1 00:00:05,360 --> 00:00:08,760 Speaker 1: This is Emily, and you're listening to stuff Mom never 2 00:00:08,840 --> 00:00:23,200 Speaker 1: told you. Today we are tackling a little topic that 3 00:00:23,800 --> 00:00:26,239 Speaker 1: has been making a few headlines here and there isn't 4 00:00:26,239 --> 00:00:28,440 Speaker 1: that right, bab. It's been a hot button topic in 5 00:00:28,480 --> 00:00:32,040 Speaker 1: the news. You might have seen it, and we've decided, 6 00:00:32,120 --> 00:00:34,479 Speaker 1: for the first time since taking the reins here at 7 00:00:34,520 --> 00:00:36,640 Speaker 1: stuff I've never told you that this is a topic 8 00:00:37,360 --> 00:00:43,239 Speaker 1: Silicon Valley sexism, that warrants a double episode, y'all, So 9 00:00:43,360 --> 00:00:46,279 Speaker 1: buckle up because there are lots of stories we want 10 00:00:46,320 --> 00:00:52,040 Speaker 1: to tell around the evolution of gender bias in computer science, 11 00:00:52,520 --> 00:00:57,120 Speaker 1: in technology, in the world's most profitable industry right now 12 00:00:57,720 --> 00:01:02,360 Speaker 1: that is resulting in women leaving stem fields and really 13 00:01:02,400 --> 00:01:06,400 Speaker 1: tech in particular, at twice the rate than men are. 14 00:01:07,000 --> 00:01:11,360 Speaker 1: And we need to uncover why Silicon Valley especially feels 15 00:01:11,360 --> 00:01:15,280 Speaker 1: like such a toxic place for professional women to thrive, 16 00:01:15,800 --> 00:01:18,960 Speaker 1: and how not only we can make a difference on 17 00:01:18,959 --> 00:01:23,120 Speaker 1: that front, but how really courageous efforts are taking place 18 00:01:23,240 --> 00:01:26,839 Speaker 1: at tech companies across the country, across the globe, thanks 19 00:01:26,840 --> 00:01:30,640 Speaker 1: to a lot of courageous whistleblowers who are demanding those 20 00:01:30,720 --> 00:01:33,880 Speaker 1: changes be implemented so that women and men can both 21 00:01:33,920 --> 00:01:36,880 Speaker 1: thrive equally. Yeah, I think one of the things that 22 00:01:37,000 --> 00:01:39,520 Speaker 1: I like to ground this issue in is the idea 23 00:01:39,640 --> 00:01:43,400 Speaker 1: of why this matters. A lot of folks say things like, oh, 24 00:01:43,520 --> 00:01:45,839 Speaker 1: so women are leaving this field, who cares a big deal? 25 00:01:46,160 --> 00:01:48,920 Speaker 1: But when you think about technology, these are people who 26 00:01:49,000 --> 00:01:51,720 Speaker 1: are poised to tackle some of the most critical problems 27 00:01:51,720 --> 00:01:54,240 Speaker 1: that are facing our world, huge challenges. And if you 28 00:01:54,280 --> 00:01:58,240 Speaker 1: don't have inclusive, diverse teams of people who are tackling 29 00:01:58,240 --> 00:02:01,480 Speaker 1: those challenges, we all could for yeah, think about all 30 00:02:01,480 --> 00:02:04,080 Speaker 1: of the specific challenges that we've faced. If you don't 31 00:02:04,080 --> 00:02:07,440 Speaker 1: have women, people of color, folks, all along different spectrums 32 00:02:07,840 --> 00:02:10,320 Speaker 1: tackling those issues, we could all be missing out. Think 33 00:02:10,320 --> 00:02:12,720 Speaker 1: of all the tech innovation that women could be bringing 34 00:02:12,800 --> 00:02:15,480 Speaker 1: us that they aren't because they're leaving this field exactly. 35 00:02:15,560 --> 00:02:19,200 Speaker 1: Tech has been just regularly disruptive to the ways that 36 00:02:19,440 --> 00:02:22,200 Speaker 1: we are creating a new kind of normal or creating 37 00:02:22,200 --> 00:02:24,919 Speaker 1: a new future for the world. And so the fact 38 00:02:25,000 --> 00:02:29,440 Speaker 1: that women are making up just about a quarter of 39 00:02:29,480 --> 00:02:33,280 Speaker 1: those in tech, or the fact that the numbers of 40 00:02:33,280 --> 00:02:37,200 Speaker 1: women in computer science programs have actually been declining since 41 00:02:37,240 --> 00:02:40,280 Speaker 1: the eighties, is a problem, and we want to make 42 00:02:40,320 --> 00:02:43,520 Speaker 1: sure that not not only are we filling the pipeline 43 00:02:43,760 --> 00:02:46,720 Speaker 1: into stem fields and into tech in particular, but that 44 00:02:46,800 --> 00:02:51,639 Speaker 1: we really take a critical look at how tech companies 45 00:02:51,680 --> 00:02:56,240 Speaker 1: and how the cultures of inclusion can be developed to 46 00:02:56,360 --> 00:02:59,800 Speaker 1: retain that kind of female talent and diverse talent along 47 00:03:00,040 --> 00:03:05,120 Speaker 1: different kinds of spectrums. Now, there was a huge article 48 00:03:05,400 --> 00:03:08,960 Speaker 1: written the cover story of The Atlantic magazine back in 49 00:03:09,080 --> 00:03:12,919 Speaker 1: April that really blew the lid off of this issue 50 00:03:13,040 --> 00:03:16,440 Speaker 1: and in so many different ways, thanks in large part 51 00:03:16,480 --> 00:03:19,000 Speaker 1: two some very courageous women whose stories were going to 52 00:03:19,120 --> 00:03:22,160 Speaker 1: tackle UH in part two here. But first, I think 53 00:03:22,160 --> 00:03:25,000 Speaker 1: it's important that we get a lay of the land. 54 00:03:25,120 --> 00:03:27,320 Speaker 1: I like to think of it as the terrible, horrible, 55 00:03:27,400 --> 00:03:32,040 Speaker 1: no good, very bad land for women, very sexist land 56 00:03:32,200 --> 00:03:35,200 Speaker 1: in the world of tech right now. But this article, 57 00:03:35,480 --> 00:03:37,400 Speaker 1: what was what was the title of this article? First 58 00:03:37,400 --> 00:03:40,040 Speaker 1: of all, the title of this article is why is 59 00:03:40,040 --> 00:03:43,840 Speaker 1: Silicon Valley so awful to women? And that really should 60 00:03:43,840 --> 00:03:45,920 Speaker 1: give you a sense of what's happening here. It might 61 00:03:45,960 --> 00:03:48,920 Speaker 1: sound hyperbolic, it might sound like there's no way it 62 00:03:48,960 --> 00:03:52,280 Speaker 1: could really be this bad. Y'all read this article because 63 00:03:52,360 --> 00:03:55,640 Speaker 1: it's really that bad. It really is, And just to 64 00:03:55,680 --> 00:03:58,320 Speaker 1: give you a quick glimmer into some of the stories 65 00:03:58,400 --> 00:04:01,760 Speaker 1: that women professionals in hack shared through that article. The 66 00:04:01,800 --> 00:04:03,880 Speaker 1: Atlantic came up with a great short video that we 67 00:04:03,920 --> 00:04:06,760 Speaker 1: want to sample from right now. I was about to 68 00:04:06,760 --> 00:04:09,360 Speaker 1: make a presentation at a tech conference when a male 69 00:04:09,400 --> 00:04:13,440 Speaker 1: attendee gave me this unsolicited piece of advice. Don't be nervous. 70 00:04:13,480 --> 00:04:15,800 Speaker 1: You're hot. No one expects you to do well. I'm 71 00:04:15,840 --> 00:04:18,800 Speaker 1: a software engineer and I'm regularly asked to take notes 72 00:04:18,839 --> 00:04:21,560 Speaker 1: and meetings. None of the men are asked to do that. 73 00:04:21,839 --> 00:04:25,000 Speaker 1: After giving a talk, I received abusive emails from men 74 00:04:25,040 --> 00:04:27,479 Speaker 1: who sit things like I jerked off to the video 75 00:04:27,480 --> 00:04:32,200 Speaker 1: of your talk, so as you can hear through some 76 00:04:32,720 --> 00:04:36,760 Speaker 1: intense interviews with senior women in tech, The Atlantic journalist 77 00:04:37,000 --> 00:04:41,040 Speaker 1: Lisa Mundy describes a series of workplace environments, not just 78 00:04:41,120 --> 00:04:44,400 Speaker 1: at one company, but at a series of companies throughout 79 00:04:44,400 --> 00:04:48,200 Speaker 1: Silicon Valley and through the tech industry space that are 80 00:04:48,320 --> 00:04:52,599 Speaker 1: only described as hostile towards women. And you might be thinking, yeah, 81 00:04:52,600 --> 00:04:56,240 Speaker 1: but aren't so many industries really hostile for women? Isn't 82 00:04:56,279 --> 00:05:01,719 Speaker 1: the law, isn't media, isn't h hospitals? Aren't there lots 83 00:05:01,720 --> 00:05:04,760 Speaker 1: of different industries that are really hostile to women, and 84 00:05:04,800 --> 00:05:10,080 Speaker 1: I would counter by saying yes, and yet there's something 85 00:05:10,480 --> 00:05:14,240 Speaker 1: very significant about the ways in which tech seems to 86 00:05:14,400 --> 00:05:19,240 Speaker 1: exaggerate sexism and make it almost like a very acceptable, 87 00:05:19,320 --> 00:05:22,360 Speaker 1: browy space. Well, that's one of the things I found 88 00:05:22,360 --> 00:05:26,320 Speaker 1: so troubling about her her account. It's this idea of 89 00:05:26,400 --> 00:05:31,320 Speaker 1: it being just part of the infrastructure of the tech industry. 90 00:05:31,360 --> 00:05:34,719 Speaker 1: So in other industries, sure there is sexism and it's awful, 91 00:05:35,000 --> 00:05:37,200 Speaker 1: but the ways in which she makes it sounds foundational 92 00:05:37,240 --> 00:05:40,000 Speaker 1: to the tech industry, where people don't even think twice 93 00:05:40,000 --> 00:05:42,359 Speaker 1: about it. It's not even something that's going on behind 94 00:05:42,400 --> 00:05:45,200 Speaker 1: closed doors or an open secret. She's talking about men 95 00:05:45,240 --> 00:05:48,400 Speaker 1: putting a hand on her thigh in public. She's talking 96 00:05:48,440 --> 00:05:50,760 Speaker 1: about a culture where women know to protect their drinks 97 00:05:50,800 --> 00:05:53,440 Speaker 1: because God only knows what someone's gonna do to it. 98 00:05:53,440 --> 00:05:55,960 Speaker 1: It's it's this idea of it being out in the 99 00:05:56,040 --> 00:05:58,440 Speaker 1: open and having it be an accepted part of culture 100 00:05:58,560 --> 00:06:00,640 Speaker 1: that people are no longer even I'm going to call 101 00:06:00,680 --> 00:06:05,240 Speaker 1: out and perhaps don't even find troubling or problematic or toxic. Exactly. 