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Speaker 1: Welcome to two Percent. I'm your host, Michael Easter. This

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Speaker 1: is a podcast where we talk about improving your performance.

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Speaker 1: I am an author, and today I am extremely pleased

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Speaker 1: to bring on a fellow author whose work I very

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Speaker 1: much admire. We're going to be talking to David Epstein. So,

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Speaker 1: David started as a sports reporter, specifically in the realm

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Speaker 1: of the science of sports at Sports Illustrated Now. He

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Speaker 1: became famous there and had a big moment of attention

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Speaker 1: when he broke the A Rod steroid scandal. So, if

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Speaker 1: you are a New York Yankees fan, you probably hate

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Speaker 1: the guy already, but please bear with me, because this

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Speaker 1: guy is a very, very fascinating thinker who can tell

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Speaker 1: us a lot about improving our work life, improving our

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Speaker 1: performance in the gym and on the road if we're

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Speaker 1: a runner, and just improving our thinking across the board

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Speaker 1: in a way that we can live better. And David

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Speaker 1: has a new book which we are going to be

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Speaker 1: diving into today. It is called Inside the Box, How

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Speaker 1: Constraints Make Us Better, and it argues that even though

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Speaker 1: we often do not like constraints and we want as

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Speaker 1: much freedom as possible, he argues that constraints are actually

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Speaker 1: what lead us into better outcomes at work, better outcomes

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Speaker 1: in our wellness, better outcomes across the board. Let's get

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Speaker 1: into it. David Epstein, thanks for coming on the show Man.

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Speaker 2: It is my absolute pleasure.

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Speaker 1: So the new book Inside the Box looks at the

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Speaker 1: idea of how constraints can actually be beneficial. And there's

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Speaker 1: one story that I loved.

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Speaker 3: That you open the book with. It's about this company

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Speaker 3: called General Magic.

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Speaker 2: Yeah. Well, so they have the people who designed the

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Speaker 2: original Mac. You know.

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Speaker 4: It's the company was so visionary the iPhone, like in

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Speaker 4: basically the IP.

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Speaker 3: Yeah, and this was in the late eighties, early nineties.

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Speaker 4: The sketch the CEO has a notebook his named Mark

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Speaker 4: Parratt in nineteen eighty nine where he sketches a thin

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Speaker 4: glass rectangle with no protruding buttons and a touchscreen where

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Speaker 4: you can download apps and it'll be a phone in

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Speaker 4: a computer. Nineteen eighty nine, only fifteen percent of Americans

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Speaker 4: had computers.

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Speaker 2: Yeah, and the Internet didn't exist.

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Speaker 4: He saw all of this stuff, Like he envisioned a

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Speaker 4: virtual meeting space where different devices could connect, and they

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Speaker 4: called it the cloud in nineteen ninety like they they

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Speaker 4: were ahead, and again they had the designers, the original MAC.

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Speaker 2: It was so everything was.

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Speaker 4: So alluring, their vision, their talent that Goldman Sachs actually

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Speaker 4: took them public in the first so called concept ipo,

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Speaker 4: where they went public with an idea.

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Speaker 1: Nothing to without a product, nothing to actually sell, just like, hey,

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Speaker 1: check out this idea.

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Speaker 3: Let's roll.

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Speaker 4: I mean they had an idea that they had the

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Speaker 4: idea plus the talent, plus a seventeen member what they

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Speaker 4: called the Alliance, which was basically other companies that had

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Speaker 4: invested in them. So this was seventeen companies from around

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Speaker 4: the world. It was the largest consortium of international businesses

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Speaker 4: in American business history. Each had given millions of dollars

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Speaker 4: in investment. And we're going to be part of the team.

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Speaker 2: And so like.

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Speaker 1: Apple White, these are giants are like Sony, Right, there's

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Speaker 1: all kinds of.

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Speaker 4: Grapples Sony, Panasonic, you know, AT and T like all

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Speaker 4: the It was actually they covered so much of the

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Speaker 4: communications technology world that their meetings had to begin with

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Speaker 4: an antitrust lawyer listing all the topics they weren't allowed

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Speaker 4: to discuss in their meetings because they covered everything and

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Speaker 4: they have this vision. So Mark Parrat, the CEO. He

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Speaker 4: raises all this money early, a stock price doubles on

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Speaker 4: the first day. It's a Wall Street darling. And he

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Speaker 4: says his goal in raising all that money so quickly

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Speaker 4: was to create what he called heaven for engineers, where

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Speaker 4: they could play and create and be limited only by

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Speaker 4: their imaginations. As he said, what more could anyone else

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Speaker 4: ask for? And I think the answer in retrospect turned

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Speaker 4: out to be a little less freedom because they could

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Speaker 4: do anything.

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Speaker 2: So they did do anything.

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Speaker 4: Everyone who had a good idea, they did it like

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Speaker 4: they any They built and built. They had no clear

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Speaker 4: they they defined their customer as Joe sixpack, which is

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Speaker 4: very vague. So after a few years of miss deadlines,

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Speaker 4: they turned around and realized nobody knew the guy or

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Speaker 4: what they were building from, or what problem they were solving.

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Speaker 3: Like beer.

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Speaker 4: Yeah, yeah, just like it's like saying Joe Schmo, you know,

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Speaker 4: like random guy. So they didn't take any time to

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Speaker 4: define their actual customer. So they ended up building for

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Speaker 4: each other and the project just grew and grew and grew.

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Speaker 4: They couldn't ever decide what not to do, and so

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Speaker 4: it ends up being this huge disaster. They ended up

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Speaker 4: selling three thousand units of their personal communicator when it

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Speaker 4: comes out. It has so many features that the battery

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Speaker 4: life's terrible, the user experience is choppy, it's expensive, it's confusing.

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Speaker 4: But there was one interview that I think kind of

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Speaker 4: encapsulated their problems, and it was at this guy named

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Speaker 4: this engineer named Steve Pearlman, whose job was to create

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Speaker 4: a calendar function.

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Speaker 2: For their operating system.

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Speaker 4: And so he creates it to run from nineteen oh

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Speaker 4: four to twenty ninety six and checks it in and

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Speaker 4: is like, all right, I'm done. And then one of

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Speaker 4: the managers comes to him and says, look, somebody might

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Speaker 4: build apps that go way back in history or way

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Speaker 4: into the future. You have to make it longer than that.

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Speaker 4: So he opens it up again. He goes back to

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Speaker 4: year one, fine, thinks he's done. Then another team comes

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Speaker 4: to him and says, look, why are you starting with

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Speaker 4: that arbitrary religious context. You should go back to the

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Speaker 4: beginning of astronomical time. So he builds the calendar app

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Speaker 4: from the beginning of the universe way into the future.

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Speaker 4: And as he said, if he had stuck from nineteen

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Speaker 4: oh four to twenty ninety six, it would have been

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Speaker 4: four lines of code and he could have moved on,

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Speaker 4: and instead it dragged on for months.

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Speaker 2: And this is how everything worked there.

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Speaker 4: Because they didn't put boundaries in place, everything grew and

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Speaker 4: grew and grew until it just totally collapsed under its

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Speaker 4: own weight.

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Speaker 1: So this becomes this like big metaphor for the book.

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Speaker 1: We often think that freedom is like the most desirable

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Speaker 1: thing for creatives. For businesses, it's like, just take some

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Speaker 1: smart people, you let him figure it out.

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Speaker 3: They'll do it.

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Speaker 1: But this becomes this great metaphor for the fact that

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Speaker 1: even when you have the greatest teams ever, you need

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Speaker 1: some sort of boundaries because without boundaries, things just turn

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Speaker 1: into chaos totally.

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Speaker 4: And in fact, in one way I would say that

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Speaker 4: General Magic was actually a success, which is it's so traumatized,

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Speaker 4: you know, in a business sense some of the people

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Speaker 4: that were there, especially some of the.

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Speaker 2: Younger employees, that.

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Speaker 4: They learned all these lessons about the importance of constraints

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Speaker 4: that they then took in their next stops in their

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Speaker 4: careers and did things like led Google Maps and built

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Speaker 4: the Apple Watch, co founded Android, LinkedIn, eBay, you know

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Speaker 4: all these other companies that you've heard of or the

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Speaker 4: guy who became an absolute zealot for Constraints, a guy

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Speaker 4: named Tony Fidell who general Magic was his first job

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Speaker 4: out of college and these were his heroes and so

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Speaker 4: he was just devastated when the company collapsed. I was

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Speaker 4: actually connected with him by the famous venture capitalist Bill Gurley,

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Speaker 4: who's famously invested in Uber and Zillow. I told him

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Speaker 4: I was interested in constraints and Bill said, oh, we

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Speaker 4: have a saying and venture more startups dive of indigestion

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Speaker 4: than starvation, like too much, not too little. He said,

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Speaker 4: you got to talk to my friend Tony. So he

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Speaker 4: connects me to Tony Fidel. The first time I talked

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Speaker 4: to him, he's like yelling at me. If you don't

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Speaker 4: have constraints, make up constraints. He's like a very intense guy.

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Speaker 4: And he went on to lead the design of the iPod,

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Speaker 4: and when he showed Steve Jobs a styrofoam model in

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Speaker 4: March of two thousand and one, got the green light

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Speaker 4: and said we are shipping by Christmas. Gave like ten

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Speaker 4: weeks for the first design and then stop and collect

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Speaker 4: your lessons and go on. And it forced the team

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Speaker 4: to think creatively and repurpose technology. So the famous scroll

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Speaker 4: wheel is something that they basically repurposed from a Danish

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Speaker 4: cordless phone because they were saying, look, we can't build

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Speaker 4: everything from scratch like they had done in General Magic.

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Speaker 4: Then Fidel goes on and he co founds Nest, the

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Speaker 4: smart thermostat company, where he forces the company to work

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Speaker 4: inside a literal box. He makes them prototype the box

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Speaker 4: before the product, because he says, this shows what we

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Speaker 4: want to communicate to the end user, and if it's

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Speaker 4: not in this box, it's not one of our priorities.

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Speaker 4: And it was just so interesting to see his arc

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Speaker 4: from this like the trauma of General Magic, to becoming

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Speaker 4: this absolute zelo for constraints, which is why I wanted

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Speaker 4: to give his narrative some air.

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Speaker 1: Well. One thing that I thought was really fascinating is

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Speaker 1: you could almost see I think the typical point of

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Speaker 1: view would be constraints are going to constrain creativity, right,

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Speaker 1: and so when you have these products like the iPod,

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Speaker 1: the first iPod, the Master, you're like, wow, that must

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Speaker 1: have taken a lot of creativity, a lot of thought,

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Speaker 1: and just kind of figuring things out. But you also

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Speaker 1: write about this idea called is it the Green Eggs

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Speaker 1: and Ham effect where having constraints can actually enhance creativity,

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Speaker 1: because we'll I'll let you explain it.

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Speaker 2: You know it reliably does. In fact, there was just this.

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Speaker 4: I cite this recent survey by psychologists around the world

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Speaker 4: of known creativity myths things that we know from psychological

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Speaker 4: research are not true, And the second most popular one

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Speaker 4: is that people are most creative when they're most free.

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Speaker 4: And as you mentioned, psychologists know this isn't true. There's

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Speaker 4: actually something called the green Eggs and Ham effect, which

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Speaker 4: is named for the fact that Theodore Gaizel aka doctor

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Speaker 4: Seuss wrote Green Eggs and Ham on a bet that

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Speaker 4: he couldn't write a book using only fifty words, and

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Speaker 4: it forced him to experiment with rhythm because he couldn't

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Speaker 4: use vocabulary. Even before Green eg and Ham, he had

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Speaker 4: been given a task to write a children's book using

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Speaker 4: only two hundred words from a kid's vocabulary list, and

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Speaker 4: at first he starts looking at the list, he starts

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Speaker 4: complaining to his wife. He says, there are no adjectives,

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Speaker 4: and then he says, I think in fine Susian form,

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Speaker 4: it's like trying to make a strudle with no Strudels,

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Speaker 4: which I think is hilarious because it's like he was

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Speaker 4: the same guy in his personal life as his books.

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Speaker 4: And then he just decides, throws his hands up and says,

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Speaker 4: I'm just going to take the first two rhyming words

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Speaker 4: on the list and make a book. And the first

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Speaker 4: two rhyming words are cat and hat, and that kind

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Speaker 4: of changed children's literature forever. It gets to this idea

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Speaker 4: that cognitive psychologists have really fleshed out now that you know,

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Speaker 4: as the cognitive scientist Daniel Willingham has put it, you

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Speaker 4: may think your brain is made for thinking, but it's

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Speaker 4: actually made to prevent you from having to think whenever possible,

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Speaker 4: because thinking is energetically costly, and so if you're not forced,

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Speaker 4: you'll just go down what cognitive psychologists call the path

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Speaker 4: of least resistance, meaning you'll just reach for ideas that

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Speaker 4: you've already used, or that you've seen, what's familiar, what's easy,

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Speaker 4: and so unless, in many cases, unless the normal thing

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Speaker 4: is actually blocked, it becomes incredibly hard and sometimes impossible

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Speaker 4: to be creative.

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Speaker 1: Yeah, and that was one that one surprised me at first,

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Speaker 1: and then two I thought about my own work and

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Speaker 1: when I'm doing a book, like I just finished this

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Speaker 1: a draft of another book, and as I was writing that,

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Speaker 1: I have sections where I'd be like, oh, this kind

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Speaker 1: of worked before in my last word, and I'd start

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Speaker 1: to do that and They're like, yeah, but you can't

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Speaker 1: do that same sort of thing. This is a new book.

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Speaker 1: And so it was like I needed that sort of

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Speaker 1: constraint to not just default to the easy thing, even

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Speaker 1: though it was originally somewhat creative, but now that I'm

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Speaker 1: doing it again, it no longer becomes creative.

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Speaker 2: Absolutely, I mean to your point.

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Speaker 4: We were talking before we started recording about my process

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Speaker 4: a little bit for the book this time around, and

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Speaker 4: this was the first time I ever created an architectural

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Speaker 4: plan for the how I was going to order the

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Speaker 4: information before I started writing the book.

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Speaker 2: I have deportion of this book is mesearch.

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Speaker 4: I was terrible at putting constraints in place and wanted

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Speaker 4: to get better at it. That's often the case for

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Speaker 4: a lot of the things I'm researching is I'm bad

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Speaker 4: at it, didn't want to get better, and so I

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Speaker 4: wrote way over length in my previous two books, and

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Speaker 4: it just incredibly inefficient. So this time I made this

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Speaker 4: one page outline. I forced myself to outline the whole

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Speaker 4: book on one page. As you can see, I ended

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Speaker 4: up writing very very small my own attempt, my brain's

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Speaker 4: attempt to defeat my own system.

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Speaker 2: But if it's not on this page, it's not in

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Speaker 2: the book.

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Speaker 4: So this is the first time I wrote the length

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Speaker 4: of a book to get a book, and the book

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Speaker 4: is tighter than my other's, about twenty percent shorter. But

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Speaker 4: it also blocked the kind of some of the methods

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Speaker 4: that I was used to because I had never laid

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Speaker 4: out a plan ahead of time where I wanted the

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Speaker 4: beginning and the end of the book to kind of

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Speaker 4: come full circle in a way that the other ones didn't.

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Speaker 4: And so I think that was really helpful because especially

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Speaker 4: and it's exactly what you're saying, like, we've gotten competent

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Speaker 4: at this thing, which is great, but competency can also

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Speaker 4: be a trap from getting better.

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Speaker 2: You know.

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Speaker 4: It's like you end up lifting the same weights the

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Speaker 4: same number of times every day, which means you may

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Speaker 4: not get worse, but you're also not going to get better.

