1 00:00:01,080 --> 00:00:05,640 Speaker 1: I want to thank every Amazon employee and every Amazon 2 00:00:05,760 --> 00:00:10,719 Speaker 1: customer because you guys paid progress. You guys paid probles. 3 00:00:12,640 --> 00:00:16,599 Speaker 1: This is Megacorp, an investigative podcast exposing some of the 4 00:00:16,640 --> 00:00:23,120 Speaker 1: world's most unethical corporations. This series is about Amazon. I'm 5 00:00:23,239 --> 00:00:30,400 Speaker 1: Jake Hanrahan, journalists and documentary filmmaker. Megacorp is produced by 6 00:00:30,680 --> 00:00:40,720 Speaker 1: H eleven for Cool Zone Media. So after eight episodes 7 00:00:40,800 --> 00:00:44,600 Speaker 1: now diving into the many scandals at the heart of Amazon, 8 00:00:45,320 --> 00:00:47,560 Speaker 1: I thought it was time to talk about the man 9 00:00:47,640 --> 00:00:52,320 Speaker 1: who built the empire, Jeff Bezos. As we know, Bezos 10 00:00:52,479 --> 00:00:56,120 Speaker 1: is the richest man on Earth. He founded Amazon in 11 00:00:57,920 --> 00:01:00,320 Speaker 1: and now it's one of the biggest company k needs 12 00:01:00,360 --> 00:01:03,800 Speaker 1: on the planet. Is also, as we've discovered, one of 13 00:01:03,840 --> 00:01:08,399 Speaker 1: the most controversial. But how did Bezos get to this point? 14 00:01:08,800 --> 00:01:12,240 Speaker 1: But if there's anyone that knows that, it's journalist Robert Evans. 15 00:01:12,720 --> 00:01:16,119 Speaker 1: He runs the Behind the Bastards podcast, which looks into 16 00:01:16,120 --> 00:01:19,360 Speaker 1: the lives of some of the worst bastards on Earth. 17 00:01:19,760 --> 00:01:24,880 Speaker 1: So I spoke to Robert Evans about Jeff Bezos. Robert 18 00:01:24,880 --> 00:01:28,520 Speaker 1: goes into a lot of detail in this so mostly 19 00:01:28,920 --> 00:01:33,880 Speaker 1: I'm just gonna let the interview run first. Maybe if 20 00:01:33,920 --> 00:01:37,080 Speaker 1: you can just kind of go into the early life 21 00:01:37,200 --> 00:01:41,280 Speaker 1: of Jeff Bezos, because he didn't start out as Jeff Bezos. Yeah, 22 00:01:41,360 --> 00:01:46,000 Speaker 1: he was born Jeffrey Preston Jourgensen um. And one of 23 00:01:46,000 --> 00:01:48,240 Speaker 1: the things that is I think the first and most 24 00:01:48,280 --> 00:01:51,440 Speaker 1: amazing thing about the Jeff Bezos story is that his 25 00:01:51,440 --> 00:01:54,560 Speaker 1: his actual father abandoned the family. And this is not 26 00:01:55,360 --> 00:01:57,600 Speaker 1: this is not like your normal story of a dude 27 00:01:57,680 --> 00:02:00,520 Speaker 1: winding up without a dad, where like it there's there's 28 00:02:00,560 --> 00:02:04,080 Speaker 1: something like really unsettling or or dark here. It's that 29 00:02:04,280 --> 00:02:08,120 Speaker 1: his his actual father is bio dad um. Theodore Jourgensen 30 00:02:08,760 --> 00:02:12,359 Speaker 1: was just like kind of a uh, kind of a 31 00:02:12,360 --> 00:02:16,520 Speaker 1: ship head carney who really really really loved unicycling. He 32 00:02:16,560 --> 00:02:19,880 Speaker 1: was a high wire unicyclist um, and having a kid 33 00:02:19,960 --> 00:02:22,640 Speaker 1: got in the way of his unicycling dreams. Yes, you 34 00:02:22,680 --> 00:02:26,840 Speaker 1: did hear that right, Jeff Bezos. His biological father abandoned 35 00:02:26,919 --> 00:02:29,720 Speaker 1: him when he was young to fulfill a dream of 36 00:02:29,760 --> 00:02:34,399 Speaker 1: his as a high wire unicyclist. Now, it's not funny 37 00:02:34,440 --> 00:02:38,239 Speaker 1: when somebody abandons the family, but what a strange start 38 00:02:38,320 --> 00:02:41,919 Speaker 1: that is. Anyway, Robot explains more, he could not get 39 00:02:41,960 --> 00:02:45,520 Speaker 1: on board with being Jeffrey's father and eventually kind of 40 00:02:45,720 --> 00:02:48,280 Speaker 1: bounced the funk out of his life and in fact 41 00:02:48,440 --> 00:02:52,480 Speaker 1: did not know that his son was Jeff Bezos and 42 00:02:52,600 --> 00:02:56,280 Speaker 1: who Jeff Bezos was really until a journalist tracked him 43 00:02:56,280 --> 00:02:59,399 Speaker 1: down like decades later when Amazon was a huge deal 44 00:02:59,440 --> 00:03:01,799 Speaker 1: and was like, you know, your kids like super rich 45 00:03:01,880 --> 00:03:04,600 Speaker 1: in creating what's turning into this monster company. He was 46 00:03:04,800 --> 00:03:07,520 Speaker 1: very surprised. Um, and I think is still kind of 47 00:03:07,520 --> 00:03:11,440 Speaker 1: a deadbeat unicyclist dude. So Jeff by the time he 48 00:03:11,560 --> 00:03:15,360 Speaker 1: was pretty young, Um, his mom had met a a 49 00:03:15,520 --> 00:03:22,000 Speaker 1: new dude, UM whose name was Miguel Angel Bezos Perez, 50 00:03:22,200 --> 00:03:25,640 Speaker 1: who was a Cuban who kind of his version of 51 00:03:25,639 --> 00:03:27,760 Speaker 1: the story. And I don't know about much about his 52 00:03:27,960 --> 00:03:31,000 Speaker 1: family back in Cuba or kind of like where they 53 00:03:31,040 --> 00:03:34,520 Speaker 1: stood and whatnot. But he was apparently as a teenager 54 00:03:34,760 --> 00:03:38,000 Speaker 1: like painting anti Castro graffiti, which got him in trouble 55 00:03:38,000 --> 00:03:41,160 Speaker 1: and at age sixteen he had to flee the country. Um, 56 00:03:41,240 --> 00:03:43,640 Speaker 1: and so he wound up in the United States. Uh. 57 00:03:43,680 --> 00:03:46,320 Speaker 1: He did his undergrad work at the University of Albuquerque, 58 00:03:46,360 --> 00:03:49,320 Speaker 1: So he wound up in because Bezos comes from the Southwest, right, 59 00:03:49,360 --> 00:03:51,200 Speaker 1: That's where his his family's like a lot in New 60 00:03:51,240 --> 00:03:54,520 Speaker 1: Mexico because his mom's side of the family were really 61 00:03:54,560 --> 00:03:57,840 Speaker 1: heavily involved in um, the US nuclear programs and like 62 00:03:57,920 --> 00:04:00,640 Speaker 1: nuclear missiles and and all that, all that and stuff. 63 00:04:01,240 --> 00:04:03,560 Speaker 1: Um and Miguel wound up in the same region because 64 00:04:03,600 --> 00:04:07,200 Speaker 1: the University of Albuquerque was offering free scholarships to Cuban refugees, 65 00:04:07,520 --> 00:04:12,080 Speaker 1: and so he met Jeffrey's mom, Jacqueline Um, when he 66 00:04:12,120 --> 00:04:14,240 Speaker 1: was working as a clerk at a bank that she 67 00:04:14,320 --> 00:04:16,440 Speaker 1: worked at two and they fell in love and married 68 00:04:16,480 --> 00:04:19,240 Speaker 1: and I think before jeff really, I don't think jeff 69 00:04:19,240 --> 00:04:21,880 Speaker 1: really remembers much time before mcguel, because Miguel's just kind 70 00:04:21,880 --> 00:04:24,640 Speaker 1: of always been his dad, And so his mom changed 71 00:04:24,760 --> 00:04:27,839 Speaker 1: the changed his last name to Bezos and that's how 72 00:04:27,880 --> 00:04:31,200 Speaker 1: he became Jeffrey bezos Is because this this this Cuban 73 00:04:31,240 --> 00:04:34,960 Speaker 1: immigrant dude kind of uh stepped in to his life 74 00:04:34,960 --> 00:04:37,520 Speaker 1: and took over as his dad. So he got pretty 75 00:04:37,600 --> 00:04:40,440 Speaker 1: lucky there. Um, it's a little bit of a winding story. 76 00:04:40,480 --> 00:04:43,719 Speaker 1: I think it's very funny that his dad abandoned him 77 00:04:43,760 --> 00:04:47,000 Speaker 1: to be a unicyclist, but also I don't I think 78 00:04:47,040 --> 00:04:49,040 Speaker 1: it's probably too much to say that had an effect 79 00:04:49,080 --> 00:04:51,240 Speaker 1: on him, because I don't think he really it doesn't 80 00:04:51,279 --> 00:04:53,360 Speaker 1: seem like he had any memory of that, you know, 81 00:04:53,520 --> 00:04:56,920 Speaker 1: so it's unlikely that that like that laid some deep 82 00:04:56,960 --> 00:05:00,840 Speaker 1: seated wound that is responsible for anything that Amazon has done. Right, 83 00:05:00,880 --> 00:05:03,640 Speaker 1: And this this guy, Miguel kind of took him under 84 00:05:03,640 --> 00:05:06,120 Speaker 1: his wing, right, Like, he seems to be quite instrumental 85 00:05:06,720 --> 00:05:09,680 Speaker 1: in you know, the way Bezos jeff Bezos so the 86 00:05:09,720 --> 00:05:12,840 Speaker 1: world growing up. Yeah, And I think that's why Jeffrey 87 00:05:12,920 --> 00:05:16,280 Speaker 1: kind of grows up as a a very very committed 88 00:05:16,360 --> 00:05:20,240 Speaker 1: capitalist um. Obviously, his dad is like an anti castro 89 00:05:20,520 --> 00:05:23,359 Speaker 1: activist as a kid um and then as an you know, 90 00:05:23,440 --> 00:05:25,920 Speaker 1: as an adult once he's graduated and out in the world. 