102 00:06:05,760 --> 00:06:08,560 Speaker 1: In the article, she interviews Susan Woo, an entrepreneur and 103 00:06:08,680 --> 00:06:12,200 Speaker 1: investor who after years of just ignoring the bs and 104 00:06:12,240 --> 00:06:15,120 Speaker 1: trying to fly above it, right the idea of don't 105 00:06:15,240 --> 00:06:17,800 Speaker 1: let the you know bastards get you down right, just 106 00:06:18,040 --> 00:06:20,680 Speaker 1: keep focused on your work and really ignoring the kind 107 00:06:20,680 --> 00:06:24,040 Speaker 1: of overt and not so overt sexism that she witnessed 108 00:06:24,480 --> 00:06:27,760 Speaker 1: in the workplace, things like being asked to fetch coffee 109 00:06:27,800 --> 00:06:31,520 Speaker 1: even though you're the senior developmental engineer on this project, 110 00:06:31,760 --> 00:06:35,360 Speaker 1: or overhearing that hiring women or people of color entailed 111 00:06:35,640 --> 00:06:39,200 Speaker 1: quote lowering the bar, or this idea that women still 112 00:06:39,240 --> 00:06:44,359 Speaker 1: felt silenced or attacked when expressing their opinions online, through 113 00:06:44,680 --> 00:06:48,440 Speaker 1: tech bass or in the office. She finally says that 114 00:06:48,520 --> 00:06:51,760 Speaker 1: something inside of her broke. She goes quote maybe it 115 00:06:51,839 --> 00:06:55,640 Speaker 1: was at tech conferences and hearing herself the quote elder 116 00:06:55,760 --> 00:06:59,599 Speaker 1: stateswoman warning younger women to cover up their drinks because 117 00:06:59,640 --> 00:07:04,080 Speaker 1: such confernces, known for alcohol after parties and hot women 118 00:07:04,120 --> 00:07:07,839 Speaker 1: at product booths, had been breeding grounds for unwanted sexual 119 00:07:07,880 --> 00:07:10,840 Speaker 1: advances and assaults, and you never knew whether some jerk 120 00:07:11,080 --> 00:07:14,240 Speaker 1: might put something in your cocktail. At one party, the 121 00:07:14,240 --> 00:07:16,960 Speaker 1: founder of a startup told Woo she needed to spend 122 00:07:17,200 --> 00:07:20,440 Speaker 1: intimate time with him to get in on his deal, 123 00:07:20,920 --> 00:07:24,240 Speaker 1: and angel investor leading a different deal told her something similar. 124 00:07:24,800 --> 00:07:29,120 Speaker 1: She became the master of warm but firm self extrication. 125 00:07:29,960 --> 00:07:34,400 Speaker 1: And really, just like you were saying, Bridget, these continuous 126 00:07:34,560 --> 00:07:38,840 Speaker 1: unwanted sexual advances put senior women in tech in this 127 00:07:39,080 --> 00:07:42,320 Speaker 1: very bizarre position. Well, I also think what's kind of 128 00:07:42,480 --> 00:07:45,240 Speaker 1: telling is that she talks about how most women in 129 00:07:45,280 --> 00:07:48,080 Speaker 1: tech have learned how to hone this specific skill of 130 00:07:48,240 --> 00:07:51,840 Speaker 1: brushing off a man's sexual advances without hurting his ego. 131 00:07:52,040 --> 00:07:53,880 Speaker 1: So imagine if that was if you worked in an 132 00:07:53,920 --> 00:07:56,120 Speaker 1: industry where that was a skill that women just knew 133 00:07:56,160 --> 00:07:58,320 Speaker 1: they had to develop if they wanted to get ahead. 134 00:07:58,520 --> 00:08:01,440 Speaker 1: And she describes this this you know, this land of 135 00:08:01,880 --> 00:08:04,720 Speaker 1: Angela investors and people who are who have the possibility 136 00:08:04,760 --> 00:08:07,160 Speaker 1: to give you money to fund your great project, and 137 00:08:07,640 --> 00:08:09,720 Speaker 1: all of that kind of hinges on your ability to 138 00:08:09,800 --> 00:08:12,040 Speaker 1: make him not feel like his ego as a bruise 139 00:08:12,080 --> 00:08:13,960 Speaker 1: if you don't want to have sex with him. Yeah, 140 00:08:14,000 --> 00:08:16,400 Speaker 1: I mean that is that's not covered in the soft 141 00:08:16,440 --> 00:08:20,960 Speaker 1: skills classes that I don't I think it is, And 142 00:08:21,080 --> 00:08:23,080 Speaker 1: why the hell should it be. You know, what I'm 143 00:08:23,120 --> 00:08:27,040 Speaker 1: saying really shouldn't have to be now, you might be thinking, okay, Emily, okay, 144 00:08:27,040 --> 00:08:29,560 Speaker 1: bridgets So what So We've got a handful of women's 145 00:08:29,560 --> 00:08:34,360 Speaker 1: stories who are facing unwanted sexual advances and being hit 146 00:08:34,440 --> 00:08:40,439 Speaker 1: on by their colleagues. That's pretty standard for the workplace, right. Unfortunately, 147 00:08:41,040 --> 00:08:45,680 Speaker 1: there's a lot of data coming out, especially recently from 148 00:08:45,720 --> 00:08:48,680 Speaker 1: this new study done called the Elephant in the Valley, 149 00:08:49,000 --> 00:08:53,640 Speaker 1: groundbreaking survey that interviewed over two hundred women, each with 150 00:08:53,720 --> 00:08:57,200 Speaker 1: a decade of experience in tech. These are basically senior 151 00:08:57,360 --> 00:09:02,520 Speaker 1: tech women of whom currently reside in Silicon Valley, and 152 00:09:02,559 --> 00:09:07,560 Speaker 1: the findings were dramatic, so pretty much sexism is alive 153 00:09:07,720 --> 00:09:11,640 Speaker 1: and well in the valley. Surprising, nobody of women have 154 00:09:11,679 --> 00:09:15,320 Speaker 1: been told they're too aggressive, have witnessed sexist behavior at 155 00:09:15,360 --> 00:09:18,640 Speaker 1: a company or an industry conference. Eight percent of witness 156 00:09:18,679 --> 00:09:21,960 Speaker 1: clients or colleagues ask questions to their male colleagues that 157 00:09:21,960 --> 00:09:24,640 Speaker 1: should have been addressed to them eight seven percent I've 158 00:09:24,640 --> 00:09:27,920 Speaker 1: received a meeting comments from male colleagues. Sixty of women 159 00:09:27,960 --> 00:09:31,640 Speaker 1: in tech have reported unwanted sexual advances. That number, to 160 00:09:31,720 --> 00:09:35,240 Speaker 1: me is staggering, more than half. I mean, I always 161 00:09:35,240 --> 00:09:38,400 Speaker 1: think about the up and arms rallying cries we're hearing 162 00:09:38,400 --> 00:09:42,520 Speaker 1: about sexual assault on college campuses of being epidemic levels. 163 00:09:42,960 --> 00:09:46,480 Speaker 1: Then you look at these adult human beings who are 164 00:09:46,520 --> 00:09:51,800 Speaker 1: supposed to be mature enough to treat each other fairly. Oh, 165 00:09:51,800 --> 00:09:55,880 Speaker 1: by the way, sexual assault is a crime, right, We're 166 00:09:55,880 --> 00:09:59,360 Speaker 1: talking about a criminal situation here. Criminal behavior is just 167 00:09:59,520 --> 00:10:02,120 Speaker 1: a thing thing that's happening in this industry. Yeah, that 168 00:10:02,200 --> 00:10:05,320 Speaker 1: over half of women in tech report happening. Now here's 169 00:10:05,320 --> 00:10:09,400 Speaker 1: the scary part. Of those six who reported these unwanted 170 00:10:09,440 --> 00:10:14,080 Speaker 1: sexual advances, sixty five percent of them received those advances 171 00:10:14,520 --> 00:10:18,520 Speaker 1: from a superior And this is where the power dynamic 172 00:10:18,600 --> 00:10:23,319 Speaker 1: in tech is so huge. Tech is disrupting the world 173 00:10:23,760 --> 00:10:27,439 Speaker 1: those people have rightfully, so in some ways a god 174 00:10:27,520 --> 00:10:32,240 Speaker 1: complex the vcs involved in funding the future of how 175 00:10:32,320 --> 00:10:36,320 Speaker 1: the entire world works. You know, this hyperbolic kind of 176 00:10:36,600 --> 00:10:40,480 Speaker 1: Steve Jobs esque We're going to change the world. Is 177 00:10:40,520 --> 00:10:43,240 Speaker 1: that that's what they eat for breakfast every day. So 178 00:10:43,280 --> 00:10:47,200 Speaker 1: these pseudo gods among men come into the office feeling 179 00:10:47,240 --> 00:10:50,000 Speaker 1: like I am personally responsible for changing how the entire 180 00:10:50,040 --> 00:10:53,440 Speaker 1: world works. I'm personally going to fund this. So of 181 00:10:53,480 --> 00:10:56,720 Speaker 1: course I'm entitled to the bodies of the women I 182 00:10:56,720 --> 00:10:58,880 Speaker 1: am around. Why that is so messed up to me 183 00:10:59,040 --> 00:11:02,320 Speaker 1: is that the the certainly everybody who is in a 184 00:11:02,320 --> 00:11:06,080 Speaker 1: lot of these spaces is smart, capable, you know, great thinkers, 185 00:11:06,120 --> 00:11:08,840 Speaker 1: all of that. Why is it that men they get 186 00:11:08,840 --> 00:11:12,080 Speaker 1: the god complex? Women they're just as I mean, if 187 00:11:12,120 --> 00:11:14,800 Speaker 1: you're a woman working at Google, you're smart and capable 188 00:11:14,840 --> 00:11:16,680 Speaker 1: and great. Why are we just giving Why are we 189 00:11:16,720 --> 00:11:19,200 Speaker 1: just making space for men to have this god complex 190 00:11:19,200 --> 00:11:23,280 Speaker 1: when the goddess complex, Yeah, we do. I love it. 191 00:11:23,280 --> 00:11:27,280 Speaker 1: It's like instead of the impostor syndrome, a goddess and 192 00:11:27,480 --> 00:11:30,440 Speaker 1: goddess syndrome. Much that's much, that's much less troubling. For 193 00:11:30,840 --> 00:11:35,400 Speaker 1: that describes my We're going to change the world one 194 00:11:35,440 --> 00:11:39,320 Speaker 1: sminty podcast. Yeah, sminty listeners, y'all are all goddesses. Yeah, 195 00:11:39,360 --> 00:11:43,480 Speaker 1: I love it. Um. Furthermore, looking even deeper into these 196 00:11:43,520 --> 00:11:48,400 Speaker 1: women who have experienced such assault and unwanted advances, fifty 197 00:11:48,800 --> 00:11:51,720 Speaker 1: of them reported this kind of thing happening more than once, 198 00:11:52,080 --> 00:11:54,680 Speaker 1: so this isn't a complete rarity. Over the course of 199 00:11:54,760 --> 00:11:59,760 Speaker 1: their careers, thirty three percent of them have feared for 200 00:12:00,040 --> 00:12:05,480 Speaker 1: their personal safety during and or because of a work circumstance. 201 00:12:05,760 --> 00:12:08,960 Speaker 1: That's awful. So if we want to unleash the full 202 00:12:09,040 --> 00:12:14,400 Speaker 1: potential of our geniuses and tech. But thirty of these 203 00:12:14,440 --> 00:12:17,160 Speaker 1: women who are reporting assaults are in fear for their 204 00:12:17,160 --> 00:12:20,360 Speaker 1: personal safety. Are those the kind of working conditions that 205 00:12:20,400 --> 00:12:22,760 Speaker 1: are going to set our men and women to achieve 206 00:12:22,760 --> 00:12:25,400 Speaker 1: their full potential in disrupting the world. Of course not. 207 00:12:25,480 --> 00:12:28,160 Speaker 1: And I also think, I mean, nobody deserves to feel 208 00:12:28,360 --> 00:12:31,320 Speaker 1: unsafe or scared for their own personal safety while they're 209 00:12:31,320 --> 00:12:34,440 Speaker 1: at work, exactly. And here's the deal. It's not just 210 00:12:34,720 --> 00:12:40,520 Speaker 1: overt and seemingly frequent sexual assaults and harassment that's happening. 211 00:12:40,600 --> 00:12:44,200 Speaker 1: It doesn't have to be that overt for unconscious bias 212 00:12:44,280 --> 00:12:47,720 Speaker 1: and sexism that's pervasive in tech to be really problematic. 