1 00:00:01,800 --> 00:00:06,280 Speaker 1: Robert Evans. Here awake the earliest we have ever been 2 00:00:06,320 --> 00:00:15,800 Speaker 1: awake to record a podcast. I take you seriously exhausted. 3 00:00:16,239 --> 00:00:21,040 Speaker 1: Just this is Behind the Bastards, a podcast where one intrepid, 4 00:00:21,160 --> 00:00:27,080 Speaker 1: dogged journalist braves the difficulty of being up barely. What 5 00:00:27,200 --> 00:00:30,720 Speaker 1: is it five and a half hours after dawn? My god? Literally? 6 00:00:30,840 --> 00:00:34,160 Speaker 1: Now it is love and twenty three. Yeah. Well, our 7 00:00:34,200 --> 00:00:37,400 Speaker 1: guest today on this very special episode of Behind the 8 00:00:37,400 --> 00:00:45,000 Speaker 1: Bastards is the great Jake Hanrahan. How you doing, buddy, Yeah, 9 00:00:45,040 --> 00:00:47,640 Speaker 1: I'm good man, I'm good good, Yeah, feeling good man? Yeah? 10 00:00:48,600 --> 00:00:51,440 Speaker 1: Is it? Is it an ungodly early hour over there too? No, 11 00:00:51,600 --> 00:00:55,000 Speaker 1: it's like seven pm, seven pm. You know, I'm gonna 12 00:00:55,000 --> 00:00:58,360 Speaker 1: see that. That's that's a nice reasonable hour to be awake. 13 00:00:58,880 --> 00:01:03,720 Speaker 1: Mm hmm. Speaking of reasonable, Jake, how do you feel 14 00:01:03,720 --> 00:01:10,320 Speaker 1: about Amazon? Well, it's funny you ask, because I'm then 15 00:01:10,400 --> 00:01:13,600 Speaker 1: this new series called Mega cool. But what about really? Um? 16 00:01:13,680 --> 00:01:17,960 Speaker 1: Yeah yeah, yeah, um yeah. They're like I was saying 17 00:01:17,959 --> 00:01:19,480 Speaker 1: to you guys before we kind of went on, are 18 00:01:19,520 --> 00:01:21,480 Speaker 1: like the more research are doing this, it's just like 19 00:01:22,000 --> 00:01:24,600 Speaker 1: it's almost like comically villainous to a point, you know 20 00:01:24,640 --> 00:01:26,280 Speaker 1: what I mean? And I'm not really wanted to be 21 00:01:26,400 --> 00:01:28,920 Speaker 1: shrill like that, but it actually is like that. It's 22 00:01:29,319 --> 00:01:32,880 Speaker 1: mad like what they're getting up to, it's deeply unsettling, 23 00:01:32,920 --> 00:01:34,840 Speaker 1: and it's kind of weird when you actually realize how 24 00:01:34,920 --> 00:01:38,640 Speaker 1: recent like they became so dominant so quickly that people 25 00:01:38,680 --> 00:01:41,640 Speaker 1: don't think about like twos and fourteen, Amazon was not 26 00:01:42,560 --> 00:01:44,959 Speaker 1: the huge deal, Like it's just like it was not 27 00:01:45,040 --> 00:01:47,760 Speaker 1: a small company, but like, yeah, they were more than 28 00:01:47,800 --> 00:01:50,880 Speaker 1: books by that, but they weren't this like beheemoth that 29 00:01:50,960 --> 00:01:54,520 Speaker 1: was doing everything. And like it's it's weird how quickly 30 00:01:54,520 --> 00:01:57,880 Speaker 1: a lot of this stuff slotted into place, and you've 31 00:01:57,880 --> 00:01:59,880 Speaker 1: got mega corps going on, which is great, and you're 32 00:02:00,040 --> 00:02:03,640 Speaker 1: kind of going through methodically the crimes of Amazon, and 33 00:02:03,680 --> 00:02:06,280 Speaker 1: the crimes of Amazon are are also one way or 34 00:02:06,280 --> 00:02:10,880 Speaker 1: the other, the crimes of the founder of Amazon, Jeff Bezos. 