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

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Speaker 1: This week on the podcast, I have an extra special guest.

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Speaker 1: His name is Josh Wolfe, and this week is a

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Speaker 1: tour to forced discussion on venture capital and technology and

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Speaker 1: behavioral psychology in the world of deploying risk capital in

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Speaker 1: the sciences. It's absolutely fascinating. If you are at all

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Speaker 1: interested in the way venture capitalists think, uh, how new

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Speaker 1: technologies are found and developed and exploited, and how they

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Speaker 1: are adapted and eventually just become part of the everyday usage,

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Speaker 1: and what the future might look like ten forty a

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Speaker 1: hundred years off, then you're gonna find this to be

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Speaker 1: an absolutely fascinating conversation. So, with no further ado, my

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Speaker 1: interview with Josh Wolfe of Lux Capital. This is mess

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Speaker 1: There's in Business with Barry Ridholts on Bloomberg Radio. My

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Speaker 1: special guest today is Josh wolf He is the co

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Speaker 1: founder and managing partner of Lux Capital, a venture capital

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Speaker 1: firm that supports basic science and scientists and entrepreneurs who

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Speaker 1: are pursuing counter conventional solutions to some of the world's

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Speaker 1: most vexiting problems. He is a founding investor and board

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Speaker 1: member with Bill Gates in Kai Metta. He is a

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Speaker 1: co investor with folks such as Mark Andreson and Peter Thiel,

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Speaker 1: and he's a director at firms such as Shapeways, Stratio,

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Speaker 1: Scalio Control Labs, and Variant. Josh Wolf, Welcome to Bloomberg.

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Speaker 1: Very great to be with you. So you have an

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Speaker 1: unusual background. You're You're like a molecular biologist coming out

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Speaker 1: of Cornell and your early work is on aids immuno

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Speaker 1: pathology research. How does that translate to finance? And then

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Speaker 1: venture capital? Venture capital is investing the people who are

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Speaker 1: inventing the future, and people that are inventing the future

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Speaker 1: tend to be technologists and engineers and scientists, and so

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Speaker 1: you have to speak their language. But when I was

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Speaker 1: growing up, I was going to be a doctor. I

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Speaker 1: was gonna go get an m d and then an

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Speaker 1: m d, pH d. And then I met people who

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Speaker 1: were actually making money, and I got way more enamored

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Speaker 1: with capital markets. And I remember doing my internships on

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Speaker 1: Wall Street, and then I was at the Summer's doing

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Speaker 1: uh scientific research, and my mentor was actually trading futures

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Speaker 1: and options in the science lab, and I got so excited.

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Speaker 1: I said, you know, what are you doing? And he

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Speaker 1: explained and and so I got way more enamored with

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Speaker 1: capital markets than than science itself. But venture capital was

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Speaker 1: the perfect hybrid because you get to bet on scientists

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Speaker 1: who are inventing the future, but you just gotta understand

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Speaker 1: what they're doing. So how much of this being enamored

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Speaker 1: on of finance comes from your background? You you grew

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Speaker 1: up in in the hood in Brooklyn? Yeah, well, I

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Speaker 1: have a buddy you grew up in c Gate. I

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Speaker 1: know that area really well, Um, did that affect the

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Speaker 1: way you looked at the world of of money and

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Speaker 1: capital or was it just your upgring Well, first of all,

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Speaker 1: if you don't grow up with money, you want it.

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Speaker 1: So that's always a virtuous thing. And I actually think

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Speaker 1: that the best entrepreneurs the kind of people that we

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Speaker 1: back and we look forward. If you can find the

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Speaker 1: people that have the chip on their shoulder that they

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Speaker 1: came from some sort of messed up background, it's almost

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Speaker 1: always predictive that they're going to be very ambitious, hungary

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Speaker 1: and ambitious. Now you want them to be ethical and

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Speaker 1: ambitious because you also get a lot of hustlers and hucksters. Now,

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Speaker 1: you grew up in Coney Island right by the seaside Carnival,

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Speaker 1: and you are filled with hucksters. So I would say,

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Speaker 1: if anything, growing up in Coney Island made me super

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Speaker 1: skeptical and cynical of human nature. I'm always fond of

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Speaker 1: the Shakespearean quote that there's daggers and men smiles, and

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Speaker 1: you grow up with this distrusting, slightly squinty eyed. You know,

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Speaker 1: what's this guy's angle? What's the agenda that he's got?

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Speaker 1: The boardwalk? Did that to you? Yeah, you've got New

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Speaker 1: York City people talk about is diverse, but the reality

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Speaker 1: is you've got Brighton Beach with Russians, and you've got

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Speaker 1: Coney Island that's primarily African American Latino, and you've got

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Speaker 1: Korean on clothes. But you go to Coney Island on

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Speaker 1: the boardwalk and it is just the absolute melting pot

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Speaker 1: of New York. That's that's quite quite fascinating. So so

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Speaker 1: you end up in finance, who work your way through

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Speaker 1: a couple of well known firms. How did you end

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Speaker 1: up pivoting to use the VC word into venture cap

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Speaker 1: It's funny because the word pivoting is totally banded lux.

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Speaker 1: We only pirouette. We don't pivot. We pirouett. I got

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Speaker 1: very lucky, and everything in my life I was describes

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Speaker 1: randomnessnoptionality and ex post factor. I can explain everything. Is

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Speaker 1: this perfectly near chain a priority? You never know and

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Speaker 1: you recognize that most people are similarly situated, but are

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Speaker 1: somewhat oblivious to that fact. Well, but again, if you're

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Speaker 1: gonna be skeptical about other people, then you have to

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Speaker 1: be a little bit skeptical of yourself too. So intellectual honesty,

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Speaker 1: I think is a is a good virtue, but it's hard.

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Speaker 1: So I got lucky, and I met a guy named

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Speaker 1: Bill Conway. And Bill is one of the three founders. Yeah, exactly,

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Speaker 1: Carlisle Group founder David Rubinstein, famous one. He raises the

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Speaker 1: money bill Less, famous by by design and invests it.

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Speaker 1: And he's just an incredible human. He decided, And again

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Speaker 1: I don't know the circumstances of the day or the

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Speaker 1: twenty four hours that preceded our meeting, but he was

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Speaker 1: in a good mood and we pitched him and I said,

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Speaker 1: you know, we want to build this great firm, And

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Speaker 1: he said, I hope you make a billion, and he

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Speaker 1: invested with us. In my life, There's not nothing that

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Speaker 1: I could have pointed to in the path dependence of

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Speaker 1: this internship or this job, or going to Cornell or

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Speaker 1: this class I took that would ever lead to that meeting.

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Speaker 1: And so I'm humbled by what I call randomness. That

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Speaker 1: is truly truly random, isn't it. So you and I

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Speaker 1: met at a dinner not too long ago, hosted by

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Speaker 1: any Duke on an educational um project that she's working on.

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Speaker 1: But I always kind of scan the people who are

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Speaker 1: going to be at these dinners, and there was something

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Speaker 1: in your bio that really made me laugh. And you

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Speaker 1: describe your venture background as having an interest in science

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Speaker 1: fiction like technology. I love that descriptor. But you're gonna

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Speaker 1: have to explain what you mean by that, okay. So,

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Speaker 1: so everything that is around us today was invented by somebody.

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Speaker 1: Somebody came up with the idea, and that idea started

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Speaker 1: quite literally as a fiction. It did not exist. It

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Speaker 1: was in somebody's imagination. Now, if you accept the premise

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Speaker 1: that a lot of the inspiration for technology from other

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Speaker 1: people who were inspired but didn't invent it, they were

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Speaker 1: the people who literally wrote the science fiction books, they

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Speaker 1: wrote the graphic novels, they wrote the comic books, they

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Speaker 1: made the movie, they did the special effects, and they

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Speaker 1: imagined what could be. And it turns out you fast

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Speaker 1: forward and you look at the Luxe portfolio today, a

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Speaker 1: huge number of the companies that we've invested in the

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Speaker 1: ideas behind them, the technologies, the design even was modeled

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Speaker 1: on things that happened twenty four years ago in science fiction.

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Speaker 1: And I've actually kept a archive of some of the

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Speaker 1: sci fi archives, so if we went through these quickly.

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Speaker 1: You've got the motorola, probably from Star Trek. It was

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Speaker 1: called the star tack phone, right, that was one of

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Speaker 1: the first things. Right then in the rotors you can

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Speaker 1: get to sing the word Star Trek and not invite

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Speaker 1: a trademark dispute exactly. Okay, Now you've got today, you've

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Speaker 1: got Siri, and you look at the image and the

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Speaker 1: thing for Siri, it's just a silver version of how

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Speaker 1: from Space Odyssey. You've got Michael Douglas disclosure. He goes

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Speaker 1: into this room and he three D scans himself and

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Speaker 1: he enters this virtual reality world. This was around the

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Speaker 1: same time as like on More Man. If you remember

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Speaker 1: that okay, not not a fan favorite, but one of mine,

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Speaker 1: and that sort of presaged the virtual reality landscape. You've

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Speaker 1: got pod racing from Star Wars today. That's drone racing Lee.

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Speaker 1: You've got robotic surgery when Luke Skywalker's hand is severed

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Speaker 1: by Vader. That we got both Intuitive Surgical and then Orse,

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Speaker 1: which was one of our companies that we sold to

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Speaker 1: Johnson Johnson earlier this year for six billion dollars plus,

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Speaker 1: not too bad. And and all of these things were

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Speaker 1: born in somebody's imagination. And if you think about really

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Speaker 1: what venture capital is, it is believing before other people understand.

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Speaker 1: And what are you believing? You're believing in somebody's vision.

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Speaker 1: Now the differences, you have to make sure that they're

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Speaker 1: not full of it, that you don't get a theo nos.

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Speaker 1: And you have to have literally literally just wrote down

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Speaker 1: the word there and and and that is now. Now

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Speaker 1: here's the intellectually honest thing. You do not know at

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Speaker 1: the moment of inception or conception of a company, whether

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Speaker 1: the person is malicious or delusional, whether they are intellectually

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Speaker 1: honest or not. And so you put up a little money.

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Speaker 1: It's like an anti in a poker game. You're trying

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Speaker 1: to figure out is this person for real? And you

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Speaker 1: try to find what are the technological milestones that will

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Speaker 1: tell me that this is for real and not BS.

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Speaker 1: And the biggest thing if you walk into my firm

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Speaker 1: and you sit in our Monday partners meeting, if you

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Speaker 1: asked any of the partners, what is Josh going to

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Speaker 1: ask about the technology or what is he thinking? The

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Speaker 1: second that somebody is pitching it, I am thinking, whether

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Speaker 1: I say publicly or in thinking it privately, is this

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Speaker 1: a fraud? So you raised Sarah nos It's such an

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Speaker 1: interesting question because my read of that entire situation from

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Speaker 1: the get go is that it was both delusional and

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Speaker 1: a fraud simultaneously. She had zero medical background. She had

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Speaker 1: and it's amazing how all the healthcare and medical device

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Speaker 1: vcs passed and everybody else came in didn't know better.

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Speaker 1: But it appeared that she believed she could legitimately do this,

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Speaker 1: but with no basis in fact. So at what point

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Speaker 1: does delusion become fraud? Well, when the technology doesn't work.

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Speaker 1: I had a friend who is at a very large

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Speaker 1: deck a billion investment fund that was doing crossover investments

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Speaker 1: to their public market portfolio. They have forty billion dollars.

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Speaker 1: They were making private investments. They were going to visit

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Speaker 1: there on us and they said, what's the number one

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Speaker 1: question that you would ask if you were us doing diligence?

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Speaker 1: And I said, does it work? And you'd be surprised

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Speaker 1: how many people just want to believe the narrative of

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Speaker 1: the story goes back to science fiction and not verifying

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Speaker 1: if it's science fact. Now, you had all these other

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Speaker 1: telltale signs with her, Uh, the octygen are in boards,

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Speaker 1: which all signaling value people that had you know, great

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Speaker 1: military or geopolitical and and just tolly inappropriate from medical technology.

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Speaker 1: But to your point, there was nobody there that really

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Speaker 1: had any sophisticated biotype perform and experience. Now, the truth

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Speaker 1: is liquid biopsy, the ability to detect from a small

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Speaker 1: drop of blood analytes that are indicative of cancer or

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Speaker 1: something else will eventually from someone see the light of day.

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Speaker 1: But you think about the scar that it has put

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Speaker 1: on the industry in the sector and how hard it

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Speaker 1: is for a legitimate entrepreneur that's developing legitimate technology. Today

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Speaker 1: they've raised this significantly, So it's not just that she

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Speaker 1: was a fraud, and her partners were frauds. They've literally

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Speaker 1: set medical technology back because now everyone's gonna be skeptical

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Speaker 1: even of legitimate breakthroughs. And now, realistically, is it set

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Speaker 1: back one year or two years or three years, or

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Speaker 1: has it raised the cost of capital for that? How

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Speaker 1: many people will die over the course of that setback.

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Speaker 1: So you can argue that there's blood on our Yeah,

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Speaker 1: I'm not a fan. In case you can't tell, I'm

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Speaker 1: with you. And look, it's easy for all of us

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Speaker 1: to say back then, and I believe, by the way,

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Speaker 1: because I know that you are a fellow skeptic that

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Speaker 1: was a fraud. It's harder today both socially to come

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Speaker 1: out and say I think that another company is a

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Speaker 1: specific fraud. Now there's an entire realm of quantum computing

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Speaker 1: today that I think is going to be wrought with frauds.

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Speaker 1: It has all of the criteria for a fraud. People

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Speaker 1: don't understand it. They have fomo if you're missing out,

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Speaker 1: they think that it's gonna be something really big. They

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Speaker 1: don't want to feel stupid because they don't understand it.

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Speaker 1: And so people are parting with money and I think

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Speaker 1: that you're going to see at least one and maybe

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Speaker 1: as many as five public frauds related to quantum computing.

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Speaker 1: The tragedy of the comments finds its way even to

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Speaker 1: venture capital. Tell us a little bit about your team

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Speaker 1: at LUX, who are your partners and what what does

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Speaker 1: everybody do? Well, let's start with my founder, my co founder,

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Speaker 1: who's Peter Abert. Peter is my dispositional opposite, which I

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Speaker 1: think if you are going to have a firm as

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Speaker 1: a critical thing. Now it's interesting because in hedge funds

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Speaker 1: you typically have one PM and they make all the decisions.

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Speaker 1: In private equity you tend to have teams. So Peter

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Speaker 1: and I are the co founders. And the reason that

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Speaker 1: it's important is he is the optimist. I am the pessimist.

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Speaker 1: I'm sitting before you, and I am wearing all dark.

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Speaker 1: I wear all black, and it's just my disposition. I

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Speaker 1: expect the worst. It helps pessimistic. But for the skulls

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Speaker 1: all over them. Correct Pete. You will catch him in

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Speaker 1: nantucket reds, in pastel colors. He is much more like

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Speaker 1: my wife, who happens to be an activist hedge fund manager.

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Speaker 1: She's more perma smile me as they say you know,

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Speaker 1: I've got RB you know, resting be face, so so

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Speaker 1: I expect the worst. He expects the best. And if

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Speaker 1: you had an entire firm that was like me, we

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Speaker 1: would be a bunch of cynical short sellers just trying

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Speaker 1: to spot the frauds and doubting everybody. If you had

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Speaker 1: a firm entirely like him, we would be lemming growth investors,

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Speaker 1: just paying any price and going off the cliff. The

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Speaker 1: balance that we've had as friends for twenty five years

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Speaker 1: and his partners for twenty has made culturally the firm

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Speaker 1: what it is now. From us flows everybody else. And

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Speaker 1: so you've got ten other investment professionals, and everybody is

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Speaker 1: intellectually and ethnically and gender diverse. Now what is it?

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Speaker 1: What does that mean for me? We've got people that

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Speaker 1: are Kashmiri, Pakistani, Iranian, Brazilian, Australian, You've got a bunch

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Speaker 1: of white New York Jews. But the intellectual mix of

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Speaker 1: people is so different. You've got electrical engineer PhDs. You've

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Speaker 1: got people that have stem cell PhDs, people in material

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Speaker 1: science PhDs, people that have no technical background and are

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Speaker 1: excellent at capital raising and marketing. And when we descend

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Speaker 1: upon a company the entire And it's funny because I

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Speaker 1: used this analogy the other day and people had no

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Speaker 1: idea what Vultron was. And I feel like I'm a

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Speaker 1: young guy, but jeez, it dated me. But it's like

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Speaker 1: Vultron coming together. You get all the robots come together

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Speaker 1: into this Megatron and uh, it's just, uh, you know,

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Speaker 1: a diverse group of people that all think differently. I

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Speaker 1: always say that if two people think the same, one

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Speaker 1: of them is unnecessary, and uh, it's it's a lot

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Speaker 1: of fun. So you're concerned that your cultural references are

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Speaker 1: out of date, and you're like, what almost fort Okay,

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Speaker 1: wait until you're in your fifties and you'll drop a

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Speaker 1: Monty Python or a Caddy check reference in the whole

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Speaker 1: table of millennials look at you like perfect um. So

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Speaker 1: so you focus a lot on um. Basic science to

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Speaker 1: some degrees tell us the sort of companies luxe investment.

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Speaker 1: So it really is something that's in the cutting edge

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Speaker 1: of an area that we think that other people haven't found. Now,

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Speaker 1: the reason we do that is we want really high

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Speaker 1: scientific technical complexity, not because we want to tackle things

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Speaker 1: that are really hard. It's because we don't want competition.

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Speaker 1: It is way easier in venture capital to go find

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Speaker 1: the next company that is developing an app for the

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Speaker 1: smartphone or some social media software or something that is

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Speaker 1: relatively easy. But the problem with funding easy things is

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Speaker 1: you get hundreds of competitors, talk about random outcomes. Of

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Speaker 1: those hundreds, who really is going to know which is

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Speaker 1: the next Snapchat or Instagram or whatever. It's practically in random. Now,

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Speaker 1: I know that you're a student of skill verse lock,

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Speaker 1: and the great question always, I think in this domain

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Speaker 1: is can you fail on purpose? Something that Michael Mobison

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Speaker 1: has and and in in in picking amongst a field

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Speaker 1: of five hundred competitors in software, you know you've got

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Speaker 1: let's say, equal probability one chance. If you can find

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Speaker 1: something where there's only three or four or five competitors,

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Speaker 1: you can look a lot smarter because now maybe you

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Speaker 1: have a twenty chance, assuming all things equal, of picking

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Speaker 1: the winner. Can you can you invest in all five

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Speaker 1: or two out of the five or anything? You're typically conflicted,

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Speaker 1: but sometimes depending on the stage of things, you might

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Speaker 1: invest in one and then later on something else comes

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Speaker 1: along and you might invest in it, but because you

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Speaker 1: know that it should really be part of the first

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Speaker 1: thing that you invest in, and by being able to

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Speaker 1: influence that outcome and say, you know what, don't split

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Speaker 1: the baby, don't try to split and and and recruit

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Speaker 1: competing talent, don't try to go to customers and and

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Speaker 1: confuse them. Be part of one enterprise. And it's better

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Speaker 1: owned something smaller, of something much bigger than to try to,

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Speaker 1: you know, compete head to head. So sometimes we will

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Speaker 1: influence those outcomes, but by and large, the stuff that

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Speaker 1: we're investing is that the cutting edge of science and technology.

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Speaker 1: It's hard, there's barriers to entry, there's intellectual property that

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Speaker 1: imposes a negative right on people, so that you have

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Speaker 1: a moat. Again, all things that and I am psychotic

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Speaker 1: about competitive advantage give the company that you're investing in

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Speaker 1: a better chance than just pure luck. It makes a

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Speaker 1: lot of sense, uh, when I think about batting averages

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Speaker 1: for the typical VC funds, usually, and I'm going back

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Speaker 1: to the you know, the John Dwarves of the world, um,

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Speaker 1: the eighties and nineties vcs they tend to spread spread

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Speaker 1: a lot of money around, most of which did not

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Speaker 1: generate a positive yield. And then there's a handful of

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Speaker 1: not just winners, but giant outside winners, hundred cks. You know,

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Speaker 1: think about eBay and Apple and and Amazon going down

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Speaker 1: the list. Given this area and and the more basic

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Speaker 1: science that you're focusing on, is it a similar distribution

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Speaker 1: or how do the numbers shake out? You're looking for

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Speaker 1: one home run to subsidize the next fifty or is

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Speaker 1: it the distribution different? So let's let's look at the

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Speaker 1: macro on venture. You've got two extremes, one of which

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Speaker 1: we say is spray and pray exactly what you're exactly

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Speaker 1: what you're describing, Just bet a lot of lottery tickets.

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Speaker 1: You have intellectual honesty or told naiveta you don't know

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Speaker 1: what's gonna work, and in rhymes, so you know it's

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Speaker 1: true exactly. The the other extreme stick with rhyming, wait

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Speaker 1: and pay. Okay, so you wait until a winner emerges.

