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Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: This week. On the podcast, I have Matt Wallert and

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Speaker 1: he is uh former director at Microsoft Ventures. Has done

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Speaker 1: a number of startups, but his background is really in

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Speaker 1: behavioral psychology, which makes for an interesting and eclectic mix.

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Speaker 1: Regular listeners know of my interest in behavioral psychology, so

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Speaker 1: this is right in my sweet spot. We actually did

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Speaker 1: not get to talk about how we met. I go

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Speaker 1: to lots of conferences as a speaker, and so I'm

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Speaker 1: always terribly reluctant to go as an attendee. It's sort

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Speaker 1: of once you've seen behind the curtain, it's like, oh,

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Speaker 1: now I know how the magic is done. It ruins

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Speaker 1: the you know, it ruins the special effects. Once you

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Speaker 1: know how the you mean, he's really not sawing a

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Speaker 1: woman in half, it ruins the show for you what.

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Speaker 1: On occasion, I'll go to um some specific conference where

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Speaker 1: there's a few people I want to see your specific

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Speaker 1: individual And it was in the t I A. Kreft

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Speaker 1: Building conference facility, which is on like third and fifty one.

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Speaker 1: It's on the thirtieth floor. It's a really interesting facility,

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Speaker 1: having a conference center not in the basement, but in

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Speaker 1: the sky, surrounded by buildings, and I was there to

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Speaker 1: see somebody else, and Matt, Matt was right before, right

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Speaker 1: after the person I had come to see, and he

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Speaker 1: just takes the stage and you know, choose up the

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Speaker 1: scenery destroys the place. And it was really a fascinating concept,

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Speaker 1: a fascinating speech about here's what behavioral psychology is, and

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Speaker 1: why aren't we applying this in the real world. We

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Speaker 1: know this works, We know we can affect people's behavior

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Speaker 1: and have them either make better decisions or end up

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Speaker 1: doing things that brings them more satisfaction, more happiness. At

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Speaker 1: a corporate level, if you're selling widgets, you don't want

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Speaker 1: people just to buy your widgets. You want them to

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Speaker 1: buy your widgets and have a higher level of satisfaction

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Speaker 1: and become a repeat customer. And behavior and psychology can

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Speaker 1: really lead companies to finding that right mix that really

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Speaker 1: generates not just sales, but sales with happy customers, and

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Speaker 1: happy customers become repeat buyers. And I thought it was

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Speaker 1: so obvious and so ridiculously. Wait, we're not doing this,

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Speaker 1: And you stop and think about it, and suddenly it's wow,

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Speaker 1: we're not doing this. You're you. You look at all

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Speaker 1: the things in the news recently about some horribly embarrassing

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Speaker 1: corporate behavior, and it's pretty clear that most of these

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Speaker 1: companies could desperately use a chief behavioral officer and they

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Speaker 1: don't have one. And so I thought his conversation was

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Speaker 1: so interesting. I said, hey, let's get him into the

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Speaker 1: studio and have a conversation. I think that was like

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Speaker 1: eight months ago. It took us a long time to

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Speaker 1: find a date that worked. He's a busy guy. But

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Speaker 1: I think you will find this to be a fascinating conversation.

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Speaker 1: If you're all interested in startups, ventures, or behavioral psychology,

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Speaker 1: you're going to really enjoy this conversation. So, with no

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Speaker 1: further ado, my interview with Matt Wallert. My special guest

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Speaker 1: today is Matt Wallert. He is a behavioral psychologist and entrepreneur.

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Speaker 1: He has built a number and sold a number of startups.

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Speaker 1: He went to Microsoft to work on products that he

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Speaker 1: could build at larger scale, eventually becoming a director at

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Speaker 1: Microsoft Ventures. Matt Wallert, Welcome to Bloomberg Verry. Thanks for

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Speaker 1: having me. Man. So let's talk a little bit about

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Speaker 1: your background, which is very eclectic. You studied both science

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Speaker 1: and psychology. You you work in the tech field, but

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Speaker 1: you're also a behaviorist, which came first. I was definitely

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Speaker 1: so Ion's kid and not a psychology kid. I'm from

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Speaker 1: very rural Oregon and had, if anything, I was anti psychology. Right.

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Speaker 1: I'm a true like mill and Nowhere kid where well,

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Speaker 1: And your impression of psychologist is like, these are people

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Speaker 1: who tell you what to do right, like or tell

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Speaker 1: you what you think right? You have this impression from television.

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Speaker 1: I don't think there was a psychologist in sixty miles

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Speaker 1: of where I was. Right, It's not like New York

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Speaker 1: where you grew up. Everybody's got a therapist, everybody knows

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Speaker 1: what this is like. It was a totally unknown quantity.

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Speaker 1: And so I actually it was really anti psych um.

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Speaker 1: And then when I went to Swarthmore, I took uh

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Speaker 1: intro Psychic and it was taught by a wonderful, wonderful

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Speaker 1: professor who's a great research professor, rible teacher, like you

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Speaker 1: couldn't even get up and leave the room kind of

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Speaker 1: teacher because you would throw them off. But then I

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Speaker 1: took another side class with arguably the best teaching professor.

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Speaker 1: It's worthmore and I had this amazing experience where they

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Speaker 1: reviewed a study and I sort of said to him,

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Speaker 1: you know, I don't I don't really agree with their

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Speaker 1: interpretation of the results. Not that I disagree with the date,

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Speaker 1: I just don't think they're interpreting right. And he said, well,

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Speaker 1: this is the most the important thing any teacher could do.

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Speaker 1: He said, Look, the rest of the field agrees with

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Speaker 1: this interpretation, but this is science, and so there's an

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Speaker 1: orderly way for you to argue. You do an experiment

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Speaker 1: that proves your side right, you provide countervailing and like

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Speaker 1: you can like a scientific method that allows you to

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Speaker 1: challenge the prevailing belief. And I was cooked at that moment.

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Speaker 1: That was the moment where I went from you know,

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Speaker 1: so turned off by well we just debate things and

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Speaker 1: whoever is a better order or kind of wins, to like, no,

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Speaker 1: we can go and you know you don't disagree with somebody,

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Speaker 1: you think they're interpreting it wrong. Great, go run a study.

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Speaker 1: So you get your undergraduate degree it's uh Swarthmore UM

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Speaker 1: in psychology and education, and then you apply to the

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Speaker 1: PhD program at Cornell for psychology, Yeah, social psychology, So

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Speaker 1: I um, it's actually funny. I was sort of getting

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Speaker 1: recruited by Stanford, Cornell, and the University of Colorado, and

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Speaker 1: a chose University of Colorado, and then my advisor calls

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Speaker 1: me says, hey, I'm moving to Cornell. So either you

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Speaker 1: can come to Colorado but I won't be here, or

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Speaker 1: you can go with me to Cornell. So I went

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Speaker 1: to Cornell. And you're a startup guy, why not Stanford?

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Speaker 1: You would think that's a natural fit. But I'm not

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Speaker 1: a startup guy, right, I'm a first generation college kid.

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Speaker 1: I've never The way I got into startups was was

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Speaker 1: ab Carnanni's fault, right, like I'm I was in in

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Speaker 1: the PC program at Cornell, and he uh sort of

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Speaker 1: uh called me up and he said, look, we're running

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Speaker 1: the startup. We think you could give us some advice.

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Speaker 1: And I said, literally, I don't know anything about startups.

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Speaker 1: I don't know anything about tech. I don't know anything

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Speaker 1: about finance. And his personal finance was min dot Com.

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Speaker 1: So with his competitor called Thrive, we sold the landing

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Speaker 1: tree and I said, I don't know anything about any this.

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Speaker 1: He said, don't worry, We'll figure out how to get

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Speaker 1: the value out of you and then a year later

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Speaker 1: I was there head a product. His name what was

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Speaker 1: his name? So that's Av Carnanni. He was the CEO

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Speaker 1: of Thrive and and he was the founder. He was

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Speaker 1: the he was the co founder with with Orage Knaps.

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Speaker 1: He was the tech guy. And he brought you in

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Speaker 1: in what role? Uh as so as the lead scientists

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Speaker 1: and sort of had a product. They had they had

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Speaker 1: built some thing. It wasn't They had this inkling that

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Speaker 1: it might not really be the right thing, and so

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Speaker 1: I sort of came in and said, yep, not sort

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Speaker 1: of behavior from able science perspective, this is probably not

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Speaker 1: You're a little bit like Mitt. You're probably not going

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Speaker 1: to move the needle. How do we go and build

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Speaker 1: something that cam? And then we built it? So let's

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Speaker 1: back up a few steps. You're in the PhD program

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Speaker 1: at Cornell. This is before Twitter, before Facebook, before social media.

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Speaker 1: I'm not that old. Facebook existed sort of not broadly,

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Speaker 1: not the way it did today. And by the way,

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Speaker 1: when I say it's before all these things, I'm not

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Speaker 1: talking about the nineteen twenties. This is a decade ago.

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Speaker 1: And how do they find you? I had bugglish on

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Speaker 1: papers they actually called Dick Taylor. That's what I love

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Speaker 1: about academics, right, like they listen there for now sailor

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Speaker 1: is is Charlie at Chicago, but he was at one

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Speaker 1: point in time, he was in California. He was at

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Speaker 1: Chicago at the time. He still because it doesn't that long.

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Speaker 1: He was a previous guest. I find his work fascinating

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Speaker 1: and he's an endlessly entertaining guy. He is, and and look, Dick,

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Speaker 1: I mean, has in some really amazing work. And what

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Speaker 1: I love about academics is like, you know, they just

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Speaker 1: put their phone number and you know, and so they

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Speaker 1: called him up and he answered the phone, and he

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Speaker 1: gave him like an hour's worth of advice and then

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Speaker 1: they yeah, for free. And then because this is before

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Speaker 1: the days of startup mania, right, this is before everybody's

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Speaker 1: going to be an advisor of something, right, they just

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Speaker 1: he was just sort of like sure, And that's I

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Speaker 1: actually think. You know, one of the things I talked

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Speaker 1: about often is, you know, if you need advice, go

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Speaker 1: find a grad Students like that, you know, they don't

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Speaker 1: charge very much, like they don't know, you know, they

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Speaker 1: don't know the true value of their information, and they're

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Speaker 1: so passionate and happy to talk to you about it.

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Speaker 1: So they called it Taylor. They got to the end

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Speaker 1: of the hour and they were sort of like, would

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Speaker 1: be advisor, and he was like, no, you gotta be

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Speaker 1: kidding me. Man, like I'm too busy, but go find

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Speaker 1: a grad student. And so I was the grad student.

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Speaker 1: They sort of came up. So how do they approach

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Speaker 1: you about this? I mean, so we just got on

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Speaker 1: a call and they said, he sort of, here's what

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Speaker 1: we're building. This is after the dot com collapse, but

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Speaker 1: you're sort of in the recovery before the financial before

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Speaker 1: the before the financial collapse, which was which was key

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Speaker 1: for us, right because here we are in this sort

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Speaker 1: of personal finance space and we can acquire by lending

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Speaker 1: to read just as the sort of financial collapses. What

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Speaker 1: was that publicly announced? What the dollar amount was? No,

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Speaker 1: it was it was a like so breaks and was

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Speaker 1: what was the Yeah? Good try Like once again you're

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Speaker 1: talking to the science guy, right, Like I paid no

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Speaker 1: attention to the cap table. As a matter of fact,

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Speaker 1: I actually so when they sent me, I remember walking

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Speaker 1: through the West Village and nobody should ever do this,

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Speaker 1: by the way, like you should know what you're signing

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Speaker 1: before you sign it. But I had to talk to

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Speaker 1: a V. Carnani about, like, what's a basis point You've

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Speaker 1: written me this offica letter with these it's fantastic, but

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Speaker 1: like one of these things we'll give you at least

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Speaker 1: five of them. We're walking through the West Village and

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Speaker 1: he's like explaining, like I knew nothing. He was a

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Speaker 1: hundred basis points fantastic lot, you know. So I actually

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Speaker 1: remember running around when I got my offer letter from them,

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Speaker 1: running around being so happy about the salary because again,

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Speaker 1: first generation college kid, like even startup money seemed like

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Speaker 1: a lot of money to me. And so then they

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Speaker 1: came to me then there was this equity part, and

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Speaker 1: I literally was like, I have no idea what this

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Speaker 1: means actually, And years later, um I went on to

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Speaker 1: build a feminist tool called salary or equity dot com,

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Speaker 1: because women are less likely to accept start up offers

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Speaker 1: because they want salary, not because they want salary, or

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Speaker 1: they don't know what equity means, or it seems riskier

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Speaker 1: than it is, and so we actually built a little

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Speaker 1: calculator where you put in your offer letter and it

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Speaker 1: basically says like that's kind of about so let's let's

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Speaker 1: talk a little bit about behavioral science and how it

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Speaker 1: fits into these various UM startup roles that you've had.

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Speaker 1: What is the job of chief behavioral officer? So this

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Speaker 1: is something I've actually written about and proposed sort of lately,

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Speaker 1: and I'm trying really to resolve this um you know,

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Speaker 1: this fundamental gap. If you go to any sort of

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Speaker 1: C level exec, they'll say, oh, I love behavioral science.

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Speaker 1: You know, I love Dana RELI, I love you know,

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Speaker 1: thinking fast and slow, I love nudging, I love all

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Speaker 1: this stuff. And then I say, okay, well where's your team?

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Speaker 1: And they're like, what team? Right is? So's implementing these

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Speaker 1: ideas in your actual business? That's right? Whose job is this?

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Speaker 1: And and because we know if it's not somebody's job,

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Speaker 1: it doesn't get done right and so and there are

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Speaker 1: a smattering people who have not taken that title but

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Speaker 1: are doing interesting things. So Steve went on morning side, Um,

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Speaker 1: you know there are people who are doing sort of this.

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Speaker 1: So what I sort of said was I actually don't

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Speaker 1: think this is business is fault. I think this is

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Speaker 1: psychology's fault. Right. This is something actually we were just

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Speaker 1: talking about a minute ago. You know, in the break.

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Speaker 1: I think we haven't done a good job of explaining

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Speaker 1: how do you go put it into an organization, in

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Speaker 1: part because we stayed in academia, right, we didn't come

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Speaker 1: out into business. And so I think a chep April

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Speaker 1: officer really has three jobs. One of them is the

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Speaker 1: application of behavioral science internally, right, So take it for meaning,

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Speaker 1: how do we get our employees to do what we

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Speaker 1: think is in their own best interests and invest in

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Speaker 1: the country of people say they're disengaged at their jobs.

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Speaker 1: Who's addressing that? Right? Like hr, yeah, good luck? Right?

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Speaker 1: Like women massive attrition. We know that like companies that

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Speaker 1: have senior women much much better return profile. They do

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Speaker 1: better on Wall Street, but women to treat a lot.

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Speaker 1: HRS had fifty years to solve this problem and has

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Speaker 1: done nothing. Like maybe we should let behavioral science take

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Speaker 1: a crack, Like how do we go change those behavioral things?

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Speaker 1: So internal stuff, motivation, retention, recruitment, all, you know, work

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Speaker 1: life balance, all of these pieces external stuff. Um, you know,

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Speaker 1: actually revamping product and depends what your product is. One

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Speaker 1: of the example who runs their behavioral science lab is

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Speaker 1: you know, people spend you know, let's call it ninety

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Speaker 1: minutes a session in a Walmart, they're in line for

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Speaker 1: five minutes, but if you survey them afterwards, that five

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Speaker 1: minutes loom super large. There six forever recent the effect.

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Speaker 1: It's the last thing they experienced, one that has the

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Speaker 1: biggest impact. That's right. You actually wrote about the checkout

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Speaker 1: experience in hotels. Why you giving me water when I exit?

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Speaker 1: Give it when I arrive, give it to me when

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Speaker 1: I leave, And I'm left with, oh, isn't that nice?

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Speaker 1: As you as you head out? That's right. It's a

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Speaker 1: peak end theory, right, I want those endpoints to really matter.

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Speaker 1: And instead, you know, I actually was on the road

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Speaker 1: this week. It feels like you're like a thief. You're

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Speaker 1: like stealing out of your hotel. You don't even go

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Speaker 1: to the desk anymore because you right out. You're just like,

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Speaker 1: you know, it's like dying and dash exactly. You're like

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Speaker 1: a bag and run out. I feel like I'm doing

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Speaker 1: something wrong when I like sneak out of my hotel

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Speaker 1: to go to the airport. Um, so those sorts of things,

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Speaker 1: so external stuff, and then now let me interrupt, do

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Speaker 1: you say that this isn't all that well developed externally?

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Speaker 1: But when I read about the science behind supermarkets and

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Speaker 1: what's eye level and and what is how the whole

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Speaker 1: circumference of the store, of the fresh foods and the

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Speaker 1: interior of the store all the package goods and what's here,

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Speaker 1: and how they make you go to the far opposite

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Speaker 1: corner of the store to get milk, which is the

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Speaker 1: number one item people come in. And the it just

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Speaker 1: works out. The longer you're in the supermarket, the more

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Speaker 1: stuff you're gonna buy. So if you're there for one

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Speaker 1: item and you have to go as far as humanly

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Speaker 1: possible from the entrance, you're gonna end up doing more

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Speaker 1: than just buy milk. Yes, And there have been like

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Speaker 1: that has become a whole science, right, There have been

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Speaker 1: isolated pockets of sort of applied behavioral science, you know,

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Speaker 1: in health, for example, they're starting to do some really

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Speaker 1: good health decision making work. There are these pockets where

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Speaker 1: we've sort of seen stuff. And I think the problem

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Speaker 1: is too often companies are like, well, that's great for them,

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Speaker 1: and they don't see how it applies to their product, Right,

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Speaker 1: it's great for those people over there. Right at this point,

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Speaker 1: even I think you know, we talked earlier about you know,

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Speaker 1: we're talking about behavior economics and people sort of paying

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Speaker 1: attention on the stock market. Like, I think, actually finance

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Speaker 1: has done a better job. But the problem with finance

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Speaker 1: doing a good job is like CpG has gone and said, well,

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Speaker 1: that's great for finance, but it's a finance thing, right,

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Speaker 1: Behavioral economics totally applies in finance, But what could they

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Speaker 1: possibly be doing for us. It's basically figuring out how

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Speaker 1: to have lead people to the right decision and then

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Speaker 1: giving them all the tools and pathways to get there.