91 00:05:25,960 --> 00:05:28,599 Speaker 1: Miguel works as a petroleum engineer for Exxon, So he 92 00:05:28,760 --> 00:05:31,680 Speaker 1: is like very much into kind of some of the 93 00:05:31,800 --> 00:05:38,400 Speaker 1: morally grayer or morally blacker aspects of of of of capitalism. Um. Like, 94 00:05:38,520 --> 00:05:42,599 Speaker 1: he's very on board with big business, and I think 95 00:05:43,120 --> 00:05:46,960 Speaker 1: his attitudes towards kind of capitalism versus socialism certainly have 96 00:05:47,080 --> 00:05:50,760 Speaker 1: an impact on young Jeffrey as he grows into an adult. 97 00:05:57,279 --> 00:06:00,040 Speaker 1: When did we see these things kind of manifest He 98 00:06:00,080 --> 00:06:02,760 Speaker 1: didn't go straight to Amazon. Right, he built this kind 99 00:06:02,760 --> 00:06:04,600 Speaker 1: of built you know, he built a good life for 100 00:06:04,680 --> 00:06:07,479 Speaker 1: himself already before that. Yeah. Yeah, so he's you know, 101 00:06:07,600 --> 00:06:10,200 Speaker 1: he goes to his family has number one. His his 102 00:06:10,600 --> 00:06:13,880 Speaker 1: maternal family is loaded, right, They've got he He spends 103 00:06:13,920 --> 00:06:16,240 Speaker 1: his summers as a kid on a twenty five thousand 104 00:06:16,320 --> 00:06:19,120 Speaker 1: acre ranch in Texas, which is, you know, land is 105 00:06:19,200 --> 00:06:21,760 Speaker 1: cheaper in Texas, but you're not poor if your family's 106 00:06:21,760 --> 00:06:26,000 Speaker 1: got twenty five thousand, twenty five. There are countries in 107 00:06:26,040 --> 00:06:31,520 Speaker 1: Europe smaller than this far um. But it also means 108 00:06:31,520 --> 00:06:33,960 Speaker 1: that Jeff like spends his childhood with his grandpa doing 109 00:06:33,960 --> 00:06:36,400 Speaker 1: a lot like practical engineering, you know, learning how to 110 00:06:36,440 --> 00:06:38,839 Speaker 1: like not just put up fences, but like build different 111 00:06:38,839 --> 00:06:41,839 Speaker 1: feeding things for livestock, doing a lot of handyman work, 112 00:06:41,880 --> 00:06:45,039 Speaker 1: repairing engines and stuff. So he grows up with this 113 00:06:45,160 --> 00:06:50,640 Speaker 1: like really um, like this really caught like a lot 114 00:06:50,680 --> 00:06:54,160 Speaker 1: of experience making things and and working hard and this 115 00:06:54,320 --> 00:06:57,400 Speaker 1: kind of like he's very molded both by his his 116 00:06:57,640 --> 00:07:02,160 Speaker 1: adopted father's um attitude towards free enterprise and by this 117 00:07:02,279 --> 00:07:07,440 Speaker 1: kind of very idyllic rural American chunk of his upbringing 118 00:07:07,480 --> 00:07:09,479 Speaker 1: where he's you know working on a farm and self 119 00:07:09,520 --> 00:07:11,760 Speaker 1: reliance and all this stuff. And of course it's self 120 00:07:11,800 --> 00:07:14,680 Speaker 1: reliance within the context of it's his grandpa's hobby farm. 121 00:07:14,800 --> 00:07:16,960 Speaker 1: You know, his grandpa does not have to make a 122 00:07:17,000 --> 00:07:19,679 Speaker 1: living with this farm. His grandfather was in the US 123 00:07:19,800 --> 00:07:23,680 Speaker 1: nuclear program for decades UM and then retired, and this 124 00:07:23,760 --> 00:07:26,040 Speaker 1: farm is kind of like his hobby as as a 125 00:07:26,080 --> 00:07:29,600 Speaker 1: retired man. So there's there's you get two sides of 126 00:07:29,640 --> 00:07:32,320 Speaker 1: it both, Like Jeff is kind of convinced, I've grown 127 00:07:32,400 --> 00:07:36,720 Speaker 1: up with this kind of traditional American rural self supporting, 128 00:07:36,840 --> 00:07:39,680 Speaker 1: like you tell take care of yourself, the government doesn't attitude. 129 00:07:39,680 --> 00:07:43,840 Speaker 1: But also the reason why there it's so much more 130 00:07:43,880 --> 00:07:45,800 Speaker 1: pleasant than a lot of people who grow up in 131 00:07:45,800 --> 00:07:48,520 Speaker 1: a rural agricultural setting is that they're rich. You know, 132 00:07:48,920 --> 00:07:51,600 Speaker 1: I grew up in a farming community, and it's it's 133 00:07:51,600 --> 00:07:54,520 Speaker 1: not most people do not have access to the resources 134 00:07:54,560 --> 00:07:56,320 Speaker 1: he had. But I'm not sure he's really aware of that. 135 00:07:56,400 --> 00:07:59,280 Speaker 1: I think he kind of sees himself as having assault 136 00:07:59,280 --> 00:08:02,280 Speaker 1: of the earth up bringing, even though that's really not 137 00:08:02,360 --> 00:08:05,800 Speaker 1: the case. Um. And he he benefits as well because 138 00:08:05,840 --> 00:08:09,000 Speaker 1: he's in he's farming in the summers and then during 139 00:08:09,040 --> 00:08:11,000 Speaker 1: the rest of the year, he's in Houston. Um, that's 140 00:08:11,000 --> 00:08:13,760 Speaker 1: where his family kind of winds up and he goes 141 00:08:13,840 --> 00:08:17,040 Speaker 1: to this very special He's in a public school, but 142 00:08:17,200 --> 00:08:19,280 Speaker 1: his school district has money for something. They call it 143 00:08:19,280 --> 00:08:22,440 Speaker 1: the Vanguard Program, and it's like this basically this super 144 00:08:22,480 --> 00:08:25,440 Speaker 1: special gifted and talented program where they're kind of experimenting 145 00:08:25,480 --> 00:08:30,680 Speaker 1: with with different ways of of taking care of of 146 00:08:30,680 --> 00:08:33,280 Speaker 1: of or or of teaching kids, and of course sort 147 00:08:33,280 --> 00:08:36,960 Speaker 1: of like molding the curriculum of the program to the 148 00:08:37,000 --> 00:08:38,760 Speaker 1: gifts of the children. And it it seems like a 149 00:08:38,760 --> 00:08:41,720 Speaker 1: good idea. It works really well for Jeff. And it's 150 00:08:42,200 --> 00:08:45,480 Speaker 1: it's you hear about stuff like this too with um 151 00:08:45,520 --> 00:08:49,160 Speaker 1: with with Bill Gates in particular, and I think Steve 152 00:08:49,240 --> 00:08:51,240 Speaker 1: Jobs kind of benefited from something similar when he was 153 00:08:51,240 --> 00:08:55,480 Speaker 1: a kid. Ditto um Wozniak. Where these are at the 154 00:08:55,600 --> 00:08:59,880 Speaker 1: very least upper middle class kids who benefit from a 155 00:09:00,200 --> 00:09:04,120 Speaker 1: specific kind of educational program that is not available to 156 00:09:04,160 --> 00:09:07,560 Speaker 1: most kids. So Jeff has very early access to computers. UM, 157 00:09:07,600 --> 00:09:10,120 Speaker 1: he's able to do what he wants in school rather 158 00:09:10,160 --> 00:09:12,079 Speaker 1: than kind of like do what the school wants him 159 00:09:12,080 --> 00:09:15,000 Speaker 1: to learn. The school is kind of go looking at Okay, 160 00:09:15,040 --> 00:09:17,320 Speaker 1: here's what Jeff's interested and here's the stuff that he's 161 00:09:17,400 --> 00:09:21,760 Speaker 1: he thinks is fascinating. Let's mold his um, his educational 162 00:09:21,840 --> 00:09:25,000 Speaker 1: program to it. UM. And he's a very competitive kid. 163 00:09:25,080 --> 00:09:28,559 Speaker 1: There's a writer who's sort of evaluating the school program 164 00:09:28,559 --> 00:09:31,480 Speaker 1: when he's like twelve or so. UM and you know, 165 00:09:31,600 --> 00:09:34,480 Speaker 1: decades later, she still had her notes on him because 166 00:09:34,520 --> 00:09:40,559 Speaker 1: he really struck her as UM, very intelligent, very competitive. 167 00:09:41,080 --> 00:09:43,600 Speaker 1: He was working on a science project at the time 168 00:09:43,640 --> 00:09:46,400 Speaker 1: called an infinity cube, which was this battery powered thing 169 00:09:46,440 --> 00:09:49,440 Speaker 1: with rotating mirrors that made the optical illusion of an 170 00:09:49,520 --> 00:09:51,760 Speaker 1: endless tunnel. And it was a thing he'd seen in 171 00:09:51,760 --> 00:09:54,040 Speaker 1: a store but was expensive, so he like made a 172 00:09:54,120 --> 00:09:57,160 Speaker 1: version of it for himself for cheaper. UM. He was 173 00:09:57,400 --> 00:09:59,800 Speaker 1: entering a bunch of local science competitions and winning them. 174 00:09:59,800 --> 00:10:03,120 Speaker 1: He's he's a very very like bright kid. Everyone kind 175 00:10:03,120 --> 00:10:05,640 Speaker 1: of pegs this, this kid out as special and they 176 00:10:05,679 --> 00:10:08,079 Speaker 1: just this whole community because again this is a public 177 00:10:08,080 --> 00:10:11,679 Speaker 1: school program. This whole community in Houston pours resources into 178 00:10:11,760 --> 00:10:15,400 Speaker 1: Jeff when he's a little kid. UM and he graduates, Uh, 179 00:10:15,520 --> 00:10:17,040 Speaker 1: he and his you know, I think when he's in 180 00:10:17,160 --> 00:10:21,320 Speaker 1: Florida by the time he graduates. Um, but he uh, 181 00:10:22,080 --> 00:10:25,040 Speaker 1: he starts this kind of like after school program the 182 00:10:25,080 --> 00:10:27,400 Speaker 1: summer after he graduates with another kid where they're like 