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Speaker 1: You pay a very high price for the higher chance

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Speaker 1: of being correct. But obviously the higher the price you pay,

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Speaker 1: the lower you're expected return. So in one case you're saying, well,

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Speaker 1: let's bet in fifty or a hundred or two hundred

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Speaker 1: companies and make small investments buying lottery tickets, and the

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Speaker 1: other you say we're gonna load up into the ones

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Speaker 1: that are winners, but we're gonna do it at such

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Speaker 1: a high price, then maybe we're only gonna get a double. Now,

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Speaker 1: for a successful venture fund, your look for three x

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Speaker 1: or four x cash on cash over the ten year

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Speaker 1: period that you've got locked up money. Now, I always

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Speaker 1: say the great advantage that we have as venture capitalist,

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Speaker 1: let's say, over peers and other alternatives like hedge funds.

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Speaker 1: Hedge funds might have quarterly, monthly, maybe annual liquidity redemptions,

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Speaker 1: and that's a hard thing because it creates an institutional

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Speaker 1: imperative where the manager is focused on the short term.

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Speaker 1: We get to focus on five, six, seven years because

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Speaker 1: our investors capital is patiently locked up for a decade. Okay,

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Speaker 1: if everything goes well, you might get three to four

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Speaker 1: x cash on cash. Now some funds you might have

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Speaker 1: a ten x fund, and some of you might have

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Speaker 1: a too, But on average, that's what you're gaming for.

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Speaker 1: There's two ways to cut it. First is you assume

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Speaker 1: that one or two of your companies return the entire fund,

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Speaker 1: all the capital that you've raised one time. Over our

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Speaker 1: newest fund five million dollars, we've got a pair of them.

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Speaker 1: It's it's we recently closed a billion dollars, but the

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Speaker 1: five million dollars. You might have one company where you

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Speaker 1: own ten percent of it and it sells for five

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Speaker 1: billion dollars. You've got five hundred million dollars back and

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Speaker 1: growth proceeds. It's returning the fund one time over. You're

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Speaker 1: next five companies in aggregate proceeds, return the fund another

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Speaker 1: time over. And in our case where you build a

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Speaker 1: portfolio about companies, then the ensuing fifteen or twenty companies

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Speaker 1: will turn it another time and you end up with

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Speaker 1: that three x cash on cash. The other way to

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Speaker 1: think about it, and again simple math. A third of

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Speaker 1: your companies are total zeros. You lose everything, a third

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Speaker 1: you make back give or take a dollar, and the

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Speaker 1: third you make ten x, and you end up with

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Speaker 1: a three x cash on cash. The problem is you

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Speaker 1: don't know which companies when you invest, are gonna be.

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Speaker 1: If you did, you can only invest in the three

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Speaker 1: I'm only going to put the money in the ten

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Speaker 1: x correct. Now, of course, what we do wherever your

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Speaker 1: confidence in your conviction is high, you upsize, and you

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Speaker 1: know portfolio allocation, You put more money in the things

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Speaker 1: that you think are going to be great, you try

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Speaker 1: to put less money and but put so much of

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Speaker 1: this is reputational. And so you mentioned John Doer before

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Speaker 1: and some of the great venture capitalists of the past,

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Speaker 1: and it was their reputation in backing a winner that

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Speaker 1: begot them the next deal. It's very much like when

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Speaker 1: you sit in a movie theater and they say from

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Speaker 1: the director that brought you you know, or from the

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Speaker 1: producers of and it's that signal. It's the same signal

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Speaker 1: of why elite you know, universities attract elite performers and

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Speaker 1: you get this path positive feedback effect. But so much

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Speaker 1: of our business, if you are intellectually honest, is luck.

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Speaker 1: There can be no doubt about that. Um. Although if

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Speaker 1: you go with this, can you fail on purpose? I

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Speaker 1: can guarantee you that I can fail on purpose. I

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Speaker 1: can pick the absolute worst entrepreneurs that cannot raise money.

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Speaker 1: I can absolutely pick fraudulent companies, and I can absolutely

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Speaker 1: pick teams that can never deliver. And I could fail

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Speaker 1: on purpose. Well, but that just says that there is

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Speaker 1: a skill component that doesn't by itself eliminate the lu

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Speaker 1: So the threshold for having a fl decent venture fund

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Speaker 1: is hey, if you can eliminate a lot of that fraud,

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Speaker 1: that's how you end up with a three or four

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Speaker 1: x as opposed to a one and a half for

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Speaker 1: a two s well. And really the best is can

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Speaker 1: you develop the reputation and can you actually be able

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Speaker 1: to help companies so that you get the best entrepreneurs.

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Speaker 1: Because the truth is what we do in the end

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Speaker 1: of the day of security selection and a little bit

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Speaker 1: of competitive intelligence gathering and some smart capital allocation, but

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Speaker 1: the most important thing that we can do is attract

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Speaker 1: the best founders because they are the people who do everything.

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Speaker 1: At the end of the day, limited partners give us

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Speaker 1: money and they allocate to us, and we in turn

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Speaker 1: give it to the entrepreneur, and the entrepreneur in turn

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Speaker 1: decides who they're gonna hire and who they're gonna fire,

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Speaker 1: and what technologies they're going to prioritize and how they're

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Speaker 1: going to sell. And the single best trait of an

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Speaker 1: entrepreneur is somebody that can tell a story, somebody that

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Speaker 1: has that narrative power, going back to science fiction and

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Speaker 1: just being able to back those individuals. They are the

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Speaker 1: people that recruit talent, They are the people that raise

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Speaker 1: money They are the people that garner attention and wind deals,

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Speaker 1: and that is what makes the metric capital business quite

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Speaker 1: quite fascinating. Earlier we were talking about UM science fiction

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Speaker 1: and how that led to your interest in venture capital.

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Speaker 1: But you've developed a philosophy that I think is somewhat

429
00:20:50,040 --> 00:20:54,120
Speaker 1: atypical from some of those other vcs UH and business

430
00:20:54,119 --> 00:20:56,600
Speaker 1: people I've mentioned. One of the one of the things

431
00:20:56,640 --> 00:21:01,040
Speaker 1: in your bio UM stood out to me EO. Wilson.

432
00:21:01,320 --> 00:21:03,879
Speaker 1: You said he might be the person that had the

433
00:21:03,920 --> 00:21:08,600
Speaker 1: single greatest impact on humanity. Explain that, well, totally biased view,

434
00:21:08,640 --> 00:21:11,680
Speaker 1: but but he had the biggest impact on me intellectually.

435
00:21:12,080 --> 00:21:15,480
Speaker 1: This was modern Renaissance thinking. The book Concilience, which I

436
00:21:15,560 --> 00:21:19,000
Speaker 1: probably read and there about so over twenty years ago.

437
00:21:19,560 --> 00:21:22,840
Speaker 1: Was this idea in science that turned me on. In

438
00:21:22,840 --> 00:21:26,960
Speaker 1: turn to Charlie Monger, these two people are our compatriots.

439
00:21:27,400 --> 00:21:31,400
Speaker 1: Charlie's view of Renaissance thinking and worldly mental models and EO.

440
00:21:31,440 --> 00:21:34,919
Speaker 1: Wilson's view of the unity of the hard sciences and

441
00:21:34,960 --> 00:21:39,919
Speaker 1: the soft sciences, between psychology and physics, between economics and geology,

442
00:21:39,920 --> 00:21:44,359
Speaker 1: and finding patterns lets you identify some universal truths, and

443
00:21:44,400 --> 00:21:46,560
Speaker 1: so if you can continue to find those first principal

444
00:21:46,720 --> 00:21:48,840
Speaker 1: universal truths. I think it sets you on a good

445
00:21:48,880 --> 00:21:51,919
Speaker 1: path for making good decisions. Quite quite interesting. I assume

446
00:21:51,960 --> 00:21:55,280
Speaker 1: you've worked your way through Port Charlie's Almanact. I've given

447
00:21:55,320 --> 00:21:57,400
Speaker 1: so many copies of that away. I've I've created more

448
00:21:57,560 --> 00:22:01,159
Speaker 1: doorstops and the coffee table book gifts, and he's one

449
00:22:01,160 --> 00:22:02,880
Speaker 1: of my whales. If you, if you can get around

450
00:22:02,920 --> 00:22:04,720
Speaker 1: to introducing us, I would love to. You know, I

451
00:22:05,080 --> 00:22:08,120
Speaker 1: have a funny story. Uh. My wife and I went

452
00:22:08,359 --> 00:22:10,840
Speaker 1: years ago to the Berkshire Annual Meeting and we were

453
00:22:10,880 --> 00:22:12,840
Speaker 1: at the I forget if it was the days into

454
00:22:12,840 --> 00:22:15,960
Speaker 1: the Marriot, whatever the hotel was, and before I actually

455
00:22:16,000 --> 00:22:17,879
Speaker 1: had invested with Bill Gates, there was a dinner and

456
00:22:17,880 --> 00:22:19,840
Speaker 1: it was Bill Gates, it was Buffett and it was Monger.

457
00:22:20,600 --> 00:22:22,159
Speaker 1: I picked my wife up from the airport because I

458
00:22:22,240 --> 00:22:24,600
Speaker 1: arrived earlier, and we go out to grab her bag

459
00:22:24,640 --> 00:22:27,360
Speaker 1: and the car was broken into. Your car, our car

460
00:22:27,440 --> 00:22:29,119
Speaker 1: was broken into and everything was stolen, the laptops and

461
00:22:29,160 --> 00:22:31,680
Speaker 1: everything was gone. In Omaha. I go to the front

462
00:22:31,680 --> 00:22:33,360
Speaker 1: desk and while we're going to the front desk, who

463
00:22:33,400 --> 00:22:36,080
Speaker 1: was settling the bill for their private dinner restaurant Charlie

464
00:22:36,119 --> 00:22:38,959
Speaker 1: Munger and here I am. My wife is really upset.

465
00:22:39,000 --> 00:22:40,720
Speaker 1: You know, all our stuff was just stolen from the car.

466
00:22:41,119 --> 00:22:44,560
Speaker 1: And there's Charlie Munger at the front and she looks

467
00:22:44,600 --> 00:22:46,360
Speaker 1: at me and she's like, go ahead. You know, it's

468
00:22:46,359 --> 00:22:48,480
Speaker 1: just she knew that Charlie was gonna trump, you know,

469
00:22:48,560 --> 00:22:52,520
Speaker 1: the front. So um, and you had a conversation. I did,

470
00:22:53,000 --> 00:22:55,760
Speaker 1: and it's a highlight that you have great memories of. Well,

471
00:22:55,800 --> 00:22:58,040
Speaker 1: you know, he's not a very loquacious person, you know,

472
00:22:58,200 --> 00:23:01,520
Speaker 1: nothing to add but h but I think that his

473
00:23:01,520 --> 00:23:04,440
Speaker 1: his rigor in being you know, I remember there was

474
00:23:04,440 --> 00:23:07,560
Speaker 1: a story that somebody asked him what is the single

475
00:23:07,640 --> 00:23:10,840
Speaker 1: thing that you would attribute your success to? And he

476
00:23:10,840 --> 00:23:14,440
Speaker 1: said being rational? Fair enough, right, And that's if you

477
00:23:14,480 --> 00:23:17,320
Speaker 1: can try to be as rational as possible, which means

478
00:23:17,320 --> 00:23:21,200
Speaker 1: identifying all the points where you are irrational. Um and mindful.

479
00:23:21,320 --> 00:23:23,640
Speaker 1: You know. We were at dinner together with Anni Duke

480
00:23:23,680 --> 00:23:29,160
Speaker 1: and Danny Kneman and and Kaneman's partner's wife, Amos Tversky's wife,

481
00:23:29,160 --> 00:23:32,200
Speaker 1: Barbara Correct, who also just wrote an amazing book. And um,

482
00:23:32,720 --> 00:23:36,199
Speaker 1: it's really interesting because Danny has identified all of the

483
00:23:36,240 --> 00:23:39,679
Speaker 1: mental biases, the cognitive biases, that we have and it

484
00:23:39,720 --> 00:23:42,320
Speaker 1: doesn't matter because even being aware of them, you still

485
00:23:42,400 --> 00:23:46,359
Speaker 1: he will still fall victim to them. It's called the bias. Bias.

486
00:23:46,400 --> 00:23:50,199
Speaker 1: Even knowing your own awareness of of these cognitive issues

487
00:23:50,640 --> 00:23:53,040
Speaker 1: is not sufficient to defeat them. Looking at an illusion,

488
00:23:53,119 --> 00:23:55,520
Speaker 1: even knowing it as an illusion, it still works on you.

489
00:23:56,359 --> 00:23:59,320
Speaker 1: It's how you wired. You know, we were very well

490
00:23:59,680 --> 00:24:03,560
Speaker 1: does line to adapt to the savannah, But these more

491
00:24:03,640 --> 00:24:08,760
Speaker 1: complex capital risking ventures where risk is out there but

492
00:24:08,840 --> 00:24:11,800
Speaker 1: the dangle of reward, our brains just go on the

493
00:24:11,800 --> 00:24:15,399
Speaker 1: fritz when that's presented to us, at least our instinctual brains.

494
00:24:15,560 --> 00:24:18,680
Speaker 1: Getting past that, you have some shot. But it's not easy.

495
00:24:18,760 --> 00:24:21,640
Speaker 1: But it's interesting because you know, we we respond to

496
00:24:21,680 --> 00:24:24,200
Speaker 1: the bustle in the hedgerow right, the little little sound

497
00:24:24,200 --> 00:24:25,960
Speaker 1: that's there, and is that a tiger you know? Or

498
00:24:25,960 --> 00:24:27,960
Speaker 1: is it just the wind in my world? I have

499
00:24:28,040 --> 00:24:31,040
Speaker 1: to respond to that with heightened sensitivity. Is that a

500
00:24:31,080 --> 00:24:33,440
Speaker 1: little signal over there? Is that entrepreneur into something big?

501
00:24:33,640 --> 00:24:35,960
Speaker 1: And so we're constantly overreacting, in part because it is

502
00:24:36,000 --> 00:24:38,520
Speaker 1: so zero. Some I do not have the opportunity to

503
00:24:38,560 --> 00:24:40,720
Speaker 1: invest in the public markets where I can say, you

504
00:24:40,760 --> 00:24:42,080
Speaker 1: know what, I really like that, and it's up ten

505
00:24:42,119 --> 00:24:44,080
Speaker 1: per cent or it's down ten percent. There is a

506
00:24:44,200 --> 00:24:47,960
Speaker 1: zero sum nature where I am racing to beat everybody

507
00:24:48,000 --> 00:24:51,119
Speaker 1: else to the big scientific technological breakthrough and own it

508
00:24:51,160 --> 00:24:54,240
Speaker 1: before everybody else can. I like that answer, and kudos

509
00:24:54,359 --> 00:24:58,200
Speaker 1: on the Zeppelin reference. I appreciate that. UM. There's another

510
00:24:58,240 --> 00:25:00,520
Speaker 1: quote of yours. I like you. You refer two things

511
00:25:00,600 --> 00:25:04,520
Speaker 1: as minnows and mega's. Explain what that means. This is

512
00:25:04,560 --> 00:25:06,639
Speaker 1: a phenomenon right now, the minnows and the mega's and

513
00:25:06,720 --> 00:25:10,120
Speaker 1: venture capital UM. Historically, you had lots of firms that existed,

514
00:25:10,119 --> 00:25:12,359
Speaker 1: and maybe they were two hundred, four hundred, five hundred

515
00:25:12,359 --> 00:25:15,280
Speaker 1: million dollar firms. What you've had in the past few

516
00:25:15,359 --> 00:25:18,280
Speaker 1: years is a phenomenon that has created a barbell of

517
00:25:18,359 --> 00:25:21,840
Speaker 1: capital allocation to very small funds, and many of them

518
00:25:21,920 --> 00:25:24,040
Speaker 1: what I call the minnows. These are people who have

519
00:25:24,160 --> 00:25:27,520
Speaker 1: raised ten or twenty or fifty or a hundred million dollars.

520
00:25:27,760 --> 00:25:29,800
Speaker 1: They are a single or in some cases a duo

521
00:25:30,000 --> 00:25:32,399
Speaker 1: g P general partner, and this is a spray and

522
00:25:32,440 --> 00:25:34,879
Speaker 1: prey approach to VC. In some cases they are, but

523
00:25:34,920 --> 00:25:39,200
Speaker 1: more importantly, they're very It's like the institutionalization of Angel investing. Okay,

524
00:25:39,359 --> 00:25:42,080
Speaker 1: that's fair. Why is this happening in part because there

525
00:25:42,119 --> 00:25:45,040
Speaker 1: is a junior person at a major fund who found

526
00:25:45,080 --> 00:25:47,480
Speaker 1: the hot deal that made the fund a lot of money.

527
00:25:47,840 --> 00:25:49,720
Speaker 1: But the problem is that person is not part of

528
00:25:49,720 --> 00:25:52,640
Speaker 1: the succession of the partnership. They don't have the economics,

529
00:25:52,640 --> 00:25:55,040
Speaker 1: They're not gonna get paid. So the limited partners recognized,

530
00:25:55,080 --> 00:25:57,520
Speaker 1: you know what, Jane Smith or Joe Smith, they were

531
00:25:57,560 --> 00:25:59,879
Speaker 1: really the deal maker here. Let's put them in business

532
00:26:00,000 --> 00:26:01,760
Speaker 1: because they are the future of venture capital. And the

533
00:26:01,920 --> 00:26:04,359
Speaker 1: LPs are thinking, how do I get a big allocation

534
00:26:04,600 --> 00:26:06,800
Speaker 1: in the next climate Perkins or Sequoia, and how do

535
00:26:06,800 --> 00:26:10,240
Speaker 1: I get that early? So that has created what is

536
00:26:10,280 --> 00:26:12,359
Speaker 1: intelligent to do on a small basis with a handful

537
00:26:12,359 --> 00:26:14,119
Speaker 1: of these people. But again a prior you don't know

538
00:26:14,160 --> 00:26:16,679
Speaker 1: which ones are going to be really successful hundreds and

539
00:26:16,760 --> 00:26:18,960
Speaker 1: hundreds of minnows. Now, the way that we look at

540
00:26:18,960 --> 00:26:20,440
Speaker 1: that as a firm is, these people are our friends.

541
00:26:20,400 --> 00:26:22,000
Speaker 1: They are a source of deal flow. We can be

542
00:26:22,000 --> 00:26:23,719
Speaker 1: a source of capital to them. Some of them are

543
00:26:23,720 --> 00:26:26,600
Speaker 1: going to turn into great franchises. Nearly impossible to predict

544
00:26:26,640 --> 00:26:29,880
Speaker 1: which ones. At the other extreme, you have the megas,

545
00:26:29,960 --> 00:26:32,760
Speaker 1: the megas are the people that are raising billions of dollars.

546
00:26:33,160 --> 00:26:36,280
Speaker 1: Now the eight hundred pound guerrilla or Godzilla, depending on

547
00:26:36,320 --> 00:26:39,800
Speaker 1: your view, is soft Bank? Sure SoftBank has cosion funds.

548
00:26:39,880 --> 00:26:43,600
Speaker 1: Is just it's a hundred billion dollar guerrilla. Well, it

549
00:26:43,720 --> 00:26:46,639
Speaker 1: is a complex beast, and I would call it Godzilla.

550
00:26:47,119 --> 00:26:51,280
Speaker 1: And it has completely changed the undulating landscape. Why because again,

551
00:26:51,440 --> 00:26:55,480
Speaker 1: in public markets you have relative efficiency at times. Of course,

552
00:26:55,520 --> 00:26:57,520
Speaker 1: you know you have massive inefficiencies at the times, but

553
00:26:57,600 --> 00:27:00,520
Speaker 1: there is no market more inefficient than ven your capital.

554
00:27:00,760 --> 00:27:03,159
Speaker 1: Where in the private markets, a single price setter can

555
00:27:03,200 --> 00:27:05,399
Speaker 1: come and just decide what the price of something is.

