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

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

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

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

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Speaker 1: dot org. This is Masters in Business with Barry Ridholts

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Speaker 1: on Bloomberg Radio. This week on the podcast, I have

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Speaker 1: a special guest. His name is Chris Anderson. He is

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Speaker 1: the founder of three D Robotics. He was formerly editor

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Speaker 1: in chief of Wired magazine, and he is the author

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Speaker 1: of the book The Long Tail. Chris Anderson has been

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Speaker 1: at the forefront of technology for a good couple of decades.

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Speaker 1: He saw pretty much where the industry has been going,

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Speaker 1: long before the bulk of the public has figured it out.

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Speaker 1: He has tremendous insight into why certain things are as

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Speaker 1: valuable as they are and why certain products, industries, sectors, software, gadgets, etcetera.

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Speaker 1: Are capable of achieving mass acceptance. I find him to

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Speaker 1: be consistently insightful and forward looking, and I don't know

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Speaker 1: anybody else who's quite had his vantage point throughout the nineties,

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Speaker 1: two thousands and beyond. He really has seen a lot

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Speaker 1: of things coming before anybody else did. Three three D Robotics,

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Speaker 1: as an example, is a started out as a d

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Speaker 1: I Y drone company and they just got so of

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Speaker 1: warm with orders that they pivoted to being a drone

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Speaker 1: manufacturer and now they're a drone software creator. Really really

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Speaker 1: fascinating stuff. If you are at all interested in technology, drones, software,

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Speaker 1: this is sort of conversation that you're going to really enjoy. So,

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Speaker 1: with no further ado, my interview with Chris Anderson. My

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Speaker 1: special guest today is someone I've known for a long

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Speaker 1: time and have been a big fan of his work

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Speaker 1: for all these many years. His name is Chris Anderson.

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Speaker 1: He is the former editor in chief at Wired magazine,

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Speaker 1: worked as a journalist and editor at the Economist for

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Speaker 1: seven years. He is the author of several books, including

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Speaker 1: the New York Times bestseller The Long Tail. Presently, he

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Speaker 1: is CEO and founder of three D Robotics, which is

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Speaker 1: a UH drone and software company, The Long Tale. You

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Speaker 1: probably are familiar with it from either the original Wired

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Speaker 1: article or the book itself. The book won the Lobe

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Speaker 1: Award for the Best Business Book of the Year in

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Speaker 1: two thousand and seven. He has been named to all

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Speaker 1: man or of lists, one of Time magazines one hundred

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Speaker 1: most Influential People, Tech forty, the most influential minds in technology,

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Speaker 1: foreign policies, global thinkers. Chris Anderson, welcome to Bloomberg Radio,

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Speaker 1: and let me add thank you for having us here

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Speaker 1: in the Three D Robotics headquarters. There is just great

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Speaker 1: to have you on our coast. It's it's a pleasure

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Speaker 1: out here to be out here. And I travel all

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Speaker 1: over and don't frequently say I could I could live

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Speaker 1: in this climate. And this is one of those places where, yeah,

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Speaker 1: I could live here if I had to adapt. It's

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Speaker 1: worth noting. You're in Berkeley, which is the nicest part

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Speaker 1: of the pay area, is it? Do you say so? Yeah?

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Speaker 1: It's a little cooler here than down in normally what's

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Speaker 1: cooler than Palaul? A bit warmer than the city. Okay,

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Speaker 1: there you go. Um, so let's let's jump right into it.

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Speaker 1: You have a really unusual background. You studied quantum mechanics

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Speaker 1: and science journalism. Some people do science and some people

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Speaker 1: do journalism. Not a lot of people do both. How

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Speaker 1: did you find your way to marry those two fields.

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Speaker 1: I didn't actually study either of them properly. I might

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Speaker 1: my degrees in computational physics, okay, but it just so

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Speaker 1: happened that in writing up my my resume, I happened

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Speaker 1: to take have taken a class in science journalism, which

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Speaker 1: at the time seemed the only link I had to journalism,

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Speaker 1: so I stuck it on my on my resume. But

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Speaker 1: but no, my degrees in computation. So your whole career

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Speaker 1: is a sharade? Is that what you're it is? It

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Speaker 1: is no you know in it's in it's big dimensions.

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Speaker 1: It doesn't make a lot of sense. I went from

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Speaker 1: from physics, um, to ultimately end up working for a

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Speaker 1: fashion company. Um, it's conde NaSTA. But fashion advertising is

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Speaker 1: the main revenue. Okay, But they're a little more than

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Speaker 1: a fashion there. There are a lot more fine publication indeed, indeed,

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Speaker 1: and it's not fair to conde NaSTA call it. But

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Speaker 1: but when you're in the you know, the elevators of

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Speaker 1: when the time was four times square and it's supermodels

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Speaker 1: up and down, you ask yourself, how did I get here?

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Speaker 1: It is? It does require some explaining, to say the least,

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Speaker 1: so computational physics. I don't want to jump too far ahead.

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Speaker 1: The idea of how drones operate and managed to both

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Speaker 1: fly within three dimensional space as well as do analytics

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Speaker 1: of what's around them. That seems to be like, oh,

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Speaker 1: computational physics to drones. But the middle so years are

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Speaker 1: a little aberrational. So let's start with with the economists.

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Speaker 1: You were there for seven years a writer and editor,

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Speaker 1: is that right? Yeah? I ran Faius bureaus, but it

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Speaker 1: was mostly writing, as the case for most people. The economist,

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Speaker 1: I mean the the You're absolutely right, there's a straight

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Speaker 1: line from from physics to to drones and measuring the

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Speaker 1: world and analyzing an etcetera. The wiggly in between bits

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Speaker 1: which consists of a about thirty years UM had to do. Um.

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Speaker 1: It's kind of interesting paths. So physics in the late

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Speaker 1: eighties e early nineties, which is when I was there, UM,

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Speaker 1: we were all inspired by the way to physicists of

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Speaker 1: the twentieth century, the you know, the fiem etcetera. UM,

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Speaker 1: and that really was that the noble science. Unfortunately, UM,

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Speaker 1: the quest of physics was to get closer and closer

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Speaker 1: to the Big Bang, to understand the origins of the universe,

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Speaker 1: and closer to the Big Bang means higher energies, which

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Speaker 1: means bigger particle accelerators, which means more money. And eventually

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Speaker 1: we just scaled out of government budgets and so there

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Speaker 1: were no more particle accelerators in the US that we're

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Speaker 1: going to be built, and so and so you had

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Speaker 1: two choices. You could either like wait in line for

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Speaker 1: you know, the LHC and CERN in Switzerland, wait in

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Speaker 1: line for like twenty years and maybe your experiment would

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Speaker 1: be would be a success, maybe wouldn't. Or you could

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Speaker 1: take the skills that you learned in physics and apply

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Speaker 1: them elsewhere. So the skills were when you think about

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Speaker 1: the internet was invented in the physics world to connect labs.

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Speaker 1: The web was invented at CERN, a physics laboratory, and

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Speaker 1: the first big data was physics data supercomputers using accelerator data,

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Speaker 1: and so you really only had some Most people sort

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Speaker 1: of said, well, what do I got here? I got

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Speaker 1: I got you know, statistical skills, I can crunch big data.

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Speaker 1: I'll go to Wall Street be a quant That was

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Speaker 1: the natural one. A lot of people went went and

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Speaker 1: did that for the money? Go for for the money.

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Speaker 1: Some of us said, well, Okay, yeah, we can do that.

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Speaker 1: But there's also this Internet thing, which seems kind of interesting.

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Speaker 1: The time, it was really just a network to connect

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Speaker 1: scientific laboratories. Well, the original Dark Book concept was, hey,

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Speaker 1: we have to have the capacity to send launch instructions

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Speaker 1: in the event that we're hit first. Well sure, but

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Speaker 1: I mean at the time this was a little bit

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Speaker 1: past then. The true the Internet was largely to connect

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Speaker 1: supercomputer centers for things like climate modeling. You know, this

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Speaker 1: is the part where Al Gore you know, invented it,

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Speaker 1: um which you know, he actually had a lot He

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Speaker 1: didn't invent it, but he had a lot to do

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Speaker 1: with them with encouraging its growth during that era. Um. So,

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Speaker 1: so those of us, you know, who were sitting here

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Speaker 1: and say, well, I don't want to go to Wall Street,

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Speaker 1: but I do have some some Internet skills. What's the

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Speaker 1: internet good for? Along comes this magazine in called Wired

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Speaker 1: with like fuluorescent ink and saying, hey, this Internet thing

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Speaker 1: is going to be big one day. One day. It's

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Speaker 1: not just you know, a tool to connect labs. It's

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Speaker 1: gonna be cultural revolution at cetera. And so I decided

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Speaker 1: to make my career the Internet, and the first step

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Speaker 1: you know, there was I went. I was working for

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Speaker 1: scientific journals as an editor, and then the Economist asked

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Speaker 1: me to take over their technology beat and I said sure,

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Speaker 1: but I'd like to cover the Internet. And they're like why,

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Speaker 1: I don't know, whatever, whatever, you say, what do you need?

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Speaker 1: And I like I needed internet connection? This is this

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Speaker 1: was ninety three. Yeah, and and at what point did

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Speaker 1: you end up leaving so the economists you weren't necessarily

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Speaker 1: covering covering economics and business. I was, I was originally

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Speaker 1: and then I went to China to to run the

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Speaker 1: bureau there and China from just before the handover of

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Speaker 1: Hong Kong Um. I guess that was ninety seven two

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Speaker 1: after wto in two thousand and one. Fascinating time to

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Speaker 1: be in China, got to serve to see China emerge.

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Speaker 1: And there was in New York where you and I

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Speaker 1: spent time together and them was recruited from there too

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Speaker 1: by Condyn asked to run Wired. Um so the only

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Speaker 1: publication I would leave the economists. You were in the

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Speaker 1: econom New York Bureau, and that's what led to Conde

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Speaker 1: Nast and Wired. So you take over Wired, which was

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Speaker 1: it was always a good magazine, but it was kind

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Speaker 1: of a hodgepodge for a while. Well it was, it

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Speaker 1: was you know, it basically created this sort of esthetic

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Speaker 1: and philosophical foundations of the Internet is going to be big.

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Speaker 1: It's like the biggest things since electricity. That was kind

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Speaker 1: of the wired concept. UM. I took over in two

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Speaker 1: thousand and one. So this is after the stock after

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Speaker 1: the dot com bust, and six weeks before September eleventh, UM,

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Speaker 1: excellent time. Well, so you know, at that point everyone

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Speaker 1: was dismissing the Internet as as as a fraud or

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Speaker 1: at least or at least wildly over high. But that's

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Speaker 1: what I mean by excellent time. You come in with

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Speaker 1: very low expectations. People aren't torturing you to why don't

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Speaker 1: he we have any results this quarter? Precisely so, so

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Speaker 1: I was betting that the Internet was real and that

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Speaker 1: the dot com bust was about Wall Street, not about

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Speaker 1: Silicon Valley. UM. And And as you say, when you

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Speaker 1: when buy at the bottom, you get three wonderful things. Um.

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Speaker 1: First of all, your failures are discard cloaked by the marketplaces.

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Speaker 1: It's just everybody's can't succeed in that in that in

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Speaker 1: that environment, Secondly, a lot of talent had come to

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Speaker 1: Silicon Valley during the boom and they were available, so

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Speaker 1: I was able to hire really good people on the

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Speaker 1: cheap on the cheap um, well, they were available. And Thirdly,

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Speaker 1: my year on years, I had a exactly the next

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Speaker 1: year I was just going to blow through my n

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Speaker 1: So before we move too forward past two thousand and one,

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Speaker 1: having lived through the nineties and all the enthusiasm and

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Speaker 1: all the excitement and then the collapse, how did that

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Speaker 1: color your view of technology and the Internet or did it?

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Speaker 1: It was? It was I think I was my um,

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Speaker 1: my faith and technology was completely unshaken. Um. You know,

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Speaker 1: as a physicist, you basically think in terms of like,

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Speaker 1: you know, what's like fundamental in the world, and there's

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Speaker 1: something about the Internet that just struck me as fundamental.

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Speaker 1: It struck me as in the same category as democracy

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Speaker 1: and evolution. It's kind of one of these one of

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Speaker 1: these truisms. And as a matter of fact, when I

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Speaker 1: was at the Economists my first they had these things

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Speaker 1: called surveys, which are these big sixteen page special features.

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Speaker 1: It's the only time you get to use your name

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Speaker 1: as an author, and which is by the way, is

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Speaker 1: unique in the world of journalism, pretty much unique exactly.

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Speaker 1: I mean, where there's no byline, you write an article

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Speaker 1: and it's by the economy. The notion of the collective voice. Yeah.

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Speaker 1: Um so, my my first survey on the Internet was

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Speaker 1: entitled the Accidental super Highway. Um, and the notion of

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Speaker 1: things happening, you know, by accident, suggests that really they're

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Speaker 1: happening by nature. There's that that that you know, this

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Speaker 1: this emerged it was an emergent organic universe exactly. And

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Speaker 1: the fact that it was an emergent rather than synthetic,

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Speaker 1: meant that it was more real. Huh. That that that's

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Speaker 1: quite fascinating. The the thing I thought you were heading towards,

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Speaker 1: and I think I'm mangling this is it met Metcalf's

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Speaker 1: law that does the each node multiplies the value. That

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Speaker 1: quickly became obvious that every time you add another user

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Speaker 1: to the Internet the value increased. Explanab absolutely. So that's

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Speaker 1: the value element. But the governance structure, the fact that

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Speaker 1: it was not centrally controlled. I mean, there's some trivial

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Speaker 1: central control, but that's that's that's that's a very lowest level.

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Speaker 1: But the terms of the content itself, the fact that

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Speaker 1: it was the fact that it is, you know, truly

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Speaker 1: democratic ameritocratic system. Um. That is, that's actually the more

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Speaker 1: important rule um. And that's why we've worked so hard

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Speaker 1: to protect the openness of the Internet is because the

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Speaker 1: emergent properties come from that openness. So one of the

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Speaker 1: questions I wanted to ask you, you kind of answered already,

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Speaker 1: So I want to expand on it. You've been quite

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Speaker 1: a student at forecasting let's call it near trends, extrapolating

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Speaker 1: from just very nascent things not well formed. Well, at

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Speaker 1: this point, I think we all can agree mobile is

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Speaker 1: going to be big one thing. But on the other hand,

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Speaker 1: you were looking at things that were the trend wasn't clear,

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Speaker 1: It wasn't let's just extrapolate this in infinity. You identified

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Speaker 1: seedlings and said this is gonna be significant. Is it

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Speaker 1: simply the organic nature of it that court your eye?

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Speaker 1: Or how did you How are you able to say, oh,

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Speaker 1: there's huge potential. You know, you're just you know the

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Speaker 1: people who, especially after the crash, people said, you know,

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Speaker 1: it's too good to be true, this is just overhyped.

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Speaker 1: What made you turn around and say, no, this is

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Speaker 1: substantial forget the finance part of it. The technology part

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Speaker 1: of it is real. Yeah, I mean things like the

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Speaker 1: long tail or or drones, which I would do right now.

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Speaker 1: So basically it's there's two elements. One of them is

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Speaker 1: that it's just exciting. You know, it's just shiny and

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Speaker 1: exciting and you know, my heart sings and all that

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Speaker 1: kind of stuff. But that's that's necessary, but not suf issued.

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Speaker 1: Um what sufficient is that also has to align with

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Speaker 1: my world view of what what works, this sort of

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Speaker 1: notion of openness or emergence or or you know the

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Speaker 1: fact that it benefits and democratization. Um. So when you

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Speaker 1: so like my first book, The long Tail, I mean

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Speaker 1: the an article as well. I mean the way that

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Speaker 1: happened was not It wasn't an idea. Um. What happened

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Speaker 1: is that I'm sitting here, you know, it's it's two

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Speaker 1: thousand two. Um, I'm I'm a physicist in Silicon Value, right,

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Speaker 1: I'm now running a magazine. I'm not really trained as

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Speaker 1: a journalist. I'm not, you know, I was. I had

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Speaker 1: a staff of brilliant narrative storytellers, but I was just

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Speaker 1: kind of a nerd, you know, with number cruncher indeed indeed,

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Speaker 1: so I need some numbers to crunch. And I was

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Speaker 1: I was thinking, you know, I was like, Okay, look,

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Speaker 1: I believe that this is the you know, the seeds

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Speaker 1: of the century being formed in the Googles and Amazons

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Speaker 1: and ebays around me. And I'll bet that in those

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Speaker 1: servers is the data that can illustrate what the century

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Speaker 1: it looks like. Let's just ask them for the data.

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Speaker 1: And has anybody previously asked them, Hey, we'd like to

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Speaker 1: see you know? And well, no, I mean because typically

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Speaker 1: the academics weren't looking at that, because that was considered

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Speaker 1: sort of cultural and trivial. Um. The companies themselves were

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Speaker 1: just starting to bring up the data scientists and the

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Speaker 1: commist was quite early, and UM and and journalists just

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Speaker 1: typically weren't weren't quantitative exactly. So there was a brief

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Speaker 1: window if you, if you asked nicely and you know, science,

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Speaker 1: some n D s et cetera, that you could actually

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Speaker 1: get that data. And so I I got the dat

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Speaker 1: different places, like it was a rhapsody I think was

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Speaker 1: my music services original one, but I also got sort

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Speaker 1: of scrubbed data from from eBay and elsewhere. I just

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Speaker 1: started analyzing, and the long tailed popped right out of

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Speaker 1: it. It It was clearly obviously there was a kind of

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Speaker 1: a power law distribution, and that the size of the tail,

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Speaker 1: you know, depending on how you split it, was about

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Speaker 1: the size of the head, which suggested that there was

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Speaker 1: a market and a submerged marketplace out there beyond beyond.

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Speaker 1: And what's interesting that about a year later after I

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Speaker 1: started got all this data, then you may be remember

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Speaker 1: this M A O L. You know, then people started

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Speaker 1: academics started saying, hey, you know, there's some interesting data here,

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Speaker 1: let's ask for it. A O. L. Released a bunch

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Speaker 1: of data and anonymized it, but didn't anonymize it well enough.

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Speaker 1: It got de anonymized, um you know, privacy issues, and

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Speaker 1: then became impossible to get dat again. So it was

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Speaker 1: like a window of about two years where you could

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Speaker 1: have done the long tale, and I just happened to

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Speaker 1: be there. Then I was a subscriber. I still have

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Speaker 1: a subscribe to Wired for it's like a decade plus now.

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Speaker 1: And and the thing that always stood out to me

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Speaker 1: about Wired was the marriage of great content with the

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Speaker 1: technology slant with fantastic design ethics. The the visual impact

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Speaker 1: of the magazine felt like the Internet and a print

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Speaker 1: magazine had a baby. It was. It was so visual

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Speaker 1: in ways that other magazines weren't. Was the thinking beyond that,

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Speaker 1: So I can take no credit for that whatsoever. Is

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Speaker 1: that that was the vision of the original founding team

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Speaker 1: A Lewis Rosetto, Jane Metcalfe and John Plunkett, who was

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Speaker 1: the original designer and their inspiration at the time, the

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Speaker 1: dear friends right now. As a matter of fact, every

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Speaker 1: time there's a new editor, Nick Thompson from the New

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Speaker 1: Yorkers now the In and we all had dinner together,

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Speaker 1: we always kind of, you know, infuse the new editor

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Speaker 1: with the creation myth and all that. But the idea.

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Speaker 1: You know, if you if you ask them with the story, um,

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Speaker 1: they'll say, well, you know, we were this was the

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Speaker 1: emergence of computers and and so print ink has got

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Speaker 1: reflective properties, but screens have a missive properties. So pixels

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Speaker 1: emit light, and so you know, we wanted to we

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Speaker 1: want to use an aesthetic that seemed emittive, emissive. It

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Speaker 1: like like it glowed, and a lot of the page,

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Speaker 1: a lot of the neon colors and other things. Very much.

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Speaker 1: We were like a screen amidden absolutely. Um So I

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Speaker 1: think that I think they'll forgive me for for saying

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Speaker 1: that this is a little bit of a kind of

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Speaker 1: a of a of a post facto expo nation. I

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Speaker 1: think what really happened at the time is there was

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Speaker 1: a new printing process, Like I again any about this.

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Speaker 1: It was a five color printing process, and it was

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Speaker 1: very expensive, and some printing plants that invested in these

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Speaker 1: new kind of five color processes were looking for some

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Speaker 1: examples to show them off. And it just basically that

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Speaker 1: like a fancy ink and like fluorescent became available affordably,

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Speaker 1: and so they took advantage of it. And it makes

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Speaker 1: perfect sense. And the magazine always popped. You turned the

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Speaker 1: pages and it really it really was alive. So you

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Speaker 1: take over wired with the air coming out of the bubble.

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Speaker 1: It was mid collapse, um. And it would get worse

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Speaker 1: and and got worse. How hard was it to stay

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Speaker 1: positive as things just went down the tubes? Um? For

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Speaker 1: me it was it was super easy. Um So. Um.

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Speaker 1: I would say that if you and I, you know,

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Speaker 1: I think you're gonna be talking to Mark andresen As

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Speaker 1: as well, you know, those of us who were there

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Speaker 1: then never never doubted that the Internet was real. We

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Speaker 1: understood the distinction between the this this stock market valuations

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Speaker 1: and the underlying technology was the West Coast view. Oh,

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Speaker 1: the guys in New York, really they really messed us up.

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Speaker 1: You know, they took valuations too high. No surprise, Choris

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Speaker 1: crashed into the sea. I was actually in China during

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Speaker 1: the bubble, uh to the dot com bubble, So I

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Speaker 1: can't really say what they with the collective view was,

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Speaker 1: but I do think that, you know, the underlying belief

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Speaker 1: there was something fundamental going on here with the networks,

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Speaker 1: um was was never doubted. Now this is of course,

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Speaker 1: you know, you know how the Internet works, like there's

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Speaker 1: the layers, right, There's like the hardware layer with the

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Speaker 1: fiber opter cables, and then there's like you know, then

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Speaker 1: the software layers and content layers. A lot of the

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Speaker 1: valuations were at the higher level of the content, and

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Speaker 1: the notion was the content was ephemeral, would come and go.

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Speaker 1: But the underlying notion of connecting the world was was

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Speaker 1: really important. Um And so you know, the the I

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Speaker 1: think when I got there, the consensus was that Wall

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Speaker 1: Street had financed an extraordinary infrastructure build out which made

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Speaker 1: fiber capacity super cheap and this was the best thing

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Speaker 1: ever for restarting. That's suddenly the cost of enter the barriers,

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Speaker 1: the cost of entry was very low, thank you, thank

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Speaker 1: you Wall Street, and that we would just now, you know,

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Speaker 1: build for the next two years without you know, the

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Speaker 1: financial microscope and see where it goes. Dan Gross wrote

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Speaker 1: a book called pop While Bubbles are better than people

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Speaker 1: realizing and the example use whether it's railroads or telegrams

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Speaker 1: or in this case dark fiber, Global crossing and Metromedia

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Speaker 1: fiber and go down the list. They laid miles and

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Speaker 1: miles of dark fiber at thousands of dollars per whatever

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Speaker 1: it was, and it sells for pennies on the dollar.

