:58:41,600
Speaker 1: time some of our companies we do end up with

1361
00:58:41,720 --> 00:58:44,360
Speaker 1: professional CEOs, and then we look for the professional CEO.

1362
00:58:44,440 --> 00:58:45,800
Speaker 1: Is that not only have that skill, but also then

1363
00:58:45,840 --> 00:58:48,000
Speaker 1: go deep on products and technology, and so you're and

1364
00:58:48,120 --> 00:58:50,160
Speaker 1: and and those are both exceptions like those are these

1365
00:58:50,160 --> 00:58:53,160
Speaker 1: are not on both sides, these are rare people. So

1366
00:58:53,360 --> 00:58:55,240
Speaker 1: so it makes me wonder is it easy to take

1367
00:58:55,320 --> 00:58:58,160
Speaker 1: someone who has the technical skills and teach them to

1368
00:58:58,280 --> 00:59:01,440
Speaker 1: be a CEO, because it sounds like gets challenging to

1369
00:59:01,560 --> 00:59:03,400
Speaker 1: take a CEO and say, by the way, on the side,

1370
00:59:03,440 --> 00:59:05,920
Speaker 1: we want you to get your you know, your ms

1371
00:59:06,000 --> 00:59:09,560
Speaker 1: in in computer science over at Stanford at night, that

1372
00:59:09,760 --> 00:59:12,800
Speaker 1: that doesn't seem like your first breakthrough product. At the

1373
00:59:12,880 --> 00:59:14,840
Speaker 1: same time, the same time, I would think it's easier

1374
00:59:14,920 --> 00:59:22,000
Speaker 1: to teach the inexperienced, unskilled but really sophisticated technology technologist

1375
00:59:22,760 --> 00:59:25,720
Speaker 1: the people skills then vice versa. So in the tech industry,

1376
00:59:25,800 --> 00:59:28,280
Speaker 1: we firmly believe exactly what you said from the general

1377
00:59:28,320 --> 00:59:29,959
Speaker 1: pattern as we want to find the great technical founder

1378
00:59:29,960 --> 00:59:32,040
Speaker 1: and helping become a great CEO, and that then, exactly

1379
00:59:32,200 --> 00:59:34,120
Speaker 1: at your point, that's easier than finding a great CEO

1380
00:59:34,200 --> 00:59:37,160
Speaker 1: and trying to turn them into a great product technology visionary.

1381
00:59:37,560 --> 00:59:39,480
Speaker 1: And I think the history of our industry confirms this,

1382
00:59:39,640 --> 00:59:42,280
Speaker 1: like nine out of ten roughly of the big successful

1383
00:59:42,360 --> 00:59:45,040
Speaker 1: tech companies in the last frankly hundred years have been

1384
00:59:45,160 --> 00:59:46,680
Speaker 1: run by their founders for a long periods of time

1385
00:59:46,720 --> 00:59:49,040
Speaker 1: for this reason, starting with you Litt Packard and Intel

1386
00:59:49,120 --> 00:59:50,880
Speaker 1: and you know many many many others you know now

1387
00:59:50,920 --> 00:59:53,600
Speaker 1: today Facebook and Google, right, um, And so that's the

1388
00:59:53,720 --> 00:59:56,880
Speaker 1: thing now because it's easier though doesn't mean it's easy, right,

1389
00:59:57,200 --> 00:59:59,120
Speaker 1: So you know, it is a real like for these

1390
00:59:59,120 --> 01:00:01,840
Speaker 1: people to learning um. You know, for for an engineer

1391
01:00:01,880 --> 01:00:04,120
Speaker 1: who has not managed a large organization before to learn

1392
01:00:04,160 --> 01:00:05,800
Speaker 1: how to do that, like that's a big thing that

1393
01:00:05,880 --> 01:00:07,720
Speaker 1: they really have. They have to really want to do it,

1394
01:00:08,120 --> 01:00:09,640
Speaker 1: and some of them just simply don't. And then you

1395
01:00:09,720 --> 01:00:11,600
Speaker 1: pair them up with somebody who them come up with

1396
01:00:11,640 --> 01:00:13,640
Speaker 1: a pair configuration where they can work with somebody who

1397
01:00:13,640 --> 01:00:15,800
Speaker 1: can help them with that UM. And then some of

1398
01:00:15,840 --> 01:00:17,720
Speaker 1: them just simply can't write. They just hit the wall

1399
01:00:17,760 --> 01:00:19,280
Speaker 1: where it's just not something that they're going to be

1400
01:00:19,320 --> 01:00:21,320
Speaker 1: able to do. And so that there there, there in

1401
01:00:21,440 --> 01:00:24,240
Speaker 1: lies the difficulty of implementing the theory. But that's that's

1402
01:00:24,440 --> 01:00:26,840
Speaker 1: that's our that's what we do. So so two things

1403
01:00:26,920 --> 01:00:28,960
Speaker 1: immediately come to mind when you bring that up. The

1404
01:00:29,040 --> 01:00:32,280
Speaker 1: first is you have Larry and surgery, who are technologists

1405
01:00:32,320 --> 01:00:39,760
Speaker 1: who eventually UM partner up with with their chairman, Eric Schmidt,

1406
01:00:39,800 --> 01:00:43,080
Speaker 1: who who basically brings them along and teaches them how

1407
01:00:43,120 --> 01:00:45,560
Speaker 1: to be c e O S or co c e

1408
01:00:45,680 --> 01:00:47,040
Speaker 1: O S or what if we want to call them.

1409
01:00:47,120 --> 01:00:51,040
Speaker 1: But the other example that leaps to minds is Marcus

1410
01:00:51,080 --> 01:00:54,080
Speaker 1: Zuckerberg and Facebook. So let's let's talk about that for

1411
01:00:54,160 --> 01:00:56,160
Speaker 1: a moment. You're a friend of his, you're a mentor.

1412
01:00:56,480 --> 01:01:00,080
Speaker 1: Tell us how that relationship came about and how how

1413
01:01:00,160 --> 01:01:05,960
Speaker 1: have you helped Zuckerberg become one of the leading CEOs

1414
01:01:06,200 --> 01:01:08,880
Speaker 1: in the world. So, first of all, I should disclaim

1415
01:01:08,920 --> 01:01:11,440
Speaker 1: most credit on this, which is Mark has done most

1416
01:01:11,480 --> 01:01:13,880
Speaker 1: of this on Zone. He has been advised and helped

1417
01:01:13,880 --> 01:01:15,760
Speaker 1: along the way by a lot of people. So I'm

1418
01:01:15,760 --> 01:01:17,200
Speaker 1: not I'm not going to take any sort of giant,

1419
01:01:17,520 --> 01:01:19,640
Speaker 1: giant credit on this one. Um I did that. I

1420
01:01:19,880 --> 01:01:21,200
Speaker 1: did get to I did get to see it, and

1421
01:01:21,440 --> 01:01:22,480
Speaker 1: you know I was I did get to be in

1422
01:01:22,520 --> 01:01:24,680
Speaker 1: the room for a fair amount of it. So um

1423
01:01:24,920 --> 01:01:27,200
Speaker 1: So I met Mark through Peter tele after he first

1424
01:01:27,240 --> 01:01:29,200
Speaker 1: invested in the company, and this was when Facebook was

1425
01:01:29,600 --> 01:01:33,160
Speaker 1: four or five people sitting in bean bags. UM in

1426
01:01:33,320 --> 01:01:35,920
Speaker 1: Palo alto uh in an office with very colorful graffiti

1427
01:01:36,000 --> 01:01:38,920
Speaker 1: on the walls in the very early days. Um, and

1428
01:01:39,320 --> 01:01:41,200
Speaker 1: it was just crystal clear from the start. I mean