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Speaker 1: That's it doesn't matter what the field is. It's it's

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Speaker 1: not about the widget, you know, the I think the

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Speaker 1: the Hitchcock movie used to call it the McGuinty or

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Speaker 1: the mcguffin. Whatever they were chasing was irrelevant. It was

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Speaker 1: the chase that mattered. So if the mcguffin was a

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Speaker 1: multese falcon or something completely different, it didn't matter. It

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Speaker 1: was the chase that h well, and and I think

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Speaker 1: that's actually a really good sort of summation of why

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Speaker 1: behavioral science matters. Right, It's a process be hero scientists

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Speaker 1: are trained interventionists. Right. Let's take the Walmart line example.

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Speaker 1: If you ask engineers, you say to the engineers while

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Speaker 1: the lines are too long, they're like, well, let's rejigger

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Speaker 1: the POS system so people move through faster. Right, And

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Speaker 1: if you ask like the store ops people, they'll tell you, well,

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Speaker 1: let's put in more checkout counter so they're shorter, and

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Speaker 1: those kind of things work. But the raw truth is

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Speaker 1: people are in line for five minutes. It's not that

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Speaker 1: they're in line for too long. It feels too long.

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Speaker 1: So why don't you behavior some with ways to either

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Speaker 1: entertain them or keep them otherwise, engage distracted involved so

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Speaker 1: it doesn't feel like it's a long process. Exactly, Like

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Speaker 1: let's do what we did in the New York subway system.

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Speaker 1: So New York Subway, you know, we put in these

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Speaker 1: signs that tell people when the subways are coming. People

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Speaker 1: to think the subways are running way faster. That absolutely.

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Speaker 1: I just took a subway here, and the first thing

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Speaker 1: you notice, the express is one minute, the local is

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Speaker 1: three minutes. But I don't want to walk up that

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Speaker 1: giant set of stairs, so I'll take the extra three minutes.

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Speaker 1: And you can make a decision, right, and and it's

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Speaker 1: three minutes and not like you know, let's say you

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Speaker 1: were running late to this thing. You know, you're looking

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Speaker 1: at your watch going is it now? Is it now?

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Speaker 1: Is it now? Should I run up stairshow over the Yeah,

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Speaker 1: like the edge of the subway? When is that train

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Speaker 1: coming and you're looking at a screen, O, it's two

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Speaker 1: minutes away? That's right. And the nice thing about that

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Speaker 1: is it removes ambiguity, and two minutes just doesn't sound

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Speaker 1: very long, right, so it contextualizes it really isn't that long.

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Speaker 1: So imagine doing that in line. What if we don't

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Speaker 1: change the pos and add new lines, we just say, hey,

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Speaker 1: you're gonna be out of here in two minutes. That's interesting.

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Speaker 1: You you call scientists train interventionists. Are these interventionists working

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Speaker 1: companies or are they still in academic? I think they're

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Speaker 1: largely still in academia. And I think that's because we

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Speaker 1: haven't gone to two companies and helped explain why this happens. Right,

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Speaker 1: So if you go to Walmart example, Right, if you

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Speaker 1: go to the engineers, they'll have an engineering solution. If

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Speaker 1: you go to the marketers, they'll have a marketing solution.

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Speaker 1: There's nobody who's solution agnostic and his problem focused, right,

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Speaker 1: And that's what science really is, right, when you think

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Speaker 1: about any scientists, when you're running a bunch of experience ements, right,

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Speaker 1: you're essentially running an intervention seeing if it works, running

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Speaker 1: an intervention seeing if it works. It's not the experiment

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Speaker 1: that matters. It's the outcome that you care about. So

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Speaker 1: you know, in the pivot, the fixed point is the outcome,

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Speaker 1: and then you pivot around experiments that help you test

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Speaker 1: around that outcome. And so scientists almost accidentally become really

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Speaker 1: good product people because what they're doing is, you know,

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Speaker 1: I know what I'm trying to test. I know what

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Speaker 1: I'm trying to test. I know what I'm trying to test,

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Speaker 1: and I'll just test until I sort of I'm able

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Speaker 1: to get my hands around the edges. I don't have

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Speaker 1: a predefined sort of you know, this is the way

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Speaker 1: that I operate against that. Let's talk a little bit

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Speaker 1: about startups and culture, and I have to begin by

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Speaker 1: asking you. I love the show Silicon Valley. It just

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Speaker 1: started the new season. Do you watch it? I don't don't.

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Speaker 1: Was how accurate is it? Here's why there's a reason

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Speaker 1: I don't watch it. I've seen episodes. There's you know, I

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Speaker 1: I don't watch it. And it actually was challenging for

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Speaker 1: me to watch the first season of Girls for the

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Speaker 1: same reason, which is it's it's clearly farcical, but if

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Speaker 1: you're in if you're in that part of the world,

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Speaker 1: it's just a little too close to home for me. Right,

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Speaker 1: It's like, I don't like uncomfortable comedy. Right, I'm like,

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Speaker 1: you're not a Larry David fat, Yeah, I don't. I

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Speaker 1: don't do well with uncomfortable comedy. And and it is

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Speaker 1: a little uncomfortable with me. And it's why, you know,

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Speaker 1: I help a lot of startups, but I try to

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Speaker 1: make you know, I try to spend as much time

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Speaker 1: outside of that world as I can, because I think

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Speaker 1: that's where, you know, if you want to be a

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Speaker 1: good product person, you've got to be exposed to the problems.

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Speaker 1: And I think one of the problems of Silicon Valley

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Speaker 1: is has so insulated itself from problem sets. You know,

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Speaker 1: it's just solving its own problems at this point. You know,

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Speaker 1: there's two things that I have to share with you. First,

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Speaker 1: I'm not the target demographic for Girls. Watch the first show,

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Speaker 1: and basically, you know, sorry tapping tapping out really early

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Speaker 1: on that one. But the fascinating thing about Mike Judge,

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Speaker 1: who is behind Silicon Valley. He started out doing the

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Speaker 1: broadest idiocracy, the broadest dumbest comedy, and then narrowed it down,

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Speaker 1: narrowed it down. I think he did Office Office Space,

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Speaker 1: which was a little ridiculous, but not quite as ridiculous.

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Speaker 1: This is the one that you know, it's clearly parody,

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Speaker 1: but it seems to venture into cinema verity quite often,

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Speaker 1: and it it to someone who's not out there, it

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Speaker 1: really rings true. Yeah, I mean and it is, and

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Speaker 1: it's hilarious, and it was really funny and really well acted,

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Speaker 1: and I think, uh, they've done it. Yeah, they've done it.

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Speaker 1: I think casting really was what made that show right there.

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Speaker 1: They're believable characters that are able to carry it off

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Speaker 1: as opposed to like, um uh, what's the other skiky

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Speaker 1: science show that's more slapstick Big Bang? Right? That is

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Speaker 1: clear sort of laugh track comedy, right, you know, the

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Speaker 1: characters are so over the top, so unbelief. Well you're

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Speaker 1: not supposed to feel like you know those people, um

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Speaker 1: in Silicon Valley. I really do know of those people.

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Speaker 1: So so let's let me ask the same question differently,

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Speaker 1: how does start up culture get unfairly mischaracterized in general,

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Speaker 1: not just HBO, but in general. What do we misunderstand

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Speaker 1: about startup culture educate the listening public if you can. Uh. So,

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Speaker 1: that's an interesting and hard question. I think it's not

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Speaker 1: uh And I want to sort of caveat this carefully, right, So,

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Speaker 1: I think it it. People really do think of Silicon Valley.

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Speaker 1: They think of young white males doing these things, and

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Speaker 1: in reality, there are you know, we know lots of

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Speaker 1: people of color. That's right, Old, older entrepreneurs with families

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Speaker 1: are more likely to have sort of better exits, right, Like,

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Speaker 1: we actually know there's a bunch of other variable there's

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Speaker 1: a lot of really interesting start ups in the middle

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Speaker 1: of the kind entree, you know. There there are a

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Speaker 1: lot of things. Now, there's not as many people of

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Speaker 1: color as we'd want. There's not as many women as

424
00:21:05,960 --> 00:21:08,720
Speaker 1: we'd want. That's a serious issue that we're I spent

425
00:21:08,800 --> 00:21:11,080
Speaker 1: a huge amount of my time working on. And that's

426
00:21:11,119 --> 00:21:12,240
Speaker 1: but it's a little bit of a see it be

427
00:21:12,280 --> 00:21:15,400
Speaker 1: it problem, right, if we see it be it problems, yeah,

428
00:21:15,480 --> 00:21:17,680
Speaker 1: CB A problem is like, well, if I never see

429
00:21:17,680 --> 00:21:20,439
Speaker 1: a female scientist as a little girl, how do I

430
00:21:20,480 --> 00:21:22,760
Speaker 1: ever have the opportunity to think, oh, I might be

431
00:21:22,880 --> 00:21:24,840
Speaker 1: so it's a cycle that that has to be broke,

432
00:21:25,000 --> 00:21:28,920
Speaker 1: that's right. And so we need to be fiercely critical

433
00:21:29,160 --> 00:21:32,440
Speaker 1: of the homogeney of startups while at the same time

434
00:21:32,480 --> 00:21:35,159
Speaker 1: recognizing all of the people that are outside of that

435
00:21:35,240 --> 00:21:37,720
Speaker 1: homogeny that you know, and we need to hold those

436
00:21:37,800 --> 00:21:40,399
Speaker 1: ups so that we get more of them. Right. That

437
00:21:40,600 --> 00:21:44,399
Speaker 1: that's really uh, that's really interesting. UM. So let's talk

438
00:21:44,440 --> 00:21:47,720
Speaker 1: a bit about the gender bias sets out in Silicon Valley.

439
00:21:47,920 --> 00:21:50,640
Speaker 1: There's been a couple of high profile lawsuits. We've seen

440
00:21:50,680 --> 00:21:54,879
Speaker 1: the turmoil at uber because of it. Um even a

441
00:21:54,880 --> 00:21:59,439
Speaker 1: big venture capital firm like Climate Perkins had had a litigation.

442
00:22:00,160 --> 00:22:03,120
Speaker 1: Is there a gender bias issue? And I mean there's no,

443
00:22:03,160 --> 00:22:06,520
Speaker 1: there's no. To me, that's a that's like saying, is

444
00:22:06,520 --> 00:22:10,240
Speaker 1: they're global warming? Right of economists will tell you that

445
00:22:10,280 --> 00:22:13,520
Speaker 1: there is a gender wage gap, right like you know,

446
00:22:14,119 --> 00:22:16,240
Speaker 1: to me, denying the science, it's amazing to me the

447
00:22:16,240 --> 00:22:17,800
Speaker 1: people that deny the science of it, that they think

448
00:22:17,840 --> 00:22:20,240
Speaker 1: that's a problem of opinion. I did this research actually

449
00:22:20,320 --> 00:22:22,240
Speaker 1: with with pay Scale, where they said, hey, do you

450
00:22:22,280 --> 00:22:23,440
Speaker 1: want to put anything in our service? Said ya, I

451
00:22:23,480 --> 00:22:25,600
Speaker 1: wanna ask two questions, Um, do you think men women

452
00:22:25,640 --> 00:22:27,879
Speaker 1: have equal opportunities in the workplace in general? And do

453
00:22:27,880 --> 00:22:30,399
Speaker 1: you think men and women have equal opportunities? Um in

454
00:22:30,440 --> 00:22:33,960
Speaker 1: your workplace? And if you look at men, only one

455
00:22:33,960 --> 00:22:37,119
Speaker 1: in five men see sexism both near them and in

456
00:22:37,160 --> 00:22:40,359
Speaker 1: the world. Right, So only one in five men something

457
00:22:40,400 --> 00:22:43,920
Speaker 1: that we know is scientifically true, like actually are able

458
00:22:43,960 --> 00:22:46,119
Speaker 1: to see that. Another one in five see it in

459
00:22:46,160 --> 00:22:48,280
Speaker 1: the world but not near them, And three or five

460
00:22:48,320 --> 00:22:51,680
Speaker 1: men just don't think it exists out there or in here,

461
00:22:52,800 --> 00:22:56,080
Speaker 1: three and five. My special guest today is Matt Wallert.

462
00:22:56,520 --> 00:23:01,600
Speaker 1: He is an entrepreneur behavioral psychologist, and previously he was

463
00:23:01,640 --> 00:23:05,840
Speaker 1: a director at Microsoft Ventures, where he did a variety

464
00:23:05,840 --> 00:23:10,600
Speaker 1: of work, including some behavioral psychology at Bing. Is that correct?

465
00:23:10,800 --> 00:23:13,360
Speaker 1: So that was actually prior to joining Ventures. I joined

466
00:23:13,440 --> 00:23:16,600
Speaker 1: Ventures when I came back to New York. Um, I

467
00:23:16,680 --> 00:23:18,960
Speaker 1: have a lovely eighteen month old and my wife said,

468
00:23:18,960 --> 00:23:20,280
Speaker 1: you know, we were out in Seattle, said I want

469
00:23:20,280 --> 00:23:21,439
Speaker 1: to co back to New York. My family is. I

470
00:23:21,440 --> 00:23:23,159
Speaker 1: said to Microsoft, hey, I'm gonna go back, and they

471
00:23:23,160 --> 00:23:26,199
Speaker 1: said Ventures. I said okay, and uh, And so I

472
00:23:26,240 --> 00:23:28,439
Speaker 1: spent a good time there. But previous to that had

473
00:23:28,480 --> 00:23:30,000
Speaker 1: done a bunch of products. What did you do with

474
00:23:30,119 --> 00:23:33,640
Speaker 1: BING and and how does behavioral psychology apply to being?

475
00:23:33,680 --> 00:23:37,920
Speaker 1: My favorite thing about BING has been the three dimensional

476
00:23:37,960 --> 00:23:42,520
Speaker 1: Bird's eye View. Was the first mapping software that had that.

477
00:23:43,160 --> 00:23:46,320
Speaker 1: And when you're looking for a house, you're like, oh

478
00:23:46,359 --> 00:23:47,960
Speaker 1: my god, I could see the park, I could see

479
00:23:47,960 --> 00:23:49,840
Speaker 1: the house, I could see the water, I could It's

480
00:23:49,880 --> 00:23:52,679
Speaker 1: amazing the level of detail that you could zoom in

481
00:23:52,720 --> 00:23:55,800
Speaker 1: on with that product. I was fascinated by that. Yeah,

482
00:23:55,840 --> 00:23:58,359
Speaker 1: I mean mapping in general has always been fascinating to me, since,

483
00:23:58,400 --> 00:23:59,920
Speaker 1: you know, coming from a rural place, I didn't have

484
00:24:00,040 --> 00:24:01,520
Speaker 1: expose you know, I think you didn't get to go

485
00:24:01,560 --> 00:24:03,199
Speaker 1: as many places as you do when you're on these

486
00:24:03,240 --> 00:24:06,040
Speaker 1: coast teas. You travel around and things, and uh so

487
00:24:06,080 --> 00:24:07,760
Speaker 1: it was a fun, really fun way to you know.

488
00:24:07,800 --> 00:24:09,040
Speaker 1: I looked at a lot of maps as a kid

489
00:24:09,040 --> 00:24:10,480
Speaker 1: as a way of like sort of visualizing the rest

490
00:24:10,480 --> 00:24:12,560
Speaker 1: of the world. Um so, so I really when I

491
00:24:12,560 --> 00:24:14,960
Speaker 1: first came to Microsoft, they sort of said, look, you know,

492
00:24:15,160 --> 00:24:18,399
Speaker 1: our our product process of very engineering driven, um which

493
00:24:18,440 --> 00:24:21,000
Speaker 1: sometimes means that we make things that people aren't actually

494
00:24:21,000 --> 00:24:23,680
Speaker 1: interested in using. So we want to sort of reverse that.

495
00:24:23,720 --> 00:24:25,239
Speaker 1: How do you think about what people want to use.

496
00:24:25,280 --> 00:24:26,879
Speaker 1: And so the first thing I actually ever worked on

497
00:24:26,880 --> 00:24:30,080
Speaker 1: at Microsoft was putting cling On into the Bing translator.

498
00:24:30,720 --> 00:24:33,360
Speaker 1: Oh yeah, and it was It was actually a really

499
00:24:33,400 --> 00:24:36,000
Speaker 1: hard project because Klingon is actually a uh you know,

500
00:24:36,000 --> 00:24:38,760
Speaker 1: it's a created language, and the linguist that created at

501
00:24:38,760 --> 00:24:42,439
Speaker 1: Mark okrand Um, you know, basically want to break all

502
00:24:42,480 --> 00:24:45,560
Speaker 1: the earthly rules. So like all Trio terrestrial languages, real

503
00:24:45,640 --> 00:24:49,159
Speaker 1: languages have a way of saying hello, the Clingon way

504
00:24:49,200 --> 00:24:51,360
Speaker 1: of saying hello is just what do you want? Right?

505
00:24:51,560 --> 00:24:53,760
Speaker 1: And so when you try and do that in a translator,

506
00:24:55,200 --> 00:24:57,320
Speaker 1: what what do you want from me? It's not that

507
00:24:57,440 --> 00:24:59,240
Speaker 1: far off. Yeah, And so it was very it was

508
00:24:59,320 --> 00:25:01,199
Speaker 1: very funny, I mean, hard to make a translator. But

509
00:25:01,240 --> 00:25:03,879
Speaker 1: the point was how do we make something that takes

510
00:25:03,920 --> 00:25:07,720
Speaker 1: a an audience that normally wouldn't engage with our translation

511
00:25:08,119 --> 00:25:09,679
Speaker 1: and sort of start to look at it in a

512
00:25:09,720 --> 00:25:13,439
Speaker 1: different way. And so that was the kind of thing

513
00:25:13,440 --> 00:25:15,520
Speaker 1: that I did at Microsoft. So, you know, I created

514
00:25:15,520 --> 00:25:17,640
Speaker 1: our what's now known as our Being in the Classroom program,

515
00:25:17,960 --> 00:25:20,120
Speaker 1: and so they what is being in the class well?

516
00:25:20,160 --> 00:25:22,399
Speaker 1: So so so they started with a key data insight,

517
00:25:22,440 --> 00:25:24,320
Speaker 1: which was we're not seeing as much church traffick from

518
00:25:24,320 --> 00:25:26,760
Speaker 1: schools as we would think we do. And so they

519
00:25:26,800 --> 00:25:29,480
Speaker 1: had these plans. The marketing department decided, well, kids aren't

520
00:25:29,480 --> 00:25:31,560
Speaker 1: currying us enough, and so we're gonna have these plans

521
00:25:31,560 --> 00:25:34,520
Speaker 1: go run a campaign around like encouraging curiosity and kids.

