1 00:00:00,280 --> 00:00:03,760 Speaker 1: Don't let your legacy I T systems cost you money, innovation, 2 00:00:03,880 --> 00:00:06,080 Speaker 1: and a place at the digital table of the future. 3 00:00:06,360 --> 00:00:08,959 Speaker 1: You can change your systems and the economics of it 4 00:00:09,520 --> 00:00:12,840 Speaker 1: with software from red Hat See how at red hat 5 00:00:13,119 --> 00:00:18,319 Speaker 1: dot com. Today's episode is about a guy called Mitch 6 00:00:18,360 --> 00:00:22,480 Speaker 1: al Lee. He runs a startup called Penny in San Francisco. 7 00:00:23,160 --> 00:00:25,759 Speaker 1: We'll tell you later about what Penny does. For now, 8 00:00:25,840 --> 00:00:27,800 Speaker 1: what you need to know is Penny is almost two 9 00:00:27,840 --> 00:00:31,880 Speaker 1: years old and it's really small. It's Mitch, his co founder, Alex, 10 00:00:32,080 --> 00:00:36,800 Speaker 1: and two employees. They're all software engineers, and big surprise, 11 00:00:37,080 --> 00:00:40,840 Speaker 1: they're all guys. A few months ago, Mitch started looking 12 00:00:40,920 --> 00:00:44,160 Speaker 1: for a fifth employee, and he wanted that fifth person 13 00:00:44,240 --> 00:00:47,480 Speaker 1: to be different from him and the rest of the team. 14 00:00:47,520 --> 00:00:49,600 Speaker 1: And since this is a podcast and we can't just 15 00:00:49,640 --> 00:00:52,800 Speaker 1: show you a picture of Mitch, we asked Mitch's sister 16 00:00:52,960 --> 00:00:57,920 Speaker 1: Christina to describe what he looks like. He's always got 17 00:00:58,240 --> 00:01:01,360 Speaker 1: some like really soft hoodie or T shirt on and 18 00:01:01,480 --> 00:01:04,440 Speaker 1: jeans or corduroys. If he was walking down the street, 19 00:01:04,480 --> 00:01:10,640 Speaker 1: you probably wouldn't necessarily notice. He blends in pretty well. Basically, 20 00:01:10,800 --> 00:01:13,480 Speaker 1: Mitch looks just like a lot of other people in 21 00:01:13,520 --> 00:01:17,520 Speaker 1: Silicon Valley. He's young, he's white, he's straight, and he 22 00:01:17,560 --> 00:01:20,120 Speaker 1: went to a top college. Yeah, he even has this 23 00:01:20,319 --> 00:01:23,760 Speaker 1: neatly trimmed beard, and perhaps because he's a cyclist like 24 00:01:23,800 --> 00:01:26,640 Speaker 1: the rest of San Francisco, he has a nice tan too. 25 00:01:27,480 --> 00:01:30,600 Speaker 1: In Silicon Valley, most programmers are white and Asian men 26 00:01:30,760 --> 00:01:34,759 Speaker 1: who have computer science degrees from elite universities. But for 27 00:01:34,920 --> 00:01:38,240 Speaker 1: Mitch's next higher he's committed to looking outside that pool 28 00:01:38,280 --> 00:01:42,280 Speaker 1: of people. That's pretty unusual for a company of penny size. 29 00:01:42,800 --> 00:01:46,400 Speaker 1: Some people would call it affirmative action hiring, because Mitch 30 00:01:46,560 --> 00:01:49,800 Speaker 1: is going to be actively considering the candidates background when 31 00:01:49,800 --> 00:01:53,240 Speaker 1: he's deciding who to hire. And it's a very touchy topic. 32 00:01:53,920 --> 00:01:56,680 Speaker 1: As we'll find out, not everyone agrees that it's the 33 00:01:56,840 --> 00:02:07,280 Speaker 1: right thing to do him Ako and I'm Ellen Hewitt, 34 00:02:07,520 --> 00:02:10,359 Speaker 1: and this week Undecrypted, we're going to be talking about 35 00:02:10,400 --> 00:02:13,680 Speaker 1: something that every technology company says they want to do 36 00:02:13,800 --> 00:02:17,960 Speaker 1: something about, which is diversity in the workforce. Well, they 37 00:02:18,080 --> 00:02:20,720 Speaker 1: want to talk about it in these lofty slogans, but 38 00:02:20,840 --> 00:02:24,480 Speaker 1: when you actually drill into the specifics, things start to 39 00:02:24,520 --> 00:02:27,760 Speaker 1: get uncomfortable. We found one of the few guys in 40 00:02:27,800 --> 00:02:31,160 Speaker 1: the industry, willing to speak completely openly, willing to get 41 00:02:31,360 --> 00:02:35,239 Speaker 1: really uncomfortable with us. Someone who's trying to do something 42 00:02:35,280 --> 00:02:38,360 Speaker 1: to fix the lack of diversity and tech from within 43 00:02:38,520 --> 00:02:49,040 Speaker 1: his own tiny startup. Oh cool, I look at this 44 00:02:49,120 --> 00:02:58,160 Speaker 1: giant chess board. The guy's penny work in a small 45 00:02:58,240 --> 00:03:03,040 Speaker 1: coworking space in downtown San Francisco. They're on a shoestring budget. 46 00:03:03,160 --> 00:03:05,960 Speaker 1: It's really not a glamorous place. They don't have free snacks, 47 00:03:06,040 --> 00:03:09,320 Speaker 1: there isn't very good natural light, and their office is 48 00:03:09,360 --> 00:03:13,880 Speaker 1: about the size of the bedroom room, A spacious, lovely 49 00:03:15,680 --> 00:03:17,600 Speaker 1: I was telling her, I'm excited for her to see 50 00:03:17,639 --> 00:03:22,520 Speaker 1: how like the other half of startups live, this is 51 00:03:22,560 --> 00:03:25,200 Speaker 1: the don't spend a lot of money half of stars. 