1429
01:01:41,280 --> 01:01:42,840
Speaker 1: it was not I don't think clear that Facebook was

1430
01:01:42,880 --> 01:01:45,200
Speaker 1: going to become you know, the heam come because just

1431
01:01:45,280 --> 01:01:47,160
Speaker 1: how how often does that ever happen? And how how

1432
01:01:47,240 --> 01:01:48,960
Speaker 1: can you actually predict that? Do you want you want

1433
01:01:48,960 --> 01:01:50,560
Speaker 1: me to go through the list of how often that's

1434
01:01:50,600 --> 01:01:52,720
Speaker 1: happened with you? Or should we save that for a

1435
01:01:52,840 --> 01:01:54,960
Speaker 1: later segment? Not not very not very freaking often. I

1436
01:01:54,960 --> 01:01:58,320
Speaker 1: can tell you that. So um so uh. I will say, like,

1437
01:01:58,400 --> 01:01:59,840
Speaker 1: it was very clear from the beginning that Mark was

1438
01:01:59,880 --> 01:02:02,000
Speaker 1: a I call him a learning machine. Um, he just

1439
01:02:02,120 --> 01:02:04,560
Speaker 1: siphons in information. And then this this goes to what

1440
01:02:04,640 --> 01:02:07,640
Speaker 1: I mentioned earlier. It's it's it's it's technical founders. It's both. Um,

1441
01:02:07,800 --> 01:02:09,480
Speaker 1: it's both can they do it? But also do they

1442
01:02:09,520 --> 01:02:10,960
Speaker 1: want to do it? And a big part of that

1443
01:02:11,120 --> 01:02:13,600
Speaker 1: for both of those is can they learn? And by

1444
01:02:13,640 --> 01:02:15,320
Speaker 1: the way, there there are people who just simply hit

1445
01:02:15,360 --> 01:02:16,960
Speaker 1: the wall where they just do not want to learn anymore,

1446
01:02:17,080 --> 01:02:18,480
Speaker 1: Like and we've all dealt with people like that where

1447
01:02:18,800 --> 01:02:21,360
Speaker 1: say they're impervious to new information, right, because they've deliberately

1448
01:02:21,360 --> 01:02:23,920
Speaker 1: set themselves up where it's like any new information at

1449
01:02:23,960 --> 01:02:25,320
Speaker 1: this point would be a threat. And so I'm just

1450
01:02:25,440 --> 01:02:28,440
Speaker 1: I'm done. They tap out and finished. They just they

1451
01:02:28,480 --> 01:02:30,240
Speaker 1: piked like they just peeked in terms of everything that

1452
01:02:30,280 --> 01:02:31,880
Speaker 1: they're ever going to know and absorbed like that's just

1453
01:02:32,000 --> 01:02:33,600
Speaker 1: the way that they've decided to live their lives. And

1454
01:02:33,720 --> 01:02:35,520
Speaker 1: so to do what Mark has done, you need to

1455
01:02:35,520 --> 01:02:37,440
Speaker 1: be these that opposite of that. You need to just

1456
01:02:37,560 --> 01:02:40,880
Speaker 1: constantly be pulling in new information until actually curious devouring

1457
01:02:41,080 --> 01:02:43,760
Speaker 1: whatever comes cross every possible topic that you can imagine

1458
01:02:43,800 --> 01:02:45,200
Speaker 1: it's going to be relevant to what you're doing. And

1459
01:02:45,240 --> 01:02:47,600
Speaker 1: so Mark, Mark has just been the way I think Marcus,

1460
01:02:47,600 --> 01:02:49,680
Speaker 1: he's the most systematic person I think I've ever worked

1461
01:02:49,720 --> 01:02:53,120
Speaker 1: with it, just pulling it and just extracting the information. Um.

1462
01:02:53,280 --> 01:02:55,520
Speaker 1: And that was true when he was really young, right

1463
01:02:55,640 --> 01:02:57,680
Speaker 1: and was not well known, um and he was very

1464
01:02:58,000 --> 01:02:59,960
Speaker 1: determined to go get the information from people who knew.

1465
01:03:00,440 --> 01:03:02,439
Speaker 1: And then it's it's only magnified out as he's grown

1466
01:03:02,480 --> 01:03:03,760
Speaker 1: and by the way he continue know he's out on

1467
01:03:03,800 --> 01:03:05,680
Speaker 1: this tour right now, you know, touring all fifty states,

1468
01:03:05,760 --> 01:03:08,240
Speaker 1: like from Mark standpoint that there's all kinds of speculation

1469
01:03:08,280 --> 01:03:10,120
Speaker 1: on that. But from Mark standpoint, the whole purpose of

1470
01:03:10,200 --> 01:03:12,880
Speaker 1: that is learning, not that he's running for president when

1471
01:03:12,880 --> 01:03:17,160
Speaker 1: he turns he's where he swears not, but yes, whole

1472
01:03:17,400 --> 01:03:19,280
Speaker 1: when he talks about that, like when you talked about

1473
01:03:19,280 --> 01:03:20,520
Speaker 1: that with us, when he said he's gonna go do

1474
01:03:20,560 --> 01:03:22,080
Speaker 1: it's all I'm gonna go learn. I'm gonna go learn

1475
01:03:22,080 --> 01:03:23,919
Speaker 1: about the Country'm gonna learn about every state. I'm gonna

1476
01:03:23,960 --> 01:03:25,800
Speaker 1: learn about people who aren't like me, and and so

1477
01:03:26,040 --> 01:03:28,000
Speaker 1: and for me. So for me, it was not surprising

1478
01:03:28,040 --> 01:03:30,400
Speaker 1: in the sense of it's completely consistent with how he operates.

1479
01:03:30,640 --> 01:03:32,240
Speaker 1: And I think this is an area in which he

1480
01:03:32,400 --> 01:03:34,800
Speaker 1: is a much He's a real role model on this,

1481
01:03:34,960 --> 01:03:36,720
Speaker 1: Like I I really think, like I think a lot

1482
01:03:36,760 --> 01:03:38,320
Speaker 1: of founders like look at Mark and they try to

1483
01:03:38,440 --> 01:03:40,000
Speaker 1: drive like how do I get good at I don't know,

1484
01:03:40,120 --> 01:03:42,680
Speaker 1: viral marketing or data da whatever product? He just wants

1485
01:03:42,720 --> 01:03:44,200
Speaker 1: to take it all. I think if more people just

1486
01:03:44,240 --> 01:03:45,640
Speaker 1: look at Mark and said, how do I learn like that,

1487
01:03:46,920 --> 01:03:48,640
Speaker 1: they would be much better. You're on the board of

1488
01:03:48,680 --> 01:03:52,120
Speaker 1: directors of Facebook. What's the reaction when the CEO and

1489
01:03:52,240 --> 01:03:55,760
Speaker 1: founder says, hey, guys, I'm going around the country. I'm

1490
01:03:55,800 --> 01:03:57,920
Speaker 1: gonna go to all fifty states because I think people

1491
01:03:58,440 --> 01:04:01,200
Speaker 1: have interesting things to say and I want to learn.