522
00:25:34,560 --> 00:25:36,520
Speaker 1: And I said, look, I had a dasy degree, Like,

523
00:25:36,600 --> 00:25:38,600
Speaker 1: let's just go look at some stuff. And you know,

524
00:25:39,119 --> 00:25:40,920
Speaker 1: kids are plenty curious. You go out to a classroom,

525
00:25:40,920 --> 00:25:42,800
Speaker 1: you could see kids are curious. Like, that's transparently not

526
00:25:42,840 --> 00:25:44,439
Speaker 1: the problem. So when you go out and look, right,

527
00:25:44,440 --> 00:25:46,000
Speaker 1: one of the tenants of behavior science, go look at

528
00:25:46,000 --> 00:25:48,960
Speaker 1: how people are behaving. You know, it was really teacher driven,

529
00:25:49,000 --> 00:25:51,800
Speaker 1: and it was actually less about they wanted to search.

530
00:25:51,960 --> 00:25:54,800
Speaker 1: It was what we call inhibiting pressures. So it was

531
00:25:54,880 --> 00:25:58,119
Speaker 1: concerns about advertising, right, you know, schools are advertising free zones,

532
00:25:59,400 --> 00:26:02,439
Speaker 1: search engines or not. Privacy. I don't really know what

533
00:26:02,520 --> 00:26:05,040
Speaker 1: Google is doing with my data, but I have concerns

534
00:26:05,080 --> 00:26:07,440
Speaker 1: about it, right, have concerns about them taking my kids data.

535
00:26:07,880 --> 00:26:11,000
Speaker 1: And then um, even classroom searches. I mean, of all

536
00:26:11,040 --> 00:26:14,040
Speaker 1: the things, well, that's the problem, right, It's all identifiable, right,

537
00:26:14,240 --> 00:26:16,879
Speaker 1: And if they were using particularly if you're using Google

538
00:26:16,920 --> 00:26:19,160
Speaker 1: in the classroom, So signing into Gmail and those sorts

539
00:26:19,160 --> 00:26:23,000
Speaker 1: of things then you're searching are getting identified that profile, right,

540
00:26:23,040 --> 00:26:25,480
Speaker 1: So it's all getting sucked into some profile and I

541
00:26:25,520 --> 00:26:26,919
Speaker 1: don't know what they're doing with it. And then the

542
00:26:26,960 --> 00:26:29,600
Speaker 1: third concern was adult content. So what we did was

543
00:26:29,720 --> 00:26:31,400
Speaker 1: roll out a version of being being in the classroom

544
00:26:31,440 --> 00:26:33,760
Speaker 1: that's free for schools to sign up for. And then

545
00:26:33,800 --> 00:26:37,880
Speaker 1: when you search from schools, it's ad free. Um, not tracked, yeah,

546
00:26:38,000 --> 00:26:41,320
Speaker 1: not tracked, and and safe search is locked on and

547
00:26:41,400 --> 00:26:43,760
Speaker 1: so um. And then Google eventually did the same thing,

548
00:26:43,840 --> 00:26:47,560
Speaker 1: sort of following on to us UM and so but

549
00:26:47,600 --> 00:26:51,800
Speaker 1: again you saw massive behavioral change increases in school searches.

550
00:26:51,880 --> 00:26:54,080
Speaker 1: That's a giant number. But that's the thing, Like, it's

551
00:26:54,160 --> 00:26:57,720
Speaker 1: so often people think that the problem is what we

552
00:26:57,760 --> 00:27:00,600
Speaker 1: call a promoting pressure like curiosity. People don't They're like, oh,

553
00:27:00,640 --> 00:27:02,959
Speaker 1: why aren't people searching? Well, they don't want to, right,

554
00:27:03,080 --> 00:27:04,840
Speaker 1: and so we're gonna encourage them to want to. So

555
00:27:04,960 --> 00:27:08,680
Speaker 1: that's not the problem. Why are you working for United Airlines? Don't?

556
00:27:08,720 --> 00:27:12,440
Speaker 1: Don't they need a chief behavioral psychologist, especially for their

557
00:27:12,440 --> 00:27:15,919
Speaker 1: own people's decision making? Is this is The goal of

558
00:27:15,960 --> 00:27:18,040
Speaker 1: my CBO sort of crusade that I'm on is I

559
00:27:18,080 --> 00:27:20,560
Speaker 1: do think airlines. I've actually done work with Southwest like,

560
00:27:22,080 --> 00:27:24,680
Speaker 1: which is a totally different experience. And it's funny. Actually

561
00:27:24,680 --> 00:27:27,600
Speaker 1: I got kicked off the United flight. Oh for what? Um?

562
00:27:27,640 --> 00:27:30,159
Speaker 1: So I this was? Actually this was that was a

563
00:27:30,200 --> 00:27:32,280
Speaker 1: puddle jumper. I heard you mentioned this. Yeah, so I

564
00:27:32,359 --> 00:27:34,640
Speaker 1: got I got kicked off of a puddle jumper years ago.

565
00:27:35,000 --> 00:27:38,280
Speaker 1: Um because the stewardess was abusing the guy in front

566
00:27:38,320 --> 00:27:40,200
Speaker 1: of me, and I said, excuse me, what's your name?

567
00:27:40,240 --> 00:27:43,080
Speaker 1: And she knew in that moment he's gonna report me,

568
00:27:43,440 --> 00:27:45,320
Speaker 1: and she said, I want you guys off my plane.

569
00:27:45,520 --> 00:27:47,399
Speaker 1: And it turns out a stewardess can kick whoever they

570
00:27:47,400 --> 00:27:49,520
Speaker 1: want off the gate agent kid for any reason. Gate

571
00:27:49,560 --> 00:27:52,159
Speaker 1: agent can't do anything about it. Uh, and so so

572
00:27:52,200 --> 00:27:54,080
Speaker 1: they kicked me off. And it's funny because did you

573
00:27:54,119 --> 00:27:57,359
Speaker 1: get her name? I? Did you know? I? I? She

574
00:27:57,680 --> 00:27:59,200
Speaker 1: say her name and then she can be off the plane.

575
00:27:59,400 --> 00:28:02,280
Speaker 1: And it's funny. United was not. They basically think if

576
00:28:02,280 --> 00:28:04,040
Speaker 1: you get kicked off a plane, it's your own fault, right,

577
00:28:04,160 --> 00:28:07,200
Speaker 1: But they and and interestingly they've not been very apologized.

578
00:28:07,240 --> 00:28:09,159
Speaker 1: You know, they're sort of like this guy deserve what

579
00:28:09,280 --> 00:28:12,919
Speaker 1: he got, you know. Uh, they're this whole thing has

580
00:28:12,960 --> 00:28:15,520
Speaker 1: been so bungled, you know from a from dot Galilee

581
00:28:15,600 --> 00:28:18,600
Speaker 1: at n y U has just reamed United for there

582
00:28:18,600 --> 00:28:21,359
Speaker 1: are three rules of this. You not only violated each

583
00:28:21,359 --> 00:28:24,480
Speaker 1: of the rules. And I'm doing this from memory, admit

584
00:28:24,560 --> 00:28:28,439
Speaker 1: your error, offered to accept responsibility, offered to repair it

585
00:28:28,800 --> 00:28:30,879
Speaker 1: each step along the way. You just made it worse.

586
00:28:30,960 --> 00:28:34,679
Speaker 1: It's it's astonishing, and it really, I mean, I have

587
00:28:34,760 --> 00:28:36,320
Speaker 1: to say I could drag back up their old their

588
00:28:36,359 --> 00:28:39,000
Speaker 1: old like apology letter to me, but it really was

589
00:28:39,040 --> 00:28:41,080
Speaker 1: the same sort of thing that we're like, it's the

590
00:28:41,120 --> 00:28:43,640
Speaker 1: corporate culture there. Yeah, and it was and and that's

591
00:28:43,640 --> 00:28:47,040
Speaker 1: where culture really matters. And it's funny. Actually a friend

592
00:28:47,040 --> 00:28:48,760
Speaker 1: of mine, dance Storms, great product guy here in the

593
00:28:48,800 --> 00:28:50,800
Speaker 1: city tweeted at me today he said, oh, it looks

594
00:28:50,840 --> 00:28:53,560
Speaker 1: like United was listening, because I had tweeted at Alaska

595
00:28:53,760 --> 00:28:56,800
Speaker 1: and virgin and said, hey, you know, you should put

596
00:28:56,840 --> 00:28:59,480
Speaker 1: into your employee app a way that makes it much

597
00:28:59,480 --> 00:29:02,680
Speaker 1: easier for them to make decisions like giving somebody free

598
00:29:02,720 --> 00:29:05,240
Speaker 1: food or free entertainment or miles on the spot when

599
00:29:05,280 --> 00:29:07,760
Speaker 1: something bad happens, and United today said hey, we're going

600
00:29:07,840 --> 00:29:09,960
Speaker 1: to do that. Oh really, that's that's part of it,

601
00:29:10,080 --> 00:29:12,000
Speaker 1: part of the their announcement. I think this morning, where

602
00:29:12,080 --> 00:29:14,840
Speaker 1: yesterday was, we put it in our app I think

603
00:29:14,920 --> 00:29:17,200
Speaker 1: to use from now this will have blown over. But

604
00:29:17,600 --> 00:29:19,960
Speaker 1: this is this is a giant drag on them for

605
00:29:20,200 --> 00:29:23,160
Speaker 1: god knows how long. And it's the thing that's amazing

606
00:29:23,280 --> 00:29:26,880
Speaker 1: is it's so avoidable. It's such a self inflicted wounds.

607
00:29:26,920 --> 00:29:28,239
Speaker 1: And this is the thing I think, you know, this

608
00:29:28,280 --> 00:29:30,440
Speaker 1: is why I'm on this sort of CBO sort of

609
00:29:30,760 --> 00:29:34,200
Speaker 1: question Chief Behavioral Office, here's g P A officer, because

610
00:29:34,640 --> 00:29:36,400
Speaker 1: I think there are so many things that we see

611
00:29:36,400 --> 00:29:40,400
Speaker 1: as FATA company that that really are actually avoidable. Right.

612
00:29:40,440 --> 00:29:43,040
Speaker 1: I think that you know, we were going back to

613
00:29:43,040 --> 00:29:45,719
Speaker 1: the gender issue, right, like we think, oh, women get

614
00:29:45,720 --> 00:29:47,280
Speaker 1: pregnant and they leave the workplace and they don't want

615
00:29:47,280 --> 00:29:48,920
Speaker 1: to come back. That's not true at all, Right, Like

616
00:29:48,960 --> 00:29:51,000
Speaker 1: there are all these systematic structural things. We don't have

617
00:29:51,040 --> 00:29:54,280
Speaker 1: to accept these things as true. Right, everything is on

618
00:29:54,320 --> 00:29:57,000
Speaker 1: the table for change. So I've tried to bring a

619
00:29:57,000 --> 00:30:00,280
Speaker 1: lot of women onto the show, and they're is a

620
00:30:00,360 --> 00:30:03,560
Speaker 1: chicken and egg issue in that I want to bring

621
00:30:04,400 --> 00:30:08,560
Speaker 1: senior women with experience and and expertise, and there are

622
00:30:08,600 --> 00:30:12,600
Speaker 1: plenty of them, but they're now in such demands that

623
00:30:12,640 --> 00:30:16,360
Speaker 1: either they don't respond or I've had ten or fifteen

624
00:30:16,400 --> 00:30:19,240
Speaker 1: percent of the hundred and fifty or so guests are women.

625
00:30:19,320 --> 00:30:22,040
Speaker 1: But you know, it's no a near half. And I

626
00:30:22,040 --> 00:30:25,920
Speaker 1: don't even think it's proportionate to how the industry is structured.

627
00:30:26,160 --> 00:30:29,720
Speaker 1: But you're still starting out with an industry that at

628
00:30:29,760 --> 00:30:32,640
Speaker 1: the senior level, forget fifty fifty. I don't even think

629
00:30:32,640 --> 00:30:38,600
Speaker 1: we're in finance and and some other related fields. It's astonishing.

630
00:30:38,640 --> 00:30:42,800
Speaker 1: And so your example of having a woman a kid,

631
00:30:43,040 --> 00:30:47,080
Speaker 1: see a female scientist, there is this sort of which

632
00:30:47,160 --> 00:30:50,440
Speaker 1: comes from Yeah, there's the problem, and and you know,

633
00:30:50,720 --> 00:30:53,360
Speaker 1: I this is this is why it is so frusting.

634
00:30:53,400 --> 00:30:55,400
Speaker 1: The three and five men don't think sexism exists, like

635
00:30:56,280 --> 00:30:58,680
Speaker 1: I think people don't draw things to their natural conclusion. Right.

636
00:30:59,200 --> 00:31:01,880
Speaker 1: If you're saying is there's no sexism, there's no systematic

637
00:31:01,880 --> 00:31:05,080
Speaker 1: sum in the world. Yet yet we only have sort

638
00:31:05,120 --> 00:31:08,680
Speaker 1: of like ten percent female CEOs. You're telling me that

639
00:31:08,720 --> 00:31:14,280
Speaker 1: there is some genetic, gendered reason that women can't run companies.

640
00:31:14,600 --> 00:31:17,120
Speaker 1: And if that's a statement, making you just I can't

641
00:31:17,160 --> 00:31:20,160
Speaker 1: say things on there like you're just wrong, right, like

642
00:31:20,280 --> 00:31:23,320
Speaker 1: that you The natural conclusion of those two facts is

643
00:31:23,360 --> 00:31:26,480
Speaker 1: that we are genetically born to be a CEO. Something

644
00:31:26,520 --> 00:31:29,000
Speaker 1: about the genetics that makes you a man also makes

645
00:31:29,040 --> 00:31:31,120
Speaker 1: you CEO worthy, Right, that's what you're saying if you're

646
00:31:31,120 --> 00:31:34,160
Speaker 1: saying sexism doesn't exist, and the status quo that we

647
00:31:34,200 --> 00:31:38,400
Speaker 1: have is is less less women. So let's talk about

648
00:31:38,440 --> 00:31:41,520
Speaker 1: some of the startups that you've done that are geared

649
00:31:41,600 --> 00:31:44,760
Speaker 1: towards making the world a better place. Tell us about

650
00:31:44,920 --> 00:31:49,840
Speaker 1: salary or equity? So I, uh have a like the

651
00:31:49,840 --> 00:31:51,560
Speaker 1: product guy in me. Every time I build something, I

652
00:31:51,560 --> 00:31:53,080
Speaker 1: recognize how I screwed it up, and then I have

653
00:31:53,120 --> 00:31:56,120
Speaker 1: to build something to sort of correct my screwing cure.

654
00:31:56,240 --> 00:31:59,400
Speaker 1: That's right, and so, um, you know, salary or equity

655
00:32:00,080 --> 00:32:01,600
Speaker 1: is a really a reaction to that conversation we were

656
00:32:01,600 --> 00:32:03,480
Speaker 1: talking earlier, right where I got my first offer and

657
00:32:03,520 --> 00:32:06,080
Speaker 1: I didn't know anything about equity, right, And so there

658
00:32:06,080 --> 00:32:09,760
Speaker 1: are whole underrepresented groups who when we bring them into

659
00:32:09,760 --> 00:32:12,720
Speaker 1: the startup environment because they didn't, you know, grow with

660
00:32:12,760 --> 00:32:15,480
Speaker 1: an engineer parent, they didn't grow up with a Silicon

661
00:32:15,600 --> 00:32:17,560
Speaker 1: Valley parent. They didn't grow up with these things. I

662
00:32:17,600 --> 00:32:20,560
Speaker 1: don't know what equity is, right, And so we've actually

663
00:32:20,560 --> 00:32:22,400
Speaker 1: created a little tool where you come in and you

664
00:32:22,720 --> 00:32:24,040
Speaker 1: sort of put in some of the variabs from your

665
00:32:24,080 --> 00:32:25,920
Speaker 1: offer letter and it will give you a risk adjusted

666
00:32:26,000 --> 00:32:28,920
Speaker 1: sort of. Yeah. It's kind of like twenty year in salary, right,

667
00:32:28,960 --> 00:32:31,520
Speaker 1: so that you can start to compare offers um in

668
00:32:31,560 --> 00:32:33,880
Speaker 1: better ways. You know, we built get Raised, which is

669
00:32:33,880 --> 00:32:37,360
Speaker 1: helping different different startup than startup equity. But yeah, we're

670
00:32:37,360 --> 00:32:40,320
Speaker 1: working in a sort of an adjacent space, right, and

671
00:32:40,360 --> 00:32:42,720
Speaker 1: so this is the you know, uh, we've held women

672
00:32:42,720 --> 00:32:45,160
Speaker 1: are two point three billion dollars in raises um by

673
00:32:45,200 --> 00:32:48,920
Speaker 1: sort of um creating a structure where they can come

674
00:32:48,920 --> 00:32:49,920
Speaker 1: in and tell me what you do, where you work,

675
00:32:49,960 --> 00:32:51,480
Speaker 1: how much you make. We'll tell you if you're underpaid,

676
00:32:52,120 --> 00:32:54,120
Speaker 1: and then you so you're comparing this against a database

677
00:32:54,160 --> 00:32:56,960
Speaker 1: of that that job. That's yeah exactly. We use bureaulabor

678
00:32:56,960 --> 00:32:58,880
Speaker 1: statistics data, we use our own data, we use public

679
00:32:58,880 --> 00:33:01,840
Speaker 1: market data. And then you answer a few more and

680
00:33:01,840 --> 00:33:03,600
Speaker 1: it generates a letter that you can give to your boss,

681
00:33:03,600 --> 00:33:06,640
Speaker 1: basically laying out a like here's market here, open job

682
00:33:06,680 --> 00:33:08,960
Speaker 1: postings in the area. Here's what I've done. Here's what

683
00:33:08,960 --> 00:33:11,640
Speaker 1: I'm going to do, Like, here's how I provide business value.

