1 00:00:02,600 --> 00:00:05,720 Speaker 1: Hey everyone, it's Aki. This week, we're re airing an 2 00:00:05,760 --> 00:00:11,039 Speaker 1: episode that we first published in January. Stick around because 3 00:00:11,039 --> 00:00:15,360 Speaker 1: we'll be back at the end with an update. Today's 4 00:00:15,360 --> 00:00:19,840 Speaker 1: episode is about a guy called Mitchel Lee. He runs 5 00:00:19,840 --> 00:00:22,799 Speaker 1: a startup called Penny in San Francisco. We'll tell you 6 00:00:22,880 --> 00:00:25,480 Speaker 1: later about what Penny does. For now, what you need 7 00:00:25,520 --> 00:00:27,600 Speaker 1: to know is Penny is almost two years old and 8 00:00:27,640 --> 00:00:31,319 Speaker 1: it's really small. It's Mitch, his co founder, Alex, and 9 00:00:31,440 --> 00:00:36,520 Speaker 1: two employees. They're all software engineers, and big surprise, they're 10 00:00:36,560 --> 00:00:40,199 Speaker 1: all guys. A few months ago, Mitch started looking for 11 00:00:40,240 --> 00:00:43,479 Speaker 1: a fifth employee, and he wanted that fifth person to 12 00:00:43,520 --> 00:00:46,640 Speaker 1: be different from him and the rest of the team. 13 00:00:46,680 --> 00:00:48,760 Speaker 1: And since this is a podcast and we can't just 14 00:00:48,840 --> 00:00:51,960 Speaker 1: show you a picture of Mitch, we asked Mitch's sister 15 00:00:52,120 --> 00:00:57,120 Speaker 1: Christina to describe what he looks like. He's always got 16 00:00:57,440 --> 00:01:00,560 Speaker 1: some like really soft hoodie or two shirt on in 17 00:01:00,680 --> 00:01:03,640 Speaker 1: jeans or corduroys. If he was walking down the street, 18 00:01:03,640 --> 00:01:09,839 Speaker 1: you probably wouldn't necessarily notice. He blends in pretty well. Basically, 19 00:01:09,959 --> 00:01:12,640 Speaker 1: Mitch looks just like a lot of other people in 20 00:01:12,720 --> 00:01:16,720 Speaker 1: Silicon Valley. He's young, he's white, he's straight, and he 21 00:01:16,760 --> 00:01:19,319 Speaker 1: went to a top college. Yeah, he even has this 22 00:01:19,480 --> 00:01:22,920 Speaker 1: neatly trimmed beard, and perhaps because he's a cyclist like 23 00:01:23,000 --> 00:01:25,840 Speaker 1: the rest of San Francisco, he has a nice tan too. 24 00:01:26,640 --> 00:01:29,800 Speaker 1: In Silicon Valley, most programmers are white and Asian men 25 00:01:29,920 --> 00:01:33,960 Speaker 1: who have computer science degrees from elite universities. But for 26 00:01:34,120 --> 00:01:37,440 Speaker 1: Mitch's next higher he's committed to looking outside that pool 27 00:01:37,440 --> 00:01:41,440 Speaker 1: of people. That's pretty unusual for a company of penny size. 28 00:01:41,959 --> 00:01:45,560 Speaker 1: Some people would call it affirmative action hiring, because Mitch 29 00:01:45,760 --> 00:01:48,920 Speaker 1: is going to be actively considering the candidates background when 30 00:01:49,000 --> 00:01:52,400 Speaker 1: he's deciding who to hire. And it's a very touchy topic. 31 00:01:53,080 --> 00:01:55,880 Speaker 1: As we'll find out, not everyone agrees that it's the 32 00:01:56,000 --> 00:02:06,840 Speaker 1: right thing to do him and I'm Ellen Hewitt, and 33 00:02:06,960 --> 00:02:10,040 Speaker 1: this week Undecrypted, we're gonna be talking about something that 34 00:02:10,160 --> 00:02:13,600 Speaker 1: every technology company says they want to do something about, 35 00:02:13,960 --> 00:02:17,600 Speaker 1: which is diversity in the workforce. Well, they want to 36 00:02:17,639 --> 00:02:20,360 Speaker 1: talk about it in these lofty slogans, but when you 37 00:02:20,480 --> 00:02:24,720 Speaker 1: actually drill into the specifics, things start to get uncomfortable. 38 00:02:25,160 --> 00:02:27,640 Speaker 1: We found one of the few guys in the industry 39 00:02:27,680 --> 00:02:31,800 Speaker 1: willing to speak completely openly, willing to get really uncomfortable 40 00:02:31,840 --> 00:02:34,919 Speaker 1: with us, someone who's trying to do something to fix 41 00:02:35,000 --> 00:02:38,160 Speaker 1: the lack of diversity and tech from within his own 42 00:02:38,280 --> 00:02:49,359 Speaker 1: tiny startup. Oh cool, I look at this giant chess board. 43 00:02:55,400 --> 00:02:58,600 Speaker 1: The guy's penny work in a small coworking space in 44 00:02:58,639 --> 00:03:02,800 Speaker 1: downtown San Francisco. They're on a shoestring budget. It's really 45 00:03:02,840 --> 00:03:05,360 Speaker 1: not a glamorous place. They don't have free snacks. There 46 00:03:05,400 --> 00:03:08,800 Speaker 1: isn't very good natural light, and their office is about 47 00:03:08,840 --> 00:03:14,920 Speaker 1: the size of the bedroom room, A spacious, lovely I 48 00:03:15,000 --> 00:03:16,960 Speaker 1: was telling her, I'm excited for her to see how 49 00:03:17,000 --> 00:03:21,800 Speaker 1: like the other half of startups live, this is the 50 00:03:22,360 --> 00:03:25,800 Speaker 1: don't spend a lot of money half of starposts. Inside 51 00:03:25,800 --> 00:03:29,320 Speaker 1: this tiny office, there are four guys sitting side by side. 52 00:03:30,320 --> 00:03:33,119 Speaker 1: My name's Mitch. I grew up in San Jose. I'm 53 00:03:33,200 --> 00:03:37,440 Speaker 1: a mid twenties white guy. I'm Alex and I just 54 00:03:37,520 --> 00:03:42,080 Speaker 1: Stanford is American. My name's Andrew Dennis. I'm half black, 55 00:03:42,080 --> 00:03:46,640 Speaker 1: half white. UM I'm married and I'm my dad. I 56 00:03:46,760 --> 00:03:51,720 Speaker 1: am Jonathan and I was born in Taiwan and I 57 00:03:51,760 --> 00:03:55,119 Speaker 1: grew up in Maryland. All four of them believe it's 58 00:03:55,120 --> 00:03:58,680 Speaker 1: the right move for their company to be prioritizing diversity now, 59 00:03:59,480 --> 00:04:03,040 Speaker 1: not because it's the right thing to do, they also 60 00:04:03,080 --> 00:04:05,840 Speaker 1: believe it will help their product. Yeah, Penny is this 61 00:04:05,920 --> 00:04:08,640 Speaker 1: app that links to your bank accounts and can give 62 00:04:08,640 --> 00:04:12,240 Speaker 1: you financial advice based on your spending patterns. Here's Mitch. 