556
00:27:05,480 --> 00:27:07,800
Speaker 1: They create the market clearing price. There is no short selling,

557
00:27:07,840 --> 00:27:11,480
Speaker 1: there's no counter offers, there's very little liquidity. So soft

558
00:27:11,560 --> 00:27:14,520
Speaker 1: Bank has come and created all of these unicorns and

559
00:27:14,680 --> 00:27:17,720
Speaker 1: deck accords. And what's crazy is the money that they're

560
00:27:17,720 --> 00:27:20,439
Speaker 1: putting to work. In some cases they are pricing up

561
00:27:20,480 --> 00:27:23,359
Speaker 1: their own investments. Now, put on the tinfoil hat for

562
00:27:23,400 --> 00:27:27,080
Speaker 1: a moment. I have a more slightly nefarious view that

563
00:27:27,200 --> 00:27:29,520
Speaker 1: a reason that soft Bank is doing this. Understanding the

564
00:27:29,600 --> 00:27:32,639
Speaker 1: motive is that they're doing this to be able to

565
00:27:32,760 --> 00:27:36,520
Speaker 1: create paper assets and increases and paper valuations that can

566
00:27:36,600 --> 00:27:40,800
Speaker 1: serve as collateral against indebtedness. That it's north of a

567
00:27:42,000 --> 00:27:45,240
Speaker 1: billion dollars. So by being able to invest in we

568
00:27:45,359 --> 00:27:47,320
Speaker 1: work at ten billion dollars and then pricing it up

569
00:27:47,359 --> 00:27:50,080
Speaker 1: to twenty, you just showed that you had gain and

570
00:27:50,119 --> 00:27:52,560
Speaker 1: your billion dollar or two billion dollar investment. And if

571
00:27:52,600 --> 00:27:54,320
Speaker 1: you look at their earnings over the past one or

572
00:27:54,320 --> 00:27:57,520
Speaker 1: two or three quarters, a significant portion of the profits

573
00:27:57,560 --> 00:28:00,159
Speaker 1: that they are reported is from these paper gains I

574
00:28:00,200 --> 00:28:03,480
Speaker 1: liquid and then when one of these companies actually exits

575
00:28:03,560 --> 00:28:06,399
Speaker 1: and you have liquidity, like an uber or gardens, they

576
00:28:06,520 --> 00:28:09,119
Speaker 1: take those proceeds instead of distributing to the investors, They say,

577
00:28:09,119 --> 00:28:11,400
Speaker 1: you know what, we're gonna borrow against this. We're gonna

578
00:28:11,440 --> 00:28:14,280
Speaker 1: issue debt and sell it to retail. So I think

579
00:28:14,320 --> 00:28:16,720
Speaker 1: the entire complex, which is really run by a bunch

580
00:28:16,760 --> 00:28:19,399
Speaker 1: of Deutsche Bank X credit structure credit. You know, Deutsche

581
00:28:19,440 --> 00:28:21,560
Speaker 1: Bank is the most straight up bank there is, of course,

582
00:28:21,680 --> 00:28:23,320
Speaker 1: I mean, there's no risk there that you know, you

583
00:28:23,400 --> 00:28:25,879
Speaker 1: have total collapse and uh so, so I think that

584
00:28:26,000 --> 00:28:29,920
Speaker 1: this is for venture capital systemic risk and one of

585
00:28:29,960 --> 00:28:34,320
Speaker 1: the poster children of illiquidity. I think that the narrative

586
00:28:34,400 --> 00:28:37,640
Speaker 1: about singularity in the future. All of that is great

587
00:28:37,680 --> 00:28:40,600
Speaker 1: for society because it funds all kinds of experiments. But

588
00:28:40,800 --> 00:28:42,800
Speaker 1: for the soft bank and soft bingk investors, I would

589
00:28:42,800 --> 00:28:45,600
Speaker 1: be very, very nervous. So soft bank is a Ponzi scheme.

590
00:28:45,720 --> 00:28:48,280
Speaker 1: Quote unquote, says Josh Wolfe. I'm gonna put that down

591
00:28:48,360 --> 00:28:51,440
Speaker 1: and put those words in your mouth. Um. The minnow

592
00:28:51,640 --> 00:28:55,640
Speaker 1: and mega model, by the way, very much reminds me

593
00:28:56,000 --> 00:28:58,360
Speaker 1: of one of the smartest things that one of the

594
00:28:58,440 --> 00:29:01,760
Speaker 1: smartest banks does, which is Goldman Sacks. Are you familiar?

595
00:29:02,320 --> 00:29:04,360
Speaker 1: You know when when Goldman Sacks has a hot trader

596
00:29:04,440 --> 00:29:07,800
Speaker 1: or they have a manager who's killing it, rather than

597
00:29:07,880 --> 00:29:10,200
Speaker 1: have that person slip out and launched their own thing,

598
00:29:10,320 --> 00:29:12,120
Speaker 1: they'll tap and say, hey, have you have a thought

599
00:29:12,160 --> 00:29:14,960
Speaker 1: of starting a hedge fund? Will help fund you. We'll

600
00:29:14,960 --> 00:29:17,240
Speaker 1: give you first billion dollars, will help you raise capital

601
00:29:17,320 --> 00:29:20,200
Speaker 1: owned by the way, where your prime broker, and we're

602
00:29:20,200 --> 00:29:23,160
Speaker 1: gonna have a piece of the GP. But go out

603
00:29:23,320 --> 00:29:26,560
Speaker 1: and um, a thousand minnows have spawned, and that's how

604
00:29:26,640 --> 00:29:29,280
Speaker 1: you end up with where we twelve thousand hedge funds

605
00:29:29,320 --> 00:29:33,120
Speaker 1: these days, most of which barely earned their keep um

606
00:29:33,680 --> 00:29:37,600
Speaker 1: including fees. Beyond fees it's it's not necessarily a money maker,

607
00:29:37,960 --> 00:29:43,360
Speaker 1: but that description of VC funding minnows is what Goldman

608
00:29:43,360 --> 00:29:45,200
Speaker 1: has been doing for I don't know, twenty years. It's

609
00:29:45,240 --> 00:29:46,880
Speaker 1: actually very interesting because on the hedge fund side, you know,

610
00:29:47,040 --> 00:29:52,120
Speaker 1: between Citadel and Millennium and bally Asney and SEC, you've

611
00:29:52,160 --> 00:29:54,040
Speaker 1: seen that phenomenon right where they say, Okay, we're gonna

612
00:29:54,040 --> 00:29:55,360
Speaker 1: do risk management of the top, we're gonna have a

613
00:29:55,400 --> 00:29:56,880
Speaker 1: lot of people will blow them out if they lose

614
00:29:56,960 --> 00:29:59,280
Speaker 1: you know, teen percent or whatever. The was notorious for

615
00:29:59,360 --> 00:30:01,960
Speaker 1: that in in venture capital. First of all, the time

616
00:30:02,040 --> 00:30:04,200
Speaker 1: frame to know if somebody has made money is so long.

617
00:30:04,640 --> 00:30:06,640
Speaker 1: But um, but you haven't had that kind of institutional

618
00:30:06,720 --> 00:30:09,520
Speaker 1: complex because the time that it takes to find out

619
00:30:09,560 --> 00:30:12,080
Speaker 1: if somebody is right or wrong, the pay schemes, all this,

620
00:30:12,240 --> 00:30:14,360
Speaker 1: it's just it's it's very complicated. But you do have

621
00:30:14,440 --> 00:30:19,000
Speaker 1: this bifurcation in many many investors that are making small bets,

622
00:30:19,440 --> 00:30:21,400
Speaker 1: and that is good for the angel investor, the person

623
00:30:21,480 --> 00:30:23,240
Speaker 1: that's starting up. It is easier than ever if you

624
00:30:23,320 --> 00:30:26,680
Speaker 1: want to start a business to find capital, and that

625
00:30:26,800 --> 00:30:30,480
Speaker 1: has an important footnote which is the beneficiary of that.

626
00:30:30,560 --> 00:30:34,240
Speaker 1: Over the past few years has been we work because

627
00:30:34,320 --> 00:30:36,200
Speaker 1: ever I see, I thought you're gonna say the public,

628
00:30:36,320 --> 00:30:39,760
Speaker 1: why why we Because the public gets subsidized. The public

629
00:30:39,880 --> 00:30:42,640
Speaker 1: is always the beneficiary in the end, even during bubbles, right.

630
00:30:42,680 --> 00:30:45,600
Speaker 1: I mean, of course pensioners and retirees lose money, but

631
00:30:45,680 --> 00:30:49,800
Speaker 1: the reality is we continue progress. Right in the Jim

632
00:30:49,880 --> 00:30:52,400
Speaker 1: Sarrewicki had a great quote many years ago in The

633
00:30:52,440 --> 00:30:55,120
Speaker 1: New Yorker which said, in greed and average lies the

634
00:30:55,160 --> 00:30:58,160
Speaker 1: hope of progress. Sure, and it's true because that's what happens. Right.

635
00:30:58,160 --> 00:31:00,000
Speaker 1: Everybody finds something, they overdo it in the short term,

636
00:31:00,240 --> 00:31:03,520
Speaker 1: they underestimated the long term. But in that greed and avarice,

637
00:31:03,800 --> 00:31:06,200
Speaker 1: lots of stuff gets laid down. The book that best

638
00:31:06,240 --> 00:31:10,240
Speaker 1: summings up that sir Wicki quote is um Pop Why

639
00:31:10,320 --> 00:31:13,479
Speaker 1: bubbles are great for the economy. So you look at

640
00:31:13,600 --> 00:31:17,240
Speaker 1: railroads and televisions and cars and computers and fiber optics.

641
00:31:17,720 --> 00:31:21,560
Speaker 1: Every with the exception of financial bubbles like the Great

642
00:31:21,600 --> 00:31:26,000
Speaker 1: Financial Crisis just leaves behind debt. But every other bubble. Look,

643
00:31:26,440 --> 00:31:30,360
Speaker 1: so you probably remember Global Crossing and Metromedia, Fiber and

644
00:31:30,440 --> 00:31:34,480
Speaker 1: all the fiber optics companies that were laying unlit cable

645
00:31:34,680 --> 00:31:38,200
Speaker 1: at something like two thousand dollars a mile, and it

646
00:31:38,440 --> 00:31:41,640
Speaker 1: end they'll go bankrupt, and it gets bought the next

647
00:31:41,720 --> 00:31:45,600
Speaker 1: generation for pennies a mile. And that's what makes YouTube

648
00:31:45,680 --> 00:31:49,640
Speaker 1: and Facebook and Netflix and all these other bandwidth intensive

649
00:31:50,320 --> 00:31:56,520
Speaker 1: applications viable because some losing investment basically made the Broadman

650
00:31:56,560 --> 00:31:58,160
Speaker 1: available for free. So you hit it, you hit on

651
00:31:58,160 --> 00:32:00,760
Speaker 1: and I think two really important things and percent right

652
00:32:00,800 --> 00:32:03,880
Speaker 1: right late nineties, you had a narrative promulgated by George Gilder,

653
00:32:03,880 --> 00:32:06,560
Speaker 1: who happens to be a friend. Yeah, but but George

654
00:32:06,680 --> 00:32:09,400
Speaker 1: god Guilder used to literally say I worked with a

655
00:32:09,440 --> 00:32:12,360
Speaker 1: guy used to get the Guilder Telecosm newsletter, and I

656
00:32:12,400 --> 00:32:14,080
Speaker 1: would read it and I would always come away with,

657
00:32:14,840 --> 00:32:17,800
Speaker 1: what is this? I don't do price nonsense. Price is

658
00:32:17,880 --> 00:32:20,520
Speaker 1: what matters in the public markets. How can you not

659
00:32:20,640 --> 00:32:24,160
Speaker 1: do price? George was directionally right about technology, okay, and

660
00:32:24,200 --> 00:32:26,800
Speaker 1: there's certain directions that's a tough call to make. Hey,

661
00:32:27,120 --> 00:32:30,719
Speaker 1: the the fault position of human technological advancement is up.

662
00:32:31,160 --> 00:32:32,840
Speaker 1: I could save you two thousand dollars a year in

663
00:32:32,880 --> 00:32:36,000
Speaker 1: the newsletter, But you had that Guilder effect right where

664
00:32:36,160 --> 00:32:38,160
Speaker 1: he would come out and because everybody else was looking

665
00:32:38,200 --> 00:32:39,640
Speaker 1: at it. It was a big bold letter. Whatever the

666
00:32:39,680 --> 00:32:42,440
Speaker 1: public company, the thing would be upft. Remember Nortel, he

667
00:32:42,520 --> 00:32:45,440
Speaker 1: got behind it exploded, and go down the list of

668
00:32:46,200 --> 00:32:48,719
Speaker 1: all of the j D S Uni phase and all

669
00:32:48,720 --> 00:32:51,080
Speaker 1: these guys. Okay, so so, but you're right, Gary Winnick

670
00:32:51,320 --> 00:32:55,040
Speaker 1: and Global Crossing. Now Hype got high cost a capital,

671
00:32:55,120 --> 00:32:57,240
Speaker 1: God low, like you said, hundreds of miles of dark

672
00:32:57,240 --> 00:32:59,080
Speaker 1: fiber optic God laden lid. And the winner from all

673
00:32:59,120 --> 00:33:01,000
Speaker 1: of that was the third world who got connected to

674
00:33:01,000 --> 00:33:03,640
Speaker 1: the Internet for free. The losers were the growth investors,

675
00:33:03,680 --> 00:33:07,400
Speaker 1: the public investors. The winners were the distressed equity guys

676
00:33:07,440 --> 00:33:09,720
Speaker 1: who came and picked up all the assets for cents

677
00:33:09,720 --> 00:33:12,640
Speaker 1: on the dollar. Now you go back almost a decade

678
00:33:12,840 --> 00:33:15,720
Speaker 1: in venture capital, the same thing was happening with solar,

679
00:33:16,240 --> 00:33:19,640
Speaker 1: an alternative energy. Everybody was funding solar, and you didn't

680
00:33:19,640 --> 00:33:20,800
Speaker 1: have to be a genius to predict this. You just

681
00:33:20,840 --> 00:33:22,320
Speaker 1: hadn't know a little bit of history going back ten

682
00:33:22,400 --> 00:33:25,400
Speaker 1: or fifteen years, which was optical networking was the only

683
00:33:25,480 --> 00:33:27,240
Speaker 1: to be. If history doesn't repeat it rhymes, it was

684
00:33:27,280 --> 00:33:29,560
Speaker 1: going to be the same thing as solar. And so

685
00:33:29,720 --> 00:33:31,840
Speaker 1: in solar we said sit out the ride, don't invest

686
00:33:31,920 --> 00:33:34,480
Speaker 1: You're gonna have massive hype. It's gonna lower the cost

687
00:33:34,520 --> 00:33:36,640
Speaker 1: of capital. People are going to do uneconomic things, and

688
00:33:36,720 --> 00:33:39,560
Speaker 1: the winner will be the third world that gets connected. Now,

689
00:33:39,640 --> 00:33:42,120
Speaker 1: my anticipation of the time was that the private equity

690
00:33:42,120 --> 00:33:43,840
Speaker 1: guys would come in and scoop up these assets for

691
00:33:43,880 --> 00:33:46,480
Speaker 1: cents on the dollars. And I was wrong, really because

692
00:33:46,520 --> 00:33:48,280
Speaker 1: the people that scooped up the assets for cents on

693
00:33:48,320 --> 00:33:51,360
Speaker 1: the dollar were the Chinese. Huh, and they now dominate

694
00:33:51,440 --> 00:33:55,480
Speaker 1: the solar electric quite quite fascinating. Let's let's talk about

695
00:33:55,640 --> 00:33:58,959
Speaker 1: some of the technologies that are out there and how

696
00:33:59,040 --> 00:34:02,320
Speaker 1: they're doing. You know, when three D printing first came out,

697
00:34:02,800 --> 00:34:07,400
Speaker 1: what is it almost fifteen years ago? Is so the

698
00:34:07,560 --> 00:34:11,120
Speaker 1: promise was we'd all have these fun two D three

699
00:34:11,200 --> 00:34:13,800
Speaker 1: D printers. I need a part, you can print just

700
00:34:13,920 --> 00:34:17,160
Speaker 1: about anything. We'd be printing heart valves. So we've been

701
00:34:17,200 --> 00:34:21,040
Speaker 1: printing all this stuff And did we get our our

702
00:34:21,239 --> 00:34:23,719
Speaker 1: hopes up too high? Is that still somewhere off in

703
00:34:23,760 --> 00:34:27,520
Speaker 1: the future or has three D printing really been an

704
00:34:27,600 --> 00:34:31,120
Speaker 1: overhyped bust? Yes? Yes, and yes so really yes, I

705
00:34:31,239 --> 00:34:33,680
Speaker 1: wasn't expecting it has been an overhyped bust. But This

706
00:34:33,960 --> 00:34:35,920
Speaker 1: is predictive if you know your history of technology, what

707
00:34:36,000 --> 00:34:39,800
Speaker 1: you do, you look at Karlotta Perez and the diffusion

708
00:34:39,800 --> 00:34:43,520
Speaker 1: of technology through through history. You go through a um

709
00:34:43,800 --> 00:34:46,359
Speaker 1: installation phase where everybody gets the thing and people are

710
00:34:46,440 --> 00:34:49,640
Speaker 1: tinkering for the past twenty years, and those typically near

711
00:34:50,200 --> 00:34:52,560
Speaker 1: a generational long thing, maybe it's three quarters of a generation,

712
00:34:52,600 --> 00:34:55,279
Speaker 1: but between years. So for the past twenty years you've

713
00:34:55,320 --> 00:34:58,360
Speaker 1: had the installation phase where people are getting lots of

714
00:34:58,440 --> 00:35:00,719
Speaker 1: different three D printers. You have top ones that do

715
00:35:00,800 --> 00:35:02,719
Speaker 1: cost a few thousand dollars, but they really do don't

716
00:35:02,719 --> 00:35:05,080
Speaker 1: do very much. They print plastics and it's good for

717
00:35:05,560 --> 00:35:08,360
Speaker 1: schools and universities and tinkers and this kind of stuff.

718
00:35:08,640 --> 00:35:11,320
Speaker 1: But then you've had the domain of prototyping, and so

719
00:35:11,440 --> 00:35:13,560
Speaker 1: that's also been another you know, early that's a big

720
00:35:13,640 --> 00:35:16,400
Speaker 1: cost saving the two thousand hour or three the bigger

721
00:35:16,520 --> 00:35:19,000
Speaker 1: units that you could design a part and say, let's

722
00:35:19,000 --> 00:35:20,719
Speaker 1: see what it looks like in three days. Yes, but

723
00:35:20,840 --> 00:35:22,960
Speaker 1: it's still modest. It isn't the boom that we've all

724
00:35:23,000 --> 00:35:25,080
Speaker 1: been looking for. Three printing. Now, I do think that

725
00:35:25,120 --> 00:35:26,959
Speaker 1: over the past three or four years we've been entering

726
00:35:27,239 --> 00:35:29,800
Speaker 1: this deployment phase so you go from installation, which is

727
00:35:29,840 --> 00:35:32,840
Speaker 1: twenty years to now deployment. We invested in a company

728
00:35:32,920 --> 00:35:35,120
Speaker 1: called desktop Metal, and when we invested it was about

729
00:35:35,160 --> 00:35:37,920
Speaker 1: three and a half years ago. The industry for spare

730
00:35:38,000 --> 00:35:42,720
Speaker 1: parts and UH end use parts was maybe five billion dollars.