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Speaker 1: The theory is, but for this collapsing, you don't end

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Speaker 1: up with YouTube and Facebook and all these other band

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Speaker 1: with intensive intensive things. Yeah, now, Dan, Dan, Dan super

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Speaker 1: smart And then the book's great um And I think

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Speaker 1: when you look back now at that decade, it's almost

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Speaker 1: impossible to see the bust when you look at the

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Speaker 1: underlying adoption curves and data curves, etcetera. It's weird. I mean,

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Speaker 1: you can obviously see it on the nineties or in

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Speaker 1: the if you look back today, if you look back

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Speaker 1: at internet adoption trends, you almost can't see the dip

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Speaker 1: right because the end customers continued to be added at

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Speaker 1: a tremendous rate because there was something real there. People

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Speaker 1: wanted what we were making in the fact that was

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Speaker 1: cheaper and accelerated the often. So let's let's talk about Wired.

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Speaker 1: When you take over Wired, it's kind of uh we

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Speaker 1: I said a Hodgepodge. I think at one point they

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Speaker 1: didn't even own their own domain. So if you wanted

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Speaker 1: to do internet stuff, how did that come about? Again?

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Speaker 1: Before my time? But but yeah, so, um so Wired

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Speaker 1: was you know, sort of the before the dot com bubble.

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Speaker 1: There was there were previous bubbles, and Wired was the

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Speaker 1: hottest thing around in the you know, in the late nineties.

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Speaker 1: UM basically created a lot of online media. So like Apache,

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Speaker 1: the web server's use was actually created um, you know

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Speaker 1: in the Wired offices. The first at the first banner

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Speaker 1: ad was on Wired. You know, a lot of the

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Speaker 1: things we take for granted today we're actually being pioneered

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Speaker 1: in the laboratories that were Wired. UM and they filed

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Speaker 1: to go public UM and Wired as a standalone stand

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Speaker 1: company again I'm gonna say nineties seven ish, maybe ninety eight, um,

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Speaker 1: and didn't work. Filed again, didn't work. Had taken out

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Speaker 1: some finance and debt on anticipation that they would go

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Speaker 1: public and ended up having to kind of fire sale

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Speaker 1: some of the bits and and the the magazine. The

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Speaker 1: print bit went to Conde Nast and the digital bit

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Speaker 1: went to Licos, which was which then got bought by

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Speaker 1: some Korean media company. Anyway, it was. It was. It

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Speaker 1: was like one of those like you know, the children

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Speaker 1: got adopted by different families kind of deal, and you

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Speaker 1: helped bring them both under the same I didn't the

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Speaker 1: call credit goes to contact. It was clearly indeed to

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Speaker 1: be done, and just you know, years went by and

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Speaker 1: evaluations kind of stabilized, and kind of appropriately I bought it,

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Speaker 1: brought it back, so you really, you really turned the

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Speaker 1: entire magazine around and turned it into this award winning affair.

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Speaker 1: I I actually, um, it was very much award winning

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Speaker 1: before I got there. Um, I just Magazine in the Decade,

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Speaker 1: not Editor of the Year. Those are pretty substantial awards

426
00:23:11,960 --> 00:23:14,240
Speaker 1: in the industry. I think what we did is we

427
00:23:14,359 --> 00:23:16,800
Speaker 1: believed in the underlying story. The reason I left the Economist,

428
00:23:16,800 --> 00:23:19,119
Speaker 1: which is the best gig ever. By the way, was

429
00:23:19,160 --> 00:23:21,560
Speaker 1: that Wired had changed my life? Um that I'm as

430
00:23:21,600 --> 00:23:24,560
Speaker 1: as as a as a physicist sitting there nerding away

431
00:23:24,680 --> 00:23:27,160
Speaker 1: on these networks, you know, not believing there were any

432
00:23:27,200 --> 00:23:30,720
Speaker 1: more than than than physics. Wired it opened my eyes

433
00:23:30,720 --> 00:23:32,960
Speaker 1: to the potential technology to change the world. I know

434
00:23:33,000 --> 00:23:34,879
Speaker 1: it's a very kind of cliche to say that, but

435
00:23:35,400 --> 00:23:38,119
Speaker 1: that's what it did. And um, And so when I

436
00:23:38,119 --> 00:23:40,119
Speaker 1: had the opportunity to run Wired, I said, well, this

437
00:23:40,200 --> 00:23:43,280
Speaker 1: is the biggest story of my lifetime. Um. The ability

438
00:23:43,359 --> 00:23:45,240
Speaker 1: to be there in the middle Silicon Valley and tell

439
00:23:45,320 --> 00:23:48,200
Speaker 1: this story is um. You know that that that story

440
00:23:48,280 --> 00:23:50,680
Speaker 1: is not done. Um. And all I did is just

441
00:23:50,760 --> 00:23:54,080
Speaker 1: kept the faith. So you're running the magazine, you're the

442
00:23:54,200 --> 00:23:58,720
Speaker 1: editor in chief, you're overseeing publication, and you're writing both

443
00:23:58,880 --> 00:24:03,200
Speaker 1: articles and books. How do you juggle any one of

444
00:24:03,240 --> 00:24:04,960
Speaker 1: those things or a full time job? How do you

445
00:24:05,040 --> 00:24:09,720
Speaker 1: juggle all of that at once? Um? Yeah? And you,

446
00:24:09,800 --> 00:24:11,560
Speaker 1: by the way, you're married and have five kids. And

447
00:24:11,720 --> 00:24:13,280
Speaker 1: you am married and have five kids and a lot

448
00:24:13,280 --> 00:24:15,560
Speaker 1: of hobbies because they ended up starting starting a drone

449
00:24:15,560 --> 00:24:20,040
Speaker 1: company maker dad, and then you had a lot of

450
00:24:20,119 --> 00:24:22,639
Speaker 1: bulls in the airs. I did, Um, well, I mean

451
00:24:22,680 --> 00:24:25,040
Speaker 1: I think I was, you know, I mean obviously I

452
00:24:25,080 --> 00:24:27,080
Speaker 1: had a good staff, and you know that my general

453
00:24:27,119 --> 00:24:31,040
Speaker 1: philosophy on on on, you know, leading teams is to

454
00:24:31,119 --> 00:24:33,680
Speaker 1: kind of point them in a direction, set some some

455
00:24:33,800 --> 00:24:36,800
Speaker 1: you know, some guidelines of what success looks, measurable targets

456
00:24:36,800 --> 00:24:39,400
Speaker 1: and let them, let them, let them do their job.

457
00:24:40,240 --> 00:24:42,280
Speaker 1: You know. One of the things that Wired as a

458
00:24:42,280 --> 00:24:44,600
Speaker 1: as a as a bully pulpit, I guess for the

459
00:24:44,640 --> 00:24:46,320
Speaker 1: technology stories, one of the things that you know that

460
00:24:46,400 --> 00:24:50,320
Speaker 1: the editors can do is to be a public persona um,

461
00:24:50,640 --> 00:24:53,000
Speaker 1: and to do their own writing. And you know, because

462
00:24:53,000 --> 00:24:55,040
Speaker 1: I mean, unlike most media, which is which is an

463
00:24:55,040 --> 00:24:57,439
Speaker 1: American media, which is required to be impartial and just

464
00:24:57,560 --> 00:25:00,159
Speaker 1: passionate and not point take a point of view, the

465
00:25:00,200 --> 00:25:02,119
Speaker 1: Commist and Wired shared one thing, which is that they

466
00:25:02,160 --> 00:25:03,680
Speaker 1: do have a point of view. They have a worldview.

467
00:25:03,880 --> 00:25:07,240
Speaker 1: The economists one famously is British liberalism, which is free

468
00:25:07,240 --> 00:25:10,280
Speaker 1: markets and free people. Um. Wired basically took the same approach,

469
00:25:10,320 --> 00:25:13,320
Speaker 1: but then had this evangelism for technology. So the notion

470
00:25:13,320 --> 00:25:16,600
Speaker 1: of being evangelical that being a preacher um, which meant

471
00:25:16,600 --> 00:25:18,720
Speaker 1: also being a doer U was kind of core to

472
00:25:18,800 --> 00:25:21,400
Speaker 1: the to the Wired credo and so, um, the reason

473
00:25:21,440 --> 00:25:23,240
Speaker 1: I did all these side things and you know, did

474
00:25:23,240 --> 00:25:25,240
Speaker 1: the research that led to the long tail and and

475
00:25:25,359 --> 00:25:28,199
Speaker 1: free and and makers was that that was the hands on,

476
00:25:28,280 --> 00:25:29,960
Speaker 1: that was the doing. I mean, that's what Silicon Valley

477
00:25:30,040 --> 00:25:32,679
Speaker 1: is all about. It's about it's about doers um And

478
00:25:32,680 --> 00:25:34,040
Speaker 1: I just didn't feel like I could tell the story

479
00:25:34,080 --> 00:25:38,320
Speaker 1: if I wasn't doing it. That's quite fascinating. You mentioned, uh,

480
00:25:38,720 --> 00:25:44,400
Speaker 1: the free and the freemium model that was pretty prescient

481
00:25:44,440 --> 00:25:46,720
Speaker 1: when you when you put that out. What do you

482
00:25:46,760 --> 00:25:51,080
Speaker 1: think of the freemium model today? Is it's still working? Um?

483
00:25:51,160 --> 00:25:55,159
Speaker 1: And and how has it negatively impacted other industries like

484
00:25:55,160 --> 00:25:58,080
Speaker 1: like print journalism? Yeah, I know exactly. Um So, I mean,

485
00:25:58,119 --> 00:25:59,760
Speaker 1: just to be clear, I've written three books that I'll

486
00:25:59,760 --> 00:26:03,520
Speaker 1: basic the same book, so the long tail hold on

487
00:26:03,560 --> 00:26:07,320
Speaker 1: a second. So, so the long tail, that's a very

488
00:26:07,400 --> 00:26:11,600
Speaker 1: specific book that an eBay or an Amazon or any

489
00:26:11,680 --> 00:26:17,199
Speaker 1: online built, any online retailer. Netflix has the ability to

490
00:26:17,280 --> 00:26:20,959
Speaker 1: have essentially an infinite warehouse versus a brick and mortar

491
00:26:21,359 --> 00:26:25,399
Speaker 1: seers which is circling the drain, cannot have every or

492
00:26:25,440 --> 00:26:28,080
Speaker 1: Barnes and Noble. For that matter, they can have every

493
00:26:28,080 --> 00:26:32,200
Speaker 1: single book available in the store. That's a very different

494
00:26:32,840 --> 00:26:38,240
Speaker 1: article argument. Then all right, give this stuff away for free,

495
00:26:38,560 --> 00:26:42,320
Speaker 1: and some percentage of those users will pay you a

496
00:26:42,359 --> 00:26:47,040
Speaker 1: small fee for a higher level of whatever it is. Um,

497
00:26:47,080 --> 00:26:50,520
Speaker 1: you've articulated the thesis of those very well. Um, but

498
00:26:50,640 --> 00:26:52,640
Speaker 1: let me suggest that the more connected than you they

499
00:26:52,640 --> 00:26:57,679
Speaker 1: may suggest. Okay, So if infinite shelf space creates infinite choice,

500
00:26:58,320 --> 00:27:02,240
Speaker 1: and therefore we can measure people's actual inter actual likes

501
00:27:02,240 --> 00:27:04,520
Speaker 1: and dislikes in a world of infinite choice, and how

502
00:27:04,560 --> 00:27:06,679
Speaker 1: do you get infinite shelf space. Well, the only way

503
00:27:06,680 --> 00:27:08,359
Speaker 1: you can get infinite shelf space is the cost of

504
00:27:08,359 --> 00:27:11,480
Speaker 1: shelf space, the marginal cost to zero or certainly as

505
00:27:11,520 --> 00:27:15,359
Speaker 1: close to zero, close to zero. So what are the

506
00:27:15,400 --> 00:27:20,200
Speaker 1: economics of zero cost markets? You know, these are markets

507
00:27:20,280 --> 00:27:23,840
Speaker 1: where where essentially free is a is an attainable price.

508
00:27:24,280 --> 00:27:26,920
Speaker 1: What are the economics are free Well, there weren't any. Um.

509
00:27:26,920 --> 00:27:31,040
Speaker 1: You know, economics by definition is monetary economics, non monetary economics,

510
00:27:31,119 --> 00:27:34,960
Speaker 1: or something else, psychology, sociology, sociology exactly. So, but what

511
00:27:35,000 --> 00:27:37,760
Speaker 1: if you applied economic principles to try to create an

512
00:27:37,760 --> 00:27:40,320
Speaker 1: economic framework for free to treat it the same way

513
00:27:40,320 --> 00:27:43,240
Speaker 1: you would treat monetary markets. UM, then you could explain

514
00:27:43,359 --> 00:27:46,200
Speaker 1: not only not only you know how things could be free,

515
00:27:46,400 --> 00:27:49,160
Speaker 1: but how maybe you would use free as the way

516
00:27:49,280 --> 00:27:52,440
Speaker 1: as a form marketing, that you would basically give things

517
00:27:52,440 --> 00:27:54,600
Speaker 1: away for free and that and that unlike traditional market

518
00:27:54,680 --> 00:27:56,359
Speaker 1: where you can give out you know, you can get

519
00:27:56,359 --> 00:27:58,920
Speaker 1: free tastes of brownies, but not many, but a finite

520
00:27:58,960 --> 00:28:02,080
Speaker 1: amount of a fixed cost to free exactly so. And

521
00:28:02,119 --> 00:28:04,640
Speaker 1: so in worlds where you have non zero marginal costs,

522
00:28:04,640 --> 00:28:06,760
Speaker 1: you can give away one percent and you must sell

523
00:28:06,880 --> 00:28:09,520
Speaker 1: ninety nine. Where you have near the marginal costs, you

524
00:28:09,520 --> 00:28:13,080
Speaker 1: can give away to sell one. And just that notion

525
00:28:13,160 --> 00:28:15,600
Speaker 1: that there was an underlying economic model behind the long

526
00:28:15,600 --> 00:28:19,240
Speaker 1: tail that drove the long tail led me to two free. Um.

527
00:28:19,640 --> 00:28:22,840
Speaker 1: The so freemium, you're correct, is is the is the

528
00:28:23,359 --> 00:28:25,840
Speaker 1: is the business model around it. And when I wrote it,

529
00:28:26,119 --> 00:28:29,399
Speaker 1: freemium was you're kind of starting to be known in

530
00:28:29,480 --> 00:28:32,359
Speaker 1: kind of business to business software, etcetera. UM. It was

531
00:28:32,560 --> 00:28:36,440
Speaker 1: just before the app store on you know and mobile um,

532
00:28:36,520 --> 00:28:38,320
Speaker 1: and you could start to see the beginnings of it

533
00:28:38,360 --> 00:28:40,880
Speaker 1: was mostly in web games that you were seeing freemium

534
00:28:40,920 --> 00:28:43,200
Speaker 1: as a as a model. But this is an example

535
00:28:43,240 --> 00:28:47,440
Speaker 1: of you identifying something when it was just germinating and saying, oh,

536
00:28:47,480 --> 00:28:49,960
Speaker 1: this is gonna be big one day. It was a

537
00:28:50,280 --> 00:28:52,240
Speaker 1: you know, most people have one of those in their

538
00:28:52,240 --> 00:28:55,280
Speaker 1: lifetime if they're lucky. Here it is not long after

539
00:28:55,320 --> 00:28:57,920
Speaker 1: the long tail, and here's the next big thing, and

540
00:28:57,960 --> 00:29:01,280
Speaker 1: you were dead right about it. I mean, I I

541
00:29:02,200 --> 00:29:05,400
Speaker 1: have to admit that I didn't do anything. I mean,

542
00:29:05,440 --> 00:29:07,640
Speaker 1: this was it was happening around me, and there was

543
00:29:07,680 --> 00:29:10,880
Speaker 1: a bazillion great entrepreneurs. Fred Wilson from Union Square Eventure

544
00:29:10,920 --> 00:29:12,440
Speaker 1: I think was that it was the person who deserves

545
00:29:12,440 --> 00:29:15,520
Speaker 1: the most credit for popularizing freemium, but all I did.

546
00:29:15,520 --> 00:29:17,840
Speaker 1: Did he he gave it the name, or did he

547
00:29:17,920 --> 00:29:21,560
Speaker 1: just popularize one of his entrepreneurs I think coined the term,

548
00:29:21,600 --> 00:29:24,920
Speaker 1: but freemium freemium, which is the combination of free in

549
00:29:25,040 --> 00:29:28,080
Speaker 1: premium um. So you give all this away for free.

550
00:29:28,160 --> 00:29:31,560
Speaker 1: And if you want to exactly and you can actually

551
00:29:31,560 --> 00:29:34,600
Speaker 1: measure the metrics, here's here's our user research. Here's how

552
00:29:34,600 --> 00:29:37,040
Speaker 1: many Here's how many people open it, Here's how many

553
00:29:37,040 --> 00:29:40,080
Speaker 1: people engage, log in, sign up, and here's how many

554
00:29:40,120 --> 00:29:42,680
Speaker 1: of those convert to the paying model in app purchases

555
00:29:42,760 --> 00:29:45,360
Speaker 1: being the perfect example. So so today freemium is the

556
00:29:45,360 --> 00:29:49,440
Speaker 1: canonical business model of online of web mobile apps, which

557
00:29:49,480 --> 00:29:51,840
Speaker 1: is to say, most apps are free, especially games, etcetera.

558
00:29:51,920 --> 00:29:54,280
Speaker 1: And there's all these in app purchases that encourage some

559
00:29:54,400 --> 00:29:58,200
Speaker 1: people to to to pay upgrade you get secret levels

560
00:29:58,320 --> 00:30:01,120
Speaker 1: or more weapons or whatever it. So again, I didn't

561
00:30:01,200 --> 00:30:03,520
Speaker 1: you know, I didn't coin anything. I didn't I didn't

562
00:30:03,520 --> 00:30:06,920
Speaker 1: invent anything. What I saw, though, is that there was

563
00:30:07,240 --> 00:30:10,240
Speaker 1: a smart people were starting to act in a certain way,

564
00:30:10,520 --> 00:30:12,479
Speaker 1: and I just created a framework for it. All right,

565
00:30:12,520 --> 00:30:16,520
Speaker 1: So let's go from longtail to two free, two makers,

566
00:30:17,000 --> 00:30:20,680
Speaker 1: which again I'm gonna argue as a totally different same

567
00:30:20,720 --> 00:30:24,920
Speaker 1: book book, the long tailed physical goods. So you have

568
00:30:25,000 --> 00:30:27,440
Speaker 1: the ability to put so when I was a kid,

569
00:30:27,440 --> 00:30:29,760
Speaker 1: and we're not that far apart in age I was

570
00:30:29,800 --> 00:30:32,520
Speaker 1: a kid, you could have these little scientific what was

571
00:30:32,560 --> 00:30:35,280
Speaker 1: it called the company that put up Yeah, you you

572
00:30:35,280 --> 00:30:37,600
Speaker 1: would either get a chemistry set or there were a

573
00:30:37,680 --> 00:30:41,480
Speaker 1: variety of electronics set. I remember the electronics set, right,

574
00:30:41,560 --> 00:30:43,920
Speaker 1: and you could put together. You can combine these things

575
00:30:43,920 --> 00:30:46,600
Speaker 1: in different ways. Oh look, I could make a little

576
00:30:47,040 --> 00:30:49,560
Speaker 1: home radio kit. Just by combining these different things. You

577
00:30:49,600 --> 00:30:53,600
Speaker 1: didn't go much beyond that, right you point out that, hey,

578
00:30:53,640 --> 00:30:57,240
Speaker 1: the cost of these things, including little electric engine little

579
00:30:57,280 --> 00:31:00,840
Speaker 1: electric engines that you could order from China for next

580
00:31:00,880 --> 00:31:03,960
Speaker 1: to nothing, here's what you could do today, and it's

581
00:31:04,160 --> 00:31:07,400
Speaker 1: nothing like it was back then. So the fundamental difference

582
00:31:07,560 --> 00:31:10,080
Speaker 1: there was not just the cost of the goods, because

583
00:31:10,080 --> 00:31:12,000
Speaker 1: that they were already pretty cheap. It's just that when

584
00:31:12,040 --> 00:31:13,800
Speaker 1: you and I were tinkering in our in our in

585
00:31:13,800 --> 00:31:16,840
Speaker 1: our bedrooms or workshops or basements, etcetera, were essentially re

586
00:31:16,920 --> 00:31:18,920
Speaker 1: all doing the same things while doing the same kids,

587
00:31:18,920 --> 00:31:21,640
Speaker 1: but weren't kind of weren't doing anything new innovating. We

588
00:31:21,640 --> 00:31:23,520
Speaker 1: weren't building on other people's work because we didn't have

589
00:31:23,520 --> 00:31:26,400
Speaker 1: an internet back then. So we had a we had

590
00:31:26,440 --> 00:31:28,720
Speaker 1: a book, and we followed the instructions of the book

591
00:31:28,720 --> 00:31:30,520
Speaker 1: and we but yeah, that was it. We weren't doing

592
00:31:30,520 --> 00:31:34,560
Speaker 1: anything innovative. Um. The virtue. So what was new about

593
00:31:34,600 --> 00:31:36,560
Speaker 1: the maker movement? In two thousand and seven was the

594
00:31:36,640 --> 00:31:39,400
Speaker 1: key year in the maker movement? Um? A bunch of

595
00:31:39,400 --> 00:31:42,720
Speaker 1: things happened that year. Um, you know, first of all, um,

596
00:31:42,760 --> 00:31:45,160
Speaker 1: and on all those things were standing on the shoulders

597
00:31:45,160 --> 00:31:48,400
Speaker 1: of the web and oh and communities and open source, etcetera.

598
00:31:48,520 --> 00:31:50,479
Speaker 1: But you had things like our duino, which was an

599
00:31:50,520 --> 00:31:53,960
Speaker 1: open source computing board that allowed you to to to

600
00:31:54,120 --> 00:31:56,000
Speaker 1: use your coding skills but actually move things in the

601
00:31:56,000 --> 00:31:58,400
Speaker 1: physical world. There was the three D printing that allowed

602
00:31:58,440 --> 00:32:00,520
Speaker 1: you to fabricate things you couldn't do on your own.

603
00:32:00,800 --> 00:32:03,880
Speaker 1: There was you started to see these and the sensors

604
00:32:04,360 --> 00:32:06,080
Speaker 1: with thinks in like a wee control or in my

605
00:32:06,120 --> 00:32:07,960
Speaker 1: case it was Lego mind Storms that you allowed to

606
00:32:08,000 --> 00:32:10,560
Speaker 1: measure the real world with the extraordinary little chips that

607
00:32:10,600 --> 00:32:15,000
Speaker 1: could measure things. Um, there was Maker Make magazine, there

608
00:32:15,040 --> 00:32:18,960
Speaker 1: was the Maker Fair and behind it all. That was

609
00:32:18,960 --> 00:32:21,880
Speaker 1: the year the iPhone was released. And it wasn't clear

610
00:32:21,920 --> 00:32:25,240
Speaker 1: initially that the iPhone had a connection to this. But

611
00:32:25,280 --> 00:32:27,080
Speaker 1: when we said why is it that, you know? So

612
00:32:27,120 --> 00:32:30,000
Speaker 1: I started with my kids. Um, we made a drone

613
00:32:30,200 --> 00:32:33,120
Speaker 1: on the dining room table out of Lego parts, you know,

614
00:32:33,360 --> 00:32:36,440
Speaker 1: fully autonomous, GPS guided, you know. And this is like

615
00:32:36,680 --> 00:32:39,160
Speaker 1: you know a few years earlier that was like military industrial,

616
00:32:39,320 --> 00:32:42,560
Speaker 1: classified export control stuff. And we did leg we did

617
00:32:42,560 --> 00:32:46,080
Speaker 1: with Legos. You know, Lego mind Storms is pretty cool. Um.