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Speaker 1: It's like putting bumpers up. If you don't have the bumpers,

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Speaker 1: you're probably going to get it in the gutter sometimes,

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Speaker 1: and I find out in my own writing where I

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Speaker 1: will find a thread that it's I'm like, I don't

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Speaker 1: know if it works for the book, but it's kind

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Speaker 1: of interesting. And then I'm writing, you know, a thousand

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Speaker 1: words on this, and then I read it and I go, yeah,

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Speaker 1: this is interesting, but what the hell does it have

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Speaker 1: to do with this book I'm writing? Or if I

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Speaker 1: could just keep down the lane, it would be a

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Speaker 1: much more efficient process.

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Speaker 2: I have that problem in spades.

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Speaker 4: I mean, I have a very psychologist I was interviewing

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Speaker 4: once told me that I have a what he called

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Speaker 4: a flat associative hierarchy, which means that I see lots

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Speaker 4: of disparate ideas as kind of connected. It's easy for

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Speaker 4: me to connect them. And that can be nice because

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Speaker 4: I maybe find things that aren't obvious to other people.

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Speaker 4: But it also means that I can be incredibly prone

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Speaker 4: to doing what you're describing, which is going down these

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Speaker 4: rabbit holes of things that I think are interesting and

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Speaker 4: they're really not that well connected in a way that

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Speaker 4: will make sense to other people.

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Speaker 2: And so I really need.

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Speaker 4: Structure to kind of prevent myself from writing books that

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Speaker 4: are just all over the place.

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Speaker 1: Yeah, what did this What did report in this book

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Speaker 1: make you think about when people get too many resources,

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Speaker 1: like specifically financial resources.

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Speaker 3: It kind of made me think that.

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Speaker 1: A lot of times you find the people who sort

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Speaker 1: of have it all almost have nothing because of a

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Speaker 1: sort of aimlessness sets in. It's like, when you can

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Speaker 1: have everything, why go after anything?

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Speaker 4: Yeah, I mean I think it's actually don't think it's healthy.

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Speaker 4: For as Jonathan Hyde told me in one of the

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Speaker 4: interviews in the book, it's not healthy for anyone to

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Speaker 4: have everything everywhere all the time. And a lot of us,

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Speaker 4: even if we're not rich, are kind of in a

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Speaker 4: situation like that in the digital world now, And so

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Speaker 4: I think when it comes to businesses, there are a

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Speaker 4: bunch of you know, there are examples in the book

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Speaker 4: where people are just sloppy, like when they have too much, right,

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Speaker 4: it leads to sloppiness, It leads to not feeling like

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Speaker 4: you need to define these boundaries. And I think one

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Speaker 4: of the things this gets at one of the things

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Speaker 4: that I hope maybe the mindset shift that I hope

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Speaker 4: the book engenders, which is from seeing limits as only

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Speaker 4: bad to seeing as to seeing them as opportunities to

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Speaker 4: clarify priorities and launch productive exploration. And I think when

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Speaker 4: people have too much, like to Bill Gurley's more startups

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Speaker 4: die of indigestion than starvation. Quote, you're not forced to

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Speaker 4: be resourceful and you're not forced to clarify priorities, and

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Speaker 4: so you don't. And I actually don't think that's a

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Speaker 4: good thing for people, right. It's like, I don't think

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Speaker 4: it's good for work, and I don't think it's good

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Speaker 4: for having meaning in your life to kind of always

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Speaker 4: have your options infinitely open.

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Speaker 2: I feel like I'm conscious that it may.

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Speaker 4: Sound like sometimes I'm contradicting at least the title of

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Speaker 4: the book sounds like I'm contradicting range my previous book,

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Speaker 4: which is about broad experiences. But it actually felt like

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Speaker 4: a natural next question to me, where it's, okay, you

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Speaker 4: get this broad tool set, at some point you have

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Speaker 4: to focus this into something, into achievement, hopefully into meaning

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Speaker 4: and satisfaction. And I've kind of found that in a

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Speaker 4: lot of really talented or hardworking people, they may over

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Speaker 4: index on optionality, like keeping their options open all the

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Speaker 4: time because they can maybe they're very talented or they're

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Speaker 4: very hard working or lucky or whatever it is. But

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Speaker 4: sometimes I think they can actually really backfire if people

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Speaker 4: start making decisions, if keeping your options open becomes an

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Speaker 4: end douneto itself. And so I think I've seen some

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Speaker 4: very talented peers and friends endlessly keep their options open

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Speaker 4: in a way that actually doesn't doesn't help them reach

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Speaker 4: better satisfaction. I'm sure I'm articulating that well.

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Speaker 3: I think I hear what you're saying.

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Speaker 1: It's like there comes a sort of phobia towards commitment,

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Speaker 1: and you sort of tell yourself that if I commit

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Speaker 1: to this one thing, I'm saying no to all these

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Speaker 1: other possible things that could land on my plate, and

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Speaker 1: so it leads to someone being unfocused. I think I

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Speaker 1: think a good example would be something like marriage totally.

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Speaker 1: It's like that is sort of the ultimate commitment. I

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Speaker 1: feel like you see a lot of men are like

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Speaker 1: they're afraid to get married because like, well, what could happen?

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Speaker 3: I'm going to be tied down.

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Speaker 1: But I think when you survey to people in general

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Speaker 1: at a population level, marriage people tend to be happier

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Speaker 1: because it's like I was talking to my friend John Deloney,

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Speaker 1: who has a podcast on relationships and where he's doing

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Speaker 1: a book about marriage, and he asked me, He's like, well,

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Speaker 1: why did you get married? Like you don't have to,

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Speaker 1: you know, he's asking all these people this, and I

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Speaker 1: was like, you know, that's a good question.

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Speaker 3: And the way I thought of it was, now, you're

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Speaker 3: doing the.

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Speaker 1: Crossword puzzle in ink, so you got to like, you

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Speaker 1: know what I mean, Like, you're committed, You're into this thing,

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Speaker 1: and you're into it for the law.

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Speaker 4: Think a lot harder about what your decisions if you're

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Speaker 4: doing in ink totally.

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Speaker 1: One thing you brought up in the book too, is

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Speaker 1: that so many people think about, well what should I do,

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Speaker 1: when oftentimes a better question to ask yourself is what

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Speaker 1: should I not do?

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Speaker 3: In different situations? Where did you see that manifest I.

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Speaker 4: Mean again, that was like part of general Magic's big

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Speaker 4: problem was deciding what not to do. But I think

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Speaker 4: about that all the time in things like our information diet, right,

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Speaker 4: Like people are overwhelmed.

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Speaker 2: There's so many things that seem interesting.

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Speaker 4: There's so much information coming at you, and I think

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Speaker 4: it's a constant question of I'm curious, I want to

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Speaker 4: learn things about how can I stay sane? And I

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Speaker 4: mentioned I describe this in one genetics lab in the book,

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Speaker 4: where they take post it notes and put them on

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Speaker 4: the wall, and each one representing one of their current

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Speaker 4: commitments or projects. And the first thing that happens is

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Speaker 4: once they put them on the wall, so making all

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Speaker 4: their current commitments visual is they realize there's way more

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Speaker 4: than they could ever get done already in process. And

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Speaker 4: so immediately they see it and say, we have to

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Speaker 4: start moving some of this stuff out or will never

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Speaker 4: get anything done. And they have like a hopper where

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Speaker 4: they don't say, we don't have to just throw that

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Speaker 4: idea out the window. We can put in a holding place,

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Speaker 4: but it's back burnered, right. And then they implement this

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Speaker 4: rule where you can't start a new project in the

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Speaker 4: funnel unless one moves out of the funnel, so it's contained.

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Speaker 4: So they're making these choices about what not to do,

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Speaker 4: and some of those things that they decide not to

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Speaker 4: do maybe good ideas, but doesn't matter because they weren't

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Speaker 4: going to get to anything if they had all these ideas.

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Speaker 4: And I kind of took that and did that for

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Speaker 4: myself because I thought that idea of making all your

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Speaker 4: current commitments visible was really interesting and I did the

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Speaker 4: same thing for my own commitments and to immediately realize,

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Speaker 4: immediately start looking at it and saying, this is a

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Speaker 4: lot of stuff, and these ones are a lot more

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Speaker 4: important than those ones, and I like all of these

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Speaker 4: and if there were ten of me, I'd be happy

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Speaker 4: to do all of them, but there aren't, and so

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Speaker 4: it forces you to kind of prioritize. And then I

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Speaker 4: did the same thing with my information diet, where I

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Speaker 4: logged all the sources over a month that I turned to,

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Speaker 4: did the post its on the wall, said which of

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Speaker 4: these do I feel was worthwhile and kept that in

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Speaker 4: and then occasionally new sources come in and then I'll

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Speaker 4: move something else out of the funnel, and it builds up,

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Speaker 4: you know, so I have to kind of do it regularly.

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Speaker 1: So I say this a ton in the wellness space

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Speaker 1: in the sense that when people go I want to

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Speaker 1: get healthier, I want to improve my life, they start

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Speaker 1: immediately adding. They started saying, Okay, I'm going to drink

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Speaker 1: this protein shake every morning, I'm gonna drink this greens powder,

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Speaker 1: I'm going to do xyz. But oftentimes, what I've noticed

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Speaker 1: is that simply figuring out your worst habit and then

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Speaker 1: subtracting that out usually moves the ball way downfield farther

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Speaker 1: than would adding all this stuff. It's like, I compare

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Speaker 1: it to if you're trying to get somewhere and you

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Speaker 1: have your foot on the brake, hammering the gas is

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Speaker 1: not going to be an efficient way to do it.

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Speaker 1: It's much easier to take your foot off the break

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Speaker 1: and then give a little bit of gas. I feel

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Speaker 1: like that's just something that people frequently overlook.

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Speaker 4: I think, especially like you mentioned in the health and

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Speaker 4: wellness space, where there's so much content about optimizing this

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Speaker 4: and that and that, you would you could schedule every

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Speaker 4: second of your day to optimize if you took all

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Speaker 4: of this kind of advice that I think can be

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Speaker 4: so overwhelming that even really well intentioned people do start

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Speaker 4: and then maybe fall prey to and this is an

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Speaker 4: actual psychological term, the what the hell effect, where you're

440
00:21:06,440 --> 00:21:08,840
Speaker 4: trying to optimize all this stuff, you miss once and

441
00:21:08,840 --> 00:21:10,280
Speaker 4: then you're like, what the hell I missed, and you

442
00:21:10,320 --> 00:21:12,160
Speaker 4: just throw everything out the window. So I think that's

443
00:21:12,200 --> 00:21:15,000
Speaker 4: a danger too, So subtracting and starting with simplicity. I mean,

444
00:21:15,000 --> 00:21:17,240
Speaker 4: for me, I wanted to convert myself into a morning

445
00:21:17,280 --> 00:21:22,960
Speaker 4: person when I had a kid, and because I was

446
00:21:23,040 --> 00:21:25,359
Speaker 4: very much a night all before. And one simple thing

447
00:21:25,400 --> 00:21:27,879
Speaker 4: I did is and it's a little embarrassing, but whatever is.

448
00:21:27,920 --> 00:21:30,679
Speaker 4: I started going to sleep in running clothes or workout clothes.

449
00:21:31,520 --> 00:21:34,680
Speaker 4: And because then I wake up in the morning and

450
00:21:34,680 --> 00:21:36,879
Speaker 4: I look in the mirror and I'm like, am I

451
00:21:36,920 --> 00:21:38,520
Speaker 4: going to take these off now?

452
00:21:39,000 --> 00:21:41,639
Speaker 2: No? I'm not. I'm going to go work out. And

453
00:21:41,680 --> 00:21:42,800
Speaker 2: I've worked out every day.

454
00:21:42,640 --> 00:21:45,280
Speaker 4: In the morning for you know, like two years, and

455
00:21:45,320 --> 00:21:48,080
Speaker 4: having us an alarm clock, and so these are really

456
00:21:49,160 --> 00:21:51,800
Speaker 4: simple things where you know, it doesn't have to be

457
00:21:51,840 --> 00:21:54,920
Speaker 4: some crazy change, just like just like a tiny constraint

458
00:21:54,920 --> 00:21:55,760
Speaker 4: that can be helpful. Yeah.

459
00:21:55,760 --> 00:21:57,920
Speaker 1: I see this a lot in this trend of morning

460
00:21:58,000 --> 00:22:01,119
Speaker 1: routines where you see people posting like fifty seven step

461
00:22:01,200 --> 00:22:05,720
Speaker 1: morning routine online, And for me, I always ask, what

462
00:22:05,880 --> 00:22:08,320
Speaker 1: is the ultimate goal of this routine? So I'll take

463
00:22:08,359 --> 00:22:11,199
Speaker 1: myself as an example. My goal is to get up

464
00:22:11,240 --> 00:22:13,080
Speaker 1: and start writing because I need to write a book.

465
00:22:14,480 --> 00:22:18,840
Speaker 1: What does meditating for ten minutes, yoga for five minutes,

466
00:22:18,880 --> 00:22:23,080
Speaker 1: breath work, insert the seventeen other things that you see online,

467
00:22:23,119 --> 00:22:25,280
Speaker 1: What the hell does that have to do with words?

468
00:22:25,280 --> 00:22:27,679
Speaker 3: Getting on a page. It's like nothing.

469
00:22:28,320 --> 00:22:30,959
Speaker 1: What gets word words on a page is sitting down

470
00:22:31,000 --> 00:22:33,520
Speaker 1: in front of a typewriter and putting words on a page.

471
00:22:33,560 --> 00:22:37,399
Speaker 1: So stripping out the things that you can't directly have

472
00:22:37,440 --> 00:22:40,280
Speaker 1: a one to one relationship with this bigger goal you're after,

473
00:22:40,359 --> 00:22:42,399
Speaker 1: I think becomes important totally.

474
00:22:42,480 --> 00:22:44,320
Speaker 4: I mean, and then it's like it's like doing all

475
00:22:44,320 --> 00:22:46,560
Speaker 4: those other things. You can argue that maybe they get

476
00:22:46,600 --> 00:22:48,840
Speaker 4: you in the space to then be more productive when

477
00:22:48,880 --> 00:22:51,600
Speaker 4: you do that, but it's such a roundabout way, right,

478
00:22:51,760 --> 00:22:54,080
Speaker 4: you should first attack the thing that you're thinking about

479
00:22:54,080 --> 00:22:57,040
Speaker 4: attacking instead of all this other stuff around it. And

480
00:22:57,119 --> 00:22:59,320
Speaker 4: I think it one of the reasons I think some

481
00:22:59,359 --> 00:23:02,359
Speaker 4: of the stimization culture is insidious is because it praise

482
00:23:02,440 --> 00:23:05,400
Speaker 4: on really good instincts in people of like self improvement,

483
00:23:05,560 --> 00:23:11,479
Speaker 4: wanting to be better. But it's like in so many cases,

484
00:23:11,480 --> 00:23:15,040
Speaker 4: I think it's just distracting from from the main thing.

485
00:23:15,400 --> 00:23:19,680
Speaker 4: Yeah and yeah, and that's that's just like frustrating for

486
00:23:20,240 --> 00:23:23,160
Speaker 4: good impulses to be I think sort of preyed upon

487
00:23:23,200 --> 00:23:23,840
Speaker 4: in that way.

488
00:23:23,680 --> 00:23:27,119
Speaker 1: Basically totally. So one thing that General Magic did not

489
00:23:27,240 --> 00:23:31,040
Speaker 1: have was managers. Now I think the average employee would

490
00:23:31,040 --> 00:23:32,800
Speaker 1: hear that and be like, oh my god, I want.

491
00:23:32,680 --> 00:23:33,520
Speaker 3: To work at this place.

492
00:23:34,359 --> 00:23:36,560
Speaker 1: So make a case for why managers are important in

493
00:23:36,560 --> 00:23:39,199
Speaker 1: the world where not many people love their manager.