63 00:04:12,880 --> 00:04:15,920 Speaker 1: As we were working on that core product, we were realizing, 64 00:04:16,400 --> 00:04:19,599 Speaker 1: we're both two software engineers that grew up in the 65 00:04:19,640 --> 00:04:22,560 Speaker 1: Bay Area. How are we going to get a product 66 00:04:22,640 --> 00:04:26,520 Speaker 1: that does well in Montana and Kansas and Maine. And 67 00:04:27,480 --> 00:04:30,880 Speaker 1: our answer to that was, we should be solving this 68 00:04:30,960 --> 00:04:34,640 Speaker 1: by building out a diverse team that can empathize with 69 00:04:34,680 --> 00:04:37,760 Speaker 1: people from different parts of the country, from different genders 70 00:04:37,880 --> 00:04:41,400 Speaker 1: or different ethnicities. And you use the app by messaging 71 00:04:41,560 --> 00:04:45,120 Speaker 1: this chat bot, this computer program that texts back and 72 00:04:45,200 --> 00:04:47,960 Speaker 1: forth with you. The chat bot is called Penny, and 73 00:04:48,040 --> 00:04:51,760 Speaker 1: Penny has a female face. But at the beginning, everything 74 00:04:51,800 --> 00:04:55,000 Speaker 1: Penny said was written by two guys, Alex and Mitch. 75 00:04:55,680 --> 00:05:01,120 Speaker 1: Here's Mitch's fiance Lizzie Wagner, explaining one way that when awry, 76 00:05:01,520 --> 00:05:03,200 Speaker 1: so they started to use a little bit of like 77 00:05:03,240 --> 00:05:05,400 Speaker 1: a snarky tone. You know, when you use like a 78 00:05:05,400 --> 00:05:08,640 Speaker 1: winky emoji, it can also be considered flirty. So I 79 00:05:08,680 --> 00:05:11,080 Speaker 1: told him I just read this conversation and I think 80 00:05:11,120 --> 00:05:13,839 Speaker 1: Penny was flirting with me, and he was like, no way, 81 00:05:14,160 --> 00:05:17,440 Speaker 1: it's a computer, it can't flirt, and I was like, no, 82 00:05:17,720 --> 00:05:20,240 Speaker 1: it's a flirty conversation. He's like, this is exactly why 83 00:05:20,240 --> 00:05:23,320 Speaker 1: we need more diverse perspectives, because Alex and I never 84 00:05:23,400 --> 00:05:29,719 Speaker 1: even thought about that comment being taken that way. And 85 00:05:30,120 --> 00:05:33,560 Speaker 1: building a relatable chatbot is super important when you're guiding 86 00:05:33,600 --> 00:05:37,360 Speaker 1: customers through something as personal and sensitive and daunting as 87 00:05:37,400 --> 00:05:40,919 Speaker 1: your finances. When a user writes in and says, I 88 00:05:41,080 --> 00:05:44,599 Speaker 1: overdraft a lot, how do you respond? And some people 89 00:05:44,800 --> 00:05:50,120 Speaker 1: will respond with, well, they should stop spending that much money. 90 00:05:50,440 --> 00:05:52,600 Speaker 1: Other people will put themselves in their shoes and say 91 00:05:52,640 --> 00:05:56,599 Speaker 1: that really sucks. That level of empathy doesn't come if 92 00:05:56,760 --> 00:05:59,880 Speaker 1: everyone thinks and acts the same way in a room, 93 00:06:00,120 --> 00:06:03,360 Speaker 1: They're all just gonna confirm each other's opinions of like, well, 94 00:06:03,360 --> 00:06:06,520 Speaker 1: that person shouldn't be spending money they don't have. And 95 00:06:06,640 --> 00:06:09,440 Speaker 1: Penny stands out from the rest of startups and Silicon 96 00:06:09,560 --> 00:06:13,560 Speaker 1: Valley because it's focusing on hiring for diversity so early 97 00:06:13,680 --> 00:06:17,799 Speaker 1: in the company's history. I think the default for early 98 00:06:17,839 --> 00:06:23,400 Speaker 1: stage companies is not necessarily an aversion to diversity. It's 99 00:06:23,400 --> 00:06:26,680 Speaker 1: not an active process of saying we are only going 100 00:06:26,760 --> 00:06:29,200 Speaker 1: to hire people that look and sound just like us. 101 00:06:30,720 --> 00:06:34,760 Speaker 1: It's the idea that we want to move fast, and 102 00:06:34,800 --> 00:06:36,800 Speaker 1: the fastest way to hire people is to pull from 103 00:06:36,800 --> 00:06:39,359 Speaker 1: our network. They get this big paycheck from a venture 104 00:06:39,360 --> 00:06:41,200 Speaker 1: capital firm and they say, great, we're going to spend 105 00:06:41,240 --> 00:06:44,640 Speaker 1: it immediately. They expand their team from two or four 106 00:06:44,720 --> 00:06:49,760 Speaker 1: people to eight or twelve. If you punt the issue 107 00:06:49,760 --> 00:06:52,840 Speaker 1: of diversity down the line, it becomes much harder because 108 00:06:52,839 --> 00:06:56,240 Speaker 1: when you have eleven men on your team on a 109 00:06:56,240 --> 00:07:00,880 Speaker 1: twelve person team, it becomes a hostile work environment for 110 00:07:01,000 --> 00:07:04,240 Speaker 1: women trying to enter into that team. When you have 111 00:07:04,480 --> 00:07:08,320 Speaker 1: a group of all white or all Asian people sitting 112 00:07:08,360 --> 00:07:11,960 Speaker 1: in the same room together, it makes it hostile for 113 00:07:12,720 --> 00:07:20,800 Speaker 1: other minority groups to join that environment. These kinds of 114 00:07:20,840 --> 00:07:25,240 Speaker 1: companies really do exist. We talked to Jennifer Barbattini, as 115 00:07:25,360 --> 00:07:28,280 Speaker 1: software engineer who interviewed with Penny in September, though she 116 00:07:28,360 --> 00:07:32,520 Speaker 1: didn't get the job. I remember there was one company 117 00:07:32,560 --> 00:07:36,360 Speaker 1: that I interviewed with. It was a smallish company like 118 00:07:36,680 --> 00:07:41,040 Speaker 1: fifteen to twenty. They had ten engineers. All ten were male. 119 00:07:41,200 --> 00:07:45,560 Speaker 1: And all ten were from Stanford, and I was like, 120 00:07:46,960 --> 00:07:49,440 Speaker 1: I don't know if I'm a good fit here, like, 121 00:07:49,640 --> 00:07:52,320 Speaker 1: and the hiring person was telling me, We're we're trying 122 00:07:52,360 --> 00:07:55,640 Speaker 1: to be diverse, and I'm like, well, okay, but still 123 00:07:55,680 --> 00:07:58,640 Speaker 1: like this is a little intimidating. I don't have a 124 00:07:58,640 --> 00:08:02,840 Speaker 1: Stanford credential. Um, I'm I'm definitely not. I can't brow talk. 125 00:08:04,600 --> 00:08:07,080 Speaker 1: If it's a problem at a startup with twenty people, 126 00:08:07,320 --> 00:08:10,520 Speaker 1: imagine what it's like at a Google or Facebook or Twitter. 