213 00:12:48,240 --> 00:12:52,200 Speaker 1: Just imagine, like many of these senior women in tech 214 00:12:52,240 --> 00:12:57,960 Speaker 1: have reported, your credentials being constantly called into question, this 215 00:12:58,040 --> 00:13:03,000 Speaker 1: idea of senior engineers being told in interviews, you know, 216 00:13:03,320 --> 00:13:06,240 Speaker 1: I'm just not sure you have the technical experience, even 217 00:13:06,280 --> 00:13:08,920 Speaker 1: though you're a full stack developer, even though you've been 218 00:13:08,920 --> 00:13:12,600 Speaker 1: working in that capacity for a decade. Maybe you should 219 00:13:12,640 --> 00:13:15,720 Speaker 1: look at something more in marketing. And I think that's 220 00:13:15,800 --> 00:13:19,040 Speaker 1: it's just so disgusting because coupled with all of this 221 00:13:19,160 --> 00:13:22,400 Speaker 1: overt gross sexual harassment that is rampant in these places. 222 00:13:22,640 --> 00:13:25,000 Speaker 1: On top of that, also having your credentials being called 223 00:13:25,000 --> 00:13:27,320 Speaker 1: into question day in and day. It is like a 224 00:13:27,400 --> 00:13:29,480 Speaker 1: death of a by a thousand cuts, you know, where 225 00:13:29,520 --> 00:13:31,920 Speaker 1: it's so many little things happening all at once, and 226 00:13:32,000 --> 00:13:34,720 Speaker 1: after a while it just breaks you, exactly. It starts 227 00:13:34,760 --> 00:13:37,440 Speaker 1: to chip away your own sense of self and confidence. 228 00:13:37,440 --> 00:13:41,480 Speaker 1: And that's the that's the power of microaggressions. Now, beyond 229 00:13:41,480 --> 00:13:44,320 Speaker 1: your credentials being called into question, there's also the element 230 00:13:44,480 --> 00:13:48,600 Speaker 1: of your mere presence in the office is kind of 231 00:13:48,720 --> 00:13:51,679 Speaker 1: raining on our boys parade. And I remember being the 232 00:13:51,760 --> 00:13:55,920 Speaker 1: only woman on a campaign, a congressional campaign, and literally 233 00:13:55,960 --> 00:13:58,560 Speaker 1: one of my colleagues saying to me, when you got 234 00:13:58,600 --> 00:14:01,120 Speaker 1: to the office, when you joined the team, you really 235 00:14:01,120 --> 00:14:03,560 Speaker 1: messed everything up. You messed our whole vibe up. And 236 00:14:03,559 --> 00:14:05,400 Speaker 1: I was like, really, shoute, you're going to be that 237 00:14:05,480 --> 00:14:07,520 Speaker 1: over he was. He thought he was being friendly, and 238 00:14:07,679 --> 00:14:09,480 Speaker 1: he thought he was being kind for telling me that, Well, 239 00:14:09,480 --> 00:14:11,800 Speaker 1: this is my thing. I don't trust any industry where 240 00:14:11,800 --> 00:14:13,880 Speaker 1: they're not a lot of women in high up rankings, 241 00:14:13,920 --> 00:14:16,640 Speaker 1: and you look at them, the military, the NFL. If 242 00:14:16,679 --> 00:14:18,400 Speaker 1: you have an organization. There's not a lot of women 243 00:14:18,440 --> 00:14:21,680 Speaker 1: around making choices and being decision makers. Men left to 244 00:14:21,720 --> 00:14:23,240 Speaker 1: their own devices. I don't trust it. I don't know 245 00:14:23,320 --> 00:14:25,160 Speaker 1: how I don't I don't know how many industries you're 246 00:14:25,160 --> 00:14:31,200 Speaker 1: gonna trust them because it's not so This amazing excerpt 247 00:14:31,280 --> 00:14:36,600 Speaker 1: from Ellen Powe's forthcoming groundbreaking book, and don't you worry. 248 00:14:36,640 --> 00:14:38,960 Speaker 1: We're gonna talk more about Ellen pou. She you might 249 00:14:39,000 --> 00:14:41,640 Speaker 1: recognize her name as someone who was involved in a 250 00:14:41,800 --> 00:14:46,480 Speaker 1: very intense lawsuit on this very issue. But for now, 251 00:14:46,520 --> 00:14:48,720 Speaker 1: before we dive into those details, I just want to 252 00:14:48,760 --> 00:14:51,720 Speaker 1: sample from one of the chapters that was released through 253 00:14:51,880 --> 00:14:55,080 Speaker 1: the cut in New York Magazine, and she described that 254 00:14:55,120 --> 00:14:57,320 Speaker 1: feeling of just not the boys not wanting her to 255 00:14:57,400 --> 00:15:01,040 Speaker 1: be around. Once she was airborne on a plane with 256 00:15:01,360 --> 00:15:04,280 Speaker 1: the CEO of a company and a couple of her 257 00:15:04,320 --> 00:15:08,280 Speaker 1: other VC partners and co workers in the firm that 258 00:15:08,320 --> 00:15:11,880 Speaker 1: she was working on. Here's here's what went down. Quote. 259 00:15:12,200 --> 00:15:14,640 Speaker 1: Once we were airborne, the CEO, who brought along a 260 00:15:14,680 --> 00:15:18,320 Speaker 1: few bottles of wine, started bragging about meeting Jenna Jamison, 261 00:15:18,800 --> 00:15:21,880 Speaker 1: talking about her career as the world's greatest porn star 262 00:15:22,000 --> 00:15:23,800 Speaker 1: and how he had taken a photo with her at 263 00:15:23,800 --> 00:15:26,800 Speaker 1: the Playboy Mansion. He asked if I knew who she was, 264 00:15:27,080 --> 00:15:29,640 Speaker 1: and then proceeded to describe her pay per view series, 265 00:15:29,920 --> 00:15:33,800 Speaker 1: Jenna's American Sex Star, on which women competed for porn 266 00:15:33,840 --> 00:15:38,360 Speaker 1: movie contracts by performing sex acts before a live audience. Nope, 267 00:15:38,520 --> 00:15:42,360 Speaker 1: I said, not a show I'm familiar with. Then the 268 00:15:42,400 --> 00:15:46,080 Speaker 1: CEO switched topics to sex workers. He asked Ted what 269 00:15:46,200 --> 00:15:49,680 Speaker 1: kind of girls he liked. Ted said he preferred white girls, 270 00:15:50,080 --> 00:15:54,040 Speaker 1: Eastern European to be specific. Eventually we all moved to 271 00:15:54,040 --> 00:15:56,840 Speaker 1: the couch for a working session to help the text CEO. 272 00:15:56,960 --> 00:15:59,200 Speaker 1: He was trying to recruit a woman to his all 273 00:15:59,280 --> 00:16:02,600 Speaker 1: male board. I suggested Marissa Mayor, but the CEO looked 274 00:16:02,640 --> 00:16:05,720 Speaker 1: at me dismissively and said, nah, too controversial. Then he 275 00:16:05,800 --> 00:16:08,000 Speaker 1: grinned at Ted and added, though I would let her 276 00:16:08,120 --> 00:16:13,520 Speaker 1: join the board because she's hot, and then she says Somehow, 277 00:16:13,560 --> 00:16:16,800 Speaker 1: I got the distinct vibe that the group couldn't wait 278 00:16:16,960 --> 00:16:19,720 Speaker 1: to ditch me, and once we landed at teder Borough, 279 00:16:20,040 --> 00:16:21,800 Speaker 1: the guys made plans to go to a club while 280 00:16:21,840 --> 00:16:25,040 Speaker 1: I headed into Manhattan alone. Taking your seat at the 281 00:16:25,080 --> 00:16:27,880 Speaker 1: table doesn't work so well, I thought, when no one 282 00:16:28,120 --> 00:16:32,560 Speaker 1: wants you there, that is so powerful and awful and disgusting. 283 00:16:34,160 --> 00:16:36,880 Speaker 1: So that's the kind of vibe that we're picking up 284 00:16:36,920 --> 00:16:42,360 Speaker 1: from countless senior women in tech, that this is a 285 00:16:42,400 --> 00:16:47,240 Speaker 1: wild wild West mad Men era like vibe that is 286 00:16:47,280 --> 00:16:50,720 Speaker 1: overtly hostile to women. And what strikes me about that 287 00:16:50,760 --> 00:16:53,200 Speaker 1: story is that these guys seemingly did not think that 288 00:16:53,280 --> 00:16:55,600 Speaker 1: was a problematic thing. They didn't think, oh, I probably 289 00:16:55,600 --> 00:16:57,440 Speaker 1: shouldn't be saying this. I had a few drinks, it 290 00:16:57,520 --> 00:17:00,920 Speaker 1: wasn't They didn't even maybe feel the need you walk 291 00:17:00,920 --> 00:17:03,320 Speaker 1: it back or not at all. That's It's just it's 292 00:17:03,320 --> 00:17:05,760 Speaker 1: just an open part of this climate. And these people 293 00:17:05,800 --> 00:17:11,320 Speaker 1: are extremely powerful, extremely wealthy, extremely well connected, and can 294 00:17:11,359 --> 00:17:13,639 Speaker 1: you just imagine having to humor them along and like 295 00:17:14,280 --> 00:17:16,560 Speaker 1: try to be pleasant while this is the kind of 296 00:17:17,160 --> 00:17:20,760 Speaker 1: status quo conversation to be expected. Now here's the thing. 297 00:17:21,240 --> 00:17:24,439 Speaker 1: Women are leaving this field at more than twice the 298 00:17:24,520 --> 00:17:27,919 Speaker 1: rate of men. That is the glaring statistic to me 299 00:17:27,960 --> 00:17:32,439 Speaker 1: that I find especially problematic. And whether this culture of 300 00:17:32,880 --> 00:17:37,960 Speaker 1: unconscious gender bias is overt or covert, whether it's involving 301 00:17:38,119 --> 00:17:43,240 Speaker 1: malintent or not, the reality is that quote studies show 302 00:17:43,760 --> 00:17:46,280 Speaker 1: that women who work in tech are interrupted in meetings 303 00:17:46,280 --> 00:17:49,480 Speaker 1: more often than men, they're evaluated on their personality in 304 00:17:49,520 --> 00:17:51,720 Speaker 1: a way that men are not. They are less likely 305 00:17:51,760 --> 00:17:55,560 Speaker 1: to get funding from venture capitalists, who studies also show 306 00:17:55,760 --> 00:18:00,960 Speaker 1: find pitches delivered by men, especially handsome men, more persuasive, 307 00:18:01,440 --> 00:18:06,119 Speaker 1: and in a particularly cruel irony. Women's contributions to open 308 00:18:06,160 --> 00:18:09,879 Speaker 1: source software are accepted more often than men's are, but 309 00:18:10,040 --> 00:18:13,600 Speaker 1: only when their gender is unknown. Wow. This reminds me 310 00:18:13,640 --> 00:18:15,760 Speaker 1: so much of that of those two women who recently 311 00:18:15,920 --> 00:18:18,760 Speaker 1: revealed that they had come up with a fake male 312 00:18:19,320 --> 00:18:22,040 Speaker 1: CEO of their organization to get and it helped them 313 00:18:22,040 --> 00:18:25,280 Speaker 1: get funding. I can only imagine what it must be 314 00:18:25,320 --> 00:18:27,320 Speaker 1: like to be in a field where you know, the 315 00:18:27,400 --> 00:18:30,000 Speaker 1: lay of the land is so crappy for women that 316 00:18:30,040 --> 00:18:32,520 Speaker 1: you have to have all of these really clever ways 317 00:18:32,560 --> 00:18:35,280 Speaker 1: of navigating the space because it's just so awful. There's 318 00:18:35,280 --> 00:18:38,000 Speaker 1: no other way to get ahead, exactly. And some would say, 319 00:18:38,000 --> 00:18:41,800 Speaker 1: wait a second, aren't women just leaving tech because they 320 00:18:41,840 --> 00:18:44,240 Speaker 1: want to drop out to become parents or they want 321 00:18:44,240 --> 00:18:46,879 Speaker 1: to spend more time in their personal lives? And what 322 00:18:46,920 --> 00:18:49,240 Speaker 1: would you say to that? I can understand why people 323 00:18:49,280 --> 00:18:51,959 Speaker 1: would think that, but the research says otherwise, it's actually 324 00:18:51,960 --> 00:18:54,399 Speaker 1: not why women are dropping out. They're actually going to 325 00:18:54,480 --> 00:18:56,960 Speaker 1: other fields where they can use their their hard tech 326 00:18:57,000 --> 00:18:59,879 Speaker 1: skills in other ways. A report