684
00:33:11,880 --> 00:33:15,600
Speaker 1: And that's been incredibly effective at getting women raises. Um

685
00:33:15,600 --> 00:33:17,760
Speaker 1: and so two or three billion dollars so far. And

686
00:33:17,800 --> 00:33:20,760
Speaker 1: you guys take of that is it's all free. We

687
00:33:20,840 --> 00:33:24,760
Speaker 1: have been speaking with Matt Walert, formally of Microsoft Ventures

688
00:33:24,800 --> 00:33:29,080
Speaker 1: and a variety of other enterprises. Check out my daily

689
00:33:29,160 --> 00:33:33,080
Speaker 1: column at Bloomberg dot com, slash View, or follow me

690
00:33:33,160 --> 00:33:37,240
Speaker 1: on Twitter at rid Halts. I'm Barry rid Halts. You're

691
00:33:37,280 --> 00:33:58,560
Speaker 1: listening to Masters in Business on Bloomberg Radio. Welcome to

692
00:33:58,600 --> 00:34:01,520
Speaker 1: the podcast. Matt. Thank you so much for for doing this.

693
00:34:01,640 --> 00:34:04,320
Speaker 1: Thank thanks for having me. It's been fun. Uh. We

694
00:34:04,320 --> 00:34:07,760
Speaker 1: were talking during the break about the various names you

695
00:34:07,880 --> 00:34:13,520
Speaker 1: have for startups get raised, salary or equity churn less, Thrive.

696
00:34:14,200 --> 00:34:17,600
Speaker 1: Who creates these? You know? Uh so some of them.

697
00:34:17,640 --> 00:34:20,439
Speaker 1: I remember exactly where it came from. Thrive was before

698
00:34:20,520 --> 00:34:25,600
Speaker 1: my time. Um uh, Turnless. I actually was the CMO

699
00:34:25,719 --> 00:34:27,920
Speaker 1: of Thrive. We were I remember having to treat with him.

700
00:34:27,920 --> 00:34:30,960
Speaker 1: Any Drive was what was sold to lending treaty Mint.

701
00:34:31,040 --> 00:34:33,480
Speaker 1: Yeah exactly. How is that the opposite? Because if you

702
00:34:33,520 --> 00:34:36,640
Speaker 1: go to Mint, you can't find a thrive tab. Is

703
00:34:36,680 --> 00:34:41,200
Speaker 1: it just integrated and and mean I stand alone? Yeah,

704
00:34:41,200 --> 00:34:43,200
Speaker 1: So they renamed it money right, and then I think

705
00:34:43,239 --> 00:34:45,799
Speaker 1: shuttered it in one of their sort of is that

706
00:34:45,840 --> 00:34:48,440
Speaker 1: the natural end game for all these acquisitions or are

707
00:34:48,440 --> 00:34:51,840
Speaker 1: they really just aquahiers they want the engineering town. No.

708
00:34:51,960 --> 00:34:53,799
Speaker 1: I think there was real product there. But I think

709
00:34:53,840 --> 00:34:56,040
Speaker 1: you know what happened at a time, you know, when

710
00:34:56,719 --> 00:34:58,520
Speaker 1: when the collapse came along, and I think, you know,

711
00:34:58,560 --> 00:35:01,200
Speaker 1: business had to reorient the they built. They built it,

712
00:35:01,440 --> 00:35:03,759
Speaker 1: bought it primarily because we could show that we could

713
00:35:03,800 --> 00:35:06,720
Speaker 1: raise people's credit scores substantially in a fairly short amount

714
00:35:06,719 --> 00:35:08,439
Speaker 1: of times. So you get, yeah, you get credit score

715
00:35:08,480 --> 00:35:11,240
Speaker 1: increases on the twenty to fifty point range in six months.

716
00:35:11,560 --> 00:35:13,359
Speaker 1: And so their idea was, look, you know, a six

717
00:35:13,440 --> 00:35:15,319
Speaker 1: hundred mortgage lead isn't worth nearly as much as a

718
00:35:15,320 --> 00:35:17,919
Speaker 1: six fifty. Let's send our people who are not really

719
00:35:17,960 --> 00:35:20,600
Speaker 1: ready to you. You'll turn them around, get them ready,

720
00:35:20,640 --> 00:35:24,799
Speaker 1: and then bring them back when they're when they're that's fascinating.

721
00:35:25,160 --> 00:35:28,600
Speaker 1: What is churnalists? So journalists? This was so I actually

722
00:35:28,960 --> 00:35:31,040
Speaker 1: it's one of the few names I actually really like.

723
00:35:31,600 --> 00:35:34,000
Speaker 1: Uh And and it was and everybody knows what churn is.

724
00:35:34,080 --> 00:35:37,200
Speaker 1: It's you go to Netflix. They added so many subscribers,

725
00:35:37,200 --> 00:35:39,520
Speaker 1: but they lost so many. And so I was having

726
00:35:40,560 --> 00:35:44,040
Speaker 1: a drink with the CMO of Thrive after the acquisition.

727
00:35:44,040 --> 00:35:45,600
Speaker 1: He had left to Thrive, and he and I were

728
00:35:45,640 --> 00:35:48,040
Speaker 1: just talking, and you know, we were talking about what

729
00:35:48,040 --> 00:35:50,439
Speaker 1: makes a good company good, and you know, I said,

730
00:35:50,440 --> 00:35:53,040
Speaker 1: good companies their churnless, right, like, you know, no one

731
00:35:53,040 --> 00:35:55,600
Speaker 1: ever has to leave because it meets their needs, right.

732
00:35:55,640 --> 00:35:57,600
Speaker 1: And I was like, and that's how you employees that

733
00:35:57,600 --> 00:36:00,200
Speaker 1: could be customers. I was like, you know that because

734
00:36:00,200 --> 00:36:03,799
Speaker 1: I'm a behavior guy, right, and so you know, churn

735
00:36:03,920 --> 00:36:06,759
Speaker 1: is the ultimate sort of like measure of behavior, right,

736
00:36:06,760 --> 00:36:10,680
Speaker 1: because it's literally am I gonna do it? Or not

737
00:36:10,800 --> 00:36:13,080
Speaker 1: do it? And so I sort of was like, oh, really,

738
00:36:13,080 --> 00:36:14,719
Speaker 1: good companies don't have turn and he was like, you

739
00:36:14,719 --> 00:36:16,680
Speaker 1: should call your next company turnless. And I was like,

740
00:36:16,920 --> 00:36:19,040
Speaker 1: all right, And we did and didn't have anything to

741
00:36:19,080 --> 00:36:21,799
Speaker 1: do with reducing churning company. And so we were while

742
00:36:21,800 --> 00:36:24,320
Speaker 1: we were building, you know, we built sort of behavioral

743
00:36:24,400 --> 00:36:27,160
Speaker 1: interventions for lots of people. We built the AIRPS retirement tools,

744
00:36:27,200 --> 00:36:30,759
Speaker 1: we built um um some various interventions around sort of

745
00:36:31,360 --> 00:36:34,239
Speaker 1: choice in in food and exercise and other things. And

746
00:36:34,239 --> 00:36:36,880
Speaker 1: it was always about behavior, and so the idea really

747
00:36:36,960 --> 00:36:42,080
Speaker 1: was to build interesting, sticky, behavior modifying things for the world.

748
00:36:42,120 --> 00:36:43,520
Speaker 1: That's where get Raised. You know, we built that on

749
00:36:43,560 --> 00:36:46,040
Speaker 1: the side while we were there, Right, it was building

750
00:36:46,080 --> 00:36:49,920
Speaker 1: those sorts of things, and um, salary equity you went

751
00:36:50,000 --> 00:36:53,760
Speaker 1: over that. So get Raised generates a letter which individual

752
00:36:53,840 --> 00:36:57,479
Speaker 1: takes their boss sally equity is really just informational. Yeah,

753
00:36:57,680 --> 00:36:59,920
Speaker 1: there's the value of the offer you've gotten. Yeah. So

754
00:37:00,040 --> 00:37:02,799
Speaker 1: then the net result of salary equity is basically taking

755
00:37:02,800 --> 00:37:05,000
Speaker 1: the equity portion of your offer, and it gives you

756
00:37:05,040 --> 00:37:07,000
Speaker 1: a sentence that says you should think you sort of

757
00:37:07,000 --> 00:37:10,040
Speaker 1: think of this as like x number of dollars per year, right,

758
00:37:10,080 --> 00:37:13,040
Speaker 1: So it'said. It basically takes into account investing um and

759
00:37:13,120 --> 00:37:14,880
Speaker 1: sort of risk adjusted return and sort of breaks that

760
00:37:14,960 --> 00:37:17,360
Speaker 1: into a yearly salary, so that if you're comparing, for example,

761
00:37:17,360 --> 00:37:19,719
Speaker 1: a startup offer and a Microsoft offer, you can say

762
00:37:19,840 --> 00:37:23,640
Speaker 1: the equity component is worth blank. So here's the question

763
00:37:23,920 --> 00:37:26,440
Speaker 1: I have to ask you. And I found this fascinating

764
00:37:27,040 --> 00:37:30,719
Speaker 1: um in your bio. So you you keep saying I'm

765
00:37:30,719 --> 00:37:34,280
Speaker 1: a behavior guy, I'm a psychologist. I'm behavioral psychology guy.

766
00:37:34,400 --> 00:37:37,239
Speaker 1: But all the speeches I've seen you've given, most of

767
00:37:37,280 --> 00:37:41,719
Speaker 1: the writings I've seen you've made, they invariably have a

768
00:37:41,800 --> 00:37:47,160
Speaker 1: focus or at least an element of this, technology, this, startup, this.

769
00:37:47,840 --> 00:37:50,640
Speaker 1: And you said when I left Microsoft, I got far

770
00:37:50,719 --> 00:37:54,800
Speaker 1: more recruiter inbound based on my background in startups and

771
00:37:54,920 --> 00:37:59,120
Speaker 1: ventures than I did in behavioral science, despite being considerably

772
00:37:59,160 --> 00:38:03,239
Speaker 1: better the ladder. So why is that, you know? I

773
00:38:03,280 --> 00:38:05,320
Speaker 1: think it goes back to this. I don't think companies

774
00:38:05,360 --> 00:38:07,520
Speaker 1: are are There's a few companies out there looking for

775
00:38:07,560 --> 00:38:10,319
Speaker 1: behavioral scientists, UM, but most of them I don't think

776
00:38:10,320 --> 00:38:12,239
Speaker 1: I've woken up to the fact that they don't have

777
00:38:12,320 --> 00:38:14,279
Speaker 1: teams and they need to write that this is going

778
00:38:14,320 --> 00:38:15,719
Speaker 1: to be the sort of next way they're going to

779
00:38:15,840 --> 00:38:19,920
Speaker 1: generate value in their business, both internally and externally. Uh.

780
00:38:19,960 --> 00:38:22,200
Speaker 1: And so I think that's, you know, a part of

781
00:38:22,239 --> 00:38:25,759
Speaker 1: what's behind that. I think I leaned towards um technology.

782
00:38:26,239 --> 00:38:29,919
Speaker 1: You know, technology when I meet with people, I built

783
00:38:29,920 --> 00:38:31,920
Speaker 1: lots of things in the world that are not technology focused.

784
00:38:32,000 --> 00:38:35,680
Speaker 1: They just you know, often their integrations into other programs, right,

785
00:38:35,680 --> 00:38:38,920
Speaker 1: technology often build standalone things, and so they're easier to

786
00:38:38,960 --> 00:38:40,960
Speaker 1: point to UM, and I know how to build them

787
00:38:41,000 --> 00:38:43,279
Speaker 1: fast and cheap and come from that world, and so

788
00:38:43,800 --> 00:38:45,759
Speaker 1: you know, it's natural that I gravitate there. But I

789
00:38:45,760 --> 00:38:48,080
Speaker 1: don't think it has to be technology. Right, We're talking

790
00:38:48,120 --> 00:38:51,000
Speaker 1: earlier about the subway signs, Right, you know, I think

791
00:38:51,000 --> 00:38:52,960
Speaker 1: putting up a sign in a Walmart line is not

792
00:38:53,040 --> 00:38:56,080
Speaker 1: a you know, there's nothing tech about that. UM, it's

793
00:38:56,120 --> 00:38:59,440
Speaker 1: just a you know, it's just it's a good behavioral intervention. Right,

794
00:38:59,440 --> 00:39:02,200
Speaker 1: It's good behave real interventions. So there was something fascinating

795
00:39:02,280 --> 00:39:04,360
Speaker 1: that I read that I have to ask you. You.

796
00:39:04,600 --> 00:39:07,120
Speaker 1: I know, you do a lot of speaking gigs, but

797
00:39:07,239 --> 00:39:13,279
Speaker 1: you refuse to accept speaking fees. And just in the

798
00:39:13,280 --> 00:39:17,800
Speaker 1: news was Obama being criticized for taking a four hundred

799
00:39:17,880 --> 00:39:21,359
Speaker 1: thousand dollar speaking fee from a Wall Street firm. The

800
00:39:21,440 --> 00:39:26,280
Speaker 1: rest of us, who are either in academia, authors, writing books,

801
00:39:26,760 --> 00:39:31,959
Speaker 1: or just otherwise public figures, it's a lucrative side gig.

802
00:39:32,480 --> 00:39:35,040
Speaker 1: You know, the greatest thing I could do during lunch

803
00:39:35,120 --> 00:39:37,839
Speaker 1: is walk out of my office, walk three blocks, walk

804
00:39:37,880 --> 00:39:40,840
Speaker 1: into a conference room, go blah blah blah for forty

805
00:39:40,840 --> 00:39:43,480
Speaker 1: five minutes, pick up a check for grand and go

806
00:39:43,520 --> 00:39:48,120
Speaker 1: back to lunch. Why have you decided I'm not interested

807
00:39:48,320 --> 00:39:51,120
Speaker 1: in taking speaking fees. I mean, I think what you

808
00:39:51,200 --> 00:39:54,040
Speaker 1: choose to get paid for is really an important way

809
00:39:54,040 --> 00:39:58,040
Speaker 1: of of how your approach life, right like you know,

810
00:39:58,120 --> 00:40:01,800
Speaker 1: we are almost I don't disagree with you. Almost every

811
00:40:01,920 --> 00:40:04,719
Speaker 1: everything that you could do is is potentially monetize herble

812
00:40:05,080 --> 00:40:08,520
Speaker 1: right like almost you know? Okay, I mean listen, I

813
00:40:08,640 --> 00:40:11,439
Speaker 1: speak it at nonprofits all the time. I don't charge them.

814
00:40:11,680 --> 00:40:14,040
Speaker 1: I speak at universities. I've spoken at n y U,

815
00:40:14,160 --> 00:40:16,680
Speaker 1: at m I t at Harvard at Columbia. I never tried,

816
00:40:16,760 --> 00:40:19,520
Speaker 1: even though Harvard is richer than you know, crotious. I

817
00:40:19,600 --> 00:40:23,600
Speaker 1: never charge them a speaking fee because it's an educational thing.

818
00:40:24,440 --> 00:40:27,000
Speaker 1: But if it's a conference where they're selling tickets and

819
00:40:27,040 --> 00:40:30,560
Speaker 1: people are doing it for more than a break, even

820
00:40:31,360 --> 00:40:34,480
Speaker 1: what why wouldn't you? And and by the way, there

821
00:40:34,520 --> 00:40:37,759
Speaker 1: are things that other people get paid for that I've

822
00:40:37,760 --> 00:40:40,440
Speaker 1: elected not to get paid for. Plus it allows me

823
00:40:40,520 --> 00:40:44,840
Speaker 1: to stay objective and neutral and except to reject what

824
00:40:44,880 --> 00:40:46,960
Speaker 1: I want because it's what I want as opposed to

825
00:40:47,000 --> 00:40:49,640
Speaker 1: going and earning a buck and to enjoy it, right,

826
00:40:49,719 --> 00:40:51,960
Speaker 1: and to enjoy it, and I want to enjoy the

827
00:40:52,000 --> 00:40:55,200
Speaker 1: spread of information. I think spreading information is incredibly important

828
00:40:55,239 --> 00:40:59,880
Speaker 1: and I think you know, uh, the places that do

829
00:41:00,280 --> 00:41:02,440
Speaker 1: that do Um, there's some places to sort of say,

830
00:41:02,440 --> 00:41:03,680
Speaker 1: we want to pay you a speaker fee, and that's

831
00:41:03,719 --> 00:41:05,239
Speaker 1: just part of how we work. So I asked them

832
00:41:05,239 --> 00:41:07,080
Speaker 1: to donate to a domestic violence shelter. So actually just

833
00:41:07,160 --> 00:41:10,480
Speaker 1: came back from a passo yesterday where an agency had

834
00:41:10,480 --> 00:41:13,319
Speaker 1: a fee structure and they donated to a domestic violence

835
00:41:13,360 --> 00:41:16,080
Speaker 1: shelter on my behalf. Um, so so there are you know,

836
00:41:16,120 --> 00:41:19,640
Speaker 1: sometimes it happens, but generally speaking, like, I think people

837
00:41:19,719 --> 00:41:24,840
Speaker 1: should get paid for doing right unless unless the unless

838
00:41:24,840 --> 00:41:27,319
Speaker 1: sharing knowledge is your job. Right Like if my job

839
00:41:27,400 --> 00:41:29,719
Speaker 1: is a journalist, yeah, I get paid for that. Right.

840
00:41:29,760 --> 00:41:31,520
Speaker 1: If your job is to be a speaker, get paid

841
00:41:31,560 --> 00:41:33,240
Speaker 1: for that. I just don't want my job to be

842
00:41:33,080 --> 00:41:35,359
Speaker 1: being a speaker. I always want my job to be

843
00:41:35,440 --> 00:41:38,839
Speaker 1: building things. So actually saying that changes what you will

844
00:41:38,920 --> 00:41:40,960
Speaker 1: or won't accept, because now it's like, who do I

845
00:41:41,000 --> 00:41:42,760
Speaker 1: want to talk to. I don't care about the salary.

846
00:41:42,800 --> 00:41:45,000
Speaker 1: I don't care about this. It's oh, that's an audience

847
00:41:45,000 --> 00:41:48,960
Speaker 1: I want to address wherever, and you know, and so

848
00:41:49,040 --> 00:41:51,040
Speaker 1: I do you know, speak eternally. A lot of speaking

849
00:41:51,040 --> 00:41:52,800
Speaker 1: engagements send them off to other people that that I

850
00:41:52,880 --> 00:41:56,160
Speaker 1: think would be better or more interesting. You know, I'll

851
00:41:56,160 --> 00:41:57,799
Speaker 1: actually correct something you said earlier, refer to me as

852
00:41:57,800 --> 00:41:59,800
Speaker 1: a cold and so I don't consult either, right, No,

853
00:42:00,440 --> 00:42:06,400
Speaker 1: I only what is I believe in, you know, coming

854
00:42:06,440 --> 00:42:08,480
Speaker 1: to get to change your mind, to how to get

855
00:42:08,480 --> 00:42:11,600
Speaker 1: you to think differently about behavioral science. He's part of

856
00:42:11,600 --> 00:42:13,400
Speaker 1: what's important to me in the world, and I think

857
00:42:13,440 --> 00:42:15,080
Speaker 1: it generates a better world. And I want to do

858
00:42:15,120 --> 00:42:16,759
Speaker 1: it for as many people as I can. What I

859
00:42:16,880 --> 00:42:19,719
Speaker 1: charge money for is coming in actually doing it, right.