731
00:35:42,800 --> 00:35:45,880
Speaker 1: Today it's nine, so rapid growth in the industry. It's

732
00:35:45,920 --> 00:35:49,440
Speaker 1: probably going to ninety over the next decade. Son x

733
00:35:50,200 --> 00:35:52,440
Speaker 1: ten x and over ten years. Now. Why, if you

734
00:35:52,520 --> 00:35:56,040
Speaker 1: look at most of the economics of manufacturing, it is

735
00:35:56,120 --> 00:35:59,600
Speaker 1: spent on tooling, but the vast majority of the value

736
00:36:00,080 --> 00:36:03,239
Speaker 1: and use parts and these spare parts, if you can

737
00:36:03,320 --> 00:36:05,759
Speaker 1: change the economics of how you make this stuff. Historically

738
00:36:05,800 --> 00:36:07,960
Speaker 1: you ship them and you ship it three ways see

739
00:36:08,360 --> 00:36:10,560
Speaker 1: air or land, but there's a fourth way to ship

740
00:36:10,600 --> 00:36:14,320
Speaker 1: apart digital digital. So eventually my mechanic is going to

741
00:36:14,400 --> 00:36:17,200
Speaker 1: tell me my turbo charger needs a new fan and

742
00:36:17,280 --> 00:36:18,880
Speaker 1: he'll be able to print it and slap it in

743
00:36:18,920 --> 00:36:21,160
Speaker 1: instead of waiting six weeks for it to show up

744
00:36:21,239 --> 00:36:23,600
Speaker 1: from Germany. And in fact desktop Metal is working with

745
00:36:23,680 --> 00:36:26,120
Speaker 1: I think seven different automotive companies for exactly this. These

746
00:36:26,160 --> 00:36:30,399
Speaker 1: small bespoke parts, a water impeller, UH something is part

747
00:36:30,440 --> 00:36:32,640
Speaker 1: of a motor where it does not make sense to

748
00:36:32,760 --> 00:36:36,359
Speaker 1: spend the fixed cost to tool and die a part

749
00:36:36,400 --> 00:36:38,239
Speaker 1: where you're not gonna make ten thousand or a hundred

750
00:36:38,239 --> 00:36:40,319
Speaker 1: thousand or a million of them. You just need one

751
00:36:40,400 --> 00:36:42,400
Speaker 1: or two of these parts. So I think that that

752
00:36:42,600 --> 00:36:44,080
Speaker 1: is going to be a big trend, and particularly if

753
00:36:44,120 --> 00:36:45,759
Speaker 1: you look at a company like Desktop Metal is a

754
00:36:45,800 --> 00:36:49,880
Speaker 1: Boston based company. It has grown very significantly, got BMW

755
00:36:50,080 --> 00:36:54,040
Speaker 1: and g E and and uh Saudi Aramco and whole

756
00:36:54,120 --> 00:36:57,680
Speaker 1: Slive Strategics that are investors alongside us. But the other

757
00:36:57,760 --> 00:37:00,400
Speaker 1: piece of this is wait a second, you said sodium

758
00:37:00,440 --> 00:37:03,880
Speaker 1: ram comes on interrupt. So what you're that you just

759
00:37:04,000 --> 00:37:06,600
Speaker 1: made me think of is you have some a crew

760
00:37:06,800 --> 00:37:10,480
Speaker 1: out at a offshore oil rig that needs and something

761
00:37:10,600 --> 00:37:13,680
Speaker 1: breaks and to get something flown out to them could

762
00:37:13,719 --> 00:37:16,000
Speaker 1: take a week and they lose a week of production,

763
00:37:16,200 --> 00:37:19,520
Speaker 1: or they have a machine, they manufacture the part and

764
00:37:19,560 --> 00:37:21,600
Speaker 1: they're down for four hours and that's it. Correct. Now,

765
00:37:21,600 --> 00:37:23,000
Speaker 1: maybe it's not four hours, maybe it's a day and

766
00:37:23,040 --> 00:37:27,000
Speaker 1: a half, but but yes, because the the installation phase

767
00:37:27,280 --> 00:37:29,279
Speaker 1: where you had a bunch of these printers there that

768
00:37:29,440 --> 00:37:32,160
Speaker 1: we're doing plastic, there's no way that an industrial company

769
00:37:32,239 --> 00:37:33,640
Speaker 1: is gonna be able to use that. Now, when you

770
00:37:33,719 --> 00:37:37,600
Speaker 1: have the sophisticated laser centering metal three D printers, it's real,

771
00:37:37,800 --> 00:37:40,680
Speaker 1: it's real pieces, it's real technology for real applications. So

772
00:37:40,960 --> 00:37:42,920
Speaker 1: that's one. And you're seeing this in jeff In in

773
00:37:43,400 --> 00:37:46,360
Speaker 1: jet engines, in automotive, you are seeing it. If you

774
00:37:46,440 --> 00:37:49,799
Speaker 1: get a knee implant, if you get a hearing aid,

775
00:37:50,920 --> 00:37:55,120
Speaker 1: it is a good chance over that that is three

776
00:37:55,200 --> 00:37:58,360
Speaker 1: D printed product. Now, this is not a biological product.

777
00:37:58,480 --> 00:38:01,120
Speaker 1: This is still some form of play sticks and or no,

778
00:38:01,280 --> 00:38:03,320
Speaker 1: it's it's it could be titanium for your knee. And

779
00:38:03,600 --> 00:38:05,560
Speaker 1: what I mean by by it's not a it's not

780
00:38:05,600 --> 00:38:09,799
Speaker 1: an organic product, a metal or plastic. So um, how

781
00:38:09,920 --> 00:38:12,200
Speaker 1: far off is the Hey and need a new aotic

782
00:38:12,280 --> 00:38:14,600
Speaker 1: card valve? And I don't want one from a cow.

783
00:38:14,760 --> 00:38:17,560
Speaker 1: Almost all of this stuff is still structural. When you

784
00:38:17,640 --> 00:38:20,120
Speaker 1: start thinking about the other component, which is function, you're

785
00:38:20,200 --> 00:38:22,680
Speaker 1: really far off. There's a guy, I think wake Forest

786
00:38:22,719 --> 00:38:25,640
Speaker 1: Tony Atala who's doing three D printing of organs. Now

787
00:38:25,719 --> 00:38:28,800
Speaker 1: that's that's the sort of vision I'm thinking. Now. I

788
00:38:28,880 --> 00:38:31,600
Speaker 1: think the best need to kidney print. They can't do kidney,

789
00:38:31,640 --> 00:38:33,359
Speaker 1: but the best that they've done is a bladder because

790
00:38:33,400 --> 00:38:37,360
Speaker 1: it's basically just structural right, it's hold old fluid. So

791
00:38:37,520 --> 00:38:40,040
Speaker 1: in other words, it doesn't have the mechanical functions. Even

792
00:38:40,080 --> 00:38:43,400
Speaker 1: if you're working with stem cells, you can't maybe in

793
00:38:43,440 --> 00:38:45,600
Speaker 1: a lab, but today, like being honest as an investor,

794
00:38:45,680 --> 00:38:47,719
Speaker 1: you we are far far away from being able to

795
00:38:47,760 --> 00:38:51,640
Speaker 1: introduce structure into that alright, So so maybe years, a

796
00:38:51,719 --> 00:38:53,839
Speaker 1: hundred of years. I think it's possible. I would say

797
00:38:53,920 --> 00:38:57,080
Speaker 1: high probability. In an hundred years, I think it's more likely.

798
00:38:57,160 --> 00:38:59,000
Speaker 1: Rather than printing it, we're probably going to harvest them.

799
00:38:59,040 --> 00:39:00,880
Speaker 1: We're gonna grow them, and that approp I'm not. We're

800
00:39:00,880 --> 00:39:02,640
Speaker 1: going to grow them inside of animals. Now, there's gonna

801
00:39:02,640 --> 00:39:04,120
Speaker 1: be ethics about that. But today if you want to

802
00:39:04,360 --> 00:39:06,800
Speaker 1: no ethics about growing kidneys in front side of animals,

803
00:39:07,040 --> 00:39:11,000
Speaker 1: if you wanted to take it from yes, but as

804
00:39:12,120 --> 00:39:15,240
Speaker 1: for for a pig or or who are very similar

805
00:39:15,280 --> 00:39:18,360
Speaker 1: biologically to humans or or a chimp, it's a nicer

806
00:39:18,480 --> 00:39:25,439
Speaker 1: life for them than they becoming bacon. Yes, but it's

807
00:39:25,520 --> 00:39:29,520
Speaker 1: a there's ethical debate. Okay, thanks, take take let's take

808
00:39:29,560 --> 00:39:31,680
Speaker 1: care of that ethical debate. You're gonna save millions and

809
00:39:31,760 --> 00:39:34,960
Speaker 1: millions of lives and inconvenience a few chimps. How is

810
00:39:35,040 --> 00:39:36,920
Speaker 1: that debate going to go much further than that? I

811
00:39:37,440 --> 00:39:40,239
Speaker 1: think that if you can, if you can appeal to

812
00:39:40,360 --> 00:39:45,120
Speaker 1: the default morality of are you reducing suffering, then you

813
00:39:45,239 --> 00:39:47,239
Speaker 1: have an entire camp with people that increasingly and this

814
00:39:47,360 --> 00:39:50,759
Speaker 1: is this is observable. It's not speculative that more and

815
00:39:50,840 --> 00:39:53,840
Speaker 1: more people are embracing animal rights and saying, you know what,

816
00:39:54,400 --> 00:39:58,600
Speaker 1: it's sentient, it's suffering. Look, there's nothing more I love. Actually,

817
00:39:58,640 --> 00:40:00,439
Speaker 1: this is not fully true. Nothing more I than bacon.

818
00:40:00,640 --> 00:40:02,640
Speaker 1: The only thing I love more than bacon is peanut

819
00:40:02,680 --> 00:40:04,400
Speaker 1: butter and bacon, which I know sounds disgusting, but it

820
00:40:04,480 --> 00:40:06,640
Speaker 1: does sound disgusted, but it is amazing. Okay, So I

821
00:40:06,680 --> 00:40:09,480
Speaker 1: have a friend who's a vegan who eats bacon, and

822
00:40:09,520 --> 00:40:11,319
Speaker 1: I'm like, you know this comes from a pig, right,

823
00:40:11,640 --> 00:40:14,920
Speaker 1: And the answer is, but it is delicious, so so.

824
00:40:15,160 --> 00:40:18,000
Speaker 1: And by the way, pigs are actually much smarter than

825
00:40:18,120 --> 00:40:21,000
Speaker 1: doors or horses, and that hasn't slowed down the human

826
00:40:21,080 --> 00:40:24,400
Speaker 1: onslaught of pork. But doesn't make it right. So okay,

827
00:40:24,480 --> 00:40:27,880
Speaker 1: but you can you can make the we're digressing, but

828
00:40:28,440 --> 00:40:32,520
Speaker 1: the ethical argument that it's wrong. We can intellectually agree

829
00:40:32,560 --> 00:40:35,640
Speaker 1: with it, but in the marketplace that is a giant

830
00:40:35,840 --> 00:40:39,640
Speaker 1: losing argument, is my point today. And you are seeing

831
00:40:39,880 --> 00:40:42,279
Speaker 1: both as evidence. If you look back in the arc

832
00:40:42,360 --> 00:40:45,200
Speaker 1: of history, I think that what we're seeing with Beyond Meat,

833
00:40:45,239 --> 00:40:48,320
Speaker 1: you will see you know this uh til ray like phenomenon.

834
00:40:48,400 --> 00:40:49,880
Speaker 1: I think this is a company that ends up. You know,

835
00:40:50,000 --> 00:40:53,839
Speaker 1: that's we value eight but after it ran up, after

836
00:40:53,960 --> 00:40:56,120
Speaker 1: run up five et. But the market is saying something

837
00:40:56,239 --> 00:40:57,759
Speaker 1: right now, maybe you've got algois and maybe you've got

838
00:40:57,800 --> 00:41:00,040
Speaker 1: momentum investors and maybe got people, but there's demand and

839
00:41:00,200 --> 00:41:02,480
Speaker 1: an interest in this, and it's it's if you believe

840
00:41:02,560 --> 00:41:04,960
Speaker 1: that markets tell you something. Right. Markets are there to

841
00:41:05,000 --> 00:41:07,080
Speaker 1: serve you, not to tell you. But but if if

842
00:41:07,160 --> 00:41:10,000
Speaker 1: you believe that there's something in the ascendency of Beyond

843
00:41:10,080 --> 00:41:13,800
Speaker 1: Meat a single in the same way that the ascendency

844
00:41:13,800 --> 00:41:15,640
Speaker 1: of global crossing and these other things, going back to

845
00:41:15,719 --> 00:41:18,600
Speaker 1: that pridor the ascendency of these things tells you that

846
00:41:18,680 --> 00:41:20,800
Speaker 1: there's something there. And I do think that we're a

847
00:41:20,920 --> 00:41:22,680
Speaker 1: rounding air in the world of food. Let's you and

848
00:41:22,760 --> 00:41:24,560
Speaker 1: I make a bet right now, when is the first

849
00:41:24,680 --> 00:41:29,240
Speaker 1: year in our lifetime, when the annual pork consumption short

850
00:41:29,280 --> 00:41:32,959
Speaker 1: of a worldwide you know, disease phenomena like bovine won't

851
00:41:32,960 --> 00:41:35,440
Speaker 1: won't happen all right? How about on a per capita basis?

852
00:41:35,520 --> 00:41:37,520
Speaker 1: What about per capita even it won't happen in our

853
00:41:37,520 --> 00:41:39,600
Speaker 1: all Okay, So now that we've taken care of the

854
00:41:39,640 --> 00:41:42,680
Speaker 1: ethical issue, which is an interesting debate with no real

855
00:41:42,840 --> 00:41:46,960
Speaker 1: market thing um, which brings us back to I don't

856
00:41:47,000 --> 00:41:49,040
Speaker 1: even know how we got three D printing to three

857
00:41:49,120 --> 00:41:53,160
Speaker 1: D printing. So if it's not if it's not the organs, um,

858
00:41:55,040 --> 00:41:56,960
Speaker 1: it's not the organs, But it is the parts inside

859
00:41:57,000 --> 00:41:58,759
Speaker 1: the body. It is the parts inside the aircraft, it

860
00:41:58,880 --> 00:42:01,080
Speaker 1: is the parts inside the ship. Where the and it's

861
00:42:01,120 --> 00:42:04,400
Speaker 1: really interesting because the you change the economics of manufacturing,

862
00:42:04,440 --> 00:42:06,719
Speaker 1: it has geopolitical implications. If you're just shipping a cat

863
00:42:06,800 --> 00:42:09,600
Speaker 1: file instead of having spare parts in inventory sitting in

864
00:42:09,640 --> 00:42:12,200
Speaker 1: a port of which there are trillions of dollars sitting imports,

865
00:42:13,000 --> 00:42:15,839
Speaker 1: there's interesting implications. Same thing you're saving on storage, you're

866
00:42:15,840 --> 00:42:19,120
Speaker 1: saving on transportation. It's a greener approach for sure, Even

867
00:42:19,200 --> 00:42:23,000
Speaker 1: whatever energy consumed by the printer, it's better than manufacturing

868
00:42:23,040 --> 00:42:25,879
Speaker 1: and storing and shipping or whatever. It sounds like it's

869
00:42:26,239 --> 00:42:29,440
Speaker 1: a no brainer if it if it can successfully penetrate

870
00:42:29,560 --> 00:42:31,279
Speaker 1: that mark, I think the no brainers gonna come in

871
00:42:31,320 --> 00:42:33,719
Speaker 1: it's economics. So we have another company here in New

872
00:42:33,800 --> 00:42:35,680
Speaker 1: York and half the operations are here in New York,

873
00:42:35,680 --> 00:42:38,480
Speaker 1: half are in ninehoven In in the Netherlands. They have

874
00:42:38,719 --> 00:42:42,800
Speaker 1: made twelve million unique parts, over a million users and

875
00:42:42,880 --> 00:42:46,960
Speaker 1: customers hundred thirty countries. They'll print two million parts this year,

876
00:42:47,120 --> 00:42:51,600
Speaker 1: unique parts, individual unique things. If you are a small

877
00:42:51,680 --> 00:42:54,759
Speaker 1: business and you have a room that's not dissimilar from

878
00:42:54,760 --> 00:42:56,320
Speaker 1: the room that we're sitting in, you know, matter a

879
00:42:56,360 --> 00:42:59,320
Speaker 1: few hundred square feet or whatever it is, that's filled

880
00:42:59,360 --> 00:43:02,040
Speaker 1: with inventory, and you think about the cash conversion, sure,

881
00:43:02,320 --> 00:43:03,759
Speaker 1: and how much is locked up in that. If you

882
00:43:03,840 --> 00:43:05,920
Speaker 1: can free that by instead of having the inventories and

883
00:43:06,000 --> 00:43:08,440
Speaker 1: just uploading that as a CAD file and printing it

884
00:43:08,520 --> 00:43:12,120
Speaker 1: and shipping it on demand, it will unlock capital. Anytime

885
00:43:12,120 --> 00:43:15,080
Speaker 1: you unlock capital, I think you create value sure for sure.

886
00:43:15,440 --> 00:43:17,480
Speaker 1: So so let's talk about some of the other things,

887
00:43:17,680 --> 00:43:20,640
Speaker 1: some of the other areas that you focus on. And

888
00:43:20,760 --> 00:43:25,120
Speaker 1: I have not mentioned um the company that you one

889
00:43:25,160 --> 00:43:29,200
Speaker 1: of your early investments that was in nuclear waste for mediation. Um,

890
00:43:29,600 --> 00:43:31,520
Speaker 1: let's talk about that, and then I'll have to ask

891
00:43:31,560 --> 00:43:34,120
Speaker 1: you about thorium. Tell us about what you guys did

892
00:43:34,200 --> 00:43:37,879
Speaker 1: with nuclear waste remediation at a time when everybody else

893
00:43:37,960 --> 00:43:40,200
Speaker 1: was looking at green alternate event. So, so if you

894
00:43:40,360 --> 00:43:42,040
Speaker 1: listen to and there were very smart guys and very

895
00:43:42,040 --> 00:43:44,839
Speaker 1: successful older venture capitalists, but John Door and Venode coastle

896
00:43:44,920 --> 00:43:46,680
Speaker 1: are they with the legends, and they were writing the

897
00:43:46,719 --> 00:43:48,719
Speaker 1: op eds and they were crying on ted stages, and

898
00:43:48,760 --> 00:43:50,640
Speaker 1: they were really promoting the idea with Al Gore and

899
00:43:50,680 --> 00:43:52,440
Speaker 1: others that the most important thing you could find were

900
00:43:52,480 --> 00:43:55,960
Speaker 1: solar wind, bile fields, ethanol battery electric cars. Okay, the

901
00:43:56,040 --> 00:43:58,160
Speaker 1: problem was everybody agreed with that. And the number one

902
00:43:58,160 --> 00:44:01,359
Speaker 1: thing that is predictive of returns is not whether there's

903
00:44:01,360 --> 00:44:03,200
Speaker 1: a hockey stick growth curve that Gartner tells you this

904
00:44:03,280 --> 00:44:04,680
Speaker 1: is going to be a big industry, but how much

905
00:44:04,719 --> 00:44:07,279
Speaker 1: money is going into the industry. More money that floods in,

906
00:44:07,600 --> 00:44:09,480
Speaker 1: the higher the price of the assets, it'll be great

907
00:44:09,480 --> 00:44:12,560
Speaker 1: for consumers, lower returns. So we said, where is nobody looking?

908
00:44:12,760 --> 00:44:13,800
Speaker 1: And that's the thing that I love to do. I

909
00:44:13,840 --> 00:44:16,040
Speaker 1: love to understand where is the consensus and where's the

910
00:44:16,120 --> 00:44:18,200
Speaker 1: varying perception. What's the thing that nobody else is looking at.

911
00:44:18,560 --> 00:44:21,359
Speaker 1: Nobody was talking about nuclear. You watched Al Gore's movie

912
00:44:21,400 --> 00:44:23,840
Speaker 1: back then, Inconvenient Truth, doesn't even mention the word nuclear

913
00:44:23,920 --> 00:44:26,719
Speaker 1: because it was taboo. It was politically taboo. So we

914
00:44:26,800 --> 00:44:28,839
Speaker 1: looked at nuclear. We spent a year and a half

915
00:44:29,600 --> 00:44:31,560
Speaker 1: looking at every part of the fuel cycle. We started

916
00:44:31,600 --> 00:44:34,120
Speaker 1: with the uranium miners. Maybe there's something there. Turns out

917
00:44:34,120 --> 00:44:36,880
Speaker 1: there all hucksters and fraudsters in New Mexico, Nevada, so

918
00:44:37,160 --> 00:44:39,200
Speaker 1: the Coney Island and me said, you know what, stay away.

919
00:44:39,360 --> 00:44:41,880
Speaker 1: You and I have had the Mark Twain quote. But

920
00:44:42,080 --> 00:44:45,239
Speaker 1: my favorite discussion, but the old time grid is Mark

921
00:44:45,280 --> 00:44:47,759
Speaker 1: Twain quote is what is a mine? It's a hole

922
00:44:47,800 --> 00:44:50,400
Speaker 1: in the ground with a liar standing there, exactly exactly. So,

923
00:44:50,520 --> 00:44:51,839
Speaker 1: so that was the same thing, right. And and by

924
00:44:51,880 --> 00:44:54,799
Speaker 1: the way, uranium mining, if if, if it took off.

925
00:44:55,280 --> 00:44:58,399
Speaker 1: Uranium itself was such a small portion of the cost

926
00:44:58,480 --> 00:45:00,600
Speaker 1: of operating a nuclear plan. It was relative in consequential,

927
00:45:00,640 --> 00:45:02,840
Speaker 1: whereas the marginal cost of nat gas and oil it

928
00:45:02,920 --> 00:45:05,280
Speaker 1: was primarily driven by the underlying material to the commodity.

929
00:45:05,880 --> 00:45:07,880
Speaker 1: So we we said no to irany minors. Then you

930
00:45:07,920 --> 00:45:10,600
Speaker 1: looked at modular reactors. This is a good idea smaller

931
00:45:10,800 --> 00:45:14,040
Speaker 1: reactors that could be moved easily in and and built,

932
00:45:14,080 --> 00:45:16,520
Speaker 1: you know, uh, incrementally. So instead of building a billion

933
00:45:16,560 --> 00:45:20,160
Speaker 1: dollar giga watt you know, a power plant for to

934
00:45:20,200 --> 00:45:22,520
Speaker 1: serve a million people, you will build an array of

935
00:45:22,600 --> 00:45:25,160
Speaker 1: say thirty thirty megawatt plants, each one maybe cost you

936
00:45:25,160 --> 00:45:27,160
Speaker 1: a hundred million dollars, and you build it over time. Now,

937
00:45:27,480 --> 00:45:29,400
Speaker 1: the problem with that is is really for the domain

938
00:45:29,760 --> 00:45:34,680
Speaker 1: of very long uh tenure investors, maybe sovereigns, billionaires, people

939
00:45:34,680 --> 00:45:37,640
Speaker 1: that could wait five years not for the technology to develop,

940
00:45:37,880 --> 00:45:41,000
Speaker 1: but because the regulatory Okay, so we said no to that.

