1 00:00:01,960 --> 00:00:08,400 Speaker 1: Afro tech World, the Child has managing partner and precursor 2 00:00:08,520 --> 00:00:11,480 Speaker 1: Vengures is on the virtual stage talking with Lisa Watson, 3 00:00:11,760 --> 00:00:15,280 Speaker 1: co founders CEO Squad and it comes to no surprise 4 00:00:15,320 --> 00:00:17,760 Speaker 1: that when you're investing early in the startup life cycle, 5 00:00:18,160 --> 00:00:21,120 Speaker 1: many will find out what they originally started building has 6 00:00:21,200 --> 00:00:25,160 Speaker 1: to morph into something new. What does Child think about 7 00:00:25,200 --> 00:00:28,159 Speaker 1: this pivot? Does he get concerned when he invested in 8 00:00:28,280 --> 00:00:32,320 Speaker 1: one thing and it becomes anohing? Yeah, the pivoting question 9 00:00:32,360 --> 00:00:34,479 Speaker 1: is a good one. So I do care about the 10 00:00:34,520 --> 00:00:38,160 Speaker 1: initial idea. I I know some precedency investors who don't 11 00:00:38,200 --> 00:00:40,159 Speaker 1: care about the initial idea. They're just like, this is 12 00:00:40,159 --> 00:00:43,280 Speaker 1: an amazing person that The challenge we have is that 13 00:00:43,400 --> 00:00:46,120 Speaker 1: most of the companies we've acted are raising five two 14 00:00:46,200 --> 00:00:48,080 Speaker 1: million dollars. It's not a lot of money in the 15 00:00:48,120 --> 00:00:52,360 Speaker 1: grand scheme of things. So if your first original idea 16 00:00:52,680 --> 00:00:55,680 Speaker 1: is really off base, you might not have enough time 17 00:00:55,760 --> 00:01:00,440 Speaker 1: to actually pivot to a new idea. Pivot are pretty 18 00:01:00,440 --> 00:01:03,080 Speaker 1: common for us to precede normally because we're backing somebody 19 00:01:03,240 --> 00:01:06,320 Speaker 1: pre product with the hypothesis, and sometimes you roll out 20 00:01:06,360 --> 00:01:09,120 Speaker 1: the hypothesis in the form of a product and it 21 00:01:09,160 --> 00:01:11,200 Speaker 1: turns out that the market responds to it in a 22 00:01:11,360 --> 00:01:16,240 Speaker 1: very different way than you had expected. So whenever that happens, 23 00:01:16,240 --> 00:01:19,160 Speaker 1: I always try to take founders through a set of 24 00:01:19,240 --> 00:01:23,440 Speaker 1: questions to figure out did we get something wrong about 25 00:01:23,440 --> 00:01:28,600 Speaker 1: our initial hypothesis, And sometimes it happens. Sometimes the hypothesis 26 00:01:28,720 --> 00:01:31,920 Speaker 1: was correct, but the market is less interesting than we 27 00:01:32,040 --> 00:01:34,039 Speaker 1: thought once we get interest. So, hey, there's people who 28 00:01:34,080 --> 00:01:36,560 Speaker 1: want to buy this product. They like it, song to 29 00:01:36,640 --> 00:01:38,880 Speaker 1: them is kind of a pain. Servicing them's kind of 30 00:01:38,880 --> 00:01:42,240 Speaker 1: a pain. There's a market here, but not one that 31 00:01:42,280 --> 00:01:45,360 Speaker 1: like we as a company are like super excited to tackle. 32 00:01:47,040 --> 00:01:48,880 Speaker 1: And then sometimes you launch it and you go the 33 00:01:48,920 --> 00:01:52,200 Speaker 1: product we launched was okay, and it's fine, but in 34 00:01:52,280 --> 00:01:54,760 Speaker 1: launching it, we discovered something that we think is way 35 00:01:54,840 --> 00:01:57,880 Speaker 1: more interesting. So the way I think about is it's 36 00:01:57,960 --> 00:02:00,840 Speaker 1: always okay, I think to of it if you're doing 37 00:02:00,880 --> 00:02:04,600 Speaker 1: it from a position of knowledge. What I worry about sometimes, though, 38 00:02:04,720 --> 00:02:07,560 Speaker 1: is people who are flailing. They launched a product, it 39 00:02:07,640 --> 00:02:10,080 Speaker 1: doesn't work immediately, so they launched another product that doesn't 40 00:02:10,080 --> 00:02:12,680 Speaker 1: work immediately. Sometimes you just have to like take a 41 00:02:12,720 --> 00:02:14,640 Speaker 1: breath and figure out what did you learn? And sometimes 42 00:02:14,639 --> 00:02:17,520 Speaker 1: the answer is the product is right, the market's just 43 00:02:17,600 --> 00:02:19,960 Speaker 1: not ready for it. So do we want to hunker 44 00:02:20,000 --> 00:02:22,960 Speaker 1: down and be patient and wait for the market to 45 00:02:23,000 --> 00:02:25,440 Speaker 1: catch up with what we think or do we want 46 00:02:25,440 --> 00:02:27,640 Speaker 1: to just say, hey, you know, we're way too early here. 47 00:02:28,040 --> 00:02:30,240 Speaker 1: We should probably abandon this and find something new. So 48 00:02:30,280 --> 00:02:31,760 Speaker 1: a lot of what I try to get people to 49 00:02:32,520 --> 00:02:35,400 Speaker 1: think through is what did we actually learn from this launch? 50 00:02:35,480 --> 00:02:37,720 Speaker 1: What have we actually learned about the customer? And are 51 00:02:37,720 --> 00:02:40,400 Speaker 1: we doing this from a position of information and knowledge 52 00:02:40,400 --> 00:02:44,680 Speaker 1: and not from a position of like fearer panting. I'm well, Lucas, 53 00:02:44,919 --> 00:02:48,519 Speaker 1: this is black tech, free money. I'm going to answer 54 00:02:48,600 --> 00:02:50,680 Speaker 1: this you to some of the biggest names somebody brightness, 55 00:02:50,760 --> 00:02:54,160 Speaker 1: minds and brilliant ideas. If you black and building simply 56 00:02:54,280 --> 00:03:02,760 Speaker 1: using techis and Chiga back this podcast. This fee Barry 57 00:03:02,840 --> 00:03:06,720 Speaker 1: Williams this chief operating officer of Bandwagon, fan club data 58 00:03:06,720 --> 00:03:09,760 Speaker 1: and identity analytics tech company focus on the sports and 59 00:03:09,880 --> 00:03:14,160 Speaker 1: entertainment space. She's also a diversity inclusion and corporate social 60 00:03:14,200 --> 00:03:18,639 Speaker 1: responsibility consultant. Prior to Bandwagon, she held executive roles at 61 00:03:18,639 --> 00:03:22,280 Speaker 1: companies like al Turtles, Stuff Up, and Facebook. Bar is 62 00:03:22,320 --> 00:03:25,120 Speaker 1: a finger, not a coder, and I wanted to gather 63 00:03:25,240 --> 00:03:27,000 Speaker 1: her thoughts on why it seems to be harder for 64 00:03:27,080 --> 00:03:29,720 Speaker 1: black people who are non technical roles at companies that 65 00:03:29,840 --> 00:03:33,840 Speaker 1: bill tech to see themselves as being in tech. Alternatively, 66 00:03:34,000 --> 00:03:35,960 Speaker 1: if we say we work in HR are legal at 67 00:03:35,960 --> 00:03:38,920 Speaker 1: a healthcare and energy company, we're much quicker to say 68 00:03:39,080 --> 00:03:42,240 Speaker 1: we work in those sectors because there's still some the 69 00:03:42,320 --> 00:03:46,280 Speaker 1: mystification that it needs to happen about what building tech means. Yeah, 70 00:03:46,280 --> 00:03:52,240 Speaker 1: there probably is um level of disconnect. I guess I 71 00:03:52,280 --> 00:03:55,200 Speaker 1: would call it in terms of are you really in 72 00:03:55,240 --> 00:04:00,200 Speaker 1: tech or are you tech? A Jason and I think 73 00:04:00,320 --> 00:04:03,920 Speaker 1: I feel like, particularly in my field, like being a lawyer, 74 00:04:03,960 --> 00:04:07,320 Speaker 1: I feel like you're still in tech because there are 75 00:04:07,360 --> 00:04:09,680 Speaker 1: certain things that you have to do and double check 76 00:04:09,720 --> 00:04:12,680 Speaker 1: in terms and make sure the product doesn't violate any laws, 77 00:04:13,520 --> 00:04:19,200 Speaker 1: um that your privacy is kept private, that you know 78 00:04:19,360 --> 00:04:22,440 Speaker 1: something doesn't happen where the company ends up getting sued 79 00:04:22,520 --> 00:04:27,039 Speaker 1: because of some discriminatory element of what it is that 80 00:04:27,080 --> 00:04:29,720 Speaker 1: you created, or even if it's looking at it from 81 00:04:29,720 --> 00:04:34,520 Speaker 1: the lens of how inclusive is the product, like kind 82 00:04:34,560 --> 00:04:38,960 Speaker 1: a blind person useless. So if the answers know, you 83 00:04:39,040 --> 00:04:41,960 Speaker 1: may be opening yourself up not just two lawsuits. But 84 00:04:42,000 --> 00:04:45,160 Speaker 1: people also neglect the fact that, you know, a tech 85 00:04:45,200 --> 00:04:49,160 Speaker 1: company can do something and get horrible pr and get dragged, 86 00:04:49,200 --> 00:04:51,320 Speaker 1: and that has absolutely nothing to do with whether the 87 00:04:51,360 --> 00:04:54,200 Speaker 1: product was good, bad, or and different. It could be 88 00:04:54,520 --> 00:04:58,080 Speaker 1: you had a really terrible marketing campaign, but if you 89 00:04:58,160 --> 00:05:01,160 Speaker 1: might have had a black marketer in that room, they 90 00:05:01,160 --> 00:05:03,680 Speaker 1: would have told you that that Ancestry DNA where a 91 00:05:03,720 --> 00:05:06,920 Speaker 1: slave runs away with her masters, not necessarily the best 92 00:05:06,920 --> 00:05:10,760 Speaker 1: way to get us sign up for Ancestry DNA care. So, 93 00:05:11,680 --> 00:05:15,160 Speaker 1: you know, sometimes it's about saving the company from itself, 94 00:05:15,320 --> 00:05:19,719 Speaker 1: even if you don't think that that is a an 95 00:05:19,760 --> 00:05:22,440 Speaker 1: adequate tech role. And I'd say that because I'm married 96 00:05:22,480 --> 00:05:25,320 Speaker 1: to a product manager, so he is very in and 97 00:05:25,360 --> 00:05:29,599 Speaker 1: of tech and not tech adjacent. So but I still 98 00:05:29,640 --> 00:05:32,360 Speaker 1: don't think that, you know, I don't think that those 99 00:05:32,480 --> 00:05:34,960 Speaker 1: roles are tech adjacent. They're just as important to keep 100 00:05:35,000 --> 00:05:38,239 Speaker 1: the company going is anything else. Do you think there's 101 00:05:38,320 --> 00:05:41,440 Speaker 1: any level of feeling not included because we may not 102 00:05:41,480 --> 00:05:43,839 Speaker 1: be able to code if we work at a tech 103 00:05:43,839 --> 00:05:46,800 Speaker 1: company and we can't say we work in tech. Yeah, 104 00:05:46,839 --> 00:05:48,800 Speaker 1: I definitely think people will look at it that way. 105 00:05:48,880 --> 00:05:50,320 Speaker 1: There are lots of people who look at it that 106 00:05:50,360 --> 00:05:52,159 Speaker 1: way in a sense that oh, well, you're not really 107 00:05:52,800 --> 00:05:55,680 Speaker 1: you're not really in the industry because you can't write 108 00:05:55,720 --> 00:05:57,760 Speaker 1: a line of code. I sure can't, but I could 109 00:05:58,040 --> 00:06:00,599 Speaker 1: tell you. You show me how what you've written and 110 00:06:00,680 --> 00:06:02,520 Speaker 1: how it works, and I can tell you whether that's 111 00:06:02,600 --> 00:06:09,960 Speaker 1: legal or not valuable. So I rebuke that whole idea 112 00:06:09,960 --> 00:06:12,120 Speaker 1: in the name of Jesus, But I understand that some 113 00:06:12,160 --> 00:06:15,279 Speaker 1: people don't. UM. But I do think that there's definitely 114 00:06:15,400 --> 00:06:18,040 Speaker 1: kind of a there's definitely a hierarchy, and people look 115 00:06:18,080 --> 00:06:20,800 Speaker 1: at it. The people who can code, or the people 116 00:06:20,800 --> 00:06:23,400 Speaker 1: who are product managers, are the people who are product designers. 117 00:06:23,839 --> 00:06:26,360 Speaker 1: Those are the people that are really in tech. All 118 00:06:26,440 --> 00:06:29,960 Speaker 1: your you know, your g n A functions, you're legal, 119 00:06:30,080 --> 00:06:36,120 Speaker 1: your sales, your marketing UM hr dn I. They look 120 00:06:36,160 --> 00:06:39,119 Speaker 1: at that as like it's not really you're not really 121 00:06:39,160 --> 00:06:43,479 Speaker 1: moving the product forward. You know, you've done a lot 122 00:06:43,480 --> 00:06:45,960 Speaker 1: of advocacy for being in the room and trying to 123 00:06:45,960 --> 00:06:47,800 Speaker 1: hold the door open for other people to be able 124 00:06:47,839 --> 00:06:51,279 Speaker 1: to get in the room. But how can you admonish 125 00:06:51,320 --> 00:06:57,080 Speaker 1: other folks to both strategically and successfully advocate for having 126 00:06:57,120 --> 00:07:00,760 Speaker 1: more faces, you know, brown faces, black faces in the room. 127 00:07:00,880 --> 00:07:03,479 Speaker 1: UM when everybody don't want us in the room. So 128 00:07:03,520 --> 00:07:06,080 Speaker 1: how how can we do that and not feel like 129 00:07:06,080 --> 00:07:10,800 Speaker 1: we're in a precarious situation, you know, putting ourselves in jeopardy. Yeah, 130 00:07:10,880 --> 00:07:13,720 Speaker 1: and I think that they're I wouldn't even say that. 131 00:07:13,760 --> 00:07:15,640 Speaker 1: A lot of people don't want us. I said, most 132 00:07:15,640 --> 00:07:18,440 Speaker 1: of them probably don't want us in the room because 133 00:07:18,440 --> 00:07:21,000 Speaker 1: we're gonna point out things that they don't see because 134 00:07:21,440 --> 00:07:23,960 Speaker 1: a lot of these people their coding and building products 135 00:07:24,000 --> 00:07:27,440 Speaker 1: that it's it's solely focused on their lens and view 136 00:07:27,440 --> 00:07:31,720 Speaker 1: of the world, which is not the global population. Sorry 137 00:07:31,880 --> 00:07:35,000 Speaker 1: to say that is not what the global population looks like. 138 00:07:35,600 --> 00:07:39,680 Speaker 1: Y'all are actually in the minority. So they don't want 139 00:07:39,760 --> 00:07:42,240 Speaker 1: us in the room. But I think what's important is, 140 00:07:42,320 --> 00:07:45,280 Speaker 1: and I always lead from a diversity standpoint, is if 141 00:07:45,280 --> 00:07:48,640 Speaker 1: you tell people how much money they can make or 142 00:07:48,640 --> 00:07:51,200 Speaker 1: how much money they will lose, that is when they 143 00:07:51,240 --> 00:07:54,360 Speaker 1: will listen. That is when they will care. They don't 144 00:07:54,440 --> 00:07:57,800 Speaker 1: want to hear like I never ever start a conversation 145 00:07:57,880 --> 00:07:59,640 Speaker 1: with oh, well it's the right thing to do. Well, 146 00:07:59,640 --> 00:08:01,360 Speaker 1: I have hell, we all know it's the right thing 147 00:08:01,360 --> 00:08:04,000 Speaker 1: to do. But if people really cared about doing the 148 00:08:04,080 --> 00:08:07,520 Speaker 1: right thing, it would have already been done. So but 149 00:08:07,680 --> 00:08:10,320 Speaker 1: if I tell you, if you put out this type 150 00:08:10,320 --> 00:08:14,160 Speaker 1: of marketing campaign with these visuals and this narrative, you're 151 00:08:14,160 --> 00:08:16,640 Speaker 1: gonna get dragged on Twitter. That ad is gonna have 152 00:08:16,720 --> 00:08:19,440 Speaker 1: to come down. You will have lost five thousand dollars. 153 00:08:19,480 --> 00:08:22,520 Speaker 1: And however, many men and woman hours creating this ad. 154 00:08:24,120 --> 00:08:27,280 Speaker 1: So there's that angle. Or it's if you make it 155 00:08:27,280 --> 00:08:30,120 Speaker 1: a product that is inclusive and has multiple use cases 156 00:08:30,120 --> 00:08:34,080 Speaker 1: and functions and also maybe has an audio function, has 157 00:08:34,160 --> 00:08:36,400 Speaker 1: a function that you know will allow that to have 158 00:08:36,520 --> 00:08:39,760 Speaker 1: a blind person use it, you're gonna gain that much 159 00:08:39,760 --> 00:08:42,880 Speaker 1: more traction because now more people can use your product. 160 00:08:44,559 --> 00:08:49,599 Speaker 1: So that's more revenue. You know, you testified before Congress 161 00:08:50,400 --> 00:08:55,000 Speaker 1: about AI bias, artificial intelligence bias, and the congressman asked 162 00:08:55,040 --> 00:08:57,640 Speaker 1: you about how that could be. You know, when you're 163 00:08:57,640 --> 00:09:01,400 Speaker 1: just developing this this this software, and I guess some 164 00:09:01,440 --> 00:09:04,000 Speaker 1: people might imagine that it's not inherently racist. It's just 165 00:09:04,360 --> 00:09:06,880 Speaker 1: cold and right. But you know, can you speak to 166 00:09:07,040 --> 00:09:11,360 Speaker 1: what's happening with the development of AI that should be 167 00:09:11,760 --> 00:09:14,360 Speaker 1: potentially cause a concern for black and brown people as 168 00:09:14,400 --> 00:09:19,360 Speaker 1: we get more into a world that relies on artificial intelligence. Yeah, 169 00:09:19,440 --> 00:09:22,000 Speaker 1: And when he asked me that question, I kind of 170 00:09:24,320 --> 00:09:26,360 Speaker 1: I just remember sitting in church with my mom and 171 00:09:26,400 --> 00:09:28,320 Speaker 1: she would just pinch me and say, fix your face, 172 00:09:28,360 --> 00:09:31,040 Speaker 1: Fix your face. Because I was like, how, what do 173 00:09:31,040 --> 00:09:33,320 Speaker 1: you mean? How is it? How could it possibly be 174 00:09:33,400 --> 00:09:37,320 Speaker 1: inherently biases code? Well, who makes the code? People? It's 175 00:09:37,720 --> 00:09:41,480 Speaker 1: unconscious biases, Like it's not like the code magically came 176 00:09:41,520 --> 00:09:44,960 Speaker 1: from the sky. A lot of things that go into 177 00:09:45,000 --> 00:09:48,360 Speaker 1: making these algorithms that will tell you your credit score 178 00:09:49,000 --> 00:09:52,480 Speaker 1: or your availability for a certain mortgage amount. It's based 179 00:09:52,480 --> 00:09:55,760 Speaker 1: on stale data. So the example that I gave was, 180 00:09:55,800 --> 00:09:58,520 Speaker 1: if you are basing this off of data from nineteen 181 00:09:58,559 --> 00:10:02,600 Speaker 1: sixty five, well, they were still red lining in black neighborhoods, 182 00:10:02,640 --> 00:10:05,960 Speaker 1: saying we couldn't having restrictive covenants and deeds and said 183 00:10:06,000 --> 00:10:08,679 Speaker 1: we couldn't buy in certain areas. So if you're using 184 00:10:08,720 --> 00:10:11,719 Speaker 1: that data, it's going to be inherently biased because it's 185 00:10:11,760 --> 00:10:15,199 Speaker 1: based on racist laws. And so if you are not 186 00:10:15,559 --> 00:10:19,360 Speaker 1: updating that data or using additional data to corroborate what 187 00:10:19,520 --> 00:10:23,920 Speaker 1: you have already coded, then you're doing it wrong. And 188 00:10:24,120 --> 00:10:26,679 Speaker 1: the way that the easy summation of that for me 189 00:10:26,760 --> 00:10:31,520 Speaker 1: is who fact checks the fact checkers because in this instance, 