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Speaker 1: Hey, Brian, Hi Katie, Well, I think it's safe to

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Speaker 1: say that we've never seen a year like the one

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Speaker 1: we've witnessed over the last what six to nine months

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Speaker 1: in terms of women's empowerment in this country. I've been

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Speaker 1: working for a long time, and I remember so many

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Speaker 1: instances when we declared it the Year of the Woman,

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Speaker 1: or when we thought everything was going to change, and

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Speaker 1: of course nothing really did. And now there's some serious

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Speaker 1: soul searching going on about Hollywood, about Silicon Valley, and

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Speaker 1: about every business in between. And Katie, you know what

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Speaker 1: really staggered me to watch your hour on nat Gio.

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Speaker 1: Do you think we've promoted your hour on nat Gio enough? Anyway?

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Speaker 1: The effect of sexism in Silicon Valley and Hollywood isn't

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Speaker 1: just in those industries. It's on all of us who

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Speaker 1: are consumers of those industry. For example, there's research that

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Speaker 1: you brought out that the more hours of TV a

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Speaker 1: girl watches, the fewer options she believes she has, and

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Speaker 1: the more hours of TV a boy watches, the more

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Speaker 1: sexist he actually becomes. And so this is a contagion.

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Speaker 1: It's not just a problem relegated to these industries. Absolutely,

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Speaker 1: we asked folks to share their experiences with gender bias

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Speaker 1: and discrimination at work. People like Dr mommy Kay that's

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Speaker 1: her display name on Twitter wrote in medicine, it is

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Speaker 1: very common to be seen as a nurse if a

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Speaker 1: female physician doesn't put on her white coat, and even

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Speaker 1: then it's not presumed. But most men in scrubs are doctors.

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Speaker 1: So many inequities in medicine. Hashtag too much to tweet That,

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Speaker 1: of course is true. You know, there's that old riddle

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Speaker 1: that I used to hear as a kid, and it

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Speaker 1: goes along these lines of father and his son are

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Speaker 1: in a car accident. The doctor comes in and exclaim animes,

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Speaker 1: I can't operate on this child. Why not, the nurse asked,

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Speaker 1: because he's my son. The doctor responds, how is this possible?

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Speaker 1: And of course, still in two thousand eighteen, people are

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Speaker 1: left scratching their heads. Okay, everyone, how is it possible?

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Speaker 1: The doctor is the mother? Hello, everybody, the doctor is

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Speaker 1: a woman. But it just shows how deeply ingrained these

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Speaker 1: gender biases are in US even today. Yeah, I have

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Speaker 1: to admit it took me a few seconds to get that, so,

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Speaker 1: you know, shame on me. Katie. Also on Twitter, someone

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Speaker 1: named Kristin Malloy wrote, I was the only female in

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Speaker 1: management in twenty two local locations of a restaurant was

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Speaker 1: told I wasn't bubbly enough to be GM. Pretty sure

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Speaker 1: bubbly was not a qualification for all the males, and

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Speaker 1: that's true. I've never been criticized for my lack of bubblinus,

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Speaker 1: and I don't think I'm particularly bubbly, and I'm nothing.

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Speaker 1: Men are ever called perky either, Brian, Just to pick

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Speaker 1: a random example, Yeah, exactly. We also got a male

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Speaker 1: perspective from Joe and Can Dada, who works in healthcare.

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Speaker 1: He emailed us saying he feels he's held to a

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Speaker 1: different standard of behavior than his female colleagues. He writes,

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Speaker 1: I cannot and would not make comments about a particularly

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Speaker 1: attractive nurse or visitor, But when, for instance, the firefighters

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Speaker 1: have to come in the building, the women go wild

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Speaker 1: and make all sorts of sexual comments. I hear the

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Speaker 1: same comments from them about attractive doctors. I feel like

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Speaker 1: if I said half of what came out of their

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Speaker 1: amounts at a nurse's desk, at best, I would be

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Speaker 1: looked at like a pervert. At worst, I would lose

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Speaker 1: my career. I mean, I think that's actually a very

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Speaker 1: interesting and somewhat valid point. There is a bit of

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Speaker 1: a double standard that I think women need to be

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Speaker 1: aware of when they're hyper sexualizing men in the office

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Speaker 1: or making sort of loot or sexual comments. Um, what's

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Speaker 1: good for the goose should be good for the gander.

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Speaker 1: What are your views on this, Brian, Um, I agree

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Speaker 1: with that. That said, the overwhelming problem here is a

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Speaker 1: lack of opportunity for women in the workplace and a

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Speaker 1: lack of flexibility for women who are still the primary parents. Um.

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Speaker 1: And so you know, I think this is true, but

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Speaker 1: to me, this doesn't seem like the brunt of the problem. Now,

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Speaker 1: I agree, but it's something just to be cognizant of,

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Speaker 1: I think. But all I have to say is go

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Speaker 1: Rustle and Karen Goldsmith. You have officially raised a feminist here.

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Speaker 1: But it's true. I think we're talking about opportunities. Obviously

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Speaker 1: a lot of it is about behavior in the tone set,

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Speaker 1: but how do these translate into doors closing or opening

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Speaker 1: for everyone, not just for women, but for more diversity.

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Speaker 1: And I think I hope that things are changing. In

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Speaker 1: today's show, we're delving into gender inequality and bias, specifically

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Speaker 1: in the tech industry. Of course, Silicon Valleys generated headlines

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Speaker 1: about sexual harassment and pay in equity for some time.

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Speaker 1: Our guest today is journalist Emily Chang. She's one of

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Speaker 1: the most respected interviewers in the tech industry, and she's

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Speaker 1: out with a new book called ro Topia, which is

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Speaker 1: all about the boys club culture of Silicon Valley. We

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Speaker 1: talked with Emily about the dam stats when it comes

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Speaker 1: to diversity and inclusion in tech, the Czech glitterati she

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Speaker 1: interviewed for her book, and the shocking sex parties attended

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Speaker 1: by Silicon Valley big wigs, which really, honestly sound like

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Speaker 1: such a throwback to Mad Men days. I mean, I

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Speaker 1: just wanted to grab a bottle up Purel while she

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Speaker 1: was even talking about these things. Yeah, I felt pretty gross.

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Speaker 1: And folks, if you don't tune in about wild Silicon

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Speaker 1: Valley sex parties, don't you what we can do? Anyway.

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Speaker 1: Today's conversation is actually an extension of an episode of

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Speaker 1: Katie's nat GEO doc series called The Revolt, where she

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Speaker 1: looked into gender inequality, or as some might call it,

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Speaker 1: the hostile male culture in both Hollywood and Silicon Valley.

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Speaker 1: So you kick things off, Katie by asking, are we

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Speaker 1: paying too much attention to these two arenas tech and entertainment. Certainly,

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Speaker 1: sexism and sexual harassment exists everywhere, It's in every industry,

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Speaker 1: but you know, these are the industries where women have

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Speaker 1: been the loudest, and Hollywood much more so than Silicon Valley.

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Speaker 1: But I sort of like to think of the me

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Speaker 1: too movements starting in Silicon Valley with Ellen Powe, you know,

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Speaker 1: five six years ago when she sued her venture capital firm,

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Speaker 1: Kleiner perkins Um, and she lost in court, but over time,

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Speaker 1: she's sort of won in the court of public opinion.

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Speaker 1: And I wonder if she had filed her suit today,

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Speaker 1: would the outcome have been different? And I actually I

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Speaker 1: asked her this question, and she said, you know, there's

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Speaker 1: no way to answer that question. But I almost think

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Speaker 1: we wouldn't be here if I hadn't done it when

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Speaker 1: I did, and she helped open the doors for so

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Speaker 1: many others. I did her first interview. I was just

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Speaker 1: over and it was fascinating to me because so much

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Speaker 1: of it was sort of intangible things that Ellen experienced

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Speaker 1: when she was working at Kleiner Perkins for example, really

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Speaker 1: being excluded being marge a lies, not being part of

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Speaker 1: the quote unquote boys club, not getting to go on

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Speaker 1: ski weekends or golf outings, and things like that. You

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Speaker 1: also talked to her boss for your book. What was

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Speaker 1: his take, because I did not get the opportunity to

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Speaker 1: do that, right. I spoke with John Doer, who hired

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Speaker 1: her and was in his view her champion um and

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Speaker 1: really gave her an opportunity when some of the other

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Speaker 1: partners didn't want her to have that opportunity. And you

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Speaker 1: know what's interesting is Kleiner Perkins was actually kind of

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Speaker 1: at the forefront in venture capital when it came to

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Speaker 1: hiring women. There are so many firms who still don't

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Speaker 1: have any women, and he made a concerted effort to

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Speaker 1: bring women into the fold. What they did wrong is

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Speaker 1: they didn't make the environment inclusive enough for them, and

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Speaker 1: they didn't create a place where women could really thrive

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Speaker 1: and and really succeed. And so it was kind of

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Speaker 1: a case of where good intentions aren't enough, you know,

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Speaker 1: and good intentions aren't enough, and Ellen experienced so many

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Speaker 1: of those things that are just so much more difficult

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Speaker 1: to pinpoint and call out, you know, it's a lot

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Speaker 1: easier to talk about these egregious forms of sexual harassment,

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Speaker 1: and I agree that that is a huge problem. But

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Speaker 1: I actually think the bigger problem is, you know, the

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Speaker 1: systemic forces that are really working against all women that

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Speaker 1: are so hard to describe and also hard to fix,

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Speaker 1: which is why you know it was worth writing a

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Speaker 1: three page book about Emily. You wrote that Silicon Valley

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Speaker 1: has become increasingly toxic for women. Do you think the

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Speaker 1: problem is actually getting worse and not better? M um?

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Speaker 1: You know, in some ways I think it is getting worse,

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Speaker 1: and in some ways I think it's getting better. Um.

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Speaker 1: And by getting worse, I mean the fact that we

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Speaker 1: just haven't seen movement for so so long has led

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Speaker 1: so many people to believe, you know, this is just

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Speaker 1: how it is, or it's not my problem. This is

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Speaker 1: a pipeline problem. This has to do with how many

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Speaker 1: women are studying computer science or how many resumes I

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Speaker 1: get in my inbox. And you know what, that's an excuse.

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Speaker 1: You know, I argue in my book that the tech

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Speaker 1: industry created the pipeline problem by having such a narrow

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Speaker 1: idea of who could do this job. And you know,

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Speaker 1: they were doing these personality tests and these aptitude tests

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Speaker 1: in the sixties and seventies to find good engineers, and

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Speaker 1: they decided that good engineers quote don't like people. Well, um,

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Speaker 1: there's no evidence to support this idea that people who

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Speaker 1: don't like people are better at this job than people

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Speaker 1: people do. People who don't like people have a little

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Speaker 1: bit of a tradition, Emily of Katie singing in every episode.