52 00:03:26,240 --> 00:03:29,560 Speaker 1: Inside this tiny office, there are four guys sitting side 53 00:03:29,560 --> 00:03:33,480 Speaker 1: by side. My name's Mitch. I grew up in San Jose. 54 00:03:33,760 --> 00:03:38,800 Speaker 1: I'm a mid twenties white guy. I'm alex and Stanford 55 00:03:39,480 --> 00:03:43,480 Speaker 1: is American. My name's Andrew Donnas. I'm half black, half white. 56 00:03:43,960 --> 00:03:48,320 Speaker 1: Um I'm married, and I'm my dad. I am Jonathan, 57 00:03:48,600 --> 00:03:52,960 Speaker 1: and I was born in Taiwan and I grew up 58 00:03:53,000 --> 00:03:56,360 Speaker 1: in Maryland. All four of them believe it's the right 59 00:03:56,400 --> 00:04:00,560 Speaker 1: move for their company to be prioritizing diversity now. Not 60 00:04:00,680 --> 00:04:03,840 Speaker 1: just because it's the right thing to do, they also 61 00:04:03,920 --> 00:04:06,680 Speaker 1: believe it will help their product. Yeah, Penny is this 62 00:04:06,760 --> 00:04:09,440 Speaker 1: app that links to your bank accounts and can give 63 00:04:09,480 --> 00:04:13,080 Speaker 1: you financial advice based on your spending patterns. Here's Mitch. 64 00:04:13,680 --> 00:04:16,760 Speaker 1: As we were working on that core product, we were realizing, 65 00:04:17,240 --> 00:04:20,440 Speaker 1: we're both two software engineers that grew up in the 66 00:04:20,440 --> 00:04:23,440 Speaker 1: Bay Area. How are we going to get a product 67 00:04:23,480 --> 00:04:27,359 Speaker 1: that does well in Montana and Kansas and Maine. And 68 00:04:28,320 --> 00:04:31,680 Speaker 1: our answer to that was, we should be solving this 69 00:04:31,760 --> 00:04:35,440 Speaker 1: by building out a diverse team that can empathize with 70 00:04:35,520 --> 00:04:38,600 Speaker 1: people from different parts of the country, from different genders 71 00:04:38,680 --> 00:04:42,240 Speaker 1: or different ethnicities. And you use the app by messaging 72 00:04:42,360 --> 00:04:45,960 Speaker 1: this chap bot, this computer program that texts back and 73 00:04:46,000 --> 00:04:48,800 Speaker 1: forth with you. The chat bot is called Penny, and 74 00:04:48,839 --> 00:04:52,560 Speaker 1: Penny has a female face. But at the beginning, everything 75 00:04:52,600 --> 00:04:55,839 Speaker 1: Penny said was written by two guys, Alex and Mitch. 76 00:04:56,480 --> 00:05:01,799 Speaker 1: Here's Mitch's fiance, Lizzie Wagner, explaining one way that went awry. 77 00:05:02,320 --> 00:05:04,000 Speaker 1: So they started to use a little bit of like 78 00:05:04,040 --> 00:05:06,200 Speaker 1: a snarky tone, you know when you use like a 79 00:05:06,240 --> 00:05:09,479 Speaker 1: winky emoji, it can also be considered flirty. So I 80 00:05:09,520 --> 00:05:11,920 Speaker 1: told him I just read this conversation and I think 81 00:05:11,920 --> 00:05:14,640 Speaker 1: Penny was flirting with me. And he was like, no way, 82 00:05:14,960 --> 00:05:18,200 Speaker 1: it's a computer. It can't flirt, and I was like, no, 83 00:05:18,520 --> 00:05:21,040 Speaker 1: it's a flirty conversation. He's like, this is exactly why 84 00:05:21,080 --> 00:05:24,159 Speaker 1: we need more diverse perspectives, because Alex and I never 85 00:05:24,200 --> 00:05:30,640 Speaker 1: even thought about that comment being taken that way. And 86 00:05:30,960 --> 00:05:34,400 Speaker 1: building a relatable chatbot is super important when you're guiding 87 00:05:34,400 --> 00:05:38,160 Speaker 1: customers through something as personal and sensitive and daunting as 88 00:05:38,200 --> 00:05:41,760 Speaker 1: your finances. When a user writes in and says, I 89 00:05:41,880 --> 00:05:45,440 Speaker 1: overdraft a lot, how do you respond? And some people 90 00:05:45,600 --> 00:05:50,920 Speaker 1: will respond with, well, they should stop spending that much money. 91 00:05:51,240 --> 00:05:53,440 Speaker 1: Other people will put themselves in their shoes and say 92 00:05:53,480 --> 00:05:57,440 Speaker 1: that really sucks. That level of empathy doesn't come if 93 00:05:57,560 --> 00:06:01,159 Speaker 1: everyone thinks and acts the same in a room. They're 94 00:06:01,200 --> 00:06:04,160 Speaker 1: all just gonna confirm each other's opinions of like, well, 95 00:06:04,200 --> 00:06:07,359 Speaker 1: that person shouldn't be spending money they don't have. And 96 00:06:07,440 --> 00:06:10,159 Speaker 1: Penny stands out from the rest of startups and Silk 97 00:06:10,240 --> 00:06:13,880 Speaker 1: on Valley because it's focusing on hiring for diversity so 98 00:06:14,000 --> 00:06:18,240 Speaker 1: early in the company's history. I think the default for 99 00:06:18,320 --> 00:06:23,960 Speaker 1: early stage companies is not necessarily in aversion to diversity. 