190 00:10:32,360 --> 00:10:35,000 Speaker 1: the people that are coding the algorithms that will determine 191 00:10:35,040 --> 00:10:39,360 Speaker 1: your credit worthiness will also determine um. They also use 192 00:10:39,480 --> 00:10:44,120 Speaker 1: this same kind of functionality in policing algorithms to tell 193 00:10:44,200 --> 00:10:47,520 Speaker 1: police where they should go on certain times of days. 194 00:10:47,559 --> 00:10:51,679 Speaker 1: And it's based off of previous crime data history. It's 195 00:10:51,720 --> 00:10:55,680 Speaker 1: based off of moon phases, it's based off temperature records, 196 00:10:56,320 --> 00:10:59,240 Speaker 1: it's based off of like randomness. So you're just gonna say, 197 00:10:59,280 --> 00:11:01,800 Speaker 1: because in nineteen eighty five it was a d eight 198 00:11:01,840 --> 00:11:03,959 Speaker 1: degrees in East Oakland, So now we need to send 199 00:11:04,240 --> 00:11:08,200 Speaker 1: twenty cars to patrol there tonight. Yeah. Like oh, and 200 00:11:08,200 --> 00:11:11,920 Speaker 1: it's also based on sports team records, So I guess 201 00:11:12,640 --> 00:11:16,200 Speaker 1: how do you gauge that? Because how many cop cars 202 00:11:16,200 --> 00:11:17,679 Speaker 1: do you send in Order Dame when they win a 203 00:11:17,720 --> 00:11:21,040 Speaker 1: big game and those kids storm the field versus the 204 00:11:21,080 --> 00:11:24,240 Speaker 1: Warriors lose and you have had a bajillion cars around 205 00:11:24,280 --> 00:11:29,000 Speaker 1: the coliseum, So like, how do you gauge? And a 206 00:11:29,040 --> 00:11:31,960 Speaker 1: lot of that is gonna be you know, unconscious bias. 207 00:11:32,240 --> 00:11:34,040 Speaker 1: You're gonna send the twenty cop cars so where the 208 00:11:34,040 --> 00:11:36,880 Speaker 1: majority of the black people are, but you know, those 209 00:11:36,920 --> 00:11:39,280 Speaker 1: white kids storm in the field. Oh, they're just celebrating. 210 00:11:40,360 --> 00:11:42,440 Speaker 1: Can you enlighten things on fire? Can you? Can you 211 00:11:42,440 --> 00:11:44,800 Speaker 1: talk to me like a little bit about what's what's 212 00:11:44,800 --> 00:11:49,200 Speaker 1: happening in the actual technology that allows it to be biased, 213 00:11:49,200 --> 00:11:52,720 Speaker 1: like seeing certain faces versus other faces, thinking some people 214 00:11:52,760 --> 00:11:56,800 Speaker 1: look at like versus other people looking at like. Oh, well, 215 00:11:56,840 --> 00:11:59,240 Speaker 1: if you're talking about like the unconscious bias and their 216 00:11:59,240 --> 00:12:02,760 Speaker 1: actual test of that where you have to decide and 217 00:12:02,760 --> 00:12:05,360 Speaker 1: that you get like I think three seconds to scroll through, 218 00:12:05,920 --> 00:12:08,920 Speaker 1: um and I've taken this course. You have three seconds 219 00:12:08,960 --> 00:12:12,600 Speaker 1: to decide if this person is like a good person 220 00:12:12,840 --> 00:12:17,760 Speaker 1: or a bad person. And it's interesting because you could see, 221 00:12:18,200 --> 00:12:22,319 Speaker 1: you know, seek men with with their hair wrapped, or 222 00:12:22,400 --> 00:12:24,679 Speaker 1: you could see a black woman with a natural hair, 223 00:12:24,800 --> 00:12:27,320 Speaker 1: or your who's dark skin, or you could see a 224 00:12:27,400 --> 00:12:30,559 Speaker 1: lighter skin black woman with straight hair, or you see 225 00:12:30,679 --> 00:12:35,760 Speaker 1: a white lady, or you see a dark skin black man, 226 00:12:36,000 --> 00:12:38,040 Speaker 1: and you literally just have to press the button and 227 00:12:38,080 --> 00:12:43,080 Speaker 1: figure out, oh is this person not sure? And even 228 00:12:43,120 --> 00:12:44,960 Speaker 1: just like not just looking at their face, but also 229 00:12:45,000 --> 00:12:47,600 Speaker 1: the clothing. Some of them had on college sweatshirts, which 230 00:12:47,679 --> 00:12:50,320 Speaker 1: is gonna give you a whole difference view of that person, right, like, 231 00:12:51,000 --> 00:12:53,880 Speaker 1: so maybe I might be afraid of your face, but 232 00:12:54,080 --> 00:12:56,120 Speaker 1: if I see the fact that you're wearing a shirt 233 00:12:56,120 --> 00:13:00,360 Speaker 1: and says Stanford, maybe I'm less afraid. And it's little 234 00:13:00,400 --> 00:13:03,840 Speaker 1: subtle cues like that too. So you you're dealing with 235 00:13:03,880 --> 00:13:07,120 Speaker 1: people who are taking those tests and to some extent 236 00:13:07,160 --> 00:13:11,120 Speaker 1: are failing them. Uh, and those are the people who 237 00:13:11,120 --> 00:13:14,480 Speaker 1: are actually doing the coding. And it's then understanding that 238 00:13:14,520 --> 00:13:18,240 Speaker 1: even little things like the hand dispensers in um in 239 00:13:18,440 --> 00:13:21,320 Speaker 1: public bathrooms, like you go to the airport and I 240 00:13:21,360 --> 00:13:24,559 Speaker 1: have to stick my hand under that thing three times 241 00:13:24,559 --> 00:13:26,920 Speaker 1: before SoC will come out. And it's because it was 242 00:13:27,000 --> 00:13:32,840 Speaker 1: designed for people with lighter skin hands and little simple 243 00:13:32,920 --> 00:13:36,040 Speaker 1: things where it's not that you think people are doing 244 00:13:36,080 --> 00:13:39,520 Speaker 1: it on purpose, it's just that is what they're used to, 245 00:13:39,679 --> 00:13:42,280 Speaker 1: and so that is how they design the product, and 246 00:13:42,320 --> 00:13:45,480 Speaker 1: the same thing is transferred to code. Well, if this 247 00:13:45,559 --> 00:13:47,680 Speaker 1: is the data that I've been given in order to 248 00:13:48,320 --> 00:13:51,760 Speaker 1: base your credit score off of, and this is historical 249 00:13:51,840 --> 00:13:54,920 Speaker 1: data of what the credit scores and certain based on 250 00:13:55,000 --> 00:13:58,800 Speaker 1: your zip code, based on your age, if this is 251 00:13:58,800 --> 00:14:02,320 Speaker 1: the information that we've had for the last ten years, well, 252 00:14:02,400 --> 00:14:06,320 Speaker 1: then that's what I'm gonna use so we've already established 253 00:14:06,320 --> 00:14:07,960 Speaker 1: in this conversation that you know, they don't want to 254 00:14:07,960 --> 00:14:09,560 Speaker 1: send the room in the first place, you know, in 255 00:14:09,679 --> 00:14:11,520 Speaker 1: order to the people that you're advocating for, they don't 256 00:14:11,559 --> 00:14:14,000 Speaker 1: want to send the room. And then at the same time, 257 00:14:14,080 --> 00:14:18,360 Speaker 1: having us not in the room causes these biased situations 258 00:14:18,679 --> 00:14:20,680 Speaker 1: to proliferate. You know, if to use a big word, 259 00:14:20,720 --> 00:14:23,920 Speaker 1: not respecting the probably a bigger word than necessary. But 260 00:14:24,920 --> 00:14:27,680 Speaker 1: so the million dollar question is is how do we 261 00:14:27,760 --> 00:14:30,400 Speaker 1: get more of us in the room in order to 262 00:14:30,440 --> 00:14:33,400 Speaker 1: be able to solve these problems for our future. I 263 00:14:33,440 --> 00:14:37,200 Speaker 1: think the answer to that is probably twofold one. It's 264 00:14:37,200 --> 00:14:40,800 Speaker 1: going to take people willing to bring people into the 265 00:14:40,880 --> 00:14:44,800 Speaker 1: rooms that are not carbon copies of themselves. And I 266 00:14:44,960 --> 00:14:48,000 Speaker 1: say that because I think the status something like fifty 267 00:14:48,160 --> 00:14:53,360 Speaker 1: three of tech employees are referrals. And let's just say, 268 00:14:53,400 --> 00:14:59,480 Speaker 1: like if the company is probably six white males and 269 00:14:59,600 --> 00:15:03,120 Speaker 1: the other is a mix of everybody else. But also 270 00:15:03,240 --> 00:15:06,440 Speaker 1: in that forty percent would be predominantly Asian. Well, that 271 00:15:06,520 --> 00:15:11,840 Speaker 1: leaves us with at about probably five at best. And 272 00:15:11,880 --> 00:15:13,520 Speaker 1: I know the one thing that I did when I 273 00:15:13,560 --> 00:15:16,080 Speaker 1: worked at Facebook is every single person that I referred 274 00:15:16,440 --> 00:15:19,920 Speaker 1: was a black woman. Even when I left, I still 275 00:15:19,960 --> 00:15:24,000 Speaker 1: would float their resumes, give them to people who I 276 00:15:24,080 --> 00:15:28,240 Speaker 1: knew were allies internally, and the good news is they 277 00:15:28,240 --> 00:15:32,160 Speaker 1: all got hired. But it's because I knew who to 278 00:15:32,240 --> 00:15:35,320 Speaker 1: pick and choose in terms of like advocating for them internally. 