from the Center for 327 00:19:00,040 --> 00:19:02,280 Speaker 1: Talent Innovation found that when women drop out of tech, 328 00:19:02,440 --> 00:19:04,920 Speaker 1: it's usually not for family reasons, nor do they drop 329 00:19:04,920 --> 00:19:07,320 Speaker 1: out because they dislike the work. To the contrary, they 330 00:19:07,400 --> 00:19:09,800 Speaker 1: enjoy it and in many cases take jobs and sectors 331 00:19:09,880 --> 00:19:12,480 Speaker 1: where they can use their tech skills. Rather, the report 332 00:19:12,560 --> 00:19:15,720 Speaker 1: concludes that quote workplace conditions, a lack of access to 333 00:19:15,840 --> 00:19:18,520 Speaker 1: key creative roles, and a sense of feel installed in 334 00:19:18,520 --> 00:19:21,679 Speaker 1: one's career are the main reasons why women leave. Undermining 335 00:19:21,720 --> 00:19:25,000 Speaker 1: behavior for managers is also a major factor. So basically, 336 00:19:25,240 --> 00:19:27,200 Speaker 1: these women are not dropping out of the workforce to 337 00:19:27,280 --> 00:19:30,640 Speaker 1: be to, you know, start families, become parents. They're staying 338 00:19:30,720 --> 00:19:34,200 Speaker 1: in sectors that allow them to use their incredibly specialized skills, 339 00:19:34,480 --> 00:19:35,919 Speaker 1: but they just don't want to do it in an 340 00:19:35,920 --> 00:19:38,880 Speaker 1: industry that treats them like crap. Right, And that's why 341 00:19:39,200 --> 00:19:43,040 Speaker 1: so many tech companies right now are struggling to figure 342 00:19:43,040 --> 00:19:47,240 Speaker 1: out how can we adapt, what can we do to 343 00:19:47,440 --> 00:19:50,480 Speaker 1: make what is already a very appealing sector in terms 344 00:19:50,520 --> 00:19:53,000 Speaker 1: of the kind of salaries that you can get, the 345 00:19:53,040 --> 00:19:55,800 Speaker 1: kind of benefits, the kind of workplace flexibility that's offered 346 00:19:55,800 --> 00:19:59,760 Speaker 1: at tech companies. All of those benefits are pretty compelling 347 00:19:59,800 --> 00:20:02,919 Speaker 1: and pretty attractive. So how can we make sure that 348 00:20:02,960 --> 00:20:08,320 Speaker 1: our culture isn't driving women in tech away? So when 349 00:20:08,359 --> 00:20:10,639 Speaker 1: we come back, we're going to share the inspiring and 350 00:20:10,680 --> 00:20:15,119 Speaker 1: truly courageous actions that are being taken by women in 351 00:20:15,320 --> 00:20:18,639 Speaker 1: tech who won't stand idly by and let the status 352 00:20:18,720 --> 00:20:21,960 Speaker 1: quo persist. And it's really due to whistleblowers like these 353 00:20:21,960 --> 00:20:25,880 Speaker 1: women that companies are starting to not just make promises, 354 00:20:26,160 --> 00:20:28,840 Speaker 1: but really put their money where their mouths are on this. 355 00:20:29,200 --> 00:20:31,760 Speaker 1: We'll be right back after a quick word from our sponsors, 356 00:20:39,720 --> 00:20:43,840 Speaker 1: and we are back, and like you listeners are, blood 357 00:20:43,880 --> 00:20:47,960 Speaker 1: is boiling thinking about what it must feel like to 358 00:20:47,960 --> 00:20:51,240 Speaker 1: be a woman in STEM who's constantly questioned on your credentials, 359 00:20:51,280 --> 00:20:54,440 Speaker 1: on your mere presence in the office being somewhat annoying 360 00:20:54,480 --> 00:20:57,760 Speaker 1: to your male colleagues, if not the subject of their 361 00:20:57,800 --> 00:21:01,600 Speaker 1: sexual desire. But before where we go any further, we 362 00:21:01,800 --> 00:21:04,879 Speaker 1: have to talk about Ellen Pow and the lawsuit that 363 00:21:04,960 --> 00:21:08,840 Speaker 1: she filed after working for six years at the Silicon 364 00:21:08,960 --> 00:21:13,959 Speaker 1: Valley VC firm Kleiner Perkins, Caufield and Buyers. She had 365 00:21:14,000 --> 00:21:16,880 Speaker 1: been a junior partner and chief of staff for managing 366 00:21:16,960 --> 00:21:20,399 Speaker 1: partner John Doer and Kleiner. The firm she was working 367 00:21:20,440 --> 00:21:23,719 Speaker 1: at was then one of the three most powerful venture 368 00:21:23,920 --> 00:21:27,480 Speaker 1: capital firms in the world. So this VC firm was 369 00:21:27,520 --> 00:21:32,639 Speaker 1: involved in hugely disruptive tech companies and making those ideas possible. 370 00:21:33,040 --> 00:21:35,600 Speaker 1: Pal published a sample chapter from her book in The 371 00:21:35,640 --> 00:21:38,639 Speaker 1: Cut dot com describing some of the sexual harassment that 372 00:21:38,680 --> 00:21:42,600 Speaker 1: she faced while working there, and her reports are so troubling. 373 00:21:42,920 --> 00:21:45,800 Speaker 1: She says, seven months later, I would sue Kleiner Perkins 374 00:21:45,840 --> 00:21:49,120 Speaker 1: for sexual harassment and discrimination in a widely publicized case 375 00:21:49,160 --> 00:21:53,280 Speaker 1: in which I would often be cast as the villain. Incompetent, greedy, aggressive, 376 00:21:53,480 --> 00:21:56,240 Speaker 1: in cold My husband and I were both directed the mud. 377 00:21:56,400 --> 00:21:59,560 Speaker 1: Are privacy destroyed. It's really shocking to me how she 378 00:21:59,600 --> 00:22:02,920 Speaker 1: described was starting out being so happy about this job. 379 00:22:03,200 --> 00:22:05,719 Speaker 1: She talks about the job description sounded like it was 380 00:22:05,800 --> 00:22:09,240 Speaker 1: basically her resume, but then she finds all of these 381 00:22:09,280 --> 00:22:12,520 Speaker 1: cracks in what seemed to be this great situation. She 382 00:22:12,560 --> 00:22:16,680 Speaker 1: says that her boss specifically requested an Asian woman because 383 00:22:16,720 --> 00:22:19,560 Speaker 1: he liked the idea of a tiger mom. I can't 384 00:22:19,600 --> 00:22:21,919 Speaker 1: imagine hearing that and then still feeling good about a 385 00:22:22,000 --> 00:22:23,440 Speaker 1: job that I was supposed to be doing, And it 386 00:22:23,520 --> 00:22:27,679 Speaker 1: was like an early warning. She goes on to say, 387 00:22:27,760 --> 00:22:30,320 Speaker 1: sometimes the whole world felt like a nerdy frat house. 388 00:22:30,560 --> 00:22:32,840 Speaker 1: People in the venture world spoke so fondly about the 389 00:22:32,840 --> 00:22:35,560 Speaker 1: early shenanigans at big companies. A friend told me how 390 00:22:35,600 --> 00:22:38,560 Speaker 1: I used to sublit an office space for Facebook only 391 00:22:38,560 --> 00:22:40,520 Speaker 1: if I'm people having sex there on the floor in 392 00:22:40,560 --> 00:22:42,919 Speaker 1: the main public area, and that was looked back on 393 00:22:42,960 --> 00:22:46,080 Speaker 1: it with like delightful charm and nostalgia, like, isn't this 394 00:22:46,119 --> 00:22:48,800 Speaker 1: a fun workplace environment? Remember wh when I used to 395 00:22:48,840 --> 00:22:50,760 Speaker 1: have sex on the floor in public at work, the 396 00:22:50,800 --> 00:22:54,600 Speaker 1: good old days before the women came in and ruined 397 00:22:54,640 --> 00:22:57,160 Speaker 1: it all. I mean, that's that's really the vibe you get, 398 00:22:57,240 --> 00:22:59,920 Speaker 1: is that it is like a frat party, and that's 399 00:23:00,040 --> 00:23:02,920 Speaker 1: almost what's appealing about it. There's almost an element of 400 00:23:03,960 --> 00:23:08,720 Speaker 1: the mythology of early startup culture like Facebook's rise. Having 401 00:23:08,720 --> 00:23:10,840 Speaker 1: worked in Silicon Valley, what I see time and time 402 00:23:10,880 --> 00:23:14,359 Speaker 1: again being lifted in her story is that in Silicon Valley, 403 00:23:14,680 --> 00:23:17,800 Speaker 1: the early kind of rag tag wild world West. People 404 00:23:17,840 --> 00:23:21,520 Speaker 1: don't look back on those times with disgust or horror 405 00:23:21,600 --> 00:23:23,480 Speaker 1: or saying, oh, gee, we sure didn't have it figured out. 406 00:23:23,520 --> 00:23:25,920 Speaker 1: Then they look back on it with glee and with 407 00:23:25,920 --> 00:23:28,480 Speaker 1: with they think it's very charming. And so plenty of 408 00:23:28,480 --> 00:23:30,639 Speaker 1: startups start and they have kind of a jankie culture. 409 00:23:30,680 --> 00:23:33,159 Speaker 1: They don't have any start department. There are three people 410 00:23:33,280 --> 00:23:35,760 Speaker 1: in a in a basement or whatever. But to look 411 00:23:35,800 --> 00:23:37,959 Speaker 1: back on all the wild times you had when you 412 00:23:37,960 --> 00:23:40,080 Speaker 1: didn't know what you were doing, you should be looking 413 00:23:40,119 --> 00:23:43,120 Speaker 1: back on it at least being a little bit embarrassed, 414 00:23:43,280 --> 00:23:45,199 Speaker 1: not looking back and saying, gee, those were the good 415 00:23:45,200 --> 00:23:47,040 Speaker 1: old days and we could just do whatever and there 416 00:23:47,040 --> 00:23:50,359 Speaker 1: were no rules. Yeah, And and the central characters to 417 00:23:50,400 --> 00:23:52,360 Speaker 1: a lot of those early stories are all men. That's 418 00:23:52,400 --> 00:23:54,439 Speaker 1: the thing. It's like, no one's aware of who's not 419 00:23:54,520 --> 00:23:57,320 Speaker 1: at the table. No one's cognizant of what didn't happen, 420 00:23:57,359 --> 00:23:59,919 Speaker 1: which is getting the input of a diverse array of 421 00:24:00,040 --> 00:24:03,080 Speaker 1: people at the start of our companies, and not every company, 422 00:24:03,119 --> 00:24:05,200 Speaker 1: but a lot of companies. Look at the movie about 423 00:24:05,240 --> 00:24:09,040 Speaker 1: the invention of Facebook, it's like that is seen as cool, 424 00:24:09,119 --> 00:24:12,000 Speaker 1: it's seen as exciting. All of all those shenanigans are 425 00:24:12,040 --> 00:24:13,800 Speaker 1: it's seen as a good thing. And here's the thing, 426 00:24:13,840 --> 00:24:16,560 Speaker 1: the shenanigans you're talking about, they were alive and while 427 00:24:16,680 --> 00:24:19,760 Speaker 1: in her venture capital firm, the third largest in the world, 428 00:24:20,240 --> 00:24:23,480 Speaker 1: she went on a business trip when her colleague, a Jeet, 429 00:24:24,080 --> 00:24:27,640 Speaker 1: basically starts hitting on her pretty aggressively, tries to get 430 00:24:27,680 --> 00:24:31,440 Speaker 1: into her hotel room, is expressing all of this sadness 431 00:24:31,480 --> 00:24:35,080 Speaker 1: over his marriage that's not sexually satisfying, and making the 432 00:24:35,080 --> 00:24:37,720 Speaker 1: case for why they should have an affair or be together. 