941
00:45:41,320 --> 00:45:42,640
Speaker 1: But then you look around and you say, GE's the

942
00:45:42,640 --> 00:45:45,200
Speaker 1: biggest unsolved problem with nuclear is not the political it's

943
00:45:45,280 --> 00:45:47,120
Speaker 1: it's what do you do with the waste it's disposed?

944
00:45:47,400 --> 00:45:49,440
Speaker 1: And so you've got, you know, this whole push for

945
00:45:49,560 --> 00:45:52,680
Speaker 1: Yucca Mountain, which would be a geological repository. We have

946
00:45:52,760 --> 00:45:55,440
Speaker 1: spent tens of billions of dollars on Yucca Mountain. Do

947
00:45:55,480 --> 00:45:57,680
Speaker 1: you know how much waste has gone in zero So

948
00:45:57,960 --> 00:45:59,560
Speaker 1: we looked at that and said, okay, that's sort of interesting. Now,

949
00:45:59,600 --> 00:46:01,200
Speaker 1: what about the the way that you store nuclear waste

950
00:46:01,200 --> 00:46:03,120
Speaker 1: on site? And it turns out that there are basically

951
00:46:03,200 --> 00:46:06,280
Speaker 1: two companies. One that makes a vertical cask like almost

952
00:46:06,440 --> 00:46:08,399
Speaker 1: a casket to put the rods, and one that makes

953
00:46:08,400 --> 00:46:09,960
Speaker 1: a horizonto one, so you can make a vertical water,

954
00:46:10,000 --> 00:46:11,400
Speaker 1: you can make a horizon on one. But that was

955
00:46:11,440 --> 00:46:14,200
Speaker 1: basically some innovation, right, that's the innovation. Now what happens

956
00:46:14,280 --> 00:46:17,520
Speaker 1: is these rods inside the reactor, you know, they go

957
00:46:17,560 --> 00:46:19,960
Speaker 1: through a nuclear chain reaction, They heat the water, the

958
00:46:20,040 --> 00:46:22,080
Speaker 1: water turns the turbines, that's hod of nuclear power. Then

959
00:46:22,200 --> 00:46:23,960
Speaker 1: when you're cooling them, they sit in a pool of

960
00:46:24,000 --> 00:46:26,040
Speaker 1: water for five years and then they're pulled out and

961
00:46:26,040 --> 00:46:28,239
Speaker 1: they put into these little caskets five years just to

962
00:46:28,280 --> 00:46:32,640
Speaker 1: cool down. No, no additional reaction, that's just a very

963
00:46:32,719 --> 00:46:35,680
Speaker 1: low level background. Water is a natural neutron observer. And

964
00:46:35,880 --> 00:46:38,440
Speaker 1: but but there's all this low level waste that is

965
00:46:38,480 --> 00:46:42,800
Speaker 1: sitting there. So everything from worker dose radiation to parts

966
00:46:42,880 --> 00:46:44,800
Speaker 1: and and and there's a big market for that. But

967
00:46:44,920 --> 00:46:46,520
Speaker 1: the bigger market and this was the thing that really

968
00:46:46,520 --> 00:46:48,759
Speaker 1: got us. If you actually sit and I promise you.

969
00:46:48,880 --> 00:46:51,000
Speaker 1: This is not scintillating reading, but it was insightful because

970
00:46:51,040 --> 00:46:52,560
Speaker 1: nobody was looking at it. If you read the d

971
00:46:52,680 --> 00:46:57,080
Speaker 1: OE budget billion dollars a year, six billion of it

972
00:46:57,680 --> 00:47:00,600
Speaker 1: is spent a nuclear waste clean up, you have you

973
00:47:00,640 --> 00:47:03,080
Speaker 1: read the Fifth Risk by Michael Lewis. Yes, the whole

974
00:47:03,440 --> 00:47:06,239
Speaker 1: that last third of the book is all about what

975
00:47:06,440 --> 00:47:08,640
Speaker 1: the people think the Department of Energy is about energy.

976
00:47:08,719 --> 00:47:12,600
Speaker 1: It's not. It's about nuclear nuclear weapon clean up and

977
00:47:12,840 --> 00:47:15,279
Speaker 1: some nuclear energy. Now that that book was written well

978
00:47:15,360 --> 00:47:19,960
Speaker 1: after this. So this is two thousand. Uh. Hanford, Savannah River,

979
00:47:20,080 --> 00:47:23,759
Speaker 1: Idaho National Vannah River is giant, huge people. People are

980
00:47:23,840 --> 00:47:26,600
Speaker 1: unaware of how there is an entire city in Hanford,

981
00:47:26,680 --> 00:47:30,320
Speaker 1: Washington to Washington. You fly in there, there's an airport

982
00:47:30,400 --> 00:47:32,719
Speaker 1: dedicated to this one bar. It's called the Three Eyed

983
00:47:32,800 --> 00:47:35,480
Speaker 1: Fish Bar, like out of the Simpsons, and and the

984
00:47:35,760 --> 00:47:39,320
Speaker 1: entire thing is a community and a complex dedicated to

985
00:47:39,480 --> 00:47:41,160
Speaker 1: nuclear waste clean up. Now, the people that are making

986
00:47:41,160 --> 00:47:42,520
Speaker 1: all the money, and when I tell you they make money,

987
00:47:42,520 --> 00:47:44,839
Speaker 1: it's billions of dollars a year basically shoveling waste from

988
00:47:44,840 --> 00:47:46,279
Speaker 1: one side to the other. And this will be going

989
00:47:46,320 --> 00:47:48,960
Speaker 1: on for decades it's U r S, it's c H

990
00:47:49,040 --> 00:47:51,440
Speaker 1: two M hill, it's floor, it's the big engineering primes.

991
00:47:51,680 --> 00:47:53,160
Speaker 1: So we looked at this and said, not only do

992
00:47:53,200 --> 00:47:54,239
Speaker 1: you have this in the U S, but you have

993
00:47:54,320 --> 00:47:56,120
Speaker 1: this in the UK with a site called Cella Field,

994
00:47:56,160 --> 00:47:58,480
Speaker 1: you have this in France with log Is there an

995
00:47:58,480 --> 00:48:01,160
Speaker 1: opportunity for a high tech solution that could win contracts

996
00:48:01,200 --> 00:48:03,160
Speaker 1: by doing this faster and cheaper. We looked around. We

997
00:48:03,200 --> 00:48:05,800
Speaker 1: couldn't find anything nobody else's and that nobody was in

998
00:48:05,840 --> 00:48:07,719
Speaker 1: the space. And I gotta tell you, going around for

999
00:48:07,920 --> 00:48:10,719
Speaker 1: a year to nuclear waste conferences, I was certainly the

1000
00:48:10,800 --> 00:48:13,600
Speaker 1: only person under fifty years old, uh, and I was

1001
00:48:13,640 --> 00:48:15,960
Speaker 1: certainly the only venture capitalists there. So we go and

1002
00:48:16,040 --> 00:48:17,840
Speaker 1: we find the best technologists that we can, and we

1003
00:48:17,880 --> 00:48:19,400
Speaker 1: find the best people that were under the age of

1004
00:48:19,440 --> 00:48:22,719
Speaker 1: sixty because they weren't that entrepreneurial in this space. And

1005
00:48:23,040 --> 00:48:24,479
Speaker 1: to be honest, we found people that were like fifty

1006
00:48:24,480 --> 00:48:26,600
Speaker 1: eight or fifty nine, and we end up blocking him.

1007
00:48:26,600 --> 00:48:28,880
Speaker 1: The best technologies, which were a combination of material science

1008
00:48:28,880 --> 00:48:32,000
Speaker 1: and chemistry and physics, materials that could grab the worst

1009
00:48:32,120 --> 00:48:35,400
Speaker 1: radio activelopments like caesium and strawntium acium, uranium, plutonium. And

1010
00:48:35,480 --> 00:48:38,160
Speaker 1: then we had a second technology called vitrification, which in

1011
00:48:38,239 --> 00:48:40,640
Speaker 1: Layman's terms is glassmaking. Take the stuff, lock it up

1012
00:48:40,640 --> 00:48:42,920
Speaker 1: into a glassmate, putting it in in silicon or some

1013
00:48:43,040 --> 00:48:45,560
Speaker 1: other silicate is glass. And but you're heating at about

1014
00:48:45,600 --> 00:48:49,080
Speaker 1: seventred degrees, it turns into this molten form. It can't leak,

1015
00:48:49,120 --> 00:48:53,000
Speaker 1: re leach into the radio acci radious, not cranking. Haven't

1016
00:48:53,000 --> 00:48:55,279
Speaker 1: transmutated it. You know that that doesn't really exist. But

1017
00:48:55,560 --> 00:48:59,200
Speaker 1: but but in gold and we know that does exactly.

1018
00:48:59,239 --> 00:49:01,000
Speaker 1: And then and then oh, start a mining company and

1019
00:49:01,080 --> 00:49:04,640
Speaker 1: taking public so with a liar exactly. So so we

1020
00:49:04,920 --> 00:49:07,320
Speaker 1: we ended up starting a company. We named it Curryon

1021
00:49:07,560 --> 00:49:10,400
Speaker 1: after Madame Curie, and in part because with a K

1022
00:49:10,600 --> 00:49:12,480
Speaker 1: to be cute, and and uh in part because I

1023
00:49:12,600 --> 00:49:14,520
Speaker 1: read the article in the about the Department of Energy

1024
00:49:14,719 --> 00:49:16,640
Speaker 1: and it said that there's billions and billions of curries,

1025
00:49:16,640 --> 00:49:18,560
Speaker 1: which is the measure of radiation. And that was the inspiration.

1026
00:49:18,719 --> 00:49:21,479
Speaker 1: So we started the company and with very little money,

1027
00:49:21,600 --> 00:49:24,080
Speaker 1: and uh, I had two of our LPs who were

1028
00:49:24,239 --> 00:49:26,399
Speaker 1: prominent hedge fund managers and Austin. We put a total

1029
00:49:26,400 --> 00:49:27,880
Speaker 1: of three million dollars into it. And I put a

1030
00:49:27,920 --> 00:49:29,480
Speaker 1: million and a half in from our fund at the time,

1031
00:49:29,840 --> 00:49:32,719
Speaker 1: and we owned thirty five percent of the business. We

1032
00:49:32,840 --> 00:49:35,040
Speaker 1: stake these guys. They go off to work and the

1033
00:49:35,080 --> 00:49:37,319
Speaker 1: first year they did about a million revenue. Second year,

1034
00:49:37,719 --> 00:49:40,000
Speaker 1: black Swan, you had a negative event, which was the

1035
00:49:40,000 --> 00:49:42,719
Speaker 1: seismic event that led to the earthquake that led to

1036
00:49:42,719 --> 00:49:45,120
Speaker 1: the tsunami that led to the Fukushiman disaster. And lo

1037
00:49:45,239 --> 00:49:47,400
Speaker 1: and behold, the only company picked in the US for

1038
00:49:47,480 --> 00:49:49,759
Speaker 1: this cleanup was this little company, Kuran. So we went

1039
00:49:49,760 --> 00:49:52,239
Speaker 1: from a million dollars in revenue to forty then eight

1040
00:49:54,200 --> 00:49:56,600
Speaker 1: still working there now it's still still are and and

1041
00:49:56,680 --> 00:49:59,200
Speaker 1: they actually sold to Veolia, which was a French giant.

1042
00:49:59,239 --> 00:50:01,440
Speaker 1: We sold for four million dollars, going to third of

1043
00:50:01,440 --> 00:50:03,759
Speaker 1: the business. We made an excess of forty times our

1044
00:50:04,000 --> 00:50:06,239
Speaker 1: early money where suring the entirety of that fund. And

1045
00:50:06,480 --> 00:50:08,799
Speaker 1: it was gratifying because we like to sound says as

1046
00:50:08,920 --> 00:50:11,839
Speaker 1: as santimonious as it sounds, that we like to invest

1047
00:50:11,880 --> 00:50:14,640
Speaker 1: in matter that matters. It was meaningful because you actually

1048
00:50:14,680 --> 00:50:17,120
Speaker 1: did something to reduce all the radiation from this disaster site.

1049
00:50:17,400 --> 00:50:19,080
Speaker 1: And we got to make our investors a lot of money.

1050
00:50:19,360 --> 00:50:21,759
Speaker 1: We have been speaking with Josh Wolf. He is the

1051
00:50:21,840 --> 00:50:25,400
Speaker 1: co founder and managing partner of Lux Capital. If you

1052
00:50:25,520 --> 00:50:27,560
Speaker 1: enjoy this conversation, we'll be sure and come back for

1053
00:50:27,640 --> 00:50:30,480
Speaker 1: the podcast extras, where we keep the tape rolling and

1054
00:50:30,520 --> 00:50:35,000
Speaker 1: continue discussing all things technology and venture related. You can

1055
00:50:35,080 --> 00:50:40,240
Speaker 1: find that at iTunes, Overcast, Spotify, Google Podcast, Bloomberg, wherever

1056
00:50:40,320 --> 00:50:44,000
Speaker 1: you're finner. Podcasts are sold. We love your comments, feedback

1057
00:50:44,040 --> 00:50:48,040
Speaker 1: and suggestions right to us at m IB podcast at

1058
00:50:48,080 --> 00:50:51,680
Speaker 1: Bloomberg dot net. Check out my weekly column on Bloomberg

1059
00:50:51,760 --> 00:50:55,040
Speaker 1: dot com. Sign up from my daily reading list at

1060
00:50:55,120 --> 00:50:58,120
Speaker 1: Rid Halts dot com. Follow me on Twitter at Rid Halts.

1061
00:50:58,400 --> 00:51:01,520
Speaker 1: I'm Barry Hults. You're listening to Masters in Business on

1062
00:51:01,640 --> 00:51:07,239
Speaker 1: Bloomberg Radio. Welcome to the podcast, Josh. Thank you so

1063
00:51:07,360 --> 00:51:09,360
Speaker 1: much for doing this. I've been looking forward to this

1064
00:51:09,920 --> 00:51:12,640
Speaker 1: for a while, UM, since you and I sat at

1065
00:51:12,719 --> 00:51:16,359
Speaker 1: what was a fascinating dinner UM and I hope something

1066
00:51:16,440 --> 00:51:20,560
Speaker 1: comes out of it. Uh, really interesting idea that any

1067
00:51:20,680 --> 00:51:23,880
Speaker 1: Duke is working on. You mentioned my friend Michael Mobison

1068
00:51:24,400 --> 00:51:29,080
Speaker 1: was also there. It was really a murderers rogue of people.

1069
00:51:29,120 --> 00:51:32,359
Speaker 1: There are quite quite a gallery of intellect. I don't

1070
00:51:32,400 --> 00:51:34,399
Speaker 1: know what's going to come of it? But I thought

1071
00:51:34,400 --> 00:51:37,239
Speaker 1: it was fascinating and I thought what you discussed was

1072
00:51:37,320 --> 00:51:40,239
Speaker 1: really interesting. I have no idea what it was, but

1073
00:51:40,320 --> 00:51:42,719
Speaker 1: it led me to say I should have him come.

1074
00:51:42,800 --> 00:51:46,040
Speaker 1: Here's an interesting guy to sit and chat with. So

1075
00:51:46,239 --> 00:51:50,840
Speaker 1: what do you recall what your big idea was in that? That? Meaning?

1076
00:51:51,080 --> 00:51:53,320
Speaker 1: You know what I actually think at least one of

1077
00:51:53,400 --> 00:51:56,080
Speaker 1: the ideas was as they were thinking about how do

1078
00:51:56,200 --> 00:51:59,919
Speaker 1: you get more broad distribution about decision making for young people,

1079
00:52:00,239 --> 00:52:02,960
Speaker 1: how to train people to make better decisions and while

1080
00:52:03,000 --> 00:52:05,279
Speaker 1: they're in the junior high school level, and and and

1081
00:52:06,760 --> 00:52:08,680
Speaker 1: the first principles approach that you would want to take

1082
00:52:08,680 --> 00:52:10,239
Speaker 1: to this is, well, how do you reach those people?

1083
00:52:10,719 --> 00:52:13,200
Speaker 1: And the old school thinking is let's put it into

1084
00:52:13,239 --> 00:52:15,560
Speaker 1: the curriculum of the schools. And every time that you've

1085
00:52:15,600 --> 00:52:17,480
Speaker 1: tried that, you know it's typically failed. And so you know,

1086
00:52:17,680 --> 00:52:19,840
Speaker 1: go where these people are. So if if you've got

1087
00:52:19,880 --> 00:52:21,600
Speaker 1: social kid, yeah, or that you know, if that you've

1088
00:52:21,600 --> 00:52:23,880
Speaker 1: got young kids it's on roadblocks, you know, have gamify

1089
00:52:24,000 --> 00:52:25,880
Speaker 1: this so that young people can be on roadblocks and

1090
00:52:25,960 --> 00:52:28,200
Speaker 1: you know, playing decision making games and looking at things

1091
00:52:28,239 --> 00:52:30,440
Speaker 1: maybe even starting to learn to think of probabilities or

1092
00:52:31,040 --> 00:52:35,160
Speaker 1: slightly older generation it's it's podcast, or it's uh, it's

1093
00:52:35,840 --> 00:52:37,960
Speaker 1: this is this is now old school media. I used

1094
00:52:37,960 --> 00:52:40,000
Speaker 1: to think this was so cutting edge. You're telling me

1095
00:52:40,160 --> 00:52:42,880
Speaker 1: this is now old People that are listening here no voltron,

1096
00:52:42,960 --> 00:52:45,839
Speaker 1: they know Caddy Shock, you know they younger, younger people

1097
00:52:45,880 --> 00:52:48,960
Speaker 1: maybe not, that's uh. I've had a number of NBA

1098
00:52:49,040 --> 00:52:53,440
Speaker 1: professors tell me they assigned m various episodes of this

1099
00:52:53,600 --> 00:52:58,480
Speaker 1: is homework. So there's some right, it's it's content you have,

1100
00:52:58,640 --> 00:53:00,880
Speaker 1: you have amazing access to amazing people. Why not, it's

1101
00:53:00,960 --> 00:53:03,880
Speaker 1: it's quite it's quite insane. Talk about dumb luck. That

1102
00:53:03,960 --> 00:53:06,799
Speaker 1: will have a longer conversation about that. At another time,

1103
00:53:07,200 --> 00:53:11,320
Speaker 1: we were just talking about nuclear waste remediation, and I

1104
00:53:11,440 --> 00:53:16,480
Speaker 1: have to ask you about the concept of thorium powered reactors.

1105
00:53:16,560 --> 00:53:19,360
Speaker 1: I don't know how many years ago, this was a

1106
00:53:19,560 --> 00:53:22,800
Speaker 1: huge article in Wired magazine if you were coll wired

1107
00:53:23,360 --> 00:53:26,880
Speaker 1: um about all the advantages of thorium and how productive

1108
00:53:26,920 --> 00:53:29,320
Speaker 1: it is and how low grade the waste is, and

1109
00:53:29,520 --> 00:53:32,560
Speaker 1: yet nothing's ever seemed to happen with that. I'll give

1110
00:53:32,600 --> 00:53:35,640
Speaker 1: you a probability in a timeframe, there's a fifty chance

1111
00:53:35,680 --> 00:53:37,520
Speaker 1: that we see something in the next fifty years, and

1112
00:53:38,040 --> 00:53:39,560
Speaker 1: a ten percent chance that you see something in the

1113
00:53:39,640 --> 00:53:42,000
Speaker 1: next ten years, and if you do, over seventy percent

1114
00:53:42,080 --> 00:53:46,120
Speaker 1: chance that comes from China. The Modi family has been

1115
00:53:46,480 --> 00:53:48,600
Speaker 1: backers of an effort on the East Coast. I think

1116
00:53:48,600 --> 00:53:50,480
Speaker 1: it was called thorium power. We looked at a bunch

1117
00:53:50,480 --> 00:53:54,279
Speaker 1: of efforts. The problem is it is very long, very expensive,

1118
00:53:54,440 --> 00:53:57,960
Speaker 1: regulatory fraud. It's just it's it's just gonna take too

1119
00:53:58,040 --> 00:54:00,200
Speaker 1: too long. The virtues of thorium are great. You know,

1120
00:54:00,480 --> 00:54:04,320
Speaker 1: it's cheap, cheaper, lower, probably cheaper than uranium or or

1121
00:54:04,600 --> 00:54:08,040
Speaker 1: any of the other and lower. The biggest virtue of

1122
00:54:08,120 --> 00:54:11,040
Speaker 1: it is you have less probability of proliferation because one

1123
00:54:11,040 --> 00:54:15,280
Speaker 1: of the outputs of traditional nuclear is plutonium. No plutonium thorium,

1124
00:54:15,360 --> 00:54:19,279
Speaker 1: which is really interesting. And um, let's talk about there's

1125
00:54:19,280 --> 00:54:22,320
Speaker 1: a few text subjects we didn't get to. I have

1126
00:54:22,480 --> 00:54:26,000
Speaker 1: to ask you about chrono biology. Yes, so let's talk

1127
00:54:26,000 --> 00:54:28,600
Speaker 1: a little bit about And my understanding of this is, Hey,

1128
00:54:28,680 --> 00:54:31,359
Speaker 1: we could take the RNA sequence and clip off the ends,

1129
00:54:31,400 --> 00:54:35,440
Speaker 1: which tells people tells other cells when they're supposed to die,

1130
00:54:35,880 --> 00:54:39,279
Speaker 1: and theoretically we have an infinite lifespan there. There's there's

1131
00:54:39,320 --> 00:54:41,880
Speaker 1: many different dimensions of this idea of chrono biology, and

1132
00:54:41,920 --> 00:54:44,439
Speaker 1: the biggest one is that different cells in your body

1133
00:54:44,560 --> 00:54:47,240
Speaker 1: are different ages, and there are markers on those cells

1134
00:54:47,320 --> 00:54:49,240
Speaker 1: that can tell how old something is. So the Prokinje

1135
00:54:49,320 --> 00:54:51,560
Speaker 1: cells in your brain are probably twenty five years old,

1136
00:54:51,719 --> 00:54:54,720
Speaker 1: oh mine much older than that. They feel it anyway.