100 00:06:24,000 --> 00:06:27,400 Speaker 1: It's not an active process of saying we are only 101 00:06:27,440 --> 00:06:30,040 Speaker 1: going to hire people that look and sound just like us. 102 00:06:31,520 --> 00:06:35,560 Speaker 1: It's the idea that we want to move fast, and 103 00:06:35,600 --> 00:06:37,600 Speaker 1: the fastest way to hire people is to pull from 104 00:06:37,600 --> 00:06:40,200 Speaker 1: our network. They get this big paycheck from a venture 105 00:06:40,200 --> 00:06:42,080 Speaker 1: capital firm and they say, great, we're going to spend 106 00:06:42,080 --> 00:06:45,480 Speaker 1: it immediately. They expand their team from two or four 107 00:06:45,520 --> 00:06:50,559 Speaker 1: people to eight or twelve. If you punt the issue 108 00:06:50,560 --> 00:06:53,640 Speaker 1: of diversity down the line, it becomes much harder because 109 00:06:53,680 --> 00:06:57,039 Speaker 1: when you have eleven men on your team on a 110 00:06:57,080 --> 00:07:01,760 Speaker 1: twelve person team, it becomes a hostile work environment for 111 00:07:01,839 --> 00:07:05,080 Speaker 1: women trying to enter into that team. When you have 112 00:07:05,279 --> 00:07:09,160 Speaker 1: a group of all white or all Asian people sitting 113 00:07:09,160 --> 00:07:12,640 Speaker 1: in the same room together, it makes it hostile for 114 00:07:13,560 --> 00:07:21,640 Speaker 1: other minority groups to join that environment. These kinds of 115 00:07:21,640 --> 00:07:26,080 Speaker 1: companies really do exist. We talked to Jennifer Barbattini, as 116 00:07:26,200 --> 00:07:29,120 Speaker 1: software engineer who interviewed with Penny in September, though she 117 00:07:29,160 --> 00:07:33,360 Speaker 1: didn't get the job. I remember there was one company 118 00:07:33,400 --> 00:07:37,160 Speaker 1: that I interviewed with. It was a smallish company like 119 00:07:37,480 --> 00:07:41,880 Speaker 1: fifteen to twenty. They had ten engineers, all ten were male, 120 00:07:42,040 --> 00:07:46,360 Speaker 1: and all ten were from Stanford, And I was like, 121 00:07:47,760 --> 00:07:50,240 Speaker 1: I don't know if I'm a good fit here, like, 122 00:07:50,480 --> 00:07:53,160 Speaker 1: and the hiring person was telling me we're we're trying 123 00:07:53,160 --> 00:07:56,480 Speaker 1: to be diverse, and I'm like, well, okay, but still 124 00:07:56,520 --> 00:07:59,440 Speaker 1: like this is a little intimidating. I don't have a 125 00:07:59,440 --> 00:08:03,679 Speaker 1: Stanford eventual, Um, I'm I'm definitely not. I can't brow talk. 126 00:08:05,440 --> 00:08:07,880 Speaker 1: If it's a problem at a startup with twenty people, 127 00:08:08,160 --> 00:08:11,320 Speaker 1: imagine what it's like at a Google or Facebook or Twitter. 128 00:08:12,120 --> 00:08:16,320 Speaker 1: They have thousands, sometimes tens of thousands of employees, only 129 00:08:16,320 --> 00:08:20,320 Speaker 1: about thirty are women, and only about six percent are 130 00:08:20,400 --> 00:08:24,320 Speaker 1: black or Latino. These companies can hire only people from 131 00:08:24,320 --> 00:08:27,200 Speaker 1: those groups for the next year and their numbers would 132 00:08:27,200 --> 00:08:30,840 Speaker 1: barely move. So it makes sense to start early. But 133 00:08:31,080 --> 00:08:33,640 Speaker 1: the problem for startups is a pressure they're under in 134 00:08:33,679 --> 00:08:36,760 Speaker 1: the very early days. You have maybe a year of 135 00:08:36,840 --> 00:08:39,360 Speaker 1: funding in the bank, you have a bunch of competitors, 136 00:08:39,400 --> 00:08:43,200 Speaker 1: and if you can't hire quickly to build quickly your toast. 137 00:08:44,520 --> 00:08:48,640 Speaker 1: The consensus opinion is start worrying about it. When your 138 00:08:48,640 --> 00:08:50,960 Speaker 1: product is successful enough that you know that you're going 139 00:08:51,000 --> 00:08:53,000 Speaker 1: to be building out a team for the long run. 140 00:08:54,440 --> 00:08:57,719 Speaker 1: Even someone like Mitch who's really determined to focus on this, 141 00:08:58,040 --> 00:09:02,080 Speaker 1: he's run into a lot of obstacle. Remember, Mitch isn't 142 00:09:02,120 --> 00:09:05,920 Speaker 1: this diversity or HR expert. He's just a programmer trying 143 00:09:05,920 --> 00:09:09,439 Speaker 1: to figure it out as he goes along. From a 144 00:09:09,520 --> 00:09:15,520 Speaker 1: macro perspective of actively pursuing diversity, I'm almost but implementation wise, 145 00:09:15,600 --> 00:09:18,600 Speaker 1: I'm i am just throwing darts on board and and 146 00:09:18,720 --> 00:09:21,560 Speaker 1: hoping that we'll learn enough from those to get better. 