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Speaker 1: You know, so I don't really want to get into

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Speaker 1: we can have like a sing off Emily. Anyway, this

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Speaker 1: is a stereotic. This is a stereotype that shuts out

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Speaker 1: more than half the population. And you know, there's a

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Speaker 1: great argument to be made that we need people who

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Speaker 1: do like people and care about people who understand the

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Speaker 1: users whose problems they are trying to solve to be

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Speaker 1: doing this job. And so, in my i view, the

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Speaker 1: tech industry created the pipeline problem, but also is reinforcing

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Speaker 1: that problem today by you know, it's not just about hiring,

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Speaker 1: it's about retention, it's about progression. It's about making sure

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Speaker 1: the women that are already working in this industry want

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Speaker 1: to stay, because women do leave tech at twice the

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Speaker 1: rate as men. And it's not because what we would

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Speaker 1: think maybe issues with work life balance. It's actually because

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Speaker 1: of toxic cultures. And why is the culture so toxic?

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Speaker 1: I mean, you hit on some of it in the book.

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Speaker 1: Some of these crazy parties that people have. Is it

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Speaker 1: because it feels so uncharted in a way innovative, and

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Speaker 1: so they feel they have to be risk takers, not

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Speaker 1: only in business but in every aspect of their lives.

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Speaker 1: So there's this idea that Silicon Valley and everyone there

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Speaker 1: is changing the world and making the world a better place,

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Speaker 1: and so much wealth and power has been amassed in

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Speaker 1: such a short span of time that it has unfortunately

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Speaker 1: led to a sense of entitlement and quite frankly arrogance

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Speaker 1: and this sort of unwillingness to admit that they're part

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Speaker 1: of the problem. Um and I think that that has

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Speaker 1: honestly gone on too long. And you do have a

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Speaker 1: lot of people who are very young. We've made a

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Speaker 1: lot of money very fast, and it comes without the

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Speaker 1: same sort of socialization that would happen over a longer career,

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Speaker 1: and not a lot of emotional intelligence. If they're hiring

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Speaker 1: people based on sort of the fact that their nerds,

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Speaker 1: they don't like people, they're super analytical, it would only

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Speaker 1: make sense that those people are not necessarily really great

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Speaker 1: at interpersonal skills or emotional intelligence, reading a room, understanding

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Speaker 1: how their behavior affects others. Right, And if you were

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Speaker 1: stuffed in your locker as a kid, it can be

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Speaker 1: hard to admit that you're discriminating against someone today. And

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Speaker 1: it's not just about having one kind of intelligence or

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Speaker 1: having people who don't like people who want to be

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Speaker 1: alone coding in a room. You need all kinds of people. Well,

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Speaker 1: you do need that emotional intelligence because billions and billions

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Speaker 1: of people are using these products. And by the way,

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Speaker 1: half of them are women. And so you know, this

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Speaker 1: isn't just Silicon Valley's problem. This isn't just a problem

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Speaker 1: for people who want to work in tech. This is

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Speaker 1: everybody's problem. This is my problem, your problem, this is

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Speaker 1: our children's problem. I mean, these are products that are

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Speaker 1: changing our lives every day. This is an industry that

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Speaker 1: is inventing the future. In fact, let's talk about artificial intelligence.

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Speaker 1: You know, one of the things that Kara Swisher said,

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Speaker 1: and she was featured in my hour on this is

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Speaker 1: that the people who are programming the content for the

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Speaker 1: future and what's going to be on our screens, Like

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Speaker 1: nine of the people in artificial intelligence are men. Can

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Speaker 1: you explain in Layman's terms how that influences how that

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Speaker 1: gender bias plays into content creation online and really in

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Speaker 1: product creation as well. So AI and machine learning, these

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Speaker 1: things aren't biased. Algorithms are the products of the people

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Speaker 1: who created them and the people who use them and

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Speaker 1: the people who use that. Right, because isn't often it

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Speaker 1: it's called from a set of information that is pulled

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Speaker 1: from how frequently people use information. Does that make in

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Speaker 1: a way it's crowdsourced, right, But if it's better for

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Speaker 1: certain people, then it is for others. That's also crowdsourced.

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Speaker 1: And so I think about face i D facial recognition technology,

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Speaker 1: which is the way that you get into your iPhone. Well,

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Speaker 1: facial recognition technology is already sexist and racist and doesn't

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Speaker 1: recognize women and people of color as easily as it

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Speaker 1: does white men, in part because you have mostly men

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Speaker 1: who are designing these who are not aware of their biases.

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Speaker 1: That's part of the problems. So they're not actually it's not,

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Speaker 1: they're not. They're not compensating for the implicit bias that

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Speaker 1: they might have in creating this equipment. For example, Siri

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Speaker 1: doesn't recognize women's voices as much as men, and that's

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Speaker 1: not accidental. It's a perfect example. Maybe it is talking

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Speaker 1: about Well, that's the thing is. I don't think it

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Speaker 1: is malicious, but at a certain point, ignorance can only

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Speaker 1: be willful. And we know that diverse teams create better products.

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Speaker 1: We know that, you know, having a women on your

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Speaker 1: team when you're building voice assistance and facial recognition technology

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Speaker 1: makes a lot of good sense. So at a certain point,

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Speaker 1: if you're not doing that, if you're not building teams

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Speaker 1: that have people from a variety of backgrounds, then maybe

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Speaker 1: it does become intentional and you can't say this is

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Speaker 1: not my problem. I didn't create this problem. You have

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Speaker 1: a responsibility if you're creating these products that are used

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Speaker 1: by billions and billions of people, to make sure that

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Speaker 1: they can actually serve billions and billions of people. You know,

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Speaker 1: Amazon is you know equally as male dominated as Apple,

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Speaker 1: and women drive sevent of consumer purchases. Don't they want

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Speaker 1: to reflect their customer base? Um, you know this is

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Speaker 1: about not just equality, and I think equality is important.

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Speaker 1: It's about com it's about commerce, it's the smart thing

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Speaker 1: to do and not just the right thing to do.

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Speaker 1: And by the way, they're not reflecting that customer base

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Speaker 1: at all. Today. Here's some of the stats from Katie's

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Speaker 1: show to really back that up. Of board seats at

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Speaker 1: the Unicorn companies, the companies valued at more than a

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Speaker 1: billion dollars are men. Sixty percent of Unicorns have no

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Speaker 1: women board members at all, and of course goes without

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Speaker 1: saying most of the executive leadership positions are held by men.

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Speaker 1: You wouldn't necessarily know this because the media focuses on,

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Speaker 1: you know, two or three very prominent women in the valley,

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Speaker 1: but the vast majority are men and white men. And

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Speaker 1: Katie had struck me when Karas Swisher said to you

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Speaker 1: in Your Hour, the tech companies think they're meritocracies, but

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Speaker 1: they're more like mirror autocracies. They reflect the people who

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Speaker 1: are making hires are the people who have already been

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Speaker 1: successful in the valley, and so Emily, how do you

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Speaker 1: think that can change? Well, first of all, I think

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Speaker 1: we need to acknowledge that a true meritocracy is impossible

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Speaker 1: to achieve. And I love Kara's term mirror autocracy because

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Speaker 1: it's so perfectly encapsulates this idea that we are people

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Speaker 1: who look like us. Um. You know, the reality is

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Speaker 1: that we all come to the plate with different kinds

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Speaker 1: of privileges and access, and the escalator of life is

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Speaker 1: moving far faster for some than it is for others.

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Speaker 1: And in fact, when you believe that you are operating

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Speaker 1: in a meritocracy, you can actually be more anti meritocratic.

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Speaker 1: And s already use that kind of jargon, but that's

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Speaker 1: sort of the best way to put it. Well, actually,

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Speaker 1: there's a study that shows people who believe they work

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Speaker 1: in a meritocracy or the least meritocratic of all because

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Speaker 1: they're not. They don't acknowledge their biases. And everybody, what

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Speaker 1: is the avenue queue. Everyone's a little bit racist. I mean,

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Speaker 1: everybody has these biases and if you're not cognizant of them,

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Speaker 1: then you can't compensate for them. And by the way,

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Speaker 1: a fun fact about meritocracy, um it actually originated in

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Speaker 1: the Han dynasty, but the actual word meritocracy was coined

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Speaker 1: by a British sociologist named Michael Young in the nineteen

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Speaker 1: fifties to warn about a future in which people use

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Speaker 1: their success to justify their success essentially, and so um,

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Speaker 1: the success in the education of the winners comes becomes

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Speaker 1: the sort of reason to dismiss the forces that are

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Speaker 1: working against everyone else. And fifty years later he actually

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Speaker 1: wrote an op ed in The Guardian and said, I've

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Speaker 1: been very disturbed by this use of the word meritocracy.

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Speaker 1: Um that I meant as a warning, when in fact

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Speaker 1: it's been It's been used to propagate the very problems

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Speaker 1: that I was so very worried about. So how do

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Speaker 1: you improve the numbers? Is it a leadership issue? Is

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Speaker 1: that the problem? I mean, I can't believe that sixty

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Speaker 1: eight percent of unicorns have zero women on their boards.

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Speaker 1: That is outrageous, including air B and B and we

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Speaker 1: work it is it is outrageous. We should be protesting

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Speaker 1: in the streets about this, to be honest, or not

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Speaker 1: using their products. Um. Look, you know, I do think

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Speaker 1: leadership is incredibly important. And I like to point to

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Speaker 1: the example of Slack, where you've got a white male

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Speaker 1: CEO who's had sort of every door opened for him

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Speaker 1: that you could possibly imagine, and he recognizes that. You know,

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Speaker 1: he doesn't use words like meritocracy, but he has made

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Speaker 1: hiring and promoting women and underrepresented minorities a top priority.

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Speaker 1: And it's not just you know, something he said at

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Speaker 1: the beginning, it's something he talks about all the time,

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Speaker 1: lip service. It's not like we have a diversity task

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Speaker 1: force and everybody knows this is something he cares about.

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Speaker 1: So they have diversified their recruiting teams, they're sourcing from

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Speaker 1: underrepresented schools, they're going to new regions. They've you know,

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Speaker 1: standardized their interview questions, they standardized their feedback and review systems.

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Speaker 1: And by the way, they're beating the pipeline. So they

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Speaker 1: just came out with new numbers. Forty four percent of

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Speaker 1: workers at Slack are women and they started with a

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Speaker 1: company of like fifty men, and so they were starting

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Speaker 1: at at a disadvantage. Um forty eight percent of managers

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Speaker 1: are women, thirty some percent. Uh, women count for thirty

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Speaker 1: percent of technical worlds, and so you know, they have

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Speaker 1: proven that you can do this if you commit to it.

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Speaker 1: And I think it's really that commitment that is so important.