279 00:15:35,720 --> 00:15:37,800 Speaker 1: So that's one thing that it's hard to It's like 280 00:15:38,360 --> 00:15:41,320 Speaker 1: nine times I'd attend if you have an internal advocate, 281 00:15:41,400 --> 00:15:44,320 Speaker 1: that will get you farther down the line, But because 282 00:15:44,320 --> 00:15:46,560 Speaker 1: there are a few of us there, it's hard to 283 00:15:46,560 --> 00:15:49,240 Speaker 1: find an internal advocate. So usually it's a friend of 284 00:15:49,280 --> 00:15:52,280 Speaker 1: a friend or somebody from that went to your college 285 00:15:52,880 --> 00:15:55,160 Speaker 1: or your law school or your business school or what 286 00:15:55,280 --> 00:16:00,080 Speaker 1: have you, and doesn't always have to be a a 287 00:16:00,160 --> 00:16:02,680 Speaker 1: internal advocate, but somebody who knows how to navigate that 288 00:16:02,760 --> 00:16:06,920 Speaker 1: system and we'll we'll champion it for you. And I 289 00:16:06,960 --> 00:16:11,960 Speaker 1: think the other piece to that is also just making 290 00:16:12,000 --> 00:16:16,440 Speaker 1: sure that you know if if you get inside, you 291 00:16:16,520 --> 00:16:19,440 Speaker 1: have to be to me. I always look at um. 292 00:16:20,040 --> 00:16:23,680 Speaker 1: There was a line in my black graduation from Berkeley, 293 00:16:23,800 --> 00:16:26,800 Speaker 1: and I think it was Michael Eric Dyson who gave 294 00:16:26,840 --> 00:16:28,720 Speaker 1: the speech, and he always said he at the end 295 00:16:28,760 --> 00:16:32,600 Speaker 1: of the speech, he said, be a trojan horse. So 296 00:16:33,600 --> 00:16:35,560 Speaker 1: that's kind of how I look at everywhere I go 297 00:16:36,200 --> 00:16:39,680 Speaker 1: is And I've seen two types of black people in 298 00:16:39,760 --> 00:16:43,440 Speaker 1: these companies. One is the type that's a trojan horse, 299 00:16:44,040 --> 00:16:46,520 Speaker 1: and it's gonna hold the door open and advocate for 300 00:16:46,560 --> 00:16:49,320 Speaker 1: other people to get inside, or if there are people 301 00:16:49,320 --> 00:16:51,400 Speaker 1: already there, you advocate for them to get promoted, or 302 00:16:51,440 --> 00:16:54,360 Speaker 1: you advocate for them to get better work assignments. And 303 00:16:54,400 --> 00:16:56,920 Speaker 1: the second type of person I've seen is the type 304 00:16:56,960 --> 00:17:00,160 Speaker 1: that wants to be the special Negro snowflake and they 305 00:17:00,160 --> 00:17:03,640 Speaker 1: want to make sure the door slammed because they want 306 00:17:03,640 --> 00:17:05,480 Speaker 1: to be one of a few. Like that makes you 307 00:17:05,520 --> 00:17:10,560 Speaker 1: feel special. Now that doesn't make me feel special, but 308 00:17:10,560 --> 00:17:12,880 Speaker 1: but for some of those people, it's that is their 309 00:17:12,920 --> 00:17:16,720 Speaker 1: calling card. That so the less the less of us 310 00:17:16,720 --> 00:17:20,040 Speaker 1: that are there, the more special they feel. And so 311 00:17:20,320 --> 00:17:22,119 Speaker 1: they want to make sure that the door is shut. 312 00:17:22,720 --> 00:17:26,040 Speaker 1: And I have even seen d I practitioners like that, 313 00:17:26,480 --> 00:17:29,080 Speaker 1: which to me is like, how do you have this job? Well, 314 00:17:29,119 --> 00:17:31,240 Speaker 1: I mean, I know how you have a job because 315 00:17:31,240 --> 00:17:35,840 Speaker 1: you're gonna be a yes person for the majority. But 316 00:17:36,320 --> 00:17:40,760 Speaker 1: it just seems like a a cruel bait and switch. 317 00:17:41,560 --> 00:17:44,480 Speaker 1: But I would say it's it's gonna be that twofold. 318 00:17:45,040 --> 00:17:47,040 Speaker 1: You have to have people that are on the inside 319 00:17:47,119 --> 00:17:49,560 Speaker 1: that are gonna be trojan horses, and you also have 320 00:17:49,600 --> 00:17:52,400 Speaker 1: to make sure that you have internal advocates and work 321 00:17:52,400 --> 00:17:56,280 Speaker 1: your network. You know, tech is still, you know, a 322 00:17:56,400 --> 00:17:59,280 Speaker 1: very new industry, may in may in the mainstream context. 323 00:17:59,320 --> 00:18:02,359 Speaker 1: You know, if you think fifteen years ago, you just 324 00:18:02,400 --> 00:18:05,480 Speaker 1: started to get things like Facebook and etcetera. And then 325 00:18:05,840 --> 00:18:07,879 Speaker 1: you know, so let's say let's call it twenty years 326 00:18:08,040 --> 00:18:10,840 Speaker 1: that tech has been like in the dominant, you know, 327 00:18:11,119 --> 00:18:16,160 Speaker 1: mainstream conversation, there are so many attorneys who maybe work 328 00:18:16,160 --> 00:18:20,080 Speaker 1: in real estate law or maybe they work in criminal law, 329 00:18:20,400 --> 00:18:23,400 Speaker 1: but they want to be in to this tech thing. 330 00:18:24,440 --> 00:18:29,200 Speaker 1: How can they find their way into helping startups? Yeah, 331 00:18:29,280 --> 00:18:34,760 Speaker 1: I would say there are lots of startups that um 332 00:18:35,080 --> 00:18:38,840 Speaker 1: can't retain large firms. So like if you think of 333 00:18:38,920 --> 00:18:46,439 Speaker 1: like a MOFO or Cooley, UM, you know, they just 334 00:18:46,520 --> 00:18:50,159 Speaker 1: can't afford the larger companies. And so the answer to 335 00:18:50,240 --> 00:18:54,840 Speaker 1: that would be, maybe that person offers them discounted services 336 00:18:55,560 --> 00:18:57,719 Speaker 1: just to get the experience to say that they've been 337 00:18:57,760 --> 00:19:00,919 Speaker 1: advising startups or helping startups. I think that's one thing 338 00:19:00,960 --> 00:19:03,440 Speaker 1: that actually would be great in terms of bolstering your resume, 339 00:19:03,520 --> 00:19:06,200 Speaker 1: and then who knows if that startup actually gets more 340 00:19:06,200 --> 00:19:08,000 Speaker 1: funding than you would be the person that they would 341 00:19:08,080 --> 00:19:11,320 Speaker 1: end up hiring as their first attorney, whether that's a 342 00:19:11,359 --> 00:19:15,600 Speaker 1: general counsel role or some other um. The other thing 343 00:19:15,640 --> 00:19:17,320 Speaker 1: you can do is I always tell people to make 344 00:19:17,400 --> 00:19:20,800 Speaker 1: your experience analogous to whatever it is that you're applying for. 345 00:19:21,400 --> 00:19:23,400 Speaker 1: So people tend to look at resumes and they don't 346 00:19:23,440 --> 00:19:25,959 Speaker 1: really or they look at job descriptions rather and they 347 00:19:25,960 --> 00:19:28,840 Speaker 1: don't change their resume to fit the narrative of what 348 00:19:28,960 --> 00:19:31,600 Speaker 1: the job description is looking for. That is a huge 349 00:19:31,800 --> 00:19:35,359 Speaker 1: no no. So for every bullet point that is on 350 00:19:35,600 --> 00:19:39,639 Speaker 1: that job description, your resume needs to detail how you 351 00:19:39,880 --> 00:19:43,800 Speaker 1: have either done something that is just like that and 352 00:19:43,840 --> 00:19:46,040 Speaker 1: here are the results that I got from doing that work, 353 00:19:46,640 --> 00:19:51,400 Speaker 1: or it needs to be an analogous argument meaning I 354 00:19:51,480 --> 00:19:54,160 Speaker 1: didn't you know, I didn't necessarily do it this way, 355 00:19:54,200 --> 00:19:56,320 Speaker 1: but this is something that I did that is very 356 00:19:56,359 --> 00:19:59,040 Speaker 1: akin to what it is that you're looking for. And 357 00:19:59,800 --> 00:20:01,960 Speaker 1: I don't even I would start off with I haven't 358 00:20:02,000 --> 00:20:05,400 Speaker 1: done this. I would just say, you know, in accordance 359 00:20:05,480 --> 00:20:07,800 Speaker 1: with you know, X, Y and Z type of experience, 360 00:20:07,920 --> 00:20:11,040 Speaker 1: I've done something analogists such as blah blah blah blah blah, 361 00:20:11,119 --> 00:20:14,479 Speaker 1: and here are the results from that. So always make 362 00:20:14,520 --> 00:20:17,720 Speaker 1: sure that you are reading the job description and making 363 00:20:17,760 --> 00:20:20,760 Speaker 1: sure that your resume lines up with it. A lot 364 00:20:20,760 --> 00:20:23,840 Speaker 1: of people simply just send the same generic resume out 365 00:20:23,920 --> 00:20:27,719 Speaker 1: over and over again. And also they need to be 366 00:20:27,760 --> 00:20:32,200 Speaker 1: mindful that the way the job descriptions are written, it's 367 00:20:32,359 --> 00:20:35,080 Speaker 1: you know, sometimes you can't tell what is I bucket 368 00:20:35,119 --> 00:20:37,600 Speaker 1: them in three ways something what's a what's a must have? 369 00:20:38,280 --> 00:20:40,480 Speaker 1: What is a nice to have? And what is a moonshot? 