147 00:09:24,880 --> 00:09:29,040 Speaker 1: Every Monday, Mitch logs onto recruiting sites like angel Lists, Hired, 148 00:09:29,160 --> 00:09:32,280 Speaker 1: and triple Bite. He's looking for candidates or approach, and 149 00:09:32,320 --> 00:09:35,480 Speaker 1: he'll spend one or two hours on each platform. He 150 00:09:35,520 --> 00:09:38,160 Speaker 1: said it takes him maybe six hours on Monday to 151 00:09:38,240 --> 00:09:40,800 Speaker 1: do that outreach, and then the follow up throughout the 152 00:09:40,840 --> 00:09:43,800 Speaker 1: week brings his time to about fifteen hours every week 153 00:09:44,360 --> 00:09:47,880 Speaker 1: just to fill one role. We spent one such Monday 154 00:09:47,960 --> 00:09:50,880 Speaker 1: with Mitch. As you look through a fresh batch of candidates, 155 00:09:51,040 --> 00:09:54,120 Speaker 1: these ones he found and hired this week, there were 156 00:09:54,160 --> 00:09:59,120 Speaker 1: forty two that match to search criteria. The very first 157 00:09:59,120 --> 00:10:04,640 Speaker 1: candidate has real experience, which is great, comes from a university, 158 00:10:04,679 --> 00:10:08,480 Speaker 1: I've never heard of also great and then has experience 159 00:10:08,640 --> 00:10:12,360 Speaker 1: in different areas, and you see the person's photos, and 160 00:10:12,679 --> 00:10:15,480 Speaker 1: you see the person's photo and their name, UM, and 161 00:10:15,679 --> 00:10:19,199 Speaker 1: actually what salary they're interested in, which is pretty interesting. 162 00:10:20,600 --> 00:10:24,120 Speaker 1: So I will start looking through this. Fortunately, when you 163 00:10:24,160 --> 00:10:27,440 Speaker 1: have forty two people every week on this platform, plus 164 00:10:27,480 --> 00:10:30,800 Speaker 1: the hundreds across, a lot of startups look at these 165 00:10:30,800 --> 00:10:33,800 Speaker 1: sites when they're trying to hire. But after a while 166 00:10:34,000 --> 00:10:36,880 Speaker 1: Mitch realized that these job sites, they're only good for 167 00:10:36,960 --> 00:10:40,640 Speaker 1: finding a certain kind of candidate. Angel List is great, UM, 168 00:10:41,320 --> 00:10:46,160 Speaker 1: but not a good place to look for diversity. Same 169 00:10:46,200 --> 00:10:50,120 Speaker 1: with many of the hiring platforms that we tried. I've 170 00:10:50,160 --> 00:10:53,080 Speaker 1: asked them about that in the past, and the typical 171 00:10:53,120 --> 00:10:57,440 Speaker 1: answer is, sorry, this is just what's available. This is 172 00:10:57,480 --> 00:11:01,559 Speaker 1: the pool of candidates. It's predominantly male, it's predominantly white origin. 173 00:11:02,840 --> 00:11:05,400 Speaker 1: So Mitch started to look for ways to find people 174 00:11:05,480 --> 00:11:09,760 Speaker 1: with more varied profiles, things like newsletters and meet ups 175 00:11:09,840 --> 00:11:13,319 Speaker 1: for women and engineering. He also looked for new graduates 176 00:11:13,320 --> 00:11:15,720 Speaker 1: from coding boot camps, which teach you how to code 177 00:11:15,760 --> 00:11:18,920 Speaker 1: in a short period of time. These people tend to 178 00:11:18,960 --> 00:11:22,360 Speaker 1: come from unusual backgrounds, but boot camps didn't turn out 179 00:11:22,400 --> 00:11:25,600 Speaker 1: to be particularly helpful for Mitch. He found that a 180 00:11:25,640 --> 00:11:28,800 Speaker 1: lot of these graduates didn't have enough experience to start 181 00:11:28,840 --> 00:11:32,840 Speaker 1: contributing right away, and Penny, as a young startup, doesn't 182 00:11:32,840 --> 00:11:39,440 Speaker 1: have the resources to train them. To apply to Penny, 183 00:11:39,600 --> 00:11:42,280 Speaker 1: you have to complete a coding questionnaire, kind of like 184 00:11:42,320 --> 00:11:45,319 Speaker 1: a take home test, and Mitch found that some people 185 00:11:45,360 --> 00:11:48,760 Speaker 1: who started it weren't finishing, and a lot of the 186 00:11:48,760 --> 00:11:51,560 Speaker 1: people who were dropping out were people who didn't have 187 00:11:51,600 --> 00:11:55,480 Speaker 1: computer science degrees, or were women, or were programmers of color. 188 00:11:56,000 --> 00:11:58,760 Speaker 1: And it was often these candidates that Mitch was most 189 00:11:58,800 --> 00:12:02,679 Speaker 1: interested in. Mid suspected it might have something to do 190 00:12:02,800 --> 00:12:06,560 Speaker 1: with a lack of confidence, like these people with non 191 00:12:06,600 --> 00:12:10,520 Speaker 1: traditional backgrounds are taking themselves out of the running before 192 00:12:10,559 --> 00:12:14,480 Speaker 1: they even tried. Maybe the job description will say you 193 00:12:14,559 --> 00:12:17,880 Speaker 1: have to have at least three years of experience, and 194 00:12:18,000 --> 00:12:20,640 Speaker 1: let's say often the men with two years of experience 195 00:12:20,679 --> 00:12:25,400 Speaker 1: will apply anyway, but the women wouldn't. Submitch, he tried 196 00:12:25,480 --> 00:12:32,840 Speaker 1: something new, but first a word from our sponsor. Inside 197 00:12:32,880 --> 00:12:36,520 Speaker 1: the most successful organizations, i T has gone from supporting 198 00:12:36,520 --> 00:12:39,360 Speaker 1: the business to driving the business. But the costs of 199 00:12:39,440 --> 00:12:43,960 Speaker 1: legacy infrastructure can impede this progress. Budgets can't stretch enough 200 00:12:44,000 --> 00:12:47,360 Speaker 1: to pay for digital innovation at the speed required. No 201 00:12:47,400 --> 00:12:49,920 Speaker 1: one gets a blank check. The answer is to change 202 00:12:49,920 --> 00:12:53,000 Speaker 1: the economics