183 00:10:27,840 --> 00:10:31,120 Speaker 1: teaching younger kids science stuff, doing like star gazing and 184 00:10:31,120 --> 00:10:34,120 Speaker 1: whatnot with them. Um. He has a really early interest 185 00:10:34,160 --> 00:10:37,439 Speaker 1: in space travel. Uh. He's his favorite show is Star Trek. 186 00:10:37,480 --> 00:10:39,920 Speaker 1: As a kid, he's got kind of some of these 187 00:10:40,000 --> 00:10:42,840 Speaker 1: utopian dreams and it's weird because there's this mix of 188 00:10:42,880 --> 00:10:46,000 Speaker 1: like these sort of utopian and Star Trek is coming 189 00:10:46,000 --> 00:10:49,880 Speaker 1: at utopia from a very left wing sort of perspective. Um, 190 00:10:50,200 --> 00:10:53,440 Speaker 1: and he's fascinated by that. But all of his heroes 191 00:10:53,520 --> 00:10:57,040 Speaker 1: are these businessmen Walt Disney and Thomas Edison, these guys 192 00:10:57,080 --> 00:11:00,440 Speaker 1: who are um, real like capitalist he os. And he 193 00:11:00,480 --> 00:11:04,640 Speaker 1: actually he prefers Disney to Edison, um because he thinks 194 00:11:04,920 --> 00:11:07,600 Speaker 1: Disney was better at building a team and working together, 195 00:11:07,760 --> 00:11:10,199 Speaker 1: like making them work together in a concerted direction. And 196 00:11:10,400 --> 00:11:12,360 Speaker 1: it makes sense that Disney is kind of the person 197 00:11:12,400 --> 00:11:16,200 Speaker 1: he idolizes, right, because Amazon is definitely has that sort 198 00:11:16,240 --> 00:11:19,600 Speaker 1: of thing that Disney has where they're pulling everything in 199 00:11:19,640 --> 00:11:22,080 Speaker 1: the world to them and making this kind of capitalist 200 00:11:22,160 --> 00:11:26,440 Speaker 1: katamari that that just owns everything in a space, right, Yeah, yeah, 201 00:11:26,480 --> 00:11:29,920 Speaker 1: so he's come from he comes from a very privileged background. 202 00:11:29,960 --> 00:11:33,280 Speaker 1: He acts like the way he talks, I agree with you. 203 00:11:33,320 --> 00:11:35,760 Speaker 1: It's as if I don't even think he's kind of 204 00:11:35,760 --> 00:11:39,480 Speaker 1: trying to present a fight narrative. He just thinks because 205 00:11:39,480 --> 00:11:42,520 Speaker 1: he was on a farm that he's from some like, oh, 206 00:11:42,559 --> 00:11:44,920 Speaker 1: I'm the salt of the earth, I'm from the dirt. Like, no, 207 00:11:45,000 --> 00:11:47,040 Speaker 1: you're from the dirt on like a twenty five thousand 208 00:11:47,120 --> 00:11:49,480 Speaker 1: acre farm. That's very different. You know, if he didn't 209 00:11:49,480 --> 00:11:51,880 Speaker 1: want to, if his granddad decided he was sick of 210 00:11:51,920 --> 00:11:54,559 Speaker 1: that farm, it's not like they would have starved, right, Yeah, 211 00:11:54,600 --> 00:11:56,320 Speaker 1: they could have. They could have stopped all of the 212 00:11:56,360 --> 00:11:58,920 Speaker 1: farming and kept living comfortably in the ranch house and 213 00:11:58,920 --> 00:12:01,360 Speaker 1: would have would have been And I think it's easy 214 00:12:01,480 --> 00:12:02,920 Speaker 1: when you're in the kind of position he was as 215 00:12:02,920 --> 00:12:05,480 Speaker 1: a kid to note that, like, well, we worked hard. 216 00:12:05,559 --> 00:12:07,319 Speaker 1: You know, I was up at dawn every day, we 217 00:12:07,360 --> 00:12:09,280 Speaker 1: didn't stop working until night. We did a bunch of 218 00:12:09,320 --> 00:12:13,320 Speaker 1: really physically and mentally difficult tasks. So that means like 219 00:12:14,000 --> 00:12:18,360 Speaker 1: I had kind of this working class experience and upbringing, 220 00:12:18,400 --> 00:12:21,160 Speaker 1: and the reality is that you're not. You don't really 221 00:12:21,240 --> 00:12:23,680 Speaker 1: know any of that, you're not really having the authentic 222 00:12:23,720 --> 00:12:26,920 Speaker 1: experience if there's not the fear of failure. I think 223 00:12:26,960 --> 00:12:29,960 Speaker 1: it's it's easy because he's working hard as a kid 224 00:12:30,000 --> 00:12:34,600 Speaker 1: for him to forget that. He also has a safety 225 00:12:34,640 --> 00:12:37,600 Speaker 1: net that about like half a percent of kids in 226 00:12:37,640 --> 00:12:40,520 Speaker 1: America maybe benefit from, you know. So so he goes 227 00:12:40,600 --> 00:12:42,680 Speaker 1: on then too, he doesn't exactly end up working in 228 00:12:42,679 --> 00:12:44,760 Speaker 1: the farming industry or anything like that, right, he kind 229 00:12:44,760 --> 00:12:48,560 Speaker 1: of follows his dreams of these kind of like capitalist heroes. Yeah, 230 00:12:48,559 --> 00:12:50,480 Speaker 1: he follows his dreams. He does start like a little 231 00:12:50,559 --> 00:12:53,280 Speaker 1: kind of educational business that does seem like a decent 232 00:12:53,360 --> 00:12:56,400 Speaker 1: thing to do. Um, and then he goes off to college. 233 00:12:56,640 --> 00:12:59,600 Speaker 1: He goes to Princeton. He seemed most of the people 234 00:12:59,640 --> 00:13:01,800 Speaker 1: he went to journalists have found other folks who were 235 00:13:01,840 --> 00:13:04,520 Speaker 1: in his class. He made really no impression on on 236 00:13:04,600 --> 00:13:08,280 Speaker 1: most people. He's very much like kind of a forgettable dude. 237 00:13:08,600 --> 00:13:11,000 Speaker 1: And in fact, one of the classmates who like sets 238 00:13:11,080 --> 00:13:13,200 Speaker 1: him up with a job after college, so you would 239 00:13:13,200 --> 00:13:16,560 Speaker 1: think this guy knew him well. Like when the interviewed 240 00:13:16,640 --> 00:13:18,559 Speaker 1: later after Jeff is rich and famous, it's like, yeah, 241 00:13:18,600 --> 00:13:20,720 Speaker 1: I don't remember anything but that he was smart, like 242 00:13:20,760 --> 00:13:23,520 Speaker 1: he does. He doesn't really Um, he doesn't really leave 243 00:13:23,600 --> 00:13:26,400 Speaker 1: much of an impact like bright, but not a dude 244 00:13:26,400 --> 00:13:29,959 Speaker 1: whose personality like you remember. Um and there's some weird 245 00:13:30,360 --> 00:13:33,920 Speaker 1: he doesn't like music. Um and it doesn't appear to 246 00:13:33,960 --> 00:13:35,320 Speaker 1: be a thing where like some people have like an 247 00:13:35,360 --> 00:13:39,079 Speaker 1: auditory processing issue right where it's uncomfortable, like for whatever reason, 248 00:13:39,200 --> 00:13:42,079 Speaker 1: music just like it doesn't feel good in their ears. 249 00:13:42,120 --> 00:13:44,120 Speaker 1: I don't think it's that because they'll play music for 250 00:13:44,160 --> 00:13:46,480 Speaker 1: other people and stuff. So with that in mind, let's 251 00:13:46,559 --> 00:13:48,959 Speaker 1: take a look at a tweet Jeff Bezos put out 252 00:13:49,120 --> 00:13:53,560 Speaker 1: on February three, twenty next to a picture of the 253 00:13:53,640 --> 00:13:58,280 Speaker 1: singer Lizzo, Bezos wrote quote, I just took a DNA tist. 254 00:13:58,679 --> 00:14:03,959 Speaker 1: Turns out I'm one hun present Lizzo's biggest fan. End quote. Now, 255 00:14:03,960 --> 00:14:07,400 Speaker 1: obviously that's very cringe to say. But also this is 256 00:14:07,440 --> 00:14:11,560 Speaker 1: from a guy that apparently doesn't like music. He's either lying, 257 00:14:11,840 --> 00:14:15,840 Speaker 1: disingenuous or he does actually like music. Who knows which 258 00:14:15,880 --> 00:14:17,560 Speaker 1: one it is? Just doesn't get it, you know, he 259 00:14:17,640 --> 00:14:20,720 Speaker 1: just doesn't understand why people like music, which is which 260 00:14:20,760 --> 00:14:22,880 Speaker 1: is interesting that some folks will claim that this is 261 00:14:22,960 --> 00:14:26,480 Speaker 1: why Apple beats Amazon to the digital music game, right, 262 00:14:26,520 --> 00:14:29,600 Speaker 1: and and then Spotify later. Like Amazon is for all 263 00:14:29,600 --> 00:14:31,680 Speaker 1: the things, they're ahead of the curvan, kind of always 264 00:14:31,720 --> 00:14:35,360 Speaker 1: behind on digital music, um, which there's a weird you know, 265 00:14:35,400 --> 00:14:38,000 Speaker 1: the stuff with Joe Rogan and Neil Young and the 266 00:14:38,040 --> 00:14:41,280 Speaker 1: fight over Spotify. There's kind of a weird parallel there 267 00:14:41,280 --> 00:14:45,560 Speaker 1: because Neil Young when he deletes his catalog from Spotify, 268 00:14:45,680 --> 00:14:47,680 Speaker 1: I'm sure he has an issue with Joe Rogan, but 269 00:14:47,720 --> 00:14:49,480 Speaker 1: a big part of what Young is doing is he's 270 00:14:49,600 --> 00:14:51,880 Speaker 1: he's got a deal with Amazon Music, right, and so 271 00:14:51,920 --> 00:14:56,280 Speaker 1: he's plugging Amazon Music too, you know. Convenient. They're all millionaires, right, 272 00:14:56,400 --> 00:14:58,640 Speaker 1: Like you