370 00:20:41,240 --> 00:20:43,600 Speaker 1: Like we want you to have these, you know, five 371 00:20:43,680 --> 00:20:46,240 Speaker 1: imperative skills. It would be great if you have these 372 00:20:46,280 --> 00:20:48,600 Speaker 1: three that are nice to have. And then like also 373 00:20:48,640 --> 00:20:53,720 Speaker 1: if you speak Arabic and Japanese like we love it now, 374 00:20:53,760 --> 00:20:56,360 Speaker 1: you probably are going to get that, but the other 375 00:20:56,400 --> 00:21:01,000 Speaker 1: things you probably can. And um also be mindful that 376 00:21:01,040 --> 00:21:03,560 Speaker 1: though the words and the phrasings that they're using in 377 00:21:03,560 --> 00:21:06,040 Speaker 1: those job descriptions, those are the keywords that they're going 378 00:21:06,080 --> 00:21:09,040 Speaker 1: to be searching for in their portal when they're looking 379 00:21:09,359 --> 00:21:12,320 Speaker 1: to advance people to screening interviews. And so again, if 380 00:21:12,320 --> 00:21:14,240 Speaker 1: you're just sending out a generic resume and it doesn't 381 00:21:14,280 --> 00:21:16,919 Speaker 1: have any of those buzzwords in it. There you're not 382 00:21:17,160 --> 00:21:20,199 Speaker 1: going to get past the screen. So let's take the 383 00:21:20,200 --> 00:21:23,040 Speaker 1: other arguments or the other case to this, and how 384 00:21:23,080 --> 00:21:27,600 Speaker 1: does it start up that has potential court an advisor 385 00:21:27,640 --> 00:21:29,480 Speaker 1: who may be a lawyer, like how to like what 386 00:21:29,560 --> 00:21:32,200 Speaker 1: gets Barri's attention to for you to be an advisor 387 00:21:32,240 --> 00:21:36,280 Speaker 1: to my thing? A good idea, Like honestly, I'm not 388 00:21:36,600 --> 00:21:41,320 Speaker 1: someone who focuses solely on you know, how how fast 389 00:21:41,359 --> 00:21:43,159 Speaker 1: can they go public or how much money can they 390 00:21:43,200 --> 00:21:46,040 Speaker 1: make or how much money have they already raised? And 391 00:21:46,119 --> 00:21:49,240 Speaker 1: for me it's it's also the journey and like how 392 00:21:49,280 --> 00:21:52,760 Speaker 1: passionate are these these people are this team about what 393 00:21:52,800 --> 00:21:56,720 Speaker 1: it is that they're doing. And also I always try 394 00:21:56,760 --> 00:21:59,440 Speaker 1: to ask people, you know, what what is the difference 395 00:22:00,000 --> 00:22:04,800 Speaker 1: ferentiation factor between you and your competitors? Because at that point, 396 00:22:04,800 --> 00:22:07,960 Speaker 1: I've probably looked at who who's your competition? How is 397 00:22:08,000 --> 00:22:11,919 Speaker 1: this different? Like what what's gonna be the factor that 398 00:22:12,040 --> 00:22:16,040 Speaker 1: gets you noticed and gets you investment dollars and not 399 00:22:16,160 --> 00:22:18,560 Speaker 1: crash and burn as opposed to these people who are 400 00:22:18,560 --> 00:22:21,760 Speaker 1: already doing something similar may not be exact, but if 401 00:22:21,760 --> 00:22:25,240 Speaker 1: it's similar enough people will pass on it. UM. So 402 00:22:25,320 --> 00:22:29,520 Speaker 1: those are the things that really catch my attention. And 403 00:22:29,760 --> 00:22:33,439 Speaker 1: UM and a deck, what is your what does your 404 00:22:33,480 --> 00:22:37,000 Speaker 1: deck look like? If you have a thirty slide deck? 405 00:22:37,080 --> 00:22:42,680 Speaker 1: I'm not I'm not reading all that. And I can 406 00:22:42,720 --> 00:22:45,800 Speaker 1: tell you from working with UM, working with startups and 407 00:22:45,800 --> 00:22:49,240 Speaker 1: then working with people working with investors, that's literally the 408 00:22:49,240 --> 00:22:51,080 Speaker 1: first thing they say they want a deck that is 409 00:22:51,720 --> 00:22:54,239 Speaker 1: in ten slides or less, and ten is pushing it. 410 00:22:55,280 --> 00:22:58,919 Speaker 1: It's the seven that's even better. How should start us 411 00:22:58,960 --> 00:23:02,200 Speaker 1: be thinking about compensating advisors? Like is there a compensation 412 00:23:02,280 --> 00:23:05,359 Speaker 1: for an advisory role? Is there an equity package? Like 413 00:23:05,440 --> 00:23:08,520 Speaker 1: how do you get you know, folks, really good attorneys 414 00:23:08,560 --> 00:23:12,000 Speaker 1: to come on and give them some incentive to be there? 415 00:23:12,440 --> 00:23:15,240 Speaker 1: Or do you need an incentive to be there? Um, 416 00:23:15,280 --> 00:23:17,800 Speaker 1: it's gonna be on an individual basis. And always I 417 00:23:17,840 --> 00:23:22,480 Speaker 1: always say you get what you negotiate. So that's something 418 00:23:22,480 --> 00:23:24,320 Speaker 1: that people should always be mindful of. Is it's not 419 00:23:24,359 --> 00:23:27,080 Speaker 1: gonna necessarily you don't You're not necessarily gonna always get 420 00:23:27,119 --> 00:23:29,400 Speaker 1: what you deserve. You're gonna get what you can negotiate. 421 00:23:30,080 --> 00:23:32,760 Speaker 1: And for me, when I have done it, it's been 422 00:23:32,800 --> 00:23:38,959 Speaker 1: a mix of actual just equity, and in compensation. Some 423 00:23:39,000 --> 00:23:43,280 Speaker 1: people choose just compensation, some people choose just equity. It's 424 00:23:43,320 --> 00:23:45,560 Speaker 1: really gonna depend on what that person wants and what 425 00:23:45,640 --> 00:23:49,359 Speaker 1: it is that the startup can provide. I would say 426 00:23:49,520 --> 00:23:51,800 Speaker 1: nine times i'd a tend it's easier for them to 427 00:23:51,880 --> 00:23:54,199 Speaker 1: provide equity because they may not have the cash on 428 00:23:54,240 --> 00:23:58,760 Speaker 1: hand yet because they haven't raised So but if you're 429 00:23:58,800 --> 00:24:02,359 Speaker 1: getting equity and they're doing a preseed round or anything else, 430 00:24:02,440 --> 00:24:05,399 Speaker 1: like you're you're gonna be one of the few people 431 00:24:05,480 --> 00:24:08,240 Speaker 1: that that's on the cap table, and that's a good 432 00:24:08,280 --> 00:24:11,040 Speaker 1: place to be, particularly if they end up getting funding 433 00:24:11,080 --> 00:24:13,440 Speaker 1: and they do go public or they're acquired. That's the 434 00:24:13,480 --> 00:24:15,840 Speaker 1: other thing people don't think about. Everyone's always so like, 435 00:24:16,720 --> 00:24:20,120 Speaker 1: you know, short term, focused on how how long can 436 00:24:20,160 --> 00:24:22,119 Speaker 1: we get to I p O? Well, maybe I p 437 00:24:22,280 --> 00:24:24,880 Speaker 1: O isn't necessarily the best route to go. Sometimes it's 438 00:24:24,880 --> 00:24:27,960 Speaker 1: perfectly fine to just get acquired by a larger company. 439 00:24:28,440 --> 00:24:30,040 Speaker 1: I was looking at your website, and so many people 440 00:24:30,040 --> 00:24:32,720 Speaker 1: have blogs on their website that do not get updated 441 00:24:33,359 --> 00:24:36,040 Speaker 1: because people got stuff to do. But like, you really 442 00:24:36,080 --> 00:24:40,639 Speaker 1: go in on on your writing, right, And I wonder, 443 00:24:40,680 --> 00:24:45,760 Speaker 1: like what makes you prioritize the thoughts that you have 444 00:24:45,880 --> 00:24:51,920 Speaker 1: to say to publish them regularly. Yeah, I think because 445 00:24:51,920 --> 00:24:55,280 Speaker 1: there's always something going on, particularly in the last I 446 00:24:55,320 --> 00:24:59,320 Speaker 1: would say three years, there's there's always been something going on, 447 00:25:00,119 --> 00:25:01,800 Speaker 1: whether it's in the tech space or it has to 448 00:25:01,800 --> 00:25:03,840 Speaker 1: do with social justice. And like that was the thing 449 00:25:03,880 --> 00:25:08,320 Speaker 1: that I wrote um last was talking about. You know, 450 00:25:08,400 --> 00:25:12,480 Speaker 1: all these tech companies made these pledges like after George Floyd, like, oh, 451 00:25:12,560 --> 00:25:15,800 Speaker 1: we care so deeply, and we're gonna invest in black founders. 452 00:25:15,800 --> 00:25:18,439 Speaker 1: We're gonna do business, you know, we're gonna do have 453 00:25:18,520 --> 00:25:22,680 Speaker 1: more supplier diversity funds deviated and set aside for black 454 00:25:22,720 --> 00:25:26,320 Speaker 1: owned businesses, and we're gonna have these hiring targets where 455 00:25:26,359 --> 00:25:30,320 Speaker 1: we're gonna have of our leadership is gonna be black. 456 00:25:30,400 --> 00:25:32,840 Speaker 1: And it's like okay, And then you read the fine 457 00:25:32,880 --> 00:25:37,760 Speaker 1: print and it's like by twenty six it's like, okay, 458 00:25:37,800 --> 00:25:39,439 Speaker 1: well that's fine. At least you gave me something to 459 00:25:39,440 --> 00:25:42,320 Speaker 1: work with, right, like I have, there's a metric and 460 00:25:42,320 --> 00:25:46,560 Speaker 1: then there's a day. But that was my gripe. As 461 00:25:46,600 --> 00:25:48,640 Speaker 1: it's been a year and no one has reported out 462 00:25:48,680 --> 00:25:52,600 Speaker 1: on any of the the things that they've done, and like, 463 00:25:52,800 --> 00:25:56,640 Speaker 1: where where is it? Because I could say I'm gonna 464 00:25:56,640 --> 00:25:59,720 Speaker 1: do a whole bunch of things today and but I 465 00:25:59,760 --> 00:26:03,399 Speaker 1: won't have them done until so. Does that mean you're 466 00:26:03,440 --> 00:26:07,600 Speaker 1: not gonna start working on them until so? For me, 467 00:26:07,640 --> 00:26:09,000 Speaker 1: it's one of those things where it should be a 468 00:26:09,000 --> 00:26:11,840 Speaker 1: constant progress report. And the other thing that gave me 469 00:26:11,880 --> 00:26:14,600 Speaker 1: such pause about that was the idea of saying, oh, well, 470 00:26:14,640 --> 00:26:17,399 Speaker 1: we're going to self audit and report out to the 471 00:26:17,440 --> 00:26:21,960 Speaker 1: press was like, you know, no, that literally is like 472 00:26:22,040 --> 00:26:24,600 Speaker 1: what I said. That's like if I punish my son 473 00:26:24,720 --> 00:26:26,639 Speaker 1: and then tell him, well, what do you want your 474 00:26:26,640 --> 00:26:30,040 Speaker 1: punishment to be? And he says, I want cookies and 475 00:26:30,160 --> 00:26:33,960 Speaker 1: ice cream and my Nintendo switch, and I say okay, 476 00:26:34,359 --> 00:26:36,840 Speaker 1: and then I'm just gonna report out that he was punished. 