1137
00:54:54,840 --> 00:54:56,879
Speaker 1: The gut and skin cells you have are maybe days

1138
00:54:57,239 --> 00:55:00,520
Speaker 1: at most weeks old. So different parts of your are

1139
00:55:01,000 --> 00:55:04,520
Speaker 1: basically regenerating and growing and dying at different times. You're

1140
00:55:04,560 --> 00:55:07,000
Speaker 1: not like there's not one Barry, right, You're made up

1141
00:55:07,000 --> 00:55:08,719
Speaker 1: of lots of different things at different ages, and so

1142
00:55:09,000 --> 00:55:11,279
Speaker 1: being able to do a body clock to understand those

1143
00:55:11,320 --> 00:55:13,960
Speaker 1: different things is important. Second, it turns out, of course

1144
00:55:14,000 --> 00:55:15,880
Speaker 1: we have circadian rhythms, right, you get tired at night,

1145
00:55:15,920 --> 00:55:18,759
Speaker 1: You have different levels of hormones at different times of

1146
00:55:18,800 --> 00:55:21,239
Speaker 1: the day. Different people might be night owls people might

1147
00:55:21,280 --> 00:55:24,399
Speaker 1: be but there's something to that physiologically throughout the day.

1148
00:55:24,520 --> 00:55:27,440
Speaker 1: The third is that there's now evidence and scientific papers

1149
00:55:27,440 --> 00:55:29,279
Speaker 1: are coming out showing that if you give chemo at

1150
00:55:29,320 --> 00:55:31,560
Speaker 1: certain times of the day to certain types of people,

1151
00:55:31,760 --> 00:55:33,800
Speaker 1: that it might be more effective than others. Really, so

1152
00:55:33,880 --> 00:55:36,520
Speaker 1: there's something in the body about when you're being reactivate.

1153
00:55:36,640 --> 00:55:38,640
Speaker 1: Look you you, I'm sure have a time when you

1154
00:55:38,760 --> 00:55:41,359
Speaker 1: feel that you are your most alert when caffeine works

1155
00:55:41,400 --> 00:55:43,440
Speaker 1: on you, Whereas you might after lunch go into your

1156
00:55:43,440 --> 00:55:45,480
Speaker 1: food coma at three o'clock or something and caffeine just

1157
00:55:45,520 --> 00:55:47,239
Speaker 1: doesn't work as well as it does, say at nine thirty,

1158
00:55:47,480 --> 00:55:50,359
Speaker 1: when you know your hormones and and metabolites are are

1159
00:55:50,400 --> 00:55:53,120
Speaker 1: spiking at a different rate. So the idea of chronobiology

1160
00:55:53,239 --> 00:55:57,320
Speaker 1: is within the cells, between the cells, and even between people.

1161
00:55:57,880 --> 00:56:00,760
Speaker 1: Is there something about the dimension of time that plays

1162
00:56:00,800 --> 00:56:04,719
Speaker 1: relevance for medicine in the body. That's that's quite fascinating. Um,

1163
00:56:05,120 --> 00:56:09,960
Speaker 1: what else? Autonomous driving GPU gaming to your concept of

1164
00:56:10,400 --> 00:56:15,680
Speaker 1: of UH, intelligent machine goes from GPU from gaming to

1165
00:56:15,960 --> 00:56:20,480
Speaker 1: power artificial intelligence to machine learning. What's next? Is this

1166
00:56:20,600 --> 00:56:26,360
Speaker 1: what's going to drive fully autonomous driving, autonomous military vehicles?

1167
00:56:26,840 --> 00:56:30,120
Speaker 1: Where does this go? And explain what GPU is? So,

1168
00:56:30,320 --> 00:56:32,880
Speaker 1: So GPUs are graphic processing units. This is the ability

1169
00:56:32,960 --> 00:56:36,520
Speaker 1: to do UH large scale multidimensional processing of polygon. So

1170
00:56:36,560 --> 00:56:38,360
Speaker 1: back in the day, if you had in a Nintendo

1171
00:56:38,400 --> 00:56:40,360
Speaker 1: sixty four, it was this big revolution. If you were

1172
00:56:40,360 --> 00:56:42,160
Speaker 1: playing James Bond, back in the day, it was like, wow,

1173
00:56:42,200 --> 00:56:44,840
Speaker 1: you know like three dimensional you know Simulation Tank Command.

1174
00:56:45,000 --> 00:56:46,719
Speaker 1: I remember that game, and that was two dimensional. But

1175
00:56:46,800 --> 00:56:49,360
Speaker 1: but you can see what was all three dimensional polygons

1176
00:56:49,400 --> 00:56:51,200
Speaker 1: and that's how they created the sense of depth and

1177
00:56:51,280 --> 00:56:55,200
Speaker 1: the illusion of space exactly. But but over time you

1178
00:56:55,280 --> 00:56:57,759
Speaker 1: can see this clear progression, almost like a Moore's law

1179
00:56:58,280 --> 00:57:02,520
Speaker 1: of visualization. But we went from CPUs, the central processing units,

1180
00:57:02,520 --> 00:57:05,200
Speaker 1: which were primarily dominated by Intel, to GPUs, which were

1181
00:57:05,239 --> 00:57:08,319
Speaker 1: primarily dominated by in Vidia. Now, the narrative in public

1182
00:57:08,400 --> 00:57:12,160
Speaker 1: markets around Nvidia was this is tightly coupled to PlayStations,

1183
00:57:12,640 --> 00:57:17,000
Speaker 1: PS four and uh xboxes. But suddenly something happened a

1184
00:57:17,040 --> 00:57:19,960
Speaker 1: few years ago, about seven years ago, where they invented

1185
00:57:20,000 --> 00:57:22,480
Speaker 1: a language called Kuda c U d A, and it

1186
00:57:22,560 --> 00:57:24,400
Speaker 1: put it in the hands of academics and researchers who

1187
00:57:24,400 --> 00:57:26,960
Speaker 1: suddenly said, wait a second, instead of using CPUs for

1188
00:57:27,200 --> 00:57:30,120
Speaker 1: high throughput computing, we can instead use GPUs to do

1189
00:57:30,280 --> 00:57:33,560
Speaker 1: this processing. And they started doing the processing for neural

1190
00:57:33,640 --> 00:57:36,160
Speaker 1: networks to be able to do artificial intelligence and machine learning.

1191
00:57:36,440 --> 00:57:39,000
Speaker 1: And so you saw the shift where GPUs were primarily

1192
00:57:39,120 --> 00:57:41,840
Speaker 1: coupled to the consoles and gaming to suddenly being the

1193
00:57:41,920 --> 00:57:44,680
Speaker 1: soul of the new machine inside of things like autonomous

1194
00:57:44,760 --> 00:57:47,800
Speaker 1: vehicles and drones and simulations. This is an area when

1195
00:57:47,840 --> 00:57:50,360
Speaker 1: you say, like what's next In terms of autonomous vehicles,

1196
00:57:50,880 --> 00:57:55,280
Speaker 1: increasingly the technology stack is um is maturing. You have

1197
00:57:55,600 --> 00:57:58,120
Speaker 1: light ar, you have solid state LDAR, but the really

1198
00:57:58,240 --> 00:58:01,160
Speaker 1: valuable thing is the simulation. And this is a fascinating

1199
00:58:01,160 --> 00:58:04,520
Speaker 1: phenomenon because it is affecting many different industries, the gap

1200
00:58:04,720 --> 00:58:08,720
Speaker 1: between reality and that which is simulated, so the ability

1201
00:58:08,880 --> 00:58:13,080
Speaker 1: to render reality in physics engines, He's leading everything from

1202
00:58:13,360 --> 00:58:15,280
Speaker 1: you can play a simulator on something like Drone Racing

1203
00:58:15,320 --> 00:58:17,600
Speaker 1: League and you can actually fly a drone and it's indistinguishable.

1204
00:58:17,960 --> 00:58:20,560
Speaker 1: You can actually simulate something inside the human body using

1205
00:58:20,560 --> 00:58:22,840
Speaker 1: a CT scan of a body and have a GPS

1206
00:58:22,880 --> 00:58:26,080
Speaker 1: guided experience using a surgical robot. You can actually have

1207
00:58:26,160 --> 00:58:29,400
Speaker 1: a simulation of a street and then have an autonomous

1208
00:58:29,480 --> 00:58:32,400
Speaker 1: vehicle that is learning on the simulation as opposed to

1209
00:58:32,440 --> 00:58:36,240
Speaker 1: actually learning driving in the road, and it's increasingly indistinguishable.

1210
00:58:36,600 --> 00:58:39,280
Speaker 1: So typically that takes place on a screen. I know

1211
00:58:39,400 --> 00:58:42,000
Speaker 1: you're a sci fi guy. When do we get um

1212
00:58:42,720 --> 00:58:46,080
Speaker 1: the star Trek Wholegram room the whole of deck well,

1213
00:58:46,160 --> 00:58:48,320
Speaker 1: you've got we have a company actually called looking Glass

1214
00:58:48,320 --> 00:58:51,920
Speaker 1: that's doing volumetric display, but we're not needing three dimensional

1215
00:58:52,040 --> 00:58:54,320
Speaker 1: without a screen. Three dimensional. You have a screen, but

1216
00:58:54,440 --> 00:58:56,880
Speaker 1: you have no V R A R glasses, So it's

1217
00:58:56,920 --> 00:59:00,360
Speaker 1: a it's an optical trick like holograms are. But the

1218
00:59:00,440 --> 00:59:02,240
Speaker 1: future there is that you'll be looking at something lookin

1219
00:59:02,320 --> 00:59:05,600
Speaker 1: too uh um. You know, a pitch on a soccer field,

1220
00:59:05,640 --> 00:59:07,360
Speaker 1: then it's in three dimensional like a fish tank, and

1221
00:59:07,360 --> 00:59:09,640
Speaker 1: you're watching the game, you know, right there. H So,

1222
00:59:09,960 --> 00:59:12,880
Speaker 1: one last star trek thing I have to ask you about.

1223
00:59:13,360 --> 00:59:17,480
Speaker 1: You've talked about transporter technology, right and we're not talking

1224
00:59:17,560 --> 00:59:21,160
Speaker 1: about spooky um connections at a distance or anything on

1225
00:59:21,280 --> 00:59:24,400
Speaker 1: a on a sub atomic level. You're literally talking about

1226
00:59:24,440 --> 00:59:29,040
Speaker 1: the ability to send matter from here to there. Explain that, Well,

1227
00:59:29,120 --> 00:59:31,000
Speaker 1: I don't think you can do. I mean that is

1228
00:59:31,040 --> 00:59:33,320
Speaker 1: science fiction. What you can do is, I can take

1229
00:59:33,320 --> 00:59:35,240
Speaker 1: a picture I'm looking at a coffee cup on our

1230
00:59:35,280 --> 00:59:38,760
Speaker 1: desk here. I can three D capture a similar acorum

1231
00:59:38,800 --> 00:59:41,720
Speaker 1: of that cop I can now it's a CAD design.

1232
00:59:42,320 --> 00:59:45,720
Speaker 1: I can turn the atoms into a representation in bits,

1233
00:59:45,760 --> 00:59:47,280
Speaker 1: and I can send those bits as a cat file

1234
00:59:47,360 --> 00:59:49,840
Speaker 1: digitally to a three D printer, and I can print

1235
00:59:50,360 --> 00:59:53,200
Speaker 1: a version of that, but I am not actually transporting

1236
00:59:53,240 --> 00:59:56,000
Speaker 1: the individual atoms which is copied it. Which is why

1237
00:59:56,320 --> 00:59:59,280
Speaker 1: I will never step foot on a transporter, because people

1238
00:59:59,360 --> 01:00:03,160
Speaker 1: don't realize the transporter kills you and creates an exact

1239
01:00:03,280 --> 01:00:06,280
Speaker 1: duplicate of view wherever it goes. And I was always

1240
01:00:06,640 --> 01:00:09,080
Speaker 1: I was always surprised at Star Trek how that never

1241
01:00:09,240 --> 01:00:12,160
Speaker 1: came up other than the doc not wanting to do it.

1242
01:00:12,640 --> 01:00:15,480
Speaker 1: But every time you step on a transporter, if you

1243
01:00:15,560 --> 01:00:18,400
Speaker 1: want to make a device to execute people, and this

1244
01:00:18,480 --> 01:00:21,840
Speaker 1: would be it, right, you basically take people and disassemble

1245
01:00:21,920 --> 01:00:26,160
Speaker 1: them on a molecule and atom by anom basis. Now,

1246
01:00:26,200 --> 01:00:29,040
Speaker 1: the fact that you have the ability to reassemble them elsewhere,

1247
01:00:29,160 --> 01:00:31,800
Speaker 1: that doesn't mean they're not dead. This is just an

1248
01:00:31,840 --> 01:00:36,240
Speaker 1: exact duplicate. But I just thought that was an interesting

1249
01:00:36,360 --> 01:00:39,800
Speaker 1: But so you're talking about reporters are not an investable thing.

1250
01:00:40,000 --> 01:00:43,920
Speaker 1: You're you're talking about basically the variation of three D

1251
01:00:44,000 --> 01:00:49,360
Speaker 1: printing using at a distance, using using um a full

1252
01:00:49,440 --> 01:00:52,320
Speaker 1: three D scanner, scanning you know, and and three D scanning.

1253
01:00:52,400 --> 01:00:55,000
Speaker 1: The resolution has increased exponentially over the past few years

1254
01:00:55,040 --> 01:00:56,480
Speaker 1: and so that's going to continue, and I think that

1255
01:00:56,600 --> 01:01:00,600
Speaker 1: you can capture very discrete elements. Uh. This is another

1256
01:01:00,640 --> 01:01:03,760
Speaker 1: area where actually I'm very excited about biology, which is

1257
01:01:03,840 --> 01:01:06,120
Speaker 1: the imaging and microscopy tools that are coming out to

1258
01:01:06,120 --> 01:01:08,800
Speaker 1: be able to capture things in real time at a

1259
01:01:08,960 --> 01:01:12,000
Speaker 1: near atomic scale inside of cells. But the idea of

1260
01:01:12,000 --> 01:01:15,520
Speaker 1: teleportation is you know, today's science fiction three D scanners

1261
01:01:15,640 --> 01:01:18,480
Speaker 1: coupled with three D printers will closely approximated, but it's

1262
01:01:18,480 --> 01:01:21,600
Speaker 1: still a trick and at a molecular level, you know,

1263
01:01:21,720 --> 01:01:23,320
Speaker 1: let's we might as well talk a little bit about

1264
01:01:23,360 --> 01:01:28,600
Speaker 1: Crisper and gene editing. Um, are you describing building these

1265
01:01:28,680 --> 01:01:32,560
Speaker 1: things from scratch or are you talking about growing these things, designing,

1266
01:01:32,720 --> 01:01:35,560
Speaker 1: redesigning the genes and growing them or doing a scan

1267
01:01:35,720 --> 01:01:38,320
Speaker 1: and reproducing them. Well, there, no, no, So in biology,

1268
01:01:38,360 --> 01:01:40,800
Speaker 1: there are there are people that have gene libraries. They're

1269
01:01:40,800 --> 01:01:43,160
Speaker 1: trying to assemble nuclear tides. There are people who are

1270
01:01:43,280 --> 01:01:45,959
Speaker 1: editing them using the technique of Crisper. Crisper is really,

1271
01:01:46,600 --> 01:01:48,600
Speaker 1: I don't want to say, an overhyped technique, but it

1272
01:01:48,760 --> 01:01:51,960
Speaker 1: is a very hyped technique that was a fundamental breakthrough

1273
01:01:51,960 --> 01:01:54,400
Speaker 1: and the fundamental breakthrough was effectively control C, control V,

1274
01:01:54,560 --> 01:01:57,040
Speaker 1: and computing. It was copy and paste. You can actually

1275
01:01:57,240 --> 01:02:00,360
Speaker 1: use this technique to be able to transpose code, dawns

1276
01:02:00,520 --> 01:02:03,160
Speaker 1: or nucleotides from one part to another, and so that

1277
01:02:03,200 --> 01:02:06,760
Speaker 1: allows you to literally edit like a Microsoft word with precision.

1278
01:02:07,160 --> 01:02:09,040
Speaker 1: But we are still so far away from being able

1279
01:02:09,040 --> 01:02:11,680
Speaker 1: to deliver. There's been no crisper in a human There's

1280
01:02:11,720 --> 01:02:14,080
Speaker 1: been no technique where you say, you know, Jeez Barr

1281
01:02:14,120 --> 01:02:16,160
Speaker 1: has got a genetic defect here and let's you know,

1282
01:02:16,480 --> 01:02:18,800
Speaker 1: edit that out. It's just it's it's it's so far

1283
01:02:18,920 --> 01:02:21,760
Speaker 1: away from practical reality that it's still a lot of hip.

1284
01:02:21,840 --> 01:02:25,200
Speaker 1: But when you say so far away a thousand years, no,

1285
01:02:25,560 --> 01:02:28,320
Speaker 1: I mean, you know, a hundred years, ten years impossible

1286
01:02:28,360 --> 01:02:29,840
Speaker 1: to predict because you could have a breakthrough, you know,

1287
01:02:29,920 --> 01:02:33,200
Speaker 1: in so intellectual but realistically, fifty years from now you

1288
01:02:33,280 --> 01:02:36,120
Speaker 1: will be able to edit not you but you can

1289
01:02:36,240 --> 01:02:39,240
Speaker 1: today edit embryos. You can you know in China is

1290
01:02:39,280 --> 01:02:41,040
Speaker 1: doing this. China, and this is something I always say

1291
01:02:41,080 --> 01:02:43,640
Speaker 1: that China lacks something that we have, and because they

1292
01:02:43,720 --> 01:02:45,600
Speaker 1: lack it, they will be ascendant. And the thing that

1293
01:02:45,680 --> 01:02:48,240
Speaker 1: they lack is the ethics and regulatory apparatus that slows

1294
01:02:48,280 --> 01:02:50,120
Speaker 1: things down here in the US. Well, the way to

1295
01:02:50,440 --> 01:02:55,000
Speaker 1: let me rephrase what you just said, China prioritizes the

1296
01:02:55,560 --> 01:02:59,280
Speaker 1: group over the individual. In America, our priorities or the

1297
01:02:59,320 --> 01:03:01,800
Speaker 1: individual of the group. And that's the difference between individual

1298
01:03:01,960 --> 01:03:05,640
Speaker 1: rights or a society that's lasted five thousand years totally.

1299
01:03:05,760 --> 01:03:09,280
Speaker 1: And and the result of that, I think is that

1300
01:03:09,520 --> 01:03:13,960
Speaker 1: many of the advances in biotech will actually in the

1301
01:03:14,040 --> 01:03:18,680
Speaker 1: near future occur in China, particularly around CNS disorders Parkinsons.

1302
01:03:18,720 --> 01:03:23,080
Speaker 1: And the reason I say that is, first of all,

1303
01:03:23,600 --> 01:03:25,960
Speaker 1: with the growing demographic population we have that are going

1304
01:03:26,000 --> 01:03:28,320
Speaker 1: to get older here in the US and suffer from

1305
01:03:28,360 --> 01:03:33,560
Speaker 1: things like Alzheimer's and Parkinson's and neurodegenerative diseases and inevitability. Statistically,

1306
01:03:33,800 --> 01:03:37,480
Speaker 1: that is a huge market. We have shifted much of

1307
01:03:37,520 --> 01:03:40,760
Speaker 1: our primate research outside the US because of ethics reasons.