of your I T by shifting from ownership 203 00:12:53,080 --> 00:12:57,120 Speaker 1: to use, from licenses to subscriptions, from proprietary to open. 204 00:12:57,520 --> 00:13:00,760 Speaker 1: Change the economics of it with open software from red hat. 205 00:13:01,120 --> 00:13:10,880 Speaker 1: Learn more at red hat dot com. Before the break, 206 00:13:11,080 --> 00:13:14,920 Speaker 1: Mitch had a realization that the very candidates he was 207 00:13:15,000 --> 00:13:18,680 Speaker 1: interested in were taking themselves out of the running, maybe 208 00:13:18,679 --> 00:13:21,440 Speaker 1: because they didn't think they would get the job. Anyway, 209 00:13:22,040 --> 00:13:26,800 Speaker 1: Here's what he decided to do about it. When we 210 00:13:26,880 --> 00:13:31,440 Speaker 1: get the sense that somebody has either a confidence issue, 211 00:13:31,520 --> 00:13:34,200 Speaker 1: like just doesn't think they're a good fit for the 212 00:13:34,200 --> 00:13:36,920 Speaker 1: position or doesn't think they have the skill set required, 213 00:13:37,480 --> 00:13:40,320 Speaker 1: we will have a lot more contact with that person 214 00:13:40,760 --> 00:13:43,720 Speaker 1: to assuage their fears. That may mean a phone screen 215 00:13:43,800 --> 00:13:47,520 Speaker 1: much earlier in the process. It may mean more checking emails. 216 00:13:49,040 --> 00:13:52,000 Speaker 1: That helped him shepherd more people through the whole process, 217 00:13:52,040 --> 00:13:55,320 Speaker 1: not just the ultra confident ones. And that was a 218 00:13:55,400 --> 00:13:58,880 Speaker 1: small success, but it all came at a real cost 219 00:13:59,080 --> 00:14:02,360 Speaker 1: in the form of mid just time, the fifteen hours 220 00:14:02,400 --> 00:14:05,600 Speaker 1: a week that Mitch was spending on recruiting. That's fifteen 221 00:14:05,600 --> 00:14:09,960 Speaker 1: hours he's not coding or troubleshooting or mentoring his team. 222 00:14:10,080 --> 00:14:12,080 Speaker 1: Mitch said it was worth it, but you can see 223 00:14:12,120 --> 00:14:15,320 Speaker 1: why a lot of other founders in his position wouldn't 224 00:14:15,360 --> 00:14:17,520 Speaker 1: really have the time to do this. And that brings 225 00:14:17,600 --> 00:14:21,280 Speaker 1: us to the most controversial part. When you get to 226 00:14:21,320 --> 00:14:24,680 Speaker 1: that final stage. Would you pick one person or another 227 00:14:24,960 --> 00:14:36,360 Speaker 1: because they're a minority candidate? What would you do? I 228 00:14:36,400 --> 00:14:39,840 Speaker 1: talked to Jay Schweney Vassan. He's the CEO of Spoke, 229 00:14:39,960 --> 00:14:43,119 Speaker 1: which is a nine person enterprise startup in San Francisco. 230 00:14:44,000 --> 00:14:47,920 Speaker 1: Like Mitch, Ja says he wants a diverse team, but 231 00:14:48,280 --> 00:14:51,480 Speaker 1: he said he's not comfortable with making someone's ethnicity or 232 00:14:51,560 --> 00:14:54,720 Speaker 1: gender one of the reasons why he's hiring them. He 233 00:14:54,960 --> 00:14:58,400 Speaker 1: explicitly did not factor that into the final decision because 234 00:14:58,440 --> 00:15:01,440 Speaker 1: I think that's unfair to us as well as the 235 00:15:01,840 --> 00:15:04,880 Speaker 1: person being hired. Um at the end of the day, 236 00:15:06,320 --> 00:15:09,000 Speaker 1: we want to have the best people possible for each 237 00:15:09,080 --> 00:15:14,600 Speaker 1: role in our company, and this makes sense right. Picking 238 00:15:14,600 --> 00:15:16,840 Speaker 1: someone in part because of what they look like can 239 00:15:16,960 --> 00:15:20,520 Speaker 1: even be seen as employment discrimination, or it can feel 240 00:15:20,560 --> 00:15:23,720 Speaker 1: patronizing to the person who was hired. These are the 241 00:15:23,800 --> 00:15:26,480 Speaker 1: kind of counter arguments you hear a lot in Silicon 242 00:15:26,560 --> 00:15:30,360 Speaker 1: Valley and elsewhere. But we asked, Mitch, here are two 243 00:15:30,480 --> 00:15:36,160 Speaker 1: hypothetical candidates equally strong encoding and other important values at Penny, 244 00:15:36,200 --> 00:15:38,360 Speaker 1: but one's a white guy and the other has a 245 00:15:38,440 --> 00:15:42,760 Speaker 1: less typical background. Who would you hire? You just said 246 00:15:42,800 --> 00:15:48,520 Speaker 1: that they're equally qualified. What we're saying is that the 247 00:15:48,520 --> 00:15:51,600 Speaker 1: the people that were interested in hiring were weighing them 248 00:15:51,600 --> 00:15:54,400 Speaker 1: across all these factors, and so somebody with a diverse 249 00:15:54,440 --> 00:15:58,200 Speaker 1: background and a totally different perspective, in our opinion, is 250 00:15:58,480 --> 00:16:01,680 Speaker 1: more qualified for the position that we're trying to fill. 251 00:16:02,040 --> 00:16:05,400 Speaker 1: Giving us a perspective that we've never considered helps the 252 00:16:05,440 --> 00:16:09,320 Speaker 1: product more than somebody that has our same perspective