127 00:08:11,280 --> 00:08:15,480 Speaker 1: They have thousands, sometimes tens of thousands of employees, only 128 00:08:15,520 --> 00:08:19,480 Speaker 1: about thirty are women, and only about six percent are 129 00:08:19,560 --> 00:08:23,520 Speaker 1: black or Latino. These companies can hire only people from 130 00:08:23,520 --> 00:08:26,400 Speaker 1: those groups for the next year and their numbers would 131 00:08:26,400 --> 00:08:30,000 Speaker 1: barely move. So it makes sense to start early. But 132 00:08:30,240 --> 00:08:32,800 Speaker 1: the problem for startups is the pressure they're under in 133 00:08:32,840 --> 00:08:35,920 Speaker 1: the very early days. You have maybe a year of 134 00:08:36,000 --> 00:08:38,520 Speaker 1: funding in the bank, you have a bunch of competitors, 135 00:08:38,559 --> 00:08:42,400 Speaker 1: and if you can't hire quickly to build quickly your toast. 136 00:08:43,679 --> 00:08:47,800 Speaker 1: The consensus opinion is start worrying about it. When your 137 00:08:47,840 --> 00:08:50,120 Speaker 1: product is successful enough that you know that you're going 138 00:08:50,160 --> 00:08:52,200 Speaker 1: to be building out a team for the long run. 139 00:08:53,600 --> 00:08:56,920 Speaker 1: Even someone like Mitch, who's really determined to focus on this. 140 00:08:57,240 --> 00:09:01,240 Speaker 1: He's run into a lot of obstacles. Remember, Mitch isn't 141 00:09:01,280 --> 00:09:05,080 Speaker 1: this diversity or HR expert. He's just a programmer trying 142 00:09:05,120 --> 00:09:08,640 Speaker 1: to figure it out as he goes along. From a 143 00:09:08,720 --> 00:09:14,720 Speaker 1: macro perspective of actively pursuing diversity, I'm almost but implementation wise, 144 00:09:14,760 --> 00:09:17,800 Speaker 1: I'm i am just throwing darts on board and and 145 00:09:17,880 --> 00:09:20,760 Speaker 1: hoping that we'll learn enough from those to get better. 146 00:09:24,080 --> 00:09:28,240 Speaker 1: Every Monday, Mitch logs onto recruiting sites like angel Lists, Hired, 147 00:09:28,320 --> 00:09:31,480 Speaker 1: and Triple Bite. He's looking for candidates or approach, and 148 00:09:31,480 --> 00:09:34,640 Speaker 1: he'll spend one or two hours on each platform. He 149 00:09:34,720 --> 00:09:37,360 Speaker 1: said it takes him maybe six hours on Monday to 150 00:09:37,400 --> 00:09:40,000 Speaker 1: do that outreach, and then the follow up throughout the 151 00:09:40,000 --> 00:09:42,959 Speaker 1: week brings his time to about fifteen hours every week 152 00:09:43,520 --> 00:09:47,040 Speaker 1: just to fill one role. We spent one such Monday 153 00:09:47,120 --> 00:09:50,040 Speaker 1: with Mitch. As you look through a fresh badge at candidates. 154 00:09:50,240 --> 00:09:53,280 Speaker 1: These ones he found and hired this week, there were 155 00:09:53,320 --> 00:09:58,280 Speaker 1: forty two that matched to search criteria. The very first 156 00:09:58,320 --> 00:10:03,840 Speaker 1: candidate has really which is great, comes from a university 157 00:10:03,840 --> 00:10:07,679 Speaker 1: I've never heard of, also great, and then has experience 158 00:10:07,800 --> 00:10:11,520 Speaker 1: in different areas and you see the person's photos, and 159 00:10:11,840 --> 00:10:14,640 Speaker 1: you see the person's photo and their name, UM, and 160 00:10:14,880 --> 00:10:18,360 Speaker 1: actually what salary they're interested in, which is pretty interesting. 161 00:10:19,800 --> 00:10:23,320 Speaker 1: So I will start looking through this. Fortunately, when you 162 00:10:23,320 --> 00:10:26,600 Speaker 1: have forty two people every week on this platform, plus 163 00:10:26,679 --> 00:10:29,960 Speaker 1: the hundreds across, a lot of startups look at these 164 00:10:29,960 --> 00:10:32,960 Speaker 1: sites when they're trying to hire. But after a while, 165 00:10:33,200 --> 00:10:36,080 Speaker 1: Mitch realized that these job sites, they're only good for 166 00:10:36,120 --> 00:10:39,839 Speaker 1: finding a certain kind of candidate. Angel List is great, UM, 167 00:10:40,480 --> 00:10:45,319 Speaker 1: but not a good place to look for diversity. Same 168 00:10:45,400 --> 00:10:48,439 Speaker 1: with many of the hiring platforms that we tried. H 169 00:10:49,120 --> 00:10:51,760 Speaker 1: I've asked them about that in the past, and the 170 00:10:51,800 --> 00:10:56,480 Speaker 1: typical answer is, sorry, this is just what's available. This 171 00:10:56,559 --> 00:11:00,760 Speaker 1: is the pool of candidates. It's predominantly male, it's predominantly white, orasan. 172 00:11:02,000 --> 00:11:04,600 Speaker 1: So Mitch started to look for ways to find people 173 00:11:04,640 --> 00:11:08,920 Speaker 1: with more varied profiles, things like newsletters and meet ups 174 00:11:09,000 --> 00:11:12,480 Speaker 1: for women and engineering. He also looked for new graduates 175 00:11:12,480 --> 00:11:14,880 Speaker 1: from coding boot camps, which teach you how to code 176 00:11:14,920 --> 00:11:18,120 Speaker 1: in a short period of time. These people tend to 177 00:11:18,120 --> 00:11:21,560 Speaker 1: come from unusual backgrounds, but boot camps didn't turn out 178 00:11:21,559 --> 00:11:24,800 Speaker 1: to be particularly helpful for Mitch. He found that a 179 00:11:24,800 --> 00:11:27,920 Speaker 1: lot of these graduates didn't have enough experience to start 180 00:11:28,000 --> 00:11:31,960 Speaker 1: contributing right away, and Penny, as a young startup, doesn't 181 00:11:32,040 --> 00:11:38,640 Speaker 1: have the resources to train them. To apply to Penny, 182 00:11:38,760 --> 00:11:41,440 Speaker 1: you have to complete a coding questionnaire, kind of like 183 00:11:41,480 --> 00:11:44,720 Speaker 1: a TACOME test. And Mitch found that some people who 184 00:11:44,760 --> 00:11:48,200 Speaker 1: started it weren't finishing. And a lot of the people 185 00:11:48,200 --> 00:11:51,160 Speaker 1: who were dropping out were people who didn't have computer 186 00:11:51,240 --> 00:11:54,800 Speaker 1: science degrees, or were women, or were