1308
01:03:41,240 --> 01:03:44,600
Speaker 1: China does not have any such ethics reasons. They do

1309
01:03:44,720 --> 01:03:47,320
Speaker 1: work on prisoners that even Americans would be a guess

1310
01:03:47,360 --> 01:03:51,560
Speaker 1: that that that may be so, but they are definitively

1311
01:03:51,600 --> 01:03:54,800
Speaker 1: doing work on primates. Uh. And I think what's gonna

1312
01:03:54,840 --> 01:03:57,280
Speaker 1: end up happening is we will import from China the

1313
01:03:57,400 --> 01:04:00,320
Speaker 1: drugs much in the way that the US X boards

1314
01:04:00,400 --> 01:04:02,600
Speaker 1: Hollywood to the rest of the world. I think China

1315
01:04:02,640 --> 01:04:06,320
Speaker 1: will be exporting drugs and hopefully you know, it's well tested,

1316
01:04:06,400 --> 01:04:09,360
Speaker 1: close to FDA approved apparatus, so that we're not in

1317
01:04:09,520 --> 01:04:13,120
Speaker 1: in uh important crazy stuff. But so, what's the difference

1318
01:04:13,160 --> 01:04:16,080
Speaker 1: between doing the research? This is where the ethical slash

1319
01:04:16,200 --> 01:04:19,840
Speaker 1: economic discussion we had earlier comes to four. Is there

1320
01:04:19,880 --> 01:04:23,320
Speaker 1: a difference between doing the research or purchasing the end

1321
01:04:23,400 --> 01:04:27,520
Speaker 1: result of that research? To me, it's the same effectively,

1322
01:04:28,000 --> 01:04:30,680
Speaker 1: the research was done. You may not like it, but

1323
01:04:30,840 --> 01:04:33,800
Speaker 1: you're eating the pork or you're buying this product. How

1324
01:04:33,880 --> 01:04:36,400
Speaker 1: are you not as responsible for the research as if

1325
01:04:36,440 --> 01:04:39,560
Speaker 1: it was done here? Isn't it hypocritical to pretend otherwise?

1326
01:04:40,000 --> 01:04:42,440
Speaker 1: And this goes to whether we're talking about food or

1327
01:04:43,120 --> 01:04:45,040
Speaker 1: what have you. Well, look, it is a US citizen,

1328
01:04:45,120 --> 01:04:47,400
Speaker 1: your your tax money goes to university research, and you

1329
01:04:47,480 --> 01:04:49,400
Speaker 1: might fund some research. Whether you agree with that or not.

1330
01:04:49,520 --> 01:04:51,480
Speaker 1: I mean that's the age or the War Department or this.

1331
01:04:51,600 --> 01:04:53,800
Speaker 1: So that I mean, which itself is another ethical issue

1332
01:04:53,800 --> 01:04:55,760
Speaker 1: we should touch on. Because military technology, I think is

1333
01:04:55,760 --> 01:04:57,920
Speaker 1: going to be an absolute boone in venture capital. I

1334
01:04:57,960 --> 01:04:59,760
Speaker 1: think it's one of the most exciting and important areas

1335
01:05:00,120 --> 01:05:02,560
Speaker 1: in the next you know, five years um in part

1336
01:05:03,160 --> 01:05:06,600
Speaker 1: because the vast number of big tech companies from Google

1337
01:05:06,640 --> 01:05:09,720
Speaker 1: on down are issuing wanting to do work with military.

1338
01:05:09,800 --> 01:05:11,800
Speaker 1: They have such pressure from the HR and I R

1339
01:05:11,880 --> 01:05:13,760
Speaker 1: departments that people are saying, I don't want to work

1340
01:05:13,800 --> 01:05:16,919
Speaker 1: on this stuff. It is creating a giant, gaping, whole

1341
01:05:17,160 --> 01:05:19,920
Speaker 1: void where there is a tremendous opportunity for some of

1342
01:05:20,000 --> 01:05:22,720
Speaker 1: the smartest technologies, some of the smartest scientists and engineers,

1343
01:05:22,960 --> 01:05:25,680
Speaker 1: to work on these wicked, thorny problems in defense. Your

1344
01:05:26,080 --> 01:05:29,360
Speaker 1: pal Peter till We are co invested in a company

1345
01:05:29,440 --> 01:05:31,440
Speaker 1: called and Roll. I think it is going to be

1346
01:05:31,520 --> 01:05:34,240
Speaker 1: one of the greatest companies in defense. If you look

1347
01:05:34,280 --> 01:05:38,480
Speaker 1: at the new entrants. If you look at the new

1348
01:05:38,800 --> 01:05:42,440
Speaker 1: entrants in defense, you know, you've got Lockeed, You've got Raytheon,

1349
01:05:42,520 --> 01:05:45,280
Speaker 1: You've got be A, You've got General Dynamics. There hasn't

1350
01:05:45,280 --> 01:05:48,960
Speaker 1: been anybody over the past. They've all conglomeratized over the

1351
01:05:49,000 --> 01:05:50,920
Speaker 1: past thirty years. There used to be fifty companies in

1352
01:05:50,960 --> 01:05:52,920
Speaker 1: that space. There's seven. And in some cases you have

1353
01:05:53,000 --> 01:05:54,680
Speaker 1: people that are doing amazing things, but it takes a

1354
01:05:54,760 --> 01:05:56,800
Speaker 1: very long time, and they're working on huge, multi billion

1355
01:05:56,800 --> 01:05:59,440
Speaker 1: projects like the thirty five Joint Strike Fighter. In other cases,

1356
01:05:59,480 --> 01:06:01,640
Speaker 1: you have many, many small beltway bandits who are just

1357
01:06:01,680 --> 01:06:03,800
Speaker 1: getting these cost plus contracts because they know somebody that

1358
01:06:03,880 --> 01:06:06,600
Speaker 1: knows somebody. But true innovation the thing that I think

1359
01:06:06,640 --> 01:06:10,080
Speaker 1: gave American military might, which in turn gave our economy

1360
01:06:10,120 --> 01:06:13,000
Speaker 1: the ability to project power across the globe and control

1361
01:06:13,040 --> 01:06:16,160
Speaker 1: two oceans, and all the geographic plus military advantage we have,

1362
01:06:16,680 --> 01:06:20,720
Speaker 1: it has to be supported by continuous technological advantage. Is

1363
01:06:20,760 --> 01:06:22,440
Speaker 1: something that over the past twenty years, I think has

1364
01:06:22,480 --> 01:06:25,240
Speaker 1: slipped away. I think very few engineers want to really

1365
01:06:25,280 --> 01:06:27,200
Speaker 1: work on these problems, and I think the smartest ones

1366
01:06:27,280 --> 01:06:29,080
Speaker 1: that do are not only going to make a fortune,

1367
01:06:29,080 --> 01:06:31,600
Speaker 1: but they're going to do a tremendous patriotic duty. Quite

1368
01:06:31,680 --> 01:06:36,320
Speaker 1: quite interesting. There's a fascinating story about how a bunch

1369
01:06:36,360 --> 01:06:39,680
Speaker 1: of engineers tried to get the Navy to take radar

1370
01:06:40,320 --> 01:06:44,000
Speaker 1: in the new book um Loon Shots. It's just astonishing

1371
01:06:44,080 --> 01:06:46,040
Speaker 1: that this was an old technology by the time it

1372
01:06:46,160 --> 01:06:49,200
Speaker 1: was put to work in World War Two. Nobody could

1373
01:06:49,240 --> 01:06:52,560
Speaker 1: get the War Department, which, as it was called, back

1374
01:06:52,600 --> 01:06:55,600
Speaker 1: then to recognize the military value of this, I gotta

1375
01:06:55,600 --> 01:06:57,560
Speaker 1: think there's a million things. It's always I mean, even

1376
01:06:57,600 --> 01:07:01,240
Speaker 1: the Jim Wolsey, whose former I who was a venture

1377
01:07:01,280 --> 01:07:04,040
Speaker 1: partner on our firm Familiar really he uh you know,

1378
01:07:04,120 --> 01:07:08,760
Speaker 1: tells a story of of Amber, which was he traded

1379
01:07:08,800 --> 01:07:11,720
Speaker 1: a bunch of alpaca back blankets to um to some

1380
01:07:11,840 --> 01:07:14,280
Speaker 1: people over in eastern Europe to be able to get

1381
01:07:14,280 --> 01:07:16,120
Speaker 1: a runway and then actually fly a drone. He was

1382
01:07:16,160 --> 01:07:20,040
Speaker 1: looking at Milissovitch and uh and in that early drone

1383
01:07:20,360 --> 01:07:24,200
Speaker 1: was predator by this really entrepreneur Abe Caram at the

1384
01:07:24,280 --> 01:07:26,200
Speaker 1: time wolves. He went to the d O D and said,

1385
01:07:26,200 --> 01:07:27,360
Speaker 1: I need a drone. I want to be able to

1386
01:07:27,480 --> 01:07:30,560
Speaker 1: you know, have an unmanned pilot that could a pilot

1387
01:07:30,680 --> 01:07:32,560
Speaker 1: vehicle that could give me eyes on the ground. And

1388
01:07:32,560 --> 01:07:34,600
Speaker 1: they said it will cost five million dollars and take

1389
01:07:34,640 --> 01:07:36,240
Speaker 1: six years. And he was able to go to this

1390
01:07:36,400 --> 01:07:39,080
Speaker 1: entrepreneur and in five million dollars in six months they

1391
01:07:39,120 --> 01:07:42,320
Speaker 1: were able to create this astonishing and whatever happened with

1392
01:07:42,440 --> 01:07:46,160
Speaker 1: the company behind the Predator drone did Boeing a General

1393
01:07:46,200 --> 01:07:49,040
Speaker 1: Atomics General Stomics. Okay, that's a that's a minute. General

1394
01:07:49,120 --> 01:07:52,160
Speaker 1: dynamics or general Atomics. Really that's a great that's a

1395
01:07:52,240 --> 01:07:55,640
Speaker 1: great name, j um So I only have you for

1396
01:07:55,760 --> 01:07:59,240
Speaker 1: a fine amount of time. Let's jump to our speed

1397
01:07:59,360 --> 01:08:02,320
Speaker 1: round our favorite questions. These are what we ask all

1398
01:08:02,400 --> 01:08:05,560
Speaker 1: of our guests. Um, first car you have our own?

1399
01:08:05,640 --> 01:08:09,720
Speaker 1: You're making model Ford Explorer. It was a Hunter Green

1400
01:08:10,040 --> 01:08:12,000
Speaker 1: and uh, I think I had it for a year

1401
01:08:12,080 --> 01:08:14,600
Speaker 1: before it spun out on the highway and uh I

1402
01:08:14,760 --> 01:08:16,280
Speaker 1: never drove it again. By the way, this is my

1403
01:08:16,720 --> 01:08:20,080
Speaker 1: test question because eventually someone's gonna say, what's a what's

1404
01:08:20,120 --> 01:08:22,240
Speaker 1: the car? I don't know what you talk exactly? Uh,

1405
01:08:22,360 --> 01:08:25,080
Speaker 1: tell us the most important thing people don't know about

1406
01:08:25,200 --> 01:08:26,760
Speaker 1: Josh wall I mean, you know, there's so much that

1407
01:08:26,760 --> 01:08:28,720
Speaker 1: I don't even know about Josh Wolf right. So being

1408
01:08:28,760 --> 01:08:31,800
Speaker 1: intellectually honest, but uh, personal side of me, I love

1409
01:08:31,960 --> 01:08:34,200
Speaker 1: heavy metal and hardcore. I grew up going to you know,

1410
01:08:34,479 --> 01:08:38,320
Speaker 1: mosh pick Brooklyn, place called Lamore's and Life of Agony,

1411
01:08:38,439 --> 01:08:41,560
Speaker 1: and I like skateboarding, and I like people that just

1412
01:08:41,720 --> 01:08:45,719
Speaker 1: have this sort of gritty rebel side to them. Heavy

1413
01:08:45,760 --> 01:08:47,960
Speaker 1: metal hip hop. When you say heavy metal, like my

1414
01:08:48,080 --> 01:08:51,840
Speaker 1: hip hop following ends at Pulse Boutique with the Beastie Boys,

1415
01:08:51,920 --> 01:08:54,360
Speaker 1: but yeah, it's too commercial, like you know Black Moon.

1416
01:08:54,479 --> 01:08:57,919
Speaker 1: On the hip hop side, um uh, like two commercial

1417
01:08:58,080 --> 01:09:01,200
Speaker 1: is three eleven or or and and on the heavy

1418
01:09:01,240 --> 01:09:03,960
Speaker 1: metal side it would have been um deaf Tones, Life

1419
01:09:03,960 --> 01:09:08,640
Speaker 1: of Agony, bio hazards, you're hard for. Like, to me,

1420
01:09:08,720 --> 01:09:11,680
Speaker 1: heavy metal is Black Sabbath and that's like when I

1421
01:09:11,760 --> 01:09:14,479
Speaker 1: was coming up. But you say that today. But when

1422
01:09:14,560 --> 01:09:17,000
Speaker 1: Black Sabbath came out, people were like, what is this

1423
01:09:17,160 --> 01:09:20,840
Speaker 1: devil music? Exactly? Um, tell us about some of your

1424
01:09:20,880 --> 01:09:23,200
Speaker 1: early mentors. You obviously have a few who have helped

1425
01:09:23,320 --> 01:09:25,439
Speaker 1: shape your career. Well, it's interesting there there used to

1426
01:09:25,479 --> 01:09:27,559
Speaker 1: be this placard outside of our old office. We were

1427
01:09:27,600 --> 01:09:29,160
Speaker 1: on forty first Park in Madison that we're down on

1428
01:09:29,160 --> 01:09:32,800
Speaker 1: Broadway and you said that, Um, you know, reading great

1429
01:09:32,840 --> 01:09:34,880
Speaker 1: books is like having conversations with you know, the best

1430
01:09:34,920 --> 01:09:37,639
Speaker 1: minds of history, and so so many of my embedded

1431
01:09:37,680 --> 01:09:44,160
Speaker 1: in the ground. And so my office is forty Brian Parks, right,

1432
01:09:44,240 --> 01:09:46,519
Speaker 1: they're they're fabulous. They're all over and I don't know

1433
01:09:46,600 --> 01:09:48,519
Speaker 1: who did that, but it's genius. I love it. And

1434
01:09:48,640 --> 01:09:50,479
Speaker 1: and you know you're you're looking down, you're looking at

1435
01:09:50,520 --> 01:09:51,920
Speaker 1: your feet, you're looking at your phone or whatever, and

1436
01:09:51,960 --> 01:09:53,560
Speaker 1: you notice these things. But that one always hit me

1437
01:09:53,600 --> 01:09:55,360
Speaker 1: because so many of my mentors in a sense are

1438
01:09:55,400 --> 01:09:57,280
Speaker 1: people that I never met, that are dead right there,

1439
01:09:57,400 --> 01:09:59,360
Speaker 1: just live in the pulp and the ideas. But but

1440
01:09:59,439 --> 01:10:01,880
Speaker 1: the one human mentor that I really I feel oh

1441
01:10:02,000 --> 01:10:05,000
Speaker 1: my career and and the intellectual honesty is Bill Conway,

1442
01:10:05,640 --> 01:10:09,080
Speaker 1: the founder of Carlisle, who is just his ethics, his integrity.

1443
01:10:09,120 --> 01:10:10,920
Speaker 1: It doesn't matter what the deal docs say, it doesn't

1444
01:10:10,960 --> 01:10:13,880
Speaker 1: matter what the contract says, you just do the right thing. Um.

1445
01:10:14,240 --> 01:10:15,880
Speaker 1: The kind of questions he asks, the way that he

1446
01:10:15,960 --> 01:10:19,080
Speaker 1: speaks without saying things. You know, it's, um, what do

1447
01:10:19,120 --> 01:10:22,240
Speaker 1: you mean by speaks without saying things? Um, he has

1448
01:10:22,280 --> 01:10:25,599
Speaker 1: a diplomatic way of saying things, and sometimes the messages

1449
01:10:25,640 --> 01:10:28,120
Speaker 1: and what he doesn't say. And uh, I find that

1450
01:10:28,240 --> 01:10:30,639
Speaker 1: there's a handful of people that can communicate that way

1451
01:10:31,040 --> 01:10:33,320
Speaker 1: where you sort of intuit what they mean without them

1452
01:10:33,320 --> 01:10:38,479
Speaker 1: actually being explicit. Quite quite interesting. Um. Talk about investors,

1453
01:10:38,560 --> 01:10:41,600
Speaker 1: be at VC or otherwise, who influenced your approach to

1454
01:10:41,720 --> 01:10:45,680
Speaker 1: the world of risking capital? Well, you know, I think

1455
01:10:45,800 --> 01:10:48,080
Speaker 1: I read every bio and every article that I could

1456
01:10:48,080 --> 01:10:50,800
Speaker 1: about the early venture investors Um, so you know Tom

1457
01:10:50,840 --> 01:10:55,320
Speaker 1: Perkins and General Drio and all the early venture capitalists,

1458
01:10:55,520 --> 01:10:58,360
Speaker 1: um and and so many of these lessons are basically

1459
01:10:58,439 --> 01:11:01,760
Speaker 1: irrelevant because you have this depends there's an idiosyncratic moment.

1460
01:11:02,080 --> 01:11:04,320
Speaker 1: I remember there was a guy from a firm he

1461
01:11:04,400 --> 01:11:06,160
Speaker 1: was a legendary investor back in the day, Ben Rosen

1462
01:11:06,240 --> 01:11:08,200
Speaker 1: from Seven Rows, and he was early investor in Compact,

1463
01:11:08,280 --> 01:11:11,479
Speaker 1: which was once a significant technology investment Invincent. You just

1464
01:11:11,760 --> 01:11:14,479
Speaker 1: need one. You just need one because you get one

1465
01:11:14,600 --> 01:11:16,639
Speaker 1: hit and then that hit begets you know, a reputation

1466
01:11:16,720 --> 01:11:18,600
Speaker 1: or reputation but gets more hits and so on. So

1467
01:11:18,760 --> 01:11:20,040
Speaker 1: on the venture capital side, it was you know a

1468
01:11:20,080 --> 01:11:22,479
Speaker 1: handful of individuals. The person who I have the most

1469
01:11:22,520 --> 01:11:24,719
Speaker 1: respect for and venture capital hands down as Bill Gurley.

1470
01:11:24,920 --> 01:11:27,120
Speaker 1: I think he's a true investor. I think he came

1471
01:11:27,160 --> 01:11:29,400
Speaker 1: from the cell side on Wall Street and he understood

1472
01:11:29,400 --> 01:11:31,600
Speaker 1: how to be an analyst. First, he understands businesses, he

1473
01:11:31,640 --> 01:11:34,040
Speaker 1: understands human nature. I think he happens to be a

1474
01:11:34,160 --> 01:11:36,160
Speaker 1: towering giant because he's very tall and I'm very short.

1475
01:11:36,439 --> 01:11:38,120
Speaker 1: But um, we're both on the board in the Santa

1476
01:11:38,160 --> 01:11:41,320
Speaker 1: Fe Institute, and and I oh with Michael exactly. Michael

1477
01:11:41,400 --> 01:11:44,280
Speaker 1: chairs it and and and so, uh So, I'm very

1478
01:11:44,320 --> 01:11:47,200
Speaker 1: fond of Bill um and uh you know, but there's

1479
01:11:47,680 --> 01:11:50,360
Speaker 1: Bill Janeway is another guy who I think really interesting,

1480
01:11:50,600 --> 01:11:52,080
Speaker 1: super smart guy, and the way that he always thought

1481
01:11:52,080 --> 01:11:55,360
Speaker 1: about risk and technology was an influence. Um. There's an

1482
01:11:55,360 --> 01:11:57,519
Speaker 1: older guy, Chris Brody, who was a Warburg pinkis who

1483
01:11:57,560 --> 01:11:58,920
Speaker 1: I spent a lot of time with. I've probably learned

1484
01:11:58,920 --> 01:12:01,599
Speaker 1: how to be a good board member from watching uh Chris,

1485
01:12:01,920 --> 01:12:04,640
Speaker 1: you know, hold people accountable. So a lot of you know,

1486
01:12:05,520 --> 01:12:09,200
Speaker 1: individual lessons. But but from an investing philosophy, I mean,

1487
01:12:09,280 --> 01:12:11,320
Speaker 1: hands down, it's like the value investors because they were

1488
01:12:11,360 --> 01:12:13,960
Speaker 1: just rational and they have they wouldn't touch venture capital.

1489
01:12:14,040 --> 01:12:16,560
Speaker 1: You know, Charlie Munger and Buffett and everything that I

1490
01:12:16,600 --> 01:12:18,960
Speaker 1: could read you know that they've ever written or said,

1491
01:12:19,760 --> 01:12:22,439
Speaker 1: and all the you know, acolytes that followed from them,

1492
01:12:22,439 --> 01:12:25,000
Speaker 1: I think are just It gives you a a grounding

1493
01:12:25,120 --> 01:12:29,320
Speaker 1: sense of a true business and markets and human psychology

1494
01:12:29,439 --> 01:12:31,960
Speaker 1: and and uh so, I think even as a venture capitalist,

1495
01:12:32,000 --> 01:12:34,400
Speaker 1: you know, if you if you haven't studied those greats

1496
01:12:34,520 --> 01:12:37,599
Speaker 1: your you get a massive deficit. Let's look about books.