shouldn't expect any of them to be your hero. 273 00:14:59,160 --> 00:15:01,720 Speaker 1: But yeah, so people will say that like that is 274 00:15:01,760 --> 00:15:05,200 Speaker 1: kind of why Amazon is consistently behind the curve when 275 00:15:05,240 --> 00:15:07,880 Speaker 1: it comes to digital music, which is which is noteworthy 276 00:15:07,960 --> 00:15:10,400 Speaker 1: just because there one thing you have to give Amazon 277 00:15:10,440 --> 00:15:13,000 Speaker 1: credit on there, the head of the curve on most things, 278 00:15:13,080 --> 00:15:16,720 Speaker 1: you know, um, but Bezos just doesn't get that, and 279 00:15:16,760 --> 00:15:21,240 Speaker 1: so they're kind of consistently behind. Um And yeah, he uh. 280 00:15:21,320 --> 00:15:24,800 Speaker 1: He eventually winds up working in a company called Fittel, 281 00:15:24,840 --> 00:15:28,480 Speaker 1: which is like a finance a tech finance company. This 282 00:15:28,560 --> 00:15:30,720 Speaker 1: is in the mid eighties. I think eight four eight 283 00:15:30,920 --> 00:15:34,000 Speaker 1: five is when he gets his first big boy job. Um. 284 00:15:34,080 --> 00:15:37,960 Speaker 1: And so that's this period where computers are starting to 285 00:15:38,120 --> 00:15:41,640 Speaker 1: become standard in investment banks and brokerages, but they're still 286 00:15:41,680 --> 00:15:44,520 Speaker 1: not the norm. Right there there that that process begins, 287 00:15:45,040 --> 00:15:48,440 Speaker 1: the more forward thinking investment banks and brokerages are putting 288 00:15:48,440 --> 00:15:50,760 Speaker 1: computers in, right, but a lot of this is still 289 00:15:50,800 --> 00:15:53,200 Speaker 1: done by analog you know, today all trading is kind 290 00:15:53,240 --> 00:15:55,640 Speaker 1: of done by these machines, and there's some human input, 291 00:15:55,760 --> 00:15:58,160 Speaker 1: but like a lot of it is these computers kind 292 00:15:58,160 --> 00:16:01,520 Speaker 1: of gambling with each other. This is the V's He's 293 00:16:01,560 --> 00:16:03,840 Speaker 1: in on the ground floor of that process, and he 294 00:16:03,960 --> 00:16:07,080 Speaker 1: is working with a company that basically gets hired by 295 00:16:07,160 --> 00:16:10,360 Speaker 1: these big banks and brokerages to help them set up 296 00:16:10,560 --> 00:16:14,120 Speaker 1: these networked computer banks that are initially just kind of 297 00:16:14,160 --> 00:16:17,240 Speaker 1: augmenting human decision making, but will eventually take it over 298 00:16:17,520 --> 00:16:20,000 Speaker 1: to a very large extent in finance. You know, that's 299 00:16:20,000 --> 00:16:23,240 Speaker 1: how things work now. And he is he's he's UM. 300 00:16:23,280 --> 00:16:25,480 Speaker 1: I don't think he's necessarily a huge part because he's 301 00:16:25,520 --> 00:16:28,360 Speaker 1: not a visionary part of this, right He's working for 302 00:16:28,440 --> 00:16:30,400 Speaker 1: the visionaries who see where the future is going to 303 00:16:30,520 --> 00:16:32,720 Speaker 1: be here, but he is also as a code or 304 00:16:32,760 --> 00:16:35,720 Speaker 1: helping to build this, so he's definitely like it's a 305 00:16:35,720 --> 00:16:39,520 Speaker 1: good internship for him in terms of he's experiencing work 306 00:16:39,560 --> 00:16:41,920 Speaker 1: with these people who have seen the future and and 307 00:16:42,000 --> 00:16:44,360 Speaker 1: helping to kind of make it with people. And he 308 00:16:44,360 --> 00:16:47,400 Speaker 1: he bounces around a few of these early fintech companies 309 00:16:47,840 --> 00:16:51,640 Speaker 1: until in nineteen eighty nine, Um, he starts having a 310 00:16:51,680 --> 00:16:55,120 Speaker 1: conversation with a colleague about like, Hey, you know, we're 311 00:16:55,240 --> 00:16:58,320 Speaker 1: we're setting up these computer networks, um that are that 312 00:16:58,360 --> 00:17:01,800 Speaker 1: are connecting like brokerage and allowing them to basically gamble 313 00:17:01,880 --> 00:17:04,600 Speaker 1: a lot faster. Uh, what if we were to set 314 00:17:04,680 --> 00:17:08,639 Speaker 1: up like a a networked computer intranet thing that anyone 315 00:17:08,680 --> 00:17:10,960 Speaker 1: could use to like connect to news stories which will 316 00:17:11,000 --> 00:17:15,800 Speaker 1: be curated algorithmically based on a person's interest. Um, and 317 00:17:15,800 --> 00:17:18,120 Speaker 1: they almost get investments in the idea you know you'll 318 00:17:18,280 --> 00:17:21,639 Speaker 1: do this is basically like how Facebook and Twitter work, right, Um, 319 00:17:21,680 --> 00:17:24,320 Speaker 1: it's a version of that. It doesn't quite happen, but 320 00:17:24,359 --> 00:17:26,680 Speaker 1: the fact that Bezos is sort of thinking about this 321 00:17:27,080 --> 00:17:31,399 Speaker 1: in nineteen nine shows that he does have a really 322 00:17:31,880 --> 00:17:35,679 Speaker 1: pretty deep understanding of where networked computing is going to 323 00:17:35,720 --> 00:17:39,160 Speaker 1: go right, Like he's not just following the trend. He's predicted, Oh, 324 00:17:39,200 --> 00:17:41,000 Speaker 1: this is gonna be a big deal. Um, And he's 325 00:17:41,080 --> 00:17:43,600 Speaker 1: very much ahead of the eight ball on that um. 326 00:17:43,640 --> 00:17:47,199 Speaker 1: I think in the late eight like early nineties, he 327 00:17:47,240 --> 00:17:50,920 Speaker 1: gets a job with an investment with an investment firm 328 00:17:51,000 --> 00:17:54,320 Speaker 1: that manages a hedge fund called d E. Shaw Um, 329 00:17:54,400 --> 00:17:57,560 Speaker 1: which is this as firms go, it's one of these 330 00:17:57,560 --> 00:17:59,480 Speaker 1: ones that's really ahead of the curve. They're doing a 331 00:17:59,480 --> 00:18:01,720 Speaker 1: lot of comput uter stuff. The guy who runs it 332 00:18:01,800 --> 00:18:04,520 Speaker 1: is hiring a lot of programmers, and Bezos is a 333 00:18:04,600 --> 00:18:09,440 Speaker 1: fucking superstar there, you know, um, his colleagues and he's 334 00:18:09,480 --> 00:18:13,280 Speaker 1: both a superstar, but he also he's clearly already thinking 335 00:18:13,320 --> 00:18:15,960 Speaker 1: about his legend. Right. People who work with him will 336 00:18:16,000 --> 00:18:18,199 Speaker 1: note that he would like keep a sleeping bag in 337 00:18:18,280 --> 00:18:21,280 Speaker 1: his office so that he could work overnight if he 338 00:18:21,320 --> 00:18:23,879 Speaker 1: needed to. But the main purpose of the sleeping bag 339 00:18:24,040 --> 00:18:26,200 Speaker 1: was not for him to actually sleep at work, because 340 00:18:26,240 --> 00:18:28,119 Speaker 1: he didn't do that often. He just kept it in 341 00:18:28,240 --> 00:18:32,160 Speaker 1: view so his employees would see it and like would think, oh, Jeff, 342 00:18:32,359 --> 00:18:34,040 Speaker 1: you know, we need I need to work like that, 343 00:18:34,080 --> 00:18:37,440 Speaker 1: because Jeff is like crashing at work sometimes he's using 344 00:18:37,440 --> 00:18:39,199 Speaker 1: it as a prop right, just when through it for 345 00:18:39,200 --> 00:18:41,879 Speaker 1: a second. I think this point that Robot is explaining 346 00:18:42,040 --> 00:18:45,120 Speaker 1: is very interesting if you look at how Amazon went 347 00:18:45,280 --> 00:18:48,720 Speaker 1: on after Bezos started it. He's always saying, hope, people 348 00:18:48,760 --> 00:18:51,320 Speaker 1: need to just work harder. But as we see here, 349 00:18:51,359 --> 00:18:53,919 Speaker 1: he allegedly used props to give the impression that he 350 00:18:54,000 --> 00:18:56,480 Speaker 1: was the hardest worker buck in the day. Now the 351 00:18:56,520 --> 00:18:59,600 Speaker 1: hardest workers are on the warehouse floor and he's telling 352 00:18:59,640 --> 00:19:01,879 Speaker 1: them I need to work as hard as him. Perhaps 353 00:19:01,880 --> 00:19:04,800 Speaker 1: he's still using props now. He's very much bought into 354 00:19:04,880 --> 00:19:07,359 Speaker 1: the kind of myths we say about like how you 355 00:19:07,400 --> 00:19:09,240 Speaker 1: ought to work and kind of you know, this is 356 00:19:09,240 --> 00:19:12,199 Speaker 1: how you get ahead is by being uh, you know, 357 00:19:12,240 --> 00:19:15,280 Speaker 1: the busiest, and by you know, sleeping at your desk 358 00:19:15,359 --> 00:19:17,760 Speaker 1: and and and basically living at the office. This kind 359 00:19:17,800 --> 00:19:20,159 Speaker 1: of stuff that that is huge in the tech industry, right. 