programmers of color, 187 00:11:55,200 --> 00:11:57,960 Speaker 1: and it was often these candidates that Mitch was most 188 00:11:58,000 --> 00:12:01,640 Speaker 1: interested in. Mitch suspect did it might have something to 189 00:12:01,679 --> 00:12:05,440 Speaker 1: do with a lack of confidence, Like these people with 190 00:12:05,520 --> 00:12:08,960 Speaker 1: non traditional backgrounds are taking themselves out of the running 191 00:12:09,240 --> 00:12:13,560 Speaker 1: before they even tried. Maybe the job description will say 192 00:12:13,600 --> 00:12:16,160 Speaker 1: you have to have at least three years of experience, 193 00:12:16,840 --> 00:12:19,120 Speaker 1: and let's say often the men with two years of 194 00:12:19,200 --> 00:12:23,720 Speaker 1: experience will apply anyway, but the women wouldn't. So Mitch 195 00:12:24,160 --> 00:12:35,680 Speaker 1: he tried something new. Before the break, Mitch had a 196 00:12:35,720 --> 00:12:40,160 Speaker 1: realization that the very candidates he was interested in were 197 00:12:40,200 --> 00:12:43,800 Speaker 1: taking themselves out of the running, maybe because they didn't 198 00:12:43,840 --> 00:12:46,680 Speaker 1: think they would get the job anyway. Here's what he 199 00:12:46,760 --> 00:12:51,600 Speaker 1: decided to do about it. When we get the sense 200 00:12:51,679 --> 00:12:56,760 Speaker 1: that somebody has either a confidence issue, like just doesn't 201 00:12:56,800 --> 00:12:59,520 Speaker 1: think they're a good fit for the position or doesn't 202 00:12:59,520 --> 00:13:02,560 Speaker 1: think they have the skill set required, we will have 203 00:13:02,640 --> 00:13:06,320 Speaker 1: a lot more contact with that person to assuage their fears. 204 00:13:06,440 --> 00:13:08,960 Speaker 1: That may mean a phone screen much earlier in the process, 205 00:13:09,000 --> 00:13:13,679 Speaker 1: It may mean more check in emails. That helped him 206 00:13:13,679 --> 00:13:16,760 Speaker 1: shepherd more people through the whole process, not just the 207 00:13:16,840 --> 00:13:20,560 Speaker 1: ultra confident ones. And that was a small success, but 208 00:13:20,720 --> 00:13:23,560 Speaker 1: it all came at a real cost in the form 209 00:13:23,640 --> 00:13:27,200 Speaker 1: of Mitch's time. The fifteen hours a week that Mitch 210 00:13:27,320 --> 00:13:30,840 Speaker 1: was spending on recruiting, that's fifteen hours he's not coding 211 00:13:31,120 --> 00:13:34,800 Speaker 1: or troubleshooting or mentoring his team. Mitch said it was 212 00:13:34,840 --> 00:13:36,920 Speaker 1: worth it, but you can see why a lot of 213 00:13:36,960 --> 00:13:39,920 Speaker 1: other founders in his position wouldn't really have the time 214 00:13:39,920 --> 00:13:42,720 Speaker 1: to do this. And that brings us to the most 215 00:13:42,800 --> 00:13:46,840 Speaker 1: controversial part. When you get to that final stage, would 216 00:13:46,880 --> 00:13:50,840 Speaker 1: you pick one person or another because they're a minority candidate? 217 00:13:51,400 --> 00:14:02,000 Speaker 1: What would you do? I talked to Jay Shriney Vassan. 218 00:14:02,400 --> 00:14:05,000 Speaker 1: He's the CEO of Spoke, which is a nine person 219 00:14:05,240 --> 00:14:09,840 Speaker 1: enterprise startup in San Francisco. Like Mitch J says he 220 00:14:09,920 --> 00:14:13,720 Speaker 1: wants a diverse team, but he said he's not comfortable 221 00:14:13,800 --> 00:14:16,840 Speaker 1: with making someone's ethnicity or gender one of the reasons 222 00:14:16,880 --> 00:14:20,880 Speaker 1: why he's hiring them. He explicitly did not factor that 223 00:14:20,960 --> 00:14:23,560 Speaker 1: into the final decision because I think that's unfair to 224 00:14:24,520 --> 00:14:27,960 Speaker 1: us as well as the person being hired. UM. At 225 00:14:28,000 --> 00:14:30,840 Speaker 1: the end of the day, UM, we want to have 226 00:14:30,920 --> 00:14:35,440 Speaker 1: the best people possible for each role in our company, 227 00:14:35,960 --> 00:14:39,760 Speaker 1: and this makes sense right. Picking someone in part because 228 00:14:39,800 --> 00:14:41,760 Speaker 1: of what they look like can even be seen as 229 00:14:41,800 --> 00:14:45,680 Speaker 1: employment discrimination, or it can feel patronizing to the person 230 00:14:45,720 --> 00:14:48,880 Speaker 1: who was hired. These are the kind of counter arguments 231 00:14:48,920 --> 00:14:52,200 Speaker 1: you hear a lot in Silicon Valley and elsewhere. But 232 00:14:52,320 --> 00:14:56,760 Speaker 1: we asked, Mitch, here are two hypothetical candidates equally strong, 233 00:14:56,880 --> 00:15:00,680 Speaker 1: encoding and other important values at Penny, but one's a 234 00:15:00,720 --> 00:15:03,600 Speaker 1: white guy and the other has a less typical background. 235 00:15:03,920 --> 00:15:08,520 Speaker 1: Who would you hire? You just said that they're equally qualified. 236 00:15:08,920 --> 00:15:13,400 Speaker 1: What we're saying is that the the people that were 237 00:15:13,440 --> 00:15:16,880 Speaker 1: interested in hiring were weighing them across all these factors, 238 00:15:16,960 --> 00:15:19,760 Speaker 1: and so somebody with a diverse background and a totally 239 00:15:19,800 --> 00:15:24,360 Speaker 1: different perspective, in our opinion, is more qualified. For the 240 00:15:24,400 --> 00:15:27,160 Speaker 1: position that we're trying to fill. Giving us a perspective 241 00:15:27,160 --> 00:15:30,920 Speaker 1: that we've never considered helps the product more than somebody 242 00:15:30,960 --> 00:15:34,320 Speaker 1: that has our same perspective but as good at engineering. 243 00:15:34,760 --> 00:15:37,520 Speaker 1: Did you have put back from people who felt like, 244 00:15:38,080 --> 00:15:40,600 Speaker 1: what's unfair? Should I be punished for going to Stanford? Right? 245 00:15:41,160 --> 00:15:45,320 Speaker 1: What's exactly that should I be punished for going to Stanford? 246 00:15:46,160 --> 00:15:48,680 Speaker 1: It's it's something that I don't have a good answer to, 247 00:15:49,320 --> 00:15:54,680 Speaker 1: other than the entire playing field is leaning in your direction. 