860
00:42:19,760 --> 00:42:21,399
Speaker 1: I want to get charged for the doing. I want

861
00:42:21,400 --> 00:42:23,920
Speaker 1: to get paid on the things that I build, the implementation,

862
00:42:24,120 --> 00:42:26,719
Speaker 1: the execution, because that's what matters. Because here's the thing

863
00:42:26,719 --> 00:42:29,160
Speaker 1: about science, right, none of my ideas are original. They

864
00:42:29,160 --> 00:42:32,239
Speaker 1: come from a long lineage of people before me. You're

865
00:42:32,280 --> 00:42:35,399
Speaker 1: standing on the shoulders of giants, and many people will

866
00:42:35,400 --> 00:42:38,200
Speaker 1: stand on my shoulders after me. Right, there's this long

867
00:42:38,280 --> 00:42:41,240
Speaker 1: and and if every one of those people like again,

868
00:42:41,280 --> 00:42:44,560
Speaker 1: I like taking these to their sort of extremes because

869
00:42:44,560 --> 00:42:46,000
Speaker 1: it makes me think of, you know, sort of thing

870
00:42:46,080 --> 00:42:49,360
Speaker 1: differently about them. If all of those people before me

871
00:42:49,480 --> 00:42:53,120
Speaker 1: had refused to share their information unless it was monetized,

872
00:42:53,520 --> 00:42:56,160
Speaker 1: I could never be where I am. There's a fascinating

873
00:42:56,200 --> 00:43:00,440
Speaker 1: priceonmics article that talks about how hand and full of

874
00:43:00,440 --> 00:43:04,160
Speaker 1: people have bought up the publishers of all the scientific

875
00:43:04,280 --> 00:43:09,600
Speaker 1: journals and university UM peer reviewed things and they're charging

876
00:43:09,600 --> 00:43:14,280
Speaker 1: absurd money for subscriptions and it's really crimping the exchange

877
00:43:14,400 --> 00:43:16,480
Speaker 1: of ideas. That's something that should not be a for

878
00:43:16,680 --> 00:43:19,960
Speaker 1: profit entity. Somebody should have had the forethought of buying

879
00:43:20,000 --> 00:43:23,000
Speaker 1: it and putting it in the public sphere public domain, right.

880
00:43:23,040 --> 00:43:25,319
Speaker 1: I think that that, you know, that's the kind of

881
00:43:25,920 --> 00:43:28,600
Speaker 1: like Mike Bloomberg sort of philanthropy kind of thing is

882
00:43:28,640 --> 00:43:31,200
Speaker 1: buying up those rights. And I think sciencests responded appropriately,

883
00:43:31,200 --> 00:43:33,439
Speaker 1: which is to say, we don't. We're gonna care less

884
00:43:33,440 --> 00:43:35,520
Speaker 1: about those journals than we used to. We're gonna start

885
00:43:35,520 --> 00:43:39,520
Speaker 1: doing open science. There's a replicability product right like project like,

886
00:43:39,640 --> 00:43:41,640
Speaker 1: we're starting to do these things that say no science

887
00:43:41,640 --> 00:43:45,480
Speaker 1: should take place in public both because people draw conclusions

888
00:43:45,560 --> 00:43:47,760
Speaker 1: from its stuff and it should be replicable and important,

889
00:43:48,120 --> 00:43:51,320
Speaker 1: and because everyone should have access to those insights. I

890
00:43:51,600 --> 00:43:55,239
Speaker 1: couldn't agree more. Um, you did a ted talk that

891
00:43:55,320 --> 00:43:59,480
Speaker 1: I thought was really interesting about motivating people to do

892
00:43:59,640 --> 00:44:03,480
Speaker 1: work worth doing. Yeah, explain. So there's this quote I

893
00:44:03,480 --> 00:44:05,920
Speaker 1: love from Teddy Roosevelt. Far and away, the best prize

894
00:44:05,920 --> 00:44:07,839
Speaker 1: that life has to offer is the chance to work

895
00:44:07,880 --> 00:44:11,560
Speaker 1: hard at work worth doing. Um And I think you know,

896
00:44:11,760 --> 00:44:14,759
Speaker 1: I like money is meaningful, and I don't you know

897
00:44:14,880 --> 00:44:16,759
Speaker 1: for anyone without it, they'll tell you how meaningful it.

898
00:44:16,760 --> 00:44:19,080
Speaker 1: It's a tool that helps you achieve things that are

899
00:44:19,160 --> 00:44:21,319
Speaker 1: useful in life. But there is certain but from a

900
00:44:21,320 --> 00:44:24,520
Speaker 1: motivational perspective, what we see is meaning. Actually, you know,

901
00:44:24,560 --> 00:44:26,480
Speaker 1: once you sort of surpass a certain level of money,

902
00:44:26,560 --> 00:44:28,160
Speaker 1: meaning is far more important. You know. One of the

903
00:44:28,200 --> 00:44:29,840
Speaker 1: things I actually bring up in this talk that you know,

904
00:44:29,960 --> 00:44:32,280
Speaker 1: I think illustrates this well is if you ask seniors

905
00:44:32,280 --> 00:44:35,239
Speaker 1: in college you know what's important about your first job,

906
00:44:35,280 --> 00:44:37,680
Speaker 1: that all say salaries, Like, salary is the most important feature.

907
00:44:38,239 --> 00:44:41,120
Speaker 1: If you survey them a mere year into the job market,

908
00:44:41,400 --> 00:44:44,200
Speaker 1: meaning has become the lead experience, meaning what they've learned.

909
00:44:44,280 --> 00:44:47,160
Speaker 1: That's right, those things become the far more important piece. Right.

910
00:44:47,200 --> 00:44:49,960
Speaker 1: People get this very quickly. Um And I actually think

911
00:44:49,960 --> 00:44:51,800
Speaker 1: that's one of the crisises of mental health in the

912
00:44:51,880 --> 00:44:54,120
Speaker 1: United States is just how many people go to work

913
00:44:54,160 --> 00:44:58,040
Speaker 1: and are miserable, disengaged, you know, sort of don't find

914
00:44:58,080 --> 00:45:00,720
Speaker 1: meaning in their work. Um. This idea of meaning management,

915
00:45:00,719 --> 00:45:02,400
Speaker 1: I actually think is really important. How do you go

916
00:45:02,440 --> 00:45:04,680
Speaker 1: and say, you know? And I actually think there are

917
00:45:04,680 --> 00:45:07,480
Speaker 1: interesting tech tools you could build. So imagine something like

918
00:45:08,080 --> 00:45:10,359
Speaker 1: let's pretend you were a coder and you're far far

919
00:45:10,440 --> 00:45:12,520
Speaker 1: removed from the end user. Right, You're working at Microsoft,

920
00:45:12,560 --> 00:45:14,080
Speaker 1: so it's it's not a small startup or something. You're

921
00:45:14,120 --> 00:45:16,479
Speaker 1: far removed from the end user. What if I said

922
00:45:16,520 --> 00:45:20,080
Speaker 1: to you, hey, did you know seven million people touched

923
00:45:20,120 --> 00:45:24,279
Speaker 1: your code today? Right? What a powerful like you know,

924
00:45:24,360 --> 00:45:27,640
Speaker 1: sort of motivating investment. And it's true, right, there's almost

925
00:45:28,400 --> 00:45:30,960
Speaker 1: you know, almost everything that people do in the world,

926
00:45:31,200 --> 00:45:33,799
Speaker 1: Like for me, even the lowliest janitor. Like imagine if

927
00:45:33,800 --> 00:45:35,919
Speaker 1: I could say, after you clean this hall, hey, did

928
00:45:35,920 --> 00:45:40,560
Speaker 1: you know like ten thousand people walked this hall today? Right?

929
00:45:41,160 --> 00:45:43,600
Speaker 1: Like that's an amazing just allowing people to see the

930
00:45:43,640 --> 00:45:46,440
Speaker 1: echoes of what they do. Um, you know, and it

931
00:45:46,440 --> 00:45:48,480
Speaker 1: goes into so many things. I mean, I think one

932
00:45:48,520 --> 00:45:49,960
Speaker 1: of the points I make in this thing about sort

933
00:45:49,960 --> 00:45:52,839
Speaker 1: of college students is we sell college to people as

934
00:45:53,520 --> 00:45:55,719
Speaker 1: as a financial thing. Right, we say the reason to

935
00:45:55,760 --> 00:45:58,759
Speaker 1: go to college is to make more money, that's right,

936
00:45:58,920 --> 00:46:00,440
Speaker 1: And it is to get a job. But what it's

937
00:46:00,480 --> 00:46:02,640
Speaker 1: really about is to get a meaningful job. Right. What

938
00:46:02,680 --> 00:46:05,000
Speaker 1: college allows you to do is to is to have

939
00:46:05,080 --> 00:46:09,880
Speaker 1: the privilege of picking something that is important to you, right. Um,

940
00:46:10,000 --> 00:46:15,520
Speaker 1: and so I think that that I love science fiction. Um,

941
00:46:15,560 --> 00:46:18,560
Speaker 1: not just sort of on a personal entertainment level. But

942
00:46:18,600 --> 00:46:20,480
Speaker 1: you know when I when product people come and tell me, like,

943
00:46:20,480 --> 00:46:22,799
Speaker 1: what do you I I habitually don't read nonfiction. I

944
00:46:22,880 --> 00:46:26,360
Speaker 1: talk to people to learn things, and I read to

945
00:46:26,480 --> 00:46:28,560
Speaker 1: learn about the world that emotionally, and so I read

946
00:46:28,600 --> 00:46:31,759
Speaker 1: almost a totally fiction and I love and I love

947
00:46:31,800 --> 00:46:35,440
Speaker 1: science fiction because science fiction is this clever thought of

948
00:46:35,440 --> 00:46:38,480
Speaker 1: experiment of the world with limits removed, and it starts

949
00:46:38,520 --> 00:46:41,800
Speaker 1: to get you thinking an interesting product direction you mentioned earlier. Um,

950
00:46:41,840 --> 00:46:45,319
Speaker 1: you know, uh idiocracy, right, which I think is a

951
00:46:45,360 --> 00:46:47,759
Speaker 1: particular kind of vision of the world. But I think

952
00:46:47,760 --> 00:46:49,440
Speaker 1: there's also a vision of the world that's very star

953
00:46:49,520 --> 00:46:51,320
Speaker 1: Trek Like think about star Trek. The exist in a

954
00:46:51,360 --> 00:46:53,480
Speaker 1: sort of post need world. We've been able we can

955
00:46:53,520 --> 00:46:57,040
Speaker 1: synthesize matter out of energy, right, and so we've sort

956
00:46:57,080 --> 00:46:59,440
Speaker 1: of surpassed basic human needs. And what do they do?

957
00:47:00,080 --> 00:47:02,200
Speaker 1: Like do they sit on their couch and watch travel,

958
00:47:03,040 --> 00:47:05,680
Speaker 1: travel the universe and explore That's right, Then they do

959
00:47:05,719 --> 00:47:08,440
Speaker 1: amazing science and they build amazing art, and like, I

960
00:47:08,520 --> 00:47:10,960
Speaker 1: actually think that's the real thing. I think if you

961
00:47:11,960 --> 00:47:13,719
Speaker 1: the big change of a post need world, there's a

962
00:47:13,760 --> 00:47:16,480
Speaker 1: lot of people scared about what happens as as robots

963
00:47:16,480 --> 00:47:20,440
Speaker 1: displaced jobs. What I think the really amazing part of

964
00:47:20,480 --> 00:47:23,040
Speaker 1: that is that it frees up people to do other things.

965
00:47:23,080 --> 00:47:25,400
Speaker 1: And the real dominant question is not how do we

966
00:47:25,440 --> 00:47:27,799
Speaker 1: replace those jobs with with you know, so there other

967
00:47:27,880 --> 00:47:30,879
Speaker 1: meaningless jobs, it's what do we do with the sheer

968
00:47:30,880 --> 00:47:34,040
Speaker 1: amount of human potential that has now been unlocked? Right? Like,

969
00:47:34,239 --> 00:47:36,319
Speaker 1: how do we incorporate those people into science? How do

970
00:47:36,360 --> 00:47:38,480
Speaker 1: we incorporate them into design? How do we incorporate them

971
00:47:38,480 --> 00:47:41,359
Speaker 1: into other things that computers don't do well? Not just

972
00:47:41,400 --> 00:47:44,000
Speaker 1: to sort of employ them, but because this is our

973
00:47:44,200 --> 00:47:46,560
Speaker 1: shot at the future, right, how do we take the

974
00:47:46,600 --> 00:47:49,319
Speaker 1: ten thousand people that got displaced by you know, sort

975
00:47:49,360 --> 00:47:51,600
Speaker 1: of closing a factory and get them started thinking about

976
00:47:51,640 --> 00:47:55,759
Speaker 1: greening the world. That that's interesting. Something else that came

977
00:47:55,840 --> 00:47:58,440
Speaker 1: up in one of your public speeches. Stayed with me.

978
00:47:59,560 --> 00:48:05,160
Speaker 1: What do Eminem's tell us about human psychology? So people? Uh,

979
00:48:05,320 --> 00:48:07,439
Speaker 1: I love this example, although I will tell you that

980
00:48:07,440 --> 00:48:09,160
Speaker 1: that I'm going to find something different because the Mars

981
00:48:09,200 --> 00:48:11,480
Speaker 1: people are like refused to give me free M and

982
00:48:11,600 --> 00:48:14,000
Speaker 1: m's for life, which I think, after having mentioned it

983
00:48:14,040 --> 00:48:15,440
Speaker 1: so many times, I ought to be able to get

984
00:48:15,480 --> 00:48:17,479
Speaker 1: at least a package. Have you taken a speaking fee?

985
00:48:17,640 --> 00:48:23,160
Speaker 1: I know by my own right. So, so, the really

986
00:48:23,200 --> 00:48:25,560
Speaker 1: powerful thing I think about Eminem's as a as a

987
00:48:25,560 --> 00:48:27,960
Speaker 1: sort of metaphor for behavior change is that it shows

988
00:48:28,000 --> 00:48:30,560
Speaker 1: how people focus on the wrong thing, right, you know,

989
00:48:30,640 --> 00:48:33,800
Speaker 1: in terms of people really go think about creating new flavors,

990
00:48:33,800 --> 00:48:36,560
Speaker 1: how delicious they are, etcetera. But the dominant thing that

991
00:48:36,640 --> 00:48:38,160
Speaker 1: keeps me from eating M and m's all the time

992
00:48:38,239 --> 00:48:40,640
Speaker 1: is availability. Right, they're Eminem's right here. I'd be eating

993
00:48:40,719 --> 00:48:42,920
Speaker 1: them right now, right, and so would you, right Like,

994
00:48:42,960 --> 00:48:46,200
Speaker 1: it's that availability factor that is so interesting, And so

995
00:48:47,280 --> 00:48:54,200
Speaker 1: you know, what people forget is mostly motivating reasons already exist. Right,

996
00:48:54,320 --> 00:48:57,280
Speaker 1: let's go back to Let's use another kind of related

997
00:48:57,320 --> 00:49:00,239
Speaker 1: example that the asking for a raised piece. Right, It's

998
00:49:00,239 --> 00:49:02,040
Speaker 1: not like women don't want to get paid fairly. It's

999
00:49:02,040 --> 00:49:04,560
Speaker 1: not like I don't that Eminem's aren't delicious. The reason

1000
00:49:04,719 --> 00:49:07,160
Speaker 1: women don't ask is because it's hard, and there are

1001
00:49:07,200 --> 00:49:09,480
Speaker 1: systematic pressures that keep them from asking. The reason I

1002
00:49:09,480 --> 00:49:11,480
Speaker 1: don't eat eminem is because they're not here. There are

1003
00:49:11,520 --> 00:49:15,520
Speaker 1: systematic inhibiting pressures, and I think people don't. They're eminem's upstairs.

1004
00:49:15,520 --> 00:49:17,720
Speaker 1: We can pick we can pick them up on the way.

1005
00:49:18,680 --> 00:49:22,279
Speaker 1: This could happen and I might take some to go.

1006
00:49:22,640 --> 00:49:25,839
Speaker 1: So the question is why do people eat more eminem's

1007
00:49:25,880 --> 00:49:28,000
Speaker 1: when they're multi colored than if you would have just

1008
00:49:28,000 --> 00:49:31,279
Speaker 1: put one color in a bowl. People actually less eminem's. Yeah,

1009
00:49:31,280 --> 00:49:33,640
Speaker 1: we love those that. We love that diversity of colors.