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Speaker 1: And so many of these companies, you know, and it's

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Speaker 1: you know, not only they're not only mostly many of

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Speaker 1: them young founders, but it's just it's not a priority,

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Speaker 1: it's about growth. It's about raising that next round, it's

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Speaker 1: about getting that next product launch. It's rushing to fill

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Speaker 1: the seats that are empty. And you know, one of

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Speaker 1: the things that I like to preach is having a

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Speaker 1: little patients and taking a little bit more time to

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Speaker 1: find someone who believes in your mission but doesn't necessarily

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Speaker 1: look like you, because it's about diversity of thought and

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Speaker 1: diversity of experience, and we all bring different experiences to

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Speaker 1: the table, and sometimes that is a result of what

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Speaker 1: we look like and how we have um lived in

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Speaker 1: this world. And if you don't have a diverse team

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Speaker 1: from the beginning, it gets so much harder as you

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Speaker 1: get bigger. But you will have blind spots, you will

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Speaker 1: make mistakes, you will miss things. And if you take

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Speaker 1: the time to be more thoughtful about how you're building

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Speaker 1: the team in the beginning, you will be able to

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Speaker 1: move so much faster a few years from then when

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Speaker 1: you have that diverse team in place, it's time to

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Speaker 1: take a quick break. We're going to be right back

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Speaker 1: with Emily Chang, and now back to our conversation with

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Speaker 1: journalist Emily Chang, author of pro Topia. So, Emily, you've

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Speaker 1: got a lot of attention when the first excerpt for

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Speaker 1: bro Topia was released in Vanity Fair. You wrote about

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Speaker 1: wild monthly sex parties attended by the tech elite. The

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Speaker 1: subhead of the piece was the guys got laid, but

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Speaker 1: the women get screwed, which I thought was clever and

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Speaker 1: also a bit depressing. And can you explain these parties?

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Speaker 1: How important are they and what the effect is for

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Speaker 1: women in tech? So, for of all, one of the

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Speaker 1: interesting things about Silicon Valley that may set it apart

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Speaker 1: from some other industries is that work bleeds into life.

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Speaker 1: In fact, life is work and that's just the way

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Speaker 1: things go. Um. And so you know, if you're working

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Speaker 1: at one of these companies, you're going out for drinks

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Speaker 1: after work, maybe for drinks in the middle of the day. Um.

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Speaker 1: And you know, there's this implicit pressure to be one

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Speaker 1: of the cool kids. And you know, there is a

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Speaker 1: subset of people in Silicon Valley who believes they're not

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Speaker 1: just changing the world when it comes to the products

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Speaker 1: that they're building. They're challenging social morais and challenging traditional morality,

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Speaker 1: challenging monogamy and that's all great. The Bay Area has

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Speaker 1: been um, you know, this this this hotbed of sexual

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Speaker 1: exploration and liberation for so so long. But if you

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Speaker 1: look at actually how some of this socializing happens, and

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Speaker 1: you know, I am quite descriptive about some of these

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Speaker 1: parties where you have powerful men inviting women two to

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Speaker 1: one so that the odds are in their favor. It's

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Speaker 1: not challenging traditional morals at all. In fact, it's a

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Speaker 1: tale as old as time. You know, these women who

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Speaker 1: participate are discredited and disrespected, when for the men it's

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Speaker 1: like a networking opportunity, even though they're not trying to

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Speaker 1: do business. Business gets done. And so women sort of

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Speaker 1: feel like they're damned if they do and damned if

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Speaker 1: they don't. And for some people, this lifestyle was ever

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Speaker 1: present and they felt like they could not escape it.

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Speaker 1: And especially um some women entrepreneurs that I spoke with,

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Speaker 1: you know, they had to leave Slicon Valley moved to

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Speaker 1: New York to get away from Not to all, not

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Speaker 1: to be too purient, but tell us about these parties

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Speaker 1: and and who are these women? Would they hire prostitutes?

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Speaker 1: Would they bring in women from the office? I mean,

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Speaker 1: set the scene for us if you could. It sounds

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Speaker 1: like Plato's retreat or something. It's a range of of women.

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Speaker 1: You know, sometimes they are women who work in the

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Speaker 1: industry and entrepreneurs, and sometimes they'll bring in UM women

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Speaker 1: for from l A or or women who they've met

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Speaker 1: via adjacent industries UM in San Francisco, UM. And the

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Speaker 1: idea is that these are women who can hang and

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Speaker 1: want to party UM, and you know, some of them

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Speaker 1: are also just exploring their sexuality. UM. The problem is

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Speaker 1: that you know, they're not sort of afforded the same

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Speaker 1: freedom to explore that sexuality as the men are. And

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Speaker 1: for men it's cool, and for women, you know, it

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Speaker 1: sort of doesn't get them anywhere. In fact, they end

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Speaker 1: up moving backwards. And so I spoke to one woman,

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Speaker 1: for example, who was part of a co founding team

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Speaker 1: with with another woman, and and one of the women

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Speaker 1: got very involved in these parties and the other just

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Speaker 1: didn't want a part of it, but wanted to try

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Speaker 1: to get access to some of the men who were

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Speaker 1: part of the scene, and they essentially got shut out.

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Speaker 1: Whereas all of these men we're obviously doing business together,

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Speaker 1: any names that we would recognize that part took in

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Speaker 1: these kind of activities. So, you know, the chapter is

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Speaker 1: very largely anonymous sources for obvious reasons. You know, I

429
00:24:02,400 --> 00:24:05,639
Speaker 1: do talk about one party that was hosted at the

430
00:24:05,640 --> 00:24:08,399
Speaker 1: home of Steve Jervidson, who's a very prominent venture capitalist,

431
00:24:08,520 --> 00:24:11,119
Speaker 1: and it was actually the official after party of the

432
00:24:11,240 --> 00:24:14,280
Speaker 1: d f J Big Think Conference, so the official after

433
00:24:14,320 --> 00:24:17,480
Speaker 1: party for his VC firm. Um, and I never said

434
00:24:17,520 --> 00:24:21,200
Speaker 1: this was a sex party. But later in the evening

435
00:24:21,320 --> 00:24:24,920
Speaker 1: there was some behavior that made people uncomfortable. Um. There

436
00:24:25,000 --> 00:24:30,280
Speaker 1: was cuddle puddling, and there were drugs, and there was um,

437
00:24:30,400 --> 00:24:33,600
Speaker 1: well it's sort of like do you know, Brian lying

438
00:24:33,680 --> 00:24:38,800
Speaker 1: together in a pretty boring life. It's sort of you know,

439
00:24:39,160 --> 00:24:43,399
Speaker 1: lying together, uh, in a group on the floor, on

440
00:24:43,440 --> 00:24:45,760
Speaker 1: the couch or overlapping from the couch onto the floor.

441
00:24:45,840 --> 00:24:48,639
Speaker 1: And um, there's some heavy petting involved, and it can

442
00:24:48,680 --> 00:24:51,479
Speaker 1: be a very as I understand it, you know, an

443
00:24:51,520 --> 00:24:56,000
Speaker 1: emotional and connective experience. Um, but it's not necessarily something

444
00:24:56,040 --> 00:24:57,760
Speaker 1: you want to do at a company party. And the

445
00:24:57,800 --> 00:25:03,360
Speaker 1: point is this was aut of a company party, and DFJ,

446
00:25:03,440 --> 00:25:06,199
Speaker 1: the venture capital firm, apologized right away and said we

447
00:25:06,280 --> 00:25:08,480
Speaker 1: are so sorry if anyone was made to feel uncomfortable.

448
00:25:08,480 --> 00:25:12,320
Speaker 1: This is not behavior that we support. But the fact

449
00:25:12,359 --> 00:25:15,040
Speaker 1: is I met they didn't support getting caught, but it

450
00:25:15,119 --> 00:25:17,760
Speaker 1: was clearly behavior they supported because they were doing it.

451
00:25:19,160 --> 00:25:23,000
Speaker 1: You know, Um, when people apologize, I do like to

452
00:25:23,040 --> 00:25:26,119
Speaker 1: think that there is some meaning there and and and

453
00:25:26,359 --> 00:25:28,960
Speaker 1: you know, maybe they didn't expect the party would take

454
00:25:29,000 --> 00:25:31,720
Speaker 1: that term. Maybe some people at the firm didn't expect

455
00:25:31,720 --> 00:25:34,640
Speaker 1: the party to take that that turn, but it did.

456
00:25:35,280 --> 00:25:38,520
Speaker 1: And you know, this venture capitalist and question no longer

457
00:25:38,560 --> 00:25:43,399
Speaker 1: works at the firm. So um on there. You know,

458
00:25:43,640 --> 00:25:47,280
Speaker 1: I have been told that there has been a bit

459
00:25:47,280 --> 00:25:50,240
Speaker 1: of a pause in some circles, that people are asking

460
00:25:50,280 --> 00:25:52,880
Speaker 1: about this in board rooms. Is this happening at our company?

461
00:25:53,000 --> 00:25:55,479
Speaker 1: Do you know? Um? Do we need to discuss this?

462
00:25:55,560 --> 00:25:58,919
Speaker 1: And so? Um what my hope is that is that

463
00:25:58,960 --> 00:26:00,800
Speaker 1: if this behavior is could you need to happen, that

464
00:26:00,840 --> 00:26:05,159
Speaker 1: it happens more thoughtfully, um, and that work boundaries and

465
00:26:05,160 --> 00:26:08,600
Speaker 1: professional boundaries aren't getting crossed. Let's talk about this lunch

466
00:26:08,640 --> 00:26:12,640
Speaker 1: spot sometimes called Conference Room G. Can you describe that place?

467
00:26:13,920 --> 00:26:17,399
Speaker 1: Conference Room G is a nickname that was given by

468
00:26:17,480 --> 00:26:21,800
Speaker 1: Yelp employees to the local strip club, which is actually

469
00:26:21,840 --> 00:26:25,240
Speaker 1: called the Gold Club in Soma, the tech heavy part

470
00:26:25,280 --> 00:26:27,320
Speaker 1: of San Francisco where a lot of the startups are

471
00:26:27,400 --> 00:26:29,639
Speaker 1: right next to the Moscony Center where you've got all

472
00:26:29,680 --> 00:26:33,719
Speaker 1: the big tech conferences. And I went in with one

473
00:26:33,800 --> 00:26:37,560
Speaker 1: of my female colleagues am on a Friday, and there

474
00:26:37,600 --> 00:26:42,159
Speaker 1: was a line out the door for lunch in the

475
00:26:42,200 --> 00:26:46,040
Speaker 1: middle of the day. And you walk in and you

476
00:26:46,080 --> 00:26:49,679
Speaker 1: knows your typical strip club, except you know, the clientele

477
00:26:50,359 --> 00:26:52,600
Speaker 1: are you know, the vast majority of these people are

478
00:26:52,840 --> 00:26:55,320
Speaker 1: clearly tech workers. And you know, they've got their oodies

479
00:26:55,400 --> 00:26:57,720
Speaker 1: or their T shirts. And there's an all you canet buffet.