494
00:23:42,800 --> 00:23:44,919
Speaker 4: Yeah, I mean places have tried, you know, like Google

495
00:23:44,920 --> 00:23:46,960
Speaker 4: did this whole thing where they tried not to have managers,

496
00:23:47,880 --> 00:23:49,919
Speaker 4: and then what they found is, you know, people are

497
00:23:49,960 --> 00:23:52,960
Speaker 4: going to Larry Page with their expense reports and stuff,

498
00:23:53,000 --> 00:23:56,040
Speaker 4: and it just wasn't going to work. But in the

499
00:23:56,080 --> 00:23:58,880
Speaker 4: case of General Magic, people weren't coordinated, right. They weren't

500
00:23:58,920 --> 00:24:01,800
Speaker 4: stopping and getting their less together. They were often working

501
00:24:01,840 --> 00:24:06,960
Speaker 4: on things that were not the most important thing they

502
00:24:07,040 --> 00:24:09,360
Speaker 4: needed to be doing. They were missing things. They would

503
00:24:09,359 --> 00:24:12,159
Speaker 4: start new projects all the time, and it was in

504
00:24:12,240 --> 00:24:15,040
Speaker 4: deep I mean one of the reasons I wanted to

505
00:24:15,080 --> 00:24:20,560
Speaker 4: contrast it to Pixar in the book, where Pixar equally

506
00:24:21,040 --> 00:24:23,720
Speaker 4: large vision developing at the exact same time as General

507
00:24:23,760 --> 00:24:29,720
Speaker 4: Magic basically. So it's like these parallel parallel visions basically,

508
00:24:29,720 --> 00:24:32,800
Speaker 4: and one worked and one didn't. And I spent a

509
00:24:32,840 --> 00:24:34,919
Speaker 4: monch of time with Ed Catmoll, who was co founder

510
00:24:34,920 --> 00:24:37,920
Speaker 4: of Pixar, and he described to me something he called

511
00:24:37,960 --> 00:24:42,119
Speaker 4: the beautifully shaded penny problem at Pixar, which was artists

512
00:24:42,200 --> 00:24:44,280
Speaker 4: or directors would get obsessed over the shading on a

513
00:24:44,320 --> 00:24:46,040
Speaker 4: penny in the background of a scene that the audience

514
00:24:46,040 --> 00:24:48,720
Speaker 4: would never notice. And the way they solved this problem,

515
00:24:48,800 --> 00:24:52,120
Speaker 4: he becomes the real high tech fix was with popsicle

516
00:24:52,160 --> 00:24:55,879
Speaker 4: sticks on a board, and each popsicle stick represented the

517
00:24:56,000 --> 00:24:58,720
Speaker 4: number of the amount of work that one animator could

518
00:24:58,720 --> 00:25:01,960
Speaker 4: get done in a week. And if a director wanted

519
00:25:02,000 --> 00:25:03,800
Speaker 4: animators to keep working on that penny, he had to

520
00:25:03,800 --> 00:25:07,000
Speaker 4: start taking popsicle sticks away from like a main character

521
00:25:07,320 --> 00:25:10,840
Speaker 4: that had to get animated, And that solved the problem immediately.

522
00:25:11,320 --> 00:25:13,080
Speaker 4: But that was an important thing for managers to come

523
00:25:13,119 --> 00:25:15,359
Speaker 4: up with, whereas at General Magic it was so free

524
00:25:15,400 --> 00:25:19,760
Speaker 4: flowing that there was sort of no coherence and no

525
00:25:19,880 --> 00:25:23,560
Speaker 4: useful boundaries. And I don't think that's That's not a

526
00:25:23,640 --> 00:25:25,440
Speaker 4: kind of freedom that's actually helpful for people. You want

527
00:25:25,440 --> 00:25:29,520
Speaker 4: freedom within a framework, not this total freedom where nobody

528
00:25:29,520 --> 00:25:30,600
Speaker 4: really knows what they should be doing.

529
00:25:31,280 --> 00:25:33,399
Speaker 1: I feel like I needed to hear that, because when

530
00:25:33,400 --> 00:25:36,159
Speaker 1: I'm writing a book, I will spend an hour on

531
00:25:36,760 --> 00:25:37,440
Speaker 1: a sentence.

532
00:25:38,359 --> 00:25:40,520
Speaker 3: I don't know if you are the same way. What

533
00:25:40,640 --> 00:25:41,960
Speaker 3: they like, is this the way to do it? Is

534
00:25:42,000 --> 00:25:42,320
Speaker 3: this way?

535
00:25:42,400 --> 00:25:44,240
Speaker 1: Is this the right wording, and you're like, it's one

536
00:25:44,320 --> 00:25:49,280
Speaker 1: sentence among like three hundred thousand or whatever. The number is, right, yeah,

537
00:25:49,359 --> 00:25:51,600
Speaker 1: And so you just get obsessed with like these micro

538
00:25:51,720 --> 00:25:54,480
Speaker 1: details when it's like pulling back is going to allow

539
00:25:54,480 --> 00:25:55,600
Speaker 1: you to be way more efficient.

540
00:25:56,480 --> 00:25:59,520
Speaker 4: I mean, I one hundred percent need deadlines. When I

541
00:25:59,520 --> 00:26:02,800
Speaker 4: I mean first two books I turned in end of

542
00:26:02,840 --> 00:26:04,320
Speaker 4: the day on the day they were due, which for

543
00:26:04,400 --> 00:26:06,840
Speaker 4: something that you're looking two years ahead to, or my

544
00:26:06,880 --> 00:26:09,320
Speaker 4: first book three years I had, it's not that easy

545
00:26:09,359 --> 00:26:14,080
Speaker 4: to do. And this time around, when I signed the

546
00:26:14,119 --> 00:26:17,159
Speaker 4: contract and my agent said, you know, and always when

547
00:26:17,160 --> 00:26:18,520
Speaker 4: I sign the contract, I'm like, oh God, will I

548
00:26:18,600 --> 00:26:21,280
Speaker 4: be able to It's hard to plan to finish something

549
00:26:21,560 --> 00:26:25,000
Speaker 4: two three years later totally and be on time, and

550
00:26:25,680 --> 00:26:28,520
Speaker 4: especially when you don't know what you're gonna find yet.

551
00:26:28,760 --> 00:26:31,560
Speaker 4: And my agent was like, you know, the deadlines of

552
00:26:31,560 --> 00:26:33,320
Speaker 4: course in books are flexible. You don't have to treat

553
00:26:33,320 --> 00:26:34,800
Speaker 4: it as real. I'm like, do not tell me that

554
00:26:34,880 --> 00:26:37,080
Speaker 4: ever again, Like we have to treat this thing as

555
00:26:37,119 --> 00:26:39,640
Speaker 4: totally real. Like Duke Ellington said, I don't need time,

556
00:26:40,000 --> 00:26:41,880
Speaker 4: what I need is a deadline totally.

557
00:26:42,200 --> 00:26:45,320
Speaker 1: Another thing that I thought was interesting about Pixar is.

558
00:26:45,640 --> 00:26:49,120
Speaker 1: They seem to work in these tight teams, and when

559
00:26:49,119 --> 00:26:53,399
Speaker 1: they would do screenings of the films, it's not like

560
00:26:53,400 --> 00:26:56,240
Speaker 1: everyone in the building could walk in and provide feedback.

561
00:26:56,480 --> 00:26:59,679
Speaker 1: And they famously they wouldn't let Steve Jobs watch the

562
00:26:59,680 --> 00:27:02,720
Speaker 1: film all right, because his opinion would be weighted too heavily.

563
00:27:03,480 --> 00:27:05,600
Speaker 1: How did you think about this when you were writing

564
00:27:05,640 --> 00:27:09,000
Speaker 1: the book? Did you have anyone beyond your editor read it?

565
00:27:09,040 --> 00:27:10,879
Speaker 1: And how did you think about who are you going

566
00:27:10,960 --> 00:27:13,439
Speaker 1: to let see this? And how did that influence you?

567
00:27:13,960 --> 00:27:18,040
Speaker 4: Yeah, I should say in the past I have not

568
00:27:18,160 --> 00:27:21,119
Speaker 4: been as good as I should be in giving people stuff.

569
00:27:22,080 --> 00:27:25,359
Speaker 4: Adam Grant, psychologist, has actually given me some very healthy

570
00:27:25,359 --> 00:27:26,879
Speaker 4: criticism on that. He said, you got to show your

571
00:27:26,880 --> 00:27:29,000
Speaker 4: work to more people while you're doing it. And so

572
00:27:29,119 --> 00:27:32,000
Speaker 4: this time around I showed it more than I ever

573
00:27:32,000 --> 00:27:33,440
Speaker 4: had in the past, which included my editor. In the past,

574
00:27:33,440 --> 00:27:34,600
Speaker 4: I didn't even show it to my editor while I

575
00:27:34,600 --> 00:27:36,680
Speaker 4: was working on it. I just showed up with a book.

576
00:27:37,359 --> 00:27:39,080
Speaker 4: This time I showed her stuff while I was going.

577
00:27:39,400 --> 00:27:41,600
Speaker 4: I showed my agent, who's a great reader. I showed

578
00:27:41,640 --> 00:27:45,680
Speaker 4: my wife tons of stuff. So that's not a ton

579
00:27:45,720 --> 00:27:49,119
Speaker 4: of people, but I went from zero to several people,

580
00:27:49,200 --> 00:27:51,240
Speaker 4: this time showing them in the middle, and I think

581
00:27:51,240 --> 00:27:53,400
Speaker 4: that was really helpful, and also talking my ideas off

582
00:27:53,400 --> 00:27:56,600
Speaker 4: of my editor a lot earlier to see if I

583
00:27:56,600 --> 00:27:59,199
Speaker 4: could articulate them reasonably. So that was more of the

584
00:27:59,240 --> 00:28:03,000
Speaker 4: process this time around, where before I would show the

585
00:28:03,040 --> 00:28:06,440
Speaker 4: manuscript in this case sometimes I was showing sections or chapters,

586
00:28:06,520 --> 00:28:07,600
Speaker 4: much smaller pieces.

587
00:28:07,960 --> 00:28:09,919
Speaker 1: I think there is a point too, where when you

588
00:28:10,160 --> 00:28:15,360
Speaker 1: show it to too many people, the feedback becomes chaotic

589
00:28:16,080 --> 00:28:18,560
Speaker 1: because everyone's going to have an opinion, and if you

590
00:28:18,680 --> 00:28:23,280
Speaker 1: wait every opinion equally, you just start adding more stuff

591
00:28:23,560 --> 00:28:26,040
Speaker 1: or there'll be disagreements. When I was at Men's Health,

592
00:28:27,160 --> 00:28:31,000
Speaker 1: we would do these things called wallwalks. Okay, so the

593
00:28:31,160 --> 00:28:33,600
Speaker 1: cover lines in the headlines were very important at Men's Health,

594
00:28:33,680 --> 00:28:36,320
Speaker 1: or so we thought, and we would put the magazine

595
00:28:36,400 --> 00:28:38,600
Speaker 1: up on the wall with all the headlines and the

596
00:28:38,600 --> 00:28:40,920
Speaker 1: cover lines, and there would be like twenty five of

597
00:28:41,000 --> 00:28:44,680
Speaker 1: us who would sit around staring at these headlines and

598
00:28:44,680 --> 00:28:48,080
Speaker 1: cover lines, and someone would throw out an idea, you know,

599
00:28:48,360 --> 00:28:50,080
Speaker 1: a bunch of people would shoot it down. Someone would

600
00:28:50,120 --> 00:28:51,760
Speaker 1: throw out another idea, a bunch of people would shoot

601
00:28:51,760 --> 00:28:55,720
Speaker 1: it down. Third idea. You might have twenty three people go,

602
00:28:55,800 --> 00:28:58,360
Speaker 1: oh my god, that's it, that's fantastic, and then you'd

603
00:28:58,360 --> 00:28:59,320
Speaker 1: have one person go.

604
00:29:01,320 --> 00:29:03,080
Speaker 3: I don't know about that one. I'm not sure if

605
00:29:03,120 --> 00:29:03,600
Speaker 3: it's great.

606
00:29:04,160 --> 00:29:07,360
Speaker 1: So then we'd keep iterating and we would literally spend

607
00:29:07,640 --> 00:29:11,160
Speaker 1: like four hours on a single headline in this magazine,

608
00:29:11,560 --> 00:29:15,640
Speaker 1: and it was maddening. So there becomes this thing where

609
00:29:15,640 --> 00:29:19,880
Speaker 1: I think people overvalue a negative opinion even when the

610
00:29:19,960 --> 00:29:22,160
Speaker 1: vast majority of opinions are good, and there's always going

611
00:29:22,200 --> 00:29:24,760
Speaker 1: to be someone with a negative opinion that's interesting.

612
00:29:24,800 --> 00:29:25,480
Speaker 2: I mean, that's like.

613
00:29:27,440 --> 00:29:29,480
Speaker 4: I sort of made a mistake like that in this

614
00:29:29,560 --> 00:29:31,560
Speaker 4: case with this book, just with the title and subtitle,

615
00:29:31,600 --> 00:29:33,360
Speaker 4: where for the first time I asked for input in

616
00:29:33,400 --> 00:29:36,880
Speaker 4: the past was just just did it, and this time

617
00:29:36,960 --> 00:29:40,240
Speaker 4: asked a number of authors that I admire for input,

618
00:29:40,880 --> 00:29:42,680
Speaker 4: and that was probably a mistake because there was no

619
00:29:42,720 --> 00:29:46,240
Speaker 4: agreement whatsoever. Everyone had some different take, and that put

620
00:29:46,280 --> 00:29:48,480
Speaker 4: me in the space of now ignoring, you know, nine

621
00:29:48,520 --> 00:29:52,560
Speaker 4: out of the ten people's input, basically, But I was

622
00:29:52,600 --> 00:29:55,239
Speaker 4: on this email list of authors mostly who were not

623
00:29:55,320 --> 00:29:59,280
Speaker 4: like me, but mostly business authors, and I would feel

624
00:29:59,320 --> 00:30:01,200
Speaker 4: like an alien when they would talk about their process

625
00:30:01,520 --> 00:30:02,680
Speaker 4: where it was.

626
00:30:02,640 --> 00:30:04,040
Speaker 2: Like, I have you ever heard that thing about?

627
00:30:04,040 --> 00:30:06,680
Speaker 4: So the contrast with Google and Apples like Google will

628
00:30:06,720 --> 00:30:09,280
Speaker 4: ab test forty shades of blue and Apple will be like,

629
00:30:09,320 --> 00:30:11,280
Speaker 4: this is our vibe, this is what we're doing. And

630
00:30:11,320 --> 00:30:13,320
Speaker 4: both of those obviously worked for them, but I was

631
00:30:13,400 --> 00:30:17,040
Speaker 4: much more the Apple style, where these writers were much

632
00:30:17,080 --> 00:30:19,000
Speaker 4: more the Google style. They would have a Google doc

633
00:30:19,760 --> 00:30:23,760
Speaker 4: with kind of a focus group in real time reading

634
00:30:23,800 --> 00:30:25,320
Speaker 4: it and saying, you know, this is what I want

635
00:30:25,360 --> 00:30:28,680
Speaker 4: to and maybe I should be doing that, but I

636
00:30:28,680 --> 00:30:31,800
Speaker 4: don't want to because I a lot of the processes

637
00:30:31,840 --> 00:30:35,680
Speaker 4: for me and what I'm learning and the craftsmanship of

638
00:30:35,720 --> 00:30:39,040
Speaker 4: it that I enjoy. But also, to be honest, I

639
00:30:39,080 --> 00:30:45,000
Speaker 4: felt like it kind of homogenized their writing when you're

640
00:30:45,000 --> 00:30:49,000
Speaker 4: going for that sort of consensus in that group, and

641
00:30:49,520 --> 00:30:52,040
Speaker 4: so maybe it decreased the risk, you know, maybe it

642
00:30:52,120 --> 00:30:54,760
Speaker 4: kind of raised the floor of their writing, but I

643
00:30:54,760 --> 00:30:58,520
Speaker 4: think it lowered the ceiling for sure, because it's sort

644
00:30:58,560 --> 00:30:59,680
Speaker 4: of less unique.