618
00:32:46,240 --> 00:32:48,840
Speaker 1: Only later did I say, how is it possible that

619
00:32:48,880 --> 00:32:50,160
Speaker 1: we were able to do this? I mean, I can

620
00:32:50,160 --> 00:32:52,600
Speaker 1: see you've got a you've got a fitbit of something

621
00:32:52,600 --> 00:32:55,440
Speaker 1: on your on your arm, I've got a pebble. They're

622
00:32:55,520 --> 00:32:57,920
Speaker 1: terribly inaccurate. Um. They make me feel good because I

623
00:32:57,920 --> 00:33:00,480
Speaker 1: do twenty thou steps. Apparently every day I see, Well,

624
00:33:00,480 --> 00:33:02,640
Speaker 1: there's a little green led that's just flashing like crazy

625
00:33:02,680 --> 00:33:05,400
Speaker 1: and it's because my h it tries to measure my

626
00:33:05,440 --> 00:33:08,280
Speaker 1: heart rate and it's unable to keep up. Okay, maybe

627
00:33:08,320 --> 00:33:12,040
Speaker 1: not the best example, but the technology and this is astonishing.

628
00:33:12,200 --> 00:33:13,840
Speaker 1: Go back in time and this so what I was

629
00:33:13,880 --> 00:33:15,440
Speaker 1: I was sitting there in two thousand said, with my

630
00:33:15,560 --> 00:33:18,640
Speaker 1: kids and Lego mind storms. Um, a guy named James

631
00:33:18,680 --> 00:33:21,800
Speaker 1: Park had just gotten a wee controller. UM, and we

632
00:33:22,040 --> 00:33:23,680
Speaker 1: you know the video showing the controller and he's like,

633
00:33:23,760 --> 00:33:25,520
Speaker 1: what is this? What isn't this stick that? Let's that

634
00:33:25,560 --> 00:33:30,320
Speaker 1: cursor in the three dimensional opens us a little chip

635
00:33:30,360 --> 00:33:32,400
Speaker 1: in there and he says, awesome, what what else would

636
00:33:32,400 --> 00:33:33,800
Speaker 1: that chip be good for? And he comes up with

637
00:33:33,880 --> 00:33:37,720
Speaker 1: fitbit UM with with his colleagues. So, um, so what

638
00:33:37,960 --> 00:33:42,680
Speaker 1: happened is that a series of core technologies, UM, what's

639
00:33:42,720 --> 00:33:46,320
Speaker 1: called mem sensors. These these sensors on a chip, um

640
00:33:46,440 --> 00:33:51,959
Speaker 1: micro electro mechanical sensors and this like a sensors so

641
00:33:52,000 --> 00:33:55,920
Speaker 1: they can sense motion, direction, gravity, exact else. Um, these

642
00:33:56,200 --> 00:33:59,640
Speaker 1: you know, these incredibly fast, low power processors, the arm UM,

643
00:33:59,720 --> 00:34:05,120
Speaker 1: why I FI cameras, GPS, lithium battery technologies. All this

644
00:34:05,240 --> 00:34:08,400
Speaker 1: stuff was coming out of the iPhone and other smartphones.

645
00:34:08,800 --> 00:34:11,040
Speaker 1: And because you have these extraordinary kind of you know

646
00:34:11,320 --> 00:34:13,840
Speaker 1: the amount of R and D and economies of scale production,

647
00:34:13,960 --> 00:34:17,520
Speaker 1: those components were now available to adjacent industries. And you

648
00:34:17,520 --> 00:34:20,239
Speaker 1: know that very inexpensive, very inexpensive, and I call this

649
00:34:20,320 --> 00:34:24,000
Speaker 1: the peace dividend of the smartphone. Course. Um, what happened

650
00:34:24,080 --> 00:34:27,640
Speaker 1: is that you take you take these extraordinary technologies, these chips,

651
00:34:27,680 --> 00:34:30,680
Speaker 1: they are now available really you know, expensively. Then you

652
00:34:30,760 --> 00:34:35,319
Speaker 1: have the uh these desktop fabrication tools like three D

653
00:34:35,400 --> 00:34:37,759
Speaker 1: printers and seen C machines and laser cutters, et cetera.

654
00:34:38,000 --> 00:34:41,719
Speaker 1: And then you have open source communities. You combine them

655
00:34:41,800 --> 00:34:44,520
Speaker 1: and now you've got the germs of an industrial revolution.

656
00:34:44,560 --> 00:34:46,400
Speaker 1: And that's and that's and this this kind of this

657
00:34:46,520 --> 00:34:49,719
Speaker 1: ability for regular people to do to take take like

658
00:34:49,800 --> 00:34:52,879
Speaker 1: the long tail of physical things rather than just digital things.

659
00:34:53,360 --> 00:34:56,360
Speaker 1: Is what drove the maker movement and the kickstarter phenomenon

660
00:34:57,040 --> 00:34:59,640
Speaker 1: what we're experiencing today. So yeah, so so um so

661
00:34:59,719 --> 00:35:01,520
Speaker 1: I started by just sort of like, you know, just

662
00:35:01,520 --> 00:35:03,920
Speaker 1: just a weekends throwing together kids, and you know the

663
00:35:03,920 --> 00:35:06,040
Speaker 1: problem is that they sold out immediately and the kids

664
00:35:06,040 --> 00:35:08,279
Speaker 1: just wouldn't do it again. So I'm like, I got

665
00:35:08,280 --> 00:35:10,120
Speaker 1: better things to do than stuff pizza boxes. So I

666
00:35:10,120 --> 00:35:12,719
Speaker 1: found a guy on the internet named Jordi MNOs who

667
00:35:12,760 --> 00:35:14,880
Speaker 1: seemed really smart, and I sent him a check for

668
00:35:14,920 --> 00:35:17,000
Speaker 1: five hundred bucks and he's just started doing it instead.

669
00:35:17,520 --> 00:35:20,160
Speaker 1: And then it gets kept sending me pictures and he's like, yeah,

670
00:35:20,200 --> 00:35:22,200
Speaker 1: I've expanded the space. I haven't met him, right, have

671
00:35:22,320 --> 00:35:25,640
Speaker 1: expanded the space and hired some people, and by two

672
00:35:25,680 --> 00:35:29,080
Speaker 1: thousand and twelve he's in Mexico. Is that wrong? I

673
00:35:29,120 --> 00:35:31,960
Speaker 1: didn't know at the time. It turns out that he was,

674
00:35:32,120 --> 00:35:33,480
Speaker 1: you know, when I met him, he was a nineteen

675
00:35:33,560 --> 00:35:36,880
Speaker 1: year old in Tijuana, scraduate from high school. Um. But

676
00:35:36,960 --> 00:35:38,480
Speaker 1: by you know, by the time I actually went it

677
00:35:38,560 --> 00:35:40,160
Speaker 1: was like he keeps sending these pictures of bigger and

678
00:35:40,160 --> 00:35:42,600
Speaker 1: bigger drone factories. By the time I met him, I mean,

679
00:35:42,640 --> 00:35:46,240
Speaker 1: we're probably doing five million a year. UM. By the time,

680
00:35:46,440 --> 00:35:48,160
Speaker 1: by the you know, by around two thousand and twelve,

681
00:35:48,200 --> 00:35:51,399
Speaker 1: I think we were making more drones per month than

682
00:35:51,440 --> 00:35:55,560
Speaker 1: all of America's aerospace companies combined out of a Tijuana

683
00:35:55,640 --> 00:35:57,200
Speaker 1: drone factory, which by the way is a greating him

684
00:35:57,200 --> 00:35:59,120
Speaker 1: for a band. Do you want a drone factory? That's

685
00:35:59,120 --> 00:36:02,560
Speaker 1: absolutely and UM and so you know, so so by

686
00:36:02,560 --> 00:36:04,600
Speaker 1: the time I actually made it like not a hobby

687
00:36:04,640 --> 00:36:07,000
Speaker 1: but like my job and took venture capital, et cetera,

688
00:36:07,120 --> 00:36:09,520
Speaker 1: is already pretty de rist and so yeah, we at

689
00:36:09,520 --> 00:36:13,040
Speaker 1: this point we were making physical drones. UM. And you know,

690
00:36:13,080 --> 00:36:15,319
Speaker 1: but we started UM. I'll just finish with one last

691
00:36:15,400 --> 00:36:18,720
Speaker 1: story about this. UM in around two thousand and nine

692
00:36:18,800 --> 00:36:20,919
Speaker 1: or ten or twelve or something like that. UM, there's

693
00:36:20,960 --> 00:36:23,759
Speaker 1: the the aerospace industry has an annual conference is called

694
00:36:23,800 --> 00:36:27,040
Speaker 1: a u VS a u v s I, and it

695
00:36:27,040 --> 00:36:30,920
Speaker 1: has has Autonomous Vehicles Systems internationals like that. Anyway, it's

696
00:36:30,920 --> 00:36:33,160
Speaker 1: like you know, the Boeings and the Lockets and they

697
00:36:33,200 --> 00:36:37,080
Speaker 1: have predator drones that it's happening actually this week I

698
00:36:37,080 --> 00:36:40,040
Speaker 1: think in Las Vegas is the only one anyway, UM,

699
00:36:40,320 --> 00:36:42,440
Speaker 1: one of the more you know, innovative. Guys on the

700
00:36:42,480 --> 00:36:45,560
Speaker 1: board invited me to speak to like, you know, the

701
00:36:45,840 --> 00:36:49,680
Speaker 1: military industrial complex, and I gave I spoke and I

702
00:36:50,000 --> 00:36:52,200
Speaker 1: held up this little board that we were making called

703
00:36:52,280 --> 00:36:54,120
Speaker 1: arch Pilot, was based on our duino, and it cost

704
00:36:55,640 --> 00:36:58,239
Speaker 1: and I said, uh, yeah, so we can do you

705
00:36:58,239 --> 00:37:00,799
Speaker 1: know this point, drones cost ten million dollars end up

706
00:37:00,960 --> 00:37:02,960
Speaker 1: and I come out with this board this night five

707
00:37:02,960 --> 00:37:04,359
Speaker 1: and say, if you take this board and you stick

708
00:37:04,400 --> 00:37:06,719
Speaker 1: it in like a toy airplane, you basically got the

709
00:37:06,719 --> 00:37:09,280
Speaker 1: same thing you guys do, just for ten million dollars.

710
00:37:09,480 --> 00:37:11,320
Speaker 1: And they just couldn't figure I mean, they couldn't decide

711
00:37:11,320 --> 00:37:14,040
Speaker 1: whether I was you know, whether it was crazy or

712
00:37:14,280 --> 00:37:17,759
Speaker 1: dangerous or both. But they knew that it wasn't that

713
00:37:17,840 --> 00:37:19,719
Speaker 1: it was a joke. You know, they knew that I

714
00:37:19,800 --> 00:37:22,600
Speaker 1: was not to be taken seriously. And you know the

715
00:37:22,640 --> 00:37:24,719
Speaker 1: Gandhi phrase. They answers to ignore you, that I laugh

716
00:37:24,760 --> 00:37:26,399
Speaker 1: at you, then they fight you, then you then you've

717
00:37:26,400 --> 00:37:30,520
Speaker 1: won today. You know that board and others like that.

718
00:37:30,520 --> 00:37:31,799
Speaker 1: There are other people were doing the same thing at

719
00:37:31,800 --> 00:37:34,760
Speaker 1: the same time. I'm there now a million drones sold

720
00:37:34,880 --> 00:37:37,360
Speaker 1: per year. You can now there's never been a technology

721
00:37:37,400 --> 00:37:40,480
Speaker 1: I can think of that went from military industrial complex

722
00:37:40,520 --> 00:37:44,080
Speaker 1: to the toy shelves of Walmart faster than drones. And

723
00:37:44,120 --> 00:37:45,879
Speaker 1: when we started, people said you can't use the word

724
00:37:45,920 --> 00:37:48,279
Speaker 1: drone because it sounds like a weapon. Now they tell

725
00:37:48,360 --> 00:37:49,799
Speaker 1: us we can't use the word drone because it sounds

726
00:37:49,840 --> 00:37:52,880
Speaker 1: like a toy. So so the question, all right, so

727
00:37:52,920 --> 00:37:55,680
Speaker 1: I have to ask the question I asked when I

728
00:37:55,719 --> 00:38:02,000
Speaker 1: saw that big black drone out outside of your reception areas.

729
00:38:02,320 --> 00:38:04,880
Speaker 1: All right, so where's the hunter killer adaptation? How do

730
00:38:04,960 --> 00:38:09,120
Speaker 1: I add something too? If I need to take care

731
00:38:09,160 --> 00:38:14,160
Speaker 1: of business? What's the adaptation to It's got elements of

732
00:38:14,239 --> 00:38:17,640
Speaker 1: Skynet to it. How often do you get that idiotic question?

733
00:38:18,239 --> 00:38:21,000
Speaker 1: Because some of these things, look, it's not that idiotic.

734
00:38:21,040 --> 00:38:24,040
Speaker 1: I mean isis is using drones very much like ours?

735
00:38:24,040 --> 00:38:27,360
Speaker 1: Hopefully not ours, but very much like ours today. Um,

736
00:38:27,400 --> 00:38:30,560
Speaker 1: they did exactly what you described. They bought typically they'll

737
00:38:30,640 --> 00:38:34,799
Speaker 1: buy um uh drones from China. Um. They stick a

738
00:38:34,840 --> 00:38:36,920
Speaker 1: little a little hook on the bottom, and then they

739
00:38:37,040 --> 00:38:41,680
Speaker 1: drop bombs off of the drones. So there was another

740
00:38:41,800 --> 00:38:44,920
Speaker 1: drone I was looking at uh last weekend in the

741
00:38:44,960 --> 00:38:50,200
Speaker 1: Apple store. That it's very light, it's very small. The

742
00:38:50,320 --> 00:38:55,319
Speaker 1: um rotors are engaged in a sort of hovered drone. Yeah,

743
00:38:55,480 --> 00:38:59,640
Speaker 1: and it has his facial recognition software built in, so

744
00:38:59,680 --> 00:39:02,279
Speaker 1: when you lock it on somebody, it just tracks them.

745
00:39:02,320 --> 00:39:05,239
Speaker 1: And I'm like, oh, that's how you assassinate people. You

746
00:39:05,320 --> 00:39:11,040
Speaker 1: just need some some minor thing. So it's not that

747
00:39:11,120 --> 00:39:13,239
Speaker 1: far from skynet. Yeah, I can be pretty you know,

748
00:39:13,280 --> 00:39:16,839
Speaker 1: whatever the science fiction catastrophe you imagine. You know, there

749
00:39:16,840 --> 00:39:18,680
Speaker 1: may be catastrophe, but it won't be that. Oh, I

750
00:39:18,680 --> 00:39:21,200
Speaker 1: don't think it's a broad catastrophe. I think it's just

751
00:39:21,320 --> 00:39:24,359
Speaker 1: an efficient way to remove certain people who are problematic.

752
00:39:24,600 --> 00:39:28,200
Speaker 1: Well how far away is that that? So that Tuesday,

753
00:39:28,360 --> 00:39:30,200
Speaker 1: that particular one. So that's not going to happen for

754
00:39:30,239 --> 00:39:32,800
Speaker 1: a number of reasons. First of all, if you'd actually

755
00:39:32,840 --> 00:39:34,000
Speaker 1: take an hour of the box and turn it on,

756
00:39:34,000 --> 00:39:36,440
Speaker 1: you would have heard that it's really noisy, and you're

757
00:39:36,440 --> 00:39:39,560
Speaker 1: not gonna let this like buzzing. You know many laws,

758
00:39:39,600 --> 00:39:42,360
Speaker 1: by the time you hear it, it's too that's their tagline.

759
00:39:42,400 --> 00:39:44,479
Speaker 1: By the time you hear us coming, it's too late.

760
00:39:44,520 --> 00:39:47,560
Speaker 1: Would that that were true? You heard drums this morning?

761
00:39:47,560 --> 00:39:50,239
Speaker 1: There there there their noise. You're not gonna stealth, you know, now,

762
00:39:50,360 --> 00:39:52,080
Speaker 1: at least not. What the hellico, how far away are

763
00:39:52,120 --> 00:39:55,360
Speaker 1: we from stealthier rotors or is that just physics and

764
00:39:55,400 --> 00:39:57,520
Speaker 1: that's never going to change. Well, you know we work

765
00:39:57,560 --> 00:39:59,360
Speaker 1: hard on that. You know, we don't make the drums anymore.

766
00:39:59,360 --> 00:40:01,399
Speaker 1: We just do this off where but you know, um,

767
00:40:01,440 --> 00:40:03,759
Speaker 1: you know, up against so we can absolutely do more

768
00:40:03,840 --> 00:40:06,239
Speaker 1: with you know, there are different propeller designs and you know,

769
00:40:06,320 --> 00:40:08,719
Speaker 1: vibrations and resonant frequencies and on speeds and all this

770
00:40:08,760 --> 00:40:10,880
Speaker 1: kind of stuff, but but by large you up against physics.

771
00:40:10,880 --> 00:40:13,040
Speaker 1: Now that's not to say that an airplane, you know,

772
00:40:13,080 --> 00:40:16,239
Speaker 1: the ones with wings can't just glide their way silently.

773
00:40:16,480 --> 00:40:18,200
Speaker 1: But it's not gonna hover, So you get hover and

774
00:40:18,280 --> 00:40:21,560
Speaker 1: noise or you get non hovers, so either stealthy or

775
00:40:21,719 --> 00:40:25,160
Speaker 1: or very mobile. So you mentioned earlier the you realize

776
00:40:25,200 --> 00:40:28,439
Speaker 1: this is a real business. You um said, let's let's

777
00:40:28,560 --> 00:40:30,120
Speaker 1: most of this is the risk. Let's go to the

778
00:40:30,160 --> 00:40:33,880
Speaker 1: venture capital route. What was that process? You had VC

779
00:40:34,200 --> 00:40:38,960
Speaker 1: money from Qualcom, from Richard Venture, Richard Branson from True Ventures.

780
00:40:39,480 --> 00:40:43,000
Speaker 1: What is the process like going to a VC and saying, hey,

781
00:40:43,040 --> 00:40:44,640
Speaker 1: we have this idea for a new company, what we're

782
00:40:44,640 --> 00:40:49,160
Speaker 1: gonna build and sell this new technology. Well at this

783
00:40:49,239 --> 00:40:51,480
Speaker 1: point it wasn't a new company. Remember we bootstrapped it

784
00:40:52,200 --> 00:40:54,760
Speaker 1: to you know, to to something like five million of revenues,

785
00:40:54,880 --> 00:40:57,800
Speaker 1: and you know, it was make money and and so

786
00:40:57,920 --> 00:41:01,239
Speaker 1: that's quite unusual to do risk something that that much.

787
00:41:01,360 --> 00:41:04,520
Speaker 1: Um So, taking it to to venture capitals, I mean,

788
00:41:04,560 --> 00:41:06,600
Speaker 1: it's very early. And at this point we were roughly

789
00:41:06,719 --> 00:41:09,959
Speaker 1: basically technology. We're sort of saying, you know, we have autopilots,

790
00:41:09,960 --> 00:41:12,760
Speaker 1: which are these boxes the brains for the planes instead

791
00:41:12,800 --> 00:41:15,480
Speaker 1: we have autopilots. You know, we have some kits. Um

792
00:41:15,600 --> 00:41:18,080
Speaker 1: it was by no means the shelves of Walmart yet,

793
00:41:19,000 --> 00:41:23,359
Speaker 1: and we actually um articulated the the the ultimate goal

794
00:41:23,440 --> 00:41:26,520
Speaker 1: not being selling consumer electronic devices and Walmart, but rather

795
00:41:26,560 --> 00:41:29,600
Speaker 1: putting sensors in the sky. The intention was always to

796
00:41:29,719 --> 00:41:32,760
Speaker 1: use drones as a tool, um to extend the Internet

797
00:41:32,880 --> 00:41:35,600
Speaker 1: into physical space. And so you know, part of extending

798
00:41:35,600 --> 00:41:38,840
Speaker 1: the Internet into physical spaces to gather it's a sense

799
00:41:38,920 --> 00:41:41,200
Speaker 1: the space and the cameras, etcetera. The other is to

800
00:41:41,239 --> 00:41:43,880
Speaker 1: extend the intelligence of the Internet out so the devices

801
00:41:44,200 --> 00:41:47,440
Speaker 1: can basically harness the collective whatever, the big data in

802
00:41:47,440 --> 00:41:50,759
Speaker 1: neural networks whatever. Um So, the idea was, Okay, we're

803
00:41:50,760 --> 00:41:53,200
Speaker 1: gonna put sensors in in in in the sky. It's

804
00:41:53,239 --> 00:41:57,239
Speaker 1: gonna compliment ground sensors, it's going to complement satellites. If

805
00:41:57,239 --> 00:41:59,759
Speaker 1: you look up, this guy is empty. It's just the

806
00:42:00,120 --> 00:42:01,840
Speaker 1: just thing. I mean, it's very hard to find a

807
00:42:01,880 --> 00:42:04,480
Speaker 1: market as empty as the sky. But that's there's a

808
00:42:04,520 --> 00:42:07,080
Speaker 1: lot of sky out there in this room for plenty

809
00:42:07,120 --> 00:42:10,120
Speaker 1: of drones, and it doesn't look like you're really taking

810
00:42:10,160 --> 00:42:12,160
Speaker 1: up a lot of space exactly. And the drones are

811
00:42:12,200 --> 00:42:13,680
Speaker 1: smart in the same way, the same way. You know.

812
00:42:13,800 --> 00:42:16,080
Speaker 1: The analogy I would use is on wireless. You know,

813
00:42:16,120 --> 00:42:18,640
Speaker 1: there was a time when the f c c S

814
00:42:18,719 --> 00:42:22,279
Speaker 1: job was to give give wireless carries monopoly ownership of

815
00:42:22,360 --> 00:42:24,799
Speaker 1: certain way of certain spectrum. You know, you know, this

816
00:42:24,880 --> 00:42:27,360
Speaker 1: frequency is yours and that frequency of somebody else's, and

817
00:42:27,400 --> 00:42:29,160
Speaker 1: we have to kind of, you know, block off the

818
00:42:29,160 --> 00:42:31,680
Speaker 1: bits so they don't interfere and ruin you know, grandma's

819
00:42:31,719 --> 00:42:35,520
Speaker 1: television reception or whatever. It wasn't those then along came WiFi,

820
00:42:36,000 --> 00:42:37,960
Speaker 1: which says, hey, you know what if we have like

821
00:42:38,200 --> 00:42:40,600
Speaker 1: low power and the ability to kind of listen before

822
00:42:40,640 --> 00:42:43,480
Speaker 1: you talk, and you know, digital radios etcetera. Maybe you

823
00:42:43,520 --> 00:42:45,239
Speaker 1: could just give us a sandbox, a little sort of

824
00:42:45,239 --> 00:42:49,440
Speaker 1: you know, spectrum, just let us know play, no licenses,

825
00:42:49,520 --> 00:42:52,640
Speaker 1: no ham radio, just you know, no no monopolies, just

826
00:42:52,719 --> 00:42:54,839
Speaker 1: let us play. And that's a sense to you what

827
00:42:54,920 --> 00:42:58,759
Speaker 1: we achieved. Um, with drones, we sort of we said, hey,

828
00:42:58,800 --> 00:43:00,680
Speaker 1: you know, give us a sandbox, and sandbox is going

829
00:43:00,719 --> 00:43:04,080
Speaker 1: to be visual line of site under four hundred feet,

830
00:43:04,200 --> 00:43:06,600
Speaker 1: don't fly over people, private land and things like that.

831
00:43:06,840 --> 00:43:10,960
Speaker 1: And and recreated originally was recreational used, not commercial use.