35 00:02:11,600 --> 00:02:14,520 Speaker 1: And so I wanted to do something a little bit 36 00:02:14,520 --> 00:02:16,640 Speaker 1: different this week. Normally on Behind the Bastards, when we 37 00:02:16,680 --> 00:02:18,799 Speaker 1: cover a guy like Bezos, and we've done, you know, 38 00:02:18,840 --> 00:02:24,119 Speaker 1: our episodes on Bill Gates, on Musk, on uh Zuckerberg, 39 00:02:24,480 --> 00:02:27,240 Speaker 1: we would do like an episode covering his early life, 40 00:02:27,280 --> 00:02:29,160 Speaker 1: and then we would do two or three episodes giving 41 00:02:29,200 --> 00:02:32,800 Speaker 1: the greatest hits of the crimes, but you're going through 42 00:02:32,880 --> 00:02:35,000 Speaker 1: all of the things that are fucked up about Amazon 43 00:02:35,080 --> 00:02:37,360 Speaker 1: bit by bit. So I felt like we would do 44 00:02:37,400 --> 00:02:41,840 Speaker 1: an episode on bastards that's kind of uh leading people 45 00:02:41,919 --> 00:02:46,399 Speaker 1: into Mega corp um. So this episode is more detailed 46 00:02:46,400 --> 00:02:48,840 Speaker 1: than a lot um and we're gonna go into the 47 00:02:48,880 --> 00:02:51,200 Speaker 1: early life of Jeffrey Bezos and we're gonna end a 48 00:02:51,200 --> 00:02:53,519 Speaker 1: little bit on on some specific actions of his that 49 00:02:53,560 --> 00:02:55,600 Speaker 1: I think are horrible. But really, more than anything, I 50 00:02:55,600 --> 00:02:58,040 Speaker 1: want to give people a sense of who this guy 51 00:02:58,200 --> 00:03:00,120 Speaker 1: is so that when they listen into all of the 52 00:03:00,960 --> 00:03:03,760 Speaker 1: you know to the the union busting in, the in 53 00:03:03,840 --> 00:03:07,960 Speaker 1: the worker uh running into the ground, and the hiring 54 00:03:08,000 --> 00:03:11,120 Speaker 1: of Nazis and that sort of thing, when they get 55 00:03:11,160 --> 00:03:13,480 Speaker 1: into that on your show, they can know who the 56 00:03:13,520 --> 00:03:17,960 Speaker 1: man is that that made it all possible. Absolutely, yeah, Yeah, 57 00:03:18,160 --> 00:03:22,120 Speaker 1: what do you know about Mr Bezos? Well, to be honest, 58 00:03:22,560 --> 00:03:25,240 Speaker 1: I've been more focused at the minute on like kind 59 00:03:25,240 --> 00:03:29,040 Speaker 1: of you know, the various different scandals that the company 60 00:03:29,080 --> 00:03:32,480 Speaker 1: has birthed. But what I do know is he's very 61 00:03:32,919 --> 00:03:36,840 Speaker 1: kind of unapologic. You know, there's a lot of stuff, 62 00:03:36,880 --> 00:03:38,200 Speaker 1: you know, I think even in one of the one 63 00:03:38,200 --> 00:03:40,200 Speaker 1: of the first two episodes that's already been out, just 64 00:03:40,280 --> 00:03:43,040 Speaker 1: like quotes where he's just like, no, this is not true. 65 00:03:43,480 --> 00:03:46,960 Speaker 1: I've read several things where there's just irrefutable proof of 66 00:03:47,040 --> 00:03:50,440 Speaker 1: like horrific injuries happening, like you know, you're eight percent 67 00:03:50,480 --> 00:03:53,160 Speaker 1: more likely to get wounded in an Amazon warehouse than 68 00:03:53,280 --> 00:03:56,839 Speaker 1: any other warehouse