645
00:31:00,040 --> 00:31:03,240
Speaker 1: People wouldn't take as many swings, the swings that could

646
00:31:03,240 --> 00:31:05,600
Speaker 1: have been really interesting. Maybe there were misses, who knows,

647
00:31:05,960 --> 00:31:07,760
Speaker 1: but some of the things that really make something stand

648
00:31:07,760 --> 00:31:09,479
Speaker 1: out and be interesting probably got stripped.

649
00:31:09,480 --> 00:31:11,560
Speaker 2: Away you have to risk missing.

650
00:31:11,600 --> 00:31:14,479
Speaker 4: I mean this is like, you know, all this research

651
00:31:14,520 --> 00:31:17,920
Speaker 4: that shows that innovators are like the successful ones have

652
00:31:17,960 --> 00:31:20,800
Speaker 4: more successes, but they have more failures than the than

653
00:31:21,000 --> 00:31:21,640
Speaker 4: their peers do.

654
00:31:21,720 --> 00:31:26,160
Speaker 1: Also totally you read about this idea thinks slow work fast.

655
00:31:26,800 --> 00:31:27,560
Speaker 1: Tell us about that.

656
00:31:28,520 --> 00:31:33,720
Speaker 4: Yeah, that's a phrase from a Danish professor at Oxford

657
00:31:33,800 --> 00:31:37,640
Speaker 4: named Bent flu beer awesomely fantastic.

658
00:31:37,720 --> 00:31:38,560
Speaker 2: Yeah, I know, I know.

659
00:31:38,840 --> 00:31:41,760
Speaker 4: Obviously had to make sure I could pronounce that one.

660
00:31:43,080 --> 00:31:44,800
Speaker 4: And yeah, because if anybody's trying to google him, it

661
00:31:44,840 --> 00:31:50,719
Speaker 4: looks like it's spelled Flipberg. Yeah. And what he he

662
00:31:50,840 --> 00:31:54,560
Speaker 4: kept h he studied big projects for his whole career

663
00:31:54,920 --> 00:31:58,480
Speaker 4: and over decades he kept a database of projects big projects.

664
00:31:58,480 --> 00:32:02,360
Speaker 4: This could be anything from infrastructure to digital transformations, whatever.

665
00:32:03,320 --> 00:32:05,920
Speaker 4: And he had sixteen thousand. He led a lot of

666
00:32:05,960 --> 00:32:10,240
Speaker 4: projects himself too. He had sixteen thousand projects by the

667
00:32:10,800 --> 00:32:12,200
Speaker 4: or he worked on them, I shouldn't say lead, he

668
00:32:12,240 --> 00:32:14,800
Speaker 4: worked on them. He had sixteen thousand projects in his

669
00:32:14,880 --> 00:32:19,040
Speaker 4: database by the end. And what he found was it

670
00:32:19,160 --> 00:32:21,840
Speaker 4: only eight and a half percent of them came in

671
00:32:21,880 --> 00:32:23,680
Speaker 4: on time and on budget, and only zero point five

672
00:32:23,720 --> 00:32:26,800
Speaker 4: percent came in on time and on budget and delivered

673
00:32:26,840 --> 00:32:29,840
Speaker 4: what they had promised. And the typical pattern he found

674
00:32:29,880 --> 00:32:34,160
Speaker 4: was what he called think fast, act slow, where someone

675
00:32:34,160 --> 00:32:36,760
Speaker 4: has an idea and they kind of rushed it into

676
00:32:36,760 --> 00:32:40,200
Speaker 4: implementation before they put boundaries around and really figure out

677
00:32:40,240 --> 00:32:42,040
Speaker 4: what the priorities are and what they're doing, and so

678
00:32:42,080 --> 00:32:45,440
Speaker 4: things expand quickly. And then that fast thinking translates to

679
00:32:45,480 --> 00:32:47,640
Speaker 4: work slow because things get big quickly, and then it's

680
00:32:47,720 --> 00:32:50,200
Speaker 4: very hard to pivot and you start learning lessons more painfully.

681
00:32:50,440 --> 00:32:54,200
Speaker 4: The opposite, what he said was the ideal kind of

682
00:32:54,240 --> 00:32:57,360
Speaker 4: planning was think slow, act fast, where you keep a

683
00:32:57,400 --> 00:33:00,360
Speaker 4: team small. At the beginning, you define the boundary, what

684
00:33:00,400 --> 00:33:04,840
Speaker 4: are we not doing, what is the focus? And then

685
00:33:04,880 --> 00:33:07,120
Speaker 4: when you do move into execution, you're able to work

686
00:33:07,200 --> 00:33:10,360
Speaker 4: much faster because you're not getting surprised by as many things.

687
00:33:10,640 --> 00:33:12,200
Speaker 2: The work boundaries are much more clear.

688
00:33:12,840 --> 00:33:14,400
Speaker 4: And again one of the reasons I picked Pixar in

689
00:33:14,400 --> 00:33:18,240
Speaker 4: the book is because Ben Flubier identified Pixar as like

690
00:33:18,280 --> 00:33:22,400
Speaker 4: the apotheosis of good planning, where they would keep He

691
00:33:22,440 --> 00:33:23,800
Speaker 4: actually calls it Pixar planning.

692
00:33:24,080 --> 00:33:25,280
Speaker 2: A director could stay.

693
00:33:25,120 --> 00:33:28,720
Speaker 4: For years with a small team in story development, refining

694
00:33:28,760 --> 00:33:31,360
Speaker 4: the core of a story cutting away characters, right like

695
00:33:31,760 --> 00:33:33,960
Speaker 4: they cut away the character for Schadenfreud in the first

696
00:33:34,040 --> 00:33:36,200
Speaker 4: Inside Out because they didn't they felt like it was

697
00:33:36,200 --> 00:33:39,800
Speaker 4: getting too many characters, getting too complicated, and that might

698
00:33:39,920 --> 00:33:43,960
Speaker 4: seem inefficient to stay in a small team for years

699
00:33:44,000 --> 00:33:47,080
Speaker 4: while you're refining the story, but the costs only explode

700
00:33:47,120 --> 00:33:49,560
Speaker 4: once you move into production and bring in this much

701
00:33:49,560 --> 00:33:52,040
Speaker 4: bigger team, and so it actually, in the long run

702
00:33:52,240 --> 00:33:54,320
Speaker 4: turns out to be much more efficient. And so that

703
00:33:54,400 --> 00:33:56,720
Speaker 4: kind of thinks slow act fast, where they've spent all

704
00:33:56,800 --> 00:34:00,520
Speaker 4: that time defining the boundaries allows them to work fast

705
00:34:00,520 --> 00:34:02,959
Speaker 4: once they get into it. And I found that to

706
00:34:02,960 --> 00:34:05,320
Speaker 4: be very true for me, where I didn't write a

707
00:34:05,320 --> 00:34:07,640
Speaker 4: single word of my book for a year. Yeah, I

708
00:34:07,720 --> 00:34:10,319
Speaker 4: just planned the architecture and did the research, and then

709
00:34:10,320 --> 00:34:12,480
Speaker 4: it allowed me to write more quickly than I ever

710
00:34:12,520 --> 00:34:13,880
Speaker 4: had once I moved into execution.

711
00:34:14,000 --> 00:34:15,799
Speaker 1: How do you think people can use that sort of

712
00:34:15,840 --> 00:34:18,480
Speaker 1: in the trenches of day to day life making decisions.

713
00:34:19,120 --> 00:34:19,319
Speaker 2: Yeah.

714
00:34:19,320 --> 00:34:22,640
Speaker 4: I think whatever it is they're doing, let's say, if

715
00:34:22,680 --> 00:34:25,080
Speaker 4: it's a work project or some kind of behavior change

716
00:34:25,239 --> 00:34:30,879
Speaker 4: that you're trying to engender, is I think we've talked

717
00:34:30,880 --> 00:34:33,440
Speaker 4: about optimization a little bit and the motivation is you

718
00:34:33,440 --> 00:34:36,200
Speaker 4: see something cool, I'm going to do this tomorrow. I

719
00:34:36,200 --> 00:34:38,439
Speaker 4: think it would actually make more sense to sit down

720
00:34:38,840 --> 00:34:41,560
Speaker 4: and say, what is the goal that this is serving?

721
00:34:43,160 --> 00:34:45,120
Speaker 4: What are the blocks between me and doing this thing?

722
00:34:46,280 --> 00:34:47,759
Speaker 4: Where am I going to draw the line for now?

723
00:34:47,840 --> 00:34:50,480
Speaker 4: Like implement in a small way. What's the first small experiment,

724
00:34:50,520 --> 00:34:52,960
Speaker 4: a low stakes experiment that I can run instead of

725
00:34:53,000 --> 00:34:56,360
Speaker 4: moving straight into big implementation. And so just spend a

726
00:34:56,360 --> 00:34:58,439
Speaker 4: little time figuring out what.

727
00:34:58,360 --> 00:34:58,880
Speaker 2: Are the blocks?

728
00:34:58,880 --> 00:35:01,560
Speaker 4: What's the smallest possible way that you can prototype this

729
00:35:01,640 --> 00:35:05,400
Speaker 4: thing before you move into this bigger execution. Because the

730
00:35:05,440 --> 00:35:08,879
Speaker 4: quicker you move into making something big, the more likely

731
00:35:08,920 --> 00:35:10,879
Speaker 4: you're going to learn harder lessons, and the more likely

732
00:35:10,880 --> 00:35:12,080
Speaker 4: I think you fall prey to that what the hell

733
00:35:12,120 --> 00:35:14,600
Speaker 4: effect where it doesn't really work well and then you

734
00:35:14,680 --> 00:35:17,520
Speaker 4: just throw the throw the baby out with the bathwater,

735
00:35:17,560 --> 00:35:18,200
Speaker 4: so to speak.

736
00:35:18,440 --> 00:35:21,560
Speaker 1: The companies that you highlighted that are doings that were successful,

737
00:35:21,680 --> 00:35:25,080
Speaker 1: they all solve the problem. Yes that in the wellness

738
00:35:25,080 --> 00:35:27,400
Speaker 1: sphere that really made me think about you know, with

739
00:35:27,480 --> 00:35:29,720
Speaker 1: my sub stack, I'll get all these questions from readers

740
00:35:29,719 --> 00:35:32,920
Speaker 1: that are like should I take this supplement? Should I

741
00:35:32,960 --> 00:35:35,120
Speaker 1: do this exercise should I do X Y Z? Insert

742
00:35:35,160 --> 00:35:38,080
Speaker 1: any number of examples, and the question I usually come

743
00:35:38,120 --> 00:35:41,640
Speaker 1: back with, after much trial and error trying to give people,

744
00:35:41,800 --> 00:35:44,640
Speaker 1: you know, well, here's this tart like complicated answer. I

745
00:35:44,719 --> 00:35:46,960
Speaker 1: usually now just respond with what problem are you trying

746
00:35:46,960 --> 00:35:52,000
Speaker 1: to solve? And oftentimes are like, I don't know. I

747
00:35:52,120 --> 00:35:54,920
Speaker 1: heard about this on a you know, online or on

748
00:35:54,960 --> 00:35:57,200
Speaker 1: a podcast, and I thought it sounded interesting, but I

749
00:35:57,280 --> 00:35:59,560
Speaker 1: actually don't know what the problem that will solve for

750
00:35:59,640 --> 00:36:00,920
Speaker 1: me is, you know. And so you get them to

751
00:36:00,920 --> 00:36:03,560
Speaker 1: like pull back, and it's like, all right, if there's

752
00:36:03,600 --> 00:36:05,359
Speaker 1: not a problem that this thing is solving, it sounds

753
00:36:05,400 --> 00:36:08,279
Speaker 1: like it might just be extra work, extra noise, and

754
00:36:08,280 --> 00:36:10,279
Speaker 1: not have that big of a return for you.

755
00:36:11,239 --> 00:36:13,279
Speaker 4: Defining the problem you want to solve, whether it's an

756
00:36:13,320 --> 00:36:18,400
Speaker 4: individual or an organization, incredibly powerful and defining it like

757
00:36:18,520 --> 00:36:20,799
Speaker 4: spending some time again to that think slow and thinking

758
00:36:20,800 --> 00:36:22,800
Speaker 4: about what are you trying to do. There's this famous

759
00:36:22,800 --> 00:36:25,560
Speaker 4: saying people don't want a quarter inch drill, they want

760
00:36:25,560 --> 00:36:27,799
Speaker 4: a quarter inch hole in their wall. Right, So if

761
00:36:27,800 --> 00:36:31,000
Speaker 4: you're thinking about serving the person, what is the actual

762
00:36:31,040 --> 00:36:33,239
Speaker 4: thing that they that they want? Does that mean they

763
00:36:33,360 --> 00:36:35,120
Speaker 4: need a carpenter? Does it mean they need a drill

764
00:36:35,200 --> 00:36:35,719
Speaker 4: or something else.

765
00:36:35,800 --> 00:36:37,640
Speaker 1: All right, here's where I want to mildly push back.

766
00:36:37,680 --> 00:36:39,239
Speaker 1: And the reason I'm doing this is there could be

767
00:36:39,280 --> 00:36:42,160
Speaker 1: people who are listening to this thinking you're saying constraints

768
00:36:42,200 --> 00:36:44,480
Speaker 1: are good. I need more constraints in my life. But

769
00:36:44,560 --> 00:36:47,480
Speaker 1: they might also be thinking my life is already constrained enough.

770
00:36:47,520 --> 00:36:50,640
Speaker 1: I have bills, I have a manager who is complaining

771
00:36:50,719 --> 00:36:53,960
Speaker 1: at me all the time. I have kids, I have dogs,

772
00:36:54,040 --> 00:36:57,600
Speaker 1: I have all these different commitments and constraints. So what

773
00:36:57,800 --> 00:37:01,239
Speaker 1: argument would you make to them about why constraints are good.

774
00:37:01,680 --> 00:37:04,879
Speaker 4: I mean, for one thing, it'd be crazy to say

775
00:37:04,880 --> 00:37:08,520
Speaker 4: the constraints can't be bad. Right, even in creativity, which

776
00:37:08,520 --> 00:37:11,960
Speaker 4: we've talked about, if a constraint, if you're telling someone

777
00:37:12,200 --> 00:37:13,560
Speaker 4: what they have to do and how they have to

778
00:37:13,560 --> 00:37:16,239
Speaker 4: do it, like if they if under this constraint they

779
00:37:16,400 --> 00:37:18,720
Speaker 4: there's no way for them to surprise you or themself,

780
00:37:19,320 --> 00:37:24,840
Speaker 4: then that's bad. It's gone too far. As far as bills,

781
00:37:24,840 --> 00:37:27,400
Speaker 4: you know, nobody likes bills, but jobs and kids and

782
00:37:27,480 --> 00:37:30,360
Speaker 4: dogs and obligations actually turn out to be really important

783
00:37:30,360 --> 00:37:32,880
Speaker 4: for people's sense of well being, so you may bristle

784
00:37:32,960 --> 00:37:33,399
Speaker 4: under them.

785
00:37:33,719 --> 00:37:35,719
Speaker 2: Sometimes because they're inconvenient.