832
00:43:11,239 --> 00:43:14,680
Speaker 1: And all those things were were legal, you know, call

833
00:43:14,760 --> 00:43:17,200
Speaker 1: them loopholes or call them. You know, it was a

834
00:43:17,280 --> 00:43:20,120
Speaker 1: carve out, it was. It wasn't. I think it was

835
00:43:20,120 --> 00:43:22,799
Speaker 1: a carve out actually for radio control hobbyists rather than

836
00:43:22,840 --> 00:43:25,440
Speaker 1: for drones. That happened in the seventies. But we used

837
00:43:25,480 --> 00:43:28,400
Speaker 1: that carve out to to basically stick a million drones

838
00:43:28,440 --> 00:43:30,600
Speaker 1: in the air. And then we're like, okay, now they're here,

839
00:43:30,840 --> 00:43:33,440
Speaker 1: they're good. Now what are they good for? And this

840
00:43:33,480 --> 00:43:35,840
Speaker 1: became the commercial use. And originally I thought it was

841
00:43:35,840 --> 00:43:37,480
Speaker 1: gonna be agriculture. I thought we were going to be

842
00:43:37,520 --> 00:43:40,800
Speaker 1: you know, um, and they are currently used pretty robustly,

843
00:43:40,880 --> 00:43:44,319
Speaker 1: and they are, but not as agriculture, real estate. I

844
00:43:44,360 --> 00:43:47,920
Speaker 1: can't tell you how many homes I've seen that standard

845
00:43:48,520 --> 00:43:52,040
Speaker 1: drone and over. Oh isn't that lovely? Look at it?

846
00:43:52,080 --> 00:43:54,320
Speaker 1: I mean, that's become stand So Hollywood, of course is

847
00:43:54,360 --> 00:43:57,520
Speaker 1: that was was was one. So you guys were involved

848
00:43:57,640 --> 00:44:00,640
Speaker 1: with Fast and Furious, if I've seen some of your

849
00:44:00,640 --> 00:44:04,919
Speaker 1: toys actually, and we were involved in the Benghazi film.

850
00:44:04,920 --> 00:44:07,400
Speaker 1: But the Fast and Furious is that we have these sprints,

851
00:44:07,560 --> 00:44:11,760
Speaker 1: um and you know, so sprints are are our development

852
00:44:11,760 --> 00:44:15,440
Speaker 1: milestones are all named after Fast and Furious films. Uh,

853
00:44:15,480 --> 00:44:17,560
Speaker 1: you know, Fast and Furious exactly. And it just turns

854
00:44:17,560 --> 00:44:19,239
Speaker 1: out there making the films about as quickly as we

855
00:44:19,280 --> 00:44:21,799
Speaker 1: can develop. So so you know, I think this is

856
00:44:21,840 --> 00:44:24,279
Speaker 1: the towards the end of them at this point, this

857
00:44:24,360 --> 00:44:25,920
Speaker 1: last one. You think the number eight is going to

858
00:44:25,960 --> 00:44:28,239
Speaker 1: be the I'm hoping, well, we'll have to come up

859
00:44:28,280 --> 00:44:31,279
Speaker 1: with another theme for our sprints. So so what have

860
00:44:31,360 --> 00:44:34,000
Speaker 1: you done much in with Hollywood? Have you done much

861
00:44:34,000 --> 00:44:36,239
Speaker 1: on that side? So so we we did a tiny

862
00:44:36,280 --> 00:44:38,760
Speaker 1: bit in Hollywood. But actually there's a you know our

863
00:44:39,160 --> 00:44:40,840
Speaker 1: you know, back when we were making drones, our biggest

864
00:44:40,840 --> 00:44:42,919
Speaker 1: competitive with a Chinese company called d g I, which

865
00:44:42,960 --> 00:44:46,200
Speaker 1: was extraordinary, perhaps the best company I've I've ever seen anywhere,

866
00:44:46,239 --> 00:44:50,279
Speaker 1: not just in China. And um, they really focused on

867
00:44:50,320 --> 00:44:54,280
Speaker 1: the on the cameras, the stabilization, et cetera. And today

868
00:44:54,360 --> 00:44:57,200
Speaker 1: Ji makes the Parrot. No no, but the Parrot is

869
00:44:57,200 --> 00:45:00,359
Speaker 1: a fringe company makes the Fantom, the Phantom of and

870
00:45:00,480 --> 00:45:02,799
Speaker 1: you know it's something called the magick. I remember when

871
00:45:02,800 --> 00:45:04,640
Speaker 1: the Phantom first came out, it was like two thousand

872
00:45:04,640 --> 00:45:07,000
Speaker 1: dollars and it was twelve dollars and it was seven.

873
00:45:07,400 --> 00:45:11,680
Speaker 1: The prices just plummeted in that is that China subsidizing

874
00:45:11,960 --> 00:45:15,200
Speaker 1: this or these guys really ringing out economies of scale

875
00:45:15,200 --> 00:45:18,520
Speaker 1: like that? It's that good. Yeah, so, um, you know

876
00:45:18,560 --> 00:45:20,640
Speaker 1: I lived in China. I knew, you know when I

877
00:45:20,640 --> 00:45:22,480
Speaker 1: lived in China for those those years of the Economist,

878
00:45:22,520 --> 00:45:25,160
Speaker 1: everyone said, oh, don't worry about China. They can't do x.

879
00:45:25,800 --> 00:45:29,520
Speaker 1: X was like innovate or design or software global or

880
00:45:29,520 --> 00:45:32,240
Speaker 1: global brands or anything like that. It's a very subtle

881
00:45:33,080 --> 00:45:37,080
Speaker 1: racism built into that, which is a repeat of what

882
00:45:37,160 --> 00:45:42,560
Speaker 1: I recall hearing about Japan right Japan early on was

883
00:45:42,600 --> 00:45:45,759
Speaker 1: oh they only make junk and they're good in imitating.

884
00:45:45,800 --> 00:45:49,160
Speaker 1: They can't innovate anything, and then suddenly, wow, this is

885
00:45:49,200 --> 00:45:52,560
Speaker 1: really first class products. And if you remember when Hyundai

886
00:45:52,640 --> 00:45:55,400
Speaker 1: first came out, they were kind of junkie cars, and

887
00:45:55,440 --> 00:45:58,239
Speaker 1: now suddenly they were the only company that comes out

888
00:45:58,239 --> 00:46:01,000
Speaker 1: of the financial crisis increasing market share. I think with

889
00:46:01,080 --> 00:46:05,560
Speaker 1: them so that, oh, those guys can't do anything. That's

890
00:46:05,600 --> 00:46:09,160
Speaker 1: a very dangerous assumption for anyone to ever make. So so,

891
00:46:09,320 --> 00:46:12,319
Speaker 1: you know, everything I learned about China before going there,

892
00:46:12,400 --> 00:46:15,000
Speaker 1: I think turned out to be wrong. Everyone said, oh

893
00:46:15,080 --> 00:46:18,360
Speaker 1: you know, um, you know, the political system is unsustainable,

894
00:46:18,440 --> 00:46:22,040
Speaker 1: their economic growth is unsustainable. It's Quoe unquote. Centrifugal forces

895
00:46:22,080 --> 00:46:25,640
Speaker 1: would sort of splinter. They you know, they can't innovate,

896
00:46:25,800 --> 00:46:28,560
Speaker 1: They only copy and and I And it was it

897
00:46:28,640 --> 00:46:30,759
Speaker 1: was around two thousand eight or two thousand nine where

898
00:46:30,800 --> 00:46:34,400
Speaker 1: I spent most of my time in Guandong province around Shenzhen,

899
00:46:34,680 --> 00:46:38,520
Speaker 1: and I spent uh. I spent a night in Huawei,

900
00:46:38,560 --> 00:46:40,880
Speaker 1: which is the telecoms company at the time they were

901
00:46:40,920 --> 00:46:46,040
Speaker 1: being accused stealing from the Cisco stuff. It was way

902
00:46:46,080 --> 00:46:51,280
Speaker 1: before iPhone. I just said, this is nineties, um and uh,

903
00:46:51,320 --> 00:46:53,080
Speaker 1: and so I spent a night. You know, the engineers

904
00:46:53,080 --> 00:46:55,680
Speaker 1: are working all night and sleeping on phutons, and I

905
00:46:55,719 --> 00:46:59,920
Speaker 1: was like, these guys are awesome. They're serious. They're basically

906
00:47:00,040 --> 00:47:02,520
Speaker 1: and you know they're basically. Yes, they're making iPhones today,

907
00:47:02,560 --> 00:47:06,120
Speaker 1: but they're taking notes. And um, the company we ended

908
00:47:06,160 --> 00:47:08,640
Speaker 1: up competing with d G I started around actually a

909
00:47:08,680 --> 00:47:11,560
Speaker 1: little bit before US. Um but um, you know, the

910
00:47:11,680 --> 00:47:14,439
Speaker 1: so called in China can't do X. We haven't found

911
00:47:14,440 --> 00:47:19,840
Speaker 1: an X. So they they innovated. They raised ten billion

912
00:47:19,920 --> 00:47:25,040
Speaker 1: dollars billions of dollars of of venture capital, ten billion

913
00:47:25,080 --> 00:47:28,960
Speaker 1: dollar valuation. They went global initially, they actually targeted outside

914
00:47:28,960 --> 00:47:33,320
Speaker 1: of China before China. Their design exquisite, their marketing is fantastic.

915
00:47:33,760 --> 00:47:37,640
Speaker 1: Um you know, uh there and then the production efficiencies

916
00:47:37,719 --> 00:47:40,120
Speaker 1: because they were insins and they vertically integrated, so they're

917
00:47:40,120 --> 00:47:42,400
Speaker 1: doing everything. You know, they're you know, they weren't. They

918
00:47:42,400 --> 00:47:44,640
Speaker 1: weren't like they weren't just like buying off the shell

919
00:47:44,719 --> 00:47:51,120
Speaker 1: stuff like we were. They were making fundamental physics, you know, um, designed, etcetera.

920
00:47:51,280 --> 00:47:54,600
Speaker 1: So so you know, watching them, you know it's uh,

921
00:47:54,640 --> 00:47:58,000
Speaker 1: you know, I knew that these twenty one century Chinese

922
00:47:58,000 --> 00:47:59,600
Speaker 1: companies were coming. I knew that they were going to

923
00:47:59,680 --> 00:48:02,719
Speaker 1: be good. Um, but just watching the price decline that

924
00:48:02,800 --> 00:48:07,080
Speaker 1: came with that price decline in nine months, I think

925
00:48:07,080 --> 00:48:09,239
Speaker 1: there's a you know, we were as the quality went

926
00:48:09,320 --> 00:48:13,040
Speaker 1: up exactly and I don't think they were dumping. I

927
00:48:13,040 --> 00:48:15,080
Speaker 1: think they're just that good. So we were one of

928
00:48:15,120 --> 00:48:18,720
Speaker 1: the first Silicon Valley companies to encounter twenty one century

929
00:48:18,800 --> 00:48:22,800
Speaker 1: native Chinese company, and um, we won't be the last.

930
00:48:23,200 --> 00:48:26,240
Speaker 1: So let's talk about the pivot. You originally were designing

931
00:48:26,239 --> 00:48:29,920
Speaker 1: and manufacturing drones and doing all the software that went

932
00:48:29,960 --> 00:48:32,719
Speaker 1: into it. The concept is, hey, we we don't even

933
00:48:32,719 --> 00:48:36,040
Speaker 1: want to try and compete with this behemoth on manufacturing,

934
00:48:36,600 --> 00:48:40,759
Speaker 1: but the software sort of an Apple model of hey,

935
00:48:40,760 --> 00:48:44,000
Speaker 1: we'll let these guys build the hardware. We're gonna build

936
00:48:44,000 --> 00:48:46,560
Speaker 1: the brains of it because that's where our expertise is.

937
00:48:46,880 --> 00:48:49,520
Speaker 1: How how did that pivot come about and how is

938
00:48:49,520 --> 00:48:52,040
Speaker 1: that going? So we originally we did it all. Um,

939
00:48:52,040 --> 00:48:54,360
Speaker 1: we did the hardware, we did the software, which is

940
00:48:54,360 --> 00:48:56,279
Speaker 1: like the operating system for the drone, and we also

941
00:48:56,320 --> 00:48:58,759
Speaker 1: did the apps that it ran with it. Um we

942
00:48:58,840 --> 00:49:01,279
Speaker 1: ended up breaking into three parts. The hardware part we

943
00:49:01,400 --> 00:49:03,480
Speaker 1: just spun off and don't do that at all anymore.

944
00:49:03,560 --> 00:49:07,759
Speaker 1: And we now know support whatever hardware's out there. So

945
00:49:07,800 --> 00:49:09,120
Speaker 1: if I want to go out and buy a three

946
00:49:09,200 --> 00:49:12,279
Speaker 1: dr drone, are they still available for sale? They they

947
00:49:12,520 --> 00:49:15,279
Speaker 1: they are. UM, it's called site scanned. It's our it's

948
00:49:15,280 --> 00:49:18,680
Speaker 1: our commercial one. It's actually it's actually it is. It

949
00:49:18,760 --> 00:49:20,800
Speaker 1: is a drone that we did make. But the the

950
00:49:21,040 --> 00:49:23,800
Speaker 1: solo or the it's it's it's it's a derivative. The solo,

951
00:49:23,880 --> 00:49:26,960
Speaker 1: it's got a fancy Sony cameras, fancy GPS, etcetera nice

952
00:49:27,000 --> 00:49:30,879
Speaker 1: skimble attached to it as well, exactly. But but soon enough, UM,

953
00:49:31,200 --> 00:49:32,920
Speaker 1: if you want a different kind of drone, will support

954
00:49:32,960 --> 00:49:34,600
Speaker 1: that as well. So we're trying to be hardware agnostic

955
00:49:34,680 --> 00:49:37,000
Speaker 1: right now. We think that this particular integration with the

956
00:49:37,000 --> 00:49:39,320
Speaker 1: Sony cameras, you know, the best camera, and that's the

957
00:49:39,360 --> 00:49:40,920
Speaker 1: way to go. But I think that over time we're

958
00:49:40,920 --> 00:49:43,279
Speaker 1: going to be more flexible and if people want, you know,

959
00:49:43,320 --> 00:49:45,279
Speaker 1: a different drone, that's that'll be fine. I think that's

960
00:49:45,440 --> 00:49:48,759
Speaker 1: I'm almost all American drone companies have done the same thing.

961
00:49:48,760 --> 00:49:51,440
Speaker 1: Everyone stopped making their own hardware. Most people are supporting

962
00:49:51,480 --> 00:49:53,880
Speaker 1: d g I at this point. Um. Then there was

963
00:49:53,920 --> 00:49:57,919
Speaker 1: the operating system part um and that was mostly open

964
00:49:57,960 --> 00:49:59,840
Speaker 1: source thanks to the way we started, and so we

965
00:50:00,040 --> 00:50:02,280
Speaker 1: spun that off and that's not part of the Linux Foundation.

966
00:50:02,360 --> 00:50:04,440
Speaker 1: Is it's called drone code and so on the chairman

967
00:50:04,440 --> 00:50:07,479
Speaker 1: of that as well. That's interesting. So that's open source,

968
00:50:07,520 --> 00:50:10,319
Speaker 1: available to anyone who wants to anybody or improve it

969
00:50:10,440 --> 00:50:14,040
Speaker 1: exactly exactly. That's drone code, um. And then there's the apps,

970
00:50:14,360 --> 00:50:16,520
Speaker 1: and the apps is where we focus. This is using

971
00:50:16,520 --> 00:50:18,719
Speaker 1: the drone data. So it's less about the drone, more

972
00:50:18,760 --> 00:50:21,520
Speaker 1: about the data, using the drone data to do something useful.

973
00:50:21,760 --> 00:50:24,719
Speaker 1: In our case, we've partnered with Autodesk, who's the dominant

974
00:50:24,719 --> 00:50:29,080
Speaker 1: company in the design. Yeah, it's auto cad. It's construction

975
00:50:29,200 --> 00:50:33,319
Speaker 1: and engineering and architecture and things like that, and they,

976
00:50:33,800 --> 00:50:35,799
Speaker 1: you know, their initiative has been to sort of say, well,

977
00:50:35,880 --> 00:50:39,320
Speaker 1: you know, in construction, you start everything digital on screen cad,

978
00:50:39,640 --> 00:50:41,200
Speaker 1: and then the moment you dig a spade of dirt,

979
00:50:41,280 --> 00:50:43,680
Speaker 1: it's it's it's now analog and you can't measure it,

980
00:50:43,719 --> 00:50:45,200
Speaker 1: you can't use the same tools. So what you need

981
00:50:45,239 --> 00:50:48,239
Speaker 1: to do is redigitize the world as you're building it.

982
00:50:48,680 --> 00:50:51,000
Speaker 1: And that's called reality capture. And where the capture and

983
00:50:51,040 --> 00:50:55,360
Speaker 1: the reality capture that's interesting. I've been watching somebody putting

984
00:50:55,440 --> 00:50:57,799
Speaker 1: up it's got to be for the past I want

985
00:50:57,800 --> 00:51:01,320
Speaker 1: to say, two years, every month or every two months,

986
00:51:02,040 --> 00:51:05,240
Speaker 1: the drone flyover of the Apple campus. And I've watched

987
00:51:05,280 --> 00:51:09,840
Speaker 1: this come together. It's it's it certainly is time compressed.

988
00:51:09,840 --> 00:51:12,279
Speaker 1: It feels like cheef. They've been working on that a

989
00:51:12,280 --> 00:51:14,319
Speaker 1: long time. It seems like it's only a year or so.

990
00:51:14,440 --> 00:51:18,800
Speaker 1: I've been watching this, but it's astonishing at the clarity

991
00:51:19,040 --> 00:51:23,080
Speaker 1: and focus and amount of details that you can see

992
00:51:23,120 --> 00:51:26,200
Speaker 1: for what is essentially private property. Yeah, we're gonna go in.

993
00:51:26,280 --> 00:51:29,480
Speaker 1: And what sort of issues do you have when you're

994
00:51:29,480 --> 00:51:33,640
Speaker 1: trying to get that granular on a construction site? And

995
00:51:33,719 --> 00:51:37,560
Speaker 1: are there problems with people sending drones? So the first

996
00:51:37,640 --> 00:51:42,000
Speaker 1: question is really the first question is really how granular

997
00:51:42,040 --> 00:51:45,480
Speaker 1: can you get on a construction site and identifying how

998
00:51:45,480 --> 00:51:47,799
Speaker 1: big is this whole, how deep is this foundation? How

999
00:51:47,840 --> 00:51:50,080
Speaker 1: much sand is this here? And then I'll save the

1000
00:51:50,160 --> 00:51:53,600
Speaker 1: question about people doing flyovers for later. Yeah, and the

1001
00:51:53,600 --> 00:51:57,760
Speaker 1: answer to the first is two centimeters per pixel. Two centimeters.

1002
00:51:58,200 --> 00:52:01,800
Speaker 1: Basically we can see a quarter Okay, Um, that's pretty detail.

1003
00:52:01,800 --> 00:52:04,719
Speaker 1: It's pretty detailed. Um, So what you describe as photographs

1004
00:52:04,760 --> 00:52:08,759
Speaker 1: maybe video. That's actually not what the construction industry you're

1005
00:52:08,800 --> 00:52:13,560
Speaker 1: talking about. Actually the measurements of everything contained there, the processes,

1006
00:52:13,640 --> 00:52:15,480
Speaker 1: and you probably saw it this morning on a on

1007
00:52:15,480 --> 00:52:18,320
Speaker 1: a roof. The processes. You you take out an iPad.

1008
00:52:18,920 --> 00:52:21,680
Speaker 1: There's a drone just sitting there. You turn on the drone,

1009
00:52:22,040 --> 00:52:25,520
Speaker 1: pruss the button, take out your iPad. Um you you

1010
00:52:25,600 --> 00:52:27,600
Speaker 1: either either, but you're doing the same scan every day,

1011
00:52:27,640 --> 00:52:30,360
Speaker 1: which cakes you just swipe or you just drag a

1012
00:52:30,400 --> 00:52:32,520
Speaker 1: box around the thing you want to scan and then

1013
00:52:32,560 --> 00:52:35,200
Speaker 1: you swipe. The drone then takes off. It flies like

1014
00:52:35,239 --> 00:52:37,520
Speaker 1: a law and mower pattern or cross hatch pattern. It

1015
00:52:37,520 --> 00:52:40,640
Speaker 1: takes photographs of exactly the right time. So this photographs

1016
00:52:40,680 --> 00:52:43,480
Speaker 1: can be stitched together into a three D model or

1017
00:52:43,960 --> 00:52:47,080
Speaker 1: two D model, and then that is then you superimpose

1018
00:52:47,200 --> 00:52:50,239
Speaker 1: the CAD file, which you know the underlying design over

1019
00:52:50,400 --> 00:52:53,560
Speaker 1: this this model, and you can compare AD as designed

1020
00:52:53,560 --> 00:52:55,520
Speaker 1: with as built. So so this is a little bit

1021
00:52:55,520 --> 00:53:00,880
Speaker 1: of augmented reality. Yeah, you're essentially putting a it's almost

1022
00:53:00,920 --> 00:53:03,759
Speaker 1: like a wire map on top of the world and

1023
00:53:03,920 --> 00:53:06,680
Speaker 1: comparing it to what was supposed to be built precisely.

1024
00:53:06,719 --> 00:53:09,759
Speaker 1: And because it's all so precise. If something's off, it's

1025
00:53:09,800 --> 00:53:12,480
Speaker 1: actually off. So if you if a post is off,

1026
00:53:12,600 --> 00:53:15,600
Speaker 1: or trenches off or or you know, or one of

1027
00:53:15,640 --> 00:53:18,600
Speaker 1: the cables for the you know, for the it's called

1028
00:53:18,640 --> 00:53:21,400
Speaker 1: post tension in cables, which is a big thing in construction.

1029
00:53:21,600 --> 00:53:23,960
Speaker 1: You can actually see it's called a clash. And when

1030
00:53:24,000 --> 00:53:26,920
Speaker 1: you see that, the the that the physical you know,

1031
00:53:26,960 --> 00:53:29,480
Speaker 1: the physical site is often the design. There's only it's

1032
00:53:29,480 --> 00:53:32,920
Speaker 1: only two possibilities. Either it was an intentional change for

1033
00:53:33,040 --> 00:53:35,720
Speaker 1: good reason um and which case you need to reflect

1034
00:53:35,800 --> 00:53:38,160
Speaker 1: that change back into the model so that everyone understands

1035
00:53:38,160 --> 00:53:40,000
Speaker 1: and things have changed. Or it was an accident, in

1036
00:53:40,000 --> 00:53:41,480
Speaker 1: which case you need to catch it and fix it.

1037
00:53:41,640 --> 00:53:44,680
Speaker 1: And it sounds like it's vastly less expensive to catch

1038
00:53:44,719 --> 00:53:47,440
Speaker 1: it early than to catch it after the building is built.