433 00:24:38,400 --> 00:24:41,679 Speaker 1: She expressed nothing but protestations, tried to get him out 434 00:24:41,720 --> 00:24:45,960 Speaker 1: of her room, and years of this, like continuous kinds 435 00:24:46,040 --> 00:24:49,840 Speaker 1: of hitting on her unwanted sexual advances came to a 436 00:24:49,840 --> 00:24:53,159 Speaker 1: head when he said I left my wife and I 437 00:24:53,200 --> 00:24:55,920 Speaker 1: want to be with you, and she started to think, hey, 438 00:24:55,960 --> 00:24:59,520 Speaker 1: maybe this, maybe this is a relationship I should concede to, 439 00:24:59,600 --> 00:25:02,800 Speaker 1: Maybe this is something I should pursue, only to find 440 00:25:02,840 --> 00:25:06,240 Speaker 1: out after they hooked up that he had actually lied. 441 00:25:06,960 --> 00:25:09,320 Speaker 1: He was very much still married, he was very much 442 00:25:09,359 --> 00:25:12,800 Speaker 1: still his wife. So she cut things off. She was livid, 443 00:25:13,040 --> 00:25:16,879 Speaker 1: and basically she did not have the skill set we 444 00:25:16,960 --> 00:25:19,600 Speaker 1: described at the start of this podcast, which is how 445 00:25:19,640 --> 00:25:23,399 Speaker 1: to sexually reject her mail colleagues without bruising his ego, 446 00:25:23,720 --> 00:25:28,480 Speaker 1: because quite literally, the next morning after the first sexual 447 00:25:28,520 --> 00:25:31,959 Speaker 1: rejection she had to give to this dude, he was livid. 448 00:25:32,200 --> 00:25:35,560 Speaker 1: Quote he stormed off to the airport by himself. So 449 00:25:35,640 --> 00:25:38,840 Speaker 1: she's trying to manage relationships with her colleagues and keep 450 00:25:38,880 --> 00:25:43,880 Speaker 1: things professional. He's trying to get in her pants, And like, 451 00:25:44,080 --> 00:25:47,080 Speaker 1: can you just imagine on top of the million dollar deals, 452 00:25:47,080 --> 00:25:52,159 Speaker 1: you're navigating the potential for total industry disruption that you 453 00:25:52,200 --> 00:25:54,240 Speaker 1: have to worry about that you're also trying to figure 454 00:25:54,240 --> 00:25:57,320 Speaker 1: out how not to piss off your married co worker 455 00:25:57,359 --> 00:25:59,240 Speaker 1: who wants to sleep with you. What resonates with me 456 00:25:59,320 --> 00:26:02,160 Speaker 1: from that story is that not only is it dealing 457 00:26:02,200 --> 00:26:05,560 Speaker 1: with the super overt you know, when your boss shows 458 00:26:05,600 --> 00:26:07,240 Speaker 1: up that your hotel door in a robe in the 459 00:26:07,240 --> 00:26:09,080 Speaker 1: middle of the night, it's all and you have to 460 00:26:09,160 --> 00:26:11,120 Speaker 1: navigate that in a way that doesn't hurt his feelings, 461 00:26:11,400 --> 00:26:14,760 Speaker 1: it's also navigating an entire slew of things that you 462 00:26:14,760 --> 00:26:17,320 Speaker 1: really shouldn't be responsible for on top of your your 463 00:26:17,560 --> 00:26:21,560 Speaker 1: your job. It's not just the overt gross things, it's 464 00:26:21,600 --> 00:26:25,160 Speaker 1: also the less overt navigating all of that just because 465 00:26:25,160 --> 00:26:27,960 Speaker 1: you're a woman exactly. And she goes on to describe 466 00:26:27,960 --> 00:26:31,320 Speaker 1: how she did elevate this issue to HR. Multiple women 467 00:26:31,359 --> 00:26:33,440 Speaker 1: at the firm were being hit on by this colleague. 468 00:26:33,640 --> 00:26:37,080 Speaker 1: Multiple people were feeling harassed. They went to HR. The 469 00:26:37,119 --> 00:26:40,879 Speaker 1: firm clearly was not willing to make the hard decisions 470 00:26:41,280 --> 00:26:43,760 Speaker 1: about actually rectifying the situation so that the women at 471 00:26:43,760 --> 00:26:46,320 Speaker 1: the firm felt safe. She goes on to describe how 472 00:26:46,359 --> 00:26:51,639 Speaker 1: he became increasingly aggressive about getting between her and her business. 473 00:26:52,400 --> 00:26:55,560 Speaker 1: Her deals were being sniped on by her colleague. She 474 00:26:55,720 --> 00:26:58,679 Speaker 1: was being called by some of the ceo s for 475 00:26:58,720 --> 00:27:01,959 Speaker 1: the ventures that she backed. This was like her venture, 476 00:27:02,000 --> 00:27:04,600 Speaker 1: her project, and the CEO would call saying, hey, what 477 00:27:04,680 --> 00:27:08,000 Speaker 1: of your colleagues just called me going around you to 478 00:27:08,040 --> 00:27:11,280 Speaker 1: talk about being a board member and trying to replace you. 479 00:27:11,400 --> 00:27:14,320 Speaker 1: So she's being sniped on at all ends. Life at 480 00:27:14,359 --> 00:27:17,920 Speaker 1: Kleiner got progressively worse for her, She says. At one point, 481 00:27:18,000 --> 00:27:20,439 Speaker 1: I found out the partners had taken some CEOs and 482 00:27:20,480 --> 00:27:23,600 Speaker 1: founders on an all male ski trip. They spent fifty 483 00:27:23,960 --> 00:27:26,879 Speaker 1: dollars on the private jet to and from Veil. I 484 00:27:26,960 --> 00:27:29,840 Speaker 1: was later told that they didn't invite any women, because 485 00:27:30,000 --> 00:27:33,120 Speaker 1: women probably wouldn't want to share a condo with men. 486 00:27:33,840 --> 00:27:36,680 Speaker 1: So she ends up making a claim after an independent 487 00:27:36,720 --> 00:27:41,640 Speaker 1: investigation into widespread sexual assault and discrimination at Kleiner went 488 00:27:41,840 --> 00:27:45,879 Speaker 1: undealt with, completely undealt with by the company itself. And 489 00:27:45,920 --> 00:27:48,680 Speaker 1: her claim, which was twelve pages covering everything that had 490 00:27:48,720 --> 00:27:52,359 Speaker 1: happened to her over seven years at Kleiner, really specified 491 00:27:52,359 --> 00:27:57,520 Speaker 1: gender discrimination in promotion, in pay, retaliation against her after 492 00:27:57,560 --> 00:28:01,399 Speaker 1: she reported that harassment, and basically she says, quote I 493 00:28:01,440 --> 00:28:04,000 Speaker 1: asked for damages to cover the lost pay and prevent 494 00:28:04,080 --> 00:28:07,240 Speaker 1: them from doing it again. Meanwhile, Kleiner had notified me 495 00:28:07,320 --> 00:28:11,359 Speaker 1: that its investigation was done. It's internal investigation, the finding 496 00:28:11,440 --> 00:28:14,040 Speaker 1: was that there'd been no retaliation or discrimination at all. 497 00:28:14,320 --> 00:28:18,320 Speaker 1: Oh isn't that convenient. Basically, here's the sad part of 498 00:28:18,320 --> 00:28:23,280 Speaker 1: the story. They crushed her in court. They absolutely decimated Pal. 499 00:28:23,840 --> 00:28:28,800 Speaker 1: Their attorneys outsmarted Pal's attorneys at every turn. They turned 500 00:28:29,320 --> 00:28:34,960 Speaker 1: her story into a widespread pr campaign against this greedy, selfish, 501 00:28:35,040 --> 00:28:40,840 Speaker 1: cold heartless woman, Ellen Pal. And they created even troll 502 00:28:40,960 --> 00:28:46,800 Speaker 1: farms to create integrated networks of influence used for quote 503 00:28:47,040 --> 00:28:52,440 Speaker 1: reputation management UM. And so basically they said they made 504 00:28:52,480 --> 00:28:55,640 Speaker 1: this case in court that it's the kind of sexism 505 00:28:55,800 --> 00:28:59,520 Speaker 1: you couldn't quite legally proved, right, And I think what's 506 00:28:59,520 --> 00:29:02,400 Speaker 1: really so telling is this bit from her investigation. She says, 507 00:29:02,880 --> 00:29:05,960 Speaker 1: at one point, the investigator asked, in a gotcha tone, Well, 508 00:29:05,960 --> 00:29:07,600 Speaker 1: if they looked down on women so much, and if 509 00:29:07,600 --> 00:29:10,080 Speaker 1: they block you from opportunities, they don't include you at 510 00:29:10,120 --> 00:29:12,120 Speaker 1: their events, why do they even keep you around in 511 00:29:12,160 --> 00:29:14,720 Speaker 1: the first place. And here's what she says. I replied 512 00:29:14,760 --> 00:29:17,720 Speaker 1: slowly as the answer crystallized in my mind. If you 513 00:29:17,760 --> 00:29:21,520 Speaker 1: get the opportunity to have workers who were over educated, underpaid, 514 00:29:21,520 --> 00:29:24,440 Speaker 1: and highly experienced, whom you could dump all the menial 515 00:29:24,480 --> 00:29:26,720 Speaker 1: tax that you didn't want to do on, whom you 516 00:29:26,720 --> 00:29:28,640 Speaker 1: could get to clean up all the problems, and whom 517 00:29:28,680 --> 00:29:31,320 Speaker 1: you could create a second class out of, wouldn't you 518 00:29:31,360 --> 00:29:37,080 Speaker 1: want them to stay? Man? Yeah, that just really nails 519 00:29:37,440 --> 00:29:41,680 Speaker 1: the sort of toxic paradox of what was going on there. 520 00:29:41,960 --> 00:29:44,280 Speaker 1: And after working there, she went on to be the 521 00:29:44,280 --> 00:29:46,720 Speaker 1: CEO of Reddit again sort of came in as this 522 00:29:46,760 --> 00:29:48,560 Speaker 1: person who was supposed to clean up what was a 523 00:29:48,640 --> 00:29:52,320 Speaker 1: really toxic, gross, messed up culture, and she had already 524 00:29:52,400 --> 00:29:56,040 Speaker 1: been set up with this horrible pr campaign about how 525 00:29:56,080 --> 00:29:59,640 Speaker 1: awful she was. She was this greedy, cold person who 526 00:29:59,680 --> 00:30:02,080 Speaker 1: is being sent in to clean up the boys club 527 00:30:02,400 --> 00:30:06,160 Speaker 1: and people even you know everyday rehdators really treated her 528 00:30:06,200 --> 00:30:08,440 Speaker 1: like crap. I think because of how she was set 529 00:30:08,520 --> 00:30:12,800 Speaker 1: up in that lawsuit. I completely agree, And it's so 530 00:30:13,080 --> 00:30:18,440 Speaker 1: depressing to read about this until we realize what the 531 00:30:18,480 --> 00:30:20,800 Speaker 1: long term effect is. And this is part of the 532 00:30:20,800 --> 00:30:25,200 Speaker 1: idea of our justice system being supremely imperfect and not 533 00:30:25,360 --> 00:30:29,160 Speaker 1: always able to deliver equal justice under the law, because 534 00:30:29,240 --> 00:30:32,440 Speaker 1: what was a short term loss for Ellen Powe, in 535 00:30:32,480 --> 00:30:37,000 Speaker 1: my opinion, had a long term positive effect that some 536 00:30:37,040 --> 00:30:40,800 Speaker 1: people even call the Pow effect. She describes it as 537 00:30:40,880 --> 00:30:43,360 Speaker 1: saying that reporters came up to me with a name 538 00:30:43,440 --> 00:30:47,200 Speaker 1: for the phenomenon of women or minorities in tech suing 539 00:30:47,440 --> 00:30:50,640 Speaker 1: or speaking up, because that's what happened after her lawsuit. 540 00:30:50,920 --> 00:30:53,800 Speaker 1: Even though as a failed lawsuit, women in tech were 541 00:30:53,920 --> 00:30:57,680 Speaker 1: leaving the office to watch the hearing, people were tuning 542 00:30:57,720 --> 00:31:01,440 Speaker 1: in bringing their daughters to court to watch the briefings. 