360 00:19:20,160 --> 00:19:24,000 Speaker 1: This is why Google has sleeping rooms and massages on site, 361 00:19:24,040 --> 00:19:26,320 Speaker 1: and like they try to keep people at the office 362 00:19:26,320 --> 00:19:29,760 Speaker 1: as often as possible. Um, you know this is this 363 00:19:29,840 --> 00:19:33,879 Speaker 1: is just kind of uh, he's he wants to I 364 00:19:34,240 --> 00:19:38,720 Speaker 1: think he he understands that mythologizing as an important part 365 00:19:38,720 --> 00:19:41,040 Speaker 1: of being a founder and I think even though he's 366 00:19:41,080 --> 00:19:43,120 Speaker 1: not a founder at this point, he wants to found 367 00:19:43,440 --> 00:19:46,280 Speaker 1: a a big tech company. You know that that is 368 00:19:46,320 --> 00:19:49,560 Speaker 1: beginning to be his ambition. Um, but he's working at 369 00:19:49,600 --> 00:19:52,560 Speaker 1: D E. Shaw, He's doing great. He meets a woman 370 00:19:52,640 --> 00:19:55,600 Speaker 1: named mckenzy Tuttle while working at the Hedge Fund. She 371 00:19:55,720 --> 00:19:59,280 Speaker 1: graduated from Princeton to She actually studied with Tony Morrison 372 00:19:59,600 --> 00:20:01,639 Speaker 1: when she at an English degree, which is a is 373 00:20:01,680 --> 00:20:04,080 Speaker 1: weird that there's a connection between Tony Morrison and Jeff 374 00:20:04,119 --> 00:20:07,600 Speaker 1: Bezos that close, But there you go. Um, she's a 375 00:20:07,640 --> 00:20:10,040 Speaker 1: novelist and she's kind of working as a secretary in 376 00:20:10,080 --> 00:20:13,480 Speaker 1: this tech company and she winds up with a big 377 00:20:13,480 --> 00:20:15,320 Speaker 1: crush on Jeff. And it is one of those things 378 00:20:15,520 --> 00:20:17,880 Speaker 1: like we talked about Bill Gates. Bill Gates has gotten 379 00:20:17,880 --> 00:20:20,439 Speaker 1: in some trouble recently because he had a history of 380 00:20:20,480 --> 00:20:25,400 Speaker 1: creepily hitting on women who were his employees at Microsoft Conn. Yeah, 381 00:20:25,400 --> 00:20:27,040 Speaker 1: and some kind of connections to Epstein. We could go 382 00:20:27,040 --> 00:20:30,080 Speaker 1: out for a while about about that. Um, it does 383 00:20:30,080 --> 00:20:32,560 Speaker 1: not appear to be that kind of case with Bezos. McKenzie, 384 00:20:32,560 --> 00:20:34,680 Speaker 1: even though she's divorced him now, is adamant that like 385 00:20:34,760 --> 00:20:37,440 Speaker 1: she was the one who started hitting on him. Um. 386 00:20:37,520 --> 00:20:39,760 Speaker 1: She was the one who kind of pushed him to 387 00:20:39,840 --> 00:20:41,719 Speaker 1: go out with her and fell in love with him. 388 00:20:41,720 --> 00:20:44,240 Speaker 1: And it does seem they stayed together a long time 389 00:20:44,320 --> 00:20:46,960 Speaker 1: you know. Um, you know they've split up now, but 390 00:20:46,960 --> 00:20:50,600 Speaker 1: they were together like twenty years or so. Um. And 391 00:20:50,640 --> 00:20:52,640 Speaker 1: she was a big part of the creation of Amazon. 392 00:20:52,800 --> 00:20:56,240 Speaker 1: So whatever else, it does seem like, uh, he is 393 00:20:56,280 --> 00:20:58,920 Speaker 1: a human being who was capable of connecting with another person, 394 00:20:59,000 --> 00:21:03,520 Speaker 1: which I guess there you go. Um. Yeah, So they 395 00:21:04,600 --> 00:21:07,600 Speaker 1: she gets together with mckensey while he's working at D. E. 396 00:21:07,720 --> 00:21:10,840 Speaker 1: Shaw and he starts to this is the you know, 397 00:21:10,880 --> 00:21:13,119 Speaker 1: he's making really good money at this he's not like 398 00:21:13,160 --> 00:21:16,800 Speaker 1: a millionaire, but he's extremely comfortable. But he starts to 399 00:21:16,880 --> 00:21:19,399 Speaker 1: kind of chafe. Not because he likes his boss. He 400 00:21:19,480 --> 00:21:21,919 Speaker 1: likes what they're doing, but it's not his business. And 401 00:21:21,960 --> 00:21:25,320 Speaker 1: he's already had this idea that he really wants to 402 00:21:25,320 --> 00:21:27,679 Speaker 1: to to create one that he's he's kind of started 403 00:21:27,720 --> 00:21:30,119 Speaker 1: forming while talking with his boss at d Shaw. He 404 00:21:30,160 --> 00:21:33,760 Speaker 1: wanted to build something he called the Everything Store. Um. 405 00:21:33,840 --> 00:21:36,159 Speaker 1: So this is the early nineties, you know, this is 406 00:21:36,240 --> 00:21:38,280 Speaker 1: kind of right around the period a little before the 407 00:21:38,280 --> 00:21:41,600 Speaker 1: period that we hit eternal September, you know, where everybody 408 00:21:41,680 --> 00:21:45,480 Speaker 1: is online. This is like immediately before that happens. But 409 00:21:45,640 --> 00:21:49,000 Speaker 1: Jeff sees the potential of the Internet and recognizes that 410 00:21:49,040 --> 00:21:51,480 Speaker 1: a huge amount of commerce is going to take part 411 00:21:51,520 --> 00:21:53,280 Speaker 1: in it, and that if you, because of what the 412 00:21:53,280 --> 00:21:56,040 Speaker 1: internet is, if you could build a store with the 413 00:21:56,119 --> 00:22:00,200 Speaker 1: kind of distribution center necessary, you could sell every thing 414 00:22:00,359 --> 00:22:03,040 Speaker 1: to anyone, you know, as opposed to having to kind 415 00:22:03,040 --> 00:22:06,800 Speaker 1: of uh locally curate your your inventory and deal with 416 00:22:06,840 --> 00:22:08,359 Speaker 1: all of that. Like, you could have a store that 417 00:22:08,400 --> 00:22:12,280 Speaker 1: could sell everything cheaper than basically anyone else, um, and 418 00:22:12,359 --> 00:22:16,160 Speaker 1: undercut all these retail change chains, um. And he has 419 00:22:16,200 --> 00:22:17,840 Speaker 1: one of the the idea he has about this that's 420 00:22:17,840 --> 00:22:20,639 Speaker 1: particularly innovative. That's that's new at this point you have 421 00:22:20,680 --> 00:22:24,359 Speaker 1: to think, you know, we're talking four. He wants to 422 00:22:24,800 --> 00:22:27,600 Speaker 1: he wants the sales on the store to be heavily 423 00:22:27,680 --> 00:22:30,400 Speaker 1: driven by consumer reviews, you know. And you can see 424 00:22:30,400 --> 00:22:32,320 Speaker 1: this as kind of him branching off from this idea 425 00:22:32,400 --> 00:22:35,800 Speaker 1: he has for an algorithmically curated news service. He wants 426 00:22:35,920 --> 00:22:40,240 Speaker 1: people reviewing products to have an influence on how often 427 00:22:40,240 --> 00:22:42,520 Speaker 1: those products show up when other people go to the 428 00:22:42,560 --> 00:22:45,480 Speaker 1: site and he wants that to drive people to buy things, 429 00:22:45,520 --> 00:22:48,040 Speaker 1: like the reviews of customers, you know, and that is 430 00:22:48,080 --> 00:22:50,800 Speaker 1: again really new at this point because the internet, like 431 00:22:51,040 --> 00:22:52,960 Speaker 1: you have to think, if you're if you're shot, if 432 00:22:52,960 --> 00:22:54,560 Speaker 1: all of your shopping is like going to a mall 433 00:22:54,640 --> 00:22:57,479 Speaker 1: or a store, you're not buying things generally because like 434 00:22:57,560 --> 00:22:59,480 Speaker 1: of a review. You know, if you get a review, 435 00:22:59,560 --> 00:23:02,639 Speaker 1: it's like someone whose job, who works at Wired or whatever, 436 00:23:02,680 --> 00:23:06,200 Speaker 1: whose job is to review products, as opposed to anyone 437 00:23:06,200 --> 00:23:08,040 Speaker 1: who buys a product being able to leave a review 438 00:23:08,080 --> 00:23:10,440 Speaker 1: online and you can see, oh, eight hundred people gave 439 00:23:10,440 --> 00:23:12,200 Speaker 1: this a five star rating, and you know that means 440 00:23:12,200 --> 00:23:16,720 Speaker 1: it's probably good. Um. That's the big idea that Jeff has. Uh. 441 00:23:16,720 --> 00:23:21,840 Speaker 1: And he grows obsessed with this idea of the everything store. Um. 442 00:23:21,880 --> 00:23:24,040 Speaker 1: He starts to feel like, in the mid nineties, it's 443 00:23:24,080 --> 00:23:27,120 Speaker 1: probably time to do this, So he quits his high 444 00:23:27,160 --> 00:23:30,399 Speaker 1: paying job, so does Mackenzie, and he and his wife 445 00:23:30,640 --> 00:23:33,800 Speaker 1: drive to California. Um because they initially right, you know, 446 00:23:33,840 --> 00:23:36,679 Speaker 1: that's where most people would would start a tech business 447 00:23:36,720 --> 00:23:39,639 Speaker 1: in both this period and now, But he decides taxes 448 00:23:39,720 --> 00:23:42,520 Speaker 1: are too high. Um. Because the thing about e commerce, right, 449 00:23:42,560 --> 00:23:44,840 Speaker 1: and this is the way it's it works, or at 450 00:23:44,880 --> 00:23:46,239 Speaker 1: least it worked for a while. I think there's been 451 00:23:46,280 --> 00:23:51,040 Speaker 1: some changes. But um consumers at the time where you 452 00:23:51,040 --> 00:23:54,879 Speaker 1: would only have to pay taxes on whatever the like, 453 00:23:54,960 --> 00:23:57,040 Speaker 1: the business would only have to pay taxes based on 454 00:23:57,119 --> 00:24:00,679 Speaker 1: like the state that it resided in, you know. UM. 455 00:24:00,920 --> 00:24:04,520 Speaker 1: And so he winds up taxes in California are too high, 456 00:24:04,560 --> 00:24:07,760 Speaker 1: doesn't make sense. He goes to Washington State because it 457 00:24:07,800 --> 00:24:11,680 Speaker 1: has good infrastructure, UM. But it has a tiny population. 