1039
00:53:47,480 --> 00:53:51,279
Speaker 1: In Hey, why are we tilting in San Francisco. There's

1040
00:53:51,320 --> 00:53:54,040
Speaker 1: that famous building that's off. You guys would have caught

1041
00:53:54,120 --> 00:53:56,480
Speaker 1: that earlier, whatever was. I don't know if we would

1042
00:53:56,480 --> 00:53:59,120
Speaker 1: have cat got that one. But the construction industries the

1043
00:53:59,160 --> 00:54:01,959
Speaker 1: second largest day the stry in the world after after food,

1044
00:54:02,000 --> 00:54:05,880
Speaker 1: eight trillion dollar industry, and unlike agriculture, which is hedged

1045
00:54:05,880 --> 00:54:10,080
Speaker 1: against all sorts of inefficiencies, constructions not it's got eight

1046
00:54:10,600 --> 00:54:15,000
Speaker 1: average cost overruns, so it's like it's like a five

1047
00:54:15,040 --> 00:54:17,880
Speaker 1: trillion dollar industry that overran the self to eight trillion

1048
00:54:17,920 --> 00:54:20,719
Speaker 1: dollars UM. And those cost overruns are typically due to

1049
00:54:21,120 --> 00:54:26,239
Speaker 1: to just stuff happened right, mistakes, delays, it's a complicated

1050
00:54:26,239 --> 00:54:29,880
Speaker 1: supply chain. Any kind of delay turns into cost um

1051
00:54:29,920 --> 00:54:32,840
Speaker 1: So this is a huge efficiency created a huge cost savings.

1052
00:54:33,520 --> 00:54:37,640
Speaker 1: Is the expectation. Eventually every major contractor is using some

1053
00:54:37,800 --> 00:54:41,080
Speaker 1: form of drones to help build their don't you know

1054
00:54:41,360 --> 00:54:43,600
Speaker 1: it's it's UM. So there's lots of ways to to

1055
00:54:43,800 --> 00:54:46,120
Speaker 1: do reality capture. You can do it I'm on the

1056
00:54:46,120 --> 00:54:48,600
Speaker 1: ground with laser scanning. You can do it on the

1057
00:54:48,640 --> 00:54:51,720
Speaker 1: ground with cameras. There's inside the building and outside the building.

1058
00:54:51,960 --> 00:54:56,880
Speaker 1: So what drones scanning is best force is commercial construction UM.

1059
00:54:56,920 --> 00:55:00,719
Speaker 1: Typically some of the larger sites often not like in

1060
00:55:00,880 --> 00:55:03,480
Speaker 1: in the city center because you have fa regulations about

1061
00:55:03,480 --> 00:55:05,160
Speaker 1: where you can fly and where you can't fly. So

1062
00:55:05,200 --> 00:55:08,480
Speaker 1: it's kind of commercial sites um lucky think as skyscrapers

1063
00:55:08,680 --> 00:55:11,120
Speaker 1: in big, big complexes to be the outside of a

1064
00:55:11,160 --> 00:55:12,960
Speaker 1: city center. But still that you're talking about maybe a

1065
00:55:13,000 --> 00:55:16,319
Speaker 1: third of construction falls into that category. And then the

1066
00:55:16,320 --> 00:55:18,480
Speaker 1: most important time to do it is at the beginning,

1067
00:55:18,520 --> 00:55:21,279
Speaker 1: when you've got the site, you're digging the holes and

1068
00:55:21,400 --> 00:55:25,040
Speaker 1: the foundations, etcetera. That characterizing it because remember the drones

1069
00:55:25,080 --> 00:55:27,399
Speaker 1: just scanning the outside of the building. So you scan

1070
00:55:27,520 --> 00:55:29,560
Speaker 1: the site you scanned with foundations, and then you scan

1071
00:55:29,719 --> 00:55:31,799
Speaker 1: each layer as they build it up. So given this

1072
00:55:31,920 --> 00:55:37,320
Speaker 1: NEPHU with that leaning tower of San Francisco, how unlikely

1073
00:55:37,440 --> 00:55:39,880
Speaker 1: is it that eventually ciney planters are gonna say, hey,

1074
00:55:39,920 --> 00:55:42,320
Speaker 1: if we want to avoid this, we have to allow

1075
00:55:42,400 --> 00:55:45,239
Speaker 1: some sort of external scanning. It's being required right now.

1076
00:55:45,239 --> 00:55:48,160
Speaker 1: It's part of a So Japan has just required drone

1077
00:55:48,200 --> 00:55:53,600
Speaker 1: scanning for in major cities. Yeah, well it's for government infrastructure.

1078
00:55:53,640 --> 00:55:56,400
Speaker 1: They do a lot of roads and bridges and highways

1079
00:55:56,400 --> 00:55:58,600
Speaker 1: and things like that. Um. This is part of a

1080
00:55:59,080 --> 00:56:03,880
Speaker 1: trend called building information modeling BIM and building information modeling

1081
00:56:03,920 --> 00:56:06,600
Speaker 1: basically the notion of digitizing sites so you can manage.

1082
00:56:06,640 --> 00:56:08,200
Speaker 1: You know, you can only manage what you can measure,

1083
00:56:08,600 --> 00:56:12,279
Speaker 1: and governments are now requiring BIM, which is to say, hey,

1084
00:56:12,320 --> 00:56:14,359
Speaker 1: you know, we need you to measure these sites because

1085
00:56:14,360 --> 00:56:16,360
Speaker 1: these costs, over runs are being paid for by somebody,

1086
00:56:16,760 --> 00:56:20,080
Speaker 1: and it's either the ultimately it's the consumer, the end user.

1087
00:56:20,560 --> 00:56:23,880
Speaker 1: Who's who's going to subsidize that, unless it's a government project,

1088
00:56:24,080 --> 00:56:26,400
Speaker 1: in which case it's a tax pay it's a reasonable request.

1089
00:56:26,560 --> 00:56:29,239
Speaker 1: Measure what you're doing so that we can manage it better.

1090
00:56:29,480 --> 00:56:31,359
Speaker 1: You know, either we're gonna avoid the errors, or if

1091
00:56:31,360 --> 00:56:34,200
Speaker 1: it happens, we know why it happened, who caused it?

1092
00:56:34,520 --> 00:56:36,560
Speaker 1: Paper trail. I mean, so many construction projects and in

1093
00:56:36,640 --> 00:56:39,399
Speaker 1: lawsuits and you have this like this question, like how

1094
00:56:39,400 --> 00:56:43,959
Speaker 1: did that happen? We were discussing earlier about the low

1095
00:56:44,040 --> 00:56:48,680
Speaker 1: cost of all these chip sets and and individual chips

1096
00:56:48,680 --> 00:56:51,680
Speaker 1: that allowed you to do really incredible things at a

1097
00:56:51,800 --> 00:56:55,840
Speaker 1: very low cost. What made you realize that, hey, these

1098
00:56:55,960 --> 00:57:01,160
Speaker 1: drone things, this is more than just a cool little toy. Um.

1099
00:57:01,400 --> 00:57:04,400
Speaker 1: First of all, I didn't initially realize that. Okay, So,

1100
00:57:04,440 --> 00:57:07,240
Speaker 1: I mean my path was really simple. I've got five kids,

1101
00:57:07,280 --> 00:57:09,440
Speaker 1: and my wife and I are are former scientists. We

1102
00:57:09,480 --> 00:57:11,759
Speaker 1: try to get our kids sided about science and technology,

1103
00:57:11,760 --> 00:57:14,640
Speaker 1: and we fail and fail and fail again. Um, And

1104
00:57:14,680 --> 00:57:17,080
Speaker 1: so every weekend, i'm you know, I'm I'm trying to

1105
00:57:17,080 --> 00:57:20,600
Speaker 1: think of something that will engage them keeping busy, well,

1106
00:57:20,640 --> 00:57:22,560
Speaker 1: I mean keep them busy. I mean video games will

1107
00:57:22,600 --> 00:57:23,959
Speaker 1: keep them busy, but I want to keep their minds

1108
00:57:23,960 --> 00:57:28,600
Speaker 1: busy as well. So, UM, one weekend, an editor wired,

1109
00:57:28,720 --> 00:57:30,520
Speaker 1: you know, and I we get these like products in

1110
00:57:30,560 --> 00:57:35,000
Speaker 1: for review, and this lego mind storms and xt robotics

1111
00:57:35,080 --> 00:57:37,240
Speaker 1: kit comes in, and this like radio control airplane comes in.

1112
00:57:37,280 --> 00:57:38,760
Speaker 1: I'm like, okay, one of these two things is going

1113
00:57:38,800 --> 00:57:42,160
Speaker 1: to work. Um, either the robot building robot will be

1114
00:57:42,160 --> 00:57:44,160
Speaker 1: cool or at least flowing a plane will be cool.

1115
00:57:44,680 --> 00:57:46,760
Speaker 1: And um, you know, the kids roll their eyes and

1116
00:57:47,240 --> 00:57:49,240
Speaker 1: another one of Dad's things. Um, so we we build

1117
00:57:49,280 --> 00:57:52,320
Speaker 1: a robot, and um, it was not cool. It was

1118
00:57:52,360 --> 00:57:54,960
Speaker 1: not I thought it was cool. Um, but you know

1119
00:57:55,000 --> 00:57:57,000
Speaker 1: it's basically spent all morning building and I leg up

1120
00:57:57,040 --> 00:57:59,800
Speaker 1: pieces and it rolls very slowly. I guess it doesn't

1121
00:57:59,840 --> 00:58:02,160
Speaker 1: all if I only a wave. Its arms basically just

1122
00:58:02,200 --> 00:58:03,960
Speaker 1: had two wheels and it rolled towards a towards the

1123
00:58:04,000 --> 00:58:05,560
Speaker 1: wall and then kind of backed away. And they're like,

1124
00:58:05,560 --> 00:58:08,840
Speaker 1: come on, we've seen transformers away the lasers. Um. And

1125
00:58:08,880 --> 00:58:11,400
Speaker 1: then you know, the next then then on the next

1126
00:58:11,480 --> 00:58:13,040
Speaker 1: day we flew the airplane, which is to say I

1127
00:58:13,040 --> 00:58:16,520
Speaker 1: flew directly into a tree and then right that sucked. Um,

1128
00:58:16,560 --> 00:58:20,560
Speaker 1: so I My thought process consisted of the following, Um,

1129
00:58:20,600 --> 00:58:24,760
Speaker 1: you know one those rotten kids to that actually wasn't

1130
00:58:24,840 --> 00:58:28,160
Speaker 1: very cool. Three what would have been cooler? Ha? What

1131
00:58:28,240 --> 00:58:30,320
Speaker 1: if the robot could fly the plane? What if we

1132
00:58:30,360 --> 00:58:32,880
Speaker 1: had a flying robot? That'd be cool? What's a flying robot?

1133
00:58:33,080 --> 00:58:37,320
Speaker 1: Just came home googled flying robot. If you google flying robot,

1134
00:58:37,480 --> 00:58:41,040
Speaker 1: you will find the first result is drone And you're like, oh, ha,

1135
00:58:41,240 --> 00:58:43,600
Speaker 1: I guess the drone is a flying robot. Wait what's

1136
00:58:43,600 --> 00:58:46,880
Speaker 1: the drone? Google drone? Oh, a drone is like an

1137
00:58:46,920 --> 00:58:49,080
Speaker 1: airplane with an autopilot. It's like a plane with the brain.

1138
00:58:49,640 --> 00:58:52,160
Speaker 1: Oh yeah, I guess that makes sense. Wait, what's an autopilot?

1139
00:58:52,400 --> 00:58:56,840
Speaker 1: Google autopilot. An autopilot is like sensors and and and

1140
00:58:57,000 --> 00:59:00,120
Speaker 1: software that you know, kind of measure you know, the

1141
00:59:00,200 --> 00:59:02,920
Speaker 1: orientation on the plane and flight. It's like, oh, wait,

1142
00:59:02,960 --> 00:59:05,120
Speaker 1: sensors and software. Isn't that what's in the Lego mind

1143
00:59:05,160 --> 00:59:08,200
Speaker 1: storms box. Let's just do that. And so they're on

1144
00:59:08,240 --> 00:59:11,400
Speaker 1: the dining room table. We put together a Lego autopilot

1145
00:59:11,480 --> 00:59:13,360
Speaker 1: and we stuck it in the RC plane which I

1146
00:59:13,400 --> 00:59:16,560
Speaker 1: had retreat from the tree and took it back to

1147
00:59:16,600 --> 00:59:19,800
Speaker 1: the field, and you know, it worked just well enough

1148
00:59:20,320 --> 00:59:23,200
Speaker 1: and kind of flew autonomously, just just just long enough

1149
00:59:23,240 --> 00:59:26,200
Speaker 1: for me to say, holy cow, what have we just

1150
00:59:26,280 --> 00:59:28,560
Speaker 1: done here? The kids are like, yeah, as we said,

1151
00:59:28,640 --> 00:59:31,680
Speaker 1: that sucked dad, um, you know, and I'm like, wait,

1152
00:59:31,800 --> 00:59:35,160
Speaker 1: so there's Kittie Hawk watching the Invention of drones and

1153
00:59:35,200 --> 00:59:37,240
Speaker 1: they're like, yeah, what else you got? Yeah? Because he

1154
00:59:37,280 --> 00:59:40,920
Speaker 1: flew flights sixty? Come on, you know, where's where's my

1155
00:59:41,000 --> 00:59:44,800
Speaker 1: jet back? Um? Um? I had that T shirt? Where's

1156
00:59:44,840 --> 00:59:48,880
Speaker 1: my flying car? So? Um? Uh? So I you know,

1157
00:59:49,120 --> 00:59:51,120
Speaker 1: I the hairs went up on the back of my neck.

1158
00:59:51,200 --> 00:59:53,680
Speaker 1: I was like, I don't know what just happened there,

1159
00:59:53,680 --> 00:59:55,040
Speaker 1: but I need to find out. And so I create

1160
00:59:55,080 --> 00:59:58,880
Speaker 1: a website called d I y Drones, which was basically

1161
00:59:58,960 --> 01:00:00,840
Speaker 1: a place for me to ask them questions in public.

1162
01:00:00,880 --> 01:00:02,920
Speaker 1: And when you ask dumb questions in public, two wonderful

1163
01:00:02,920 --> 01:00:06,000
Speaker 1: things happened. Number One, people answer your dumb questions, and second,

1164
01:00:06,320 --> 01:00:08,840
Speaker 1: it liberates other people to ask their own dumb questions.

1165
01:00:08,840 --> 01:00:10,720
Speaker 1: And it was just that moment. Its two thousand and seven.

1166
01:00:10,920 --> 01:00:13,760
Speaker 1: It's the moment where all these disciplines were coming together.

1167
01:00:13,800 --> 01:00:16,720
Speaker 1: There were software, people on hardware, people on airplane people, etcetera.

1168
01:00:17,040 --> 01:00:19,000
Speaker 1: And over the course of a couple of years they

1169
01:00:19,120 --> 01:00:23,400
Speaker 1: based at the community basically created the most advanced drones.

1170
01:00:24,120 --> 01:00:26,560
Speaker 1: You know that you well, certainly most advanced drones you

1171
01:00:26,560 --> 01:00:29,560
Speaker 1: could buy, and possibly most of advanced drones in the world,

1172
01:00:29,560 --> 01:00:31,800
Speaker 1: at least the technology. And it was our jobs to

1173
01:00:31,800 --> 01:00:33,600
Speaker 1: create a company to turn them into a product. So

1174
01:00:33,600 --> 01:00:37,640
Speaker 1: so originally you were selling kits. You could get these engines,

1175
01:00:37,720 --> 01:00:41,120
Speaker 1: you could get these motors, these battery packs. Here, put

1176
01:00:41,120 --> 01:00:43,720
Speaker 1: this to your together yourself and have have some fun.

1177
01:00:44,360 --> 01:00:46,760
Speaker 1: When did that? First of all, how long did that

1178
01:00:46,800 --> 01:00:48,880
Speaker 1: go on? And how did that come about? And then

1179
01:00:49,000 --> 01:00:53,080
Speaker 1: when did that transfer to let's build drones and sales

1180
01:00:53,160 --> 01:00:55,320
Speaker 1: to the public. So it was a hobby from two

1181
01:00:55,360 --> 01:00:58,760
Speaker 1: thousand seven to thirteen oh so full six years of

1182
01:00:58,920 --> 01:01:01,200
Speaker 1: just me, just new LNG. Wait now when I say hobby,

1183
01:01:01,400 --> 01:01:04,440
Speaker 1: um uh, you know it started with me and my

1184
01:01:04,600 --> 01:01:08,480
Speaker 1: kids putting together robot blimp kids in the pizza boxes.

1185
01:01:09,240 --> 01:01:12,400
Speaker 1: Um did they ever get excited about the idea of drones?

1186
01:01:12,520 --> 01:01:16,560
Speaker 1: That never? Never, And you have some crazy cool stuff here,

1187
01:01:16,600 --> 01:01:20,160
Speaker 1: even this stuff they're like, who cares? Yeah, no, I know,

1188
01:01:20,640 --> 01:01:24,560
Speaker 1: you know you got any more kids? I brought one

1189
01:01:24,600 --> 01:01:27,520
Speaker 1: and he's walking around, eyes bugging out of his head, going,

1190
01:01:27,520 --> 01:01:29,520
Speaker 1: oh my god, look at all this stuff. But he's

1191
01:01:29,560 --> 01:01:33,840
Speaker 1: in his thirties, so this is he's the right age.

1192
01:01:33,840 --> 01:01:38,440
Speaker 1: I think they're spoiled that. Let's talk about Silicon Valley

1193
01:01:38,600 --> 01:01:42,640
Speaker 1: and the so called bubble and unicorn. So I always

1194
01:01:42,640 --> 01:01:46,680
Speaker 1: hate when people pull out the most extreme example of

1195
01:01:46,760 --> 01:01:50,280
Speaker 1: something and try and paint everybody with that brush. Is

1196
01:01:50,320 --> 01:01:55,400
Speaker 1: there a bubble and Silicon Valley today? I'm I'm the

1197
01:01:55,440 --> 01:01:59,960
Speaker 1: worst person to ask, um, So actually know it to

1198
01:02:00,280 --> 01:02:05,520
Speaker 1: evaluations really just it's it's not how we keep Doesn't

1199
01:02:05,520 --> 01:02:10,160
Speaker 1: it affect the ability to either fine talent higher source

1200
01:02:10,280 --> 01:02:12,960
Speaker 1: things that when things get crazy, it's like, hey, we

1201
01:02:13,040 --> 01:02:16,080
Speaker 1: can't hire anybody's engineers are asking for four hundred thousand

1202
01:02:16,080 --> 01:02:19,120
Speaker 1: as a base seller. Doesn't the when things get really

1203
01:02:19,160 --> 01:02:22,520
Speaker 1: out of hand doesn't affect everything? It must? I mean,

1204
01:02:22,520 --> 01:02:25,880
Speaker 1: I'm sure you're right, um. I mean the engineers that

1205
01:02:26,000 --> 01:02:29,520
Speaker 1: I've worked with in the past are motivated primarily by

1206
01:02:29,920 --> 01:02:33,280
Speaker 1: the work. They want to do the most exciting work

1207
01:02:33,280 --> 01:02:35,040
Speaker 1: of their career. I mean, there was a while when

1208
01:02:35,040 --> 01:02:39,840
Speaker 1: our recruiting technique consisted of the words flying robot, do

1209
01:02:39,920 --> 01:02:44,360
Speaker 1: I sign? Yeah? Exactly. Um, I mean, I'm sure that

1210
01:02:44,480 --> 01:02:47,880
Speaker 1: at some level the ability to you know, have upside

1211
01:02:47,880 --> 01:02:50,360
Speaker 1: in your in your stock options or sorary. I mean,

1212
01:02:50,400 --> 01:02:52,880
Speaker 1: of course that's a that's a thing, but it should

1213
01:02:52,880 --> 01:02:55,520
Speaker 1: be a second order um thing. I mean, the reason

1214
01:02:55,840 --> 01:02:59,440
Speaker 1: that you know, the Tesla's and ubers are able to

1215
01:03:00,000 --> 01:03:01,800
Speaker 1: there is a special case, let's say, the Teslas of

1216
01:03:01,840 --> 01:03:04,120
Speaker 1: the world where apples are able to hire so well

1217
01:03:04,360 --> 01:03:07,439
Speaker 1: is because you're going to do really exciting, meaningful work.

1218
01:03:08,120 --> 01:03:11,240
Speaker 1: On the way up here, we passed the Tesla factory

1219
01:03:11,440 --> 01:03:14,520
Speaker 1: and it's enormous, and it's just like, wait, what was

1220
01:03:14,560 --> 01:03:18,040
Speaker 1: there before? This was? Oh, that was just a field. No,

1221
01:03:18,280 --> 01:03:19,919
Speaker 1: it was actually no, it's a it's a great story.

1222
01:03:20,200 --> 01:03:22,800
Speaker 1: Is a really good this American Life episode. It was

1223
01:03:22,880 --> 01:03:25,480
Speaker 1: a new me. Um, the GM Toyota, I think, um

1224
01:03:26,960 --> 01:03:30,000
Speaker 1: the future of of car manufacturing. Well, it turned out

1225
01:03:30,000 --> 01:03:32,840
Speaker 1: to turn out there that's the future of it could

1226
01:03:32,880 --> 01:03:35,840
Speaker 1: be the future of cars. People don't give give enough

1227
01:03:35,840 --> 01:03:38,000
Speaker 1: credit for that particular one. He was able to buy

1228
01:03:38,040 --> 01:03:40,439
Speaker 1: that factory. It's a mile long, right, he was able

1229
01:03:40,440 --> 01:03:43,480
Speaker 1: to buy that factory for um, you know, for pennies

1230
01:03:43,520 --> 01:03:46,200
Speaker 1: on the dollar before there was any real case, and

1231
01:03:46,240 --> 01:03:49,280
Speaker 1: originally tested was like a tiny corner like one factory

1232
01:03:49,640 --> 01:03:52,080
Speaker 1: at the end. By the end, by the time he's done,

1233
01:03:52,080 --> 01:03:53,920
Speaker 1: he'll be using it, all of it and more. And

1234
01:03:53,920 --> 01:03:56,680
Speaker 1: then brought all that equipment as well for again pennies

1235
01:03:56,720 --> 01:04:00,320
Speaker 1: on the dollar. That that was so the original slow

1236
01:04:00,440 --> 01:04:03,520
Speaker 1: was buying the Lotus, taking the engine out and filling

1237
01:04:03,880 --> 01:04:07,400
Speaker 1: the floor of the car with batteries essentially, And this

1238
01:04:07,440 --> 01:04:09,680
Speaker 1: is where they're actually making the cars, and it's it's

1239
01:04:09,760 --> 01:04:14,800
Speaker 1: astonishing to drive by. It's gleaming and amazingly amazingly huge.

1240
01:04:14,960 --> 01:04:16,480
Speaker 1: And then you know, keep going, you know you'll get

1241
01:04:16,480 --> 01:04:20,520
Speaker 1: to the solar City bit and battery factory, Gigga factory.

1242
01:04:20,600 --> 01:04:23,280
Speaker 1: You keep driving south and you'll get to SpaceX. So

1243
01:04:23,320 --> 01:04:25,560
Speaker 1: there's some cool stuff happening after here. And by the way,

1244
01:04:25,560 --> 01:04:27,320
Speaker 1: you don't have to drive because he's digging a tunnel

1245
01:04:27,360 --> 01:04:29,520
Speaker 1: between the two the hyper loop. Is that really gonna

1246
01:04:29,560 --> 01:04:32,640
Speaker 1: I'm not think that the tunnel hyper different thing. Really, Yeah,

1247
01:04:32,800 --> 01:04:35,400
Speaker 1: I can't keep it's not actually taking a tunnel between

1248
01:04:35,400 --> 01:04:37,280
Speaker 1: the two. But would it would be fun if he

1249
01:04:37,440 --> 01:04:41,040
Speaker 1: if he was Um, so you don't really think about

1250
01:04:41,920 --> 01:04:49,120
Speaker 1: valuation or bubbles. What motivates people who are attracted to

1251
01:04:49,200 --> 01:04:52,120
Speaker 1: this part of the part of the world. What what

1252
01:04:52,280 --> 01:04:55,360
Speaker 1: is the driving factor? There are vast riches here, but

1253
01:04:55,400 --> 01:05:00,360
Speaker 1: there's some really cool stuff here. What what's the dominant

1254
01:05:00,400 --> 01:05:02,920
Speaker 1: meme out here? Well, you know, I did, I mean,

1255
01:05:02,960 --> 01:05:06,600
Speaker 1: I don't dominant meme is is saying a lot. I mean,

1256
01:05:06,640 --> 01:05:08,560
Speaker 1: I think in general, and it sounds like a cliche.