543 00:31:01,480 --> 00:31:04,600 Speaker 1: This had a huge impact, especially in Silicon Valley, in 544 00:31:04,720 --> 00:31:08,000 Speaker 1: terms of seeing, Okay, how accountable are we holding VC 545 00:31:08,600 --> 00:31:11,720 Speaker 1: when it comes to harassment and discrimination. Her upcoming book 546 00:31:11,760 --> 00:31:16,720 Speaker 1: called Reset by Alan Powe describes her lawsuit in the 547 00:31:16,760 --> 00:31:21,760 Speaker 1: context of what is developed since when this first went down, 548 00:31:22,480 --> 00:31:26,960 Speaker 1: And I am optimistic in hearing of some of the 549 00:31:27,120 --> 00:31:30,920 Speaker 1: incredibly courageous whistleblowing women who have come after her, notably 550 00:31:31,040 --> 00:31:34,640 Speaker 1: this year. Yeah, when you look at this Ellen Powe 551 00:31:34,720 --> 00:31:37,560 Speaker 1: with that of all these women first of all watching 552 00:31:37,680 --> 00:31:40,680 Speaker 1: her lawsuit on fold, with this kind of hope or 553 00:31:40,680 --> 00:31:43,040 Speaker 1: it's kind of you know what's going to happen attitude, 554 00:31:43,240 --> 00:31:46,120 Speaker 1: and then all of these courageous women who she inspired. 555 00:31:46,160 --> 00:31:48,120 Speaker 1: I think it really goes back to this idea that 556 00:31:48,440 --> 00:31:50,640 Speaker 1: as a woman, or a personal color or any kind 557 00:31:50,680 --> 00:31:53,640 Speaker 1: of marginalized person in this space, when you speak up, 558 00:31:53,680 --> 00:31:57,120 Speaker 1: it obviously has consequences and that's a tough choice. But 559 00:31:57,320 --> 00:31:59,440 Speaker 1: you never know who you're inspiring to do the same. 560 00:31:59,520 --> 00:32:02,200 Speaker 1: You never know what culture shift you are kickstarting. And 561 00:32:02,240 --> 00:32:05,880 Speaker 1: I really think that by having this big public lawsuit 562 00:32:06,080 --> 00:32:08,320 Speaker 1: that she was kick starting a culture shift that we're 563 00:32:08,320 --> 00:32:10,959 Speaker 1: seeing the effects of today. Yeah, And honestly, you have 564 00:32:11,080 --> 00:32:14,400 Speaker 1: to read her book or this chapter in the cut 565 00:32:14,560 --> 00:32:17,680 Speaker 1: to hear just how much of a toll the lawsuit 566 00:32:17,720 --> 00:32:19,840 Speaker 1: took on her I mean she ended up losing a 567 00:32:19,920 --> 00:32:23,520 Speaker 1: pregnancy from it. She was physically ill from the stress 568 00:32:23,560 --> 00:32:26,600 Speaker 1: and the continuous harassment. Not only are we taking about 569 00:32:26,600 --> 00:32:30,640 Speaker 1: professional losses, but her personal life was seriously dragged through 570 00:32:30,680 --> 00:32:33,600 Speaker 1: the mud in a way that was devastating. So the 571 00:32:33,680 --> 00:32:36,800 Speaker 1: sacrifices she made, we have to make sure that they 572 00:32:36,840 --> 00:32:39,440 Speaker 1: pay off. And one of the women whose name comes 573 00:32:39,440 --> 00:32:42,719 Speaker 1: to mind this year, who has carried that torch forward 574 00:32:42,760 --> 00:32:46,920 Speaker 1: of Susan Fowler. You might have read her viral account 575 00:32:47,040 --> 00:32:51,720 Speaker 1: of her workplace experience at uber that went pretty crazy 576 00:32:51,760 --> 00:32:54,280 Speaker 1: on the internet not that long ago, maybe a couple 577 00:32:54,320 --> 00:32:56,920 Speaker 1: of months ago. It was in February of this year, 578 00:32:56,960 --> 00:32:59,920 Speaker 1: and she titled her own blog post reflecting on one 579 00:33:00,240 --> 00:33:03,800 Speaker 1: very very strange year at Uber. In it, she really 580 00:33:03,840 --> 00:33:08,160 Speaker 1: meticulously documented all the kinds of ways in which her 581 00:33:08,800 --> 00:33:13,600 Speaker 1: claims in HR fell on deaf ears, how the systemic 582 00:33:13,680 --> 00:33:18,880 Speaker 1: presence of gender based discrimination made her feel hopelessly held back, 583 00:33:18,920 --> 00:33:21,040 Speaker 1: to the point where she ended up leaving because no 584 00:33:21,080 --> 00:33:24,400 Speaker 1: one was doing anything about the kind of sexual harassment 585 00:33:24,440 --> 00:33:30,160 Speaker 1: she'd been experiencing. And that viral blog post finally ended 586 00:33:30,360 --> 00:33:34,440 Speaker 1: up triggering an internal investigation that led to the firing 587 00:33:34,520 --> 00:33:37,880 Speaker 1: of twenty employees, and shortly thereafter, due to a lot 588 00:33:38,320 --> 00:33:42,320 Speaker 1: of public pressure put on Uber and twenty other women 589 00:33:42,800 --> 00:33:47,200 Speaker 1: at Uber who basically shared their comparable stories. It led 590 00:33:47,400 --> 00:33:51,000 Speaker 1: to Travis Kalani stepping down a CEO. Yeah, I mean 591 00:33:51,160 --> 00:33:53,320 Speaker 1: her her story is incredible. If you haven't read it, 592 00:33:53,320 --> 00:33:55,200 Speaker 1: you should read the whole thing. This may sound like 593 00:33:55,240 --> 00:33:57,959 Speaker 1: a small detail, but something that I that just sticks 594 00:33:58,000 --> 00:34:00,240 Speaker 1: with me from her story. It's just something word talking 595 00:34:00,280 --> 00:34:03,160 Speaker 1: about this idea of the kind of sexism you couldn't 596 00:34:03,160 --> 00:34:06,520 Speaker 1: prove in court. Her story obviously has all kinds of 597 00:34:06,560 --> 00:34:09,120 Speaker 1: super over messed up stuff, but one of the small 598 00:34:09,280 --> 00:34:11,839 Speaker 1: details that I thought, I remember thinking, God, this this 599 00:34:11,960 --> 00:34:14,800 Speaker 1: was really just crap all the way down. The company 600 00:34:14,880 --> 00:34:18,160 Speaker 1: bought special jacket for all the for all the dudes 601 00:34:18,239 --> 00:34:19,879 Speaker 1: on the team, and she didn't get it. They didn't 602 00:34:19,880 --> 00:34:22,279 Speaker 1: get any jackets for the women, and so she was like, Yo, 603 00:34:22,360 --> 00:34:24,600 Speaker 1: can we the women get jackets? And they were like, oh, well, 604 00:34:24,640 --> 00:34:26,800 Speaker 1: if y'all want jackets, you can just pay for them yourself, 605 00:34:27,239 --> 00:34:30,759 Speaker 1: and something about that. Where but in a lawsuit that 606 00:34:30,840 --> 00:34:33,759 Speaker 1: probably would be easy to make it seem like it 607 00:34:33,800 --> 00:34:36,359 Speaker 1: wasn't a big deal, Like, can you imagine if your 608 00:34:36,440 --> 00:34:39,000 Speaker 1: team if if something they got a treat or something 609 00:34:39,040 --> 00:34:41,040 Speaker 1: like that, or a perk or a gift and you 610 00:34:41,160 --> 00:34:43,920 Speaker 1: just didn't get it. Like it's so it's such a 611 00:34:44,000 --> 00:34:48,080 Speaker 1: small thing, but it really highlights how deep and how 612 00:34:48,200 --> 00:34:52,839 Speaker 1: pervasive this culture was exactly, and Susan Fowler's experience sort 613 00:34:52,840 --> 00:34:56,440 Speaker 1: of built on ellen Pow, the ellen Pow effect in 614 00:34:57,000 --> 00:34:59,799 Speaker 1: the verge, there's a really great rundown of this c 615 00:35:00,000 --> 00:35:04,360 Speaker 1: on a logical domino effect that happened saying that quote. Meanwhile, 616 00:35:04,640 --> 00:35:07,160 Speaker 1: what was happening at Uber was echoing across the industry 617 00:35:07,200 --> 00:35:10,320 Speaker 1: and other companies. In late February of female engineer sued 618 00:35:10,360 --> 00:35:14,080 Speaker 1: Tesla for sexual harassment and discrimination and was later fired 619 00:35:14,160 --> 00:35:17,640 Speaker 1: in what she claims was retaliation. Then multiple women came 620 00:35:17,680 --> 00:35:20,880 Speaker 1: forward claiming they had been harassed by venture capitalist Justin 621 00:35:20,960 --> 00:35:25,960 Speaker 1: Caldwell of Binary Capital. The accusations snowballed until called back, resigned, 622 00:35:26,000 --> 00:35:31,279 Speaker 1: and Binary Capital collapsed when investors pulled their funds. They 623 00:35:31,320 --> 00:35:33,440 Speaker 1: go on to say, with the collapse of Binary Capital, 624 00:35:33,480 --> 00:35:36,640 Speaker 1: more women came forward to accuse other venture capitalists like 625 00:35:36,719 --> 00:35:40,600 Speaker 1: Dave McClure and Chris Socca, and it all culminated in 626 00:35:40,640 --> 00:35:43,640 Speaker 1: this big new York Times expose in which senior women 627 00:35:43,640 --> 00:35:49,560 Speaker 1: in tech sat down, named names, got clear about their claims. 628 00:35:49,600 --> 00:35:52,319 Speaker 1: And this kind of whistleblowing didn't happen in a court 629 00:35:52,320 --> 00:35:55,640 Speaker 1: of law. It happened in the public domain. And going 630 00:35:55,760 --> 00:35:57,920 Speaker 1: public with these stories seems to be the only thing 631 00:35:57,960 --> 00:36:01,560 Speaker 1: that's had real impact in rammifications, and it has caused 632 00:36:01,600 --> 00:36:04,400 Speaker 1: some of these guys at least temporary career. What's so 633 00:36:04,440 --> 00:36:06,440 Speaker 1: important about what you just said is that they were 634 00:36:06,440 --> 00:36:09,360 Speaker 1: willing to say who it is. I think the woman 635 00:36:09,520 --> 00:36:12,279 Speaker 1: in fields that have been pretty much male dominated, so 636 00:36:12,400 --> 00:36:14,560 Speaker 1: much of when we share information with each other is 637 00:36:14,600 --> 00:36:17,160 Speaker 1: sort of kind of becomes kind of whisper campaigns. I 638 00:36:17,200 --> 00:36:20,520 Speaker 1: have long said there was power in gossip. Gossip is like, 639 00:36:20,600 --> 00:36:24,200 Speaker 1: don't discount gossip as you know whatever. Frivolous gossip is 640 00:36:24,200 --> 00:36:26,560 Speaker 1: all people who don't have power get the word out. 641 00:36:26,600 --> 00:36:28,920 Speaker 1: And so for a long time in my own industry, 642 00:36:29,120 --> 00:36:32,279 Speaker 1: there have been whisper campaigns about noted sexual harassers. And 643 00:36:32,360 --> 00:36:36,279 Speaker 1: it wasn't until someone vocally said this is who it is, 644 00:36:36,360 --> 00:36:39,600 Speaker 1: this is what happened. Other people say, yeah, it happened 645 00:36:39,600 --> 00:36:41,520 Speaker 1: to me, Yet it happened to me. You don't have 646 00:36:41,600 --> 00:36:44,319 Speaker 1: to have it be you know, whisper campaigns. Watch out 647 00:36:44,320 --> 00:36:46,080 Speaker 1: for this guy, watch out for that guy. When you 648 00:36:46,160 --> 00:36:48,640 Speaker 1: go on the record. It's hard when you name names, 649 00:36:48,680 --> 00:36:51,080 Speaker 1: it's tough. There are consequences, But that is the thing 650 00:36:51,120 --> 00:36:53,600 Speaker 1: that's going to be that first domino that can really 651 00:36:53,640 --> 00:36:58,960 Speaker 1: create lasting culture change. Exactly. I think that this culture 652 00:37:00,040 --> 00:37:04,040 Speaker 1: of sexism thrives in the dark, definitely, and that shedding 653 00:37:04,080 --> 00:37:05,920 Speaker 1: any kind of light on it is the first step 654 00:37:05,960 --> 00:37:08,799 Speaker 1: to reconciling what the heck we're going to do about this. 655 00:37:10,040 --> 00:37:12,840 Speaker 1: So when we come back, we're going to talk about 656 00:37:13,440 --> 00:37:17,760 Speaker 1: why an industry like tech that for all of its newness, 657 00:37:17,920 --> 00:37:23,120 Speaker 1: doesn't really have the same historical baggage with gender discrimination. 