1497
01:12:37,640 --> 01:12:40,040
Speaker 1: We've mentioned a few over the course of our conversation,

1498
01:12:40,120 --> 01:12:42,000
Speaker 1: what are some of your favorite books, be they fiction,

1499
01:12:42,080 --> 01:12:45,599
Speaker 1: non fiction, techychology. I'm a voracious reader, so so fiction nonfiction,

1500
01:12:45,720 --> 01:12:48,360
Speaker 1: so Conciliency. O. Wilson was a great Um. How the

1501
01:12:48,400 --> 01:12:52,519
Speaker 1: Mind Works Stephen Pinker. Um. I loved The Operator about

1502
01:12:52,600 --> 01:12:56,960
Speaker 1: David Geffen And I like reading biology. Uh sorry biographies? Um,

1503
01:12:57,040 --> 01:12:59,960
Speaker 1: why Zebras don't get old scar? Oh? Sure out of Stanford?

1504
01:13:00,000 --> 01:13:03,360
Speaker 1: What's this game? Poki? Right? Suppolsky Roberts Polk is he's

1505
01:13:03,360 --> 01:13:05,320
Speaker 1: a primatologist, right, but at the end of the day,

1506
01:13:05,320 --> 01:13:08,000
Speaker 1: where where he's really a behaviorist if you think about it.

1507
01:13:08,240 --> 01:13:10,559
Speaker 1: But but we we are two primates sitting here talking, right.

1508
01:13:10,600 --> 01:13:15,000
Speaker 1: And related to that, there's a great book that Robin

1509
01:13:15,080 --> 01:13:17,160
Speaker 1: Hansen wrote in the past one or two years called

1510
01:13:17,160 --> 01:13:19,640
Speaker 1: The Elephant in the Brain about signaling and if you

1511
01:13:19,800 --> 01:13:22,240
Speaker 1: understand why people the motives behind why people do things.

1512
01:13:22,320 --> 01:13:25,760
Speaker 1: I think it's really interesting. Um, let's see the on

1513
01:13:25,840 --> 01:13:27,960
Speaker 1: the fiction side, it's interesting. My wife really got me

1514
01:13:28,040 --> 01:13:31,000
Speaker 1: into fiction. Got no going back in fifteen years or

1515
01:13:31,040 --> 01:13:34,400
Speaker 1: something like that. But the Magics, um, and it was

1516
01:13:34,439 --> 01:13:37,800
Speaker 1: sort of the psychological profile. Oh. I can't remember the

1517
01:13:37,840 --> 01:13:41,040
Speaker 1: author's name, but it was m A g u s.

1518
01:13:41,160 --> 01:13:43,400
Speaker 1: It's this guy's got this like Greek island and he

1519
01:13:43,680 --> 01:13:46,400
Speaker 1: he has this teacher professor who takes this little sojourn

1520
01:13:46,479 --> 01:13:48,320
Speaker 1: there and he's just like all these mind games that

1521
01:13:48,360 --> 01:13:50,840
Speaker 1: he plays with John Fouls. Yeah, it was. It was

1522
01:13:50,960 --> 01:13:53,840
Speaker 1: dark and cool and psychologically that turned me onto a

1523
01:13:53,960 --> 01:13:57,960
Speaker 1: British writer, Rachel Kusk, who's writing like every sentence you

1524
01:13:58,000 --> 01:13:59,639
Speaker 1: know I mentioned before, I love the line that there's

1525
01:14:00,000 --> 01:14:02,960
Speaker 1: aggers and men smiles from Shakespeare and so just her

1526
01:14:03,080 --> 01:14:06,840
Speaker 1: use of language and the psychologically astute prose that she

1527
01:14:06,960 --> 01:14:10,120
Speaker 1: has is great. Um she has a trilogy that seems

1528
01:14:10,160 --> 01:14:13,240
Speaker 1: to be Outline is the first um and and Transit

1529
01:14:13,360 --> 01:14:15,599
Speaker 1: was the second, and Kudos I think is the third.

1530
01:14:16,320 --> 01:14:20,640
Speaker 1: Um fiction recently over story by Richard Powers. It's just

1531
01:14:20,880 --> 01:14:25,120
Speaker 1: brilliant pros And I really got turned onto fiction because

1532
01:14:25,120 --> 01:14:27,240
Speaker 1: I think that you can you can tell more in

1533
01:14:27,280 --> 01:14:30,280
Speaker 1: a paragraph by an amazing fiction author who understands human

1534
01:14:30,320 --> 01:14:33,760
Speaker 1: psychology than you kind reading an entire textbook. So I'm

1535
01:14:33,840 --> 01:14:36,559
Speaker 1: surprised there isn't a science fiction title on the list

1536
01:14:36,640 --> 01:14:41,400
Speaker 1: because you talked so much previously about sci fi, Neil Stevenson,

1537
01:14:41,560 --> 01:14:44,160
Speaker 1: I mean by you know by far the depth, the

1538
01:14:44,320 --> 01:14:46,920
Speaker 1: rigor the foresight. You know. The Diamond Age for me,

1539
01:14:47,000 --> 01:14:49,200
Speaker 1: probably around the time that we were starting Locks was

1540
01:14:49,280 --> 01:14:53,479
Speaker 1: just such an inspiration. Um, Diamond Age. Yeah, and I

1541
01:14:53,880 --> 01:14:59,200
Speaker 1: know that one. And he didn't he do seven Moons? Yeah? Right?

1542
01:14:59,360 --> 01:15:03,240
Speaker 1: And um yeah, and then and then TV writers like um,

1543
01:15:03,680 --> 01:15:06,759
Speaker 1: you know, David Milch and Deadwood and the current writers

1544
01:15:06,800 --> 01:15:11,000
Speaker 1: on Westworld. Um, I just I think it's absolutely brilliant,

1545
01:15:11,120 --> 01:15:16,400
Speaker 1: scintillating soliloquies that they put forth and philosophically erudite, really interesting.

1546
01:15:16,640 --> 01:15:19,760
Speaker 1: We are in a golden edge of television, I think. So, uh,

1547
01:15:19,840 --> 01:15:21,920
Speaker 1: tell us about a time you've failed and what you

1548
01:15:22,120 --> 01:15:24,840
Speaker 1: learned from the experience. Well, you know, there's business failures

1549
01:15:24,880 --> 01:15:27,280
Speaker 1: and there's personal failures. The business failures you lose some money, right,

1550
01:15:27,320 --> 01:15:29,639
Speaker 1: or you made a bad decision. So if you're losing money,

1551
01:15:29,720 --> 01:15:31,479
Speaker 1: is you know you're not gonna hit home around every

1552
01:15:31,479 --> 01:15:34,240
Speaker 1: time you're at bad may not be a failure. It's

1553
01:15:34,280 --> 01:15:37,759
Speaker 1: within the expected distribution and returns. We watched as process

1554
01:15:37,800 --> 01:15:39,600
Speaker 1: first outcome right, So so there are times where we

1555
01:15:39,640 --> 01:15:41,840
Speaker 1: actually make money, but we considered it a failure because

1556
01:15:41,840 --> 01:15:43,680
Speaker 1: we have the wrong process. So what I mean by

1557
01:15:43,720 --> 01:15:45,280
Speaker 1: that is maybe our thesis was we were going to

1558
01:15:45,320 --> 01:15:46,880
Speaker 1: make a certain amount of money or we gonna work

1559
01:15:46,880 --> 01:15:48,599
Speaker 1: for this reason, and we were. We ended up making money,

1560
01:15:48,640 --> 01:15:51,240
Speaker 1: but we're totally wrong. We consider that a process failure.

1561
01:15:51,680 --> 01:15:54,040
Speaker 1: But being you know, I don't want to sound like, um,

1562
01:15:55,320 --> 01:15:58,120
Speaker 1: I don't know to cliche here, but like, the the

1563
01:15:58,200 --> 01:16:00,280
Speaker 1: biggest failures for me are the things that have the

1564
01:16:00,320 --> 01:16:03,479
Speaker 1: permanence of regret. So choices I made, or relationships that

1565
01:16:03,479 --> 01:16:05,439
Speaker 1: I under invested in, or people that I didn't spend

1566
01:16:05,520 --> 01:16:07,599
Speaker 1: enough time with who passed, like, those to me are

1567
01:16:07,600 --> 01:16:10,240
Speaker 1: the biggest failures. And um, if there was like if

1568
01:16:10,280 --> 01:16:11,880
Speaker 1: there was a younger Josh that I can go back to,

1569
01:16:12,040 --> 01:16:14,120
Speaker 1: it would be you know, some of the relationships that

1570
01:16:14,160 --> 01:16:16,040
Speaker 1: I wish I could have fixed or or people like

1571
01:16:16,080 --> 01:16:17,479
Speaker 1: I said who passed, that I could have spent more

1572
01:16:17,520 --> 01:16:19,000
Speaker 1: time with. Those to me of the biggest failures because

1573
01:16:19,000 --> 01:16:21,160
Speaker 1: you can never fix them. You lose money and investment.

1574
01:16:21,200 --> 01:16:23,080
Speaker 1: You know, a big deal, you'll make money in another one,

1575
01:16:23,479 --> 01:16:25,160
Speaker 1: but you know, you lose a person or a friendship

1576
01:16:25,200 --> 01:16:28,040
Speaker 1: or relationship. I think those are the biggest failures. So afterwards,

1577
01:16:28,120 --> 01:16:30,479
Speaker 1: I'll give you the secret to time travel and tell

1578
01:16:30,479 --> 01:16:34,320
Speaker 1: you how you could solve those issues. Um, and I'm

1579
01:16:34,479 --> 01:16:36,160
Speaker 1: not kidding. What do you do for fun? What do

1580
01:16:36,200 --> 01:16:38,679
Speaker 1: you do when you're not reading or or at work?

1581
01:16:38,840 --> 01:16:41,120
Speaker 1: My my kids are just you know, it's an amazing adventure.

1582
01:16:41,120 --> 01:16:42,880
Speaker 1: I've got three of them. Uh you know, now there

1583
01:16:42,880 --> 01:16:44,519
Speaker 1: are nine six and three, two girls and a boy,

1584
01:16:44,600 --> 01:16:47,800
Speaker 1: and you know you get to just see the world

1585
01:16:47,840 --> 01:16:50,200
Speaker 1: through their eyes and and make all kinds of new

1586
01:16:50,280 --> 01:16:52,800
Speaker 1: mistakes over. Um. So I love my kids, I love

1587
01:16:52,880 --> 01:16:54,280
Speaker 1: my family. I was an only child, though I ever

1588
01:16:54,320 --> 01:16:56,360
Speaker 1: wanted was a big nuclear family, and my parents blew

1589
01:16:56,360 --> 01:16:57,519
Speaker 1: when I was young. So this is sort of the

1590
01:16:57,600 --> 01:17:02,200
Speaker 1: chance I've had to make it right. Um. Love reading, skateboarding, basketball,

1591
01:17:02,479 --> 01:17:05,360
Speaker 1: just still skateboarding. In fact, there's a Sunday morning crew

1592
01:17:05,439 --> 01:17:07,759
Speaker 1: of Dad's, um you know that are sort of between

1593
01:17:07,800 --> 01:17:09,720
Speaker 1: forty and five, and we go out and try back

1594
01:17:09,760 --> 01:17:11,560
Speaker 1: at the skate park. And you've got to keep the

1595
01:17:11,680 --> 01:17:15,640
Speaker 1: orthopedics busy. They need to earn a living also, UM,

1596
01:17:16,200 --> 01:17:18,519
Speaker 1: tell us what you're most optimistic about today and what

1597
01:17:18,600 --> 01:17:21,080
Speaker 1: are you most pessimistic about? You know, I think this

1598
01:17:21,240 --> 01:17:24,080
Speaker 1: is a constant. I'm always optimistic about scientists and uh,

1599
01:17:24,439 --> 01:17:26,720
Speaker 1: you know, the incremental discoveries and the big breakthroughs that

1600
01:17:26,760 --> 01:17:28,720
Speaker 1: they're going to make, because I think it's an inevitability.

1601
01:17:28,720 --> 01:17:30,919
Speaker 1: I always talk about this sort of directional hour of progress,

1602
01:17:31,400 --> 01:17:34,320
Speaker 1: and I think there's an absolute inevitability, just driven by

1603
01:17:34,560 --> 01:17:36,920
Speaker 1: human greed pursuit of status, that we are going to

1604
01:17:37,000 --> 01:17:40,880
Speaker 1: continue to discover incredible things that by definition, nobody ever anticipated.

1605
01:17:41,080 --> 01:17:44,599
Speaker 1: So I'm optimistic about science itself as a process. Um.

1606
01:17:45,000 --> 01:17:47,880
Speaker 1: I am generally pestimistic about human nature. I mean, the

1607
01:17:48,520 --> 01:17:50,559
Speaker 1: best and unfortunately the worst things that I think happened

1608
01:17:50,560 --> 01:17:53,080
Speaker 1: in the world are not because of um, you know,

1609
01:17:53,479 --> 01:17:56,000
Speaker 1: inanimate things. It's because of animated things. Is about people,

1610
01:17:56,280 --> 01:17:58,840
Speaker 1: two legged mammals who who are you know, filled with

1611
01:17:59,160 --> 01:18:01,599
Speaker 1: um too much read or too much fear, too much hate,

1612
01:18:01,680 --> 01:18:05,679
Speaker 1: or too much ignorance. And and so I'm generally pessimistic

1613
01:18:05,680 --> 01:18:09,160
Speaker 1: about human nature. And and uh, you know that classic

1614
01:18:09,240 --> 01:18:12,960
Speaker 1: Daggers and men smiles. UM. And our final two and

1615
01:18:13,120 --> 01:18:15,680
Speaker 1: most favorite questions, what sort of advice would you give

1616
01:18:15,720 --> 01:18:18,519
Speaker 1: to a millennial or recent college graduate who came to

1617
01:18:18,560 --> 01:18:21,240
Speaker 1: you and said, Hey, I'm interested in a career either

1618
01:18:21,360 --> 01:18:25,720
Speaker 1: as a technology entrepreneur or a venture capital. I think

1619
01:18:25,760 --> 01:18:27,920
Speaker 1: the single most important thing for anybody is to UM,

1620
01:18:28,200 --> 01:18:32,000
Speaker 1: to build your brand, be differentiated, be indispensable. UM, stay

1621
01:18:32,040 --> 01:18:34,160
Speaker 1: close to the money. You know, find where the capital

1622
01:18:34,280 --> 01:18:35,720
Speaker 1: is flowing and stay close to it. That was one

1623
01:18:35,760 --> 01:18:38,639
Speaker 1: of the great early advices that I got from somebody. UM.

1624
01:18:39,320 --> 01:18:42,120
Speaker 1: And and be voracious in your reading. I think you

1625
01:18:42,160 --> 01:18:43,400
Speaker 1: have to be exposed to so many things so that

1626
01:18:43,439 --> 01:18:45,320
Speaker 1: you can sort of develop your passion and then from

1627
01:18:45,360 --> 01:18:47,519
Speaker 1: your passion development expertise and be able to stand out

1628
01:18:47,600 --> 01:18:50,160
Speaker 1: so you know, all those things are in a linked UM.

1629
01:18:50,640 --> 01:18:52,960
Speaker 1: And the best advice that I probably give, again with

1630
01:18:53,080 --> 01:18:55,960
Speaker 1: regret to my older self, is I think you need

1631
01:18:55,960 --> 01:18:57,439
Speaker 1: to find the balance between having the chip on your

1632
01:18:57,439 --> 01:19:00,160
Speaker 1: shoulder and the ambition. And then UM, you know, being

1633
01:19:00,200 --> 01:19:02,240
Speaker 1: mindful that every relationship you have and every person that

1634
01:19:02,439 --> 01:19:03,920
Speaker 1: you know, at some point in the future, they're going

1635
01:19:03,960 --> 01:19:06,080
Speaker 1: to be a call option and you don't want that

1636
01:19:06,120 --> 01:19:09,479
Speaker 1: to expire. So UM, you know, be good to people.

1637
01:19:10,280 --> 01:19:12,960
Speaker 1: That works for me. And our final question, what is

1638
01:19:13,000 --> 01:19:15,639
Speaker 1: it that you know about the world of venture capital

1639
01:19:15,680 --> 01:19:18,640
Speaker 1: investing today that you wish you knew twenty or so

1640
01:19:18,880 --> 01:19:21,719
Speaker 1: years ago when you were first getting started. I wish

1641
01:19:21,840 --> 01:19:23,400
Speaker 1: that I would have known and this is gonna sound

1642
01:19:23,400 --> 01:19:27,040
Speaker 1: a bit cynical, how rigged the game is. Um, I

1643
01:19:27,120 --> 01:19:29,920
Speaker 1: think every system at every point is rigged. And if

1644
01:19:29,960 --> 01:19:33,519
Speaker 1: you can figure out you know, that little uh, you know,

1645
01:19:33,600 --> 01:19:36,439
Speaker 1: mechanical turk in the machine that's pulling the con rigged

1646
01:19:36,720 --> 01:19:42,080
Speaker 1: Yegg explained that a little bit every look in you know,

1647
01:19:42,120 --> 01:19:43,880
Speaker 1: people thought, you know, just gotta pick the winners or whatever.

1648
01:19:43,960 --> 01:19:46,160
Speaker 1: But the system was rigged. I p O s were rigged,

1649
01:19:46,200 --> 01:19:49,280
Speaker 1: the distribution of IPOs was rigged. UM, you know, housing

1650
01:19:49,320 --> 01:19:52,280
Speaker 1: market c d O s. There's always a game being played,

1651
01:19:52,360 --> 01:19:55,600
Speaker 1: and it's and there's a a a secret that the

1652
01:19:55,680 --> 01:19:58,880
Speaker 1: people who are making the most money basically keep they

1653
01:19:58,920 --> 01:20:01,640
Speaker 1: won't acknowledge public until after the fact. And so I

1654
01:20:01,720 --> 01:20:04,280
Speaker 1: think at any point in time in some domain it's happening.

1655
01:20:04,360 --> 01:20:06,479
Speaker 1: And whether it's central bankers today, or whether it was

1656
01:20:06,520 --> 01:20:08,880
Speaker 1: the housing crisis, or whether it's the soft bank stuff too,

1657
01:20:09,240 --> 01:20:11,800
Speaker 1: you know today or um, that there's always some game

1658
01:20:11,880 --> 01:20:14,720
Speaker 1: that's being played that is totally unfair and rigged. And

1659
01:20:15,160 --> 01:20:19,080
Speaker 1: UM and to sort of appreciate that and and look forward, UM,

1660
01:20:19,240 --> 01:20:21,160
Speaker 1: try to try to figure out where's the system rigged,

1661
01:20:21,400 --> 01:20:25,720
Speaker 1: because it always is somewhere quite quite cynical and fantastic, fascinating.

1662
01:20:26,200 --> 01:20:28,960
Speaker 1: We have been speaking with Josh. I almost called you

1663
01:20:29,120 --> 01:20:34,040
Speaker 1: Josh Lux Josh Wolf, managing partner and co founder of

1664
01:20:34,160 --> 01:20:37,639
Speaker 1: Lux Capital. If you enjoyed this conversation, we'll be sure

1665
01:20:37,720 --> 01:20:39,640
Speaker 1: to look up an inch with down an inch and

1666
01:20:39,920 --> 01:20:42,160
Speaker 1: on Apple iTunes and you can see any of the

1667
01:20:42,560 --> 01:20:46,200
Speaker 1: previous two hundred and fifty such conversations we've had over

1668
01:20:46,240 --> 01:20:50,000
Speaker 1: the prior five years. UH. You can find that wherever

1669
01:20:50,200 --> 01:20:54,920
Speaker 1: final podcasts are sold Spotify, Apple, Google Podcasts, etcetera. We

1670
01:20:55,040 --> 01:20:58,400
Speaker 1: love your comments, feedback and suggestions right to us at

1671
01:20:59,080 --> 01:21:02,240
Speaker 1: m IB podcast that Bloomberg dot Net. I would be

1672
01:21:02,360 --> 01:21:04,920
Speaker 1: meant remiss if I did not thank our crack staff

1673
01:21:04,960 --> 01:21:07,960
Speaker 1: who helps put this together each week. Carolin O'Brien is

1674
01:21:08,000 --> 01:21:12,280
Speaker 1: our audio engineer today. Michael Boyle is my producer. A

1675
01:21:12,439 --> 01:21:15,919
Speaker 1: tick of Val Bronn is our project manager. Michael Batnick

1676
01:21:16,080 --> 01:21:19,519
Speaker 1: is our head of research. I'm Barry Ritolts. You've been

1677
01:21:19,600 --> 01:21:22,639
Speaker 1: listening to Masters in Business on Bloomberg Radio.