1257
01:05:09,120 --> 01:05:11,120
Speaker 1: You know, the idea that this goes right back to

1258
01:05:11,120 --> 01:05:13,240
Speaker 1: the foundings of Wired, The idea that can technology that

1259
01:05:13,320 --> 01:05:17,000
Speaker 1: can change the world continues to resonate. Um. You know,

1260
01:05:17,040 --> 01:05:19,240
Speaker 1: Elon Musk is kind of everybody's hero. But you know,

1261
01:05:19,280 --> 01:05:22,520
Speaker 1: Jeff Bezos and you know Larry and Saragei from from

1262
01:05:22,560 --> 01:05:25,960
Speaker 1: Google are all in the same category. They are tackling

1263
01:05:25,960 --> 01:05:29,400
Speaker 1: the big problems. This is the environment, food, life off

1264
01:05:29,480 --> 01:05:32,280
Speaker 1: the planet, the future of transportation of energy. I mean,

1265
01:05:32,320 --> 01:05:34,320
Speaker 1: this is like, you know, say nothing of biotech and

1266
01:05:34,400 --> 01:05:37,360
Speaker 1: medicine and all that. Um, people feel like they're doing

1267
01:05:37,400 --> 01:05:40,680
Speaker 1: meaningful work. It certainly makes makes a lot of sense.

1268
01:05:41,640 --> 01:05:44,800
Speaker 1: Let's talk about all these private companies that have chosen

1269
01:05:45,080 --> 01:05:48,560
Speaker 1: not to go public. What do you again, I look

1270
01:05:48,600 --> 01:05:52,920
Speaker 1: at you as somebody who is a astute observer of

1271
01:05:52,960 --> 01:05:57,160
Speaker 1: the technology seen around you. So even if you don't

1272
01:05:57,160 --> 01:06:00,840
Speaker 1: necessarily care about valuation, what do you make of these companies?

1273
01:06:01,240 --> 01:06:03,840
Speaker 1: Go down the list of unicorns that have these huge

1274
01:06:03,960 --> 01:06:06,760
Speaker 1: valuations and just seem to show no interest in I

1275
01:06:06,920 --> 01:06:09,880
Speaker 1: p owing. What's the exit for the vcs and and

1276
01:06:10,080 --> 01:06:15,320
Speaker 1: what is the reluctance to cash out or raise more capital.

1277
01:06:15,640 --> 01:06:18,440
Speaker 1: I guess for someone like Uber they don't need more capital. Well,

1278
01:06:18,440 --> 01:06:21,280
Speaker 1: the reluctance to go public is obvious, which is there's

1279
01:06:21,320 --> 01:06:23,640
Speaker 1: that it's a hassle to be a publicly traded company. Yeah,

1280
01:06:23,640 --> 01:06:27,960
Speaker 1: but it's how you, as an individual get liquid. It's

1281
01:06:28,000 --> 01:06:31,160
Speaker 1: how your backers. Well as an individual, there's lots of

1282
01:06:31,160 --> 01:06:33,800
Speaker 1: ways to go to get liquid. You know, a Travis

1283
01:06:33,840 --> 01:06:36,680
Speaker 1: you know, there's there's plenty of secondary markets for that.

1284
01:06:37,080 --> 01:06:39,680
Speaker 1: It is for your employees, it's a little it's a

1285
01:06:39,720 --> 01:06:41,840
Speaker 1: little tougher, and you know, there's been a lot of

1286
01:06:41,880 --> 01:06:43,720
Speaker 1: me you will you'll probably you know, maybe you can

1287
01:06:43,760 --> 01:06:46,000
Speaker 1: advise me on this. It's been all this talk of like,

1288
01:06:46,040 --> 01:06:49,120
Speaker 1: you know, secondary markets that liquid, secondary markets that could

1289
01:06:49,120 --> 01:06:53,320
Speaker 1: allow people, employees, et cetera, to get liquid Um, they've

1290
01:06:53,360 --> 01:06:57,840
Speaker 1: always they've never really come to fition. Course. Well, part

1291
01:06:57,880 --> 01:07:00,160
Speaker 1: of the reason is when you're you go public, you

1292
01:07:00,200 --> 01:07:02,800
Speaker 1: go through the SEC process, You go through this and

1293
01:07:02,880 --> 01:07:06,400
Speaker 1: there's a very standard set of ways of you're locked

1294
01:07:06,480 --> 01:07:08,000
Speaker 1: up for this period of time, and now if you

1295
01:07:08,000 --> 01:07:10,240
Speaker 1: want to sell some or all of your stock, here's

1296
01:07:10,280 --> 01:07:15,400
Speaker 1: the methodology. The various secondary markets simply aren't big and

1297
01:07:15,520 --> 01:07:18,280
Speaker 1: deep and liquid enough to do that for every company

1298
01:07:18,320 --> 01:07:21,120
Speaker 1: that wants to go public or doesn't want to go.

1299
01:07:21,120 --> 01:07:23,720
Speaker 1: But that's right, and then you you also end up

1300
01:07:23,760 --> 01:07:27,360
Speaker 1: with the venture capital backers and the original founders who

1301
01:07:27,400 --> 01:07:31,160
Speaker 1: either put up money or sweat equity or both. It's

1302
01:07:31,240 --> 01:07:35,760
Speaker 1: hard to see how they capitalize on the success without

1303
01:07:35,800 --> 01:07:38,280
Speaker 1: going So is this I look at this as a

1304
01:07:38,280 --> 01:07:41,560
Speaker 1: little bit of a Koy dance and Uber will eventually

1305
01:07:41,600 --> 01:07:44,160
Speaker 1: go public, as will Lift as well all the other

1306
01:07:44,280 --> 01:07:47,520
Speaker 1: unicorns or am I you know old school? Well I

1307
01:07:47,520 --> 01:07:49,920
Speaker 1: wouldn't see the whole thing through the lens of unicorns.

1308
01:07:49,920 --> 01:07:52,520
Speaker 1: I mean, obviously, have a multibillion dollar valuation, you know,

1309
01:07:52,560 --> 01:07:54,440
Speaker 1: there aren't a lot of ways to get liquid. But

1310
01:07:54,440 --> 01:07:56,680
Speaker 1: if you have a lower valuation hundreds of millions or

1311
01:07:56,720 --> 01:07:58,960
Speaker 1: something like that, then m and a is a perfectly

1312
01:07:59,000 --> 01:08:01,880
Speaker 1: reasonable short to do it. And so I would I

1313
01:08:01,880 --> 01:08:05,000
Speaker 1: would say that you know, the the you know there

1314
01:08:05,040 --> 01:08:08,280
Speaker 1: are when you look at our investors in particular combinations

1315
01:08:08,280 --> 01:08:11,440
Speaker 1: strategics like at a desk and qualcom and then venture capitalists,

1316
01:08:11,720 --> 01:08:15,320
Speaker 1: they're all looking at creating big value down the line.

1317
01:08:15,360 --> 01:08:17,640
Speaker 1: Either it's you know, like you're looking at construction and

1318
01:08:17,760 --> 01:08:20,320
Speaker 1: we have the opportunity to be part of the you know,

1319
01:08:20,400 --> 01:08:23,360
Speaker 1: transformation of the second biggest industry in the world. Um.

1320
01:08:23,400 --> 01:08:25,519
Speaker 1: You know, if we get this right, there will be

1321
01:08:25,560 --> 01:08:28,599
Speaker 1: any number of companies that would want to buy us.

1322
01:08:28,600 --> 01:08:31,040
Speaker 1: We may not choose not to sell um. But but

1323
01:08:31,080 --> 01:08:33,080
Speaker 1: the notion of when you're creating value, you have you

1324
01:08:33,400 --> 01:08:36,320
Speaker 1: open up optionality. That's the thing. Now, you don't want

1325
01:08:36,360 --> 01:08:38,720
Speaker 1: to create your valuation so high that you that there's

1326
01:08:39,240 --> 01:08:40,479
Speaker 1: you could have created a lot of value, but there's

1327
01:08:40,479 --> 01:08:43,200
Speaker 1: only like you know, three people, three countries, three companies

1328
01:08:43,200 --> 01:08:44,679
Speaker 1: in the world that could buy you, and they won't

1329
01:08:44,800 --> 01:08:47,599
Speaker 1: because then you have to go public. UM. So it's

1330
01:08:47,640 --> 01:08:49,400
Speaker 1: it's a it's a delicate balancing act. But if you

1331
01:08:49,479 --> 01:08:51,519
Speaker 1: created value and if you get taken the time to

1332
01:08:51,560 --> 01:08:55,000
Speaker 1: sort of take a tackle a big, big industry. How

1333
01:08:55,040 --> 01:08:59,479
Speaker 1: far are we from flying robots really being uh an

1334
01:08:59,520 --> 01:09:03,360
Speaker 1: everyday part of life, ordinary life. You mentioned Bezos. There

1335
01:09:03,439 --> 01:09:05,280
Speaker 1: was a lot of buzz when he talked about using

1336
01:09:05,360 --> 01:09:09,760
Speaker 1: drones as as delivery vehicles. How serious is that? Are

1337
01:09:09,800 --> 01:09:12,640
Speaker 1: we eventually going to order something from Amazon and a

1338
01:09:12,760 --> 01:09:14,639
Speaker 1: drone is going to drop it at our house? Well,

1339
01:09:14,680 --> 01:09:17,479
Speaker 1: you know, so Bezos has the delivery. But there's also

1340
01:09:17,720 --> 01:09:20,840
Speaker 1: Uber and others are talking about air taxis. Um, you

1341
01:09:20,880 --> 01:09:23,840
Speaker 1: know we're talking about you know, drones on every construction

1342
01:09:23,920 --> 01:09:26,880
Speaker 1: side or every farm, et cetera. Um, you know, of

1343
01:09:26,920 --> 01:09:29,920
Speaker 1: all of those, I think actually sing drones as a

1344
01:09:29,960 --> 01:09:33,479
Speaker 1: conventional part of you know, construction or agriculture is probably

1345
01:09:33,520 --> 01:09:37,880
Speaker 1: the nearest. My grandfather, UM was the inventor of the

1346
01:09:37,880 --> 01:09:41,960
Speaker 1: automatic sprinkler system, and um he was my inspiration the

1347
01:09:42,000 --> 01:09:45,920
Speaker 1: automatic sprinkler system meaning if I have if I have

1348
01:09:46,000 --> 01:09:49,320
Speaker 1: timer really yeah, So he was a He was a

1349
01:09:49,360 --> 01:09:53,320
Speaker 1: Swiss engineer in Los Angeles working to bring the talkies.

1350
01:09:53,520 --> 01:09:55,599
Speaker 1: So you come from a long line of makers. In

1351
01:09:55,640 --> 01:10:00,680
Speaker 1: other words, one um, two generations back. That's pretty pretty

1352
01:10:00,800 --> 01:10:04,840
Speaker 1: good ancestry for exactly. It was certainly mind spirence. I

1353
01:10:04,880 --> 01:10:06,680
Speaker 1: spent my summers as it was kind of a rotten kid,

1354
01:10:06,680 --> 01:10:08,320
Speaker 1: and so I was sent to spend my summers with

1355
01:10:08,360 --> 01:10:12,320
Speaker 1: my grandfather and we just spent the summer silently working

1356
01:10:12,360 --> 01:10:14,559
Speaker 1: in the in the workshop. But what I love about

1357
01:10:14,560 --> 01:10:17,040
Speaker 1: the sprinkler, sprinkler is my inspiration for drones. You may

1358
01:10:17,040 --> 01:10:18,759
Speaker 1: not think so. I mean, I could see the connection.

1359
01:10:18,800 --> 01:10:21,120
Speaker 1: But a sprinkler is a robot. You know, sprinkler is

1360
01:10:21,120 --> 01:10:23,720
Speaker 1: connected to the internet, has got sensors. It's you know,

1361
01:10:23,760 --> 01:10:26,000
Speaker 1: it comes up mechanically, does its own thing, goes back.

1362
01:10:26,040 --> 01:10:27,960
Speaker 1: And well, I love about is how boring it is.

1363
01:10:28,479 --> 01:10:30,519
Speaker 1: You just put your sprinklers and you live in New York,

1364
01:10:30,560 --> 01:10:32,320
Speaker 1: so he probably no, I live in the burbs, have

1365
01:10:32,400 --> 01:10:36,360
Speaker 1: underground sprinklers. Absolutely, you're sticking war zones the rain sensors

1366
01:10:36,439 --> 01:10:38,400
Speaker 1: so it doesn't go on in its raining. It's an

1367
01:10:38,439 --> 01:10:41,760
Speaker 1: interesting little technology. It just kind of works, right, Yeah, yeah, yeah,

1368
01:10:42,000 --> 01:10:44,560
Speaker 1: to take it for granted, So I would that's that

1369
01:10:44,960 --> 01:10:47,599
Speaker 1: is the sign of success. So if someday drones are

1370
01:10:47,640 --> 01:10:50,559
Speaker 1: as boring as sprinklers, which is to say, there's a box,

1371
01:10:51,000 --> 01:10:52,720
Speaker 1: there's a box on the farm, there's a box on

1372
01:10:52,680 --> 01:10:55,920
Speaker 1: the construction site, whatever, And every morning it's like eight

1373
01:10:56,000 --> 01:10:59,040
Speaker 1: thirty a m. The box opens. The it's not a

1374
01:10:59,080 --> 01:11:01,439
Speaker 1: sprinkle coming out as a drone coming out. It surveys this,

1375
01:11:02,080 --> 01:11:05,280
Speaker 1: but it goes hone, charges itself and waits for the

1376
01:11:05,320 --> 01:11:08,439
Speaker 1: next morning. And this whole thing is completely automated. And

1377
01:11:08,520 --> 01:11:10,479
Speaker 1: you and and we I would love to be covered

1378
01:11:10,479 --> 01:11:13,600
Speaker 1: in mud. I'd like that stickers like Union stickers on it,

1379
01:11:13,720 --> 01:11:17,839
Speaker 1: something like that, you know, just absolutely conventional construction equipment.

1380
01:11:18,160 --> 01:11:19,800
Speaker 1: And no one cares about it because all they care

1381
01:11:19,840 --> 01:11:22,320
Speaker 1: about the data. What they know is that every morning

1382
01:11:22,600 --> 01:11:25,840
Speaker 1: they have a you know, an up to date, super

1383
01:11:25,960 --> 01:11:29,439
Speaker 1: high resolution three D two D model of the site

1384
01:11:29,880 --> 01:11:32,680
Speaker 1: they digitized. The site was just automatically digitized in the

1385
01:11:32,720 --> 01:11:36,919
Speaker 1: cloud every day. Now, that is not something that's decades

1386
01:11:37,000 --> 01:11:40,320
Speaker 1: or years off in the future. It's months or weeks away.

1387
01:11:40,439 --> 01:11:43,919
Speaker 1: We could do it today today. The only thing stopping

1388
01:11:44,000 --> 01:11:48,799
Speaker 1: us is that um f A A regulations currently require

1389
01:11:48,840 --> 01:11:52,280
Speaker 1: a pilot in command. Now the pilot that this is

1390
01:11:52,320 --> 01:11:53,920
Speaker 1: the p I C. The pilot command doesn't have to

1391
01:11:53,960 --> 01:11:55,719
Speaker 1: be a pilot doesn't actually have to be in command,

1392
01:11:55,840 --> 01:11:57,840
Speaker 1: but there has to be human present. And when I

1393
01:11:57,920 --> 01:11:59,400
Speaker 1: meant when I said that, the way it works that

1394
01:11:59,400 --> 01:12:02,360
Speaker 1: you open up your iPad and just swipe. That's swiping

1395
01:12:02,400 --> 01:12:07,640
Speaker 1: acts acquired by regulation. So it can't be so it

1396
01:12:07,680 --> 01:12:10,240
Speaker 1: can't quite be like a roomba. Not that I have

1397
01:12:10,280 --> 01:12:13,000
Speaker 1: a room, but but the way I understand it is

1398
01:12:13,560 --> 01:12:15,439
Speaker 1: it knows where it's to home, dock is and how

1399
01:12:15,479 --> 01:12:17,240
Speaker 1: to charge. It goes and does its thing and then

1400
01:12:17,280 --> 01:12:20,719
Speaker 1: it returns and it does it regularly, like the irrigation system.

1401
01:12:20,800 --> 01:12:24,479
Speaker 1: So the regulatory reason you need a human to to

1402
01:12:24,640 --> 01:12:26,800
Speaker 1: push a button. Yeah, so so our our our drones

1403
01:12:26,840 --> 01:12:28,720
Speaker 1: are fully autonomous. They do all that stuff all on

1404
01:12:28,720 --> 01:12:32,080
Speaker 1: their own. The only including coming back to a charging

1405
01:12:32,280 --> 01:12:34,519
Speaker 1: doc it can be it's a pad, and we don't

1406
01:12:34,560 --> 01:12:37,080
Speaker 1: happen to use the pad because because since we have

1407
01:12:37,120 --> 01:12:39,040
Speaker 1: a human there, we might as well to use them

1408
01:12:39,040 --> 01:12:41,200
Speaker 1: to change the batter. Um. But but yeah, such a

1409
01:12:41,200 --> 01:12:44,360
Speaker 1: thing exists. You can buy charging charging docks or pads

1410
01:12:44,360 --> 01:12:46,960
Speaker 1: and things like that. Um, but no the so, so

1411
01:12:47,000 --> 01:12:49,880
Speaker 1: what we're talking about is autonomy, the notion of taking

1412
01:12:49,880 --> 01:12:51,559
Speaker 1: the human out of the loop. And it's simply a

1413
01:12:51,600 --> 01:12:54,360
Speaker 1: regulatory thing. So we work with the FAA to figure

1414
01:12:54,360 --> 01:12:57,320
Speaker 1: out what what would it take to allow them, So

1415
01:12:57,400 --> 01:12:59,920
Speaker 1: like right now has to be one person, one human,

1416
01:13:00,360 --> 01:13:04,920
Speaker 1: one drone. What about one human drones? What about one

1417
01:13:05,000 --> 01:13:09,360
Speaker 1: human global fleet of drones monitored remotely. Turns out that

1418
01:13:09,360 --> 01:13:11,320
Speaker 1: that actually, you know, you can sit there on the

1419
01:13:11,320 --> 01:13:13,439
Speaker 1: ground and you know you're in command, but you're not.

1420
01:13:13,520 --> 01:13:17,120
Speaker 1: You're you're not you're checking your email, you're not looking drones.

1421
01:13:17,200 --> 01:13:19,800
Speaker 1: The drones got much better perceptual powers than you have.

1422
01:13:20,040 --> 01:13:23,519
Speaker 1: You're there, it's over there. It has cameras all over

1423
01:13:23,560 --> 01:13:26,640
Speaker 1: the place. That's sensors. It's got you know, radar and

1424
01:13:26,800 --> 01:13:29,639
Speaker 1: sonar and light are and all this kind of stuff.

1425
01:13:29,840 --> 01:13:33,000
Speaker 1: How about just let the drone, you know, stay, it

1426
01:13:33,160 --> 01:13:35,360
Speaker 1: turns away and if it's got an issue, it can

1427
01:13:35,360 --> 01:13:38,040
Speaker 1: alert you and tell you to pay attention to make sense.

1428
01:13:38,080 --> 01:13:40,080
Speaker 1: But in the meantime, why don't you do what you're

1429
01:13:40,080 --> 01:13:43,880
Speaker 1: best at, which is answering whatever it was exactly. So

1430
01:13:43,920 --> 01:13:45,800
Speaker 1: that's the process we're going through with the f A

1431
01:13:46,160 --> 01:13:48,400
Speaker 1: to figure out what what sort of evidence they need

1432
01:13:48,439 --> 01:13:51,639
Speaker 1: to allow that kind of autonomy. And that's my assumption

1433
01:13:51,720 --> 01:13:53,479
Speaker 1: is that's not all that far off in the field.

1434
01:13:53,640 --> 01:13:55,639
Speaker 1: I mean, I think when you're talking about places like farms,

1435
01:13:56,000 --> 01:13:58,639
Speaker 1: which is private land away from people, I think that's

1436
01:13:58,640 --> 01:14:01,040
Speaker 1: going to be a lower density againstity. You're talking about

1437
01:14:01,120 --> 01:14:03,960
Speaker 1: going beyond visual minus site, which is particular phrase. So

1438
01:14:04,000 --> 01:14:06,800
Speaker 1: I think it's gonna be bits where it's accepted first.

1439
01:14:06,840 --> 01:14:09,679
Speaker 1: And when getting back to delivery, um, you know the Amazon,

1440
01:14:10,040 --> 01:14:12,880
Speaker 1: you know, original video delivering in your suburbs, that's that's

1441
01:14:12,920 --> 01:14:17,400
Speaker 1: hard dogs kids, telephone lines, treats. But delivering to a

1442
01:14:17,520 --> 01:14:20,120
Speaker 1: rural area that's quite expensive for the postal service to

1443
01:14:20,160 --> 01:14:22,880
Speaker 1: get to. That actually makes more sense. Do we have

1444
01:14:22,920 --> 01:14:25,400
Speaker 1: the battery life to send something fifty miles out into

1445
01:14:25,400 --> 01:14:28,760
Speaker 1: a farming back? It seems fixed wing? An airplane you

1446
01:14:28,800 --> 01:14:32,760
Speaker 1: know said about you know, consumer drones or consumer sized

1447
01:14:32,800 --> 01:14:35,640
Speaker 1: drones flo across the Atlantic. Oh really, yeah, you know,

1448
01:14:35,680 --> 01:14:37,920
Speaker 1: so you doesn't have to elect consumer side like three

1449
01:14:37,920 --> 01:14:40,000
Speaker 1: ft across. I get like an airplane that's like a

1450
01:14:40,439 --> 01:14:43,000
Speaker 1: three ft four ft wingspans that it doesn't have to

1451
01:14:43,000 --> 01:14:46,040
Speaker 1: be a battery, but you can't use a gas power. Um,

1452
01:14:46,080 --> 01:14:48,680
Speaker 1: so yeah, it's it's not a problem. So I want

1453
01:14:48,720 --> 01:14:51,439
Speaker 1: to talk about one of the Ted talks you did. Well,

1454
01:14:51,479 --> 01:14:59,360
Speaker 1: you talk about four key stages of viable technology, right price,

1455
01:15:00,200 --> 01:15:07,520
Speaker 1: market share, the displacement of existing technology, and finally becoming ubiquitous.

1456
01:15:08,080 --> 01:15:11,600
Speaker 1: Is that four stage process something that's pretty standard, or

1457
01:15:11,640 --> 01:15:14,720
Speaker 1: do I have them wrong? Chris Anderson? I get that

1458
01:15:14,760 --> 01:15:17,200
Speaker 1: a lot. So is the joke you're making is that

1459
01:15:17,520 --> 01:15:21,200
Speaker 1: the curator of of TED is also named Chris Anderson. Here,

1460
01:15:21,600 --> 01:15:24,240
Speaker 1: how big of a pain and necka is having someone

1461
01:15:24,280 --> 01:15:26,680
Speaker 1: else in tech one plus one equals three. We have

1462
01:15:26,840 --> 01:15:30,080
Speaker 1: we have a mutual non destruction pact. Okay, that's promised

1463
01:15:30,120 --> 01:15:32,240
Speaker 1: not to ruin the Chris Anderson brand. There you go

1464
01:15:33,080 --> 01:15:37,200
Speaker 1: befward each other each other's misdirected emails without you do

1465
01:15:37,200 --> 01:15:40,560
Speaker 1: you guys get each other. So let me a quick digression.