248 00:15:56,880 --> 00:15:59,640 Speaker 1: I know that it can feel like in that specific 249 00:15:59,760 --> 00:16:05,000 Speaker 1: and sense um you're being discriminated against or you're somehow 250 00:16:05,080 --> 00:16:09,600 Speaker 1: unfairly disadvantaged, but you are unfairly advantaged everywhere else in 251 00:16:09,640 --> 00:16:13,280 Speaker 1: your life. So it's just, uh, it's just a slight 252 00:16:13,360 --> 00:16:17,480 Speaker 1: tilt of the playing field a little less in your favor, um, 253 00:16:17,680 --> 00:16:21,000 Speaker 1: which I'm okay with. I can I can sleep easy 254 00:16:21,080 --> 00:16:24,000 Speaker 1: at night knowing that, knowing that if you graduated from 255 00:16:24,000 --> 00:16:27,280 Speaker 1: Stanford you're going to be fine. I'm not worried about you. 256 00:16:29,640 --> 00:16:32,120 Speaker 1: And we just heard Mitch laughing there, But you can 257 00:16:32,160 --> 00:16:36,440 Speaker 1: tell just how carefully and deliberately Mitch has been choosing 258 00:16:36,520 --> 00:16:40,120 Speaker 1: his words through this whole interview. His face was flushed. 259 00:16:40,240 --> 00:16:43,120 Speaker 1: You could really tell that he was nervous. And I 260 00:16:43,160 --> 00:16:45,560 Speaker 1: don't blame him here. We are shoving a mic in 261 00:16:45,640 --> 00:16:48,640 Speaker 1: his face asking him to talk about gender and race 262 00:16:48,800 --> 00:16:51,840 Speaker 1: and all these other things that are so touchy. The 263 00:16:51,880 --> 00:16:54,400 Speaker 1: things going on in my mind are I need to 264 00:16:54,440 --> 00:16:56,600 Speaker 1: be careful about this. I know that people are going 265 00:16:56,640 --> 00:16:58,520 Speaker 1: to look at me and say, you're just some white 266 00:16:58,600 --> 00:17:01,680 Speaker 1: dude rattling off about diversity, but you have no idea 267 00:17:01,680 --> 00:17:05,760 Speaker 1: what you're talking about. That may be true. I may 268 00:17:05,760 --> 00:17:07,560 Speaker 1: not know what I'm talking about, but I want to 269 00:17:07,560 --> 00:17:11,119 Speaker 1: get that conversation going so that I can learn, so 270 00:17:11,119 --> 00:17:13,760 Speaker 1: that the rest of our team can learn, and so 271 00:17:13,800 --> 00:17:18,400 Speaker 1: that um we do better moving forward. We're basically just saying, 272 00:17:18,520 --> 00:17:21,600 Speaker 1: you know, Allen, I really can't remember the last time 273 00:17:21,640 --> 00:17:24,359 Speaker 1: I was this nervous trying to come up with the 274 00:17:24,400 --> 00:17:28,479 Speaker 1: most delicate way to pose these questions. Me too, and Aki. 275 00:17:28,960 --> 00:17:32,040 Speaker 1: You and I are both women of color, you're also gay. 276 00:17:32,080 --> 00:17:34,200 Speaker 1: It really makes you think back to every time you've 277 00:17:34,240 --> 00:17:38,160 Speaker 1: ever been offered a new job. Yeah, and I guess 278 00:17:38,240 --> 00:17:41,560 Speaker 1: until now this has all been theoretical. But when we 279 00:17:41,600 --> 00:17:44,040 Speaker 1: went in to interview the Penny team, they were in 280 00:17:44,080 --> 00:17:47,800 Speaker 1: the final stages with one candidate. They were talking to 281 00:17:47,840 --> 00:17:51,000 Speaker 1: a woman and she's of Indian descent. While we were 282 00:17:51,000 --> 00:17:53,399 Speaker 1: in the Penny office, we got the team altogether in 283 00:17:53,400 --> 00:17:56,240 Speaker 1: a room and asked them what they thought of her. Yeah, 284 00:17:56,280 --> 00:17:58,840 Speaker 1: they all thought she would work well with the team, 285 00:17:58,880 --> 00:18:03,080 Speaker 1: but she lacks some techical experience. Specifically, she wasn't fluent 286 00:18:03,240 --> 00:18:05,960 Speaker 1: in the main programming language that Penny is written in. 287 00:18:06,480 --> 00:18:08,840 Speaker 1: On top of that, the company was about to enter 288 00:18:08,960 --> 00:18:13,199 Speaker 1: a really busy period. Great culture fit. It's somebody that 289 00:18:13,240 --> 00:18:16,760 Speaker 1: we would all gladly have in the room working with us, 290 00:18:18,440 --> 00:18:21,639 Speaker 1: because she's articulate and well thought out, and it is 291 00:18:21,760 --> 00:18:24,760 Speaker 1: very responsive to feedback. How do you weigh that with 292 00:18:24,840 --> 00:18:28,560 Speaker 1: the fact that she has no experience shipping production Ruby code, 293 00:18:28,560 --> 00:18:32,280 Speaker 1: which is the language that we write in UM, but 294 00:18:32,400 --> 00:18:34,919 Speaker 1: seems to have the aptitude to pick that up quickly. Well, 295 00:18:36,600 --> 00:18:40,359 Speaker 1: the answer is we don't know, so ACKI. When we 296 00:18:40,560 --> 00:18:43,800 Speaker 1: visited Mention his team, everything that they were saying seemed 297 00:18:43,840 --> 00:18:46,800 Speaker 1: pretty reasonable to us. Yeah, I thought he was a 298 00:18:46,880 --> 00:18:51,280 Speaker 1: really thoughtful guy. I was just genuinely impressed. But we're 299 00:18:51,320 --> 00:18:55,200 Speaker 1: not experts on this either. So we outlined Mitch's philosophy 300 00:18:55,240 --> 00:18:59,000 Speaker 1: and tactics with my Von Hutchinson, a former labor lawyer 301 00:18:59,040 --> 00:19:01,840 Speaker 1: who is now a concip Bolton helping smaller startups on 302 00:19:01,920 --> 00:19:06,280 Speaker 1: diversity and inclusion. You get an A for enthusiasm. Mitch 303 00:19:08,320 --> 00:19:10,960 Speaker 1: for this letter grade, I would give him a beat. Um. 304 00:19:11,000 --> 00:19:14,920 Speaker 1: I think that he's doing some of the right things. Um, 305 00:19:14,920 --> 00:19:17,080 Speaker 1: he's taking a couple of risks, but he could take 306 00:19:17,119 --> 00:19:20,680 Speaker 1: bigger risks. I think that he could definitely educate theself 307 00:19:20,680 --> 00:19:22,960 Speaker 1: a little bit more. Just come through the resources and 308 00:19:22,960 --> 00:19:25,760 Speaker 1: see what's out there. My Van said that in the 309 00:19:25,840 --> 00:19:30,080 Speaker 1: long term, Penny should build relationships with organizations that are 310 00:19:30,200 --> 00:19:34,880 Speaker 1: trying to bring