658 00:37:23,600 --> 00:37:27,960 Speaker 1: So why why does this industry that's still relatively young 659 00:37:28,120 --> 00:37:32,160 Speaker 1: in terms of its historical existence have such a backwards 660 00:37:32,760 --> 00:37:37,520 Speaker 1: Mad Men era like culture of overt hostility towards women, 661 00:37:37,800 --> 00:37:40,279 Speaker 1: and of course, what we can do about it. We'll 662 00:37:40,320 --> 00:37:51,320 Speaker 1: be right back after this quick break, and we're back 663 00:37:52,120 --> 00:37:57,000 Speaker 1: and we're talking about why tech is so overtly hostile 664 00:37:57,080 --> 00:37:59,520 Speaker 1: to women in particular. We heard a little bit about 665 00:37:59,560 --> 00:38:03,759 Speaker 1: the whistle lowers, the brave change makers, Ellen Powe, who 666 00:38:03,760 --> 00:38:06,640 Speaker 1: really paved the way for other women to come out 667 00:38:06,719 --> 00:38:11,600 Speaker 1: and share their stories of hostile work environments and sexism 668 00:38:11,640 --> 00:38:15,160 Speaker 1: in tech. But first, before we go any further in 669 00:38:15,160 --> 00:38:17,760 Speaker 1: this conversation, can we just take a temperature check Bridget 670 00:38:17,760 --> 00:38:21,239 Speaker 1: and say, why is tex so sexist? To begin with? 671 00:38:21,640 --> 00:38:25,359 Speaker 1: It's not like medicine. It's not like the law, whereby 672 00:38:25,520 --> 00:38:28,839 Speaker 1: they have a long history of gentlemen only ladies forbidden 673 00:38:29,160 --> 00:38:31,640 Speaker 1: women not even being able to study these things exactly 674 00:38:31,680 --> 00:38:38,560 Speaker 1: on the books. Historically, this is supposedly data driven industry, right, 675 00:38:38,560 --> 00:38:42,960 Speaker 1: that's so obsessed with optimizing our world and disrupting the 676 00:38:43,080 --> 00:38:47,160 Speaker 1: old way of thinking. Why on earth can't this basically 677 00:38:47,239 --> 00:38:51,680 Speaker 1: pubescent industry get woke when it comes to women. I mean, 678 00:38:51,760 --> 00:38:55,480 Speaker 1: what are some of the reasons why tech seems to 679 00:38:55,520 --> 00:38:58,560 Speaker 1: really struggle with this in particular. Well, there's a few 680 00:38:58,600 --> 00:39:00,759 Speaker 1: theories behind it. One of it's just idea of the 681 00:39:00,840 --> 00:39:04,960 Speaker 1: genius fallacy. Basically, in tech, we have this idea that 682 00:39:05,000 --> 00:39:09,040 Speaker 1: people who become these big tech people are innate geniuses, 683 00:39:09,320 --> 00:39:13,960 Speaker 1: and think about other fields like surgery or astronomy or law. 684 00:39:14,280 --> 00:39:16,600 Speaker 1: Maybe you think of some people as having more aptitude 685 00:39:16,600 --> 00:39:18,680 Speaker 1: than others, but you don't think of them as being 686 00:39:18,800 --> 00:39:21,879 Speaker 1: innately born with some sort of magical quality that makes 687 00:39:21,880 --> 00:39:24,920 Speaker 1: them better or the best, But tech isn't really like that. 688 00:39:25,239 --> 00:39:28,680 Speaker 1: A twenty fifteen study published in Science confirmed that computer 689 00:39:28,760 --> 00:39:31,960 Speaker 1: science and certain other fields including physics, math, and philosophy 690 00:39:32,239 --> 00:39:36,680 Speaker 1: fetishize brilliant, cultivating the idea that potential is inborn. The 691 00:39:36,719 --> 00:39:39,600 Speaker 1: report concluded that these fields tend to be problematic for women, 692 00:39:39,920 --> 00:39:42,480 Speaker 1: owing to a stubborn assumption that genius is a male trade. 693 00:39:42,719 --> 00:39:45,680 Speaker 1: So basically, if we're all thinking that men just have 694 00:39:45,800 --> 00:39:48,919 Speaker 1: the ability to be born geniuses and women aren't really 695 00:39:49,000 --> 00:39:51,880 Speaker 1: like that, it cultivates this really toxic idea that the 696 00:39:51,920 --> 00:39:54,640 Speaker 1: men are the rulers of this industry and women are 697 00:39:54,640 --> 00:39:56,719 Speaker 1: just sort of second class citizens. I'm almost thinking of 698 00:39:56,760 --> 00:40:00,360 Speaker 1: Russell kras character in A Beautiful Mind. Oh yeah, I 699 00:40:00,400 --> 00:40:02,759 Speaker 1: mean think about it. Even in pop culture, we do 700 00:40:02,840 --> 00:40:06,640 Speaker 1: not see the not maybe we do it. If you 701 00:40:06,760 --> 00:40:08,600 Speaker 1: think of you know, shows that do this, let me know, 702 00:40:08,680 --> 00:40:12,040 Speaker 1: but thank you. But the first movie I've seen, yes, 703 00:40:12,160 --> 00:40:15,520 Speaker 1: but it hidden figures. They're not treated as now as special, 704 00:40:15,520 --> 00:40:18,239 Speaker 1: They're treated as they work super hard and blah blah blah. 705 00:40:18,520 --> 00:40:21,600 Speaker 1: I'm talking about where it's you know, the Steve jobs 706 00:40:21,640 --> 00:40:23,920 Speaker 1: origin story where he was just born and was always 707 00:40:24,000 --> 00:40:26,480 Speaker 1: different and blah blah blah. Women are not afforded that 708 00:40:26,560 --> 00:40:29,239 Speaker 1: same kind of glowing illustration of why they're good at 709 00:40:29,280 --> 00:40:32,440 Speaker 1: what they do. That same science study found that the 710 00:40:32,520 --> 00:40:36,000 Speaker 1: more a field valued giftedness, the fewer female PhDs they 711 00:40:36,000 --> 00:40:38,040 Speaker 1: would be, and at pointed out the same pattern being 712 00:40:38,040 --> 00:40:40,480 Speaker 1: true for people of color. And again, if you think 713 00:40:40,520 --> 00:40:44,080 Speaker 1: of genius as being innately white and male, and then 714 00:40:44,120 --> 00:40:46,320 Speaker 1: you don't even unpack what that means for your industry, 715 00:40:46,360 --> 00:40:48,360 Speaker 1: I think it's really problematic and probably one of the 716 00:40:48,400 --> 00:40:50,600 Speaker 1: reasons why tech is scene is so terrible for people 717 00:40:50,600 --> 00:40:56,080 Speaker 1: who aren't white men exactly, especially when venture capital. The 718 00:40:56,120 --> 00:41:00,560 Speaker 1: whole model of VC funding relies on making these sort 719 00:41:00,560 --> 00:41:03,399 Speaker 1: of gut decisions around whose companies you believe in enough 720 00:41:03,400 --> 00:41:05,839 Speaker 1: to throw your dollars behind. And so when there's those 721 00:41:05,960 --> 00:41:10,880 Speaker 1: kinds of genius fallacies impeding our ability to see women 722 00:41:11,080 --> 00:41:14,319 Speaker 1: or people of color as having that special something, it's 723 00:41:14,360 --> 00:41:18,120 Speaker 1: going to have this chain impact of not giving them 724 00:41:18,239 --> 00:41:21,839 Speaker 1: the funding to even have the shot to take their 725 00:41:21,880 --> 00:41:24,480 Speaker 1: company to the next level. And we know that people 726 00:41:24,520 --> 00:41:27,359 Speaker 1: give funding to people who remind them of themselves, so 727 00:41:27,400 --> 00:41:30,880 Speaker 1: that in the unconscious bias there too. It just it 728 00:41:31,080 --> 00:41:34,480 Speaker 1: sort of compounds time and time and time again. Another 729 00:41:34,680 --> 00:41:40,160 Speaker 1: theory behind why tech is especially struggling with sexism has 730 00:41:40,160 --> 00:41:44,400 Speaker 1: something to do with the meritocracy paradox. Here back in 731 00:41:44,440 --> 00:41:47,520 Speaker 1: the Atlantic magazine piece, they write, quote, we don't have 732 00:41:47,600 --> 00:41:50,520 Speaker 1: the same history as of exclusion, says Joel Emerson, the 733 00:41:50,560 --> 00:41:53,320 Speaker 1: founder and CEO of Paradigm, a firm in San Francisco 734 00:41:53,360 --> 00:41:58,200 Speaker 1: that advises companies undiversity and inclusion. But being new comes 735 00:41:58,239 --> 00:42:02,320 Speaker 1: with its own problems. Because Silicon Valley is a place 736 00:42:02,360 --> 00:42:06,799 Speaker 1: where a newcomer can unseat the most established player, many 737 00:42:06,880 --> 00:42:10,840 Speaker 1: people there believe, despite evidence everywhere to the contrary, that 738 00:42:10,960 --> 00:42:15,560 Speaker 1: tech is a meritocracy. People truly believe that those who 739 00:42:15,600 --> 00:42:20,920 Speaker 1: work hard and perform well will get ahead. And I know, listening, 740 00:42:21,000 --> 00:42:22,960 Speaker 1: you might think, Yeah, if I work hard and I 741 00:42:23,040 --> 00:42:25,360 Speaker 1: perform well, I'm gonna get ahead. That's our That's the 742 00:42:25,360 --> 00:42:28,040 Speaker 1: promise that's been made to me. And that's the promise 743 00:42:28,080 --> 00:42:31,760 Speaker 1: that's been perpetuated in the American lore, in the American 744 00:42:31,880 --> 00:42:37,000 Speaker 1: dream from I don't know, the Industrial Revolution on up. However, 745 00:42:37,440 --> 00:42:42,960 Speaker 1: this very belief in a meritocracy can perpetuate inequality twenty 746 00:42:43,200 --> 00:42:47,600 Speaker 1: study called the Paradox of Meritocracy and Organizations found that 747 00:42:47,640 --> 00:42:53,400 Speaker 1: in cultures that espouse this meritocracy idea, managers may, in fact, quote, 748 00:42:53,600 --> 00:42:58,880 Speaker 1: show greater bias in favor of men over equally performing women. 749 00:42:59,200 --> 00:43:03,879 Speaker 1: Surprise of Prize Want Want WA's depressing. In fact, they 750 00:43:03,920 --> 00:43:09,000 Speaker 1: did free experiments in which researchers presented participants with profiles 751 00:43:09,040 --> 00:43:12,960 Speaker 1: of similarly performing people of both genders and asked them 752 00:43:12,960 --> 00:43:17,160 Speaker 1: to award bonuses based on their performance. The researchers found 753 00:43:17,400 --> 00:43:22,680 Speaker 1: that telling participants that their company valued merit based decisions 754 00:43:23,239 --> 00:43:26,880 Speaker 1: only increased the likelihood of their giving higher bonuses to 755 00:43:26,920 --> 00:43:30,200 Speaker 1: the men. So basically, when you say I'm rewarding people 756 00:43:30,200 --> 00:43:34,160 Speaker 1: based on merit and you prime the participants in that study, 757 00:43:34,400 --> 00:43:38,319 Speaker 1: they're more likely to associate male achievement as merit based. Yeah, 758 00:43:38,360 --> 00:43:40,879 Speaker 1: that doesn't surprise me at all. And I think going 759 00:43:40,920 --> 00:43:43,560 Speaker 1: back to this idea of you know, the tech industry 760 00:43:43,680 --> 00:43:46,840 Speaker 1: loves talking about how it's data driven, it's really efficient 761 00:43:47,000 --> 00:43:50,520 Speaker 1: like blah blah blah blah blah. But belief in a 762 00:43:50,560 --> 00:43:54,120 Speaker 1: meritocracy is not data driven. There's so much data saying no, 763 00:43:54,480 --> 00:43:56,759 Speaker 1: the tech industry is not a meritoc RECEIP doesn't work 764 00:43:56,800 --> 00:43:59,759 Speaker 1: that way. Yet this industry continues to cling to this 765 00:43:59,840 --> 00:44:03,960 Speaker 1: idea despite all data proving otherwise, and while also claiming 766 00:44:04,000 --> 00:44:06,280 Speaker 1: to be very data driven and efficient. It makes no sense. 