1466
01:15:41,600 --> 01:15:44,080
Speaker 1: My last name is spelled O L t Z. We

1467
01:15:44,200 --> 01:15:47,519
Speaker 1: have cousins who don't have the second T. So I

1468
01:15:47,600 --> 01:15:50,000
Speaker 1: have a cousin named Barry Ridholts r I T H

1469
01:15:50,080 --> 01:15:53,639
Speaker 1: O l Z. He has a kid, brother, Richard, as

1470
01:15:53,680 --> 01:15:56,600
Speaker 1: do I. So there's two Barry and Richie ridholts Is.

1471
01:15:57,160 --> 01:15:59,080
Speaker 1: At one point in time, we both had offices in

1472
01:15:59,160 --> 01:16:02,640
Speaker 1: three parts and we would get each other's male and

1473
01:16:02,640 --> 01:16:06,800
Speaker 1: it was pretty hilarious. Now I'm his nightmare because he

1474
01:16:06,840 --> 01:16:09,400
Speaker 1: has to deal with me in the media and he

1475
01:16:09,400 --> 01:16:13,439
Speaker 1: he's a lawyer at a mutual fund company. So is

1476
01:16:13,479 --> 01:16:15,960
Speaker 1: one of us is a little higher profile than the other,

1477
01:16:16,040 --> 01:16:19,439
Speaker 1: so he has the bigger headache. How does this work

1478
01:16:19,800 --> 01:16:23,000
Speaker 1: with you and Chris Anderson? Shank and he's a player too,

1479
01:16:23,680 --> 01:16:27,360
Speaker 1: let's sell. So so you're both spelled with a C though,

1480
01:16:28,479 --> 01:16:33,680
Speaker 1: it's so, do you really get each other's I think

1481
01:16:33,720 --> 01:16:36,559
Speaker 1: he's full on British? He lived there for a long time.

1482
01:16:36,720 --> 01:16:40,560
Speaker 1: You were born in London. So is this a genuine

1483
01:16:41,240 --> 01:16:45,080
Speaker 1: like does genuine confusion happen with this? Um? Yeah, no,

1484
01:16:45,160 --> 01:16:47,040
Speaker 1: I mean but in the best sort of way, which

1485
01:16:47,080 --> 01:16:49,240
Speaker 1: is like people give it, give us credit for each

1486
01:16:49,280 --> 01:16:51,960
Speaker 1: other's work. It's like, you know, Chris, you know you've

1487
01:16:51,960 --> 01:16:55,040
Speaker 1: done something. You've written these books Wired three, dr Andy

1488
01:16:55,160 --> 01:16:58,160
Speaker 1: run Ted, how do you do it? It's like, well,

1489
01:16:58,200 --> 01:17:02,240
Speaker 1: you know, I have a clothe that that's hilarious. So

1490
01:17:02,960 --> 01:17:06,839
Speaker 1: the four stages of tech, any viable technology, right price,

1491
01:17:06,960 --> 01:17:12,360
Speaker 1: market share, displacing established tech, and then eventually becoming ubiquitous.

1492
01:17:12,840 --> 01:17:16,160
Speaker 1: Is that the process that we always see with technology,

1493
01:17:16,280 --> 01:17:20,000
Speaker 1: or at least for the foreseeable future. Sure, Barry, I

1494
01:17:20,080 --> 01:17:22,519
Speaker 1: have to admit I can't even remember that talk. Really,

1495
01:17:22,720 --> 01:17:24,920
Speaker 1: I'm sure you've done it several of them. Yeah, I'm

1496
01:17:24,960 --> 01:17:27,439
Speaker 1: sure it was inspired. The only reason that talk is

1497
01:17:27,960 --> 01:17:31,640
Speaker 1: on YouTube. Is that my other talks were abject failures

1498
01:17:31,920 --> 01:17:34,280
Speaker 1: And it turns out not all talks get Get, Get,

1499
01:17:34,360 --> 01:17:37,200
Speaker 1: Get put on the website. Yeah, my other ones were

1500
01:17:37,200 --> 01:17:40,519
Speaker 1: tech demos, right and fresh and Burn, Fresh and Burn. Oh,

1501
01:17:40,560 --> 01:17:43,960
Speaker 1: that's that's hilarious. We have been speaking with Chris Anderson.

1502
01:17:44,040 --> 01:17:46,720
Speaker 1: He is the founder and CEO of three D Robotics,

1503
01:17:47,160 --> 01:17:51,439
Speaker 1: as well as the author of the long tail Makers

1504
01:17:51,640 --> 01:17:55,280
Speaker 1: and Free. If you enjoy this conversation, be sure and

1505
01:17:55,360 --> 01:17:57,639
Speaker 1: check out the podcast Actus Will we keep the tape

1506
01:17:57,720 --> 01:18:02,439
Speaker 1: rolling and continue chatting about all in digital technology. We

1507
01:18:02,560 --> 01:18:06,280
Speaker 1: love your comments, feedback and suggestions right to us at

1508
01:18:06,600 --> 01:18:10,240
Speaker 1: m IB podcast at Bloomberg dot net. Follow me on

1509
01:18:10,280 --> 01:18:12,880
Speaker 1: Twitter at Dhults. You can see my daily column at

1510
01:18:12,880 --> 01:18:16,679
Speaker 1: Bloomberg View dot com. I'm Barry Hults. You've been listening

1511
01:18:16,720 --> 01:18:32,240
Speaker 1: to Masters and Business on Bloomberg Radio. If you're having

1512
01:18:32,240 --> 01:18:35,360
Speaker 1: a business dispute, the process can be slow and drawn out,

1513
01:18:35,720 --> 01:18:38,640
Speaker 1: especially if you rely on litigation in the courts. You

1514
01:18:38,680 --> 01:18:41,200
Speaker 1: can wait for years before your case is resolved, and

1515
01:18:41,240 --> 01:18:44,519
Speaker 1: the longer your case proceeds, the more your case can cost.

1516
01:18:45,040 --> 01:18:49,519
Speaker 1: Not with the American Arbitration Association arbitration or mediation with

1517
01:18:49,600 --> 01:18:53,679
Speaker 1: the American Arbitration Association is faster. In fact, nearly fifty

1518
01:18:54,040 --> 01:18:57,240
Speaker 1: of our cases settled prior to hearings. A d r

1519
01:18:57,320 --> 01:19:05,360
Speaker 1: dot org resolve faster. Welcome to the podcast, everybody. This

1520
01:19:05,400 --> 01:19:08,240
Speaker 1: is really fascinating stuff. I'm having a great time here,

1521
01:19:08,240 --> 01:19:09,800
Speaker 1: and thank you for the drone I get to take

1522
01:19:09,840 --> 01:19:14,840
Speaker 1: home with Palati drones that were sending U And we

1523
01:19:14,840 --> 01:19:17,400
Speaker 1: didn't get to do a demo that we need. If

1524
01:19:17,800 --> 01:19:20,200
Speaker 1: I'll be late going into the city. If I don't,

1525
01:19:20,400 --> 01:19:21,920
Speaker 1: I don't have time. But if you want to hang around,

1526
01:19:22,000 --> 01:19:23,760
Speaker 1: the team will happily give you a demo. I have

1527
01:19:23,840 --> 01:19:26,120
Speaker 1: to be somewhere at twelve thirty, but I would love

1528
01:19:26,160 --> 01:19:28,800
Speaker 1: to and I we watched them take off and land

1529
01:19:28,800 --> 01:19:32,960
Speaker 1: before and it was just the return to home to land.

1530
01:19:33,479 --> 01:19:36,479
Speaker 1: It literally lands on the same square inch where it

1531
01:19:36,520 --> 01:19:39,880
Speaker 1: took off, right. It's amazing. So so let's get through

1532
01:19:39,920 --> 01:19:41,920
Speaker 1: some of these standard questions before you have to head

1533
01:19:42,000 --> 01:19:46,040
Speaker 1: into the city. The most interesting thing that people don't

1534
01:19:46,080 --> 01:19:50,840
Speaker 1: know about your background. Um failed out of college and

1535
01:19:50,960 --> 01:19:53,519
Speaker 1: played in punk rock bands for most of my failed

1536
01:19:53,760 --> 01:19:57,760
Speaker 1: Would you go to school? Well, I fail. I ultimately

1537
01:19:57,800 --> 01:20:00,840
Speaker 1: went to school to George Washington study that you see Berkeley,

1538
01:20:00,840 --> 01:20:02,800
Speaker 1: which is where we are here as well. Um, I

1539
01:20:02,880 --> 01:20:06,640
Speaker 1: failed out of school. I won't mention, um. But but

1540
01:20:06,680 --> 01:20:10,240
Speaker 1: the cool bit why is that? Why won't you mention it?

1541
01:20:10,280 --> 01:20:12,439
Speaker 1: Just because it's fair to the school? Okay it was

1542
01:20:12,680 --> 01:20:15,960
Speaker 1: it was on you, it wasn't on the yea. Um.

1543
01:20:16,000 --> 01:20:18,519
Speaker 1: But ultimately you got a college degree. Yeah. No, I

1544
01:20:18,560 --> 01:20:20,720
Speaker 1: went to I was I was as a playing punk

1545
01:20:20,800 --> 01:20:23,600
Speaker 1: rock bands. I was a bicycle messenger by day. And

1546
01:20:23,640 --> 01:20:27,559
Speaker 1: at age like twenty seven, Um, you know, finally get

1547
01:20:27,560 --> 01:20:30,479
Speaker 1: back to school and get a degree. Um. I was

1548
01:20:30,560 --> 01:20:33,240
Speaker 1: such a mess up that I decided to do the

1549
01:20:33,280 --> 01:20:35,759
Speaker 1: hardest thing possible just to convince people that I actually

1550
01:20:35,760 --> 01:20:38,639
Speaker 1: had a brain. So that's why I did physics. Um.

1551
01:20:38,680 --> 01:20:40,920
Speaker 1: But the cool bit is that one of the bands

1552
01:20:40,960 --> 01:20:43,760
Speaker 1: I played in was called R E. M. But not that,

1553
01:20:44,160 --> 01:20:47,160
Speaker 1: not that, And we had a we had a very

1554
01:20:47,200 --> 01:20:50,960
Speaker 1: funny we released our albums on the same week and

1555
01:20:51,360 --> 01:20:54,680
Speaker 1: it was this coincidental like, I mean, who knew who

1556
01:20:54,720 --> 01:20:56,720
Speaker 1: are em? Well they were like these hicks from the

1557
01:20:56,760 --> 01:21:00,280
Speaker 1: Sticks and we were like these the coolest band in Washington. See,

1558
01:21:00,320 --> 01:21:03,080
Speaker 1: I know it's a bit of a non secure um.

1559
01:21:03,120 --> 01:21:04,640
Speaker 1: And so our manager thought would be funny to have

1560
01:21:04,680 --> 01:21:06,600
Speaker 1: a battle of the R E M s where the

1561
01:21:06,640 --> 01:21:09,200
Speaker 1: winner got to rename the loser. And did that ever happen?

1562
01:21:09,360 --> 01:21:12,360
Speaker 1: It did? Indeed? Oh really? And what and what did

1563
01:21:12,360 --> 01:21:15,559
Speaker 1: they rename you? I'm gonna they renamed us a good

1564
01:21:15,600 --> 01:21:20,320
Speaker 1: assumption they need renamed us Ego Slavia. That's hilarious. Under

1565
01:21:20,320 --> 01:21:24,000
Speaker 1: which name we released our only album, Ego Slavia. Oh

1566
01:21:24,120 --> 01:21:27,559
Speaker 1: my god, that's that's hilarious. So let's talk about some

1567
01:21:27,600 --> 01:21:30,120
Speaker 1: of your early mentors, one of which I have to

1568
01:21:30,160 --> 01:21:33,200
Speaker 1: assume is your grandfather. After the story you subsolutely Fred

1569
01:21:33,240 --> 01:21:36,280
Speaker 1: Fred House or Swiss immigrant l A. You know, by

1570
01:21:36,360 --> 01:21:39,240
Speaker 1: day working on in the film industry, bring sound to

1571
01:21:39,560 --> 01:21:41,840
Speaker 1: you know, bring the talkies in, and by night I'm

1572
01:21:41,880 --> 01:21:43,960
Speaker 1: inventing the automatic sprinkler system, which, by the way, is

1573
01:21:44,000 --> 01:21:47,000
Speaker 1: exactly what a Swiss engineer in l A would do.

1574
01:21:47,160 --> 01:21:49,519
Speaker 1: L A is greening the desert. The Swiss engineer would

1575
01:21:49,520 --> 01:21:51,439
Speaker 1: put a clock on a sprinkler. Did he make a

1576
01:21:51,600 --> 01:21:53,519
Speaker 1: fortune in this or was it one of those things

1577
01:21:53,560 --> 01:21:57,840
Speaker 1: that the patent did? Fortune? Alright, hey, better than no fortune.

1578
01:21:58,160 --> 01:22:01,519
Speaker 1: That that's interesting? Who um who? Well? Any other mentors

1579
01:22:01,680 --> 01:22:07,280
Speaker 1: affected the way you think about either technology or writing. Um, well,

1580
01:22:07,320 --> 01:22:09,880
Speaker 1: you know Whired and it's and its founders had it

1581
01:22:09,960 --> 01:22:12,000
Speaker 1: had a huge influence on me. So you know Kevin

1582
01:22:12,120 --> 01:22:17,200
Speaker 1: Kelly in particular, Um, you know his book, well, Culturals

1583
01:22:17,320 --> 01:22:19,200
Speaker 1: is what he's you know, one of the things he's

1584
01:22:19,240 --> 01:22:22,120
Speaker 1: known for, but one of his original books, out of Control.

1585
01:22:22,560 --> 01:22:24,960
Speaker 1: So earlier in the conversation, we're talking about emergence and

1586
01:22:24,960 --> 01:22:28,360
Speaker 1: the notion of capitalism and democracy and the Internet being

1587
01:22:28,439 --> 01:22:32,080
Speaker 1: essentially emergent you know, um phenomena. Um, well, out of

1588
01:22:32,120 --> 01:22:35,400
Speaker 1: Control extends that to biological and more net kind of

1589
01:22:35,479 --> 01:22:39,080
Speaker 1: natural examples. I mean, the notion of emergent phenomena, the

1590
01:22:39,080 --> 01:22:42,960
Speaker 1: notion that there's some general principles physics or chemistry, whatever

1591
01:22:43,280 --> 01:22:45,240
Speaker 1: that just set these rules and then everything else all that,

1592
01:22:45,320 --> 01:22:49,920
Speaker 1: you know, life and civilization emerges from these principles. That's

1593
01:22:50,160 --> 01:22:52,519
Speaker 1: that was one of the most influential ideas of my

1594
01:22:52,880 --> 01:22:58,200
Speaker 1: of my formative years. Interesting, let's talk about who else

1595
01:22:58,320 --> 01:23:02,200
Speaker 1: influenced your approach to write, because you are a quite

1596
01:23:02,240 --> 01:23:06,360
Speaker 1: an accomplished writer, not just three books, you're a very

1597
01:23:06,400 --> 01:23:11,879
Speaker 1: influential writer. Who who shaped your approach to that aha

1598
01:23:11,960 --> 01:23:15,000
Speaker 1: moment and then turning it into Hey, this is an

1599
01:23:15,120 --> 01:23:17,479
Speaker 1: article in a magazine. Oh no, there's more here. Let's

1600
01:23:17,520 --> 01:23:19,559
Speaker 1: turn this into a book. So I don't think of mice.

1601
01:23:19,640 --> 01:23:22,200
Speaker 1: I mean, thank you for saying that. I m my

1602
01:23:22,280 --> 01:23:25,280
Speaker 1: parents were writers, and so I promised to do everything

1603
01:23:25,320 --> 01:23:29,120
Speaker 1: but that and I failed once again. We become, we

1604
01:23:29,240 --> 01:23:31,760
Speaker 1: become our parents. Is no way to avoid it and

1605
01:23:32,120 --> 01:23:36,679
Speaker 1: bite everything. Genetics actually is a thing. Um. So I

1606
01:23:36,680 --> 01:23:40,719
Speaker 1: I think of I mean, I I don't. I don't

1607
01:23:40,720 --> 01:23:42,639
Speaker 1: think of myself as a writer. I think of myself

1608
01:23:42,720 --> 01:23:44,439
Speaker 1: as as a you know, a guy who can sort

1609
01:23:44,479 --> 01:23:46,680
Speaker 1: of get capture ideas and get them out there. So

1610
01:23:47,000 --> 01:23:48,960
Speaker 1: as an editor, of course, you're not typically not writing.

1611
01:23:49,000 --> 01:23:53,360
Speaker 1: You're just packaging ideas. Companies package ideas, open source communities

1612
01:23:53,400 --> 01:23:55,200
Speaker 1: package ideas, and writing is one way to do it.

1613
01:23:55,400 --> 01:23:57,840
Speaker 1: I think the perhaps the most influential writer for me

1614
01:23:57,920 --> 01:24:01,200
Speaker 1: was that Nicholas Niggerponte and his um original book Being

1615
01:24:01,240 --> 01:24:07,400
Speaker 1: Digital UM, which very early days, but sweeping notions that

1616
01:24:07,520 --> 01:24:10,120
Speaker 1: digital was different, that it almost had kind of fundamental

1617
01:24:10,200 --> 01:24:13,320
Speaker 1: physical properties UM. And I think that was probably I mean,

1618
01:24:13,320 --> 01:24:15,280
Speaker 1: I haven't read it for years, but I can't I

1619
01:24:15,280 --> 01:24:17,160
Speaker 1: can't tell you whether it's well written or not. But

1620
01:24:17,200 --> 01:24:20,080
Speaker 1: it's very powerful ideas. So since you brought up books, Uh,

1621
01:24:20,640 --> 01:24:22,880
Speaker 1: probably the question we get asked more than any other

1622
01:24:23,040 --> 01:24:26,000
Speaker 1: is hey, what are your guest favorite books? So let's

1623
01:24:26,120 --> 01:24:28,639
Speaker 1: you mentioned being digital. What other books have you found

1624
01:24:28,720 --> 01:24:35,160
Speaker 1: to be important, influential, valuable to you? Fiction, nonfiction, technology,

1625
01:24:35,240 --> 01:24:39,120
Speaker 1: non tech, it doesn't matter. Yeah. Um, Well, recently I've

1626
01:24:39,160 --> 01:24:42,280
Speaker 1: been reading. UM. I had this rule for like thirty years.

1627
01:24:42,280 --> 01:24:46,400
Speaker 1: I would only read nonfiction. UM and the books the

1628
01:24:46,439 --> 01:24:48,400
Speaker 1: Keevin Colors of the World. I've recently I've been been

1629
01:24:48,479 --> 01:24:52,000
Speaker 1: reading fiction again, but only science fiction on the grounds

1630
01:24:52,000 --> 01:24:53,599
Speaker 1: that it's say, you know, I can I can almost

1631
01:24:53,600 --> 01:24:55,320
Speaker 1: testify it because there's you know, it's kind of a

1632
01:24:55,800 --> 01:24:57,920
Speaker 1: vision of the fay. Here's here's where tech is going.

1633
01:24:58,280 --> 01:25:02,719
Speaker 1: I mean the Three Body Problem him series. UM. Uh oh,

1634
01:25:02,760 --> 01:25:06,360
Speaker 1: now you're now you're asking um it's Chinese um author

1635
01:25:06,600 --> 01:25:09,800
Speaker 1: um and it's uh something Lee. We can look it

1636
01:25:09,920 --> 01:25:15,080
Speaker 1: up Body Problems, the Three Body Problems series. UM, it's

1637
01:25:15,200 --> 01:25:18,519
Speaker 1: it is the most exciting science fiction. UM. If you

1638
01:25:18,640 --> 01:25:21,639
Speaker 1: liked Yeah, So there's the first one, the Three Body Problem.

1639
01:25:21,680 --> 01:25:23,639
Speaker 1: The second one was The Dark Forest, and the third

1640
01:25:23,640 --> 01:25:26,920
Speaker 1: one his name is Escaping Me. But with those two

1641
01:25:26,960 --> 01:25:30,679
Speaker 1: I can yeah, and it's Quinly something. No, it's not Quinly.

1642
01:25:30,840 --> 01:25:33,080
Speaker 1: I can't remember the exactly. But what's cool is that

1643
01:25:33,120 --> 01:25:37,160
Speaker 1: the guy the author like by day he's a he's

1644
01:25:37,200 --> 01:25:40,519
Speaker 1: an engineer in a like power plant in the middle

1645
01:25:40,520 --> 01:25:43,519
Speaker 1: of China, and by night he writes the most exciting

1646
01:25:43,600 --> 01:25:48,720
Speaker 1: kind of mind expanding um science fiction about you know

1647
01:25:48,800 --> 01:25:51,960
Speaker 1: about you know, what would the university like if there were?

1648
01:25:52,000 --> 01:25:55,599
Speaker 1: If there were, you know, how how would we encounter life? Um?

1649
01:25:55,640 --> 01:26:00,240
Speaker 1: Why haven't we encountered life? Um? How would we communicate? Um? Away?

1650
01:26:00,320 --> 01:26:03,800
Speaker 1: It's six in louid C I X I N L

1651
01:26:03,880 --> 01:26:08,400
Speaker 1: I U the three body problem, the dark forest, remembrance

1652
01:26:08,439 --> 01:26:12,160
Speaker 1: of Earth's past, and Death's end? Are those the three?

1653
01:26:12,280 --> 01:26:14,479
Speaker 1: Those are the three? But then on the list that

1654
01:26:14,520 --> 01:26:17,920
Speaker 1: sounds fascinating, Yeah, is this firm his paradox is that

1655
01:26:17,960 --> 01:26:22,280
Speaker 1: what he's describing or something more more forward than that,

1656
01:26:23,000 --> 01:26:27,599
Speaker 1: I it is. It is more so so one notion,

1657
01:26:27,840 --> 01:26:29,439
Speaker 1: I mean, the most interesting that I don't want to

1658
01:26:29,479 --> 01:26:31,720
Speaker 1: ruin the plot, but the dark forest, the middle one

1659
01:26:32,120 --> 01:26:35,599
Speaker 1: sort of explains the notion that if you encounter, if

1660
01:26:35,600 --> 01:26:37,559
Speaker 1: you know that there's another species out there, you assume

1661
01:26:37,640 --> 01:26:40,439
Speaker 1: that sooner or later they're going to kill you, and

1662
01:26:40,479 --> 01:26:43,519
Speaker 1: so it's really important that you that they not know

1663
01:26:43,600 --> 01:26:46,400
Speaker 1: you exist. And and he kind of walks to the logic,

1664
01:26:46,439 --> 01:26:49,599
Speaker 1: but you know, so we shouldn't be broadcasting television signals

1665
01:26:49,600 --> 01:26:52,400
Speaker 1: in space precisely. And so the reason that you know

1666
01:26:52,760 --> 01:26:55,840
Speaker 1: the case, there's the reason we haven't encountered other civilizations

1667
01:26:55,840 --> 01:26:58,439
Speaker 1: since as they're hiding because they know that it's a

1668
01:26:58,479 --> 01:27:01,599
Speaker 1: fascinating that's a fascinating On sept I like that any

1669
01:27:01,640 --> 01:27:06,840
Speaker 1: of the books you want to mention, Um, those you know,

1670
01:27:06,920 --> 01:27:09,160
Speaker 1: I I got, I mean there's m I hate to

1671
01:27:09,280 --> 01:27:13,800
Speaker 1: you know, keep using U Kevin Kelly examples. Um, those

1672
01:27:13,800 --> 01:27:18,240
Speaker 1: are the ones I'm reading right now. Um uh there's

1673
01:27:18,280 --> 01:27:21,240
Speaker 1: a new book. Well anyway that those are the ones

1674
01:27:21,280 --> 01:27:24,280
Speaker 1: I'm gonna I'm not a a highlight for now, okay. Um.