but 253 00:16:09,320 --> 00:16:11,840 Speaker 1: as good at engineering. Did you have put back from 254 00:16:11,880 --> 00:16:15,280 Speaker 1: people who felt like, what's unfair? I should I be 255 00:16:15,280 --> 00:16:20,080 Speaker 1: punished for going to Stanford's exactly that should I be 256 00:16:20,120 --> 00:16:23,240 Speaker 1: punished for going to Stanford? It's it's something that I 257 00:16:23,240 --> 00:16:27,840 Speaker 1: don't have a good answer to, other than the entire 258 00:16:28,000 --> 00:16:33,520 Speaker 1: playing field is leaning in your direction. I know that 259 00:16:33,600 --> 00:16:38,240 Speaker 1: it can feel like in that specific instance, you're being 260 00:16:38,480 --> 00:16:43,600 Speaker 1: discriminated against or you're somehow unfairly disadvantaged, but you are 261 00:16:43,720 --> 00:16:48,160 Speaker 1: unfairly advantaged everywhere else in your life. So it's just, uh, 262 00:16:48,520 --> 00:16:50,920 Speaker 1: it's just a slight tilt of the playing field, a 263 00:16:50,960 --> 00:16:55,640 Speaker 1: little less in your favor, um, which I'm okay with. 264 00:16:55,800 --> 00:16:57,880 Speaker 1: I can. I can sleep easy at night knowing that, 265 00:16:58,160 --> 00:17:00,920 Speaker 1: knowing that if you graduated from Stanford you're going to 266 00:17:01,000 --> 00:17:06,080 Speaker 1: be fine. I'm not worried about you. And we just 267 00:17:06,200 --> 00:17:09,080 Speaker 1: heard Mitch laughing there. But you can tell just how 268 00:17:09,119 --> 00:17:13,280 Speaker 1: carefully and deliberately Mitch has been choosing his words through 269 00:17:13,320 --> 00:17:16,800 Speaker 1: this whole interview. His face was flushed. You could really 270 00:17:16,800 --> 00:17:19,960 Speaker 1: tell that he was nervous. And I don't blame him. 271 00:17:19,960 --> 00:17:22,639 Speaker 1: Here we are shoving a mic in his face, asking 272 00:17:22,720 --> 00:17:25,480 Speaker 1: him to talk about gender and race and all these 273 00:17:25,520 --> 00:17:28,560 Speaker 1: other things that are so touchy. The things going on 274 00:17:28,600 --> 00:17:31,240 Speaker 1: in my mind are I need to be careful about this. 275 00:17:31,400 --> 00:17:33,159 Speaker 1: I know that people are going to look at me 276 00:17:33,200 --> 00:17:36,560 Speaker 1: and say, you're just some white dude rattling off about diversity, 277 00:17:36,640 --> 00:17:38,879 Speaker 1: but you have no idea what you're talking about. That 278 00:17:38,960 --> 00:17:42,960 Speaker 1: may be true. I may not know what I'm talking about, 279 00:17:42,960 --> 00:17:46,000 Speaker 1: but I want to get that conversation going so that 280 00:17:46,400 --> 00:17:48,879 Speaker 1: I can learn, so that the rest of our team 281 00:17:48,920 --> 00:17:53,359 Speaker 1: can learn, and so that um, we do better moving forward. 282 00:17:53,359 --> 00:17:56,439 Speaker 1: We'd basically just saying, you know, Allen, I really can't 283 00:17:56,480 --> 00:17:59,560 Speaker 1: remember the last time I was this nervous trying to 284 00:17:59,680 --> 00:18:02,000 Speaker 1: come up with the most delicate way to pose these 285 00:18:02,080 --> 00:18:05,439 Speaker 1: questions to me too. And Aki, you and I are 286 00:18:05,480 --> 00:18:08,640 Speaker 1: both women of color, you're also gay. It really makes 287 00:18:08,680 --> 00:18:11,080 Speaker 1: you think back to every time you've ever been offered 288 00:18:11,160 --> 00:18:15,000 Speaker 1: a new job. Yeah, and I guess until now this 289 00:18:15,119 --> 00:18:18,119 Speaker 1: has all been theoretical. But when we went in to 290 00:18:18,280 --> 00:18:21,240 Speaker 1: interview the Penny team, they were in the final stages 291 00:18:21,359 --> 00:18:24,439 Speaker 1: with one candidate. They were talking to a woman and 292 00:18:24,520 --> 00:18:27,960 Speaker 1: she's of Indian descent. While we were in the Penny office, 293 00:18:28,119 --> 00:18:30,240 Speaker 1: we got the team altogether in a room and asked 294 00:18:30,320 --> 00:18:32,879 Speaker 1: them what they thought of her. Yeah, they all thought 295 00:18:32,880 --> 00:18:35,600 Speaker 1: she would work well with the team, but she lacks 296 00:18:35,600 --> 00:18:39,720 Speaker 1: some technical experience. Specifically, she wasn't fluent in the main 297 00:18:39,840 --> 00:18:43,199 Speaker 1: programming language that Penny is written in. On top of that, 298 00:18:43,280 --> 00:18:46,600 Speaker 1: the company was about to enter a really busy period. 