786
00:37:36,920 --> 00:37:38,960
Speaker 4: But I think it's also pretty clear that a dense

787
00:37:39,040 --> 00:37:42,560
Speaker 4: network of obligation is actually a lot of what brings

788
00:37:42,640 --> 00:37:48,080
Speaker 4: meaning to people's lives. And so the founder Emil Durkheim,

789
00:37:48,239 --> 00:37:52,600
Speaker 4: founder of modern sociology, basically he did this famous study

790
00:37:52,600 --> 00:37:55,920
Speaker 4: on suicide when government started first keeping track of statistics,

791
00:37:56,520 --> 00:38:01,160
Speaker 4: and he found things intuitive, things like that suicide rates

792
00:38:01,200 --> 00:38:04,960
Speaker 4: would increase when economic fortunes of a country plummeted, but

793
00:38:05,040 --> 00:38:07,160
Speaker 4: he found they would also increase when the economic fortunes

794
00:38:07,200 --> 00:38:10,880
Speaker 4: of a country skyrocketed. Because anything that unmoored people from

795
00:38:10,920 --> 00:38:13,120
Speaker 4: these kinds of obligations what he called he called anime,

796
00:38:13,320 --> 00:38:17,319
Speaker 4: which means rulelessness. If people were sort of stripped of

797
00:38:17,320 --> 00:38:20,520
Speaker 4: these normal structures and rules that they lived under, they

798
00:38:20,560 --> 00:38:23,520
Speaker 4: would struggle with finding meaning in life. That's not to

799
00:38:23,520 --> 00:38:26,600
Speaker 4: say that every constraint is good, but I think the

800
00:38:26,719 --> 00:38:29,960
Speaker 4: idea that we just need more freedom then will be happier.

801
00:38:30,560 --> 00:38:32,799
Speaker 4: It actually usually looks like the opposite, that people with

802
00:38:32,880 --> 00:38:38,320
Speaker 4: more constraints are happier with married, with kids, with community obligations,

803
00:38:39,080 --> 00:38:42,160
Speaker 4: with regular rituals, and you know, for some people religion,

804
00:38:42,360 --> 00:38:45,680
Speaker 4: going to a job is inconvenient sinking up your schedule

805
00:38:45,680 --> 00:38:47,719
Speaker 4: with someone else to spend time with them is inconvenient.

806
00:38:47,920 --> 00:38:52,040
Speaker 4: Kids are incredibly inconvenient all the time. But these things

807
00:38:52,080 --> 00:38:54,560
Speaker 4: also add meaning to our life. So I think it's

808
00:38:55,080 --> 00:38:58,080
Speaker 4: it's tricky because it feels like more freedom should always

809
00:38:58,080 --> 00:39:00,719
Speaker 4: be attractive. In fact, I went to some years ago

810
00:39:00,760 --> 00:39:04,000
Speaker 4: this writer's retreat where we were all asked, the only

811
00:39:04,000 --> 00:39:05,880
Speaker 4: one I've ever been to where we're all asked, what

812
00:39:05,920 --> 00:39:08,279
Speaker 4: are you optimizing for this year? And I said autonomy

813
00:39:08,600 --> 00:39:11,359
Speaker 4: because after my last book, I became just a writer

814
00:39:11,520 --> 00:39:13,800
Speaker 4: for you know, full time. I left like having a

815
00:39:13,880 --> 00:39:18,680
Speaker 4: normal daily job, and I thought I just wanted to

816
00:39:18,680 --> 00:39:21,960
Speaker 4: spend every minute in the way that I determined. And

817
00:39:22,080 --> 00:39:23,640
Speaker 4: fast forward two years and I learned there's such a

818
00:39:23,640 --> 00:39:25,799
Speaker 4: thing as too much autonomy. Where I was like living

819
00:39:25,800 --> 00:39:28,839
Speaker 4: in an individualized world for one. And so to reel

820
00:39:28,880 --> 00:39:30,799
Speaker 4: that back, I joined the board of a nonprofit in

821
00:39:30,840 --> 00:39:33,440
Speaker 4: my community. I started going to like dance meetups with

822
00:39:33,480 --> 00:39:39,200
Speaker 4: strangers and just started inconveniencing myself a lot more in

823
00:39:39,320 --> 00:39:40,960
Speaker 4: order to add meaning back to my life.

824
00:39:41,120 --> 00:39:41,600
Speaker 3: That's cool.

825
00:39:41,680 --> 00:39:44,799
Speaker 1: It makes me wonder what you think about retirement. I

826
00:39:44,800 --> 00:39:47,720
Speaker 1: feel like when you see data on retirement and well being.

827
00:39:48,120 --> 00:39:49,759
Speaker 1: At first, people are like, oh, this is great, I

828
00:39:49,800 --> 00:39:51,520
Speaker 1: can do anything, and then there's kind of like a

829
00:39:51,960 --> 00:39:54,920
Speaker 1: drop off where they go, I don't know about this totally.

830
00:39:55,000 --> 00:39:59,120
Speaker 4: And there's also all this research that people, you know,

831
00:39:59,239 --> 00:40:02,480
Speaker 4: when they retire, all these rates of dementia and things

832
00:40:02,520 --> 00:40:04,680
Speaker 4: like this are not as cognitively engaged anymore go up.

833
00:40:06,280 --> 00:40:10,640
Speaker 4: And I think, I'm not planning on never retiring good,

834
00:40:10,719 --> 00:40:13,400
Speaker 4: but I think if someone's going to retire, like find take.

835
00:40:13,200 --> 00:40:17,880
Speaker 3: That vacation, but replace it with something, replace.

836
00:40:17,520 --> 00:40:18,680
Speaker 2: It with something that's right.

837
00:40:18,880 --> 00:40:21,440
Speaker 4: There's this I cite this research in the book from

838
00:40:21,520 --> 00:40:28,520
Speaker 4: Sweden where they look at antidepressants being dispensed throughout the country,

839
00:40:28,719 --> 00:40:30,239
Speaker 4: and one of the things they find is that when

840
00:40:30,320 --> 00:40:33,000
Speaker 4: more people in the country are on vacation at once,

841
00:40:33,440 --> 00:40:41,160
Speaker 4: antidepressant dispensing goes way down. But it's true among retirees too,

842
00:40:41,239 --> 00:40:43,840
Speaker 4: who it didn't matter like they were already on vacation essentially.

843
00:40:44,120 --> 00:40:46,040
Speaker 4: But the fact is it's when lots of people are

844
00:40:46,040 --> 00:40:48,759
Speaker 4: doing it at the same time. It's like social control

845
00:40:48,800 --> 00:40:52,320
Speaker 4: of time that has a well being benefit for everyone,

846
00:40:52,360 --> 00:40:55,480
Speaker 4: Whereas the opposite was in the Soviet Union when they

847
00:40:55,520 --> 00:40:58,080
Speaker 4: tried to in order to keep factories running all the time,

848
00:40:58,200 --> 00:41:02,719
Speaker 4: individualize everyone's work routine so that people in the same

849
00:41:02,719 --> 00:41:04,759
Speaker 4: family are on the same block. It wasn't like five

850
00:41:04,880 --> 00:41:07,120
Speaker 4: days of work and two day weekend. They did all

851
00:41:07,120 --> 00:41:09,640
Speaker 4: these different four days of work, one day weekend cycles

852
00:41:09,640 --> 00:41:14,120
Speaker 4: that were different for everybody, and it desynchronized everyone's schedule

853
00:41:14,200 --> 00:41:14,480
Speaker 4: and it.

854
00:41:14,440 --> 00:41:15,560
Speaker 2: Was a social disaster.

855
00:41:16,320 --> 00:41:19,839
Speaker 4: Interesting, that's often what we're doing to ourselves, I think,

856
00:41:20,000 --> 00:41:22,840
Speaker 4: right Like, I remember when Mark Zuckerberg first advertised the metaverse,

857
00:41:22,880 --> 00:41:25,200
Speaker 4: and he was like, it's gonna be amazing. Everyone's going

858
00:41:25,280 --> 00:41:27,759
Speaker 4: to live in their own universe, tailored just for them.

859
00:41:27,840 --> 00:41:30,440
Speaker 4: I'm like, that actually sounds like hell yeah, that sounds terrible.

860
00:41:31,520 --> 00:41:35,640
Speaker 4: Just me, myself and whatever is in this device streaming

861
00:41:35,640 --> 00:41:38,960
Speaker 4: into my brain at all times. I spent too much

862
00:41:38,960 --> 00:41:42,480
Speaker 4: of my time in my head already. So you had

863
00:41:42,480 --> 00:41:45,800
Speaker 4: a great section about health research. So the case study

864
00:41:45,840 --> 00:41:49,080
Speaker 4: here is National Heart, Lung, and Blood Institute. So before

865
00:41:49,160 --> 00:41:52,560
Speaker 4: about year two thousand, projects are getting a ton of

866
00:41:52,560 --> 00:41:56,839
Speaker 4: funding and they're similarly finding these fantastic results for health.

867
00:41:57,080 --> 00:41:59,520
Speaker 4: All these great drugs, all these good things are happening.

868
00:42:00,280 --> 00:42:02,400
Speaker 4: And then after two thousand, all of a sudden that

869
00:42:02,760 --> 00:42:06,480
Speaker 4: drops off the finding stop. Now, that might seem like

870
00:42:06,480 --> 00:42:08,040
Speaker 4: a bad thing, but in the book you argue, no,

871
00:42:08,120 --> 00:42:09,759
Speaker 4: this is actually a good thing. So walk us through

872
00:42:09,760 --> 00:42:14,600
Speaker 4: that and what it tells us about health research. Yeah,

873
00:42:14,640 --> 00:42:17,959
Speaker 4: I'm glad you asked me about this, because I don't

874
00:42:17,960 --> 00:42:19,560
Speaker 4: think many people are going to ask me about this

875
00:42:20,200 --> 00:42:22,600
Speaker 4: part of the book because it's a little complicated. But

876
00:42:23,160 --> 00:42:25,799
Speaker 4: as you said, it was all these major where there's

877
00:42:25,880 --> 00:42:29,839
Speaker 4: drugs supplements. Before two thousand, most of the results were

878
00:42:29,840 --> 00:42:33,040
Speaker 4: positive that were funded in these studies, and then starting

879
00:42:33,040 --> 00:42:35,799
Speaker 4: in two thousand, they're almost all negative. Supplements aren't working,

880
00:42:35,840 --> 00:42:40,200
Speaker 4: they're not out performing placebo anyway, drugs aren't working. And

881
00:42:40,280 --> 00:42:42,080
Speaker 4: so it looked like all of a sudden, medicine stopped

882
00:42:42,120 --> 00:42:45,200
Speaker 4: working in the year two thousand, like millennium bug. What

883
00:42:45,360 --> 00:42:48,719
Speaker 4: really happened was that researchers started facing more constraints in

884
00:42:48,760 --> 00:42:51,200
Speaker 4: their work, so they were forced to do what's called

885
00:42:51,280 --> 00:42:55,800
Speaker 4: preregistration starting in two thousand for these big supplement and

886
00:42:55,880 --> 00:42:59,759
Speaker 4: drug studies. Preregistration means you have to say what you're

887
00:42:59,760 --> 00:43:01,680
Speaker 4: act actually testing, What do you think this drug or

888
00:43:01,680 --> 00:43:04,560
Speaker 4: supplement is going to do? How are you going to

889
00:43:04,600 --> 00:43:06,959
Speaker 4: measure that, How are you going to analyze the data.

890
00:43:07,960 --> 00:43:12,920
Speaker 4: And that's counterintuitively, that's what caused positive effects to stop

891
00:43:13,640 --> 00:43:17,560
Speaker 4: popping up, because what had been happening was researchers would

892
00:43:17,560 --> 00:43:19,800
Speaker 4: make a hypothesis. They'd guess how some drug or supplement

893
00:43:19,920 --> 00:43:23,160
Speaker 4: was going to improve health. They would analyze the data

894
00:43:23,160 --> 00:43:25,840
Speaker 4: and they would see their prediction was not right. But

895
00:43:25,880 --> 00:43:28,279
Speaker 4: they had all this data, so then they would start

896
00:43:28,280 --> 00:43:33,360
Speaker 4: looking through the data for some other correlation, right, thinking well, okay,

897
00:43:33,360 --> 00:43:35,200
Speaker 4: maybe it didn't drop blood pressure, but maybe.

898
00:43:35,080 --> 00:43:36,680
Speaker 2: It improves cholesterol. Oh what do you know?

899
00:43:36,880 --> 00:43:38,480
Speaker 4: Oh, so then they published as if that's what they

900
00:43:38,520 --> 00:43:39,960
Speaker 4: were looking for in the first place, and this was

901
00:43:39,960 --> 00:43:43,040
Speaker 4: not nefarious. People were not doing this thinking it was bad.

902
00:43:43,080 --> 00:43:46,360
Speaker 4: I did this as a science gratitude, not realizing the problem.

903
00:43:46,719 --> 00:43:50,040
Speaker 4: The problem essentially is that when you're doing this thing,

904
00:43:50,080 --> 00:43:53,640
Speaker 4: which is called HARKing hypothesizing after the results are known,

905
00:43:54,760 --> 00:43:57,480
Speaker 4: it's like being The analogy is like a sharpshooter who

906
00:43:57,480 --> 00:43:59,920
Speaker 4: fires randomly at a wall and then draws a bullet,

907
00:44:00,040 --> 00:44:01,759
Speaker 4: finds a clump of bullet holes and draws a bulls

908
00:44:01,760 --> 00:44:04,799
Speaker 4: eye around them, and somebody who walks up says, wow,

909
00:44:04,840 --> 00:44:07,520
Speaker 4: what a great shooter, Not oh, there was a clump

910
00:44:07,640 --> 00:44:10,879
Speaker 4: because of random statistical variation. And so when researchers were

911
00:44:10,880 --> 00:44:13,400
Speaker 4: retroactively going through their data, and if you have a

912
00:44:13,400 --> 00:44:15,399
Speaker 4: lot of data, you're always going to find a bunch

913
00:44:15,440 --> 00:44:19,080
Speaker 4: of false positives just by chance alone. So it was

914
00:44:19,080 --> 00:44:22,120
Speaker 4: this preregistration forcing someone to stick to the initial prediction

915
00:44:22,800 --> 00:44:26,000
Speaker 4: that showed that most of these drugs and supplements weren't

916
00:44:26,040 --> 00:44:29,920
Speaker 4: working and in fact, most of the prior positive results,

917
00:44:30,400 --> 00:44:32,120
Speaker 4: many of which are drugs that are still out there.

918
00:44:32,360 --> 00:44:35,000
Speaker 4: What's an example, There's like a bunch of blood pressure

919
00:44:35,040 --> 00:44:39,840
Speaker 4: medications that were out there. There's one one very famous

920
00:44:39,840 --> 00:44:44,600
Speaker 4: one called a tenolol. That's very famous drug out there.

921
00:44:44,640 --> 00:44:46,080
Speaker 4: And one of the interesting things about a tennolol was

922
00:44:46,120 --> 00:44:47,960
Speaker 4: it was viewed as a breakthrough because it it does

923
00:44:48,080 --> 00:44:52,520
Speaker 4: lower blood pressure numbers, but people die from heart attack

924
00:44:52,560 --> 00:44:54,160
Speaker 4: and stroke at the exact same rate just with lower

925
00:44:54,200 --> 00:44:56,920
Speaker 4: blood pressure numbers. So you know, it can look good

926
00:44:56,920 --> 00:45:02,680
Speaker 4: but doesn't actually have an useful effect. And so anyway,

927
00:45:02,719 --> 00:45:06,880
Speaker 4: so it was these these greater constraints in how scientists

928
00:45:06,920 --> 00:45:10,120
Speaker 4: were operating that led them to start drawing more true conclusions.

929
00:45:10,160 --> 00:45:14,800
Speaker 4: The downside is because this is a newer era of research,

930
00:45:14,880 --> 00:45:17,799
Speaker 4: like nutrition research is just an absolute mess. So it's

931
00:45:17,840 --> 00:45:20,719
Speaker 4: this famous study that people refer to, scientists refer to

932
00:45:20,760 --> 00:45:23,840
Speaker 4: as the Everything in your Fridge Causes and Prevents Cancer study,

933
00:45:23,880 --> 00:45:26,400
Speaker 4: because it it looked at all the different studies on

934
00:45:26,440 --> 00:45:28,640
Speaker 4: a bunch of different foods and found that basically everything

935
00:45:28,680 --> 00:45:31,040
Speaker 4: had been found both to cause and prevent cancer except

936
00:45:31,040 --> 00:45:33,000
Speaker 4: for bacon's cause cancer.