1675
01:27:24,360 --> 01:27:27,719
Speaker 1: We talked about changes in the technology industry. We talked

1676
01:27:27,720 --> 01:27:31,320
Speaker 1: about shifts and uh, let me briefly ask you about

1677
01:27:31,479 --> 01:27:36,840
Speaker 1: the digital shift in journalism and the problem that genuine

1678
01:27:36,960 --> 01:27:40,760
Speaker 1: media outlets are having competing with free and digital. How

1679
01:27:40,800 --> 01:27:43,760
Speaker 1: big of a problem is that for the idea of

1680
01:27:43,800 --> 01:27:46,680
Speaker 1: a free press in America when that business model is

1681
01:27:46,760 --> 01:27:51,000
Speaker 1: under attack from everywhere. So I'm always baffled by that,

1682
01:27:51,000 --> 01:27:53,559
Speaker 1: that question. I get it all the time. Um, there

1683
01:27:53,680 --> 01:27:56,879
Speaker 1: is no shortage of media. You know, it's the golden

1684
01:27:56,880 --> 01:27:59,400
Speaker 1: age of media. Now we call that media Facebook, or

1685
01:27:59,439 --> 01:28:01,600
Speaker 1: we call it you know, you know, the web, but

1686
01:28:01,680 --> 01:28:04,720
Speaker 1: we're called social media. But there's you know, um, you know,

1687
01:28:04,720 --> 01:28:07,439
Speaker 1: we now have access to global media. Um. You know,

1688
01:28:07,520 --> 01:28:09,360
Speaker 1: I get the ft every day there you go, and

1689
01:28:09,400 --> 01:28:13,720
Speaker 1: India digitally as as well. So it's very hard to

1690
01:28:13,800 --> 01:28:18,840
Speaker 1: believe there's any kind of suppression of of of of media. Um.

1691
01:28:18,920 --> 01:28:21,280
Speaker 1: For economic reasons. I mean, you know, basically, the cost

1692
01:28:21,439 --> 01:28:25,280
Speaker 1: of distributing, creating, distributing content is very low. Um. So

1693
01:28:25,320 --> 01:28:28,120
Speaker 1: when people talk about the free press, it's a very archaic,

1694
01:28:28,160 --> 01:28:31,760
Speaker 1: almost American notion of the fourth of state. I mean,

1695
01:28:31,760 --> 01:28:34,559
Speaker 1: no other country has this sort of maybe some other countries,

1696
01:28:34,600 --> 01:28:37,800
Speaker 1: but in general, this this notion that you know that

1697
01:28:37,880 --> 01:28:40,920
Speaker 1: you have these kind of you know, natural monopolies, big

1698
01:28:40,960 --> 01:28:46,240
Speaker 1: city newspapers who are necessary to counterbalance political forces. Very weird,

1699
01:28:46,600 --> 01:28:52,200
Speaker 1: very American, very twentieth century. Doesn't seem relevant today. Tell

1700
01:28:52,280 --> 01:28:54,439
Speaker 1: us about a time you failed and what you learned

1701
01:28:54,439 --> 01:28:58,920
Speaker 1: from the experience. Where where do I start? Um? Uh

1702
01:28:59,040 --> 01:29:02,040
Speaker 1: So you know, in our in our progression UM three

1703
01:29:02,080 --> 01:29:06,439
Speaker 1: dr from the technology to the consumer electronics to the

1704
01:29:06,479 --> 01:29:11,080
Speaker 1: tool using using drones as sensors. UM. Because of regulation,

1705
01:29:11,120 --> 01:29:14,160
Speaker 1: that regulation only allowed recreational use. So the only way

1706
01:29:14,240 --> 01:29:18,599
Speaker 1: you know, we couldn't do commercial use until last September

1707
01:29:18,600 --> 01:29:20,639
Speaker 1: of last year. And so in between, the only way

1708
01:29:20,640 --> 01:29:22,800
Speaker 1: you could really get drones out there was on the

1709
01:29:22,800 --> 01:29:25,920
Speaker 1: shelves of Walmart and UM. And so we raised a

1710
01:29:25,960 --> 01:29:28,320
Speaker 1: lot of money and we spun up you know, outsourced

1711
01:29:28,320 --> 01:29:31,760
Speaker 1: manufacturing and consumer electronics and you know, perhaps the most

1712
01:29:31,880 --> 01:29:36,320
Speaker 1: complex consumer electronics device you know ever at that time.

1713
01:29:36,920 --> 01:29:43,320
Speaker 1: And we decided to you know, to do the consumer products,

1714
01:29:43,640 --> 01:29:47,200
Speaker 1: you know, the thing right with distribution and marketing and

1715
01:29:47,240 --> 01:29:51,719
Speaker 1: distributing retail shelves and all that. UM. And we designed

1716
01:29:51,720 --> 01:29:54,439
Speaker 1: a good product and we um, you know, did everything right,

1717
01:29:55,120 --> 01:29:58,559
Speaker 1: UM and UM that's when the prices fell by by

1718
01:29:58,680 --> 01:30:03,599
Speaker 1: by sevent months and and we ended up we ended

1719
01:30:03,680 --> 01:30:08,960
Speaker 1: up with negative margins. Uh. And you know your units

1720
01:30:09,000 --> 01:30:12,479
Speaker 1: called dollars to build, yeah, exactly. We were so when

1721
01:30:12,520 --> 01:30:15,000
Speaker 1: we saw when we started, the going price for an

1722
01:30:15,000 --> 01:30:18,400
Speaker 1: advanced drone was about fiftie hundred even more. And so

1723
01:30:18,560 --> 01:30:20,760
Speaker 1: you know, our bill of materials was about half that,

1724
01:30:20,880 --> 01:30:23,920
Speaker 1: which is appropriate. UM. By the time a couple of

1725
01:30:23,920 --> 01:30:26,120
Speaker 1: months after we after we launched, the going price was

1726
01:30:26,160 --> 01:30:29,560
Speaker 1: below bill materials and and as of like about a

1727
01:30:29,640 --> 01:30:32,400
Speaker 1: year ago, was down to about three D. So well,

1728
01:30:32,439 --> 01:30:35,559
Speaker 1: these were not like not like bad technology, not toys.

1729
01:30:35,600 --> 01:30:38,439
Speaker 1: These are serious, serious things. So so what did you

1730
01:30:38,520 --> 01:30:41,320
Speaker 1: learn from that? What was the takeaway? I learned that.

1731
01:30:41,520 --> 01:30:44,440
Speaker 1: I mean my instinct right now is that that essentially

1732
01:30:44,720 --> 01:30:47,360
Speaker 1: was the universe telling me what should have been obvious,

1733
01:30:47,400 --> 01:30:52,000
Speaker 1: which is that Silicon Valley should focus on software. Um.

1734
01:30:52,240 --> 01:30:55,559
Speaker 1: China is extraordinarily good at hardware. The notion of advanced

1735
01:30:55,560 --> 01:30:58,200
Speaker 1: Silicon Valley cloud software running on Chinese hardware, which by

1736
01:30:58,240 --> 01:31:02,200
Speaker 1: the way, is your phone actually, UM, is the way

1737
01:31:02,240 --> 01:31:04,880
Speaker 1: we should go. And it's kind of like, don't fight it.

1738
01:31:04,880 --> 01:31:07,080
Speaker 1: It's you know, rather than build your own hardware, go

1739
01:31:07,160 --> 01:31:09,519
Speaker 1: find some brilliant company in China that's making the hardware

1740
01:31:09,520 --> 01:31:12,840
Speaker 1: and partner lets them have all the costs and produce

1741
01:31:12,920 --> 01:31:15,200
Speaker 1: it at half. And every time I talked to you know,

1742
01:31:15,520 --> 01:31:18,200
Speaker 1: a new company and Silicon valid it's doing hardware, they're like, yeah,

1743
01:31:18,240 --> 01:31:20,040
Speaker 1: we know that the hardware is commoditized, and we know

1744
01:31:20,040 --> 01:31:21,760
Speaker 1: the chance. But we think we have five or seven years.

1745
01:31:21,760 --> 01:31:23,320
Speaker 1: And it's like, yeah, I said that too, you got

1746
01:31:23,360 --> 01:31:26,479
Speaker 1: six months, you got six months. Um, what do you

1747
01:31:26,520 --> 01:31:29,280
Speaker 1: do to keep either mentally you're physically fit, and what

1748
01:31:29,320 --> 01:31:32,479
Speaker 1: do you do to relax outside of the office? Um?

1749
01:31:33,040 --> 01:31:36,760
Speaker 1: So physically fit, UM, I just play tennis. Um, but

1750
01:31:36,840 --> 01:31:39,640
Speaker 1: I have some of my dear friends and mentors or

1751
01:31:39,760 --> 01:31:42,240
Speaker 1: my tennis partners, and so tennis is not about the tennis.

1752
01:31:42,479 --> 01:31:44,719
Speaker 1: So not spending time with you know, with with with

1753
01:31:44,720 --> 01:31:47,840
Speaker 1: with with your friends, you wouldn't otherwise schedule. Um. Intellectually

1754
01:31:47,880 --> 01:31:50,000
Speaker 1: I have I still have tons of tons of hobbies.

1755
01:31:50,040 --> 01:31:51,920
Speaker 1: And ten years ago we did d I Y drones.

1756
01:31:51,960 --> 01:31:54,120
Speaker 1: Now we're doing d I Y robot cars and applying

1757
01:31:54,120 --> 01:31:56,840
Speaker 1: the same sort of grassroots bob you know, you know,

1758
01:31:56,960 --> 01:31:58,599
Speaker 1: sort of like what what what if you made an

1759
01:31:58,600 --> 01:32:00,960
Speaker 1: autonomous car for a hundred ours now wouldn't carry a

1760
01:32:00,960 --> 01:32:03,160
Speaker 1: person in the same way of drone doesn't carry a person,

1761
01:32:03,520 --> 01:32:07,240
Speaker 1: But it's the same. It's light, our computer vision, neural networks,

1762
01:32:07,280 --> 01:32:10,080
Speaker 1: you know, Google clouds. This is now down to the

1763
01:32:10,160 --> 01:32:13,400
Speaker 1: d I Y level, the various At one point in time,

1764
01:32:13,439 --> 01:32:17,519
Speaker 1: this was a um was it a dark book? Dar

1765
01:32:18,120 --> 01:32:20,880
Speaker 1: you know, the Darker Competition, the Grand Challenge, etcetera. So

1766
01:32:20,920 --> 01:32:23,719
Speaker 1: this weekend, for example, we're gonna be doing um, robot

1767
01:32:23,760 --> 01:32:26,840
Speaker 1: to robot, wheel to wheel Thomas car racing in a

1768
01:32:27,000 --> 01:32:31,360
Speaker 1: in a huge warehouse like semi abandoned warehouse in in Oakland, California.

1769
01:32:31,520 --> 01:32:34,320
Speaker 1: And then we're gonna do robot versus human um. And

1770
01:32:34,360 --> 01:32:36,120
Speaker 1: this is not These are not toys. I mean they

1771
01:32:36,160 --> 01:32:38,439
Speaker 1: look like toys because they're like in a one tense scale,

1772
01:32:38,479 --> 01:32:41,360
Speaker 1: But the exact same technology, isn't it Tesla or And

1773
01:32:41,400 --> 01:32:43,400
Speaker 1: they could scale right up if they scale right up,

1774
01:32:43,400 --> 01:32:46,800
Speaker 1: it's basically using the same Google Neural network stuff. But

1775
01:32:47,000 --> 01:32:49,639
Speaker 1: the cost of the hardware itself is about a hundred

1776
01:32:49,680 --> 01:32:54,040
Speaker 1: dollars undred fifty dollars. And um, we made it so

1777
01:32:54,120 --> 01:32:57,559
Speaker 1: it's fun. We made it so that students could do it,

1778
01:32:58,200 --> 01:33:01,720
Speaker 1: and um, you know it's dangerous for the cars, but

1779
01:33:01,760 --> 01:33:05,559
Speaker 1: not for humans. These are they're truly autonomous. You basically

1780
01:33:05,680 --> 01:33:07,880
Speaker 1: three to one go and this one, this one just

1781
01:33:07,920 --> 01:33:09,760
Speaker 1: outside this room, we're gonna have to take a look

1782
01:33:09,800 --> 01:33:13,439
Speaker 1: at that. Now I have to ask my final two questions. First,

1783
01:33:13,920 --> 01:33:16,280
Speaker 1: what sort of advice would you give to a millennial

1784
01:33:16,360 --> 01:33:21,640
Speaker 1: or someone just starting their career in either technology or journalism.

1785
01:33:21,680 --> 01:33:23,800
Speaker 1: So you know, I have kids who are who are

1786
01:33:23,840 --> 01:33:27,439
Speaker 1: just going to college or or soon will local or

1787
01:33:27,600 --> 01:33:29,640
Speaker 1: or elsewhere. No, they refused to go locally in the

1788
01:33:29,640 --> 01:33:31,960
Speaker 1: same way. Why did I Why did I knew that? No,

1789
01:33:32,080 --> 01:33:34,000
Speaker 1: that was the answer. I mean, we live in Berkeley,

1790
01:33:34,040 --> 01:33:35,680
Speaker 1: you know, you see, you see, Berkeley is like one

1791
01:33:35,680 --> 01:33:36,760
Speaker 1: of the best schoals in the world. But they just

1792
01:33:36,840 --> 01:33:39,800
Speaker 1: won't go to it. Um. I didn't get in either,

1793
01:33:39,840 --> 01:33:41,920
Speaker 1: but they didn't. They didn't apply. I didn't in fairness,

1794
01:33:41,920 --> 01:33:45,599
Speaker 1: they didn't apply. Um uh so um. You know, if

1795
01:33:45,600 --> 01:33:48,639
Speaker 1: you ask me what I was to study today, I'm

1796
01:33:48,640 --> 01:33:50,840
Speaker 1: gonna give you an answer which sounds boring, and then

1797
01:33:50,840 --> 01:33:52,280
Speaker 1: you have to find a sexier way to sell The

1798
01:33:52,280 --> 01:33:56,320
Speaker 1: answer is statistics. Probability and statistics are not boring at all,

1799
01:33:56,680 --> 01:33:59,040
Speaker 1: but it doesn't sound super compelling. So let's call it.

1800
01:33:59,120 --> 01:34:00,559
Speaker 1: Let's call it something a sex here like it's called

1801
01:34:00,640 --> 01:34:04,520
Speaker 1: data science. I'm gonna take it a step further. Probability

1802
01:34:04,520 --> 01:34:09,559
Speaker 1: and statistics is how you discern reality from falsity, truth

1803
01:34:09,680 --> 01:34:12,360
Speaker 1: from nonsense, and so sell it to my seventeen year

1804
01:34:12,360 --> 01:34:16,559
Speaker 1: old I can't tell you how often something comes along

1805
01:34:16,600 --> 01:34:20,559
Speaker 1: and you say, no, that's you're giving me a sample

1806
01:34:20,600 --> 01:34:23,000
Speaker 1: set of two Who cares it's it's not if you

1807
01:34:23,000 --> 01:34:26,320
Speaker 1: don't know that, you cannot identify reality. So my seventeen

1808
01:34:26,400 --> 01:34:29,559
Speaker 1: year old is like, he's trying to choose a major,

1809
01:34:30,400 --> 01:34:33,080
Speaker 1: and you, Barry uncle Barry as we'll call you, for

1810
01:34:33,080 --> 01:34:35,280
Speaker 1: the purpose of this argument, are going to convince my

1811
01:34:35,360 --> 01:34:39,240
Speaker 1: seventeen year old to major in what you just said,

1812
01:34:40,360 --> 01:34:44,559
Speaker 1: sell it in a way that doesn't sound like spinach. Well,

1813
01:34:44,640 --> 01:34:46,080
Speaker 1: what do you want to do with your life? What

1814
01:34:46,120 --> 01:34:48,479
Speaker 1: do you what are you interested in? How would you

1815
01:34:48,560 --> 01:34:51,120
Speaker 1: like to have a way to identify what's real and

1816
01:34:51,200 --> 01:34:53,720
Speaker 1: what's false in that? How would you like to be

1817
01:34:53,840 --> 01:34:58,519
Speaker 1: able to inject the truth serum into anything that takes

1818
01:34:58,520 --> 01:35:01,719
Speaker 1: place in meat space and some stuff that takes place

1819
01:35:01,720 --> 01:35:04,360
Speaker 1: in the virtual world and figure out if it's real

1820
01:35:04,439 --> 01:35:07,040
Speaker 1: or it's false. And what if they say it sounds

1821
01:35:07,160 --> 01:35:10,040
Speaker 1: hard and is it even a job? Anything that's worth

1822
01:35:10,120 --> 01:35:13,640
Speaker 1: doing is hard number one, And it's a job, but

1823
01:35:13,720 --> 01:35:17,360
Speaker 1: it's only a fabulous, paying, fascinating job. So if that's

1824
01:35:17,400 --> 01:35:22,200
Speaker 1: of interest, you know the ability to And by the way,

1825
01:35:22,720 --> 01:35:26,639
Speaker 1: this is a well established science. Many people have problems

1826
01:35:26,680 --> 01:35:29,000
Speaker 1: with it. But the ability to walk in and look

1827
01:35:29,040 --> 01:35:31,960
Speaker 1: at something and say, here's how we know their story

1828
01:35:32,040 --> 01:35:37,000
Speaker 1: is false based on this is an amazing, amazing thing.

1829
01:35:37,120 --> 01:35:42,040
Speaker 1: And I'm I'm astonished more people who don't recognize it

1830
01:35:42,240 --> 01:35:44,479
Speaker 1: for what it is. So so the I agree with

1831
01:35:44,479 --> 01:35:46,439
Speaker 1: everything you just said. Um, the way I ended up

1832
01:35:46,479 --> 01:35:49,720
Speaker 1: selling it was I said, well, okay, um, let's call it.

1833
01:35:50,120 --> 01:35:52,800
Speaker 1: Let's call its computer science, and it's called data science.

1834
01:35:52,840 --> 01:35:55,479
Speaker 1: The data science is basically computer science plus statistics of

1835
01:35:55,720 --> 01:35:58,479
Speaker 1: various sorts. Um. One of my one of my sons

1836
01:35:58,520 --> 01:36:03,080
Speaker 1: is at North Northwestern and and he did economics, econometrics,

1837
01:36:03,600 --> 01:36:08,360
Speaker 1: and computer science, which is another way. So and so

1838
01:36:08,400 --> 01:36:11,840
Speaker 1: when you put econometrics and computer science together, if if,

1839
01:36:11,880 --> 01:36:14,040
Speaker 1: if the employers want to see that as data science,

1840
01:36:14,280 --> 01:36:16,559
Speaker 1: data science, it is, and that's a very hot profession

1841
01:36:16,520 --> 01:36:18,280
Speaker 1: and get paid for that. Sure to say the least

1842
01:36:18,280 --> 01:36:21,120
Speaker 1: our final question before I make you late for your

1843
01:36:21,520 --> 01:36:25,519
Speaker 1: next appointment, what is it that you know about technology

1844
01:36:25,840 --> 01:36:29,960
Speaker 1: writing journalism today you wish you knew twenty five years ago.

1845
01:36:30,479 --> 01:36:32,800
Speaker 1: I mean, I've been pretty lucky in being in the

1846
01:36:32,880 --> 01:36:38,839
Speaker 1: right place at the right time throughout im I've sometimes

1847
01:36:38,840 --> 01:36:41,479
Speaker 1: been too early. Is as bad as being wrong, and

1848
01:36:41,520 --> 01:36:47,200
Speaker 1: I've certainly been too early on on some things. UM yeah,

1849
01:36:47,240 --> 01:36:51,600
Speaker 1: I uh, I think I might have. UM, so I

1850
01:36:51,600 --> 01:36:55,559
Speaker 1: could have moved to Silicon Valley sooner. Um, but in

1851
01:36:55,640 --> 01:36:57,880
Speaker 1: that if I'd done so, I would have missed China.

1852
01:36:58,000 --> 01:36:59,840
Speaker 1: I would have missed being in China. That's a cool

1853
01:37:00,000 --> 01:37:03,080
Speaker 1: into it and that was that was cooler. I guess

1854
01:37:03,080 --> 01:37:05,920
Speaker 1: I would have. UM. The only time I regret at

1855
01:37:05,920 --> 01:37:07,640
Speaker 1: the time I spent in Washington, d C, I was

1856
01:37:07,760 --> 01:37:09,679
Speaker 1: I was too long in Washington. I saw the world

1857
01:37:09,680 --> 01:37:12,599
Speaker 1: through kind of a political lens, and I wish I'd

1858
01:37:12,600 --> 01:37:15,960
Speaker 1: gotten out of there sooner. The you know, here's the

1859
01:37:15,960 --> 01:37:19,520
Speaker 1: Looking Valley, d C fields a long way away. Governments

1860
01:37:19,520 --> 01:37:24,639
Speaker 1: seem not terribly not as important, markets and even countries

1861
01:37:24,640 --> 01:37:28,000
Speaker 1: don't seem as important as as as as trade relationships.

1862
01:37:28,320 --> 01:37:31,599
Speaker 1: So I wish i'd traveled center. We have been speaking

1863
01:37:31,600 --> 01:37:36,320
Speaker 1: with Chris Anderson, CEO of three D Robotics, author of

1864
01:37:36,360 --> 01:37:40,840
Speaker 1: The Long Tail and Makers and Free. If you enjoy

1865
01:37:40,960 --> 01:37:43,280
Speaker 1: this conversation, be showing look up an inch or down

1866
01:37:43,280 --> 01:37:47,440
Speaker 1: an inch on either Apple, iTunes, SoundCloud, Bloomberg dot com,

1867
01:37:47,479 --> 01:37:49,439
Speaker 1: and you could see our other hundred and fifty or

1868
01:37:49,479 --> 01:37:53,800
Speaker 1: so such uh recordings. I would be remiss if I

1869
01:37:53,840 --> 01:37:57,040
Speaker 1: did not think Michael Battnick, who helped prepare these questions,

1870
01:37:57,479 --> 01:38:01,000
Speaker 1: Chris ven who has been my boyfriend today on this trip.

1871
01:38:01,720 --> 01:38:03,639
Speaker 1: And Chris, thank you so much for being so generous

1872
01:38:03,640 --> 01:38:05,760
Speaker 1: with your time. I know you have other things and

1873
01:38:05,840 --> 01:38:08,960
Speaker 1: better places to do and be. I'm Barry Retults. You're

1874
01:38:09,000 --> 01:38:22,240
Speaker 1: listening to Masters in Business on Bloomberg Radio. Masters in

1875
01:38:22,280 --> 01:38:25,280
Speaker 1: Business is brought to you by the American Arbitration Association.

1876
01:38:25,640 --> 01:38:30,520
Speaker 1: Business disputes are inevitable, resolve faster with the American Arbitration Association,

1877
01:38:30,920 --> 01:38:35,280
Speaker 1: the global leader in alternative dispute resolution for over ninety years.

1878
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