480
00:26:57,760 --> 00:27:00,360
Speaker 1: It's actually the cheapest lunch in San Francisco. Mean, it's

481
00:27:00,400 --> 00:27:03,520
Speaker 1: five dollars for so much food. UM. So I'm sure

482
00:27:03,560 --> 00:27:06,040
Speaker 1: that is some part of the attraction. I'm sure they

483
00:27:06,080 --> 00:27:08,720
Speaker 1: could go to a Bob's Big Boy if they wanted,

484
00:27:08,800 --> 00:27:12,080
Speaker 1: and all you could eat the fat. But you know,

485
00:27:12,160 --> 00:27:14,720
Speaker 1: really it's the you know, it's it's the topless entertainment

486
00:27:14,800 --> 00:27:17,440
Speaker 1: and um, and women go there and men go there.

487
00:27:17,760 --> 00:27:20,760
Speaker 1: I saw very few women. I saw very few women. Um.

488
00:27:20,800 --> 00:27:23,600
Speaker 1: You know, right away, we you know, we started talking

489
00:27:23,600 --> 00:27:25,600
Speaker 1: to one of the women who worked there, and you know,

490
00:27:25,640 --> 00:27:29,440
Speaker 1: I confessed right away, like I'm a reporter, um, doing

491
00:27:29,480 --> 00:27:31,480
Speaker 1: some research. Can you tell me a little bit about

492
00:27:31,520 --> 00:27:34,240
Speaker 1: this place, and she was really helpful, Um, and she

493
00:27:34,280 --> 00:27:37,200
Speaker 1: talked a lot about how she sees, you know, groups

494
00:27:37,200 --> 00:27:39,840
Speaker 1: of men coming in with their bosses. They're talking about

495
00:27:39,920 --> 00:27:42,080
Speaker 1: deals or you know, some exact walks in the door

496
00:27:42,119 --> 00:27:44,639
Speaker 1: and everybody's flocking around him. Maybe they go up to

497
00:27:44,680 --> 00:27:49,920
Speaker 1: a private room together, and this is set lunch, you know. Um.

498
00:27:49,960 --> 00:27:52,359
Speaker 1: But again this brings me back to my question about

499
00:27:52,480 --> 00:27:55,760
Speaker 1: sort of the tone the leadership at the top sets.

500
00:27:56,440 --> 00:28:01,359
Speaker 1: And I think that without being too pure botanical and

501
00:28:01,720 --> 00:28:05,760
Speaker 1: like no fun at all, is it because they're super young,

502
00:28:06,119 --> 00:28:10,359
Speaker 1: these guys who are running these places. It just so young.

503
00:28:11,240 --> 00:28:13,280
Speaker 1: But and so I do think part of the problem

504
00:28:13,320 --> 00:28:17,840
Speaker 1: is that the behavior has been normalized and it becomes acceptable.

505
00:28:18,320 --> 00:28:20,240
Speaker 1: You know, I had women at uper who told me

506
00:28:20,280 --> 00:28:21,840
Speaker 1: they were invited to go to the Gold Club in

507
00:28:21,880 --> 00:28:24,960
Speaker 1: the middle of the day by their male bosses and

508
00:28:25,400 --> 00:28:27,200
Speaker 1: it was no big if they didn't come back until

509
00:28:27,240 --> 00:28:29,520
Speaker 1: three three am, as long as they got their work done.

510
00:28:29,880 --> 00:28:31,960
Speaker 1: And so you know, they're putting this. First of all,

511
00:28:31,960 --> 00:28:33,359
Speaker 1: people are going to strip clubs in the middle of

512
00:28:33,440 --> 00:28:36,520
Speaker 1: the day, which I still don't get. But you know,

513
00:28:36,800 --> 00:28:39,840
Speaker 1: for women especially, you're put in this super awkward position

514
00:28:39,880 --> 00:28:41,880
Speaker 1: where you know, are you the woman who goes along

515
00:28:41,920 --> 00:28:44,280
Speaker 1: and sort of um, you know, put yourself in this

516
00:28:44,360 --> 00:28:46,959
Speaker 1: really uncomfortable territory or do you not go and then

517
00:28:46,960 --> 00:28:49,000
Speaker 1: you miss out on work talk and maybe someone gets

518
00:28:49,000 --> 00:28:52,920
Speaker 1: an assignment or they decide this new feature to work on. Um,

519
00:28:52,960 --> 00:28:55,680
Speaker 1: it's it's this impossible catch twenty two, you know, similar

520
00:28:55,720 --> 00:28:58,960
Speaker 1: to the parties that we were talking about earlier. And

521
00:29:00,000 --> 00:29:02,440
Speaker 1: this is an organization if you look at Uber in particular,

522
00:29:02,560 --> 00:29:06,200
Speaker 1: just one example where you saw the CEO going to

523
00:29:06,240 --> 00:29:09,360
Speaker 1: an escort bar in South Korea. And so if this

524
00:29:09,440 --> 00:29:11,160
Speaker 1: kind of thing is happening at all the way to

525
00:29:11,160 --> 00:29:14,040
Speaker 1: the very top, just think about the fires that this

526
00:29:14,280 --> 00:29:17,560
Speaker 1: hr organization is trying to put out, and the things

527
00:29:17,600 --> 00:29:21,040
Speaker 1: like Susan Fowler's experience of being hit on over the

528
00:29:21,120 --> 00:29:24,880
Speaker 1: chat by her male manager becomes, you know, far less

529
00:29:24,880 --> 00:29:27,680
Speaker 1: of an emergency than the fact that your CEO is

530
00:29:27,720 --> 00:29:30,600
Speaker 1: at an escort bar in South Korea with his colleagues.

531
00:29:31,480 --> 00:29:34,720
Speaker 1: The number of women getting computer science degrees has steadily

532
00:29:34,760 --> 00:29:39,720
Speaker 1: declined since the mid eighties. And I'm wondering about the

533
00:29:39,760 --> 00:29:43,640
Speaker 1: pipeline problem because yes, you do need different skills, but

534
00:29:43,720 --> 00:29:47,080
Speaker 1: you also do need some people who have those basic

535
00:29:47,120 --> 00:29:50,240
Speaker 1: engineering skills to work at some of these technology companies.

536
00:29:50,680 --> 00:29:54,040
Speaker 1: So is there you know, I talked to some women

537
00:29:54,240 --> 00:29:56,960
Speaker 1: from Silicon Valley in my the hour that I did

538
00:29:56,960 --> 00:29:59,760
Speaker 1: for Nacio, and they said, there are many men who

539
00:29:59,760 --> 00:30:03,200
Speaker 1: don't have computer science degrees who get hired. So is

540
00:30:03,200 --> 00:30:05,560
Speaker 1: there a double standard when it comes to the quote

541
00:30:05,640 --> 00:30:09,800
Speaker 1: unquote you know CS degree? There absolutely is a double standard.

542
00:30:09,880 --> 00:30:12,080
Speaker 1: And one example is if you look at the Forbes

543
00:30:12,160 --> 00:30:16,120
Speaker 1: Midas list of the top venture capitalists, UM, the vast

544
00:30:16,120 --> 00:30:20,560
Speaker 1: majority of women have STEM backgrounds and a significant percentage

545
00:30:20,600 --> 00:30:23,360
Speaker 1: of men do not, which shows that the standards are

546
00:30:23,360 --> 00:30:25,840
Speaker 1: different for men and women. UM. And you'll see a

547
00:30:25,840 --> 00:30:28,600
Speaker 1: lot more men on that list like Peter Thiel and

548
00:30:28,680 --> 00:30:32,480
Speaker 1: Peter Fenton who have philosophy backgrounds, or Mike Morritts of Sequoia,

549
00:30:32,560 --> 00:30:36,360
Speaker 1: who was a journalist. And yet Mike morrets is the

550
00:30:36,440 --> 00:30:39,400
Speaker 1: very person who told me, and this is when Sequoia

551
00:30:39,480 --> 00:30:42,440
Speaker 1: had no women in their US investing business, that not

552
00:30:42,600 --> 00:30:44,840
Speaker 1: enough women were studying STEM and that they were not

553
00:30:44,920 --> 00:30:49,360
Speaker 1: prepared to lower their standards. And so, yes, the standards

554
00:30:49,360 --> 00:30:52,960
Speaker 1: are different. That said, there is a pipeline problem. And

555
00:30:53,040 --> 00:30:55,880
Speaker 1: you know, as I mentioned earlier, My argument is the

556
00:30:55,880 --> 00:30:58,920
Speaker 1: tech industry created the pipeline problem. But there is a

557
00:30:58,960 --> 00:31:01,640
Speaker 1: lot of hard work and had done and how the

558
00:31:01,680 --> 00:31:05,120
Speaker 1: tech industry, because it's such a toxic culture, people don't

559
00:31:05,120 --> 00:31:09,120
Speaker 1: gravitate towards that. So, going back to the forties and fifties,

560
00:31:09,160 --> 00:31:13,520
Speaker 1: women actually were there. They were programming computers. Men were

561
00:31:13,560 --> 00:31:17,320
Speaker 1: predominantly the hardware makers, but women were actually well represented

562
00:31:17,360 --> 00:31:20,240
Speaker 1: among software programmers right, which we saw hidden figures hidden

563
00:31:20,280 --> 00:31:23,560
Speaker 1: figures is real. This was industry wide. They were programming

564
00:31:23,560 --> 00:31:27,040
Speaker 1: computer for the military, programming computers for NASA, and then

565
00:31:27,080 --> 00:31:29,960
Speaker 1: as the industry was starting to explode in the sixties

566
00:31:29,960 --> 00:31:33,800
Speaker 1: and seventies, the tech industry was so desperate for new

567
00:31:33,840 --> 00:31:37,520
Speaker 1: talent that they were doing these personality tests and aptitude

568
00:31:37,520 --> 00:31:40,120
Speaker 1: tests to find good programmers. Hence the tests where they

569
00:31:40,160 --> 00:31:43,640
Speaker 1: decided that good programmers don't like people. Well, the research

570
00:31:43,680 --> 00:31:46,680
Speaker 1: tells us that people who quote unquote don't like people.

571
00:31:47,160 --> 00:31:49,280
Speaker 1: If you're looking for those kinds of people, you'll hire

572
00:31:49,360 --> 00:31:52,240
Speaker 1: far more men than women. And again, there's no research

573
00:31:52,280 --> 00:31:53,880
Speaker 1: to support the idea that men are better at this

574
00:31:53,960 --> 00:31:56,240
Speaker 1: job than women, but it's a stereotype that's been held

575
00:31:56,240 --> 00:31:59,080
Speaker 1: for decades. I mean, we're talking about companies as big

576
00:31:59,120 --> 00:32:03,400
Speaker 1: as IBM that using these tests, and fast forward fifty years,

577
00:32:03,400 --> 00:32:06,800
Speaker 1: you've got people like James Demore, that Google engineer who's

578
00:32:06,800 --> 00:32:10,080
Speaker 1: repeating the very same toxic assumptions. He is the guy

579
00:32:10,120 --> 00:32:12,560
Speaker 1: who wrote a memo who said that men are biologically

580
00:32:12,640 --> 00:32:16,120
Speaker 1: more suited to this. I interviewed him. I interviewed him,

581
00:32:16,160 --> 00:32:19,840
Speaker 1: and then I interviewed a neuroscientists who analyze his research

582
00:32:20,000 --> 00:32:23,959
Speaker 1: from Bernard to refute his claims. But he said, not

583
00:32:24,080 --> 00:32:27,880
Speaker 1: that women have less capacity to perform these jobs, but

584
00:32:28,000 --> 00:32:32,640
Speaker 1: they're less interested in these jobs because of prenatal testosterone,

585
00:32:32,720 --> 00:32:36,480
Speaker 1: which was totally refuted and is considered really junk science, right.