767 00:44:06,480 --> 00:44:09,279 Speaker 1: It doesn't it's it's the biggest hypocrisy of tech in 768 00:44:09,320 --> 00:44:13,840 Speaker 1: my opinion. And really this reminded me of a paragraph 769 00:44:14,000 --> 00:44:17,160 Speaker 1: in the very beginning of Lean In by Cheryl Sandberg 770 00:44:17,239 --> 00:44:20,120 Speaker 1: that stuck out to me. And I've seen almost nobody 771 00:44:20,160 --> 00:44:22,600 Speaker 1: talk about it, but to a fault, because I think 772 00:44:22,640 --> 00:44:27,320 Speaker 1: this is really important. The whole concept of leaning in, 773 00:44:27,680 --> 00:44:31,239 Speaker 1: of working hard, of saying yes, of having your seat 774 00:44:31,239 --> 00:44:35,400 Speaker 1: at the table basically assumes that there's a meritocracy, that 775 00:44:35,440 --> 00:44:38,760 Speaker 1: the harder you work, the more you know in your face, 776 00:44:38,880 --> 00:44:42,040 Speaker 1: or the more assertive you are as a woman in tech, 777 00:44:42,640 --> 00:44:45,080 Speaker 1: you'll have better outcomes. And yet in the start of 778 00:44:45,080 --> 00:44:49,200 Speaker 1: her book, here's what Cheryl Sandberg says, quote one stumbling 779 00:44:49,239 --> 00:44:51,640 Speaker 1: block is that many people believe that the workplace is 780 00:44:51,719 --> 00:44:57,000 Speaker 1: largely a meritocracy, which means we look at individuals, not groups, 781 00:44:57,080 --> 00:45:00,480 Speaker 1: and determine that differences and outcomes must be based on 782 00:45:00,640 --> 00:45:05,200 Speaker 1: merit not gender. Men at the top are often unaware 783 00:45:05,280 --> 00:45:08,680 Speaker 1: of the benefits they enjoy simply because they're men, and 784 00:45:08,680 --> 00:45:11,440 Speaker 1: this can make them blind to the disadvantages associated with 785 00:45:11,480 --> 00:45:14,880 Speaker 1: being a woman. By the way, like double this, like 786 00:45:15,080 --> 00:45:17,879 Speaker 1: ditto that for people of color. Yeah, I would say, 787 00:45:17,960 --> 00:45:20,960 Speaker 1: as you're reading, that's just like something else I know, 788 00:45:22,680 --> 00:45:26,440 Speaker 1: right exactly. She goes on to right. Women lower down 789 00:45:26,800 --> 00:45:30,880 Speaker 1: also believe that men at the top are entitled to 790 00:45:31,080 --> 00:45:33,560 Speaker 1: be there, so they try to play by the rules 791 00:45:33,600 --> 00:45:36,960 Speaker 1: and work harder to advance, rather than raise questions or 792 00:45:37,040 --> 00:45:40,360 Speaker 1: voice concerns about the possibility of bias. As a result, 793 00:45:40,960 --> 00:45:46,040 Speaker 1: everyone becomes complicit in perpetuating an unjust system. And it 794 00:45:46,120 --> 00:45:49,520 Speaker 1: really shows how we all women, men, wherever you are 795 00:45:49,560 --> 00:45:52,400 Speaker 1: on the spectrum, we're all cogs in this one messed 796 00:45:52,440 --> 00:45:55,480 Speaker 1: up system exactly. And that messed up system is not 797 00:45:55,560 --> 00:45:58,160 Speaker 1: something we're growing out of, No, we're leaning into it, 798 00:45:58,280 --> 00:46:01,279 Speaker 1: right exactly. It does something that's just going to go 799 00:46:01,360 --> 00:46:04,919 Speaker 1: away with time. I think a lot of baby boomers 800 00:46:05,040 --> 00:46:08,120 Speaker 1: and women and my grandmother's generation like to think, oh, 801 00:46:08,160 --> 00:46:11,960 Speaker 1: women are advancing, things are getting better. You don't know 802 00:46:12,000 --> 00:46:15,440 Speaker 1: what it was like to really experience sexism. But we 803 00:46:15,520 --> 00:46:17,880 Speaker 1: have to remind ourselves this is not something that we 804 00:46:17,920 --> 00:46:22,600 Speaker 1: are inevitably getting over as a society, which means we 805 00:46:22,680 --> 00:46:28,040 Speaker 1: need proactive, assertive solutions on the part of companies. And frankly, 806 00:46:28,120 --> 00:46:31,240 Speaker 1: the good news here is that we are talking about 807 00:46:31,239 --> 00:46:34,920 Speaker 1: it because this stuff is everywhere. I could in researching 808 00:46:34,960 --> 00:46:38,840 Speaker 1: for this episode, I couldn't go a day without something else, 809 00:46:39,000 --> 00:46:41,800 Speaker 1: something brand new popping up on my social media feeds, 810 00:46:42,160 --> 00:46:45,040 Speaker 1: or someone tweeting at me a brand new article that 811 00:46:45,080 --> 00:46:47,960 Speaker 1: needed to be internalized. And that is why we have 812 00:46:48,040 --> 00:46:51,280 Speaker 1: so much more to share that we need to really 813 00:46:51,520 --> 00:46:54,200 Speaker 1: uh queue up our next episode on this right. This 814 00:46:54,360 --> 00:46:57,120 Speaker 1: is a burly topic. It's a topic that's ever evolving, 815 00:46:57,160 --> 00:47:00,200 Speaker 1: ever changing, but it's not all doom and gloom is 816 00:47:00,280 --> 00:47:02,120 Speaker 1: good news on the horizon, I think for this issue. 817 00:47:02,160 --> 00:47:04,320 Speaker 1: I think it's an issue that if we talk about 818 00:47:04,560 --> 00:47:08,840 Speaker 1: we really can't get somewhere with exactly. Back in Google 819 00:47:09,480 --> 00:47:12,040 Speaker 1: was amongst the first to release data on the number 820 00:47:12,040 --> 00:47:15,120 Speaker 1: of women and minorities that it employed, thanks to organizations 821 00:47:15,120 --> 00:47:17,479 Speaker 1: like Color of Change, who actually had to push tech 822 00:47:17,600 --> 00:47:20,799 Speaker 1: organizations to disclose these numbers a lot of them. Some 823 00:47:20,920 --> 00:47:22,279 Speaker 1: of them did it right away, but a lot of 824 00:47:22,320 --> 00:47:25,200 Speaker 1: them didn't do it exactly. And once Google did it, 825 00:47:25,239 --> 00:47:27,600 Speaker 1: there was a lot more pressure, and so kudos to 826 00:47:27,640 --> 00:47:29,919 Speaker 1: Color of Change and advocates who were involved in making 827 00:47:29,920 --> 00:47:32,919 Speaker 1: that happen. Once they did release the numbers, everyone looked 828 00:47:32,920 --> 00:47:35,880 Speaker 1: at them and was like, Oh, that's not good. That 829 00:47:36,040 --> 00:47:39,600 Speaker 1: is no good for a data driven decision making company 830 00:47:39,800 --> 00:47:44,839 Speaker 1: who's uh phrases don't be evil. Right, And while we're 831 00:47:44,880 --> 00:47:47,520 Speaker 1: talking about Google, I would like to just make clear 832 00:47:47,560 --> 00:47:49,759 Speaker 1: that I have done work and been paid for that 833 00:47:49,800 --> 00:47:53,960 Speaker 1: work to help Google with its gender equity efforts in 834 00:47:54,000 --> 00:47:56,760 Speaker 1: the past. But I like the way that Google really 835 00:47:57,040 --> 00:47:59,040 Speaker 1: stepped up to kind of was a leader in this 836 00:47:59,160 --> 00:48:00,920 Speaker 1: in this practice, it's by saying, you know, we're going 837 00:48:01,000 --> 00:48:02,960 Speaker 1: to release it. It might not be pretty, but we're 838 00:48:02,960 --> 00:48:05,000 Speaker 1: gonna take a step to sort of kick start the 839 00:48:05,000 --> 00:48:08,440 Speaker 1: conversation exactly. And what came of that was that the 840 00:48:08,440 --> 00:48:11,600 Speaker 1: company's pledged to spend hundreds of millions of dollars changing 841 00:48:11,640 --> 00:48:14,640 Speaker 1: their work climates, altering the composition of their leadership, and 842 00:48:14,640 --> 00:48:19,239 Speaker 1: refining their hiring practices. So really there's a lot of 843 00:48:19,280 --> 00:48:24,160 Speaker 1: hope in that millions have been spent and more have 844 00:48:24,280 --> 00:48:28,240 Speaker 1: been pledged to solve this problem. And on our next episode, 845 00:48:28,280 --> 00:48:31,400 Speaker 1: on Part two of Silicon Valley sexism. We're going to 846 00:48:31,480 --> 00:48:35,880 Speaker 1: explore how well or sometimes not so well those efforts 847 00:48:35,920 --> 00:48:39,480 Speaker 1: are progressing. We'll talk through some of the latest proposed solutions, 848 00:48:39,920 --> 00:48:44,279 Speaker 1: the resulting pushback that we're hearing from men, especially a 849 00:48:44,360 --> 00:48:49,280 Speaker 1: certain man, a certain manifesto writing man at Google, and 850 00:48:50,040 --> 00:48:53,399 Speaker 1: some of the pushbacks that were overcoming as people who 851 00:48:53,480 --> 00:48:56,480 Speaker 1: are advocating for gender equality. We also want to make 852 00:48:56,520 --> 00:48:59,320 Speaker 1: sure in the next episode to cover how you, yes you, 853 00:48:59,600 --> 00:49:03,600 Speaker 1: every single one of us can make a difference and 854 00:49:03,680 --> 00:49:06,640 Speaker 1: have an impact to further ensure that tech is a 855 00:49:06,680 --> 00:49:11,440 Speaker 1: safe and welcoming space for women's talents and careers to 856 00:49:11,520 --> 00:49:14,319 Speaker 1: fully reach their potential. We know this is a barely 857 00:49:14,360 --> 00:49:16,960 Speaker 1: one Please stick with us to at the next episode 858 00:49:16,960 --> 00:49:19,840 Speaker 1: and we'll we'll learn and grow together tech industry, and 859 00:49:19,880 --> 00:49:21,440 Speaker 1: we want to hear what you have to say about this. 860 00:49:21,520 --> 00:49:23,279 Speaker 1: If you're a woman in tech like I was for 861 00:49:23,320 --> 00:49:25,680 Speaker 1: a while, what's your experienced, But like, if you're dude 862 00:49:25,680 --> 00:49:28,000 Speaker 1: in tech, what have you seen? Please get in touch 863 00:49:28,040 --> 00:49:30,360 Speaker 1: with us, you really want to hear your stories. You 864 00:49:30,400 --> 00:49:32,960 Speaker 1: can find us on Instagram at stuff Mom Never Told You, 865 00:49:33,080 --> 00:49:36,000 Speaker 1: on Twitter at mom Stuff Podcasts, and via email at 866 00:49:36,040 --> 00:49:48,600 Speaker 1: mom Stuff at how stuff works dot com,