937
00:45:33,160 --> 00:45:34,520
Speaker 2: Unfortunately. Yes, yeah, indeed.

938
00:45:37,320 --> 00:45:40,719
Speaker 4: And the problem was these studies just are not I mean,

939
00:45:40,800 --> 00:45:43,320
Speaker 4: nutrition is hard to study anyway for variety of reasons.

940
00:45:43,360 --> 00:45:46,920
Speaker 4: But the studies were just not well controlled. And so

941
00:45:47,040 --> 00:45:50,080
Speaker 4: the good the good news is there's a lot more preregistration.

942
00:45:50,200 --> 00:45:54,080
Speaker 4: Now the bad news is, I think this caused some

943
00:45:54,280 --> 00:45:59,720
Speaker 4: understandable mistrust from some of the scientific community where results

944
00:45:59,719 --> 00:46:03,040
Speaker 4: were not holding up. Well, the good thing is it

945
00:46:03,080 --> 00:46:06,719
Speaker 4: was scientists themselves who identified these problems and it led

946
00:46:06,760 --> 00:46:08,240
Speaker 4: to a now a better system.

947
00:46:08,440 --> 00:46:10,880
Speaker 1: Yeah that makes sense. And you use the example of

948
00:46:11,239 --> 00:46:16,000
Speaker 1: Brian Wantson. Yeah, I remember before this became a big deal.

949
00:46:16,080 --> 00:46:17,960
Speaker 1: So he was using those methods where he kind of

950
00:46:18,000 --> 00:46:19,680
Speaker 1: sort of set up these studies and then he just

951
00:46:19,760 --> 00:46:21,160
Speaker 1: combed through the data and be like, well, what can

952
00:46:21,200 --> 00:46:23,799
Speaker 1: I find that was positive? But most of it's just

953
00:46:23,840 --> 00:46:25,640
Speaker 1: going to be, like you said, by chance, you're going

954
00:46:25,680 --> 00:46:29,080
Speaker 1: to have something positive. When I was at Men's Health,

955
00:46:29,320 --> 00:46:32,680
Speaker 1: that dude was like, our are all star because this

956
00:46:32,760 --> 00:46:35,040
Speaker 1: is a health magazine with like little tidbits, and you'd

957
00:46:35,040 --> 00:46:38,040
Speaker 1: have things like if you use a smaller plate, you

958
00:46:38,080 --> 00:46:40,120
Speaker 1: will eat less, like all these little things.

959
00:46:39,719 --> 00:46:42,640
Speaker 3: And then and then it all entirely blew up totally.

960
00:46:42,719 --> 00:46:45,840
Speaker 4: And it wasn't It wasn't just health magazines by any stretch.

961
00:46:45,880 --> 00:46:48,920
Speaker 4: I mean, his work was called, you know, masterpiece in

962
00:46:48,960 --> 00:46:51,839
Speaker 4: a book by a Nobel Prize winner, like he was

963
00:46:52,040 --> 00:46:56,719
Speaker 4: informing nutrition guidelines for Americans. He to me, clearly was

964
00:46:56,800 --> 00:47:01,759
Speaker 4: not intentionally misrepresenting everything, because the problem developed for him

965
00:47:01,760 --> 00:47:04,400
Speaker 4: when he wrote a blog post about his research methods

966
00:47:05,200 --> 00:47:08,520
Speaker 4: and another scientist was like, you can't do that, because

967
00:47:08,520 --> 00:47:10,400
Speaker 4: all your results are just going to be from a

968
00:47:10,400 --> 00:47:12,480
Speaker 4: statistical chance alone. And we see this all the time

969
00:47:12,520 --> 00:47:14,000
Speaker 4: in the world around us, right. Like the example I

970
00:47:14,080 --> 00:47:15,960
Speaker 4: use in the book is if you're watching an NFL

971
00:47:16,000 --> 00:47:18,160
Speaker 4: game and you hear the announcer say, you know, the

972
00:47:18,239 --> 00:47:21,120
Speaker 4: Chiefs are undefeated, when Taylor Swift is in the audience

973
00:47:21,160 --> 00:47:23,400
Speaker 4: and they're playing in division rival on the road. You

974
00:47:23,440 --> 00:47:25,239
Speaker 4: can be sure that they first looked for are the

975
00:47:25,320 --> 00:47:28,160
Speaker 4: Chiefs undefeated when Taylor Swift is in the audience, didn't

976
00:47:28,160 --> 00:47:31,160
Speaker 4: find that, and then started adding more and more qualifications,

977
00:47:31,560 --> 00:47:33,120
Speaker 4: And every time you do that, it's more.

978
00:47:33,000 --> 00:47:34,360
Speaker 2: Likely that you find a false positive.

979
00:47:34,440 --> 00:47:37,439
Speaker 1: Basically, Yeah, I think sports is a great example because

980
00:47:37,440 --> 00:47:40,040
Speaker 1: there's so many times where you know, if it's like

981
00:47:40,080 --> 00:47:43,720
Speaker 1: the Masters, it's oh, well, this guy tends to score

982
00:47:44,120 --> 00:47:47,400
Speaker 1: less when there's this due point in the air and xyz,

983
00:47:47,480 --> 00:47:48,920
Speaker 1: and it's like you just had an intern look at

984
00:47:48,960 --> 00:47:52,479
Speaker 1: all this random stuff and give us some piece of information. Now,

985
00:47:53,160 --> 00:47:55,439
Speaker 1: that is an example where it's kind of just this fun,

986
00:47:55,480 --> 00:47:58,920
Speaker 1: stupid stuff we watch. But I will say when I

987
00:47:58,960 --> 00:48:02,239
Speaker 1: listened to sports podcast, yes I hear people using this

988
00:48:02,760 --> 00:48:06,879
Speaker 1: information as a reason to make a bet, right because

989
00:48:06,880 --> 00:48:09,719
Speaker 1: a lot of podcasts are sponsored by betting companies, And

990
00:48:09,760 --> 00:48:11,920
Speaker 1: so it becomes like, oh, well, the Chief Taylor Swiss

991
00:48:11,920 --> 00:48:13,560
Speaker 1: in the audience, she's going to be at the game.

992
00:48:13,640 --> 00:48:15,960
Speaker 1: They never lose when Taylor's there, so you got to

993
00:48:16,000 --> 00:48:17,400
Speaker 1: push those chips across the table.

994
00:48:17,400 --> 00:48:19,080
Speaker 3: And in that case, I'm like, ooh, I don't know

995
00:48:19,120 --> 00:48:19,480
Speaker 3: about that.

996
00:48:20,239 --> 00:48:25,600
Speaker 4: No, And in fat absolutely and I think also sometimes

997
00:48:25,640 --> 00:48:28,200
Speaker 4: on financial TV when I'll catch that if I'm in

998
00:48:28,200 --> 00:48:31,239
Speaker 4: a gym or something, there will be something very very

999
00:48:31,239 --> 00:48:33,960
Speaker 4: similar with someone who's brought on because of certain predictions

1000
00:48:34,000 --> 00:48:36,520
Speaker 4: they're making, and they'll start describing how they come to

1001
00:48:36,560 --> 00:48:40,920
Speaker 4: those predictions. And I don't know for sure, but you

1002
00:48:40,960 --> 00:48:45,759
Speaker 4: can tell when somebody starts adding different caveats to the category,

1003
00:48:45,840 --> 00:48:47,919
Speaker 4: like when the housing market does this, and these other

1004
00:48:47,960 --> 00:48:50,840
Speaker 4: three things happen, three things happen, here's here's you know

1005
00:48:50,880 --> 00:48:53,080
Speaker 4: what we see in the market. You can be almost

1006
00:48:53,120 --> 00:48:55,279
Speaker 4: positive that they were slicing and dicing data in a

1007
00:48:55,280 --> 00:48:57,120
Speaker 4: way that ensures that this was that this was a

1008
00:48:57,160 --> 00:48:58,240
Speaker 4: false positive totally.

1009
00:48:58,600 --> 00:49:03,960
Speaker 1: Your first book, Sports gam that was twenty thirteen. Twenty thirteen, Yeah,

1010
00:49:04,840 --> 00:49:06,920
Speaker 1: what led you to write that? A few things?

1011
00:49:06,960 --> 00:49:11,719
Speaker 4: So one, I mean, I guess the little secret of

1012
00:49:11,719 --> 00:49:15,920
Speaker 4: that book is that it was very much questions I

1013
00:49:16,080 --> 00:49:19,319
Speaker 4: had about things that I had seen in sports, either

1014
00:49:19,320 --> 00:49:21,640
Speaker 4: as a spectator or I was an eight hundred meter

1015
00:49:21,680 --> 00:49:25,200
Speaker 4: runner in college, or as a competitor. So things like

1016
00:49:25,320 --> 00:49:26,879
Speaker 4: as a runner in my high school, we had lots

1017
00:49:26,880 --> 00:49:28,879
Speaker 4: of Jamaican guys and we had an incredible track team,

1018
00:49:28,920 --> 00:49:30,920
Speaker 4: and you know, it's like a country of two to

1019
00:49:30,960 --> 00:49:33,400
Speaker 4: three million people, like what's going on over there? And

1020
00:49:33,440 --> 00:49:35,600
Speaker 4: then in college I was running against some Kenyan guys

1021
00:49:35,600 --> 00:49:39,160
Speaker 4: and realizing they were all from one tiny tribe called

1022
00:49:39,200 --> 00:49:42,960
Speaker 4: the Kallengin. And then just seeing things like why, you know,

1023
00:49:43,000 --> 00:49:45,600
Speaker 4: watching an exhibition softball game with Major League baseball players

1024
00:49:45,600 --> 00:49:47,600
Speaker 4: and realizing none of the best baseball hitters in.

1025
00:49:47,560 --> 00:49:48,400
Speaker 2: The world could hit.

1026
00:49:49,760 --> 00:49:52,239
Speaker 4: A good softball pitcher and just wondering what's going on

1027
00:49:52,320 --> 00:49:55,800
Speaker 4: with this, and so just just wanting to examine those things.

1028
00:49:55,880 --> 00:49:57,200
Speaker 2: And then there was also.

1029
00:49:58,600 --> 00:50:01,239
Speaker 4: Sort of disclaimer, sad part to the story, but this

1030
00:50:01,280 --> 00:50:03,520
Speaker 4: is kind of what led to my writing career in

1031
00:50:03,560 --> 00:50:05,759
Speaker 4: many ways. Was so I was a national level eight

1032
00:50:05,800 --> 00:50:07,520
Speaker 4: hundred meter runner and I had a training partner who

1033
00:50:07,520 --> 00:50:09,759
Speaker 4: died at the end of a race, Oh Jesus, from

1034
00:50:09,760 --> 00:50:13,560
Speaker 4: a condition called hypertrophic cardiomyopathy or HCM, almost usually the

1035
00:50:13,560 --> 00:50:17,640
Speaker 4: cause of a young athlete with no obvious prior symptoms

1036
00:50:17,719 --> 00:50:22,960
Speaker 4: dropping dead. And I had his family sign a way

1037
00:50:23,000 --> 00:50:24,600
Speaker 4: for allowing me to gather up his medical records and

1038
00:50:24,640 --> 00:50:27,040
Speaker 4: kind of investigated what had happened, and he had this

1039
00:50:27,080 --> 00:50:29,200
Speaker 4: disease had been misdiagnosed. He had a you know, it's

1040
00:50:29,239 --> 00:50:31,440
Speaker 4: caused by a single genetic mutation, and I thought there

1041
00:50:31,440 --> 00:50:34,200
Speaker 4: were some lives that could be saved with certain types

1042
00:50:34,200 --> 00:50:35,680
Speaker 4: of awareness. And so this is what led me to

1043
00:50:35,800 --> 00:50:37,600
Speaker 4: leave my track of training to be a scientist and

1044
00:50:37,640 --> 00:50:39,480
Speaker 4: try to become the science writer at Sports Illustrated to

1045
00:50:39,520 --> 00:50:41,719
Speaker 4: write about sudden cardiac death and athletes. And that's what

1046
00:50:41,760 --> 00:50:43,840
Speaker 4: got me interested in genetics in the first place.

1047
00:50:43,960 --> 00:50:46,200
Speaker 1: Yeah, what were the big takeaways from that book for

1048
00:50:46,200 --> 00:50:46,960
Speaker 1: the average person?

1049
00:50:47,960 --> 00:50:51,800
Speaker 4: I think some things that I thought were totally innate,

1050
00:50:51,880 --> 00:50:53,880
Speaker 4: like the reflexes they hit a major league fastball, are not.

1051
00:50:53,920 --> 00:50:58,360
Speaker 4: They're completely learned. And other things like the will to

1052
00:51:00,520 --> 00:51:02,520
Speaker 4: do a lot of physical activity actually has like a

1053
00:51:02,560 --> 00:51:08,640
Speaker 4: really strong innate component. But maybe the biggest takeaway, so

1054
00:51:08,680 --> 00:51:10,600
Speaker 4: the American College of Sports Medicine has this phrase, I

1055
00:51:10,640 --> 00:51:12,920
Speaker 4: don't know if they still have it, but exercises medicine.

1056
00:51:13,160 --> 00:51:16,160
Speaker 4: And just like we've learned from medical genetics that no

1057
00:51:16,239 --> 00:51:18,520
Speaker 4: two people respond to a medication the same way because

1058
00:51:18,520 --> 00:51:20,880
Speaker 4: of differences in their genetics, no two people will respond

1059
00:51:20,920 --> 00:51:23,719
Speaker 4: to a specific training exactly the same way. And so

1060
00:51:23,800 --> 00:51:27,080
Speaker 4: I think it's worth spending some time kind of experimenting

1061
00:51:27,080 --> 00:51:30,920
Speaker 4: with different training modalities because you know, you may have

1062
00:51:30,960 --> 00:51:32,560
Speaker 4: the same diet as someone else and it may not

1063
00:51:32,600 --> 00:51:35,120
Speaker 4: work as well for you, and so it may be

1064
00:51:35,160 --> 00:51:36,640
Speaker 4: worth it to be a little bit of a scientist

1065
00:51:36,680 --> 00:51:40,960
Speaker 4: of yourself and see if you can fit your your

1066
00:51:40,960 --> 00:51:43,759
Speaker 4: health routines to your physiology or improve a little bit

1067
00:51:43,800 --> 00:51:45,200
Speaker 4: over time with some experimentation.

1068
00:51:45,719 --> 00:51:48,080
Speaker 1: Yeah, and I feel like a lot of people kind

1069
00:51:48,080 --> 00:51:52,000
Speaker 1: of understand this at a basic level. Though I'm a

1070
00:51:52,000 --> 00:51:54,279
Speaker 1: better runner than I am a lifter. Like to take

1071
00:51:54,320 --> 00:51:56,400
Speaker 1: my example, I go, you know, I was at mentalit,

1072
00:51:56,400 --> 00:51:58,880
Speaker 1: so I had to do all this lifting. There was

1073
00:51:58,920 --> 00:52:01,279
Speaker 1: no amount of training I could do that would allow

1074
00:52:01,360 --> 00:52:04,680
Speaker 1: me to be super strong and like a big sense

1075
00:52:04,719 --> 00:52:07,000
Speaker 1: like that. But running, I'm like, I'm pretty good at

1076
00:52:07,000 --> 00:52:09,160
Speaker 1: that outdoor stuff like I can just hike on a

1077
00:52:09,200 --> 00:52:11,239
Speaker 1: trail for days and I'm fine where some people are

1078
00:52:11,239 --> 00:52:12,480
Speaker 1: just never going to be able to do that.

1079
00:52:12,640 --> 00:52:14,360
Speaker 3: And so I think leaning into that.

1080
00:52:14,400 --> 00:52:16,320
Speaker 1: But also I think one of the keys with exercise

1081
00:52:16,360 --> 00:52:18,120
Speaker 1: in particular is you got to find something you actually

1082
00:52:18,200 --> 00:52:21,000
Speaker 1: enjoy and if it aligns with what you're good.