1010
00:49:33,640 --> 00:49:35,440
Speaker 1: You know, even though I don't think blindfold did anyone

1011
00:49:35,440 --> 00:49:37,160
Speaker 1: could tell the difference. There is no difference there. They'll

1012
00:49:37,160 --> 00:49:39,120
Speaker 1: tell you there's no there's no taste difference. It's actually

1013
00:49:39,160 --> 00:49:41,560
Speaker 1: one of the things I always you know, when I

1014
00:49:41,600 --> 00:49:44,000
Speaker 1: try and jog people's mind with this, you know, I'm like,

1015
00:49:44,000 --> 00:49:45,520
Speaker 1: think about the fact that you have a favorite color

1016
00:49:45,560 --> 00:49:47,600
Speaker 1: of eminem. Any kid on earth will tell you what

1017
00:49:47,640 --> 00:49:50,000
Speaker 1: his favorite color color is, right, He's gonna She's gonna

1018
00:49:50,000 --> 00:49:52,040
Speaker 1: tell you, he's gonna tell you, I save the green

1019
00:49:52,080 --> 00:49:54,440
Speaker 1: ones for last because they're the best. Why the hell

1020
00:49:54,480 --> 00:49:56,120
Speaker 1: is the best? They don't taste any different than the

1021
00:49:56,200 --> 00:49:58,880
Speaker 1: other ones, but they have a favorite color. And I

1022
00:49:58,920 --> 00:50:01,040
Speaker 1: think part of beaver science, the part of reason that

1023
00:50:01,080 --> 00:50:03,200
Speaker 1: beavirol science is so important in organizations, is because it

1024
00:50:03,200 --> 00:50:06,560
Speaker 1: embraces that irrational part of ourselves. The part that says

1025
00:50:06,719 --> 00:50:08,959
Speaker 1: it's not about how long in line I am in line,

1026
00:50:08,960 --> 00:50:11,640
Speaker 1: It's about how being in line feels right. The part

1027
00:50:11,680 --> 00:50:14,000
Speaker 1: that says it's not about the fact that the Eminem's

1028
00:50:14,000 --> 00:50:16,319
Speaker 1: don't taste different. I still have a favorite color. I'm

1029
00:50:16,400 --> 00:50:18,600
Speaker 1: you have to be familiar with the brown Eminem's and

1030
00:50:18,600 --> 00:50:21,640
Speaker 1: the Van Halen Rider. Of course, I love the idea

1031
00:50:21,719 --> 00:50:25,399
Speaker 1: that that's a built in verifier of have have these

1032
00:50:25,440 --> 00:50:28,719
Speaker 1: persons actually gone through a full rider and done the

1033
00:50:28,719 --> 00:50:31,480
Speaker 1: full technology requirements that I want out of this and

1034
00:50:31,680 --> 00:50:34,480
Speaker 1: and brown Eminem's are relevant, but it's an instant check

1035
00:50:34,719 --> 00:50:36,640
Speaker 1: have they done it or not? Yes, it's a It's

1036
00:50:36,640 --> 00:50:38,400
Speaker 1: a great behavioral hack, and I think there's all sorts

1037
00:50:38,400 --> 00:50:40,759
Speaker 1: of those interesting kinds of behavioral hacks in the world.

1038
00:50:40,840 --> 00:50:43,120
Speaker 1: I actually remember when I was a kid, you know

1039
00:50:43,120 --> 00:50:46,280
Speaker 1: how many people actually remember like lessons from sixth grade,

1040
00:50:46,480 --> 00:50:50,359
Speaker 1: like not many people. And I remember science class day one.

1041
00:50:51,400 --> 00:50:53,880
Speaker 1: Mr Mars I think was his name, was the science teacher,

1042
00:50:53,880 --> 00:50:56,840
Speaker 1: and he gave us our first set of experimental directions.

1043
00:50:57,400 --> 00:51:01,480
Speaker 1: And the first the first instruction was read these experiments

1044
00:51:01,480 --> 00:51:04,719
Speaker 1: these instructions completely before doing anything. And there were about

1045
00:51:04,800 --> 00:51:09,120
Speaker 1: ten steps and they was don't do anything at all.

1046
00:51:09,400 --> 00:51:12,440
Speaker 1: And I had an exam like that, and I love that.

1047
00:51:12,520 --> 00:51:15,799
Speaker 1: I mean, I love it is stuck with me forever, right,

1048
00:51:15,840 --> 00:51:18,200
Speaker 1: and we'll stick with me forever. And I think that,

1049
00:51:18,400 --> 00:51:22,160
Speaker 1: And everybody gets to work my step, and most people

1050
00:51:22,239 --> 00:51:25,120
Speaker 1: don't follow the instructions. I think the last step was

1051
00:51:25,520 --> 00:51:28,520
Speaker 1: if you've done this step, quietly, fold your paper in half,

1052
00:51:28,800 --> 00:51:30,680
Speaker 1: bringing up to the classroom, and then you can leave

1053
00:51:30,880 --> 00:51:32,920
Speaker 1: or or something like that. So all these people are

1054
00:51:32,960 --> 00:51:35,400
Speaker 1: working away that's right, And two or three people have

1055
00:51:35,440 --> 00:51:37,400
Speaker 1: walked out of the class and nobody understands it, right.

1056
00:51:37,400 --> 00:51:40,440
Speaker 1: Everybody looks them and thinks they're crazy. And I actually

1057
00:51:40,480 --> 00:51:42,840
Speaker 1: think that's I've never thought about it this way, but

1058
00:51:42,880 --> 00:51:45,040
Speaker 1: it's a good metaphor for why I think behavioral science

1059
00:51:45,040 --> 00:51:48,000
Speaker 1: is so important because it's outcome focused right. It doesn't

1060
00:51:48,040 --> 00:51:51,399
Speaker 1: just try and apply whatever the tool is righteologically through

1061
00:51:51,440 --> 00:51:54,640
Speaker 1: these steps. It says, what happens if we break the rules,

1062
00:51:54,719 --> 00:51:56,600
Speaker 1: What happened if we get outside of these tools? What

1063
00:51:56,640 --> 00:52:00,719
Speaker 1: happens if we start with the outcome and then backwards? Huh,

1064
00:52:00,760 --> 00:52:02,960
Speaker 1: that's quite interesting. So let me get to some of

1065
00:52:03,000 --> 00:52:06,520
Speaker 1: my favorite questions. I'm gonna call you out for some

1066
00:52:06,600 --> 00:52:09,520
Speaker 1: bs that you said earlier. I'm gonna challenge you on

1067
00:52:09,520 --> 00:52:12,040
Speaker 1: some good challenge me. But let's let's let's plow this

1068
00:52:12,200 --> 00:52:14,680
Speaker 1: uh through us. So you told us about your background.

1069
00:52:14,719 --> 00:52:18,239
Speaker 1: You're working in in academic studying for your PhD. You

1070
00:52:18,320 --> 00:52:22,520
Speaker 1: go to work at a startup before Microsoft. Give us

1071
00:52:22,520 --> 00:52:25,920
Speaker 1: a run off of your background quickly? What was your

1072
00:52:25,760 --> 00:52:27,920
Speaker 1: your path? So it was was thrived, We got acquired

1073
00:52:27,960 --> 00:52:29,880
Speaker 1: by lending Tree, then it was at landing Tree for

1074
00:52:29,920 --> 00:52:32,320
Speaker 1: a while, we broke out. We did uh churn lists,

1075
00:52:33,080 --> 00:52:36,400
Speaker 1: built a bunch of things sold that. UM came to Microsoft,

1076
00:52:36,600 --> 00:52:39,279
Speaker 1: did product at Microsoft, then ventures at Microsoft, and now

1077
00:52:39,280 --> 00:52:44,520
Speaker 1: I'm chief Dad. So that's your dad, officer. UM, tell

1078
00:52:44,640 --> 00:52:46,880
Speaker 1: us about some of your early mentors. You strike me

1079
00:52:46,920 --> 00:52:51,640
Speaker 1: at somebody as somebody who has benefited greatly. Oh yeah,

1080
00:52:51,640 --> 00:52:55,440
Speaker 1: I mean I would not. I am a strong believer

1081
00:52:55,520 --> 00:52:58,439
Speaker 1: in mentorship. You know. I I think I'm a boy Scout,

1082
00:52:58,480 --> 00:53:00,120
Speaker 1: an Eagle Scout, and I think that boy Scouts had

1083
00:53:00,120 --> 00:53:01,959
Speaker 1: a profound effect on me, as having a Scout master

1084
00:53:02,120 --> 00:53:05,600
Speaker 1: had a profound effect on me. Um. You know I couldn't.

1085
00:53:05,640 --> 00:53:07,800
Speaker 1: I would be remissing. You know, Andrew warden Berry Schwartz

1086
00:53:07,800 --> 00:53:10,719
Speaker 1: both professors. It's worthmore. Barry obviously very famous for this

1087
00:53:10,760 --> 00:53:12,919
Speaker 1: sort of choice reduction stuff and all the Ted talks

1088
00:53:12,920 --> 00:53:15,920
Speaker 1: and things. Um. Andrew Ward was the professor that I

1089
00:53:15,960 --> 00:53:19,120
Speaker 1: talked about earlier that said there's an orderly way to

1090
00:53:19,239 --> 00:53:22,080
Speaker 1: sort of approach disagreement in science, which is doing an

1091
00:53:22,080 --> 00:53:24,440
Speaker 1: experiment like that's your chance to disagree is to run

1092
00:53:24,520 --> 00:53:27,160
Speaker 1: to prove it, right. Um. And I think that that

1093
00:53:27,200 --> 00:53:30,160
Speaker 1: has had a profound impact on the rest of my life. Um.

1094
00:53:30,200 --> 00:53:34,440
Speaker 1: So you know, I think my parents were amazing role models. Um.

1095
00:53:34,800 --> 00:53:37,640
Speaker 1: In part, so my mom is a nurse and my

1096
00:53:37,719 --> 00:53:41,399
Speaker 1: dad works in durable medical quipment basically, um, wheelchairs. Mom

1097
00:53:41,440 --> 00:53:43,719
Speaker 1: got her college agree right after I got mine. Um,

1098
00:53:43,840 --> 00:53:47,919
Speaker 1: so she she graduated. She was a foreign nurse forever. Uh.

1099
00:53:48,000 --> 00:53:51,319
Speaker 1: And and you know Dad didn't go to college. But

1100
00:53:51,400 --> 00:53:56,920
Speaker 1: they both are, you know, so smart and inquisitive, and

1101
00:53:56,920 --> 00:54:00,680
Speaker 1: and my house was a place where you know, I'll

1102
00:54:00,680 --> 00:54:03,080
Speaker 1: give a great example. The Oregans a ballot measure state. Right,

1103
00:54:03,120 --> 00:54:05,680
Speaker 1: So every year we get the ballot measures, and my

1104
00:54:05,719 --> 00:54:08,840
Speaker 1: parents would, from as far back as I can remember,

1105
00:54:08,840 --> 00:54:11,279
Speaker 1: five or six, we would have a family discussion about

1106
00:54:11,280 --> 00:54:13,040
Speaker 1: what we all thought about the ballot measures. Really, and

1107
00:54:13,080 --> 00:54:15,680
Speaker 1: I was disagree, Like we were free to disagree. We

1108
00:54:15,680 --> 00:54:17,239
Speaker 1: were free to like sort of like they gave us

1109
00:54:17,480 --> 00:54:19,759
Speaker 1: the pamphlet and we were how many siblings in the house,

1110
00:54:19,880 --> 00:54:22,120
Speaker 1: just one brother eighteen months older, So the two of

1111
00:54:22,160 --> 00:54:25,239
Speaker 1: you and the parents debating Oregon ballot measures. Yeah, just

1112
00:54:25,239 --> 00:54:28,759
Speaker 1: talking about I just wrote a column on on Canada's

1113
00:54:28,800 --> 00:54:33,719
Speaker 1: marijuana initiative and trace the history of legal weed in America.

1114
00:54:34,120 --> 00:54:38,480
Speaker 1: And everybody thinks it's California, but actually it's Oregon. It's

1115
00:54:38,640 --> 00:54:43,799
Speaker 1: like nineteen seventies six decriminalized medical marijuana, long before California

1116
00:54:43,840 --> 00:54:45,799
Speaker 1: did it. Yeah, sometime in the seventies, I don't remember

1117
00:54:45,800 --> 00:54:49,480
Speaker 1: the exacts. A fascinating sort of state, right in somewhat

1118
00:54:49,520 --> 00:54:52,040
Speaker 1: you know, everything east of the mountains is high desert,

1119
00:54:52,520 --> 00:54:55,120
Speaker 1: all sorts of kinds of people. It's both strangely sort

1120
00:54:55,120 --> 00:54:58,000
Speaker 1: of like a hippie mecca but also very religious. Do

1121
00:54:58,040 --> 00:55:02,080
Speaker 1: you interesting you don't watch Silicon val I do watch Portlandia.

1122
00:55:02,320 --> 00:55:05,319
Speaker 1: So my joke, My joke is, you know, I thought

1123
00:55:05,320 --> 00:55:07,560
Speaker 1: it was a sitcom, but then I've been to Portland

1124
00:55:07,640 --> 00:55:10,000
Speaker 1: a few times and I learned it was a documentary.

1125
00:55:10,120 --> 00:55:12,600
Speaker 1: That's right, I mean it is. It is sort of

1126
00:55:13,080 --> 00:55:19,000
Speaker 1: eerily like home sometimes, and that's good and fascinating and interesting,

1127
00:55:19,160 --> 00:55:22,239
Speaker 1: and I like that. Um. And the thing I really

1128
00:55:22,239 --> 00:55:26,120
Speaker 1: love about Oregon is you could sign in some ways,

1129
00:55:26,160 --> 00:55:29,520
Speaker 1: like Friday Night Lights is also a documentary Oregon absolutely right,

1130
00:55:29,560 --> 00:55:31,200
Speaker 1: Like it's about Texas, but you know, in many ways

1131
00:55:31,200 --> 00:55:32,959
Speaker 1: it's about Oregon, right, Like I came from a small

1132
00:55:33,000 --> 00:55:35,560
Speaker 1: town with the championship football team and they're a whole

1133
00:55:35,600 --> 00:55:38,400
Speaker 1: town shout up for games, and Origan is a microcos

1134
00:55:38,560 --> 00:55:41,040
Speaker 1: him up all sorts of different things, and and yet

1135
00:55:41,440 --> 00:55:44,000
Speaker 1: they've managed to live in relative sort of piece and harmony.

1136
00:55:44,040 --> 00:55:46,680
Speaker 1: There's not a ton of acrimony. Um, it's a it's

1137
00:55:46,680 --> 00:55:50,799
Speaker 1: a very changing hasn't even though I know most of

1138
00:55:50,800 --> 00:55:53,759
Speaker 1: the people who have who are in like Portland's are

1139
00:55:53,920 --> 00:55:59,360
Speaker 1: relatively newcomers. Like everybody seems to be an immigrant. And

1140
00:55:59,440 --> 00:56:01,840
Speaker 1: what I find fascinating is Portland as a town that

1141
00:56:01,920 --> 00:56:04,160
Speaker 1: used to be a mill in mining town, and all

1142
00:56:04,280 --> 00:56:08,800
Speaker 1: these old buildings and factories have been wonderfully converted to

1143
00:56:09,040 --> 00:56:13,400
Speaker 1: restaurants and theaters and and you could see the rough hewn,

1144
00:56:13,760 --> 00:56:19,400
Speaker 1: original exposed timbers. It's it's it's really fascinating. But isn't

1145
00:56:19,400 --> 00:56:22,400
Speaker 1: there now starting to be a little chafing about the gentrification?

1146
00:56:22,560 --> 00:56:24,880
Speaker 1: I think, Look, I think anytime in the crowds and

1147
00:56:24,880 --> 00:56:26,759
Speaker 1: and you guys are starting to get traffic out there,

1148
00:56:26,760 --> 00:56:29,560
Speaker 1: there's always going to be some chafing, right, Um, any

1149
00:56:29,600 --> 00:56:32,040
Speaker 1: time in a city that grows like I think that's inevitable,

1150
00:56:32,040 --> 00:56:35,400
Speaker 1: but grows like a weed. Like they are one of

1151
00:56:35,400 --> 00:56:38,040
Speaker 1: the fastest growing cities in the country. I think Austin

1152
00:56:38,160 --> 00:56:40,720
Speaker 1: is first, and they might be second, a Seattle's second.

1153
00:56:41,040 --> 00:56:43,000
Speaker 1: They're up there, yeah, and and we don't have the

1154
00:56:43,040 --> 00:56:46,520
Speaker 1: infrastructure to support it. Um. But I think the thing

1155
00:56:46,520 --> 00:56:48,480
Speaker 1: that I love about Oregon, the thing I loved about

1156
00:56:48,600 --> 00:56:53,719
Speaker 1: growing up country is the chafing is like you can

1157
00:56:53,800 --> 00:56:56,560
Speaker 1: vehemently disagree with people and still come together for like

1158
00:56:56,600 --> 00:56:59,439
Speaker 1: dinner on Friday night. You know, it was that it's

1159
00:56:59,520 --> 00:57:08,560
Speaker 1: that spirit of of disagreement but still respect. Yeah, neighborly compassion, right, Um,

1160
00:57:08,600 --> 00:57:13,200
Speaker 1: And I think that that, particularly at this era in

1161
00:57:13,239 --> 00:57:18,640
Speaker 1: American culture, that is so important. How do we disagree

1162
00:57:19,040 --> 00:57:22,560
Speaker 1: and still like love each other in an emotive and

1163
00:57:22,720 --> 00:57:26,480
Speaker 1: important way. Mayer Stateman at Santa Clara said he comes

1164
00:57:26,520 --> 00:57:30,680
Speaker 1: from Hebrew University of Jerusalem said the difference between Americans

1165
00:57:30,880 --> 00:57:36,480
Speaker 1: and Europeans and Israelis. In Europe they can disc they

1166
00:57:36,520 --> 00:57:39,760
Speaker 1: can have the political disagreement, but then put it aside

1167
00:57:39,760 --> 00:57:42,120
Speaker 1: and go to dinner. The same in Israel. In the

1168
00:57:42,200 --> 00:57:45,320
Speaker 1: United States, people don't want to talk about the disagreement.

1169
00:57:45,320 --> 00:57:49,520
Speaker 1: They'd rather not have the debate and and then go

1170
00:57:49,560 --> 00:57:52,320
Speaker 1: to dinner. If they have the debate, it seems hackles

1171
00:57:52,360 --> 00:57:55,840
Speaker 1: gets raised and people get offended. But I I derailed

1172
00:57:55,880 --> 00:57:58,280
Speaker 1: you from mentors. Were there any other mentors I wanted?

1173
00:57:58,280 --> 00:57:59,800
Speaker 1: I mean, look, they were tons of you know, I

1174
00:58:00,080 --> 00:58:02,160
Speaker 1: close out on really saying, you know, my parents were

1175
00:58:02,160 --> 00:58:04,479
Speaker 1: a key shaper for me. Um. You know, my father

1176
00:58:04,600 --> 00:58:06,160
Speaker 1: was fond of saying that my brother and I were

1177
00:58:06,160 --> 00:58:07,920
Speaker 1: his chance to change the world. He's sort of like, look,

1178
00:58:07,960 --> 00:58:09,760
Speaker 1: I didn't go to college. I don't have this sort

1179
00:58:09,800 --> 00:58:13,040
Speaker 1: of background. You guys are it, so you need to

1180
00:58:13,080 --> 00:58:16,040
Speaker 1: go be who you can be. And then that was company.

1181
00:58:16,080 --> 00:58:18,520
Speaker 1: So it was a strong sense of purpose coupled with

1182
00:58:18,600 --> 00:58:21,720
Speaker 1: a and whatever you end up being is okay, my mom,

1183
00:58:22,400 --> 00:58:25,240
Speaker 1: my mom. I think only a few years ago finally

1184
00:58:25,240 --> 00:58:26,880
Speaker 1: stopped trying to convince me to come a nurse. You

1185
00:58:26,880 --> 00:58:28,400
Speaker 1: know here I have I'm an exited startup to done

1186
00:58:28,520 --> 00:58:29,800
Speaker 1: this thing. And she's like, you'd be a great nurse.