477 00:26:37,840 --> 00:26:41,480 Speaker 1: I'm not gonna tell you all that other stuff. So that, 478 00:26:41,600 --> 00:26:43,639 Speaker 1: to me, is not a viable option. There need to 479 00:26:43,640 --> 00:26:46,560 Speaker 1: be independent auditors that are coming in and looking at 480 00:26:46,600 --> 00:26:50,720 Speaker 1: that because there's no there's no incentive for the company 481 00:26:50,840 --> 00:26:53,280 Speaker 1: to really tell us the truth, right, Like, they'll just 482 00:26:53,680 --> 00:26:56,800 Speaker 1: report out the metrics that fit their narrative that makes 483 00:26:56,800 --> 00:26:58,320 Speaker 1: it look like they did something, but they're going to 484 00:26:58,400 --> 00:27:00,840 Speaker 1: completely bypass and maybe the other are four things that 485 00:27:00,880 --> 00:27:03,080 Speaker 1: they said they were going to do. So we'll just 486 00:27:03,119 --> 00:27:06,800 Speaker 1: tell you about this one. Just focus on this one, 487 00:27:07,040 --> 00:27:08,600 Speaker 1: and then you ask them, well what about the other four? 488 00:27:08,680 --> 00:27:10,560 Speaker 1: What about them? We really did really great on this 489 00:27:10,600 --> 00:27:15,320 Speaker 1: one though. Yeah, I mean when we have I don't 490 00:27:15,320 --> 00:27:18,040 Speaker 1: want to call everybody in diversity theater, but there's a 491 00:27:18,119 --> 00:27:20,840 Speaker 1: lot of folks who do do diversity theater, and we 492 00:27:20,880 --> 00:27:23,120 Speaker 1: think when we have things like a George Floyd during 493 00:27:23,119 --> 00:27:26,520 Speaker 1: a COVID pandemic happened, and so a lot of us 494 00:27:26,560 --> 00:27:29,480 Speaker 1: do talk about how to who's holding these folks accountable 495 00:27:29,680 --> 00:27:32,160 Speaker 1: to what they said they were going to do. What 496 00:27:32,240 --> 00:27:34,440 Speaker 1: has to happen to get to a place to where 497 00:27:34,440 --> 00:27:38,800 Speaker 1: there is a body that holds people accountable to those promises? 498 00:27:38,840 --> 00:27:41,400 Speaker 1: Like what is it shareholders that have to step in 499 00:27:41,440 --> 00:27:44,080 Speaker 1: and organize, or like what has to happen in order 500 00:27:44,080 --> 00:27:45,440 Speaker 1: to get to a place that we're saying we're no 501 00:27:45,520 --> 00:27:48,760 Speaker 1: longer having to say somebody has to hold them accountable 502 00:27:48,880 --> 00:27:53,000 Speaker 1: to such and such is holding them accountable? Yeah? I 503 00:27:53,520 --> 00:27:55,720 Speaker 1: think it should be. It's going to be a mix 504 00:27:55,760 --> 00:27:57,960 Speaker 1: of shareholders and I would love to see like a 505 00:27:58,000 --> 00:28:01,280 Speaker 1: coalition of independent auditors who kind of do this work 506 00:28:02,119 --> 00:28:05,280 Speaker 1: um and then make them actually have to sign on 507 00:28:05,400 --> 00:28:09,879 Speaker 1: to handle that. But I think that that's gonna be 508 00:28:10,000 --> 00:28:12,000 Speaker 1: very difficult because these people they don't want to be 509 00:28:12,040 --> 00:28:14,200 Speaker 1: held accountable. They want to continue to do what they're 510 00:28:14,200 --> 00:28:15,719 Speaker 1: doing and do it in the dark. And when they 511 00:28:15,760 --> 00:28:17,639 Speaker 1: decide to tell you something is when they decided to 512 00:28:17,640 --> 00:28:23,359 Speaker 1: tell you something, and it's it's troublesome. But I think 513 00:28:23,400 --> 00:28:26,640 Speaker 1: that you know, press asking these questions, and that's why 514 00:28:26,720 --> 00:28:28,320 Speaker 1: you asked why I published as much as I do? 515 00:28:28,560 --> 00:28:31,879 Speaker 1: Because again, who fact checks the fact checkers or in 516 00:28:31,880 --> 00:28:34,800 Speaker 1: this case, who's gonna who's gonna tell me what you've 517 00:28:34,840 --> 00:28:37,919 Speaker 1: actually got gotten done? You had a year and like 518 00:28:38,000 --> 00:28:40,400 Speaker 1: I want to see, I want to see the receipts, 519 00:28:41,120 --> 00:28:44,600 Speaker 1: and it looks like you don't have any. So what 520 00:28:44,640 --> 00:28:46,600 Speaker 1: that tells me is you haven't started working on this, 521 00:28:46,800 --> 00:28:49,560 Speaker 1: or if you have, your results were so abysmal you 522 00:28:49,560 --> 00:28:53,480 Speaker 1: didn't even want to share them. So but I think 523 00:28:53,680 --> 00:28:56,800 Speaker 1: there's a fast company like they don't they donated, but 524 00:28:56,840 --> 00:29:02,560 Speaker 1: they they made their entire June issue around this topic. 525 00:29:03,280 --> 00:29:06,160 Speaker 1: And I think if you have more people continuously asking 526 00:29:06,200 --> 00:29:09,040 Speaker 1: the question, you can only avoid it for so long 527 00:29:09,080 --> 00:29:12,640 Speaker 1: if they're like eight or nine publications asking you the 528 00:29:12,680 --> 00:29:16,960 Speaker 1: same question. And that's the other thing I thought was 529 00:29:17,000 --> 00:29:20,120 Speaker 1: super interesting is then you have people who write independently 530 00:29:20,240 --> 00:29:23,480 Speaker 1: or I'm but I'm a Fast Company contributor. But then 531 00:29:23,480 --> 00:29:26,160 Speaker 1: you have other people who are doing things independently asking 532 00:29:26,200 --> 00:29:29,000 Speaker 1: the question. You have people creating tools that are asking 533 00:29:29,040 --> 00:29:31,720 Speaker 1: the question. My favorite tool last year is one called 534 00:29:31,840 --> 00:29:35,960 Speaker 1: layoffs dot f y I, where it listed all all 535 00:29:36,000 --> 00:29:39,120 Speaker 1: the people who got laid off during the pandemic from 536 00:29:39,240 --> 00:29:43,160 Speaker 1: UM I want to say, starting in March, and from 537 00:29:43,200 --> 00:29:45,480 Speaker 1: a whole bunch of big tech companies down to like 538 00:29:45,560 --> 00:29:49,360 Speaker 1: some smaller ones, but ones that are significantly funded. And 539 00:29:49,160 --> 00:29:52,760 Speaker 1: and in some cases you could see the names of 540 00:29:52,800 --> 00:29:56,880 Speaker 1: the people and a cursory glance of that the names 541 00:29:56,880 --> 00:29:59,360 Speaker 1: and the department that they were in. And it was 542 00:29:59,760 --> 00:30:02,360 Speaker 1: h people, it will salespeople, it was d I people, 543 00:30:02,920 --> 00:30:06,880 Speaker 1: it was all the people were typically people of color 544 00:30:06,960 --> 00:30:11,040 Speaker 1: and women congregate the most in those departments. And you 545 00:30:11,040 --> 00:30:12,480 Speaker 1: could look at some of the names and it was 546 00:30:12,640 --> 00:30:18,480 Speaker 1: very clear I could probably guess what this person is. Yeah, 547 00:30:18,800 --> 00:30:21,240 Speaker 1: But that's that's the thing. It's like, you have people 548 00:30:21,240 --> 00:30:24,040 Speaker 1: who are doing it independently you have, you know, actual 549 00:30:24,320 --> 00:30:27,920 Speaker 1: magazines and press asking the question, there's only so long 550 00:30:28,600 --> 00:30:32,000 Speaker 1: that you can hide this information. There was a blog 551 00:30:32,040 --> 00:30:34,840 Speaker 1: post you did about social media and it being an 552 00:30:34,920 --> 00:30:38,440 Speaker 1: extension of white privilege, and it was a white privilege 553 00:30:38,440 --> 00:30:40,680 Speaker 1: in the office, and I specifically wanted to hone in 554 00:30:40,760 --> 00:30:43,760 Speaker 1: on this one line you wrote. It said, Um, there's 555 00:30:43,800 --> 00:30:46,600 Speaker 1: an added layer of complexity when you add tech and 556 00:30:46,720 --> 00:30:49,760 Speaker 1: social media to this mix. Now you don't just get 557 00:30:49,800 --> 00:30:52,360 Speaker 1: to know your colleagues in the workplace, but also by 558 00:30:52,400 --> 00:30:55,760 Speaker 1: their digital presence and what they choose to comment on 559 00:30:56,000 --> 00:31:00,000 Speaker 1: or avoid. And I particularly asked this because I hire people. 560 00:31:00,080 --> 00:31:02,320 Speaker 1: And I wonder because the first time I see a resume, 561 00:31:02,880 --> 00:31:04,840 Speaker 1: I gotta look at Facebook, I got to look at Twitter. 562 00:31:04,920 --> 00:31:06,960 Speaker 1: I want to see what you're talking about and who 563 00:31:07,000 --> 00:31:10,960 Speaker 1: are you really beyond this resume that you gave. And 564 00:31:11,000 --> 00:31:15,640 Speaker 1: I wonder your thoughts on how this continues to play 565 00:31:15,680 --> 00:31:20,360 Speaker 1: out and the complexity in tech. I think that the 566 00:31:20,360 --> 00:31:23,360 Speaker 1: complexity with it is like oftentimes you're you know, you're 567 00:31:23,360 --> 00:31:25,440 Speaker 1: working with these people who are also building the tools. 