1492
01:04:02,200 --> 01:04:04,320
Speaker 1: Is there eyebrows raised or it's like, oh, that's just

1493
01:04:04,480 --> 01:04:06,760
Speaker 1: Mark being Mark. Go ahead, yeah, I mean Mark, Mark

1494
01:04:06,840 --> 01:04:08,920
Speaker 1: does things like that. Mart Market. Is My point is

1495
01:04:08,920 --> 01:04:11,040
Speaker 1: Mark has always stretched himself like he stretches. He comes

1496
01:04:11,120 --> 01:04:13,320
Speaker 1: up with a quest every year, goes at his question quest,

1497
01:04:13,360 --> 01:04:15,200
Speaker 1: the quest. A couple of years ago, I remember he

1498
01:04:15,320 --> 01:04:18,760
Speaker 1: was learning some obscure language if memories, sir, I don't

1499
01:04:18,760 --> 01:04:22,800
Speaker 1: remember what it was. Mandarin Mandarin right, well, not so obscure,

1500
01:04:22,920 --> 01:04:26,000
Speaker 1: but a complex, uh language. So he does a quest

1501
01:04:26,040 --> 01:04:28,840
Speaker 1: every year and just basically keeps learning, keeps learning. He's

1502
01:04:28,840 --> 01:04:29,880
Speaker 1: been doing this is if you know, I don't know

1503
01:04:29,920 --> 01:04:31,640
Speaker 1: for ten years now or something. It's it's and so

1504
01:04:31,800 --> 01:04:33,640
Speaker 1: there's been through a variety of things, and he talks

1505
01:04:33,640 --> 01:04:35,880
Speaker 1: about this. He's very open about this um and so

1506
01:04:36,000 --> 01:04:37,840
Speaker 1: you know, he just he just goes out in siphons.

1507
01:04:37,880 --> 01:04:39,439
Speaker 1: And so he's a guy. He's a guy who's literally

1508
01:04:39,480 --> 01:04:40,960
Speaker 1: able to learn from some of the most you know,

1509
01:04:41,000 --> 01:04:43,520
Speaker 1: experience and accomplish people in the world on certain topics.

1510
01:04:43,560 --> 01:04:44,760
Speaker 1: But you know, spend a day on a farm not

1511
01:04:44,800 --> 01:04:46,840
Speaker 1: far from where I grew up in Wisconsin and dow

1512
01:04:46,840 --> 01:04:48,360
Speaker 1: to milk cows like and he's just as far and

1513
01:04:48,400 --> 01:04:52,560
Speaker 1: he's happy to do that. He's absolutely so he's living

1514
01:04:52,600 --> 01:04:54,360
Speaker 1: the life he always wants to live. I just want

1515
01:04:54,400 --> 01:04:57,080
Speaker 1: to just suck everything I can possibly into my head

1516
01:04:57,520 --> 01:05:02,040
Speaker 1: as as rapidly as I possibly can. Well, that's pretty fascinating. Um,

1517
01:05:02,800 --> 01:05:05,920
Speaker 1: let's keep going about the deal days you do. I

1518
01:05:06,000 --> 01:05:09,040
Speaker 1: want to talk about some some more of the combination

1519
01:05:09,240 --> 01:05:13,400
Speaker 1: angel investing late stage. So you do deal days, and

1520
01:05:13,600 --> 01:05:16,080
Speaker 1: and how many people will the firm see in a

1521
01:05:16,200 --> 01:05:19,360
Speaker 1: given day or week when those are running well, we have,

1522
01:05:19,480 --> 01:05:21,360
Speaker 1: like I said, we do about two thousand a year total.

1523
01:05:21,520 --> 01:05:23,240
Speaker 1: So there's a whole funnel in the whole system, and

1524
01:05:23,240 --> 01:05:24,400
Speaker 1: there's a lot of people in the firm who meet

1525
01:05:24,400 --> 01:05:26,480
Speaker 1: companies the whole process of kind of distilling the information.

1526
01:05:26,560 --> 01:05:28,240
Speaker 1: So it's you know, it's dozens of week you know,

1527
01:05:28,360 --> 01:05:31,120
Speaker 1: kind of across the firm, and then we see pitches

1528
01:05:31,320 --> 01:05:34,560
Speaker 1: on usually Monday and Thursday, or Monday Monday Thursday generally, UM,

1529
01:05:34,840 --> 01:05:37,440
Speaker 1: and as the GP team and that's you know, I

1530
01:05:37,440 --> 01:05:40,080
Speaker 1: don't know, Monday, it was five this week on Thursday,

1531
01:05:40,080 --> 01:05:42,440
Speaker 1: I think there's two or three more. Um, and this

1532
01:05:42,600 --> 01:05:45,600
Speaker 1: is every week, so there's occasional vacations and stuff, but

1533
01:05:45,640 --> 01:05:49,560
Speaker 1: pretty much you're running through three hundred, four hundred pitches.

1534
01:05:49,720 --> 01:05:52,000
Speaker 1: The GPS will see everything the way. These are called

1535
01:05:52,040 --> 01:05:53,560
Speaker 1: the all GP pitches, and so these are ones where

1536
01:05:53,560 --> 01:05:55,720
Speaker 1: everybody's in the room, um, and then many others. There's

1537
01:05:55,720 --> 01:05:57,360
Speaker 1: many others that happened during the week where one or

1538
01:05:57,360 --> 01:05:59,280
Speaker 1: two or three GPS might see them. Right, So it's

1539
01:05:59,280 --> 01:06:01,920
Speaker 1: a giant fun It shrinks down and down until and

1540
01:06:02,000 --> 01:06:04,200
Speaker 1: out of those let's call it three hundred a year,

1541
01:06:04,880 --> 01:06:08,360
Speaker 1: you're looking at ten percent, very very seriously. Yeah, that's

1542
01:06:08,560 --> 01:06:11,040
Speaker 1: about right. And when you decide to do an investment

1543
01:06:11,080 --> 01:06:15,960
Speaker 1: in them, are these usually angel fifty thousand dollar rounds

1544
01:06:16,120 --> 01:06:18,880
Speaker 1: or these much more substantial rounds or both. Yeah, So

1545
01:06:18,960 --> 01:06:21,280
Speaker 1: we don't really do the fifty anymore. We used to

1546
01:06:21,280 --> 01:06:23,080
Speaker 1: do more of those when we got started, but we

1547
01:06:23,280 --> 01:06:25,000
Speaker 1: don't do those as we tend to get much more.

1548
01:06:25,160 --> 01:06:26,440
Speaker 1: We tend to kind of go all in or not.

1549
01:06:27,240 --> 01:06:29,000
Speaker 1: So that I could go through that, but like that,

1550
01:06:29,440 --> 01:06:32,040
Speaker 1: why not we work, well, why all in? Why not?

1551
01:06:32,280 --> 01:06:34,760
Speaker 1: Well we'll get to three other investors and will hedge

1552
01:06:34,800 --> 01:06:37,000
Speaker 1: our bets and go in partially. Well, there's still so

1553
01:06:37,200 --> 01:06:39,080
Speaker 1: there's there a couple of couple of reasons. UM. One

1554
01:06:39,240 --> 01:06:41,480
Speaker 1: is just the level of one is just an economic thing,

1555
01:06:41,520 --> 01:06:43,400
Speaker 1: which is for us to be the opportunity. So there's

1556
01:06:43,440 --> 01:06:45,840
Speaker 1: costs and then there's opportunity costs. And there's always opportunity

1557
01:06:45,880 --> 01:06:47,720
Speaker 1: costs on our time and on our ability. How many

1558
01:06:47,720 --> 01:06:49,560
Speaker 1: companies gonna work with? Is it more of the time

1559
01:06:49,720 --> 01:06:51,280
Speaker 1: or the money. It's it's more of the time. It's

1560
01:06:51,280 --> 01:06:53,400
Speaker 1: more the opportunity costs. It's every deal, every deal and

1561
01:06:53,480 --> 01:06:55,560
Speaker 1: we do fills a slot that can't be then filled

1562
01:06:55,640 --> 01:06:57,640
Speaker 1: by you know, another hundred deals that you might have

1563
01:06:57,680 --> 01:06:59,680
Speaker 1: done instead. And right, so there's a big there's a

1564
01:06:59,760 --> 01:07:01,280
Speaker 1: there's always there were any deal you do a big

1565
01:07:01,400 --> 01:07:03,520
Speaker 1: risk that you're for closing some other deal that much better,

1566
01:07:03,560 --> 01:07:05,560
Speaker 1: and so you have to just think very hard about it.