458 00:24:11,840 --> 00:24:16,800 Speaker 1: So Washington does have sales tax. But um, because it's 459 00:24:16,800 --> 00:24:19,520 Speaker 1: not a populous state. It means every state other than 460 00:24:19,560 --> 00:24:21,399 Speaker 1: Washington that people buy from in this story he's going 461 00:24:21,480 --> 00:24:24,119 Speaker 1: to be creating don't have to pay sales tax. So 462 00:24:24,160 --> 00:24:25,880 Speaker 1: this is kind of his way of minimizing the number 463 00:24:25,920 --> 00:24:27,879 Speaker 1: of customers who might have to pay sales tax on 464 00:24:27,920 --> 00:24:32,000 Speaker 1: products for what's going to become Amazon. UM. And he 465 00:24:32,080 --> 00:24:34,920 Speaker 1: starts with the plan to sell books UM. Mainly because 466 00:24:35,359 --> 00:24:38,040 Speaker 1: all books come from one or two there's one or 467 00:24:38,080 --> 00:24:41,159 Speaker 1: two companies in the United States that distribute all of 468 00:24:41,200 --> 00:24:43,159 Speaker 1: the books everywhere. You know, they sell the books to 469 00:24:43,160 --> 00:24:45,520 Speaker 1: the stores. They're the ones who actually they're not producing 470 00:24:45,560 --> 00:24:47,280 Speaker 1: the books, but they're the ones who are like gathering 471 00:24:47,320 --> 00:24:49,639 Speaker 1: them and putting them in warehouses and shipping them out 472 00:24:49,640 --> 00:24:51,679 Speaker 1: to where they need to go. So he sees, like 473 00:24:52,520 --> 00:24:57,480 Speaker 1: the book industry already has pretty good infrastructure. It would 474 00:24:57,480 --> 00:24:59,639 Speaker 1: make it easy for me to order books anywhere in 475 00:24:59,680 --> 00:25:01,879 Speaker 1: the kind tree and get them shipped to anywhere in 476 00:25:01,920 --> 00:25:05,080 Speaker 1: the country. Um. So he starts. He decides that I'm 477 00:25:05,080 --> 00:25:07,280 Speaker 1: going to start by selling books online, all books, so 478 00:25:07,320 --> 00:25:09,199 Speaker 1: it's like a bookstore. And again, this is a really 479 00:25:09,280 --> 00:25:11,280 Speaker 1: new idea at the time. You don't have to like 480 00:25:11,720 --> 00:25:14,119 Speaker 1: go to the bookstore and see if you can find something, 481 00:25:14,200 --> 00:25:16,439 Speaker 1: or like have them special orders something. Everything is just 482 00:25:16,480 --> 00:25:19,560 Speaker 1: available all the time. Um. They wanted to call it 483 00:25:19,640 --> 00:25:22,960 Speaker 1: Cadabra at first, and then make it so dot com 484 00:25:23,000 --> 00:25:26,440 Speaker 1: because again he's a big star trek nerd um. But 485 00:25:26,680 --> 00:25:29,639 Speaker 1: their friends thankfully talk them out of most of these things. 486 00:25:29,880 --> 00:25:32,880 Speaker 1: They go through a bunch of different names, um and 487 00:25:33,800 --> 00:25:36,800 Speaker 1: relentless dot Com I think is Jeff's favorite. You can 488 00:25:36,800 --> 00:25:38,879 Speaker 1: actually still go to relentless dot com today if you 489 00:25:38,880 --> 00:25:42,359 Speaker 1: type it in, it will take you to Amazon. Um. 490 00:25:42,400 --> 00:25:45,520 Speaker 1: But yeah, so they they they start this business. They 491 00:25:45,520 --> 00:25:48,920 Speaker 1: eventually land upon Amazon as a name, and it's worth 492 00:25:48,960 --> 00:25:53,159 Speaker 1: noting that like when this business uh, when when he 493 00:25:53,200 --> 00:25:55,760 Speaker 1: starts this company he puts ten grand of his own 494 00:25:55,800 --> 00:25:57,880 Speaker 1: money in it, which is a significant amount of money 495 00:25:57,920 --> 00:26:02,280 Speaker 1: in like uh, but it's also primarily funded by eighty 496 00:26:02,320 --> 00:26:05,840 Speaker 1: four thousand dollars in interest free loans UM, some of 497 00:26:05,840 --> 00:26:08,440 Speaker 1: them from his first employees who are people he met 498 00:26:08,480 --> 00:26:11,760 Speaker 1: in the financial tech industry UH, and pour money into 499 00:26:11,760 --> 00:26:14,800 Speaker 1: it UM and a lot of it comes from his parents, 500 00:26:15,080 --> 00:26:17,960 Speaker 1: and in fact, they eventually invest a hundred thousand dollars 501 00:26:17,960 --> 00:26:21,480 Speaker 1: of it in UM of their own money. So it 502 00:26:21,600 --> 00:26:23,880 Speaker 1: is this thing. It's the same deal with Elon Musk 503 00:26:23,960 --> 00:26:29,359 Speaker 1: really um where he gets his business. First business gets 504 00:26:29,359 --> 00:26:32,359 Speaker 1: started because of money that his dad UM, and his 505 00:26:32,440 --> 00:26:35,400 Speaker 1: mom and dad pump into the business. So remember, kids, 506 00:26:35,400 --> 00:26:37,480 Speaker 1: the moral of the story here is if you want 507 00:26:37,480 --> 00:26:40,600 Speaker 1: to make it big in business, all we have to 508 00:26:40,640 --> 00:26:50,879 Speaker 1: do is have rich parents. That's it. Jeff tells them 509 00:26:50,920 --> 00:26:53,480 Speaker 1: there's a seventy chance they're going to lose it all. Uh. 510 00:26:53,520 --> 00:26:55,880 Speaker 1: The fact that they're willing to invest that in him 511 00:26:55,880 --> 00:26:59,199 Speaker 1: says a lot both about their level of financial um 512 00:26:59,400 --> 00:27:02,200 Speaker 1: privilege and about you know, just they believe in their sons. 513 00:27:02,240 --> 00:27:04,959 Speaker 1: So it's this mix of like, well, that's very sweet. 514 00:27:05,080 --> 00:27:07,960 Speaker 1: But it's also this continuing story where it's like, yeah, 515 00:27:08,000 --> 00:27:10,040 Speaker 1: part of why Jeff Bezos is so successful is he 516 00:27:10,119 --> 00:27:12,399 Speaker 1: got every lucky break a motherfucker can get. You know. 517 00:27:13,040 --> 00:27:17,040 Speaker 1: It's this this consistent story with these these big tech 518 00:27:17,200 --> 00:27:21,919 Speaker 1: founders where they all have part that they all have 519 00:27:22,119 --> 00:27:25,359 Speaker 1: the best fucking luck they could possibly have had, you know, 520 00:27:26,040 --> 00:27:31,600 Speaker 1: jobs Gates musk Uh. It's it's a very consistent story 521 00:27:31,600 --> 00:27:34,680 Speaker 1: where it's like, yeah, you were rich, you had all 522 00:27:34,720 --> 00:27:38,680 Speaker 1: of the educational resources, you had, all of the backing 523 00:27:38,760 --> 00:27:42,600 Speaker 1: of your family, you had, um, you know, everything about 524 00:27:42,680 --> 00:27:45,680 Speaker 1: your life up to this point was geared at clearing 525 00:27:45,680 --> 00:27:48,520 Speaker 1: a path to you and making it easy. And again, 526 00:27:48,600 --> 00:27:50,760 Speaker 1: I think it's easy for them to miss that. I 527 00:27:50,760 --> 00:27:52,480 Speaker 1: think it's easy for a guy like Bezos to miss 528 00:27:52,520 --> 00:27:56,120 Speaker 1: that because you're still working hard. You know, um, you're 529 00:27:56,160 --> 00:27:58,920 Speaker 1: still putting in a lot of hours. But motherfucker, there's 530 00:27:58,920 --> 00:28:00,600 Speaker 1: a lot of people who work just is hard and 531 00:28:00,640 --> 00:28:02,800 Speaker 1: don't have parents who can throw a hundred thousand dollars 532 00:28:02,840 --> 00:28:06,320 Speaker 1: that their business idea. You know. So Amazon, there's a 533 00:28:06,320 --> 00:28:08,920 Speaker 1: couple of sneaky things that they start to do early on. 534 00:28:08,920 --> 00:28:11,200 Speaker 1: One of them is that this this this. These book 535 00:28:11,240 --> 00:28:13,640 Speaker 1: distributors have a rule where you have to order ten 536 00:28:13,760 --> 00:28:15,399 Speaker 1: copies of a book at a time for them to 537 00:28:15,400 --> 00:28:18,080 Speaker 1: ship it to you. You know, because they're selling two bookstores, 538 00:28:18,080 --> 00:28:21,680 Speaker 1: they're not selling to individuals. Amazon is not that big 539 00:28:21,720 --> 00:28:23,280 Speaker 1: at this point, so it's it would be a huge 540 00:28:23,280 --> 00:28:26,400 Speaker 1: waste of money for them to order ten copies of 541 00:28:26,400 --> 00:28:29,480 Speaker 1: of a single book UM or ten copies of books. 