299 00:18:47,720 --> 00:18:51,400 Speaker 1: Great culture fit. It's somebody that we would all gladly 300 00:18:51,440 --> 00:18:55,919 Speaker 1: having the room working with us, because she's articulate and 301 00:18:55,960 --> 00:19:00,000 Speaker 1: well thought out and it's very responsive to feedback. How 302 00:19:00,040 --> 00:19:01,919 Speaker 1: do you weigh that with the fact that shifts no 303 00:19:02,040 --> 00:19:06,000 Speaker 1: experience shipping production Ruby code, which is the language that 304 00:19:06,040 --> 00:19:09,600 Speaker 1: we write in UM, but seems to have the aptitude 305 00:19:09,640 --> 00:19:13,399 Speaker 1: to pick that up quickly. Well, the answer is we 306 00:19:13,440 --> 00:19:18,360 Speaker 1: don't know, so ak. When we visited Mention his team, 307 00:19:18,400 --> 00:19:22,080 Speaker 1: everything that they were saying seemed pretty reasonable to us. Yeah, 308 00:19:22,119 --> 00:19:24,560 Speaker 1: I thought he was a really thoughtful guy. I was 309 00:19:24,840 --> 00:19:28,520 Speaker 1: just genuinely impressed. But we're not experts on this either, 310 00:19:28,880 --> 00:19:33,480 Speaker 1: So we outlined Mitch's philosophy and tactics with my Von Hutchinson, 311 00:19:33,640 --> 00:19:36,720 Speaker 1: a former labor lawyer who is now a consultant helping 312 00:19:36,760 --> 00:19:41,040 Speaker 1: smaller startups on diversity and inclusion. You get an A 313 00:19:41,160 --> 00:19:45,679 Speaker 1: for enthusiasm Mitch for this letter grade, I would give 314 00:19:45,760 --> 00:19:49,399 Speaker 1: him a beat. Um. I think that he's doing some 315 00:19:49,480 --> 00:19:52,480 Speaker 1: of the right things. Um, he's taking a couple of risks, 316 00:19:52,520 --> 00:19:54,679 Speaker 1: but he could take bigger risks. I think that he 317 00:19:54,720 --> 00:19:58,120 Speaker 1: could definitely educate itself a little bit more, just comb 318 00:19:58,160 --> 00:20:00,800 Speaker 1: through the resources and see what's out there. My Van 319 00:20:00,960 --> 00:20:04,320 Speaker 1: said that in the long term, Penny should build relationships 320 00:20:04,359 --> 00:20:08,679 Speaker 1: with organizations that are trying to bring more underrepresented groups 321 00:20:08,680 --> 00:20:12,640 Speaker 1: into tech host dinners and meetups and that kind of thing. 322 00:20:13,480 --> 00:20:16,800 Speaker 1: But she also gave Mitch some practical advice for right now, 323 00:20:17,480 --> 00:20:21,119 Speaker 1: like put your pledge to diversity on your company's landing page, 324 00:20:21,280 --> 00:20:24,400 Speaker 1: not just on your job spage. Consider bringing in an 325 00:20:24,400 --> 00:20:27,159 Speaker 1: expert to help guide you instead of trying to figure 326 00:20:27,160 --> 00:20:30,600 Speaker 1: it out for yourself. And when you hire minority candidates, 327 00:20:30,800 --> 00:20:33,760 Speaker 1: don't expect them to do the work of recruiting diverse 328 00:20:33,840 --> 00:20:38,080 Speaker 1: candidates for you. I'm really happy there are guys out 329 00:20:38,119 --> 00:20:42,639 Speaker 1: here like Mitch. I think, um, sometimes we see guys 330 00:20:42,680 --> 00:20:45,520 Speaker 1: like Mitch. Don't stay like Mitch for very long. I 331 00:20:45,560 --> 00:20:47,880 Speaker 1: think it's going to be in the next few years 332 00:20:47,880 --> 00:20:52,160 Speaker 1: is going to be really hard to hold those values 333 00:20:52,560 --> 00:20:56,400 Speaker 1: and not to succumb to the temptations that are going 334 00:20:56,440 --> 00:20:58,840 Speaker 1: to be abound in the industry when it comes to 335 00:20:58,920 --> 00:21:02,320 Speaker 1: making the final call on who to hire. My Van said, 336 00:21:02,440 --> 00:21:05,240 Speaker 1: Mitch is taking the right approach by focusing on the 337 00:21:05,280 --> 00:21:08,240 Speaker 1: different perspectives that a candidate would bring to Penny. I 338 00:21:08,280 --> 00:21:10,400 Speaker 1: don't think that you should hire someone just because they're 339 00:21:10,440 --> 00:21:13,400 Speaker 1: black or just because they're a woman. That will fill 340 00:21:13,440 --> 00:21:15,080 Speaker 1: a short term goal. But that's not going to pay 341 00:21:15,119 --> 00:21:17,280 Speaker 1: off in the long run. And I feel like so 342 00:21:17,359 --> 00:21:21,000 Speaker 1: often we we equate identity with experience and it's not 343 00:21:21,080 --> 00:21:24,439 Speaker 1: the same thing, although sometimes they're tied, right, So I 344 00:21:24,440 --> 00:21:27,040 Speaker 1: think if you can figure out a way to really 345 00:21:27,160 --> 00:21:30,480 Speaker 1: capture that, to capture the experience part of the identity 346 00:21:30,480 --> 00:21:33,200 Speaker 1: as opposed to just the identity in a vacuum, like, 347 00:21:33,320 --> 00:21:35,679 Speaker 1: that's when you're kind of like more set up for 348 00:21:35,720 --> 00:21:41,160 Speaker 1: a success, Which brings us to our climax. Did Mitch, Alexandrew, 349 00:21:41,240 --> 00:21:45,040 Speaker 1: and Jonathan hire the female programmer? We followed up with 350 00:21:45,119 --> 00:21:48,040 Speaker 1: them in late November, and even though they were approaching 351 00:21:48,040 --> 00:21:50,639 Speaker 1: a busy time, they decided to give her an offer. 