more underrepresented groups into tech host dinners 311 00:19:34,920 --> 00:19:38,080 Speaker 1: and meetups and that kind of thing. But she also 312 00:19:38,160 --> 00:19:42,160 Speaker 1: gave Mitch some practical advice for right now, like put 313 00:19:42,200 --> 00:19:45,359 Speaker 1: your pledge to diversity on your company's landing page, not 314 00:19:45,480 --> 00:19:48,800 Speaker 1: just on your job spage. Consider bringing in an expert 315 00:19:48,840 --> 00:19:51,200 Speaker 1: to help guide you instead of trying to figure it 316 00:19:51,240 --> 00:19:55,080 Speaker 1: out for yourself. And when you hire minority candidates, don't 317 00:19:55,160 --> 00:19:58,359 Speaker 1: expect them to do the work of recruiting diverse candidates 318 00:19:58,440 --> 00:20:02,280 Speaker 1: for you. I'm real happy there are guys out here 319 00:20:02,359 --> 00:20:06,720 Speaker 1: like Mitch. I think, Um, sometimes we see guys like 320 00:20:06,800 --> 00:20:09,760 Speaker 1: Mitch don't stay like Mitch for very long. I think 321 00:20:09,800 --> 00:20:11,959 Speaker 1: it's going to be in the next few years it's 322 00:20:11,960 --> 00:20:16,800 Speaker 1: going to be really hard to hold those values and 323 00:20:17,640 --> 00:20:20,520 Speaker 1: not to succumb to the temptations that are going to 324 00:20:20,600 --> 00:20:23,160 Speaker 1: be abound in the industry when it comes to making 325 00:20:23,160 --> 00:20:26,320 Speaker 1: the final call on who to hire. My Vaughan said, 326 00:20:26,440 --> 00:20:29,199 Speaker 1: Mitch is taking the right approach by focusing on the 327 00:20:29,240 --> 00:20:32,240 Speaker 1: different perspectives that a candidate would bring to Penny. I 328 00:20:32,240 --> 00:20:34,400 Speaker 1: don't think that you should hire someone just because they're 329 00:20:34,400 --> 00:20:37,399 Speaker 1: black or just because they're a woman. That will fill 330 00:20:37,400 --> 00:20:39,080 Speaker 1: a short term goal, but that's not going to pay 331 00:20:39,080 --> 00:20:41,280 Speaker 1: off in the long run. And I feel like so 332 00:20:41,359 --> 00:20:45,000 Speaker 1: often we we equate identity with experience and it's not 333 00:20:45,080 --> 00:20:48,399 Speaker 1: the same thing, although sometimes they're tied, right, So I 334 00:20:48,440 --> 00:20:51,040 Speaker 1: think if you can figure out a way to really 335 00:20:51,160 --> 00:20:54,440 Speaker 1: capture that, to capture the experience part of the identity 336 00:20:54,480 --> 00:20:57,200 Speaker 1: as opposed to just the identity in a vacuum, like 337 00:20:57,280 --> 00:20:59,639 Speaker 1: that's when you're kind of like more set up for 338 00:20:59,680 --> 00:21:05,160 Speaker 1: a six US, Which brings us to our climax. Did Mitch, Alexandrew, 339 00:21:05,200 --> 00:21:09,040 Speaker 1: and Jonathan hire the female programmer? We followed up with 340 00:21:09,080 --> 00:21:12,000 Speaker 1: them in late November, and even though they were approaching 341 00:21:12,040 --> 00:21:14,639 Speaker 1: a busy time, they decided to give her an offer. 342 00:21:15,280 --> 00:21:18,720 Speaker 1: Her name's Vertica Shrivastav, and we met her just a 343 00:21:18,720 --> 00:21:22,479 Speaker 1: few days after she accepted Penny's offer. She told us 344 00:21:22,480 --> 00:21:24,800 Speaker 1: that as she's met with all these different companies, she 345 00:21:24,920 --> 00:21:31,520 Speaker 1: knew she was likely going to be an outlier. I 346 00:21:31,560 --> 00:21:35,200 Speaker 1: made it a point to ask Um how many female 347 00:21:35,240 --> 00:21:39,320 Speaker 1: engineers they had, and after I started asking, I realized 348 00:21:39,320 --> 00:21:41,760 Speaker 1: it made people uncomfortable. I didn't mean I didn't mean 349 00:21:41,800 --> 00:21:44,480 Speaker 1: it as like a point of superiority. I just wanted 350 00:21:44,520 --> 00:21:47,040 Speaker 1: to know how many female engineers because it would affect 351 00:21:47,080 --> 00:21:51,800 Speaker 1: me as someone joining their team. And it made people uncomfortable. UM. 352 00:21:51,960 --> 00:21:54,040 Speaker 1: They kind of be like, you know, well we had 353 00:21:54,119 --> 00:21:59,919 Speaker 1: this engineer. She left UM, but she didn't sense that 354 00:22:00,040 --> 00:22:04,359 Speaker 1: same discomfort at Penny. I had asked him a question UM, 355 00:22:04,400 --> 00:22:07,359 Speaker 1: saying that companies always asked me why am I interested 356 00:22:07,400 --> 00:22:09,080 Speaker 1: in them? I think it's only fair for me to 357 00:22:09,119 --> 00:22:11,879 Speaker 1: ask UM, why are you interested in me? And I 358 00:22:12,000 --> 00:22:14,239 Speaker 1: like that They didn't like tiptoe around the fact that 359 00:22:14,280 --> 00:22:17,600 Speaker 1: I'm a female engineer. They're like, diversity is something that's 360 00:22:17,600 --> 00:22:21,320 Speaker 1: really important to us, and you're clearly different because you're female, 361 00:22:21,359 --> 00:22:23,520 Speaker 1: and then also listed like other things that were a 362 00:22:23,520 --> 00:22:27,040 Speaker 1: little different about me than I guess the average software engineer. 363 00:22:27,440 --> 00:22:29,240 Speaker 1: So like that. They're honest about that, and they're not 364 00:22:29,280 --> 00:22:31,879 Speaker 1: trying to just because I'm a girl got out of 365 00:22:31,880 --> 00:22:34,560 Speaker 1: the team, like they saw something more in me other 366 00:22:34,600 --> 00:22:42,040 Speaker 1: than just that I'm a female. Fortica starts this week, 367 00:22:42,320 --> 00:22:46,719 Speaker 1: but Mitch's work isn't over. Wi Bond, the diversity consultant, 368 00:22:46,880 --> 00:22:48,800 Speaker 1: told us that it's not going to be enough to 369 00:22:48,920 --> 00:22:52,960 Speaker 1: just hire candidates with minority backgrounds. The hardest part is 370 00:22:53,000 --> 00:22:55,440 Speaker 1: making sure that the new hire feels like a real 371 00:22:55,680 --> 00:22:59,040 Speaker 1: and necessary part of a team with challenging work, but 372 00:22:59,119 --> 00:23:02,440 Speaker 1: also the right amount of support. It's a difficult balance, 373 00:23:03,520 --> 00:23:06,439 Speaker 1: Mitch just success in the long term ultimately depends on 374 00:23:06,480 --> 00:23:10,640 Speaker 1: whether Vertica stays and thrives at Penny and down the road, 375 00:23:10,840 --> 00:23:13,600 Speaker 1: whether the different people Mitch keeps hiring will make the 376 00:23:13,600 --> 00:23:17,600 Speaker 1: app more useful and enjoyable to everyone, not just people 377 00:23:17,680 --> 00:23:40,480 Speaker 1: in the Silicon Valley bubble. Okay, silent, it is June 378 00:23:41,280 --> 00:23:45,520 Speaker 1: now because birth con. She's been good. She's still at Penny. 