1083
00:52:20,880 --> 00:52:22,080
Speaker 3: At, bonus points.

1084
00:52:22,320 --> 00:52:26,000
Speaker 1: So how did your second book Range come out of

1085
00:52:26,080 --> 00:52:28,279
Speaker 1: the sports gene and quickly tell us about range too.

1086
00:52:28,560 --> 00:52:31,880
Speaker 4: So in the Sports Gene, I criticized the research underlying

1087
00:52:31,880 --> 00:52:34,520
Speaker 4: the ten thousand hours rule that Malcolm Gladwell had made

1088
00:52:34,520 --> 00:52:38,440
Speaker 4: famous because the research was poorly done, and that brought

1089
00:52:38,440 --> 00:52:41,040
Speaker 4: me into a public debate with glad Will the first

1090
00:52:41,040 --> 00:52:41,440
Speaker 4: time we met.

1091
00:52:41,520 --> 00:52:41,680
Speaker 3: Yeah.

1092
00:52:41,719 --> 00:52:43,759
Speaker 1: In the ten thousand hour rule is that you need

1093
00:52:43,800 --> 00:52:46,600
Speaker 1: to practice something for ten thousand hours to be an expert.

1094
00:52:46,920 --> 00:52:49,880
Speaker 4: Yeah, basically, And the implication is you should specialize as

1095
00:52:49,960 --> 00:52:53,560
Speaker 4: narrowly and early as humanly possible. And that brought us

1096
00:52:53,560 --> 00:52:55,800
Speaker 4: into this debate that's on YouTube at the MIT Sloane

1097
00:52:55,800 --> 00:52:59,680
Speaker 4: Sports Analytics Conference, and I put up some of the

1098
00:52:59,760 --> 00:53:02,560
Speaker 4: data showing that most future lead athletes, because we were

1099
00:53:02,560 --> 00:53:06,759
Speaker 4: talking about athletic development at that debate, actually had a

1100
00:53:06,760 --> 00:53:09,200
Speaker 4: sampling period early where they did a variety of things.

1101
00:53:09,239 --> 00:53:12,800
Speaker 4: They learned these broad general skills that scaffold later technical skills,

1102
00:53:12,880 --> 00:53:16,080
Speaker 4: They learned about their interest and abilities and delay picking

1103
00:53:16,480 --> 00:53:19,160
Speaker 4: one activity. And when we were coming off the stage,

1104
00:53:19,160 --> 00:53:21,279
Speaker 4: he said, you got me on that that doesn't fit

1105
00:53:21,480 --> 00:53:23,560
Speaker 4: with things that I've thought and written. Why don't we

1106
00:53:23,600 --> 00:53:25,320
Speaker 4: And he had been a national level runner two, so

1107
00:53:25,360 --> 00:53:27,160
Speaker 4: he said, why don't we run together? Tomorrow back in

1108
00:53:27,200 --> 00:53:29,040
Speaker 4: New York and we'll talk about it. And then we

1109
00:53:29,080 --> 00:53:32,000
Speaker 4: started talking about what we called the Roger Versus Tiger

1110
00:53:32,040 --> 00:53:36,040
Speaker 4: problem because Tiger Woods early specialization, Roger Fetter delayed specialization,

1111
00:53:38,360 --> 00:53:40,959
Speaker 4: and pretty soon we leaped out of sports and started

1112
00:53:41,040 --> 00:53:42,560
Speaker 4: jumping about in other area. So I was like doing

1113
00:53:42,640 --> 00:53:46,720
Speaker 4: research weekly for my runs with Gladwell, and that became

1114
00:53:46,760 --> 00:53:48,759
Speaker 4: the book Range about the benefits of breadth in an

1115
00:53:48,800 --> 00:53:52,440
Speaker 4: increasingly specialized world. And the introduction is called Roger Versus Tiger,

1116
00:53:52,480 --> 00:53:54,759
Speaker 4: which was exactly what we would call our arguments what

1117
00:53:54,880 --> 00:53:55,800
Speaker 4: we were running together.

1118
00:53:56,160 --> 00:54:00,960
Speaker 1: So your book Range, especially with this idea that specialization

1119
00:54:01,520 --> 00:54:05,000
Speaker 1: is not always required for growth and improving in the

1120
00:54:05,000 --> 00:54:07,920
Speaker 1: long term. In fact, it's nice to have some range.

1121
00:54:08,520 --> 00:54:10,759
Speaker 1: I feel like that's a huge one for parents with

1122
00:54:10,920 --> 00:54:14,239
Speaker 1: kids in sports. It's like I remember when I was

1123
00:54:14,239 --> 00:54:16,160
Speaker 1: at mental health. I would do a lot of I

1124
00:54:16,160 --> 00:54:18,839
Speaker 1: would use as a source this guy Eric Kressy, who

1125
00:54:18,920 --> 00:54:22,920
Speaker 1: was a baseball trainer basically, and he would work with

1126
00:54:22,960 --> 00:54:24,799
Speaker 1: like the Red Sox, all these pros. But he had

1127
00:54:24,840 --> 00:54:26,840
Speaker 1: all these people in that he was up in Boston.

1128
00:54:26,840 --> 00:54:28,600
Speaker 1: He'd have all you know, Red Sox are huge up there,

1129
00:54:28,600 --> 00:54:30,680
Speaker 1: all the kids to play baseball. All these parents sending

1130
00:54:31,160 --> 00:54:32,960
Speaker 1: their eight year olds, nine year olds to him and

1131
00:54:33,000 --> 00:54:35,600
Speaker 1: being like, you need to make this kid a professional

1132
00:54:35,640 --> 00:54:38,280
Speaker 1: athlete immediately. And he would just be like, you should

1133
00:54:38,280 --> 00:54:40,279
Speaker 1: maybe have him go do some other stuff, to play

1134
00:54:40,280 --> 00:54:41,200
Speaker 1: different sports.

1135
00:54:41,400 --> 00:54:43,719
Speaker 2: So what did you find with that? Yeah, I mean

1136
00:54:44,360 --> 00:54:44,799
Speaker 2: that's funny.

1137
00:54:44,880 --> 00:54:46,840
Speaker 4: You mentioned that reminds you of this guy, Ian Yates,

1138
00:54:46,840 --> 00:54:50,960
Speaker 4: who was a British guy who developed olympians for various sports.

1139
00:54:51,040 --> 00:54:54,160
Speaker 4: And he told me one of the problems became so

1140
00:54:54,360 --> 00:54:57,040
Speaker 4: he mentioned Bradley Wiggins is famous British cyclist, and he

1141
00:54:57,040 --> 00:54:58,799
Speaker 4: would say, I have parents coming to me now saying

1142
00:54:58,840 --> 00:55:00,880
Speaker 4: I want my twelve year old doing what Bradley Wiggins

1143
00:55:00,880 --> 00:55:02,920
Speaker 4: is doing now, not what Bradley Wiggins was doing when

1144
00:55:02,920 --> 00:55:07,360
Speaker 4: he was twelve, which was completely different. And the fact

1145
00:55:07,400 --> 00:55:13,080
Speaker 4: is the research shows that the best the most typical path.

1146
00:55:13,080 --> 00:55:14,520
Speaker 4: There are a lot of different paths, of course, but

1147
00:55:14,520 --> 00:55:18,600
Speaker 4: the most typical path becoming an elite athlete is with

1148
00:55:18,680 --> 00:55:22,320
Speaker 4: a sampling period, early variety of activities, broad general skills.

1149
00:55:22,360 --> 00:55:24,800
Speaker 4: Now some people call physical literacy those general skills.

1150
00:55:24,840 --> 00:55:26,879
Speaker 1: So you're playing you're not just playing baseball, You're also

1151
00:55:26,920 --> 00:55:28,359
Speaker 1: like I'm going to be on the basketball team, I'm

1152
00:55:28,360 --> 00:55:29,600
Speaker 1: going to run some truck, I'm going to do a

1153
00:55:29,600 --> 00:55:30,640
Speaker 1: bunch of stuff.

1154
00:55:31,040 --> 00:55:35,680
Speaker 4: Or at least diversifying your movement. So I don't know

1155
00:55:35,719 --> 00:55:37,560
Speaker 4: that it matters that you put on a basketball jersey,

1156
00:55:37,600 --> 00:55:40,919
Speaker 4: but I think there's a reason why the large, large

1157
00:55:40,960 --> 00:55:43,440
Speaker 4: majority of the top soccer players in the world grew

1158
00:55:43,480 --> 00:55:45,880
Speaker 4: up playing futsal, which has a small ball that stays

1159
00:55:45,920 --> 00:55:49,000
Speaker 4: on the ground and they play on a cobblestones one

1160
00:55:49,080 --> 00:55:50,840
Speaker 4: day and sand the next day. It's like soccer in

1161
00:55:50,840 --> 00:55:54,040
Speaker 4: a phone booth. It's like much more diversity of problem

1162
00:55:54,160 --> 00:55:57,000
Speaker 4: solving and movement, and I think that's really important. I

1163
00:55:57,000 --> 00:56:00,600
Speaker 4: do think playing multiple actual different sports is really helpful.

1164
00:56:00,600 --> 00:56:02,400
Speaker 4: So there was actually just a paper that came out

1165
00:56:02,440 --> 00:56:03,879
Speaker 4: in Science, you know, one of the probably two most

1166
00:56:03,920 --> 00:56:07,719
Speaker 4: prestigious journals in the world, scientific journals, that looked at

1167
00:56:08,320 --> 00:56:15,400
Speaker 4: thirty thousand performers in sports, science, music, and they found

1168
00:56:15,400 --> 00:56:18,960
Speaker 4: this trend in all of those things where the predictors

1169
00:56:19,600 --> 00:56:24,960
Speaker 4: of top youth performance were negative predictors of elite adult performance.

1170
00:56:25,600 --> 00:56:28,319
Speaker 4: So that happened in sports, it happened for when they

1171
00:56:28,320 --> 00:56:30,760
Speaker 4: looked at scientists who won the Nobel They actually progressed

1172
00:56:30,840 --> 00:56:34,080
Speaker 4: more slowly earlier in their careers because they're more interdisciplinary

1173
00:56:34,120 --> 00:56:35,640
Speaker 4: early on, and they get like a penalty for it

1174
00:56:35,760 --> 00:56:36,160
Speaker 4: early on.

1175
00:56:36,560 --> 00:56:40,320
Speaker 1: Yeah, and I feel like this applies to just general

1176
00:56:40,400 --> 00:56:44,640
Speaker 1: experiences that you've had in life make you more adaptable

1177
00:56:44,760 --> 00:56:47,080
Speaker 1: and able to take on new things. I'll give you

1178
00:56:47,120 --> 00:56:51,279
Speaker 1: a good example, dude, sitting right there, my producer Robbie.

1179
00:56:51,840 --> 00:56:55,560
Speaker 1: Did you graduate high school? Yeah, he graduated high school,

1180
00:56:55,600 --> 00:56:58,280
Speaker 1: didn't go to college. But in high school he starts

1181
00:56:58,280 --> 00:57:03,840
Speaker 1: touring around with a punk band. Then he gets into producing,

1182
00:57:03,920 --> 00:57:07,080
Speaker 1: correct me if I'm wrong, just yelled out producing trap

1183
00:57:07,160 --> 00:57:10,879
Speaker 1: music in Atlanta. This leads into LA where he works

1184
00:57:10,920 --> 00:57:13,120
Speaker 1: with all these different artists in the music industry. Then

1185
00:57:13,120 --> 00:57:16,040
Speaker 1: he starts working on like the Rock Project, does some

1186
00:57:16,040 --> 00:57:18,760
Speaker 1: stuff for Open AI And so as I'm looking for

1187
00:57:18,760 --> 00:57:22,240
Speaker 1: someone to help me with this podcast, I start getting

1188
00:57:22,280 --> 00:57:24,960
Speaker 1: in resumes and I'm like, there's gonna be an audio component.

1189
00:57:25,000 --> 00:57:28,400
Speaker 1: There's also gonna be a heavy video component. Robbie's resume

1190
00:57:28,480 --> 00:57:32,280
Speaker 1: comes in, there's all this weird stuff. He's clearly got

1191
00:57:32,280 --> 00:57:34,880
Speaker 1: audio but no video. And then I'm looking at other

1192
00:57:35,720 --> 00:57:38,240
Speaker 1: resumes where it's just like the perfect St're like this

1193
00:57:38,360 --> 00:57:41,280
Speaker 1: perfect pipeline of exactly what I need, and so I

1194
00:57:41,360 --> 00:57:42,760
Speaker 1: jump on the phone with a few people. But I

1195
00:57:42,760 --> 00:57:44,520
Speaker 1: talked to Robbie and he's like, yeah, I used to

1196
00:57:44,520 --> 00:57:46,640
Speaker 1: produce trap in Atlanta, and you know, these guys were

1197
00:57:46,720 --> 00:57:48,760
Speaker 1: rolling with guns and stuff, and I'm having to produce

1198
00:57:48,760 --> 00:57:51,120
Speaker 1: at two am, just all these crazy experience. I also

1199
00:57:51,200 --> 00:57:55,000
Speaker 1: work on open AI, and I'm just like, this seems

1200
00:57:55,080 --> 00:57:58,320
Speaker 1: like a person who can just figure out stuff, and

1201
00:57:58,360 --> 00:58:00,920
Speaker 1: at the end of the day, that's probably what I'm

1202
00:58:00,960 --> 00:58:03,840
Speaker 1: going to value and need more than like, here's the

1203
00:58:03,840 --> 00:58:06,439
Speaker 1: button I push. I know exactly what button to push,

1204
00:58:06,440 --> 00:58:08,120
Speaker 1: but don't ask me too much else. I'm like this, dude,

1205
00:58:08,120 --> 00:58:09,960
Speaker 1: I feel like I can probably just like give them

1206
00:58:09,960 --> 00:58:12,080
Speaker 1: stuff and he'll figure it out. And that has absolutely

1207
00:58:12,080 --> 00:58:12,640
Speaker 1: been the case.

1208
00:58:13,200 --> 00:58:14,960
Speaker 4: But that's like evidence of someone who can learn, who

1209
00:58:15,000 --> 00:58:18,440
Speaker 4: can pivot, which basically everybody has to do now. Right,

1210
00:58:18,720 --> 00:58:21,520
Speaker 4: Like the period of history where you had a discrete

1211
00:58:21,560 --> 00:58:23,800
Speaker 4: period of training followed by living off of that for

1212
00:58:23,840 --> 00:58:26,080
Speaker 4: the rest of your career is over for most people,

1213
00:58:26,160 --> 00:58:30,920
Speaker 4: if not everyone. And you reminded me of when LinkedIn

1214
00:58:31,000 --> 00:58:32,840
Speaker 4: shared with me some data when I was reporting range

1215
00:58:32,960 --> 00:58:35,440
Speaker 4: that they did this analysis of a half million members.

1216
00:58:35,640 --> 00:58:38,120
Speaker 4: They found the best predictor of someone who would rise

1217
00:58:38,160 --> 00:58:39,919
Speaker 4: high in their field was the number of different job

1218
00:58:39,960 --> 00:58:42,960
Speaker 4: functions someone had worked in. And I told them, I

1219
00:58:43,080 --> 00:58:46,240
Speaker 4: argued to them that, well, I think your guys product

1220
00:58:46,320 --> 00:58:51,360
Speaker 4: actually maybe discourages people from doing that because they want

1221
00:58:51,360 --> 00:58:55,080
Speaker 4: this very linear LinkedIn right, and you should maybe add

1222
00:58:55,080 --> 00:58:56,880
Speaker 4: more space for a narrative or something. They said, you know,

1223
00:58:56,880 --> 00:58:59,520
Speaker 4: we think we're doing fine, right, because their business is

1224
00:58:59,520 --> 00:59:04,040
Speaker 4: doing fine, So fine for them, but it obviously took

1225
00:59:04,120 --> 00:59:06,240
Speaker 4: you thinking a little differently. Is there anything that Robbie

1226
00:59:06,280 --> 00:59:08,240
Speaker 4: said kind of that that made you I mean, because

1227
00:59:08,240 --> 00:59:09,720
Speaker 4: at some point you must have been a you know,

1228
00:59:09,800 --> 00:59:11,000
Speaker 4: is he going to be able to do this job?