352 00:21:51,320 --> 00:21:54,720 Speaker 1: Her name's Vertica Shrivastav, and we met her just a 353 00:21:54,760 --> 00:21:58,479 Speaker 1: few days after she accepted Penny's offer. She told us 354 00:21:58,480 --> 00:22:00,840 Speaker 1: that as she's met with all these different companies, she 355 00:22:00,920 --> 00:22:07,520 Speaker 1: knew she was likely going to be an outlier. I 356 00:22:07,600 --> 00:22:11,200 Speaker 1: made it a point to ask Um how many female 357 00:22:11,240 --> 00:22:15,320 Speaker 1: engineers they had, And after I started asking, I realized 358 00:22:15,320 --> 00:22:17,760 Speaker 1: it made people uncomfortable. I didn't mean I didn't mean 359 00:22:17,800 --> 00:22:20,119 Speaker 1: it as like a point of like superiority. I just 360 00:22:20,200 --> 00:22:22,680 Speaker 1: wanted to know how many female engineers because it would 361 00:22:22,680 --> 00:22:25,520 Speaker 1: affect me as someone joining their team, and it made 362 00:22:25,520 --> 00:22:29,600 Speaker 1: people uncomfortable. UM. They kind of be like, you know, well, 363 00:22:29,680 --> 00:22:35,560 Speaker 1: we had this engineer she left. UM, but she didn't 364 00:22:35,560 --> 00:22:38,560 Speaker 1: sense that same discomfort at Penny. I had asked them 365 00:22:38,560 --> 00:22:42,800 Speaker 1: a question, UM, saying that companies always asked me why 366 00:22:42,800 --> 00:22:44,680 Speaker 1: am I interested in them? I think it's only fair 367 00:22:44,760 --> 00:22:47,120 Speaker 1: for me to ask why are you interested in me? 368 00:22:47,680 --> 00:22:49,840 Speaker 1: And I liked that they didn't like tiptoe around the 369 00:22:49,880 --> 00:22:53,199 Speaker 1: fact that I'm a female engineer. They're like, diversity is 370 00:22:53,200 --> 00:22:56,439 Speaker 1: something that's really important to us, and you're clearly different 371 00:22:56,480 --> 00:22:59,160 Speaker 1: because you're female, and then also listed like other things 372 00:22:59,160 --> 00:23:01,200 Speaker 1: that were a little differ and about me then I 373 00:23:01,240 --> 00:23:04,399 Speaker 1: guess the average software engineer. So like that they're honest 374 00:23:04,440 --> 00:23:06,960 Speaker 1: about that and they're not trying to just because I'm 375 00:23:07,000 --> 00:23:09,359 Speaker 1: a girl come out of the team like they saw 376 00:23:09,400 --> 00:23:11,960 Speaker 1: something more in me other than just that I'm a female. 377 00:23:16,680 --> 00:23:21,320 Speaker 1: Vertica starts this week, but Mitch's work isn't over. Wi Bond, 378 00:23:21,359 --> 00:23:24,159 Speaker 1: the diversity consultant, told us that it's not going to 379 00:23:24,280 --> 00:23:28,119 Speaker 1: be enough to just hire candidates with minority backgrounds. The 380 00:23:28,160 --> 00:23:30,879 Speaker 1: hardest part is making sure that the new hire feels 381 00:23:30,920 --> 00:23:33,960 Speaker 1: like a real and necessary part of a team with 382 00:23:34,119 --> 00:23:37,480 Speaker 1: challenging work, but also the right amount of support. It's 383 00:23:37,480 --> 00:23:41,280 Speaker 1: a difficult balance, Mitch just success in the long term 384 00:23:41,440 --> 00:23:45,200 Speaker 1: ultimately depends on whether Vertica stays and thrives at Penny 385 00:23:45,560 --> 00:23:48,640 Speaker 1: and down the road, whether the different people Mitch keeps 386 00:23:48,720 --> 00:23:52,080 Speaker 1: hiring will make the app more useful and enjoyable to everyone, 387 00:23:52,560 --> 00:24:02,600 Speaker 1: not just people in the Silicon Valley bubble. And that's 388 00:24:02,640 --> 00:24:05,840 Speaker 1: it for this week's episode of Decrypted. Thanks for listening. 389 00:24:06,440 --> 00:24:08,680 Speaker 1: You can subscribe to the show on iTunes or any 390 00:24:08,720 --> 00:24:12,160 Speaker 1: of your favorite podcast apps. While you're there, please leave 391 00:24:12,240 --> 00:24:14,960 Speaker 1: us a rating and review. I read each and every 392 00:24:15,000 --> 00:24:17,439 Speaker 1: one of these reviews. It helps us keep making the 393 00:24:17,480 --> 00:24:20,040 Speaker 1: show better, and it also helps us find new listeners 394 00:24:20,800 --> 00:24:23,160 Speaker 1: and tell us what do you think of the state 395 00:24:23,240 --> 00:24:26,560 Speaker 1: of diversity in the tech industry. I'm on Twitter at 396 00:24:26,600 --> 00:24:29,600 Speaker 1: Ellen Hewitt and I'm met aki its O seven. This 397 00:24:29,680 --> 00:24:33,320 Speaker 1: episode was produced by Pierre ged Kari Magnus Hendrickson, and 398 00:24:33,400 --> 00:24:37,240 Speaker 1: Liz Smith. Emily Buso edited Ellen's print story, which you 399 00:24:37,280 --> 00:24:41,840 Speaker 1: can find on Bloomberg dot com Slash Technology. Alec McCabe 400 00:24:41,880 --> 00:24:44,560 Speaker 1: is head of Bloomberg Podcasts. We'll see you next week. 401 00:24:57,480 --> 00:25:00,920 Speaker 1: Don't let your legacy I T systems cost you money, innovation, 402 00:25:01,040 --> 00:25:03,240 Speaker 1: and a place at the digital table of the future. 403 00:25:03,520 --> 00:25:06,159 Speaker 1: You can change your systems and the economics of it 404 00:25:06,720 --> 00:25:10,000 Speaker 1: with software from red Hat. See how at red hat 405 00:25:10,280 --> 00:25:11,760 Speaker 1: dot com.