379 00:23:45,720 --> 00:23:49,080 Speaker 1: I talked to her and Mitch this week and asked her, 380 00:23:49,760 --> 00:23:52,880 Speaker 1: you know, what her experience was like, and I was surprised. 381 00:23:52,880 --> 00:23:55,840 Speaker 1: She told me, you know, the first um part of 382 00:23:55,840 --> 00:23:59,320 Speaker 1: her time there, she felt actually pretty uncomfortable, pretty because 383 00:23:59,320 --> 00:24:02,000 Speaker 1: she was the only woman there. Yeah, and I think 384 00:24:02,040 --> 00:24:05,199 Speaker 1: because her engineering background was different. I think she just 385 00:24:05,560 --> 00:24:08,560 Speaker 1: you know, she didn't have a background in fintech. Uh. 386 00:24:08,560 --> 00:24:10,600 Speaker 1: But she told me she was really touched by how 387 00:24:10,680 --> 00:24:13,760 Speaker 1: much the company really tried to make sure she understood 388 00:24:13,800 --> 00:24:16,040 Speaker 1: they felt she belonged with them, she was part of 389 00:24:16,080 --> 00:24:18,919 Speaker 1: the team, that kind of thing. So, you know, for example, 390 00:24:18,920 --> 00:24:21,800 Speaker 1: they eat lunch together every day, and she said that 391 00:24:22,160 --> 00:24:25,320 Speaker 1: even though the conversation could sometimes move to topics that 392 00:24:25,359 --> 00:24:28,720 Speaker 1: she wasn't that interested in, she noticed that at different points, 393 00:24:28,800 --> 00:24:31,720 Speaker 1: you know, different coworkers of hers would notice and try 394 00:24:31,800 --> 00:24:34,480 Speaker 1: to move the conversation back to something that we could 395 00:24:34,480 --> 00:24:36,960 Speaker 1: that they could all participate in. So they made a 396 00:24:37,000 --> 00:24:39,840 Speaker 1: real effort. Yeah, and she felt like, you know, they 397 00:24:39,880 --> 00:24:41,920 Speaker 1: were the ones who were really pushing the message like, yes, 398 00:24:42,000 --> 00:24:44,240 Speaker 1: you're part of the team. And if anything, she said, 399 00:24:44,240 --> 00:24:46,359 Speaker 1: she was the one who was nervous about it or 400 00:24:46,480 --> 00:24:48,760 Speaker 1: or unsure that that was that that was the correct 401 00:24:48,760 --> 00:24:52,400 Speaker 1: thing to do. And she also said, you know, Mitch 402 00:24:52,440 --> 00:24:55,040 Speaker 1: made some changes to his leadership style to accommodate her 403 00:24:55,080 --> 00:24:57,360 Speaker 1: as well, Like she had told him at one point 404 00:24:57,400 --> 00:25:00,200 Speaker 1: a few months in that you know, he's really excitable. 405 00:25:00,240 --> 00:25:02,600 Speaker 1: He really he talks fast and things out loud. He 406 00:25:02,680 --> 00:25:05,600 Speaker 1: was really outgoing. Yeah, and she said he you know, 407 00:25:05,680 --> 00:25:07,920 Speaker 1: she told him at one point, you know, when people 408 00:25:07,960 --> 00:25:10,680 Speaker 1: speak loudly, it makes me feel like there's not enough 409 00:25:10,680 --> 00:25:12,919 Speaker 1: space for me to give my ideas, or it makes me, 410 00:25:13,119 --> 00:25:15,919 Speaker 1: you know, sort of pull back. And she said she 411 00:25:16,000 --> 00:25:20,400 Speaker 1: noticed Mitch tried really hard to speak more softly when 412 00:25:20,400 --> 00:25:22,920 Speaker 1: he was talking with her, and even though she was 413 00:25:22,960 --> 00:25:25,200 Speaker 1: a little embarrassed that that change had to be made 414 00:25:25,200 --> 00:25:28,120 Speaker 1: and had to be asked for, she was grateful that 415 00:25:28,200 --> 00:25:30,760 Speaker 1: it was something that people really put effort into changing 416 00:25:30,800 --> 00:25:33,080 Speaker 1: so that she could work there as well as everyone else. 417 00:25:34,280 --> 00:25:36,400 Speaker 1: One of the things that Mitch talked about in our 418 00:25:36,440 --> 00:25:40,160 Speaker 1: original episode was that hiring for diversity is not just 419 00:25:40,320 --> 00:25:43,320 Speaker 1: the morally right thing to do, it's also a good 420 00:25:43,359 --> 00:25:48,560 Speaker 1: business decision. And I'm wondering if hiring Vertica was a 421 00:25:48,600 --> 00:25:52,119 Speaker 1: good ended up being a good business decision for him. 422 00:25:52,160 --> 00:25:56,040 Speaker 1: I think he would agree, He said, for example, you know, 423 00:25:56,160 --> 00:25:59,000 Speaker 1: she would have prospective and ideas that he would have 424 00:25:59,080 --> 00:26:01,639 Speaker 1: never come up with him self. So he cited this 425 00:26:01,720 --> 00:26:06,120 Speaker 1: time when they were talking about checking accounts and saving accounts, 426 00:26:06,160 --> 00:26:08,560 Speaker 1: and he and the other guys had sort of assumed 427 00:26:08,680 --> 00:26:12,200 Speaker 1: most people knew the difference between these two Rota chimed 428 00:26:12,240 --> 00:26:14,480 Speaker 1: in and said, you know, actually I only recently learned 429 00:26:14,480 --> 00:26:17,639 Speaker 1: the difference between these accounts. Maybe we should include an 430 00:26:17,640 --> 00:26:21,520 Speaker 1: explanation for our users, and they did, and um, a 431 00:26:21,520 --> 00:26:24,280 Speaker 1: lot of users clicked on, you know, wanting to know more. 432 00:26:24,400 --> 00:26:27,159 Speaker 1: And so he was shown, you know, her perspective was 433 00:26:27,160 --> 00:26:30,359 Speaker 1: actually correct. They had made an assumption about their users 434 00:26:30,359 --> 00:26:32,880 Speaker 1: that turned out not to be so true. And so 435 00:26:33,040 --> 00:26:36,040 Speaker 1: I think