1567
01:07:05,800 --> 01:07:08,360
Speaker 1: And right, it's it's it's it's time um. And then

1568
01:07:08,600 --> 01:07:10,440
Speaker 1: UM that that that's a big part of it. UM.

1569
01:07:10,520 --> 01:07:12,720
Speaker 1: Then there's also just the economics of it UM, which

1570
01:07:12,840 --> 01:07:14,920
Speaker 1: is just as a consequence of that, when when you

1571
01:07:14,960 --> 01:07:16,560
Speaker 1: have a hit, like how much of it you actually

1572
01:07:16,560 --> 01:07:19,080
Speaker 1: own actually really drives your returns um. And so we

1573
01:07:19,160 --> 01:07:21,440
Speaker 1: have a responsibility to our parall piece to try to

1574
01:07:21,520 --> 01:07:23,880
Speaker 1: you know, really have the the hits pay off um.

1575
01:07:24,120 --> 01:07:26,080
Speaker 1: And then part of it is just a full engagement model,

1576
01:07:26,120 --> 01:07:27,880
Speaker 1: like we really don't like giving up. We really don't

1577
01:07:27,920 --> 01:07:30,000
Speaker 1: like we don't you know, there there are venture firms

1578
01:07:30,040 --> 01:07:31,720
Speaker 1: that if your company is not going well, they just

1579
01:07:31,800 --> 01:07:34,440
Speaker 1: they'll mysteriously vanage on the visa website like they just

1580
01:07:34,520 --> 01:07:37,160
Speaker 1: never get talked, they get dropped down the memory hole. Um.

1581
01:07:37,640 --> 01:07:39,600
Speaker 1: And uh, we don't tend to do that. We're just

1582
01:07:39,960 --> 01:07:41,720
Speaker 1: and it comes from the fact we've all been founders

1583
01:07:41,760 --> 01:07:43,160
Speaker 1: and CEO is like, we just we don't tend to

1584
01:07:43,200 --> 01:07:45,000
Speaker 1: give up. Um, you know what it's like when you're

1585
01:07:45,040 --> 01:07:47,760
Speaker 1: on the receiving end of unreturned phone calls. That is right, yeah,

1586
01:07:47,800 --> 01:07:49,520
Speaker 1: and so and and my view is like our we

1587
01:07:49,840 --> 01:07:51,080
Speaker 1: make our money on the ones that work, and we

1588
01:07:51,160 --> 01:07:53,240
Speaker 1: probably I think make our reputations and the ones that don't.

1589
01:07:53,720 --> 01:07:57,760
Speaker 1: That's a fascinating line right there, because the math behind

1590
01:07:58,040 --> 01:08:00,960
Speaker 1: venture investing is, look, when you go out and buy

1591
01:08:01,000 --> 01:08:03,960
Speaker 1: stocks in the market, fifty of them will do nothing,

1592
01:08:04,280 --> 01:08:06,560
Speaker 1: thirty of them will work, and uh, ten of them

1593
01:08:06,560 --> 01:08:09,360
Speaker 1: will be disasters, and a handful them will work really well.

1594
01:08:09,880 --> 01:08:13,240
Speaker 1: The odds are far more daunting with venture investing. It's

1595
01:08:13,320 --> 01:08:16,240
Speaker 1: it's almost ten x that you have and I think

1596
01:08:16,320 --> 01:08:19,640
Speaker 1: you've you've written this elsewhere. You'll make a hundred investments

1597
01:08:19,760 --> 01:08:25,040
Speaker 1: and fifty of them just crash. Okay, But it's the

1598
01:08:25,080 --> 01:08:26,600
Speaker 1: one or two at the top of the pyramid that

1599
01:08:26,680 --> 01:08:28,960
Speaker 1: pay for the other for the whole thing, right exactly.

1600
01:08:29,040 --> 01:08:31,040
Speaker 1: And that's actually roughly the math. So yeah, So fifty

1601
01:08:31,439 --> 01:08:34,040
Speaker 1: in top tier venture capital, top descile venture capital across

1602
01:08:34,320 --> 01:08:36,439
Speaker 1: forty years of data on this, which we have access

1603
01:08:36,439 --> 01:08:39,240
Speaker 1: to a couple different ways. Um, basically half the deal's

1604
01:08:39,479 --> 01:08:41,720
Speaker 1: good news is it's not a temper cent success, right,

1605
01:08:41,720 --> 01:08:43,439
Speaker 1: it's a fifty percent success right right, So it's not

1606
01:08:43,520 --> 01:08:44,880
Speaker 1: as bad as some people think. But it is a

1607
01:08:44,920 --> 01:08:47,280
Speaker 1: fifty percent success right, meaning it's a fifty percent failure,

1608
01:08:47,360 --> 01:08:51,320
Speaker 1: right um. And then meaning fifty percent failure meaning zero

1609
01:08:51,439 --> 01:08:55,960
Speaker 1: return on that half about about about half of the

1610
01:08:56,680 --> 01:08:58,559
Speaker 1: go to zero and then and then the other half

1611
01:08:58,600 --> 01:09:02,240
Speaker 1: returns between zero and one x. They return, recover some capital,

1612
01:09:02,280 --> 01:09:04,720
Speaker 1: but not all they message capital. And then the sort

1613
01:09:04,760 --> 01:09:07,920
Speaker 1: of that third quartile of sort of fifty, they return

1614
01:09:08,000 --> 01:09:10,519
Speaker 1: you know, one to three x and then it's basically

1615
01:09:10,600 --> 01:09:13,280
Speaker 1: the upper quartile that returns sort of you know, god

1616
01:09:13,320 --> 01:09:15,600
Speaker 1: Welling returns three x plus. And then if you and

1617
01:09:15,680 --> 01:09:17,559
Speaker 1: some of the three x plus is a lot more

1618
01:09:17,640 --> 01:09:19,560
Speaker 1: plus than So that's the good the good news. The

1619
01:09:19,600 --> 01:09:20,960
Speaker 1: bad news is, yeah, you do get that. You you

1620
01:09:21,040 --> 01:09:23,120
Speaker 1: do get the ones where you lose one x pretty frequently.

1621
01:09:23,200 --> 01:09:25,439
Speaker 1: The good news is they if they, if they work,

1622
01:09:25,479 --> 01:09:27,519
Speaker 1: they can make a thousand x right, and so you

1623
01:09:27,680 --> 01:09:29,559
Speaker 1: and then the LP. This is the great thing about

1624
01:09:29,600 --> 01:09:32,840
Speaker 1: kind of the venture ecosystem. The LPs don't care. So

1625
01:09:33,000 --> 01:09:34,600
Speaker 1: for example, in the data, they don't care how you

1626
01:09:34,680 --> 01:09:35,920
Speaker 1: make the money just as on as you do. So

1627
01:09:36,040 --> 01:09:38,280
Speaker 1: that in the data, for example, the top performing venture

1628
01:09:38,360 --> 01:09:41,120
Speaker 1: from strike out more often called the baby Ruth effect,

1629
01:09:41,439 --> 01:09:44,160
Speaker 1: the top performing firms have more zeros. Right, they're swinging,

1630
01:09:44,160 --> 01:09:45,720
Speaker 1: they're only swinging for home runs and so and by

1631
01:09:45,760 --> 01:09:47,599
Speaker 1: definition in venture, if you're only swinging for home runs,

1632
01:09:47,640 --> 01:09:50,600
Speaker 1: you're taking more risk on crazier ideas. Makes sense, and

1633
01:09:50,760 --> 01:09:54,439
Speaker 1: it's like it's like you know, the famously they're they're

1634
01:09:54,479 --> 01:09:55,960
Speaker 1: Google Fund. It was the fund that Google and it

1635
01:09:56,040 --> 01:09:59,479
Speaker 1: had segue right and Segway was a legendary disaster, and

1636
01:09:59,640 --> 01:10:01,640
Speaker 1: you know, out all this negative publicity and you know

1637
01:10:01,720 --> 01:10:03,360
Speaker 1: they guess what they lost one x their money in

1638
01:10:03,400 --> 01:10:08,080
Speaker 1: segue right, guess how much money they made on Google right, right?