1229
00:59:11,040 --> 00:59:13,400
Speaker 4: Was there anything in particular that he did that might

1230
00:59:13,440 --> 00:59:16,520
Speaker 4: be useful for other people to hear in the interview

1231
00:59:16,560 --> 00:59:19,840
Speaker 4: with you or or in his resume or application that

1232
00:59:19,920 --> 00:59:21,919
Speaker 4: they kind of got you over that hump of saying

1233
00:59:21,920 --> 00:59:23,000
Speaker 4: this is a risk worth taking.

1234
00:59:27,080 --> 00:59:31,720
Speaker 1: Well, I think it was the breadth of experiences someone

1235
00:59:31,760 --> 00:59:36,400
Speaker 1: who can so in my books, I'll go into kinetic places.

1236
00:59:36,440 --> 00:59:39,120
Speaker 1: You know, I've meant to iract or report scarcity brand

1237
00:59:39,120 --> 00:59:41,360
Speaker 1: into the Bolivian jungle, and I found in my own

1238
00:59:41,360 --> 00:59:43,880
Speaker 1: self like the ability to just like remain calm, learn

1239
00:59:43,920 --> 00:59:47,560
Speaker 1: from that, but be adaptable has seemed to transfer over

1240
00:59:47,600 --> 00:59:48,960
Speaker 1: to other things in my life. So when I hear

1241
00:59:48,960 --> 00:59:51,800
Speaker 1: about him in these you know, trap recording sessions where

1242
00:59:52,080 --> 00:59:54,800
Speaker 1: drugs are being dealt, guns are being shown, but he's

1243
00:59:54,840 --> 00:59:57,440
Speaker 1: like able to manage that, I'm like, Okay, well he

1244
00:59:57,520 --> 01:00:00,240
Speaker 1: can probably manage me because I'm not armed and tell

1245
01:00:00,280 --> 01:00:03,400
Speaker 1: me what I need to do right. But also there

1246
01:00:04,200 --> 01:00:08,360
Speaker 1: I would say honesty about it. He's like, look, I

1247
01:00:08,360 --> 01:00:11,960
Speaker 1: don't know. I've never done video. I did work on

1248
01:00:12,040 --> 01:00:15,880
Speaker 1: the Grock project, so I think I can figure video out,

1249
01:00:15,960 --> 01:00:19,440
Speaker 1: but I'll tell you I haven't done anything yet. But

1250
01:00:19,480 --> 01:00:21,360
Speaker 1: at the same time, I'm confident I can figure it out.

1251
01:00:21,400 --> 01:00:23,320
Speaker 1: And so I think there was like the honesty there too,

1252
01:00:23,360 --> 01:00:25,520
Speaker 1: And I would say the other people that I talked

1253
01:00:25,520 --> 01:00:28,680
Speaker 1: to were just less interesting and it was like very

1254
01:00:29,040 --> 01:00:31,920
Speaker 1: clear what I was going to get. But I felt

1255
01:00:31,920 --> 01:00:34,920
Speaker 1: like if there was other opportunities that might pop up

1256
01:00:35,120 --> 01:00:37,840
Speaker 1: that I could need help with, those people were going

1257
01:00:37,880 --> 01:00:39,280
Speaker 1: to be like, well, I don't do that. I do

1258
01:00:39,400 --> 01:00:40,680
Speaker 1: YouTube videos, you know.

1259
01:00:40,920 --> 01:00:42,680
Speaker 4: In Range, I talked about this research from a woman

1260
01:00:42,680 --> 01:00:45,200
Speaker 4: named Abby Griffin who studies serial innovators, and she said

1261
01:00:45,200 --> 01:00:48,280
Speaker 4: one of the challenges is they often look like kind

1262
01:00:48,320 --> 01:00:51,240
Speaker 4: of a square peg in a round hole because they're

1263
01:00:51,360 --> 01:00:53,240
Speaker 4: very broad and they want to learn outside their domain,

1264
01:00:53,320 --> 01:00:57,560
Speaker 4: and so it can be like a little confusing to

1265
01:00:57,640 --> 01:01:00,320
Speaker 4: an HR person. It's like is this person really the fit?

1266
01:01:00,800 --> 01:01:02,960
Speaker 4: And so they may get selected out and so they

1267
01:01:03,000 --> 01:01:05,800
Speaker 4: often sort of move between organizations to get that breadth

1268
01:01:05,800 --> 01:01:09,600
Speaker 4: that they they need to be powerful because they just don't.

1269
01:01:10,120 --> 01:01:12,200
Speaker 4: They're just like not out of central casting for whatever

1270
01:01:12,240 --> 01:01:15,480
Speaker 4: that job is. Yeah, how did range make you think about?

1271
01:01:15,720 --> 01:01:16,120
Speaker 3: Reporting?

1272
01:01:16,120 --> 01:01:18,440
Speaker 1: That book make you think about wellness and how people

1273
01:01:18,440 --> 01:01:19,520
Speaker 1: approach well being.

1274
01:01:19,800 --> 01:01:22,560
Speaker 4: I think people feel like they have to specialize. In

1275
01:01:22,560 --> 01:01:25,080
Speaker 4: many cases, they're not often doing it because they want to,

1276
01:01:25,240 --> 01:01:28,440
Speaker 4: Like people are curious and would like to have more

1277
01:01:28,520 --> 01:01:30,640
Speaker 4: variety in their life if they didn't feel like they'd

1278
01:01:30,680 --> 01:01:33,960
Speaker 4: be penalized for it. And so I think there's some

1279
01:01:34,240 --> 01:01:36,320
Speaker 4: ways that we can do things that relate to that,

1280
01:01:36,400 --> 01:01:39,480
Speaker 4: like having a hobby unrelated to your work. So there's

1281
01:01:39,480 --> 01:01:42,560
Speaker 4: studies showing that if you have a hobby that's unrelated

1282
01:01:42,600 --> 01:01:47,120
Speaker 4: to your work or loosely related, it improves your your

1283
01:01:47,120 --> 01:01:50,600
Speaker 4: self efficacy, your feeling of ability, to take on challenges,

1284
01:01:51,280 --> 01:01:53,480
Speaker 4: whereas if the hobby is too closely related to what

1285
01:01:53,560 --> 01:01:56,400
Speaker 4: you already do it work, it actually decreases self efficacy.

1286
01:01:56,560 --> 01:01:57,240
Speaker 3: Final question.

1287
01:01:57,360 --> 01:02:00,919
Speaker 1: We oftentimes will ask people about the best book they've

1288
01:02:00,960 --> 01:02:03,240
Speaker 1: read recently, but I recently tapped you for that for

1289
01:02:03,360 --> 01:02:05,960
Speaker 1: my substock posts, which I read a.

1290
01:02:05,960 --> 01:02:09,440
Speaker 2: Lot, though, so you know I can always add others.

1291
01:02:09,880 --> 01:02:12,440
Speaker 1: Here's what I'll ask you, Okay, because I thought you

1292
01:02:12,520 --> 01:02:15,959
Speaker 1: might have an interesting answer for this one. You could

1293
01:02:15,960 --> 01:02:19,160
Speaker 1: spend an entire day with someone living or dead. They

1294
01:02:19,200 --> 01:02:23,120
Speaker 1: have to be somewhat of a celebrity, so you can't say,

1295
01:02:23,160 --> 01:02:25,000
Speaker 1: you know, extra relative to pass away or whatever.

1296
01:02:25,000 --> 01:02:26,960
Speaker 4: Who would it be when you say somewhat of a celebrity?

1297
01:02:26,960 --> 01:02:28,640
Speaker 4: Can I pick a writer that I think, like a

1298
01:02:28,680 --> 01:02:32,000
Speaker 4: lot of literature people would have heard of. But okay, okay,

1299
01:02:32,240 --> 01:02:35,280
Speaker 4: and I mentioned him and inside the box. So the

1300
01:02:35,280 --> 01:02:39,040
Speaker 4: writer Jorge Luis Borges, Argentine writer I think is like

1301
01:02:39,080 --> 01:02:40,800
Speaker 4: one of the most creative minds that ever lived, and

1302
01:02:40,840 --> 01:02:43,480
Speaker 4: most of his story he only wrote short stories, and

1303
01:02:44,000 --> 01:02:48,760
Speaker 4: they're all like metaphysical thought experiments basically, and he was

1304
01:02:48,880 --> 01:02:50,919
Speaker 4: keeping up with the math and science of his day

1305
01:02:51,840 --> 01:02:54,200
Speaker 4: and would play out like sort of what it meant.

1306
01:02:54,240 --> 01:02:56,960
Speaker 4: So one of his famous stories, called the Library of Babel,

1307
01:02:57,080 --> 01:02:59,160
Speaker 4: is about and most of his pages, most of his

1308
01:02:59,200 --> 01:03:02,080
Speaker 4: books are between like sorry, his short stories are between

1309
01:03:02,080 --> 01:03:07,000
Speaker 4: like four and eight or ten pages, and that one

1310
01:03:07,080 --> 01:03:09,680
Speaker 4: is the narrator is in a universe that is basically

1311
01:03:09,760 --> 01:03:13,960
Speaker 4: a library of repeating hexagonal rooms that have all identical

1312
01:03:14,000 --> 01:03:16,720
Speaker 4: shelves with books on them, and the books all use

1313
01:03:17,400 --> 01:03:21,120
Speaker 4: the normal alphabet and appear to just have random orderings.

1314
01:03:21,120 --> 01:03:22,840
Speaker 4: But every once in a while people come across a

1315
01:03:22,880 --> 01:03:25,800
Speaker 4: word or a phrase, or a sentence even and the

1316
01:03:25,920 --> 01:03:30,000
Speaker 4: question is is that order random or not? So it's

1317
01:03:30,040 --> 01:03:33,040
Speaker 4: almost like a parable of living in a universe where

1318
01:03:33,080 --> 01:03:35,560
Speaker 4: you see signs of order in design, but you don't

1319
01:03:35,560 --> 01:03:38,080
Speaker 4: know if they're random or not. So it's really his

1320
01:03:38,120 --> 01:03:42,600
Speaker 4: stories make me think about certain human circumstances in a

1321
01:03:42,640 --> 01:03:46,640
Speaker 4: way that nothing else, even knowing these aspects of science

1322
01:03:46,800 --> 01:03:48,800
Speaker 4: and reading new scientists every week that nothing else has

1323
01:03:48,800 --> 01:03:51,360
Speaker 4: gotten me to inhabit some of those ideas the way

1324
01:03:51,360 --> 01:03:53,240
Speaker 4: that he does. And the more I read him, the

1325
01:03:53,280 --> 01:03:55,880
Speaker 4: more I realize his ideas pop up in things that

1326
01:03:56,640 --> 01:03:58,680
Speaker 4: I see all the time, Like if you've ever heard

1327
01:03:58,680 --> 01:04:02,520
Speaker 4: that express the map so detailed it became the world.

1328
01:04:02,840 --> 01:04:05,280
Speaker 4: It's like, I think it's interesting for writers because we

1329
01:04:05,320 --> 01:04:07,880
Speaker 4: have to simplify the ideas we're talking about to be useful.

1330
01:04:08,240 --> 01:04:10,320
Speaker 4: And that comes from a one page short story he

1331
01:04:10,360 --> 01:04:13,000
Speaker 4: wrote about a cartography department at a university that gets

1332
01:04:13,040 --> 01:04:15,760
Speaker 4: obsessed with making more and more detailed maps until they

1333
01:04:15,800 --> 01:04:18,440
Speaker 4: make a map that exactly recreates the territory that they're

1334
01:04:18,480 --> 01:04:21,920
Speaker 4: trying to show and becomes totally useless. And like he

1335
01:04:21,920 --> 01:04:24,440
Speaker 4: shows up in interviews Christopher Nolan if you liked Inception

1336
01:04:24,560 --> 01:04:27,200
Speaker 4: Christopher Nole, it was based on two Borges's stories, The

1337
01:04:27,240 --> 01:04:32,200
Speaker 4: Secret Miracle and the Circular Ruins, and just like just

1338
01:04:32,560 --> 01:04:36,080
Speaker 4: such an interesting thinker, and if you read his nonfiction,

1339
01:04:36,200 --> 01:04:38,720
Speaker 4: he was really ahead on sort of calling out European

1340
01:04:38,760 --> 01:04:42,440
Speaker 4: fascism before it burst into the public. And it's just

1341
01:04:42,480 --> 01:04:44,160
Speaker 4: I think one of the smartest people who ever lived,

1342
01:04:44,280 --> 01:04:48,000
Speaker 4: who seem to be just a kind, generous, fascinating soul.

1343
01:04:48,640 --> 01:04:50,480
Speaker 4: And I feel like I've been in conversation with him

1344
01:04:50,480 --> 01:04:52,640
Speaker 4: through his work and would just love to be able

1345
01:04:52,680 --> 01:04:54,240
Speaker 4: to actually spend a little time with him.

1346
01:04:54,640 --> 01:04:56,160
Speaker 3: I love it so fantastic was a.

1347
01:04:56,120 --> 01:04:57,920
Speaker 4: Very long answer. You just wanted like me to say

1348
01:04:57,960 --> 01:04:59,880
Speaker 4: that sorry was not good at Lightning Round.

1349
01:05:00,000 --> 01:05:04,000
Speaker 1: That was a great answer because I definitely believe that

1350
01:05:04,040 --> 01:05:05,680
Speaker 1: you would like to meet that guy, and I wasn't

1351
01:05:05,680 --> 01:05:07,280
Speaker 1: even really that familiar with them, so I'm going to

1352
01:05:07,400 --> 01:05:10,720
Speaker 1: do some research. David, thanks for coming on the show.

1353
01:05:10,880 --> 01:05:13,280
Speaker 1: The book is inside the box. The other two are

1354
01:05:13,400 --> 01:05:16,160
Speaker 1: Range and the Sports Genes. You also have a sub stack,

1355
01:05:16,960 --> 01:05:19,320
Speaker 1: so everyone check that out. Thanks for coming on to

1356
01:05:19,400 --> 01:05:22,280
Speaker 1: chat Man. That was fantastic. It's a total pleasure.

1357
01:05:22,320 --> 01:05:23,640
Speaker 4: I mean, you've been a fan of your work from far,

1358
01:05:23,720 --> 01:05:25,680
Speaker 4: so it's kind of a treat to get to connect

1359
01:05:25,680 --> 01:05:26,120
Speaker 4: in real time.

1360
01:05:26,360 --> 01:05:29,680
Speaker 1: Likewise, thanks for checking out the show. Keep an eye

1361
01:05:29,680 --> 01:05:32,600
Speaker 1: out for more episodes. We will be dropping two a week,

1362
01:05:32,640 --> 01:05:35,080
Speaker 1: and if you have any questions for me for our

1363
01:05:35,240 --> 01:05:38,440
Speaker 1: AMA section, please either drop them in the comments on

1364
01:05:38,480 --> 01:05:43,480
Speaker 1: YouTube or email them. Please send a video or an

1365
01:05:43,520 --> 01:05:46,040
Speaker 1: audio question. That's what we would really love. If you

1366
01:05:46,080 --> 01:05:47,760
Speaker 1: want to type it, we're good with that, but we

1367
01:05:47,760 --> 01:05:49,680
Speaker 1: would love to hear your voice or see your face

1368
01:05:50,160 --> 01:05:52,960
Speaker 1: asking that question. We will do our best to answer

1369
01:05:53,000 --> 01:05:57,080
Speaker 1: as many questions as possible. Do not forget to hit subscribe,

1370
01:05:57,280 --> 01:05:59,640
Speaker 1: and it's always have fun, don't die