he was really grateful that having people just 436 00:26:36,080 --> 00:26:38,920 Speaker 1: with a different background, a different experience, which show small 437 00:26:38,960 --> 00:26:40,919 Speaker 1: things like that that could still be really important to 438 00:26:40,920 --> 00:26:44,119 Speaker 1: how someone uses their product. It's been a year and 439 00:26:44,160 --> 00:26:47,239 Speaker 1: a half since we first aired that episode, and a 440 00:26:47,320 --> 00:26:50,200 Speaker 1: lot has happened since then in Silicon Valley and also 441 00:26:50,280 --> 00:26:53,359 Speaker 1: other industries too, about the way we think about women 442 00:26:53,359 --> 00:26:56,960 Speaker 1: in the workplace. I'm wondering if he sensed if Mitch 443 00:26:57,040 --> 00:27:00,480 Speaker 1: had changed his thinking at all about the way he 444 00:27:00,600 --> 00:27:05,159 Speaker 1: thinks about diversity. I think he probably stands in about 445 00:27:05,200 --> 00:27:08,399 Speaker 1: the same position. You know, he's pretty committed to the 446 00:27:08,440 --> 00:27:11,000 Speaker 1: ways that he views it. I think it's, you know, 447 00:27:11,280 --> 00:27:13,960 Speaker 1: speaking as a reporter, it's been interesting to view how 448 00:27:14,040 --> 00:27:17,359 Speaker 1: both you know, the me too conversation has led us 449 00:27:17,400 --> 00:27:20,520 Speaker 1: to hear a lot more stories about how harassment in 450 00:27:20,560 --> 00:27:23,800 Speaker 1: the workplace holds women back or affects the workplace dynamics. 451 00:27:24,359 --> 00:27:26,960 Speaker 1: And then we also I think I've been getting more 452 00:27:27,040 --> 00:27:31,199 Speaker 1: people speaking up on the other side, white men who 453 00:27:31,280 --> 00:27:34,359 Speaker 1: say that they are starting to be discriminated against by 454 00:27:34,400 --> 00:27:38,440 Speaker 1: over zealous diversity efforts, like the former Google engineer. Yeah, 455 00:27:38,560 --> 00:27:40,680 Speaker 1: James damm or Is is really sort of a big 456 00:27:40,720 --> 00:27:43,600 Speaker 1: player in this. And it's just been kind of crazy 457 00:27:43,600 --> 00:27:46,760 Speaker 1: to watch these two conversations come out at once and 458 00:27:46,760 --> 00:27:48,280 Speaker 1: and see how those are playing out. So I think 459 00:27:48,280 --> 00:27:51,320 Speaker 1: if you're a CEO today, you're weighing a lot of 460 00:27:51,359 --> 00:27:54,399 Speaker 1: different questions about how to approach diversity and inclusion at 461 00:27:54,440 --> 00:27:57,680 Speaker 1: your company. So do you think there's anything that those 462 00:27:57,680 --> 00:28:01,479 Speaker 1: CEOs can learn from a dis experience. I think just 463 00:28:01,600 --> 00:28:04,680 Speaker 1: that diversity and inclusion is a lot of work. Uh, 464 00:28:04,960 --> 00:28:06,760 Speaker 1: Mitch put in a lot of work to hire EVERTHCA 465 00:28:07,280 --> 00:28:09,159 Speaker 1: and a lot of work afterward to make sure she 466 00:28:09,240 --> 00:28:14,000 Speaker 1: felt comfortable and included. He told me that he feels 467 00:28:14,000 --> 00:28:16,639 Speaker 1: like it would have saved him time, you know, if 468 00:28:16,640 --> 00:28:18,639 Speaker 1: he was doing more hiring in the future, now that 469 00:28:18,720 --> 00:28:20,879 Speaker 1: they have a better idea of this process, now that 470 00:28:20,920 --> 00:28:23,320 Speaker 1: there already is a woman at the company. Yeah, and 471 00:28:23,320 --> 00:28:25,520 Speaker 1: that they know, you know, they've made some changes to 472 00:28:25,560 --> 00:28:29,440 Speaker 1: their process to help make it more accessible to different groups. Uh. 473 00:28:29,440 --> 00:28:33,240 Speaker 1: But Penny was actually acquired in March by Credit Karma, 474 00:28:33,440 --> 00:28:38,160 Speaker 1: so they won't be doing that same kind of hiring anymore. Uh. Yeah, 475 00:28:38,240 --> 00:28:41,160 Speaker 1: the whole team went over to start working at Credit Karma, 476 00:28:41,480 --> 00:28:45,320 Speaker 1: including Brethica, including birtha um and so now they're developing 477 00:28:45,760 --> 00:28:48,120 Speaker 1: a chat functionality for Credit Karma, which is a much 478 00:28:48,120 --> 00:28:50,120 Speaker 1: bigger company. So he's in a little bit of a 479 00:28:50,160 --> 00:28:53,160 Speaker 1: different position now he's not CEO of his own company. 480 00:28:53,200 --> 00:28:56,560 Speaker 1: But I think he definitely feels like the effort he 481 00:28:56,640 --> 00:29:00,400 Speaker 1: put in for that first hiring process is something that 482 00:29:00,440 --> 00:29:02,200 Speaker 1: he would have been able to reuse if he'd been 483 00:29:02,200 --> 00:29:04,400 Speaker 1: doing it again in the future. Well, best of luck 484 00:29:04,440 --> 00:29:07,400 Speaker 1: to Mention and his team, and thanks for that update. Ellen. Yeah, 485 00:29:07,520 --> 00:29:18,560 Speaker 1: you're welcome, and that's it for this week's episode of Decrypted. 486 00:29:18,720 --> 00:29:22,120 Speaker 1: Thanks for listening. Do you have a story about diversity 487 00:29:22,120 --> 00:29:24,920 Speaker 1: in Silicon Valley? You can send us a message at 488 00:29:25,040 --> 00:29:28,640 Speaker 1: Decrypted at Bloomberg dot net or I'm on Twitter at 489 00:29:28,720 --> 00:29:31,840 Speaker 1: Ellen Hewitt and I'm at aki Eto seven. If you 490 00:29:31,920 --> 00:29:35,240 Speaker 1: enjoy listening to Decrypted, please recommend us to your friends, 491 00:29:35,600 --> 00:29:37,480 Speaker 1: and if you have it already, take a moment to 492 00:29:37,520 --> 00:29:40,440 Speaker 1: write and review our show. This really helps us find 493 00:29:40,440 --> 00:29:44,440 Speaker 1: new listeners. The original episode was produced by Pia get Cary, 494 00:29:44,720 --> 00:29:48,400 Speaker 1: Liz Smith and Magnus Hendrickson. And thanks to topor Foreheads 495 00:29:48,480 --> 00:29:51,680 Speaker 1: for his help on this update. Francesco Leavie is the 496 00:29:51,680 --> 00:30:04,520 Speaker 1: head of Bloomberg Podcasts. We'll see you next week. Table 497 00:30:04,720 --> 00:30:11,680 Speaker 1: of the Abacus Robert, the last of Man of Ragus