1187
00:58:30,040 --> 00:58:31,760
Speaker 1: Come back to Oregon, be a nurse. I'm like, I

1188
00:58:31,800 --> 00:58:35,680
Speaker 1: don't think I'm on that path, right They they part

1189
00:58:35,680 --> 00:58:37,800
Speaker 1: of the reason, you know, I don't charge for speaking

1190
00:58:37,840 --> 00:58:39,640
Speaker 1: and you know, take some of my income and build

1191
00:58:39,640 --> 00:58:41,680
Speaker 1: these pro social side projects and do all these things

1192
00:58:41,760 --> 00:58:43,520
Speaker 1: is because I had these parents who sort of said,

1193
00:58:44,160 --> 00:58:48,520
Speaker 1: that's the stuff that matters. But that's really interesting. Tell

1194
00:58:48,600 --> 00:58:52,160
Speaker 1: us about some academics or others who influenced your approach

1195
00:58:52,520 --> 00:58:56,920
Speaker 1: to behavioral psychology. So you know, look, I I the

1196
00:58:57,040 --> 00:58:59,840
Speaker 1: nice thing about nice thing, the the interesting thing about

1197
00:58:59,840 --> 00:59:01,720
Speaker 1: being a real science and sort of the not modern

1198
00:59:02,080 --> 00:59:04,520
Speaker 1: you know, sort of application of behavior economics and some

1199
00:59:04,600 --> 00:59:07,400
Speaker 1: parts of judgment decision making. You know, we have the

1200
00:59:07,720 --> 00:59:11,400
Speaker 1: condiment Introversey. You know, we have these these iconic figures

1201
00:59:11,440 --> 00:59:16,920
Speaker 1: who really have advanced the work. Um, you know Danna Rellie,

1202
00:59:17,520 --> 00:59:19,400
Speaker 1: who I'm lucky enough to sort of count as a friend.

1203
00:59:19,800 --> 00:59:23,160
Speaker 1: Like Predictably Irrational was one of my favorite books. I

1204
00:59:23,200 --> 00:59:28,400
Speaker 1: mean it's just Dan so full of insight and personal um,

1205
00:59:28,440 --> 00:59:31,800
Speaker 1: just the trauma with the burn Unite amazing. Yeah, I

1206
00:59:31,800 --> 00:59:33,840
Speaker 1: means so visceral. When you read it, it's like, oh,

1207
00:59:33,880 --> 00:59:36,480
Speaker 1: this stuff really matters. Yeah, it matters to him and

1208
00:59:36,480 --> 00:59:38,640
Speaker 1: and I mean it matters to think about how we

1209
00:59:38,840 --> 00:59:41,680
Speaker 1: just these little changes, what it means to someone going

1210
00:59:41,720 --> 00:59:44,400
Speaker 1: through the burn Unit process. You could take someone in

1211
00:59:44,560 --> 00:59:48,800
Speaker 1: miserable pain and make their life appreciably better with a

1212
00:59:48,880 --> 00:59:53,360
Speaker 1: series of rational decisions. That's right shocking you and I.

1213
00:59:53,440 --> 00:59:55,400
Speaker 1: You know, I talked earlier about like why do I

1214
00:59:55,400 --> 00:59:57,560
Speaker 1: think GP Abril Officer is so important? And I sort

1215
00:59:57,560 --> 01:00:00,360
Speaker 1: of said, you know, I think it's about making all

1216
01:00:00,400 --> 01:00:03,080
Speaker 1: the rules. Everything is possible, any out we can reach,

1217
01:00:03,160 --> 01:00:06,200
Speaker 1: any outcome if we are willing to sort of take

1218
01:00:06,200 --> 01:00:09,000
Speaker 1: a step back and say every behavior is motivatable, if

1219
01:00:09,040 --> 01:00:11,200
Speaker 1: we are willing to pull all of the levers. And

1220
01:00:11,600 --> 01:00:15,080
Speaker 1: I think what I love about Dan um is that.

1221
01:00:15,320 --> 01:00:16,800
Speaker 1: You know, he was one of those people that came

1222
01:00:16,840 --> 01:00:18,720
Speaker 1: out of the lab and said, no, there's an application

1223
01:00:18,760 --> 01:00:20,960
Speaker 1: for this. You know, we talked earlier about Tom Gilovich,

1224
01:00:21,320 --> 01:00:24,360
Speaker 1: Um who I think how we know it isn't so yeah,

1225
01:00:24,480 --> 01:00:26,680
Speaker 1: great hoth hand. You know, he's just a run in

1226
01:00:26,680 --> 01:00:30,360
Speaker 1: a bunch of amazing research. Um, he's sort of presage.

1227
01:00:30,600 --> 01:00:33,280
Speaker 1: I think he's sort of way ahead of everybody else.

1228
01:00:33,680 --> 01:00:35,720
Speaker 1: And and I think he's sort of pressage that I

1229
01:00:35,720 --> 01:00:39,200
Speaker 1: was going to leave academia. I ravividly remember him calling

1230
01:00:39,200 --> 01:00:41,000
Speaker 1: me and saying, Hey, we love you at Cornell. We'd

1231
01:00:41,040 --> 01:00:42,960
Speaker 1: love to offer you this this interesting fellowship. We'd love

1232
01:00:42,960 --> 01:00:45,160
Speaker 1: for you to come. But you know, you keep saying

1233
01:00:45,160 --> 01:00:48,560
Speaker 1: applied psychology, and you know we're researchers, and you know,

1234
01:00:48,880 --> 01:00:51,720
Speaker 1: if what you care about in the world is applying psychology,

1235
01:00:51,800 --> 01:00:54,240
Speaker 1: you might not want to come into a graduate program.

1236
01:00:54,240 --> 01:00:56,360
Speaker 1: And I think ultimately he was right. I got frustrated

1237
01:00:56,400 --> 01:00:59,400
Speaker 1: with just writing research papers because it wasn't moving the

1238
01:00:59,440 --> 01:01:01,959
Speaker 1: needle that I on it. And and I think Tom

1239
01:01:02,040 --> 01:01:04,560
Speaker 1: was really right, And I wish I had had known

1240
01:01:04,600 --> 01:01:07,640
Speaker 1: better how to listen to him at the time about Hey,

1241
01:01:07,720 --> 01:01:09,360
Speaker 1: I really needed to get out of the lab and

1242
01:01:09,400 --> 01:01:11,600
Speaker 1: that I was never going to be able to just

1243
01:01:11,800 --> 01:01:14,920
Speaker 1: do that, and and and so I think there's all

1244
01:01:14,960 --> 01:01:18,720
Speaker 1: these people. You know, that's one phone call from Tom Gillich,

1245
01:01:18,760 --> 01:01:21,440
Speaker 1: and he's given me lots of things. You know, by

1246
01:01:21,480 --> 01:01:23,320
Speaker 1: the time I was a Cornelli's a great guy. But

1247
01:01:24,760 --> 01:01:26,960
Speaker 1: these little moments have big ripple effects. And so a

1248
01:01:26,960 --> 01:01:29,040
Speaker 1: lot of what what I think is important in mentorship

1249
01:01:29,200 --> 01:01:33,280
Speaker 1: is he's creating those moments, right taking the fifteen minutes

1250
01:01:33,320 --> 01:01:35,680
Speaker 1: to talk to somebody that you wouldn't normally talk to

1251
01:01:36,240 --> 01:01:39,320
Speaker 1: as an equal in an emotionally compassionate way, not to

1252
01:01:39,440 --> 01:01:42,280
Speaker 1: lecture them, but to to really engage with whatever is

1253
01:01:42,400 --> 01:01:45,000
Speaker 1: very important to them. You know. I think at the

1254
01:01:45,000 --> 01:01:47,680
Speaker 1: temptation of many mentors is well, I have my stick,

1255
01:01:47,720 --> 01:01:49,000
Speaker 1: I have what I believe, and I'm just going to

1256
01:01:49,200 --> 01:01:51,920
Speaker 1: enforce that on other people. I think so much of

1257
01:01:51,960 --> 01:01:56,960
Speaker 1: what's important about mentorship, particularly in in our given the

1258
01:01:57,040 --> 01:02:00,480
Speaker 1: challenges that America faces right now, he's about saying, what

1259
01:02:00,600 --> 01:02:03,320
Speaker 1: do you want to accomplish? And then how can I

1260
01:02:03,320 --> 01:02:05,480
Speaker 1: help you get there. It's not about me judging your goals,

1261
01:02:05,520 --> 01:02:07,440
Speaker 1: are saying your goals are the right, goals are the wrong? Goals.

1262
01:02:07,480 --> 01:02:10,800
Speaker 1: It's me saying whatever is important to you, I'm going

1263
01:02:10,840 --> 01:02:15,920
Speaker 1: to get behind helping you get there. Quite quite interesting. Um,

1264
01:02:15,960 --> 01:02:18,200
Speaker 1: I only have you for another ten or fifteen minutes.

1265
01:02:18,600 --> 01:02:22,920
Speaker 1: Let me get to some of my favorite questions. Tell

1266
01:02:23,000 --> 01:02:25,600
Speaker 1: us about failure, tell us about a time you failed

1267
01:02:25,640 --> 01:02:28,240
Speaker 1: and what you learned from the experience. Well, so you know,

1268
01:02:28,280 --> 01:02:30,160
Speaker 1: as I start pointed out to you earlier, everything I

1269
01:02:30,200 --> 01:02:32,760
Speaker 1: build as a failure, and that even when it's successful,

1270
01:02:33,240 --> 01:02:35,360
Speaker 1: it doesn't I recognize the thing I need to go

1271
01:02:35,440 --> 01:02:38,600
Speaker 1: do next. Right, So get raised is really a reaction

1272
01:02:38,640 --> 01:02:44,840
Speaker 1: to thrive. So at thrive for example, successful company. But

1273
01:02:45,360 --> 01:02:48,120
Speaker 1: when I was looking at the data, so one of

1274
01:02:48,160 --> 01:02:50,360
Speaker 1: the components that really changed behavior there is we had

1275
01:02:50,400 --> 01:02:54,160
Speaker 1: a very clear, clear behavioral score and it had sub components.

1276
01:02:54,160 --> 01:02:55,720
Speaker 1: One of some components, for example, with savings, and I

1277
01:02:55,720 --> 01:02:57,120
Speaker 1: didn't want rich people to get a high score and

1278
01:02:57,120 --> 01:02:58,800
Speaker 1: poor people to get a low score. So I said, well,

1279
01:02:58,800 --> 01:03:00,800
Speaker 1: we're gonna do this is We're gonna remitting it's income.

1280
01:03:00,880 --> 01:03:04,200
Speaker 1: So basically like savings. Yeah, percentage of your income that

1281
01:03:04,240 --> 01:03:10,440
Speaker 1: you save for ninety days, that's right, and so um,

1282
01:03:10,480 --> 01:03:12,920
Speaker 1: when you look at at saving as a percentage of income,

1283
01:03:12,960 --> 01:03:16,080
Speaker 1: women are actually significantly better savers than men. But the

1284
01:03:16,520 --> 01:03:19,200
Speaker 1: but the moment I took out the the income on

1285
01:03:19,240 --> 01:03:21,240
Speaker 1: a raw dollar basis, all of the men were doing

1286
01:03:21,320 --> 01:03:24,000
Speaker 1: much better at Thrive. Why because they were getting paid better, right,

1287
01:03:24,160 --> 01:03:28,439
Speaker 1: And so here I was, we built this exitable, successful thing,

1288
01:03:28,880 --> 01:03:31,080
Speaker 1: and I felt like an idiot because I had only

1289
01:03:31,080 --> 01:03:33,520
Speaker 1: looked at what happened post paycheck, and that's really how

1290
01:03:33,920 --> 01:03:37,120
Speaker 1: to relative to the dollar amount. And so that's where

1291
01:03:37,120 --> 01:03:40,200
Speaker 1: I sort of got to get raised. Idea was we

1292
01:03:40,240 --> 01:03:42,640
Speaker 1: only did interventions post paycheck. What if we did something

1293
01:03:42,640 --> 01:03:44,400
Speaker 1: that it directly affected your paycheck? And so that's why

1294
01:03:44,440 --> 01:03:46,120
Speaker 1: we built get Raised. At the next company, it was that,

1295
01:03:46,520 --> 01:03:50,400
Speaker 1: oh I screwed it up last time. Get Raised fantastic tool,

1296
01:03:50,440 --> 01:03:53,960
Speaker 1: two point three billion dollars and raises very successful. But

1297
01:03:54,200 --> 01:03:55,960
Speaker 1: there's many ways and things which I think I screwed

1298
01:03:56,000 --> 01:03:58,560
Speaker 1: it up. One of the important ones is it puts

1299
01:03:58,560 --> 01:04:02,560
Speaker 1: the burden on the disadvantaged. Right. It says to women, hey,

1300
01:04:02,760 --> 01:04:04,680
Speaker 1: you live in a world that is unfair to you.

1301
01:04:04,680 --> 01:04:07,000
Speaker 1: You have to go do something about that, when really

1302
01:04:07,080 --> 01:04:09,920
Speaker 1: the onus is on men, right, Like, but you can

1303
01:04:09,960 --> 01:04:11,920
Speaker 1: get women to go to a site, fill out a

1304
01:04:11,960 --> 01:04:14,600
Speaker 1: couple of forms printed out and give it to their boss.

1305
01:04:14,920 --> 01:04:16,800
Speaker 1: You can't get their boss to go to a site

1306
01:04:16,840 --> 01:04:20,600
Speaker 1: and say, here's why you you are sexist in your

1307
01:04:20,600 --> 01:04:24,960
Speaker 1: pay practices. This is where I'm gonna challenge you. Okay,

1308
01:04:25,000 --> 01:04:27,840
Speaker 1: everything is changeable. I want to live in Some things

1309
01:04:27,880 --> 01:04:30,560
Speaker 1: are more changeable than others. Yes, so there is there's

1310
01:04:30,600 --> 01:04:33,720
Speaker 1: certainly low hanging fruit that I am VMA agreement. There's

1311
01:04:33,920 --> 01:04:35,960
Speaker 1: get raised might have been the low hanging fruit. Here

1312
01:04:36,080 --> 01:04:37,640
Speaker 1: might have been the low hanging fruit. But I think

1313
01:04:37,680 --> 01:04:40,120
Speaker 1: what behavioral I think the whole point of behaviorl scientists

1314
01:04:40,120 --> 01:04:42,400
Speaker 1: to say, used to consider that as a thought question,

1315
01:04:42,560 --> 01:04:45,040
Speaker 1: what could I do to get a boss to be

1316
01:04:45,160 --> 01:04:48,400
Speaker 1: motivated to pay you fairly? Right? And we're going and

1317
01:04:48,400 --> 01:04:50,400
Speaker 1: thinking about those things like, for example, give you a

1318
01:04:50,440 --> 01:04:54,640
Speaker 1: short answer to that. You now have a huge move

1319
01:04:54,720 --> 01:04:59,160
Speaker 1: towards e s G investing, environmental sustainable governance, and companies

1320
01:04:59,200 --> 01:05:02,840
Speaker 1: are now being ranked on what sort of diversification they

1321
01:05:02,840 --> 01:05:04,720
Speaker 1: have at their highest levels at their board, if their

1322
01:05:04,720 --> 01:05:09,200
Speaker 1: senior management. And it's an objective goal that hey, you're

1323
01:05:09,200 --> 01:05:11,440
Speaker 1: not meeting this, what do you what? You're here's a

1324
01:05:11,480 --> 01:05:14,200
Speaker 1: plan just as you could get raised. Here's how to

1325
01:05:14,320 --> 01:05:19,160
Speaker 1: get diversified or whatever it is, get retained right retentions,

1326
01:05:19,160 --> 01:05:21,640
Speaker 1: and I like, let's do economic motivators. Forget the triple

1327
01:05:21,680 --> 01:05:24,360
Speaker 1: bottom line, will use your single bottom line. If you

1328
01:05:24,440 --> 01:05:29,840
Speaker 1: retain women, you make more money period period like turnover

1329
01:05:29,880 --> 01:05:33,680
Speaker 1: reduced high diversity. Like we know this, we can prove

1330
01:05:33,760 --> 01:05:36,680
Speaker 1: it empirically. So hey, if I can get you to

1331
01:05:36,760 --> 01:05:40,400
Speaker 1: regularly evaluate women's salaries to make sure that they are

1332
01:05:40,440 --> 01:05:43,960
Speaker 1: comparative so that they don't leave you, Like, that's a

1333
01:05:44,000 --> 01:05:47,640
Speaker 1: good thing, and we can go build The next generation

1334
01:05:47,680 --> 01:05:49,360
Speaker 1: of tools that I'm building is actually, you know, I

1335
01:05:49,400 --> 01:05:51,960
Speaker 1: sold you every everything I build as a failure, and

1336
01:05:51,960 --> 01:05:55,440
Speaker 1: then that pumps the next It's an interesting life perspective.

1337
01:05:56,120 --> 01:05:58,720
Speaker 1: So it's not I'm going to change the language on you.

1338
01:05:58,840 --> 01:06:01,120
Speaker 1: It's not so much a fail you're as it is

1339
01:06:01,880 --> 01:06:07,040
Speaker 1: a provisional step waiting the next hypothesis. It is incomplete.

1340
01:06:07,080 --> 01:06:09,480
Speaker 1: Science is perpetually incomplete. What's one of the things I

1341
01:06:09,520 --> 01:06:11,760
Speaker 1: love about it? And so the next set of tools

1342
01:06:11,760 --> 01:06:14,120
Speaker 1: that I'm building are actually focused on man. So here's

1343
01:06:14,120 --> 01:06:17,280
Speaker 1: where I'm gonna call BS on you. You said you

1344
01:06:17,320 --> 01:06:21,160
Speaker 1: read almost exclusively fiction, and yet I go to your

1345
01:06:21,200 --> 01:06:25,840
Speaker 1: website and there is Dan Arrowley's Predictably Irrational, Thinking Fast

1346
01:06:25,880 --> 01:06:29,040
Speaker 1: and Slow by Danny Kahneman, Stumbling on hop Happiness by

1347
01:06:29,120 --> 01:06:33,520
Speaker 1: Gilbert paraps of Choice by Barry Shortz. Essentially all of

1348
01:06:33,640 --> 01:06:40,840
Speaker 1: the things that you recommend are explicitly nonfiction. Actually, if

1349
01:06:40,880 --> 01:06:42,840
Speaker 1: you go to the recommendation page, you will notice that

1350
01:06:42,880 --> 01:06:45,600
Speaker 1: there is a section for fiction, and I didn't. If

1351
01:06:45,600 --> 01:06:48,320
Speaker 1: you scroll down, I am recommending fiction books as well.