586
00:32:36,560 --> 00:32:39,200
Speaker 1: I interviewed James to Moore as well, and he says

587
00:32:39,240 --> 00:32:41,400
Speaker 1: his paper has all these citations, but even the people

588
00:32:41,440 --> 00:32:44,800
Speaker 1: he's citing disagree with how he uses the data. And

589
00:32:45,000 --> 00:32:48,320
Speaker 1: you know what is unfortunate is I think he's repeating

590
00:32:48,720 --> 00:32:52,280
Speaker 1: an assumption that many people have and is more widely

591
00:32:52,320 --> 00:32:55,960
Speaker 1: held than we would like to believe. And that's the

592
00:32:56,120 --> 00:32:58,640
Speaker 1: kind of stereotype that we need to break down if

593
00:32:58,640 --> 00:33:00,800
Speaker 1: we're going to fix the type blind problem. And the

594
00:33:00,800 --> 00:33:03,440
Speaker 1: tech industry has a responsibility there well, if he blames

595
00:33:03,480 --> 00:33:08,239
Speaker 1: it on prenatal testosterone, then there's no onus on the

596
00:33:08,280 --> 00:33:12,520
Speaker 1: industry to change things because it can't be fixed ostensibly.

597
00:33:12,600 --> 00:33:15,200
Speaker 1: And that's but and so it's a cop out in

598
00:33:15,320 --> 00:33:18,440
Speaker 1: terms of taking any proactive measures as well. And in fact,

599
00:33:18,520 --> 00:33:21,800
Speaker 1: there's new research. There was just a great Harvard Business

600
00:33:21,880 --> 00:33:24,880
Speaker 1: Review article about this that shows that men and women

601
00:33:24,920 --> 00:33:28,680
Speaker 1: are far more alike than they're just as ambitious. Justice

602
00:33:28,760 --> 00:33:31,040
Speaker 1: driven Emily that said, do you think the more should

603
00:33:31,040 --> 00:33:35,240
Speaker 1: have been fired? Yes? And I think Google also could

604
00:33:35,240 --> 00:33:38,000
Speaker 1: have done some things better in that situation as well.

605
00:33:38,520 --> 00:33:40,800
Speaker 1: First of all, that memo was out and about hanging

606
00:33:40,840 --> 00:33:44,960
Speaker 1: around and you know Google, Google Or's email boxes for

607
00:33:45,000 --> 00:33:47,320
Speaker 1: like a month before anyone said anything, and it was

608
00:33:47,360 --> 00:33:49,240
Speaker 1: not until it leaked to the press that it became

609
00:33:49,800 --> 00:33:52,920
Speaker 1: a company emergency. But you know, companies do have a

610
00:33:53,000 --> 00:33:57,200
Speaker 1: right to decide what values they have, and you cannot

611
00:33:57,200 --> 00:34:00,360
Speaker 1: write a memo like that that makes fifty of people,

612
00:34:00,400 --> 00:34:03,160
Speaker 1: well actually thirty some percent because it's Google and women

613
00:34:03,200 --> 00:34:06,240
Speaker 1: are underrepresented. But that makes all of your female colleagues

614
00:34:06,280 --> 00:34:08,959
Speaker 1: feel like they don't belong or don't deserve to be there,

615
00:34:09,120 --> 00:34:12,040
Speaker 1: or are constantly questioning when they speak up in a room.

616
00:34:12,200 --> 00:34:16,080
Speaker 1: You know how they're being constantly being questioned? Right, you

617
00:34:16,160 --> 00:34:18,600
Speaker 1: cannot you know, freedom of speech is one thing, but

618
00:34:18,719 --> 00:34:20,480
Speaker 1: it can't come at the expense of the freedom of

619
00:34:20,480 --> 00:34:23,799
Speaker 1: speech of others. And so to play Devil's advocate for

620
00:34:23,800 --> 00:34:27,600
Speaker 1: a second. Um, does everybody working at Google, the thousands

621
00:34:27,640 --> 00:34:29,440
Speaker 1: of people working at Google, do they all have to

622
00:34:29,480 --> 00:34:33,880
Speaker 1: share Google's ideology and views on every issue? Um? I

623
00:34:34,000 --> 00:34:38,440
Speaker 1: understand if somebody is, you know, outright racist. I mean,

624
00:34:38,480 --> 00:34:41,440
Speaker 1: I mean, maybe you would argue that Demoor is outright sexist.

625
00:34:41,560 --> 00:34:45,720
Speaker 1: But where do you draw the line between unpopular views

626
00:34:45,800 --> 00:34:48,000
Speaker 1: and views so awful that we have to get rid

627
00:34:48,000 --> 00:34:50,760
Speaker 1: of this person. I do think we need to create

628
00:34:50,800 --> 00:34:53,000
Speaker 1: safe spaces for people to talk about this and if

629
00:34:53,000 --> 00:34:55,680
Speaker 1: we want people to listen, if I want people to

630
00:34:55,719 --> 00:34:57,520
Speaker 1: listen to me, I have to be willing to listen

631
00:34:57,560 --> 00:35:02,000
Speaker 1: to them. Um. But I don't think at necessarily sharing

632
00:35:02,040 --> 00:35:04,239
Speaker 1: those kinds of views in a company wide memo is

633
00:35:04,239 --> 00:35:06,799
Speaker 1: the best approach, Emily. In your book, you tell one

634
00:35:06,840 --> 00:35:10,719
Speaker 1: other instructive story about Google that in its early years, Uh,

635
00:35:10,800 --> 00:35:13,640
Speaker 1: Sergey and Larry, the founders, hired lots of women for

636
00:35:13,760 --> 00:35:18,560
Speaker 1: leadership positions. Marissa Meyer, Cheryl Sandberg, Susan Wichitski. But now

637
00:35:18,640 --> 00:35:22,839
Speaker 1: women make up only about of the key tech positions there.

638
00:35:22,920 --> 00:35:28,360
Speaker 1: So what happened, well, they lost focus. But the really

639
00:35:28,400 --> 00:35:31,160
Speaker 1: important lesson that I wanted to tell, or that I

640
00:35:31,200 --> 00:35:34,720
Speaker 1: wanted to share, is that they in the early days

641
00:35:35,239 --> 00:35:37,680
Speaker 1: put this emphasis on hiring women and got these incredible

642
00:35:37,680 --> 00:35:40,760
Speaker 1: women who you know. Susan conceived the ad business, Cheryl

643
00:35:40,840 --> 00:35:44,400
Speaker 1: scaled the ad business, Marissa Meyer designed the minimalist you

644
00:35:44,440 --> 00:35:47,040
Speaker 1: know user interface that we all use when we interact

645
00:35:47,080 --> 00:35:49,719
Speaker 1: with Google to this day. And they were critical to

646
00:35:49,800 --> 00:35:53,480
Speaker 1: making Google a success. We sort of think Google was

647
00:35:53,560 --> 00:35:56,160
Speaker 1: destined to succeed because it was the first to future,

648
00:35:56,440 --> 00:35:58,640
Speaker 1: but in fact, there were like a dozen or more

649
00:35:58,719 --> 00:36:01,000
Speaker 1: other search engines at the time line that we're competing

650
00:36:01,400 --> 00:36:04,360
Speaker 1: to be that one, and Google broke away from the

651
00:36:04,400 --> 00:36:08,040
Speaker 1: pack in part because of the diversity that they had

652
00:36:08,080 --> 00:36:10,880
Speaker 1: at the table. But that's not the story that is told,

653
00:36:11,360 --> 00:36:14,120
Speaker 1: And so what is unfortunate about it is that Larry

654
00:36:14,120 --> 00:36:16,799
Speaker 1: and Sergey didn't continue to make it a priority year

655
00:36:16,920 --> 00:36:19,799
Speaker 1: after a year after a year and communicate that same

656
00:36:19,840 --> 00:36:22,880
Speaker 1: commitment that they felt to other managers in the organization.

657
00:36:22,960 --> 00:36:25,840
Speaker 1: And as Google was exploding in size. After the I

658
00:36:25,920 --> 00:36:29,080
Speaker 1: p O. It became much more about filling the seats

659
00:36:29,080 --> 00:36:33,040
Speaker 1: as fast as possible. It also became very elitist. Um.

660
00:36:33,080 --> 00:36:36,240
Speaker 1: And then you know, after the financial crisis, which actually

661
00:36:36,320 --> 00:36:40,040
Speaker 1: Google did feel the brunt of like everybody else, because

662
00:36:40,040 --> 00:36:42,520
Speaker 1: their customers are advertisers, they sort of picked their heads

663
00:36:42,520 --> 00:36:45,279
Speaker 1: back up and said, oh my gosh, where are all

664
00:36:45,280 --> 00:36:49,719
Speaker 1: the women? Like what happened? And so it's another example

665
00:36:49,800 --> 00:36:53,839
Speaker 1: of good intentions just not being enough. Did you talk

666
00:36:53,880 --> 00:36:57,960
Speaker 1: to people like Marissa Meyer, Cheryl Samberg and Susan Wojitsky

667
00:36:58,360 --> 00:37:02,560
Speaker 1: of YouTube and what did they have to say? I

668
00:37:02,680 --> 00:37:06,400
Speaker 1: talked to all of them, and it's really interesting because

669
00:37:06,400 --> 00:37:09,719
Speaker 1: they all have completely different views about these subjects. But

670
00:37:09,760 --> 00:37:11,680
Speaker 1: I think that is so great because not all women

671
00:37:11,680 --> 00:37:14,399
Speaker 1: are the same. In fact, we need more female role

672
00:37:14,440 --> 00:37:16,719
Speaker 1: models so we can see that women can and do

673
00:37:16,880 --> 00:37:19,640
Speaker 1: lead differently and give you know, our young women more

674
00:37:19,760 --> 00:37:23,839
Speaker 1: more examples. UM. You know, they all think it's a problem. Um.

675
00:37:23,880 --> 00:37:26,680
Speaker 1: They all think it's a systemic issue. And UM, it

676
00:37:26,719 --> 00:37:30,480
Speaker 1: doesn't have to just do with with women leading in Um.

677
00:37:30,680 --> 00:37:33,600
Speaker 1: One of the really interesting things about Marissa and I

678
00:37:33,640 --> 00:37:35,560
Speaker 1: know that you know you've worked with Merissa closely is

679
00:37:35,600 --> 00:37:37,839
Speaker 1: she doesn't like to talk about this. She doesn't like

680
00:37:37,960 --> 00:37:40,320
Speaker 1: to be the woman's CEO. She wants to be the CEO,

681
00:37:40,480 --> 00:37:43,759
Speaker 1: and I completely understand that. But I do think that

682
00:37:43,800 --> 00:37:46,360
Speaker 1: I got her to open up about this particular topic,

683
00:37:46,840 --> 00:37:49,560
Speaker 1: um more than I've seen her her do in the past.