542 00:28:29,480 --> 00:28:31,080 Speaker 1: You have to order tin books at a time basically, 543 00:28:31,119 --> 00:28:33,920 Speaker 1: and they don't have enough volume initially. So what they 544 00:28:33,960 --> 00:28:35,960 Speaker 1: do is they'll order like the books that they have 545 00:28:36,119 --> 00:28:38,640 Speaker 1: orders for from their customers, and then they'll fill out 546 00:28:38,680 --> 00:28:40,440 Speaker 1: the rest of the tin with copies of books they 547 00:28:40,440 --> 00:28:43,000 Speaker 1: know are out of print, and those out of stock 548 00:28:43,120 --> 00:28:45,360 Speaker 1: orders get auto canceled, but the company will ship the 549 00:28:45,400 --> 00:28:48,239 Speaker 1: two or three orders that aren't out of stock UH 550 00:28:48,480 --> 00:28:51,800 Speaker 1: to Amazon anyway, and that saves Amazon a lot of 551 00:28:51,880 --> 00:28:54,160 Speaker 1: money kind of at the expense of these distributors that 552 00:28:54,200 --> 00:28:58,040 Speaker 1: they're working with. Um. They get another hundred and forty 553 00:28:58,080 --> 00:29:00,400 Speaker 1: five thousand dollars from his parents a year or so later, 554 00:29:00,520 --> 00:29:03,680 Speaker 1: because you know, it's an early company, it's it's burning 555 00:29:03,680 --> 00:29:08,000 Speaker 1: money very quickly. It loses three thousand dollars and ninety five. UM. 556 00:29:08,040 --> 00:29:11,680 Speaker 1: But you know, he Jeff does kind of say, hey, 557 00:29:11,720 --> 00:29:13,760 Speaker 1: this there's a good chance this will fail. Mom and dad. 558 00:29:13,840 --> 00:29:15,360 Speaker 1: Now you've put a quarter of a million bucks in. 559 00:29:15,720 --> 00:29:18,560 Speaker 1: But he also starts to investors projecting net sales of 560 00:29:18,600 --> 00:29:21,680 Speaker 1: seventy four million dollars by the year two thousand, um. 561 00:29:21,840 --> 00:29:25,080 Speaker 1: And he is way off because his actual net sales 562 00:29:25,120 --> 00:29:29,160 Speaker 1: by two thousand or one point six four billion. UM. Yeah, 563 00:29:29,200 --> 00:29:32,880 Speaker 1: Amazon grows really fucking quickly. Uh. There's all these sort 564 00:29:32,880 --> 00:29:35,840 Speaker 1: of attitudes from the early days. You know, every big 565 00:29:35,880 --> 00:29:40,640 Speaker 1: tech company kind of mythologizes it's early days. Um. Amazon's 566 00:29:40,720 --> 00:29:44,440 Speaker 1: first company motto is get big fast. UM. They have 567 00:29:44,440 --> 00:29:46,600 Speaker 1: an I p O in nine seven. They have this 568 00:29:46,720 --> 00:29:51,120 Speaker 1: legal fight with um um, with Barnes and Noble. And 569 00:29:51,280 --> 00:29:53,320 Speaker 1: while they're kind of doing this in these in these 570 00:29:53,360 --> 00:29:56,800 Speaker 1: early days, Jeff's writing his first letter to shareholders, you know, 571 00:29:56,840 --> 00:29:58,520 Speaker 1: because they have their I p O in nineties seven. 572 00:29:58,840 --> 00:30:00,640 Speaker 1: And one of the things he writes in this letter is, 573 00:30:00,640 --> 00:30:02,600 Speaker 1: this is day one for the Internet and if we 574 00:30:02,680 --> 00:30:06,160 Speaker 1: execute it well for Amazon dot com. And day one 575 00:30:06,480 --> 00:30:09,960 Speaker 1: is like a religious thing in Amazon. UM. Day one 576 00:30:10,080 --> 00:30:12,960 Speaker 1: thinking is they is the way they kind of talk 577 00:30:13,040 --> 00:30:15,040 Speaker 1: about it like you need to be thinking like it 578 00:30:15,200 --> 00:30:17,600 Speaker 1: is day one, you know, for the for the future, 579 00:30:17,640 --> 00:30:19,440 Speaker 1: for the Internet, for whatever it is you're working on. 580 00:30:19,760 --> 00:30:22,600 Speaker 1: You have to have this this um acceptance of kind 581 00:30:22,600 --> 00:30:25,760 Speaker 1: of infinite possibility. That's the attitude that he tries to 582 00:30:25,760 --> 00:30:31,200 Speaker 1: inculcate within his employees. UM. People work very very hard 583 00:30:31,600 --> 00:30:34,800 Speaker 1: at Amazon. They do not have initially, like they have 584 00:30:34,920 --> 00:30:37,080 Speaker 1: just a couple of warehouses at first, for like the 585 00:30:37,120 --> 00:30:40,760 Speaker 1: first big Christmas rushes they have UM. And he forces 586 00:30:40,800 --> 00:30:43,760 Speaker 1: his employees during one of these Christmas is to leave 587 00:30:43,840 --> 00:30:46,800 Speaker 1: their homes and families for two weeks and spend time 588 00:30:46,840 --> 00:30:50,320 Speaker 1: either working customer service lines or working in warehouses to 589 00:30:50,360 --> 00:30:53,400 Speaker 1: distribute books. UM. He keeps his workers to to a 590 00:30:53,480 --> 00:30:57,120 Speaker 1: hotel room. UM. And it's this you know, it's an 591 00:30:57,120 --> 00:31:00,440 Speaker 1: early version of what will happen later where uh yeah, 592 00:31:00,440 --> 00:31:04,200 Speaker 1: he's he doesn't you know, whatever has to happen to 593 00:31:04,320 --> 00:31:07,600 Speaker 1: keep the warehouses most efficient is what's going to happen. 594 00:31:07,640 --> 00:31:10,720 Speaker 1: And he's kind of you see, from the beginning, there's 595 00:31:10,760 --> 00:31:13,440 Speaker 1: not a whole lot of belief that Bezos has that 596 00:31:13,560 --> 00:31:16,680 Speaker 1: you ought to that your family life is more important 597 00:31:16,680 --> 00:31:20,800 Speaker 1: than Amazon shipping out products. Right, it's certainly not to him, 598 00:31:20,840 --> 00:31:22,960 Speaker 1: and he doesn't feel like it should be for anyone else. 599 00:31:23,440 --> 00:31:26,840 Speaker 1: And that's okay when you've got this tiny startup company 600 00:31:26,840 --> 00:31:30,080 Speaker 1: and everybody involved has some equity, and everybody involved is 601 00:31:30,120 --> 00:31:32,800 Speaker 1: the same kind of crazy. But as it gets bigger 602 00:31:32,800 --> 00:31:35,560 Speaker 1: and bigger, these warehouses get bigger and bigger, and these 603 00:31:35,560 --> 00:31:38,360 Speaker 1: people aren't cut into the profits. These people don't have 604 00:31:38,400 --> 00:31:42,120 Speaker 1: any kind of um buy into the organization. But he 605 00:31:42,240 --> 00:31:45,080 Speaker 1: still has the same attitude that like, well, you shouldn't 606 00:31:45,120 --> 00:31:48,760 Speaker 1: have a life outside of this if if that's going 607 00:31:48,800 --> 00:31:52,320 Speaker 1: to at all reduce the efficiency of the warehouse. It's 608 00:31:52,360 --> 00:31:55,920 Speaker 1: It's interesting because from the very early on you saw 609 00:31:56,120 --> 00:31:59,320 Speaker 1: what was just basically now the reason there is so 610 00:31:59,400 --> 00:32:02,960 Speaker 1: much bad US on Amazon, the reason um the Mega 611 00:32:03,000 --> 00:32:06,160 Speaker 1: code this podcast even exists, it started very early on, 612 00:32:06,200 --> 00:32:09,240 Speaker 1: you know what I mean. It starts at the very beginning. 613 00:32:09,240 --> 00:32:13,239 Speaker 1: There's stories that are told almost as like pride of 614 00:32:13,280 --> 00:32:18,440 Speaker 1: like employees weeping at their desks because they're so overworked. Um, 615 00:32:18,520 --> 00:32:21,320 Speaker 1: and that that's in line with like his the the 616 00:32:21,360 --> 00:32:25,360 Speaker 1: motto that he wants his employees to have is I'm peculiar, 617 00:32:25,840 --> 00:32:28,640 Speaker 1: which basically means like I'm willing to work like a 618 00:32:28,680 --> 00:32:31,920 Speaker 1: crazy person and and cut years off of my life 619 00:32:31,960 --> 00:32:35,400 Speaker 1: in order to make this company a success. UM. He 620 00:32:35,720 --> 00:32:39,160 Speaker 1: comes up with this list of fourteen rules UM that 621 00:32:39,320 --> 00:32:42,840 Speaker 1: are you know, his attitudes on on business and whatnot. 622 00:32:42,840 --> 00:32:47,240 Speaker 1: They're kind of like the religious founding of Amazon corporate culture. UM. 623 00:32:47,280 --> 00:32:49,320 Speaker 1: A lot of it's just kind of stuff like higher 624 00:32:49,320 --> 00:32:52,640 Speaker 1: and developed the best, which I think every company agrees 625 00:32:53,200 --> 00:32:56,959 Speaker 1: is important. But he also wants employees to exhibit ownership 626 00:32:57,800 --> 00:32:59,760 Speaker 1: of of the chunk of the business they're working on 627 00:33:00,320 --> 00:33:02,720 Speaker 1: UM and do deep dives to like find things that 628 00:33:02,720 --> 00:33:05,440 Speaker 1: are problems and fix them. And that is that is 629 00:33:05,640 --> 00:33:08,360 Speaker 1: that attitude of ownership I think goes both ways. He 630 00:33:08,400 --> 00:33:11,120 Speaker 1: wants employees to act as if they own the part 631 00:33:11,200 --> 00:33:13,479 Speaker 1: of Amazon that they're working on, so it's very personal 632 00:33:13,520 --> 00:33:15,880 Speaker 1: to them UM. But he also is going to act 633 00:33:16,240 --> 00:33:18,760 Speaker 1: as if he owns you, you know, like that's that's 634 00:33:18,800 --> 00:33:21,960 Speaker 1: his attitude, and yeah, you're a product. And there's this 635 00:33:22,040 --> 00:33:26,240 Speaker 1: very They developed this very mechanical method of analyzing employee 636 00:33:26,760 --> 00:33:30,320 Speaker 1: UM success or failure. They have these like fifty sixty 637 00:33:30,400 --> 00:33:33,920 Speaker 1: page long employee evaluations that have these that are filled 638 00:33:33,920 --> 00:33:37,760 Speaker 1: with numbers that are kind of like statistical uh derivations 639 00:33:37,760 --> 00:33:40,480 Speaker 1: of like how that employee is doing or this employee 640 00:33:40,480 --> 00:33:44,120 Speaker 1: is doing. Um. And employees are expected to like memorize 641 00:33:44,160 --> 00:33:47,360 Speaker 1: these different numbers that are put together by the company 642 00:33:47,400 --> 00:33:50,480 Speaker 1: to explain their success or failure and be willing to 643 00:33:50,560 --> 00:33:53,360 Speaker 1: like be able to recite them when like their manager 644 00:33:53,440 --> 00:33:55,600 Speaker 1: calls them and asks about a number. You have to 645 00:33:55,640 --> 00:33:59,360 Speaker 1: be able to have an answer to that kind of stuff. Um, yeah, 646 00:33:59,360 --> 00:34:02,680 Speaker 1: it's it's not And and Bezos become like is kind 647 00:34:02,680 --> 00:34:06,320 Speaker 1: of famous for his temper um. He's he's very commonly 648 00:34:06,360 --> 00:34:08,640 Speaker 1: screams at people in meetings. He likes to mock and 649 00:34:08,680 --> 00:34:11,279 Speaker 1: to ride employees when they when they fail or when 650 00:34:11,280 --> 00:34:13,880 Speaker 1: they say something that he doesn't think is like a 651 00:34:13,920 --> 00:34:19,160 Speaker 1: good enough answer. Um. He's very kind of like infamous 652 00:34:19,200 --> 00:34:23,839 Speaker 1: for breaking people down during meetings uh and attacking them. Um. 653 00:34:24,280 --> 00:34:27,000 Speaker 1: There's also this kind of early on attitude Amazon has 654 00:34:27,080 --> 00:34:30,400 Speaker 1: that like, if you're a woman, um, you haven't a 655 00:34:30,480 --> 00:34:35,080 Speaker 1: kid or anything is not like an excuse for uh. 656 00:34:35,239 --> 00:34:36,719 Speaker 1: And it's not even that, it's that there's this story 657 00:34:36,800 --> 00:34:39,560 Speaker 1: this woman Elizabeth Willett, who's like a former army captain 658 00:34:39,840 --> 00:34:41,799 Speaker 1: who gets a child and because she has a kid 659 00:34:41,880 --> 00:34:45,160 Speaker 1: now she wants to leave come to work earlier and 660 00:34:45,239 --> 00:34:47,000 Speaker 1: leave earlier so she can pick her baby up and 661 00:34:47,000 --> 00:34:49,040 Speaker 1: then go work at home like she's working the same 662 00:34:49,080 --> 00:34:51,880 Speaker 1: hours as everyone else. She just alters her schedule, but 663 00:34:51,960 --> 00:34:54,720 Speaker 1: she gets in trouble because it looks like she's leaving 664 00:34:54,719 --> 00:34:58,000 Speaker 1: earlier to her employees who are coming in later. Um, 665 00:34:58,040 --> 00:34:59,840 Speaker 1: and her boss is like, I'm not going to defend 666 00:34:59,880 --> 00:35:01,920 Speaker 1: you because it doesn't look good to your peers. And 667 00:35:01,960 --> 00:35:03,600 Speaker 1: this is this is you. Hear a lot of stories 668 00:35:03,640 --> 00:35:06,960 Speaker 1: like this at Amazon where um, because so much of 669 00:35:07,000 --> 00:35:10,440 Speaker 1: the employee evaluations are your coworkers, You're you're supposed to 670 00:35:10,560 --> 00:35:12,840 Speaker 1: kind of attack and poke holes in the performance of 671 00:35:12,880 --> 00:35:16,080 Speaker 1: your co workers. It's very competitive. Um, there's this really 672 00:35:16,120 --> 00:35:19,839 Speaker 1: abusive attitude towards people who you know, anything goes wrong 673 00:35:19,880 --> 00:35:21,680 Speaker 1: in their lives. If you get sick, you know, if 674 00:35:21,719 --> 00:35:24,440 Speaker 1: you have a death in the family. Um, even if 675 00:35:24,480 --> 00:35:29,480 Speaker 1: that doesn't actually impact your your actual work performance. If 676 00:35:29,520 --> 00:35:31,759 Speaker 1: you like miss a day, everyone who works with you 677 00:35:31,840 --> 00:35:33,319 Speaker 1: is going to attack you for it, and that's going 678 00:35:33,360 --> 00:35:36,040 Speaker 1: to be in your performance review. And so it leads 679 00:35:36,120 --> 00:35:39,239 Speaker 1: to people like breaking down again. This people weeping at 680 00:35:39,239 --> 00:35:42,359 Speaker 1: their desks is incredibly common. People just having these like 681 00:35:42,840 --> 00:35:47,200 Speaker 1: emotional collapses. Um, and a lot of it's driven by, 682 00:35:47,440 --> 00:35:49,680 Speaker 1: you know, because Jeff is the kind of guy who 683 00:35:49,840 --> 00:35:53,160 Speaker 1: too employees faces, will insult them and attack them and 684 00:35:53,360 --> 00:35:57,160 Speaker 1: give these very detailed breakdowns of like why they've failed him. 685 00:35:57,200 --> 00:35:59,560 Speaker 1: And he builds a system with an Amazon that is 686 00:35:59,560 --> 00:36:04,320 Speaker 1: supposed to spread that attitude out to everyone else. Like again, 687 00:36:04,560 --> 00:36:06,759 Speaker 1: Amazon is This is kind of I guess the most 688 00:36:06,760 --> 00:36:09,759 Speaker 1: important point and maybe even like the penultimate point I 689 00:36:09,760 --> 00:36:12,480 Speaker 1: want to make here. Amazon is. The way that it works, 690 00:36:12,480 --> 00:36:14,640 Speaker 1: the way that it treats people is an extension of 691 00:36:14,680 --> 00:36:18,160 Speaker 1: how Jeff Bezos treats people. Like he built it to 692 00:36:18,360 --> 00:36:21,520 Speaker 1: work that way. It is everything that is kind of 693 00:36:21,560 --> 00:36:24,920 Speaker 1: abusive an anti human about the company's labor practices is 694 00:36:24,960 --> 00:36:28,400 Speaker 1: that way because it's how Jeff thinks people should be 695 00:36:28,480 --> 00:36:31,160 Speaker 1: treated um, and it's how he treats people in person. 696 00:36:31,239 --> 00:36:35,560 Speaker 1: You know, these big, impersonal robotic systems are a reflection 697 00:36:35,600 --> 00:36:38,360 Speaker 1: of how he treats individuals who work for him, including 698 00:36:38,360 --> 00:36:41,400 Speaker 1: people who he's known for years. He's very famous for, 699 00:36:41,440 --> 00:36:44,239 Speaker 1: like somebody will like spend years and years working eighty 700 00:36:44,239 --> 00:36:47,319 Speaker 1: hour weeks and be integral to the company's success, and 701 00:36:47,360 --> 00:36:49,720 Speaker 1: then the instant they stumble, he fires them and replaces 702 00:36:49,760 --> 00:36:52,440 Speaker 1: them with someone else. There's no understanding of like and 703 00:36:52,719 --> 00:36:55,000 Speaker 1: you know, there's plenty of failures that Bezos makes. He 704 00:36:55,040 --> 00:36:57,040 Speaker 1: never has to pay for them, right, like him missing 705 00:36:57,040 --> 00:36:59,600 Speaker 1: out on Apple Music and you know, failing to understand 706 00:36:59,600 --> 00:37:02,360 Speaker 1: that that's an area for Amazon. He doesn't get fired 707 00:37:02,400 --> 00:37:04,640 Speaker 1: for that, but he lets people go where edges them 708 00:37:04,640 --> 00:37:08,080 Speaker 1: out and replaces them with someone younger for much smaller 709 00:37:08,560 --> 00:37:12,319 Speaker 1: snaffoos um. And it's yeah, it's it's I think that's 710 00:37:12,360 --> 00:37:14,600 Speaker 1: kind of the best point to end on is that 711 00:37:15,000 --> 00:37:17,160 Speaker 1: when you think about the things that are anti human, 712 00:37:17,239 --> 00:37:21,520 Speaker 1: that are really fucked up about how Amazon functions, those 713 00:37:21,560 --> 00:37:25,040 Speaker 1: things are fundamentally reflections of how Bezos treats the people 714 00:37:25,080 --> 00:37:28,400 Speaker 1: in his own life. This company is a direct reflection 715 00:37:28,480 --> 00:37:34,239 Speaker 1: of his personal morally. It was Robert Evans going into 716 00:37:34,239 --> 00:37:38,240 Speaker 1: the details of Jeff Bezos and how he formed Amazon 717 00:37:38,480 --> 00:37:43,279 Speaker 1: in his own image. In the next episode of Megacorp, 718 00:37:43,560 --> 00:37:47,960 Speaker 1: will be looking into how Amazon screwed over its flex drivers. 719 00:37:51,239 --> 00:37:55,680 Speaker 1: Megacorp is made by my production company H eleven for 720 00:37:55,880 --> 00:38:00,680 Speaker 1: Cool Zone Media. It's written, researched, and produced by myself, 721 00:38:00,760 --> 00:38:06,960 Speaker 1: Jake Hanrahan. It was also produced by Sophie Lichtman. Music 722 00:38:07,160 --> 00:38:11,240 Speaker 1: is by some black graphics by Adam Doyle and sound 723 00:38:11,280 --> 00:38:15,160 Speaker 1: engineering by Splicing Block. If you want to get in touch, 724 00:38:15,480 --> 00:38:20,680 Speaker 1: follow me on social media at Jake Underscore Hanrahan. That's 725 00:38:20,880 --> 00:38:23,960 Speaker 1: h a n a A h a n