1352
01:06:48,440 --> 01:06:55,120
Speaker 1: So let's talk about some of your favorite fiction books. So, so, I, uh,

1353
01:06:55,360 --> 01:06:57,560
Speaker 1: it's funny the things that I are my favorites are

1354
01:06:57,600 --> 01:06:59,440
Speaker 1: not necessarily things I read often. Right, I sort of

1355
01:06:59,440 --> 01:07:01,600
Speaker 1: said I read all of science fiction and things because

1356
01:07:01,600 --> 01:07:03,400
Speaker 1: I think it makes me a better product person. I

1357
01:07:03,440 --> 01:07:11,160
Speaker 1: love Blindness jose Saramago. Oh yeah, beautiful sad book. I

1358
01:07:11,160 --> 01:07:13,680
Speaker 1: will love a lot of Hemingway. You know what. I

1359
01:07:13,720 --> 01:07:15,520
Speaker 1: just read The Old Man in the City on a flight,

1360
01:07:15,720 --> 01:07:19,040
Speaker 1: and I was just I come back to, you know,

1361
01:07:19,120 --> 01:07:21,720
Speaker 1: to reread. I'm a big rereader of fiction, so I

1362
01:07:21,800 --> 01:07:25,160
Speaker 1: tried because I'm what are you reading? Uh So, so

1363
01:07:25,200 --> 01:07:27,160
Speaker 1: what I've done over the last couple of years, Actually,

1364
01:07:28,000 --> 01:07:29,800
Speaker 1: what I've done over the last couple years actually is

1365
01:07:29,800 --> 01:07:33,200
Speaker 1: is I'll pick an author and read everything they've written, um,

1366
01:07:33,240 --> 01:07:36,080
Speaker 1: regardless of the quality. That regardless of the quality, because

1367
01:07:36,080 --> 01:07:38,160
Speaker 1: there are certain authors that, hey, you should read these

1368
01:07:38,160 --> 01:07:40,800
Speaker 1: three books. You don't want to read those three books,

1369
01:07:40,880 --> 01:07:44,040
Speaker 1: that's right. But but what's interesting is even bad fiction

1370
01:07:44,080 --> 01:07:47,439
Speaker 1: teaches you something, right, Like, so I recently I would

1371
01:07:47,440 --> 01:07:50,240
Speaker 1: read through everything Stephen King had written, and there's some

1372
01:07:50,320 --> 01:07:53,400
Speaker 1: good stuff and some bad stuff some of his some

1373
01:07:53,520 --> 01:07:56,480
Speaker 1: stuff is bad. So the let me give you an example.

1374
01:07:56,480 --> 01:07:58,560
Speaker 1: And I'm gonna get nasty letters from people on this,

1375
01:07:58,840 --> 01:08:00,920
Speaker 1: and I, by the way, I know he's an immensely

1376
01:08:00,960 --> 01:08:06,440
Speaker 1: talented guy. Misery is just astonishing. But I remember reading

1377
01:08:06,480 --> 01:08:11,000
Speaker 1: The Stand, which is like eight hundred pages, and then

1378
01:08:11,040 --> 01:08:14,600
Speaker 1: suddenly it's like he put the papers down for six

1379
01:08:14,640 --> 01:08:16,840
Speaker 1: months and then picked it up and just pivoted in

1380
01:08:16,880 --> 01:08:20,160
Speaker 1: a totally different direction. And I was like, oh, what

1381
01:08:20,240 --> 01:08:22,840
Speaker 1: a terrible thing to do to your readers. We were

1382
01:08:22,880 --> 01:08:25,120
Speaker 1: with you for eight hundred pages, and now it's like

1383
01:08:25,760 --> 01:08:28,960
Speaker 1: a different ending just slapped on. And I was kind

1384
01:08:28,960 --> 01:08:31,400
Speaker 1: of the last Stephen King book I read. And so

1385
01:08:31,400 --> 01:08:35,479
Speaker 1: so I think that he certainly has a range of

1386
01:08:35,600 --> 01:08:39,200
Speaker 1: books from from what I think i've as probably even terrible, right,

1387
01:08:39,400 --> 01:08:43,679
Speaker 1: two very very good classics, amazing literature and and and

1388
01:08:44,080 --> 01:08:47,160
Speaker 1: even better movies very often. That's right. And and so

1389
01:08:47,400 --> 01:08:51,200
Speaker 1: reading the whole thing actually, like he's been writing for

1390
01:08:51,240 --> 01:08:54,920
Speaker 1: so long, you know, he stopped so good and so

1391
01:08:55,680 --> 01:08:59,880
Speaker 1: his prose is fantastic, his voice is unique. He's no

1392
01:09:00,160 --> 01:09:02,320
Speaker 1: for writing horror, but some of his non horror stuff

1393
01:09:02,360 --> 01:09:05,320
Speaker 1: is amazing. Some of the movies stand by me. Other things. Yeah,

1394
01:09:05,320 --> 01:09:08,720
Speaker 1: I mean, that's that's just one of I read that

1395
01:09:08,720 --> 01:09:12,080
Speaker 1: book so long ago and it was just you forget

1396
01:09:12,200 --> 01:09:15,240
Speaker 1: it's Stephen King's right, I think green Mile. I mean,

1397
01:09:15,280 --> 01:09:18,320
Speaker 1: I think there's a bunch of things that people forget

1398
01:09:18,360 --> 01:09:21,240
Speaker 1: or Stephen King, but because they're because they don't get

1399
01:09:21,240 --> 01:09:23,519
Speaker 1: as much attention, they don't sell as much. Yeah, exactly,

1400
01:09:23,600 --> 01:09:25,959
Speaker 1: as you know all his horror stuff, that's right. Christine

1401
01:09:26,080 --> 01:09:29,120
Speaker 1: was like it was a huge seller, that's right. And

1402
01:09:29,200 --> 01:09:31,519
Speaker 1: so and I think he even talks about that sort

1403
01:09:31,560 --> 01:09:35,679
Speaker 1: of commerciality and so reading so much of somebody, uh,

1404
01:09:35,800 --> 01:09:38,040
Speaker 1: you get to see like you know, when he got

1405
01:09:38,040 --> 01:09:39,880
Speaker 1: in a he got hit by a car very severely,

1406
01:09:39,880 --> 01:09:42,280
Speaker 1: almost died, um and which is what led to misery,

1407
01:09:42,360 --> 01:09:44,479
Speaker 1: and it just ship it was actually I think this

1408
01:09:44,560 --> 01:09:46,800
Speaker 1: is far after misery he sort of presaged his own

1409
01:09:46,800 --> 01:09:49,400
Speaker 1: life and sort of why am I thinking that? I

1410
01:09:49,439 --> 01:09:51,479
Speaker 1: think missuries long before this. This is quite late in

1411
01:09:51,479 --> 01:09:53,519
Speaker 1: his career actually, um, and he stops writing for a

1412
01:09:53,560 --> 01:09:55,559
Speaker 1: while because it's very painful for him to write. And

1413
01:09:55,800 --> 01:09:58,519
Speaker 1: the stuff he produces then is dark. It's not horror,

1414
01:09:58,640 --> 01:10:01,760
Speaker 1: is just dark. He's clearly depressed. It is clearly bad

1415
01:10:01,800 --> 01:10:05,799
Speaker 1: and scary. And uh, I mean getting that sort of

1416
01:10:06,000 --> 01:10:08,760
Speaker 1: that that long history is great, the old. I will

1417
01:10:08,800 --> 01:10:11,479
Speaker 1: admit that I broke my rule once, which was I

1418
01:10:11,520 --> 01:10:14,120
Speaker 1: started reading everything Cormick McCarthy has ever written, and I

1419
01:10:14,160 --> 01:10:15,559
Speaker 1: had to stop in the middle and take a break,

1420
01:10:15,600 --> 01:10:18,519
Speaker 1: because I will tell you, reading everything corm McCarthy has

1421
01:10:18,520 --> 01:10:21,479
Speaker 1: ever written with no breaks it tough. It makes you

1422
01:10:21,479 --> 01:10:24,160
Speaker 1: want to kill yourself. Every book is basically like, oh

1423
01:10:24,200 --> 01:10:26,480
Speaker 1: you care. He is like the he is the presage

1424
01:10:26,560 --> 01:10:28,200
Speaker 1: to get Game of Thrones. He's like, oh you care

1425
01:10:28,200 --> 01:10:30,080
Speaker 1: about this? Great? Let me kill it, like, let me

1426
01:10:30,240 --> 01:10:36,360
Speaker 1: like just destroy it. From from from Cormac McCarthy. Uh,

1427
01:10:36,400 --> 01:10:39,040
Speaker 1: I think All the White Horses is pretty amazing. I mean,

1428
01:10:39,160 --> 01:10:42,000
Speaker 1: the road is obviously amazing. You mentioned you were a

1429
01:10:42,040 --> 01:10:44,439
Speaker 1: sci fi buff. You haven't talked really about any sci

1430
01:10:44,479 --> 01:10:46,120
Speaker 1: fi and that's what I I sort of said, like,

1431
01:10:46,240 --> 01:10:48,280
Speaker 1: none of the sci fi stuff really makes my favorite

1432
01:10:48,280 --> 01:10:52,559
Speaker 1: book list. Really, yeah, it's it's so interesting. My favorite

1433
01:10:52,600 --> 01:10:56,439
Speaker 1: book list is very typecast. They're all very sad um right,

1434
01:10:56,479 --> 01:10:59,920
Speaker 1: like blindness is that they're just tragic and either something

1435
01:11:00,560 --> 01:11:02,960
Speaker 1: the tragedy stick with me. I guess really, I could

1436
01:11:02,960 --> 01:11:04,840
Speaker 1: give you a list to sci fi that's as good

1437
01:11:04,840 --> 01:11:06,640
Speaker 1: as anything else in there. And it's not that I

1438
01:11:06,680 --> 01:11:08,599
Speaker 1: got to cook for you that I bet you've never

1439
01:11:08,640 --> 01:11:12,519
Speaker 1: even read. It's a little esoteric call it out. So

1440
01:11:12,600 --> 01:11:13,960
Speaker 1: that's the other people read it too. I love it

1441
01:11:14,040 --> 01:11:18,519
Speaker 1: dying inside. So the premise of the book is he's

1442
01:11:19,160 --> 01:11:22,679
Speaker 1: got a fairly robust set of e sp powers and

1443
01:11:23,000 --> 01:11:25,360
Speaker 1: as a kid he discovers this and he gets to

1444
01:11:25,800 --> 01:11:29,280
Speaker 1: reach out and do all these things with his ability

1445
01:11:29,320 --> 01:11:33,320
Speaker 1: to read minds and shells. It's not Shell Silverstein. It's

1446
01:11:33,400 --> 01:11:36,599
Speaker 1: something dance. So the name will come to me. And

1447
01:11:36,760 --> 01:11:39,640
Speaker 1: as he gets older, as he goes through colleges, the

1448
01:11:39,680 --> 01:11:43,599
Speaker 1: ability fades and loses it. And it's a giant metaphor,

1449
01:11:43,600 --> 01:11:46,120
Speaker 1: of course. But it's a wonderful book, and you, of

1450
01:11:46,160 --> 01:11:48,320
Speaker 1: all people would really appreciate it. I'm going to do it.

1451
01:11:48,560 --> 01:11:51,000
Speaker 1: I'm definitely gonna recommend that we're running out of time.

1452
01:11:51,040 --> 01:11:53,400
Speaker 1: I got to get to my last two questions, my

1453
01:11:53,479 --> 01:11:57,800
Speaker 1: favorite questions. First, you work with a lot of young

1454
01:11:57,880 --> 01:12:01,080
Speaker 1: tech people, a lot of millennials college do. What sort

1455
01:12:01,080 --> 01:12:03,040
Speaker 1: of advice would you give someone who came to you

1456
01:12:03,080 --> 01:12:05,680
Speaker 1: and said, hey, I'm starting my career. I'm interested in

1457
01:12:05,840 --> 01:12:13,000
Speaker 1: either behavioral, behavioral psychology or technology startups. I mean, the

1458
01:12:13,040 --> 01:12:14,800
Speaker 1: device I always give people is you want to think

1459
01:12:14,800 --> 01:12:18,439
Speaker 1: into your chunks. I think people are very bad year chunk. Yeah,

1460
01:12:18,479 --> 01:12:20,880
Speaker 1: I don't. I think people are very bad. So so

1461
01:12:20,960 --> 01:12:23,040
Speaker 1: the metaphor I always use is orienteering. Are you familiar

1462
01:12:23,040 --> 01:12:25,559
Speaker 1: with It's like longteering support is a weird country person sport.

1463
01:12:25,640 --> 01:12:27,880
Speaker 1: It's a race, but it's a race from nowhere to nowhere.

1464
01:12:27,880 --> 01:12:29,679
Speaker 1: So you like, go out in the woods. You dropped

1465
01:12:29,720 --> 01:12:31,200
Speaker 1: off at point A, and they're like, here's a map

1466
01:12:31,200 --> 01:12:34,120
Speaker 1: in a compass, get to point B. But there's no path, right,

1467
01:12:34,160 --> 01:12:36,280
Speaker 1: It's not like there's our course you rise on. And

1468
01:12:36,360 --> 01:12:39,600
Speaker 1: so a lot of what we teach people today metaphorically

1469
01:12:39,600 --> 01:12:41,000
Speaker 1: is to sort of take out the map and compass

1470
01:12:41,040 --> 01:12:43,120
Speaker 1: and then walk to point B right in the most

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01:12:43,160 --> 01:12:45,880
Speaker 1: efficient possible way. And I think the right answer, and

1472
01:12:45,880 --> 01:12:48,120
Speaker 1: the way you actually win at orienteering is to say,

1473
01:12:48,479 --> 01:12:51,120
Speaker 1: I know B is over there. I'm gonna run in

1474
01:12:51,200 --> 01:12:53,080
Speaker 1: that direction until I'm no longer sure I'm going in

1475
01:12:53,120 --> 01:12:54,680
Speaker 1: the right direction, and then I'm gonna stop and take

1476
01:12:54,680 --> 01:12:57,639
Speaker 1: on my map and compass, take a point run, take

1477
01:12:57,680 --> 01:12:59,960
Speaker 1: a point run, and actually think careers work best this way.

1478
01:13:00,320 --> 01:13:01,960
Speaker 1: I know, if you look at my career right, I've

1479
01:13:01,960 --> 01:13:05,360
Speaker 1: done all sorts of very different stuff, and yet when

1480
01:13:05,400 --> 01:13:07,040
Speaker 1: you look back at it, all sounds like a story

1481
01:13:07,479 --> 01:13:10,360
Speaker 1: because it's all been united by common values. I know

1482
01:13:10,439 --> 01:13:12,439
Speaker 1: I want to change things for large groups of people.

1483
01:13:12,640 --> 01:13:14,519
Speaker 1: I know that it's about empowering people at the bottom

1484
01:13:14,520 --> 01:13:16,960
Speaker 1: of the pyramid, not the top. And and so because

1485
01:13:17,000 --> 01:13:18,599
Speaker 1: I have those values, that's my point B. And every

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01:13:18,600 --> 01:13:20,519
Speaker 1: two years I basically look up and say, am I

1487
01:13:20,600 --> 01:13:24,200
Speaker 1: still honoring those values? And then I pivot based on

1488
01:13:24,479 --> 01:13:28,479
Speaker 1: how do I question our last question? What is it

1489
01:13:28,520 --> 01:13:33,240
Speaker 1: that you know about startup venture behavioral psychology today that

1490
01:13:33,360 --> 01:13:36,160
Speaker 1: you wish you knew fifteen years or so ago when

1491
01:13:36,160 --> 01:13:38,920
Speaker 1: you were coming out of school. I will go back

1492
01:13:38,960 --> 01:13:41,080
Speaker 1: to what I've said a couple of times through this program.

1493
01:13:41,120 --> 01:13:43,920
Speaker 1: Everything is on the table right the sooner you get

1494
01:13:43,960 --> 01:13:46,679
Speaker 1: to the place and you say, there's nothing I can't challenge.

1495
01:13:46,680 --> 01:13:50,360
Speaker 1: There's no rules that we can't reconfigure, like start from

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01:13:50,400 --> 01:13:52,640
Speaker 1: the design from the world as you want it to be,

1497
01:13:52,760 --> 01:13:56,840
Speaker 1: and then work backwards. That's quite fascinating. We have been

1498
01:13:56,880 --> 01:14:01,320
Speaker 1: speaking with Matt Wallert, formally director at Microsoft Ventures and

1499
01:14:02,240 --> 01:14:05,519
Speaker 1: a a co founder or CEO of a number of

1500
01:14:05,560 --> 01:14:09,920
Speaker 1: successful startups. If you have enjoyed this conversation, be sure

1501
01:14:09,960 --> 01:14:13,680
Speaker 1: and look upward down a few inches on iTunes, SoundCloud

1502
01:14:14,000 --> 01:14:16,840
Speaker 1: or Bloomberg dot com and you could see any of

1503
01:14:16,880 --> 01:14:21,000
Speaker 1: the other hundred and fifty or so such conversations that

1504
01:14:21,040 --> 01:14:24,120
Speaker 1: we've put together over the past three years. I would

1505
01:14:24,120 --> 01:14:27,200
Speaker 1: be remiss if I did not remind you that we

1506
01:14:27,320 --> 01:14:31,920
Speaker 1: love your comments, feedback and suggestions right to us at

1507
01:14:32,200 --> 01:14:35,759
Speaker 1: m IB podcast at Bloomberg dot net. I must thank

1508
01:14:36,080 --> 01:14:39,519
Speaker 1: my head of research, Michael bat Nick. Taylor Riggs is

1509
01:14:39,600 --> 01:14:45,280
Speaker 1: my booker producer, and Medina Parwana is my ACE recording engineer.

1510
01:14:45,800 --> 01:14:49,320
Speaker 1: I'm Barry Hults. You're listening to Masters in Business on

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01:14:49,479 --> 01:15:00,360
Speaker 1: Bloomberg Radio.