1639
01:10:08,600 --> 01:10:10,800
Speaker 1: And so so. So the good news on it is

1640
01:10:10,880 --> 01:10:12,800
Speaker 1: we we and this is something that we're The US

1641
01:10:12,840 --> 01:10:14,880
Speaker 1: in particular has a real edge on this, I think

1642
01:10:15,000 --> 01:10:17,719
Speaker 1: is we we have a base of limited partners institutional

1643
01:10:17,800 --> 01:10:20,479
Speaker 1: investors in the US that deeply understand that this is

1644
01:10:20,520 --> 01:10:22,479
Speaker 1: how it works. And so when we have one that

1645
01:10:22,520 --> 01:10:24,000
Speaker 1: goes to zero, they don't call us up and yell

1646
01:10:24,040 --> 01:10:26,920
Speaker 1: at us and complain there is They're like, yeah, that's

1647
01:10:26,960 --> 01:10:28,960
Speaker 1: the cost of doing business. Um. And in fact, we've

1648
01:10:28,960 --> 01:10:30,920
Speaker 1: had helpings ask us why we actually don't have more failures?

1649
01:10:31,000 --> 01:10:33,640
Speaker 1: Like are you taking enough risk? Really, what they want

1650
01:10:33,680 --> 01:10:35,599
Speaker 1: to know is, okay, of the companies that are working,

1651
01:10:35,720 --> 01:10:38,280
Speaker 1: how well are they working? And and we call this

1652
01:10:38,320 --> 01:10:40,639
Speaker 1: to how high is up? Question? How high is high

1653
01:10:40,680 --> 01:10:43,479
Speaker 1: is up? How big could they get? Right? And then

1654
01:10:43,560 --> 01:10:45,479
Speaker 1: if and then literally, Alan venture, if you came back,

1655
01:10:45,520 --> 01:10:47,000
Speaker 1: if you were lucky enough in venture to come back

1656
01:10:47,040 --> 01:10:49,800
Speaker 1: every year with one big, giant home run, and all

1657
01:10:49,800 --> 01:10:51,800
Speaker 1: the rest are failures, they'd be totally happy like so,

1658
01:10:52,040 --> 01:10:53,960
Speaker 1: so they would not complain about that at all. And

1659
01:10:54,120 --> 01:10:55,920
Speaker 1: of course it's very, very different than how the rest

1660
01:10:55,960 --> 01:10:59,040
Speaker 1: of Wall Street operates, no doubt. Do you run into

1661
01:10:59,600 --> 01:11:01,840
Speaker 1: every time time there's that thousand X do you run

1662
01:11:01,920 --> 01:11:05,000
Speaker 1: into How many times do people say, well, this is

1663
01:11:05,040 --> 01:11:08,240
Speaker 1: the next Facebook? No, please stop saying that, do you do?

1664
01:11:08,320 --> 01:11:12,120
Speaker 1: You sort of end up in that mode where whatever

1665
01:11:12,320 --> 01:11:14,519
Speaker 1: was working, Hey, let me explain to you why our

1666
01:11:14,640 --> 01:11:17,800
Speaker 1: company is the next X, y Z. Do these cliches

1667
01:11:17,920 --> 01:11:20,040
Speaker 1: begin to make you crazy? Yeah, they do. Well, they're

1668
01:11:20,040 --> 01:11:22,439
Speaker 1: they're a tip. They usually the really great founders tend

1669
01:11:22,479 --> 01:11:25,360
Speaker 1: not to say that. I hope people are listening to

1670
01:11:26,000 --> 01:11:30,040
Speaker 1: all these suggestions. This is gonna be a how to pitch.

1671
01:11:30,360 --> 01:11:34,720
Speaker 1: Indrees and Horowitz don't go there without having heard all

1672
01:11:34,800 --> 01:11:39,200
Speaker 1: these suggestions from this guy named Mark Andres. And I

1673
01:11:39,240 --> 01:11:42,240
Speaker 1: would imagine that at a certain point you halfway through

1674
01:11:42,240 --> 01:11:43,920
Speaker 1: a pitch, do you look at each other and like,

1675
01:11:44,040 --> 01:11:46,840
Speaker 1: all right, we're done with this. Every once in a while, everyone,

1676
01:11:47,080 --> 01:11:49,280
Speaker 1: I will say every once in a while, um, but um,

1677
01:11:49,479 --> 01:11:51,960
Speaker 1: but I will say this, um. The privilege of the

1678
01:11:52,080 --> 01:11:55,639
Speaker 1: job is that these are two thousand of the smartest

1679
01:11:55,640 --> 01:11:57,599
Speaker 1: people in the world in the remains in which they're operating,

1680
01:11:57,600 --> 01:11:59,320
Speaker 1: who we get to do with every year, and so

1681
01:11:59,760 --> 01:12:01,800
Speaker 1: the other that we we learned so much and even

1682
01:12:01,880 --> 01:12:03,360
Speaker 1: the deals we're not gonna we're not gonna do it.

1683
01:12:03,360 --> 01:12:04,720
Speaker 1: There's something we've just made a mistake and we've seen

1684
01:12:04,720 --> 01:12:06,160
Speaker 1: a pitch that we shouldn't seen because we're never going

1685
01:12:06,200 --> 01:12:08,400
Speaker 1: to do the deal for some reason. Um, and there's

1686
01:12:08,439 --> 01:12:10,120
Speaker 1: you know, a dozen reasons why that could be the case. Um.

1687
01:12:10,240 --> 01:12:13,160
Speaker 1: We generally, you generally learned just a lot of contextual

1688
01:12:13,200 --> 01:12:16,360
Speaker 1: information about what's happening in that in that industry because

1689
01:12:16,360 --> 01:12:18,439
Speaker 1: these people have to get to that point. They've all

1690
01:12:18,479 --> 01:12:20,519
Speaker 1: thought these things through. Really back to the idea amazing

1691
01:12:20,520 --> 01:12:23,040
Speaker 1: that we're talking about. So that's that's what you just learned.

1692
01:12:23,080 --> 01:12:24,800
Speaker 1: You learn and that it's and by the way, once

1693
01:12:24,800 --> 01:12:26,920
Speaker 1: your experience that it's just a fire hose of it's

1694
01:12:26,960 --> 01:12:28,240
Speaker 1: just the smartest people in the world coming in and

1695
01:12:28,320 --> 01:12:30,080
Speaker 1: spending an hour telling you everything they know, like it's

1696
01:12:30,080 --> 01:12:33,439
Speaker 1: an amazing how how that was a question? Next question

1697
01:12:33,520 --> 01:12:37,040
Speaker 1: is how big of a strategic advantage is getting first

1698
01:12:37,120 --> 01:12:39,840
Speaker 1: looks at at two thousand of the smartest people in

1699
01:12:39,880 --> 01:12:41,360
Speaker 1: the world. Yeah. Yeah, I think that's a big part

1700
01:12:41,400 --> 01:12:42,640
Speaker 1: of it. And I mean, and that's why I'm so

1701
01:12:42,720 --> 01:12:46,360
Speaker 1: I've sort of become kind of semi legendary sort of optimist. Um.