568 00:31:26,160 --> 00:31:30,160 Speaker 1: And one thing that they did at Facebook is as 569 00:31:30,160 --> 00:31:32,840 Speaker 1: soon as you accept your offer, I think within forty 570 00:31:32,880 --> 00:31:37,280 Speaker 1: five minutes, of me accepting my offer. Um my soon 571 00:31:37,320 --> 00:31:40,960 Speaker 1: to be colleagues sent me Facebook requests and I was like, oh, no, 572 00:31:42,440 --> 00:31:45,720 Speaker 1: are we doing it? I don't know. I don't And 573 00:31:46,720 --> 00:31:50,760 Speaker 1: my husband specifically said, why don't you just create the 574 00:31:50,800 --> 00:31:53,720 Speaker 1: limited profile function and add them to that? And I said, honestly, 575 00:31:53,760 --> 00:31:56,720 Speaker 1: that sounds like a lot of work, Like that's me 576 00:31:56,920 --> 00:32:00,680 Speaker 1: maintaining essentially to digital presences on one plant. For him, 577 00:32:00,720 --> 00:32:03,640 Speaker 1: that's too much work. If you look, get what you're getting. 578 00:32:05,120 --> 00:32:09,200 Speaker 1: So if you like I remember that might have been 579 00:32:09,240 --> 00:32:10,960 Speaker 1: like the second or third week that I was working 580 00:32:11,000 --> 00:32:13,680 Speaker 1: there and that man who had that really cute mud 581 00:32:13,720 --> 00:32:17,280 Speaker 1: shot with the blue eyes and everyone loved him, and 582 00:32:17,320 --> 00:32:20,320 Speaker 1: I posted something about it and my general counsel swiveled 583 00:32:20,320 --> 00:32:22,840 Speaker 1: his chair because he sat at the next bank of chairs, 584 00:32:23,160 --> 00:32:28,560 Speaker 1: and he was like, so that's what you like. I'm like, oh, 585 00:32:28,600 --> 00:32:31,600 Speaker 1: you got jokes calling okay? And I said, look, I 586 00:32:31,640 --> 00:32:33,840 Speaker 1: don't want to know. I don't want a felon necessarily. 587 00:32:33,840 --> 00:32:35,960 Speaker 1: I don't want him like, but it's a it's a 588 00:32:36,000 --> 00:32:42,680 Speaker 1: cute picture. Cute what's what they calling prison bay or 589 00:32:42,720 --> 00:32:47,040 Speaker 1: something's in prison but that's the thing. Well, prison bay 590 00:32:47,120 --> 00:32:49,120 Speaker 1: ended up coming up. He became a model and married 591 00:32:49,160 --> 00:32:55,880 Speaker 1: like a millionaire, so he won, I guess so. But yeah, 592 00:32:55,880 --> 00:32:58,600 Speaker 1: it was one of those things where you know, at 593 00:32:58,600 --> 00:33:03,000 Speaker 1: any other workplace, nobody would have seen that because I 594 00:33:03,040 --> 00:33:05,400 Speaker 1: wouldn't if you send me a friend request and like 595 00:33:05,840 --> 00:33:10,680 Speaker 1: you just remain in purgatory. So I think it is 596 00:33:10,960 --> 00:33:14,120 Speaker 1: the digital presence of you know, what are the things 597 00:33:14,120 --> 00:33:15,480 Speaker 1: that you choose to comment on and what are the 598 00:33:15,480 --> 00:33:17,880 Speaker 1: things that you ignore? And it's not like people don't 599 00:33:17,920 --> 00:33:23,520 Speaker 1: notice that. So whether it is you know, you didn't 600 00:33:23,560 --> 00:33:28,320 Speaker 1: acknowledge that there was a string of people that were 601 00:33:28,400 --> 00:33:31,680 Speaker 1: killed by police or armed vigilantes in the case of 602 00:33:31,680 --> 00:33:34,840 Speaker 1: a mod Aubrey. If there you don't want to talk 603 00:33:34,840 --> 00:33:37,400 Speaker 1: about that, or you don't speak on that, or you 604 00:33:37,400 --> 00:33:40,640 Speaker 1: don't notice it, but you hear us talking about it 605 00:33:40,680 --> 00:33:43,760 Speaker 1: in the break room or something, or you hear us 606 00:33:43,760 --> 00:33:45,960 Speaker 1: talking about it outside of work, and you happen to 607 00:33:46,040 --> 00:33:49,520 Speaker 1: just you know, join in with something like that, but 608 00:33:49,600 --> 00:33:52,320 Speaker 1: you don't say anything. Don't think that when you walk 609 00:33:52,320 --> 00:33:54,080 Speaker 1: away that we weren't talking about the fact that you 610 00:33:54,080 --> 00:33:59,000 Speaker 1: didn't say anything. And you know, I've been in other 611 00:33:59,280 --> 00:34:04,760 Speaker 1: workplaces where something like that will happen, and you know, 612 00:34:05,640 --> 00:34:09,160 Speaker 1: people will just completely turned pivot and run from the conversation. 613 00:34:10,360 --> 00:34:13,640 Speaker 1: And but I've also been in a workplace. But then 614 00:34:13,680 --> 00:34:15,319 Speaker 1: this is one of the people that I say was 615 00:34:15,400 --> 00:34:19,799 Speaker 1: a great, great ally. When I was at Facebook is 616 00:34:19,840 --> 00:34:24,160 Speaker 1: this guy named Mark. And it was after Mike Brown 617 00:34:24,239 --> 00:34:27,239 Speaker 1: had been killed, and I just didn't you have an 618 00:34:27,239 --> 00:34:30,359 Speaker 1: open workspace? And I just didn't want to. I didn't 619 00:34:30,360 --> 00:34:33,000 Speaker 1: want to stand there all day and sit there like 620 00:34:33,040 --> 00:34:34,759 Speaker 1: where people could come up to me and act like 621 00:34:34,880 --> 00:34:38,880 Speaker 1: this didn't happen, like you're just coming and you know, 622 00:34:38,960 --> 00:34:41,799 Speaker 1: talking to me about whatever, and I just I don't 623 00:34:41,840 --> 00:34:45,680 Speaker 1: want to talk about that. So I went and locked 624 00:34:45,680 --> 00:34:49,240 Speaker 1: myself in an empty conference room, and he went around 625 00:34:49,320 --> 00:34:53,279 Speaker 1: the floor to all the conference rooms looking for me, 626 00:34:54,160 --> 00:34:55,680 Speaker 1: and then when he did, he just knocked on the 627 00:34:55,719 --> 00:34:58,360 Speaker 1: door and I was like sure. He was like, I 628 00:34:58,440 --> 00:35:02,520 Speaker 1: just want to know if you're okay. And I was like, 629 00:35:02,560 --> 00:35:05,880 Speaker 1: how how long did you go around this building? He 630 00:35:06,040 --> 00:35:09,279 Speaker 1: was like about ten minutes, and he's like, but I 631 00:35:09,320 --> 00:35:12,200 Speaker 1: just wanted to know if you were okay because I 632 00:35:12,239 --> 00:35:14,439 Speaker 1: saw you earlier this morning and then I didn't see 633 00:35:14,440 --> 00:35:17,520 Speaker 1: you again, and I was like, honestly, no, I'm tired 634 00:35:17,520 --> 00:35:22,200 Speaker 1: of this. It's exhausting, and he was like, I know. 635 00:35:23,320 --> 00:35:25,120 Speaker 1: I was like, if you want to talk, let me know, 636 00:35:26,080 --> 00:35:28,000 Speaker 1: But like, that's the kind of thing like that. I 637 00:35:28,160 --> 00:35:31,160 Speaker 1: clearly I noticed that versus everyone else who acted like 638 00:35:31,200 --> 00:35:35,200 Speaker 1: it didn't happen, Versus a different colleague who was like, oh, wow, 639 00:35:35,360 --> 00:35:41,160 Speaker 1: somebody was shot byl I don't even know. Okay, Like 640 00:35:41,440 --> 00:35:43,840 Speaker 1: you have the luxury to not did not know or 641 00:35:43,840 --> 00:35:47,560 Speaker 1: did not care. And that's what I mean by the 642 00:35:47,560 --> 00:35:51,600 Speaker 1: extension of that versus you see people hopping conversations about 643 00:35:52,239 --> 00:35:55,160 Speaker 1: politics or something else and then it's like, oh, he 644 00:35:55,320 --> 00:35:59,239 Speaker 1: has a different view than I thought, or she is 645 00:35:59,320 --> 00:36:04,040 Speaker 1: probably a Karen and yeah. I mean you just look 646 00:36:04,080 --> 00:36:05,920 Speaker 1: at that stuff and you notice it and you just 647 00:36:06,120 --> 00:36:08,279 Speaker 1: file it away in the back of your head. Right. 648 00:36:09,719 --> 00:36:12,359 Speaker 1: So that's what I mean by the extension of like 649 00:36:12,440 --> 00:36:14,479 Speaker 1: you get to when they say bring your whole self 650 00:36:14,480 --> 00:36:19,280 Speaker 1: to work. First off, I don't agree that necessarily everyone 651 00:36:19,320 --> 00:36:21,279 Speaker 1: should bring their whole self to work, And I don't 652 00:36:21,320 --> 00:36:24,640 Speaker 1: know one should bring a hundred. If you're gonna get 653 00:36:24,640 --> 00:36:28,719 Speaker 1: as close as you want, maybe bring eight. Even then, 654 00:36:28,800 --> 00:36:32,000 Speaker 1: that eight I think is reserved for certain types of people, 655 00:36:32,080 --> 00:36:36,480 Speaker 1: and those types of people tend to be straight able body, 656 00:36:36,680 --> 00:36:40,640 Speaker 1: white man. Yeah. Um, I was intrigued by your post 657 00:36:40,840 --> 00:36:45,400 Speaker 1: the computer generated apocalypse that never happened? And I wonder 658 00:36:46,000 --> 00:36:51,120 Speaker 1: where you see this particular trajectory progressing, um with regards 659 00:36:51,120 --> 00:36:55,319 Speaker 1: to like social media, AI, venture capital even and if 660 00:36:55,360 --> 00:36:57,600 Speaker 1: we stay on our current path, there are something that 661 00:36:57,640 --> 00:37:00,120 Speaker 1: would say, you know, the wealth gap gets brought, the 662 00:37:01,160 --> 00:37:05,440 Speaker 1: minority communities get hurt even more than they are. Is 663 00:37:05,480 --> 00:37:09,160 Speaker 1: this the same thing as the apocalypse that never happened? 