684
00:37:50,080 --> 00:37:52,960
Speaker 1: And one of the lessons that I really want people

685
00:37:52,960 --> 00:37:55,640
Speaker 1: to take away from the Cheryl and Marissa stories that

686
00:37:55,680 --> 00:37:59,520
Speaker 1: these are women who are incredibly successful yet faced outsized

687
00:37:59,640 --> 00:38:02,799
Speaker 1: criticis si ism for things that, in my view, we're

688
00:38:02,880 --> 00:38:05,880
Speaker 1: only because they were women, and we're not fair game.

689
00:38:06,040 --> 00:38:09,359
Speaker 1: You know, people debating the length of Marissa myers maternity leave,

690
00:38:09,840 --> 00:38:12,000
Speaker 1: or that she had a nursery in her office, or

691
00:38:12,040 --> 00:38:14,640
Speaker 1: that she ended work from home. I mean, if she

692
00:38:14,680 --> 00:38:16,320
Speaker 1: were a man who was about to have a child,

693
00:38:16,480 --> 00:38:18,640
Speaker 1: no one would have even known. If she were a

694
00:38:18,640 --> 00:38:21,120
Speaker 1: man who ended the work from home policy, no one

695
00:38:21,120 --> 00:38:23,560
Speaker 1: would have blinked any And so you know, there was

696
00:38:23,600 --> 00:38:27,920
Speaker 1: this one headline our Cheryl Sandberg and Marissa Meyer setting

697
00:38:27,920 --> 00:38:31,080
Speaker 1: back the cause of working moms, and I was like, what,

698
00:38:31,960 --> 00:38:34,800
Speaker 1: you know, this is just it's it is just so unfair.

699
00:38:35,120 --> 00:38:38,000
Speaker 1: We should be critical of them. They are business leaders,

700
00:38:38,040 --> 00:38:40,680
Speaker 1: they are running, you know, businesses worth you know, millions

701
00:38:40,719 --> 00:38:43,719
Speaker 1: or billions and billions of dollars. But let's be fair

702
00:38:43,760 --> 00:38:47,000
Speaker 1: about how we critique our leaders and the stories we

703
00:38:47,040 --> 00:38:48,960
Speaker 1: tell about them. You know, I look at someone like

704
00:38:48,960 --> 00:38:53,919
Speaker 1: Susan Wijitski, who built Google's ad business. Uh, she led

705
00:38:53,920 --> 00:38:57,040
Speaker 1: the acquisitions of double click and YouTube. She has five children,

706
00:38:57,080 --> 00:38:59,520
Speaker 1: and yet so many people don't even know her name,

707
00:39:00,120 --> 00:39:03,160
Speaker 1: and she is a visionary and a genius, just like

708
00:39:03,480 --> 00:39:05,719
Speaker 1: you know. We use these words to describe men over

709
00:39:05,719 --> 00:39:07,720
Speaker 1: and over again. We don't use them to describe women.

710
00:39:08,080 --> 00:39:10,520
Speaker 1: But she just doesn't fit that sort of picture perfect

711
00:39:10,560 --> 00:39:14,000
Speaker 1: idea of the young male entrepreneur in a hoodie. And

712
00:39:14,040 --> 00:39:17,080
Speaker 1: that's a tragedy. We need to change the way we're

713
00:39:17,080 --> 00:39:20,240
Speaker 1: telling the stories about these women, because they are equally

714
00:39:20,360 --> 00:39:24,560
Speaker 1: important to building our future. I understand the complaint of

715
00:39:24,600 --> 00:39:27,920
Speaker 1: women getting a separate kind of scrutiny, certainly, I've experienced

716
00:39:27,920 --> 00:39:31,160
Speaker 1: that in my professional life as well. On the other hand,

717
00:39:31,239 --> 00:39:36,080
Speaker 1: if it is such a huge problem and there's such

718
00:39:36,080 --> 00:39:40,160
Speaker 1: an epidemic of this broke culture, you can understand how

719
00:39:40,200 --> 00:39:44,279
Speaker 1: people would look to the female leaders for setting a

720
00:39:44,320 --> 00:39:49,120
Speaker 1: tone that would open the doors of opportunity to other women.

721
00:39:49,719 --> 00:39:52,960
Speaker 1: So it is kind of a double edged sword, don't

722
00:39:53,000 --> 00:39:56,200
Speaker 1: you think. Well, I mean, I understand Marissa and and

723
00:39:56,280 --> 00:39:58,200
Speaker 1: you know, she's like, look, I plan to take a

724
00:39:58,200 --> 00:40:01,520
Speaker 1: six months maternity leave at Google. It would have been glorious.

725
00:40:01,560 --> 00:40:04,160
Speaker 1: But when I got to Yahoo, like, I couldn't. I

726
00:40:04,160 --> 00:40:06,479
Speaker 1: couldn't take that much time. I don't even mean about

727
00:40:06,560 --> 00:40:10,200
Speaker 1: her personal thing, and and and some of it, of course,

728
00:40:10,320 --> 00:40:13,759
Speaker 1: is all about how it's communicated. But changing the work

729
00:40:13,800 --> 00:40:16,239
Speaker 1: from home policy, which I apparently you know, I read

730
00:40:16,280 --> 00:40:18,280
Speaker 1: a lot about that a lot of people weren't being

731
00:40:18,280 --> 00:40:22,200
Speaker 1: productive at home. It wasn't really an effective thing. But

732
00:40:22,320 --> 00:40:24,759
Speaker 1: I think that some of it is on in the

733
00:40:24,800 --> 00:40:27,960
Speaker 1: telling of a policy change. And I think you have

734
00:40:28,000 --> 00:40:30,680
Speaker 1: to be out front on some of these issues because

735
00:40:30,719 --> 00:40:33,879
Speaker 1: you're under greater scrutiny and you're expected to set a

736
00:40:33,920 --> 00:40:36,719
Speaker 1: more positive tone for other working women, right, And maybe

737
00:40:36,760 --> 00:40:39,560
Speaker 1: they didn't communicate that well to be honest, Um, and

738
00:40:39,600 --> 00:40:44,120
Speaker 1: I'm not saying certainly, you know, Yahoo wasn't perfect, and um,

739
00:40:44,200 --> 00:40:46,239
Speaker 1: you know, some people don't think the result was so great,

740
00:40:46,239 --> 00:40:48,200
Speaker 1: and some people don't think she did did a good job.

741
00:40:48,840 --> 00:40:51,200
Speaker 1: I don't think it's necessarily because they ended the work

742
00:40:51,239 --> 00:40:53,520
Speaker 1: from home right when she took a short maternity leave.

743
00:40:53,760 --> 00:40:57,000
Speaker 1: But those are the things that people remember about her tenure.

744
00:40:57,880 --> 00:41:01,560
Speaker 1: And you know, she's starting a new uh incubator UM

745
00:41:01,880 --> 00:41:03,239
Speaker 1: and there was just a story about it in the

746
00:41:03,239 --> 00:41:05,960
Speaker 1: New York Times, and people are calling her a loser,

747
00:41:06,160 --> 00:41:08,680
Speaker 1: and I just I just think it's so unfair. I

748
00:41:08,719 --> 00:41:14,759
Speaker 1: just think it's the kind of outsized, overly negative criticism

749
00:41:14,760 --> 00:41:16,120
Speaker 1: that just I don't know. I don't know if she

750
00:41:16,160 --> 00:41:18,080
Speaker 1: was a man, I don't even maybe people wouldn't pay attention,

751
00:41:18,120 --> 00:41:20,560
Speaker 1: and maybe that's another another problem. But you know, people

752
00:41:20,640 --> 00:41:23,759
Speaker 1: want to know what she's what she's doing. But let's

753
00:41:23,800 --> 00:41:25,880
Speaker 1: give her a chance. Emily. This may take us a

754
00:41:25,880 --> 00:41:29,280
Speaker 1: little further afield from what we're discussing, but I can't

755
00:41:29,560 --> 00:41:32,839
Speaker 1: have this conversation with you without asking about the relationship

756
00:41:32,880 --> 00:41:37,200
Speaker 1: between Silicon Valley and Donald Trump. UM. We talked to

757
00:41:37,280 --> 00:41:41,839
Speaker 1: Tristan Harris, who said definitively that Trump and his view

758
00:41:41,840 --> 00:41:46,360
Speaker 1: wouldn't have been elected without Facebook. How is Silicon Valley,

759
00:41:46,440 --> 00:41:48,759
Speaker 1: both the women and the men, how are they reacting

760
00:41:48,880 --> 00:41:52,800
Speaker 1: to the election of Donald Trump, the presidency of Donald Trump,

761
00:41:52,880 --> 00:41:57,640
Speaker 1: and their role in all of that. Well, obviously Trump's

762
00:41:57,680 --> 00:42:02,480
Speaker 1: election surprised a lot of people, include the President himself, UM,

763
00:42:02,520 --> 00:42:05,520
Speaker 1: and many people in Silicon Valley, and I think most

764
00:42:05,560 --> 00:42:09,440
Speaker 1: of Silicon Valley was not happy to see Trump elected, which, um,

765
00:42:09,480 --> 00:42:13,080
Speaker 1: you know is unfortunate because Silicon Valley and the White

766
00:42:13,080 --> 00:42:15,960
Speaker 1: House had made a lot of progress. And we need

767
00:42:16,040 --> 00:42:18,799
Speaker 1: the government and the tech industry to be talking to

768
00:42:18,840 --> 00:42:22,120
Speaker 1: each other because the tech industry really is inventing the

769
00:42:22,160 --> 00:42:24,800
Speaker 1: future and hugely influential. And as we've seen with Facebook,

770
00:42:25,080 --> 00:42:30,120
Speaker 1: things can go wrong, and so I hope that there

771
00:42:30,239 --> 00:42:32,279
Speaker 1: is some sort of dialogue. I mean, I know that

772
00:42:32,320 --> 00:42:35,920
Speaker 1: there have been some meetings and some attempts to communicate,

773
00:42:36,239 --> 00:42:38,439
Speaker 1: but in general, and this is not just about President Trump,

774
00:42:38,480 --> 00:42:40,920
Speaker 1: but I do think our lawmakers need to better understand

775
00:42:41,080 --> 00:42:43,520
Speaker 1: what these companies are doing. You know, if we saw,

776
00:42:43,920 --> 00:42:47,399
Speaker 1: if any of one saw Mark Zuckerberg's testimony on Capitol Hill,

777
00:42:47,440 --> 00:42:49,320
Speaker 1: and I was in that room, you know, some of

778
00:42:49,360 --> 00:42:53,160
Speaker 1: the questions from the senators were really disappointing. They just

779
00:42:53,160 --> 00:42:56,239
Speaker 1: don't know how Facebook works, UM, and we need our

780
00:42:56,239 --> 00:42:59,280
Speaker 1: government to understand or these things aren't going to get fixed.