1702
01:12:47,000 --> 01:12:49,200
Speaker 1: It's really hard, I think, to be pessimistic in this job,

1703
01:12:49,280 --> 01:12:51,519
Speaker 1: because you do see all these incredibly fired up people

1704
01:12:51,560 --> 01:12:54,800
Speaker 1: with all these incredibly vivid and brilliant ideas, um, and

1705
01:12:54,920 --> 01:12:56,760
Speaker 1: you you just you get a sense of the productive

1706
01:12:56,800 --> 01:13:01,120
Speaker 1: capacity of capitalism, of the intel rustial pursuits. By the way,

1707
01:13:01,160 --> 01:13:03,280
Speaker 1: it's hard to be too negative when you're looking at

1708
01:13:03,479 --> 01:13:06,360
Speaker 1: the most optimistic inventions in in the universe. And by

1709
01:13:06,360 --> 01:13:08,400
Speaker 1: the way, also the output of the great research universities,

1710
01:13:08,439 --> 01:13:10,000
Speaker 1: like so much of what we do is building you

1711
01:13:10,400 --> 01:13:12,040
Speaker 1: mentioned earlier, so much of what we do is building

1712
01:13:12,040 --> 01:13:13,560
Speaker 1: off and what happens to Stanford and M I. T

1713
01:13:13,640 --> 01:13:15,640
Speaker 1: in Berkeley and all these other schools, and it's just

1714
01:13:15,720 --> 01:13:18,160
Speaker 1: crystal clear, like how amazing they are. So you know,

1715
01:13:18,200 --> 01:13:20,599
Speaker 1: so that it's just this that that that's that's cut

1716
01:13:20,720 --> 01:13:22,439
Speaker 1: sort of the psychic payoff from the job is the

1717
01:13:22,479 --> 01:13:24,880
Speaker 1: ability to do that. And then it just also i think,

1718
01:13:25,000 --> 01:13:28,080
Speaker 1: just becomes clear like there are contra all that, you know,

1719
01:13:28,200 --> 01:13:30,599
Speaker 1: the headlines are just all relentless negative these days, there

1720
01:13:30,600 --> 01:13:32,320
Speaker 1: are a lot of smart people in the world. There

1721
01:13:32,320 --> 01:13:33,559
Speaker 1: are a lot of people have a lot of energy

1722
01:13:33,560 --> 01:13:35,120
Speaker 1: in the world who want to legitimately want to build

1723
01:13:35,160 --> 01:13:38,920
Speaker 1: new things. Um. It's very exciting and increasingly not just here, right,

1724
01:13:39,000 --> 01:13:40,679
Speaker 1: not just in Silicon Valley, not just in the US,

1725
01:13:40,800 --> 01:13:43,200
Speaker 1: not just in the West, all over the world, right,

1726
01:13:43,240 --> 01:13:44,560
Speaker 1: And and so that that's the other side of it,

1727
01:13:44,640 --> 01:13:47,280
Speaker 1: is this, this culture of entrepreneurship is really spreading, um.

1728
01:13:47,439 --> 01:13:49,280
Speaker 1: And you know, we're gonna benefit from that in the

1729
01:13:49,320 --> 01:13:51,040
Speaker 1: sense of more opportunities, but I think the entire world

1730
01:13:51,080 --> 01:13:53,240
Speaker 1: is going to benefit from that. So your focus mostly

1731
01:13:53,360 --> 01:13:55,880
Speaker 1: here on the West Coast, primarily Silicon Valley, you're not

1732
01:13:56,040 --> 01:13:58,000
Speaker 1: looking at China, You're not looking at India. Is that

1733
01:13:58,120 --> 01:14:02,240
Speaker 1: a fair uh servation. So all of our companies want

1734
01:14:02,320 --> 01:14:04,560
Speaker 1: to be multinationals, and all of our companies want to

1735
01:14:04,600 --> 01:14:06,400
Speaker 1: operate on the ground in all these countries, and all

1736
01:14:06,400 --> 01:14:08,280
Speaker 1: of our companies hire a huge number of immigrants, and

1737
01:14:08,360 --> 01:14:10,519
Speaker 1: so our our companies by definition, will end up with

1738
01:14:10,600 --> 01:14:13,200
Speaker 1: a lot of Chinese immigrant, Indian immigrant. And then by

1739
01:14:13,240 --> 01:14:14,840
Speaker 1: the way, a lot of them are actually built offices

1740
01:14:14,920 --> 01:14:17,080
Speaker 1: on the ground. A bunch of companies with offices in

1741
01:14:17,200 --> 01:14:20,280
Speaker 1: China building up big presences indeed these other countries. UM,

1742
01:14:20,439 --> 01:14:23,040
Speaker 1: so we're heavily exposed to the sort of knowledge flow

1743
01:14:23,120 --> 01:14:25,600
Speaker 1: and talent that way. UM, we do I guess that

1744
01:14:25,640 --> 01:14:27,479
Speaker 1: though we do think venture capital is a craft, there

1745
01:14:27,560 --> 01:14:29,760
Speaker 1: is a craft to the business UM and it's it's

1746
01:14:29,760 --> 01:14:31,519
Speaker 1: a lot of it's the relationship with the board, the

1747
01:14:31,600 --> 01:14:34,120
Speaker 1: board members, and the founders and the team UM. And

1748
01:14:34,240 --> 01:14:38,080
Speaker 1: so there's a local dynamic to it. UM. The number

1749
01:14:38,080 --> 01:14:40,120
Speaker 1: of venture firms that have successfully figured out how to

1750
01:14:40,160 --> 01:14:42,720
Speaker 1: operate in multiple geographies and stay world class, it's a

1751
01:14:42,800 --> 01:14:45,280
Speaker 1: very short list, most of the most most generally that

1752
01:14:45,360 --> 01:14:47,240
Speaker 1: doesn't work. And so we we've just we've made a

1753
01:14:47,320 --> 01:14:48,840
Speaker 1: decision at least for the time being, to try to

1754
01:14:48,880 --> 01:14:51,599
Speaker 1: be really good in Silicon Valley. So, by the way,

1755
01:14:51,640 --> 01:14:53,360
Speaker 1: we do fund companies outside the valley, but just on

1756
01:14:53,520 --> 01:14:55,799
Speaker 1: on an exception basis, like they have to be super special.

1757
01:14:55,960 --> 01:14:57,519
Speaker 1: So we do have some of those. So you you

1758
01:14:57,640 --> 01:15:02,559
Speaker 1: focus mostly local. Your you're a software absolutist, no clean tech,

1759
01:15:02,760 --> 01:15:06,479
Speaker 1: no rocket ships, no electric cars. Is that still true?

1760
01:15:06,600 --> 01:15:10,240
Speaker 1: It's it's software and software only, so software at the core.

1761
01:15:10,479 --> 01:15:12,479
Speaker 1: So the rule of the rule is software at the core,

1762
01:15:12,600 --> 01:15:14,560
Speaker 1: so there has to be We just think there's a

1763
01:15:14,600 --> 01:15:17,200
Speaker 1: particular magic around software. There's a particular magic around how

1764
01:15:17,280 --> 01:15:19,360
Speaker 1: you can create things with software. Right. It's it's it's

1765
01:15:19,400 --> 01:15:21,360
Speaker 1: I call alchemy, like you type on a keyboard and

1766
01:15:21,360 --> 01:15:23,320
Speaker 1: then things happen in the real world like that. That's

1767
01:15:23,320 --> 01:15:25,839
Speaker 1: an amazing process. There's there's just kind of huge intellectual

1768
01:15:25,920 --> 01:15:28,160
Speaker 1: leverage there, and then as a consequence, there is huge

1769
01:15:28,240 --> 01:15:31,400
Speaker 1: economic leverage, right, which is there's likes to say capital,

1770
01:15:31,600 --> 01:15:35,040
Speaker 1: software is the labor that creates capital. Software is labor

1771
01:15:35,080 --> 01:15:37,439
Speaker 1: that Okay, It's the closest thing to magic will ever have.