664 00:37:09,280 --> 00:37:12,680 Speaker 1: Or where do you see this progressing as we continue 665 00:37:12,680 --> 00:37:15,640 Speaker 1: to go down the road of AI and certain people 666 00:37:15,640 --> 00:37:18,359 Speaker 1: getting funded versus other groups of people getting funded and 667 00:37:18,360 --> 00:37:21,759 Speaker 1: and the and the like. Um. I think what you 668 00:37:21,800 --> 00:37:24,560 Speaker 1: could say in that case, I wouldn't say it would 669 00:37:24,600 --> 00:37:28,200 Speaker 1: be a computer generated apocalypse that never happened. I would 670 00:37:28,239 --> 00:37:32,960 Speaker 1: say computer generated apocalypse that did happen. Um. And I 671 00:37:33,000 --> 00:37:36,120 Speaker 1: think we we all saw it. It all happened in 672 00:37:36,200 --> 00:37:42,640 Speaker 1: real time with you know, disinformation all over everyone's social 673 00:37:42,640 --> 00:37:46,759 Speaker 1: media platforms. It's still happening in terms of just the vaccine, 674 00:37:47,080 --> 00:37:49,520 Speaker 1: and people don't want to take the vaccine because well 675 00:37:49,560 --> 00:37:52,680 Speaker 1: I read they're putting microchips, Like where are you all 676 00:37:52,719 --> 00:37:56,399 Speaker 1: getting this? But it's one of those things that if 677 00:37:57,360 --> 00:38:00,880 Speaker 1: you know you are using not you to sational outlets 678 00:38:00,920 --> 00:38:03,960 Speaker 1: to get your information, which I don't think is necessarily 679 00:38:04,000 --> 00:38:07,759 Speaker 1: always a bad thing. But like, I'm not taking any 680 00:38:07,840 --> 00:38:11,120 Speaker 1: type of, you know, medical advice from Alex Jones on 681 00:38:11,160 --> 00:38:14,360 Speaker 1: Info Wars. It's just not ever gonna happen. And I 682 00:38:14,400 --> 00:38:17,160 Speaker 1: definitely don't need Tucker Crosson to tell me that COVID 683 00:38:17,280 --> 00:38:19,680 Speaker 1: isn't isn't really real and I don't need a vaccine 684 00:38:19,719 --> 00:38:26,439 Speaker 1: while you were vaccinated, sir. So to me, it's we've 685 00:38:26,480 --> 00:38:29,000 Speaker 1: seen this already happen in real time and it's still 686 00:38:29,239 --> 00:38:33,680 Speaker 1: continuing to happen. And I think if there isn't any 687 00:38:33,719 --> 00:38:37,719 Speaker 1: type of actual regulation, I don't know that you we 688 00:38:37,760 --> 00:38:39,839 Speaker 1: can prevent it from getting worse. You. I mean, you've 689 00:38:39,840 --> 00:38:42,840 Speaker 1: already thrown an election and now you're getting people sick. 690 00:38:43,560 --> 00:38:48,760 Speaker 1: So it's it isn't that it's not happening. It is happening. 691 00:38:48,840 --> 00:38:52,239 Speaker 1: The question is how do we stop it? And I 692 00:38:52,280 --> 00:38:55,279 Speaker 1: think the best answer that you have for that is 693 00:38:56,520 --> 00:38:59,160 Speaker 1: and I don't know that social media companies in particular 694 00:38:59,160 --> 00:39:01,680 Speaker 1: will like the answer or is right now, they are 695 00:39:01,719 --> 00:39:05,760 Speaker 1: distinguished as platforms, which means they're not subject to certain regulation. 696 00:39:06,320 --> 00:39:11,560 Speaker 1: But if you publishers now they have a responsibility and 697 00:39:11,680 --> 00:39:14,719 Speaker 1: but they don't want it, which I could understand. Their 698 00:39:14,760 --> 00:39:17,280 Speaker 1: attitude is, well, we're just the mechanism and the portal 699 00:39:17,360 --> 00:39:20,600 Speaker 1: for which you post and share whatever cat videos, pictures, 700 00:39:20,680 --> 00:39:24,799 Speaker 1: or your kids or Trump lost the election or he 701 00:39:24,840 --> 00:39:29,040 Speaker 1: won the election, whichever you choose, and they don't want 702 00:39:29,040 --> 00:39:32,800 Speaker 1: to be responsible for having to take down information about 703 00:39:33,560 --> 00:39:36,040 Speaker 1: Oh well no he won the election of stolen and 704 00:39:36,320 --> 00:39:39,879 Speaker 1: you know those were loving tourists that visited on January six, 705 00:39:40,400 --> 00:39:44,360 Speaker 1: Like they don't want the responsibility of that, which I understand. 706 00:39:45,160 --> 00:39:50,560 Speaker 1: But at some point you can't just continue to proliferate 707 00:39:50,600 --> 00:39:55,400 Speaker 1: all of this, like you can't. It's that's essentially the equivalent, 708 00:39:55,440 --> 00:39:57,560 Speaker 1: the digital equivalent of throwing a rock and hide in 709 00:39:57,560 --> 00:40:01,480 Speaker 1: your hand. That's also why I there's another social media 710 00:40:01,520 --> 00:40:04,640 Speaker 1: company that I like a lot called Telepath, because you 711 00:40:04,680 --> 00:40:07,000 Speaker 1: actually have to use your real name and picture or 712 00:40:07,080 --> 00:40:10,879 Speaker 1: you cannot participate versus Twitter. It's like somebody would a 713 00:40:10,920 --> 00:40:14,680 Speaker 1: cat photo and like don one, two, three four, and 714 00:40:15,600 --> 00:40:20,279 Speaker 1: he's calling you everything but a child of God. So 715 00:40:21,880 --> 00:40:24,840 Speaker 1: I think if they're some of that would be started 716 00:40:25,440 --> 00:40:29,520 Speaker 1: if people could not hide an anonymity. There was this 717 00:40:29,640 --> 00:40:34,840 Speaker 1: period during COVID and right after the murder George Floyd, Um, 718 00:40:34,880 --> 00:40:37,000 Speaker 1: where black people, I feel like, had this window of 719 00:40:37,040 --> 00:40:41,920 Speaker 1: opportunity to capitalize is the wrong word, but for the 720 00:40:41,960 --> 00:40:45,560 Speaker 1: sake of his conversation, capitalized on the attention that we 721 00:40:45,719 --> 00:40:49,640 Speaker 1: had from VESI, capitalists, from corporations, for people who finally 722 00:40:49,719 --> 00:40:52,160 Speaker 1: saw that we weren't crying wolf all these years, but 723 00:40:52,200 --> 00:40:56,520 Speaker 1: that were they were actually actually systemic issues that kept 724 00:40:56,600 --> 00:41:00,160 Speaker 1: us from being able to progress in many different use 725 00:41:00,200 --> 00:41:04,319 Speaker 1: of our lives. Um is that window now close? Are 726 00:41:04,360 --> 00:41:06,320 Speaker 1: we are? We? Are we so far past it to 727 00:41:06,400 --> 00:41:08,640 Speaker 1: where they don't have to talk about it anymore, they 728 00:41:08,680 --> 00:41:13,160 Speaker 1: don't have to pay attention anymore. That is a good question. 729 00:41:13,200 --> 00:41:17,359 Speaker 1: And I've thought about this for a while myself. Um I. 730 00:41:17,760 --> 00:41:22,200 Speaker 1: My prediction was that it was probably gonna be over 731 00:41:23,680 --> 00:41:28,280 Speaker 1: either after the election around January, after inauguration. I would 732 00:41:28,280 --> 00:41:33,200 Speaker 1: say it's not closed, but it's closing. And the reason 733 00:41:33,280 --> 00:41:36,560 Speaker 1: being is, um, I think there's a course now at 734 00:41:36,560 --> 00:41:39,520 Speaker 1: a different added element to it, where you have all 735 00:41:39,560 --> 00:41:44,239 Speaker 1: of these voting restrictions being written into state laws, and 736 00:41:44,480 --> 00:41:49,360 Speaker 1: you also have these two Democratic senators who refused to 737 00:41:49,400 --> 00:41:53,399 Speaker 1: do away with the filibuster in order to right right 738 00:41:53,480 --> 00:41:57,640 Speaker 1: that wrong and get federal legislation passed. Like they don't 739 00:41:57,680 --> 00:41:59,920 Speaker 1: want the John exactly, they don't want for other people. 740 00:42:00,600 --> 00:42:04,000 Speaker 1: They definitely don't want the George Floyd Policing Reform Act. 741 00:42:04,280 --> 00:42:06,400 Speaker 1: But I think as long as those things are still 742 00:42:06,520 --> 00:42:09,680 Speaker 1: churning and on the table, the windows still open, And 743 00:42:10,080 --> 00:42:11,479 Speaker 1: I would just say it was like it was open 744 00:42:11,560 --> 00:42:14,720 Speaker 1: like this last summer, and it's probably open like this now, 745 00:42:16,160 --> 00:42:19,080 Speaker 1: So it's it's there. It's just closing. But I also 746 00:42:19,120 --> 00:42:21,880 Speaker 1: think that that's on us to like keep forcing it 747 00:42:21,920 --> 00:42:39,400 Speaker 1: and keep pushing it. Black Tech Green Money is the 748 00:42:39,480 --> 00:42:42,120 Speaker 1: production of Black and the Afro Tech, The Black Effect 749 00:42:42,120 --> 00:42:45,320 Speaker 1: podcast Network, and nine Hired Media. It's produced by Morgan 750 00:42:45,400 --> 00:42:49,359 Speaker 1: Debonn and me Well Lucas, with additional production supported by 751 00:42:49,400 --> 00:42:53,239 Speaker 1: Love Visas Lewis. Special thank you to Michael Davis. Since 752 00:42:53,280 --> 00:42:55,759 Speaker 1: the carsa von Jan you know like the Wine. Yes 753 00:42:55,840 --> 00:42:58,759 Speaker 1: that's his real name. Learn more about my guests and 754 00:42:58,760 --> 00:43:00,880 Speaker 1: other technist as the end of it is an afrotech 755 00:43:00,920 --> 00:43:03,359 Speaker 1: dot com. The video version of this episode will drop 756 00:43:03,400 --> 00:43:05,640 Speaker 1: the Black Tech Green Money on YouTube next week, so 757 00:43:05,640 --> 00:43:09,759 Speaker 1: I'll tap in joining Black Tech green money, leave us 758 00:43:09,760 --> 00:43:14,720 Speaker 1: a fire star rating on iTunes. Go get your money, 759 00:43:15,400 --> 00:43:16,000 Speaker 1: peace and love,