781
00:42:59,440 --> 00:43:02,279
Speaker 1: And you know, we talk a lot about regulation, but

782
00:43:02,320 --> 00:43:06,080
Speaker 1: I think the tech industry has proven that it can't

783
00:43:06,080 --> 00:43:11,279
Speaker 1: necessarily regulate itself. But we need the right regulation. We

784
00:43:11,320 --> 00:43:14,880
Speaker 1: don't want another election to be swayed. We don't want

785
00:43:14,880 --> 00:43:19,160
Speaker 1: our information stolen by you know, foreign governments. We all

786
00:43:19,200 --> 00:43:23,399
Speaker 1: want our privacy, you know. I think that Facebook has

787
00:43:23,480 --> 00:43:26,719
Speaker 1: responded as they should in a way, it's it's too little,

788
00:43:26,760 --> 00:43:28,960
Speaker 1: too late, But I am glad that there they are

789
00:43:29,000 --> 00:43:32,480
Speaker 1: reacting to it, and I I hope they get it right.

790
00:43:33,040 --> 00:43:36,239
Speaker 1: How do you think that Silicon Valley differs from what's

791
00:43:36,280 --> 00:43:41,080
Speaker 1: going on in Hollywood? Hollywood, of course, has its own problems,

792
00:43:41,120 --> 00:43:45,480
Speaker 1: not only with sexual harassment, sexual misconduct, but also just

793
00:43:45,760 --> 00:43:50,279
Speaker 1: playing opportunity. And of movie directors or men, eight three

794
00:43:50,640 --> 00:43:54,240
Speaker 1: of TV directors or men eight percent of entertainment executives

795
00:43:54,719 --> 00:43:59,520
Speaker 1: are mail. So which is worse, Silicon Valley or Hollywood?

796
00:43:59,520 --> 00:44:01,520
Speaker 1: And by the way, aren't you guys surprised that Wall

797
00:44:01,560 --> 00:44:04,840
Speaker 1: Street has gone gotten away relatively scott free in this

798
00:44:04,880 --> 00:44:08,680
Speaker 1: whole conversation totally, I cannot get over that. Um I

799
00:44:08,800 --> 00:44:11,640
Speaker 1: keep asking what's happening on Wall Street? But okay, so

800
00:44:12,320 --> 00:44:14,960
Speaker 1: before we talk about Hollywood, actually, on Wall Street, if

801
00:44:14,960 --> 00:44:17,759
Speaker 1: you look at the top banks, they're about fifty fifty. Um,

802
00:44:17,760 --> 00:44:19,040
Speaker 1: they have a lot of work to do when it

803
00:44:19,040 --> 00:44:21,960
Speaker 1: comes to women in leadership positions, but in a way,

804
00:44:22,040 --> 00:44:23,440
Speaker 1: they did a lot of the hard work in the

805
00:44:23,480 --> 00:44:26,920
Speaker 1: eighties and nineties, and you know, they've gotten to a

806
00:44:26,960 --> 00:44:30,400
Speaker 1: better place, which is certainly an example that Silicon Valley

807
00:44:30,480 --> 00:44:33,520
Speaker 1: and Hollywood can do it too. I mentioned at the

808
00:44:33,560 --> 00:44:37,239
Speaker 1: beginning that I think some of the courage from the

809
00:44:37,239 --> 00:44:40,120
Speaker 1: Me Too movement started with Allan pow And in Silicon Valley,

810
00:44:40,160 --> 00:44:43,879
Speaker 1: And in fact, we saw Susan Fowler and women entrepreneurs

811
00:44:44,320 --> 00:44:48,000
Speaker 1: coming forward in Silicon Valley months before the Harvey Weinstein

812
00:44:48,120 --> 00:44:51,560
Speaker 1: story broke. But Hollywood really like picked up the ball

813
00:44:51,600 --> 00:44:54,440
Speaker 1: and ran with it. And you know, I've been so

814
00:44:54,520 --> 00:44:57,160
Speaker 1: inspired by all of these women in Hollywood telling their stories,

815
00:44:57,480 --> 00:44:59,680
Speaker 1: and I've been a little bit disappointed that I feel

816
00:44:59,680 --> 00:45:02,360
Speaker 1: like the momentum there's been a loss of some of

817
00:45:02,360 --> 00:45:04,759
Speaker 1: that momentum in Silicon Valley. Um. You know, I do

818
00:45:04,800 --> 00:45:07,759
Speaker 1: think the reality is when you can have Reese Witherspoon

819
00:45:08,120 --> 00:45:10,719
Speaker 1: talking about it at the Golden Globes, that has a

820
00:45:10,719 --> 00:45:12,360
Speaker 1: lot of impact and a lot of people see it

821
00:45:12,400 --> 00:45:15,560
Speaker 1: and it raises awareness, and you know, these women in

822
00:45:15,600 --> 00:45:18,880
Speaker 1: Silicon Valley are often working at companies where they've signed

823
00:45:18,880 --> 00:45:21,800
Speaker 1: an n DA. They can't talk about it. They're scared

824
00:45:21,840 --> 00:45:25,440
Speaker 1: for their jobs. And so, you know, I really hope

825
00:45:25,520 --> 00:45:30,080
Speaker 1: that we will see some of the more shaking up

826
00:45:30,120 --> 00:45:33,520
Speaker 1: that's been happening in Hollywood happen in Silicon Valley, even

827
00:45:33,600 --> 00:45:38,239
Speaker 1: without you know, potentially the role models like Reese Witherspoon, Um.

828
00:45:38,239 --> 00:45:40,360
Speaker 1: But I've been, you know, I've been down in Hollywood.

829
00:45:40,440 --> 00:45:42,360
Speaker 1: There's there's some folks there that are really interested in

830
00:45:42,400 --> 00:45:44,720
Speaker 1: the book, and I've I've spoken to um, the people

831
00:45:44,760 --> 00:45:47,120
Speaker 1: involved in the Times Up movement, and I'm really really

832
00:45:47,160 --> 00:45:50,600
Speaker 1: inspired by what they're doing. But obviously the need is there,

833
00:45:50,640 --> 00:45:53,439
Speaker 1: and it's not just in Hollywood. It's it's farmers, it's

834
00:45:53,680 --> 00:45:56,400
Speaker 1: flight attendants, it's you know, all different kinds of women

835
00:45:56,400 --> 00:45:58,880
Speaker 1: and all all different walks of life they need help. Well,

836
00:45:58,920 --> 00:46:01,839
Speaker 1: let's talk about the hath Forward. One woman in the

837
00:46:01,920 --> 00:46:04,279
Speaker 1: hour I did on this subject, Emily talked about the

838
00:46:04,320 --> 00:46:07,520
Speaker 1: Rooney Rule, which was started by Dan Rooney, who owned

839
00:46:07,520 --> 00:46:10,360
Speaker 1: the Pittsburgh Steelers, and they wanted to have more diversity,

840
00:46:10,400 --> 00:46:14,800
Speaker 1: more racial diversity among coaches and management positions. So the

841
00:46:14,880 --> 00:46:19,560
Speaker 1: Rooney Rule requires that you interview one minority candidate, and

842
00:46:19,600 --> 00:46:24,279
Speaker 1: I think they increased to seventy minority positions as a

843
00:46:24,280 --> 00:46:27,719
Speaker 1: result of implementing the Rooney rules. So A, do you

844
00:46:27,760 --> 00:46:31,360
Speaker 1: think that's a good idea? And B they also suggested

845
00:46:31,480 --> 00:46:34,719
Speaker 1: many of the people on the show blind resumes, which,

846
00:46:34,760 --> 00:46:38,360
Speaker 1: of course, as have been very successful in integrating national

847
00:46:38,400 --> 00:46:43,279
Speaker 1: symphonies all over the country. So what other measures can

848
00:46:43,320 --> 00:46:47,359
Speaker 1: be taken to fix this? I think the Rooney rule

849
00:46:47,560 --> 00:46:50,480
Speaker 1: is great, And you know, I've heard some companies that

850
00:46:50,520 --> 00:46:53,319
Speaker 1: won't even start an interview process until they have at

851
00:46:53,400 --> 00:46:57,000
Speaker 1: least two qualified female candidates and two candidates of color,

852
00:46:57,000 --> 00:46:59,960
Speaker 1: and they have to be qualified candidates, like Real Canada.

853
00:47:00,040 --> 00:47:03,279
Speaker 1: It's um. It goes back to this idea that if

854
00:47:03,320 --> 00:47:06,960
Speaker 1: we just focus on unconscious biased training or raising awareness

855
00:47:07,000 --> 00:47:10,040
Speaker 1: about our bias, that won't necessarily have a lot of impact.

856
00:47:10,120 --> 00:47:12,360
Speaker 1: You know, it's really hard to tell someone just change

857
00:47:12,400 --> 00:47:14,359
Speaker 1: the way you think about women. I mean, these are

858
00:47:14,400 --> 00:47:16,840
Speaker 1: so deeply rooted. These are biases that, as you said,

859
00:47:17,280 --> 00:47:20,240
Speaker 1: we all have. But if you give people the tools

860
00:47:20,320 --> 00:47:23,279
Speaker 1: to combat their bias, that can have a lot of impacts. So,

861
00:47:23,400 --> 00:47:25,800
Speaker 1: you know, part of the problem is there's not enough research,

862
00:47:25,840 --> 00:47:29,239
Speaker 1: there's not enough people who have tried blind resumes um

863
00:47:29,280 --> 00:47:32,560
Speaker 1: to prove like, oh, this works, but it's certainly worth

864
00:47:32,600 --> 00:47:35,840
Speaker 1: a try. Another woman in the hours suggested for dealing

865
00:47:35,840 --> 00:47:39,800
Speaker 1: with the pay and equity problem is more transparent, Like

866
00:47:39,880 --> 00:47:43,920
Speaker 1: the earliest thing to solve more transparency, but no negotiations.

867
00:47:43,960 --> 00:47:47,360
Speaker 1: Once suggested that you have a job, you get paid

868
00:47:47,400 --> 00:47:50,440
Speaker 1: what you get paid for that job, almost like, I

869
00:47:50,440 --> 00:47:53,040
Speaker 1: guess if you're in the civil service, right, I've heard

870
00:47:53,200 --> 00:47:56,160
Speaker 1: no negotiation. I've heard, well, if you're going to do negotiation,

871
00:47:56,280 --> 00:47:59,480
Speaker 1: make sure that women are negotiating or encouraging them to negotiate,

872
00:47:59,520 --> 00:48:03,160
Speaker 1: and that every one is negotiating. I mean, look, either way,

873
00:48:03,480 --> 00:48:06,760
Speaker 1: you might lose some people, you might gain some people. Um.

874
00:48:06,800 --> 00:48:10,280
Speaker 1: But I also think just being aware of it is important.