1772
01:15:37,520 --> 01:15:39,240
Speaker 1: It's like typing on the keyboard is like casting a

1773
01:15:39,280 --> 01:15:41,320
Speaker 1: spell and then something happens, Right, It's like, you know,

1774
01:15:41,439 --> 01:15:43,840
Speaker 1: that's the lift. Lift Guys they type software and then

1775
01:15:43,840 --> 01:15:45,720
Speaker 1: all of a sudden, real people are being taken on

1776
01:15:45,720 --> 01:15:47,759
Speaker 1: a real rise in real cars, like just by magic,

1777
01:15:47,960 --> 01:15:50,160
Speaker 1: Like just the software just caused that to happen. And

1778
01:15:50,320 --> 01:15:53,040
Speaker 1: so we are looking for that kind of software leverage,

1779
01:15:53,200 --> 01:15:57,000
Speaker 1: the sort of software magic in every company, scalable, magical

1780
01:15:57,360 --> 01:16:01,960
Speaker 1: and the ability to convert labor into capital. And then

1781
01:16:02,080 --> 01:16:04,040
Speaker 1: as a consequence, but as a consequence of that, our

1782
01:16:04,080 --> 01:16:06,360
Speaker 1: companies do end up doing all kinds of different things.

1783
01:16:06,439 --> 01:16:09,240
Speaker 1: And so we have some companies that sell software. We

1784
01:16:09,320 --> 01:16:11,200
Speaker 1: have some companies that provide softwares a service. We have

1785
01:16:11,280 --> 01:16:13,519
Speaker 1: some companies that provide apps. We have some companies that

1786
01:16:13,640 --> 01:16:16,479
Speaker 1: are making hardware. We have some companies that are going

1787
01:16:16,520 --> 01:16:20,240
Speaker 1: after new new healthcare pursuits and biology, material science like there.

1788
01:16:20,360 --> 01:16:23,400
Speaker 1: There's lots of software is becoming so intertwined with the

1789
01:16:23,439 --> 01:16:25,320
Speaker 1: world that there are lots of ways to now use it,

1790
01:16:25,400 --> 01:16:27,560
Speaker 1: to go to leverage it in a different spaces, and

1791
01:16:27,640 --> 01:16:29,479
Speaker 1: so are The range of companies that were funding is

1792
01:16:29,479 --> 01:16:31,519
Speaker 1: broader than ever, but they all have software at the core,

1793
01:16:31,720 --> 01:16:36,759
Speaker 1: software at the core. Um I started out this segment

1794
01:16:36,840 --> 01:16:39,840
Speaker 1: talking about the firm. There's a couple of things I

1795
01:16:39,960 --> 01:16:42,479
Speaker 1: have to ask about the firm because I'm fascinated to

1796
01:16:42,560 --> 01:16:45,439
Speaker 1: buy it. So you used to blog at p Marca,

1797
01:16:45,680 --> 01:16:47,840
Speaker 1: and let me remind you that I warned you to

1798
01:16:47,880 --> 01:16:50,360
Speaker 1: pace yourself, young man. You're gonna burn yourself out. I

1799
01:16:50,360 --> 01:16:53,599
Speaker 1: don't know if you remember that. And and you tweet

1800
01:16:53,640 --> 01:16:57,960
Speaker 1: fairly actively. I know we're on hiatus now and you podcast.

1801
01:16:58,160 --> 01:17:02,120
Speaker 1: You guys have a podcast room at this doesn't sound

1802
01:17:02,240 --> 01:17:06,000
Speaker 1: like it's the typical forms of communication for the old

1803
01:17:06,120 --> 01:17:09,760
Speaker 1: line venture capital front. I mean, everybody has a website

1804
01:17:10,200 --> 01:17:12,200
Speaker 1: and there's sort of this interesting, Hey, here are all

1805
01:17:12,240 --> 01:17:14,760
Speaker 1: the things we oops, we passed on that we shouldn't have.

1806
01:17:15,200 --> 01:17:18,400
Speaker 1: It's sort of this, UM, I guess we could call

1807
01:17:18,479 --> 01:17:24,040
Speaker 1: it humility or attempt at humility. Your distult is very different. Here.

1808
01:17:24,320 --> 01:17:27,200
Speaker 1: Tell me about the thinking behind that. Yeah, so we're

1809
01:17:27,280 --> 01:17:30,439
Speaker 1: just we're trying to teach, like with yourselves, kind of

1810
01:17:30,479 --> 01:17:32,120
Speaker 1: has been fundamentally lucky to have seen a lot of

1811
01:17:32,160 --> 01:17:33,960
Speaker 1: these things, mentioning these things were trying to teach. We

1812
01:17:34,000 --> 01:17:35,519
Speaker 1: think it's by the way to our benefit to teach,

1813
01:17:35,560 --> 01:17:37,200
Speaker 1: because if more people know how to do this, then

1814
01:17:37,280 --> 01:17:38,960
Speaker 1: there's more companies for us to fund and people for

1815
01:17:39,040 --> 01:17:41,000
Speaker 1: us to work with. UM. And then part of it

1816
01:17:41,160 --> 01:17:43,479
Speaker 1: is just look, I think technology has gotten very important

1817
01:17:43,479 --> 01:17:45,320
Speaker 1: in the world, much more than it used to be,

1818
01:17:45,439 --> 01:17:47,799
Speaker 1: and so UM and it's complex and it's hard to understand,

1819
01:17:47,920 --> 01:17:49,599
Speaker 1: and so I think and it's hard to understand from

1820
01:17:49,600 --> 01:17:51,920
Speaker 1: the outside UM, and so a big part of just

1821
01:17:52,040 --> 01:17:53,679
Speaker 1: what we're trying to do is to tell the story

1822
01:17:54,000 --> 01:17:56,640
Speaker 1: of what's happening, not just in our firm, but tell

1823
01:17:56,680 --> 01:17:58,479
Speaker 1: the story of what's happening in the industry and what's

1824
01:17:58,479 --> 01:18:00,479
Speaker 1: happening with all these companies and with the aplications of

1825
01:18:00,520 --> 01:18:02,960
Speaker 1: all these technologies. So you mentioned our podcast series. So

1826
01:18:03,000 --> 01:18:05,080
Speaker 1: I get no credit for that. Our partners here, UM,

1827
01:18:05,560 --> 01:18:07,560
Speaker 1: our partners here have done all the work on that.

1828
01:18:07,720 --> 01:18:11,719
Speaker 1: But it's become like I would say, shockingly amazingly important

1829
01:18:11,720 --> 01:18:15,120
Speaker 1: and influential among people who care about how technologies affect.

1830
01:18:16,280 --> 01:18:18,439
Speaker 1: The level of response we've gotten to that has been

1831
01:18:18,479 --> 01:18:20,120
Speaker 1: like an order magnitude beyond what I would have ever

1832
01:18:20,160 --> 01:18:23,320
Speaker 1: thought was possible. UM. And the amazing thing is a

1833
01:18:23,400 --> 01:18:25,720
Speaker 1: couple of years ago people saying, well, podcast is all

1834
01:18:25,800 --> 01:18:29,360
Speaker 1: but over. I just read last week Reid Hoffman of

1835
01:18:29,439 --> 01:18:33,479
Speaker 1: LinkedIn is now hosting a podcast. So this is a

1836
01:18:33,560 --> 01:18:36,040
Speaker 1: technology that a is not going away as a form

1837
01:18:36,080 --> 01:18:39,880
Speaker 1: of communication. What is the benefit to the firm of

1838
01:18:40,000 --> 01:18:42,160
Speaker 1: being able to go out and tell a story that

1839
01:18:42,320 --> 01:18:44,799
Speaker 1: might not be Hey, here's why you should use injuries

1840
01:18:44,840 --> 01:18:47,680
Speaker 1: and Harrowitz. It's never that overt it's always here's some

1841
01:18:47,800 --> 01:18:50,080
Speaker 1: interesting things happening in the world of technology you should

1842
01:18:50,120 --> 01:18:51,800
Speaker 1: know about. Here's what's happening. A lot of people care

1843
01:18:51,800 --> 01:18:53,120
Speaker 1: about I was thinking, a lot of people care about

1844
01:18:53,160 --> 01:18:54,880
Speaker 1: this stuff. A lot of people care about the implications.

1845
01:18:54,880 --> 01:18:56,040
Speaker 1: A lot of people, by the way, are in a

1846
01:18:56,080 --> 01:18:59,840
Speaker 1: position to affect the world, and regulators and journalists and

1847
01:19:00,040 --> 01:19:03,040
Speaker 1: people at big companies, um. And so we're trying to

1848
01:19:03,080 --> 01:19:04,920
Speaker 1: get to all the people who are decision makers, you know,

1849
01:19:05,080 --> 01:19:07,400
Speaker 1: you know, the constituents and the entire ecosystem that have

1850
01:19:07,520 --> 01:19:09,920
Speaker 1: to cope with the consequences of technological change. Trying to

1851
01:19:09,960 --> 01:19:11,439
Speaker 1: get to those folks. We're trying to pull more people

1852
01:19:11,439 --> 01:19:13,560
Speaker 1: into the industry. Right, if more people all over the

1853
01:19:13,600 --> 01:19:15,280
Speaker 1: world have a sense of what's happening, then they can

1854
01:19:15,360 --> 01:19:17,679
Speaker 1: they can decide they might want to participate. We're trying

1855
01:19:17,720 --> 01:19:20,040
Speaker 1: to help people up level of their skills, become more

1856
01:19:20,120 --> 01:19:22,320
Speaker 1: knowledgeable about these things. And we I mean, we obviously

1857
01:19:22,360 --> 01:19:24,200
Speaker 1: benefit from all that, but just more broadly, like I

1858
01:19:24,240 --> 01:19:27,320
Speaker 1: think this is the topic of our time. It's absolutely

1859
01:19:27,520 --> 01:19:30,559
Speaker 1: absolutely fascinating. All right, So we're almost at a time

1860
01:19:30,960 --> 01:19:33,839
Speaker 1: there's a ton of questions that didn't get to, including

1861
01:19:33,880 --> 01:19:36,160
Speaker 1: the speed round. But what I really want to do

1862
01:19:36,320 --> 01:19:38,960
Speaker 1: is get to the standard questions. So how many minutes

1863
01:19:38,960 --> 01:19:44,000
Speaker 1: can we go over two minutes? That's gonna be tough alright.

1864
01:19:44,080 --> 01:19:47,240
Speaker 1: So so, by the way, if I forget to um

1865
01:19:47,479 --> 01:19:49,400
Speaker 1: say this, thank you so much for being so generous

1866
01:19:49,479 --> 01:19:52,480
Speaker 1: with your time and and and going through such thoughtful,

1867
01:19:53,160 --> 01:19:56,840
Speaker 1: detailed answers. These these really been been fascinating. I have

1868
01:19:57,040 --> 01:19:59,240
Speaker 1: ten favorite questions. I'm going to just go through a

1869
01:19:59,360 --> 01:20:02,080
Speaker 1: two or three of them really quickly. Tell us something

1870
01:20:02,120 --> 01:20:05,680
Speaker 1: about your background that most people don't know. Probably big one.

1871
01:20:05,680 --> 01:20:08,080
Speaker 1: I grew up in the roal Midwest. I grew up

1872
01:20:08,120 --> 01:20:10,800
Speaker 1: in Trump Country. You did, yes, absolutely, And has that

1873
01:20:10,920 --> 01:20:15,120
Speaker 1: affected your output on on the world at all? Your outlook? Yeah? Absolutely.

1874
01:20:15,479 --> 01:20:19,400
Speaker 1: Early mentors Jim Clark, Jim Barksdale, um with the two

1875
01:20:19,400 --> 01:20:23,200
Speaker 1: big ones and say, very complimentary personalities that work together

1876
01:20:23,280 --> 01:20:25,880
Speaker 1: very well, but also starkly different in their skill sets

1877
01:20:25,880 --> 01:20:27,599
Speaker 1: and orientations. And I learned a lot for both of them.

1878
01:20:27,760 --> 01:20:30,639
Speaker 1: The question I get from listeners more than anyone asked

1879
01:20:30,680 --> 01:20:33,280
Speaker 1: your guests what their favorite books are? Favorite books? So

1880
01:20:33,400 --> 01:20:36,000
Speaker 1: I have three quick nominations, and I'm getting getting the

1881
01:20:36,040 --> 01:20:41,240
Speaker 1: hook here, uh Port Charlie's Almanac, The Companion Speeches. I

1882
01:20:41,320 --> 01:20:44,400
Speaker 1: think it's tremendous. Um um. Extreme Ownership has been a

1883
01:20:44,439 --> 01:20:47,200
Speaker 1: favorite recent book. A guy named Jocko Willink is a

1884
01:20:47,240 --> 01:20:50,200
Speaker 1: former Navy seal commander UM, one of the stream ownership

1885
01:20:50,240 --> 01:20:52,759
Speaker 1: streame Ownership Um one of the best books on leadership

1886
01:20:53,000 --> 01:20:54,840
Speaker 1: I've ever I've ever read in a tremendous by the

1887
01:20:54,880 --> 01:20:58,479
Speaker 1: Way word story book as well. UM. And then what

1888
01:20:58,880 --> 01:21:01,880
Speaker 1: is the Man? There are so many there's a brand

1889
01:21:01,880 --> 01:21:03,519
Speaker 1: new book. O Roberts of Pulsky has a new book

1890
01:21:03,520 --> 01:21:05,840
Speaker 1: I've just started on human behavior of his magna Opus

1891
01:21:05,840 --> 01:21:08,880
Speaker 1: on human behavior. He's the great evolutionary theorist. And so

1892
01:21:09,120 --> 01:21:10,680
Speaker 1: that would probably be the third one that that's the

1893
01:21:10,720 --> 01:21:12,559
Speaker 1: new one, but it looks very exciting. And then our

1894
01:21:12,600 --> 01:21:15,600
Speaker 1: final question, what is it that you know about technology

1895
01:21:15,680 --> 01:21:19,280
Speaker 1: and venture capital investing today that you wish you knew

1896
01:21:20,120 --> 01:21:22,680
Speaker 1: five plus years ago when you were first starting out. Oh,

1897
01:21:22,920 --> 01:21:25,719
Speaker 1: there are no bad ideas. There are only early ideas.

1898
01:21:26,200 --> 01:21:29,240
Speaker 1: No bad ideas, just earlier ideas happen. It'll all happen.

1899
01:21:29,320 --> 01:21:31,360
Speaker 1: I've become convinced now it'll all happen. Every smart person

1900
01:21:31,360 --> 01:21:32,760
Speaker 1: who comes in here with a crazy idea, it's all

1901
01:21:32,800 --> 01:21:35,280
Speaker 1: going to happen. At some point, they all happen. It's

1902
01:21:35,320 --> 01:21:38,160
Speaker 1: just a question to win. We have been speaking with

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Speaker 1: did not thank our hosts, Mark Andreason and the crew

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Speaker 1: here at Andreason Horowitz, as well as Mike Batnick, who

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Speaker 1: is my head of research and help put these stans together.

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Speaker 1: We love your comments, feedback and suggestions right to us

1911
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Speaker 1: at m IB podcast at Bloomberg dot net. I'm Barry Ridholts.

1912
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