875
00:48:10,400 --> 00:48:13,600
Speaker 1: And for these companies, they all see the data even

876
00:48:13,600 --> 00:48:16,680
Speaker 1: though it it these companies that love data, but yet

877
00:48:16,680 --> 00:48:19,280
Speaker 1: they ignore it um when it doesn't work in their favor.

878
00:48:19,480 --> 00:48:21,480
Speaker 1: You know, you see what people get paid, and to me,

879
00:48:21,560 --> 00:48:24,640
Speaker 1: that's the easiest thing to solve, especially when you're making

880
00:48:24,640 --> 00:48:27,680
Speaker 1: a lot of money. You know, pay people fairly, pay

881
00:48:27,719 --> 00:48:30,360
Speaker 1: people what they're worth, and you know, I'm sure you

882
00:48:30,400 --> 00:48:33,000
Speaker 1: know Mark Benny Off, the CEO of Salesforce, who's they

883
00:48:33,000 --> 00:48:35,919
Speaker 1: did a two year comprehensive pay review and it took

884
00:48:35,920 --> 00:48:37,719
Speaker 1: a lot of work, because you know, by that point

885
00:48:37,719 --> 00:48:40,960
Speaker 1: Salesforce was a big organization. But now they can probably

886
00:48:41,080 --> 00:48:43,200
Speaker 1: say we don't have a pay gap, and I'm sure

887
00:48:43,200 --> 00:48:47,680
Speaker 1: that's attracting a lot of potential candidates. I guess. Bottom line, Emily,

888
00:48:47,719 --> 00:48:52,400
Speaker 1: are you optimistic about the future. I am, and I

889
00:48:52,440 --> 00:48:54,439
Speaker 1: don't think I could be doing this whole book tour

890
00:48:54,520 --> 00:48:57,840
Speaker 1: thing if I wasn't. Um. I believe that the people

891
00:48:57,840 --> 00:49:00,880
Speaker 1: who are changing the world and taking us to Mars

892
00:49:00,920 --> 00:49:04,200
Speaker 1: and building self driving cars and have given us rides

893
00:49:04,239 --> 00:49:06,120
Speaker 1: at the push of a button and connected the world,

894
00:49:06,680 --> 00:49:10,720
Speaker 1: they can hire more people, hire more women, pay them fairly,

895
00:49:10,880 --> 00:49:13,440
Speaker 1: fund their ideas. Like this is not too hard of

896
00:49:13,440 --> 00:49:15,920
Speaker 1: a problem from Silicon Valley to solve. A lot of

897
00:49:15,920 --> 00:49:18,279
Speaker 1: it is setting a tone, isn't it. You have to

898
00:49:18,320 --> 00:49:21,640
Speaker 1: make it a priority. It can't be sort of like yeah, yeah, yeah,

899
00:49:21,680 --> 00:49:24,600
Speaker 1: lip service. Oh yeah, we have this group of people

900
00:49:24,640 --> 00:49:28,799
Speaker 1: and they're responsible. Oh yeah, and they're mostly women and okay, whatever, yeah,

901
00:49:29,000 --> 00:49:31,160
Speaker 1: pat them on the head, go about and do your

902
00:49:31,520 --> 00:49:35,200
Speaker 1: altruistic work. Absolutely, it is about CEO is making this

903
00:49:35,280 --> 00:49:39,480
Speaker 1: a priority. Explicitly, it's about investors making this a priority

904
00:49:39,480 --> 00:49:42,600
Speaker 1: when it comes to funding and then the port Some

905
00:49:42,680 --> 00:49:45,759
Speaker 1: companies are demanding it um Upfront Ventures, which is a

906
00:49:45,840 --> 00:49:47,759
Speaker 1: venture capital firm based in l A, when they give

907
00:49:47,760 --> 00:49:50,400
Speaker 1: an entrepreneur term sheet. On that term sheet, they are

908
00:49:50,440 --> 00:49:52,640
Speaker 1: committing to building a diverse team. And I've talked to

909
00:49:52,680 --> 00:49:55,000
Speaker 1: some of the entrepreneurs who say, look, we're small, but

910
00:49:55,040 --> 00:49:58,320
Speaker 1: it's working. We're six people, two women, to people of color.

911
00:49:58,520 --> 00:50:01,840
Speaker 1: Like this matters. And the earlier you do it, the

912
00:50:01,960 --> 00:50:04,960
Speaker 1: easier it will be, and the better your company will

913
00:50:05,000 --> 00:50:07,600
Speaker 1: be for it, the better we all will be for it.

914
00:50:07,719 --> 00:50:09,680
Speaker 1: If we solve this problem, that will have the biggest

915
00:50:09,680 --> 00:50:12,040
Speaker 1: impact of any So is your next book going to

916
00:50:12,040 --> 00:50:17,160
Speaker 1: be Systopia? I've been thinking a lot about what does

917
00:50:17,320 --> 00:50:22,160
Speaker 1: utopia look like? Where is it working um and across industries,

918
00:50:22,320 --> 00:50:24,719
Speaker 1: and you know, some of the bright spots can be

919
00:50:24,760 --> 00:50:26,640
Speaker 1: hard to find because you know, not a lot of

920
00:50:26,640 --> 00:50:29,560
Speaker 1: people have gotten this right. But I do think we

921
00:50:29,719 --> 00:50:31,680
Speaker 1: have a lot to learn from the people who are

922
00:50:32,480 --> 00:50:36,280
Speaker 1: doing good things. And these are the workplaces of the future,

923
00:50:36,280 --> 00:50:38,680
Speaker 1: and everyone's looking for a competitive edge. We all want

924
00:50:38,680 --> 00:50:41,400
Speaker 1: to build the best businesses and be as productive as possible.

925
00:50:41,880 --> 00:50:45,600
Speaker 1: And that's about building a workforce that can do all

926
00:50:45,640 --> 00:50:47,720
Speaker 1: of those things, and a workforce that we can also

927
00:50:47,760 --> 00:50:50,719
Speaker 1: be proud of. The millennials and Gen zs are demanding it,

928
00:50:50,800 --> 00:50:53,680
Speaker 1: aren't they right. People care about this. Young people care

929
00:50:53,680 --> 00:50:56,640
Speaker 1: about this. Old people care about this. We all care

930
00:50:56,640 --> 00:51:00,440
Speaker 1: about this, So let's do something about it. Emily Shang,

931
00:51:00,600 --> 00:51:03,360
Speaker 1: author of bro Topia, breaking Up the Boys Club of

932
00:51:03,400 --> 00:51:06,120
Speaker 1: Silicon Valley. So great to have you. Thank you so much.

933
00:51:06,440 --> 00:51:09,160
Speaker 1: And also you're the host of Bloomberg Technology, which is

934
00:51:09,200 --> 00:51:13,280
Speaker 1: every day on Bloomberg Television at two am two pm.

935
00:51:13,320 --> 00:51:15,120
Speaker 1: I mean not two am, that would really be the

936
00:51:15,120 --> 00:51:20,239
Speaker 1: grave two pm Pacific, five pm Eastern streaming live. We're

937
00:51:20,280 --> 00:51:23,000
Speaker 1: on YouTube. You can get us a lot of different ways. Emily,

938
00:51:23,160 --> 00:51:24,799
Speaker 1: so great to have you here and so nice to

939
00:51:24,800 --> 00:51:28,279
Speaker 1: meet you. Thank you. It's an honor, honestly for me

940
00:51:28,400 --> 00:51:31,080
Speaker 1: as a journalist, you are always one of my role models.

941
00:51:31,120 --> 00:51:33,640
Speaker 1: So um, thank you for having me. Thank you, and

942
00:51:33,680 --> 00:51:41,279
Speaker 1: I'm so sorry to hear that. That does it for

943
00:51:41,320 --> 00:51:44,520
Speaker 1: today's episode. Everyone. Next up on the podcast will be

944
00:51:44,640 --> 00:51:50,279
Speaker 1: diving into the age of Outrage. I'm talking political correctness, microaggressions,

945
00:51:50,320 --> 00:51:55,240
Speaker 1: trigger warning, safe spaces, cultural appropriation. As usual, we really

946
00:51:55,239 --> 00:51:57,200
Speaker 1: want to hear from you. What do you think. Are

947
00:51:57,200 --> 00:52:00,360
Speaker 1: we in the midst of a long overdue course direction

948
00:52:00,760 --> 00:52:04,480
Speaker 1: or have we simply become too sensitive? So you tell us.

949
00:52:04,520 --> 00:52:06,839
Speaker 1: Call and leave us a message as always at nine

950
00:52:06,880 --> 00:52:10,000
Speaker 1: to nine to two four for six three seven, or

951
00:52:10,400 --> 00:52:14,120
Speaker 1: shoot us an email at comments at currect podcast dot com.

952
00:52:14,160 --> 00:52:16,480
Speaker 1: As usual. Our thanks to the team that puts this

953
00:52:16,520 --> 00:52:21,160
Speaker 1: podcast together, Gianna Palmer, Jared O'Connell, Nora Richie over at Stitcher,

954
00:52:21,520 --> 00:52:25,640
Speaker 1: and Alison Bresnick, Bethams and Emily Beana my posse over

955
00:52:25,680 --> 00:52:29,120
Speaker 1: at Katie correct Media. Thanks also today to the invisible

956
00:52:29,160 --> 00:52:32,280
Speaker 1: studios here in West Hollywood who helped with today's recording.

957
00:52:32,719 --> 00:52:35,600
Speaker 1: Mark Phillips wrote Artneme Music, and Katie and I are

958
00:52:35,640 --> 00:52:38,520
Speaker 1: the show's executive producers. For better or for worse, find

959
00:52:38,560 --> 00:52:41,040
Speaker 1: me on social media under Katie Currict. You know by

960
00:52:41,040 --> 00:52:44,280
Speaker 1: now that I'm addicted to Instagram, so follow me there

961
00:52:44,320 --> 00:52:47,880
Speaker 1: and give me your comments and criticisms, but mostly your comments.

962
00:52:48,120 --> 00:52:52,080
Speaker 1: Brian tweets from the handle Goldsmith b. Please subscribe to

963
00:52:52,120 --> 00:52:54,120
Speaker 1: our show if you haven't already, and for those of

964
00:52:54,160 --> 00:52:56,040
Speaker 1: you who've taken the time to leave us a review

965
00:52:56,080 --> 00:52:59,279
Speaker 1: on Apple Podcasts, we thank you. We're very grateful it

966
00:52:59,320 --> 00:53:02,279
Speaker 1: helps other people to find the show. And for the

967
00:53:02,280 --> 00:53:04,359
Speaker 1: rest of you, if you're listening at this point, what

968
00:53:04,400 --> 00:53:08,160
Speaker 1: are you waiting for? Please subscribe and leave us a review. Meanwhile,

969
00:53:08,200 --> 00:53:11,040
Speaker 1: thanks so much for listening as always, and you'll be

970
00:53:11,120 --> 00:53:12,479
Speaker 1: hearing from us next week.