in their industry. And then when when 69 00:03:56,840 --> 00:03:58,920 Speaker 1: he was speaking about this, he was just like, yeah, 70 00:03:59,040 --> 00:04:02,080 Speaker 1: the media is lying. It's it's just like crazy, Like 71 00:04:02,160 --> 00:04:04,440 Speaker 1: he's just like it's kind of he's the kind of 72 00:04:04,480 --> 00:04:06,880 Speaker 1: post the way I guess for business when it comes 73 00:04:06,880 --> 00:04:09,440 Speaker 1: to the kind of post scandalierra where he's like, yeah, no, 74 00:04:09,600 --> 00:04:11,760 Speaker 1: it's not real, and it's like, well it is. But 75 00:04:11,880 --> 00:04:14,240 Speaker 1: he just he just very much embraces that thing of 76 00:04:14,240 --> 00:04:17,760 Speaker 1: like tough luck. You know. Yeah, there's this u there's 77 00:04:17,760 --> 00:04:21,040 Speaker 1: this term people used for Steve Jobs, the reality distortion field, 78 00:04:21,640 --> 00:04:23,200 Speaker 1: And usually when they were talking about it, it it was 79 00:04:23,240 --> 00:04:26,200 Speaker 1: his ability to like kind of make get people hyped 80 00:04:26,240 --> 00:04:29,040 Speaker 1: up about products, his ability to like get his workers 81 00:04:29,080 --> 00:04:31,680 Speaker 1: to work unreasonably in order to like meet deadlines, and 82 00:04:31,920 --> 00:04:33,719 Speaker 1: his ability to make people believe that he was doing 83 00:04:33,760 --> 00:04:37,120 Speaker 1: something magic with these products he was making, and Bezos 84 00:04:37,160 --> 00:04:42,920 Speaker 1: seems to have that for the impact of his I don't. 85 00:04:42,960 --> 00:04:46,160 Speaker 1: I don't know. It's weird, like it's there's this degree 86 00:04:46,200 --> 00:04:50,360 Speaker 1: to which like everything seems to slide off him at 87 00:04:50,400 --> 00:04:52,840 Speaker 1: this point. And I don't know that it's much that 88 00:04:52,920 --> 00:04:57,160 Speaker 1: he's distorted reality. Is like he's made Amazon foundational to 89 00:04:57,560 --> 00:05:00,839 Speaker 1: daily reality for so many people that like, yeah, I 90 00:05:00,839 --> 00:05:04,000 Speaker 1: mean that's messed up, but what are we going to do? Uh? Right? 91 00:05:04,200 --> 00:05:07,120 Speaker 1: And it's so big, so much money. Is he's kind 92 00:05:07,120 --> 00:05:09,520 Speaker 1: of a point where he doesn't, he doesn't really have 93 00:05:09,600 --> 00:05:12,480 Speaker 1: to pay attention, you know, to these bad things. I mean, 94 00:05:12,560 --> 00:05:15,080 Speaker 1: if you had a soul and a heart, you would. 95 00:05:15,520 --> 00:05:18,120 Speaker 1: But you know, I think from from some of the 96 00:05:18,200 --> 00:05:20,400 Speaker 1: things I've read and the way that he allows it 97 00:05:20,440 --> 00:05:22,920 Speaker 1: to happen on Amazon, um, you know, I think he's 98 00:05:23,000 --> 00:05:25,440 Speaker 1: quite happy to just be like, yeah, tough luck. Yeah. 99 00:05:25,480 --> 00:05:27,640 Speaker 1: And we'll get into that. But it is interesting I 100 00:05:27,680 --> 00:05:30,279 Speaker 1: think that. Um. I think one thing for people to 101 00:05:30,320 --> 00:05:32,279 Speaker 1: consider at the start of this, because I've been thinking 102 00:05:32,320 --> 00:05:35,960 Speaker 1: about this myself, is how true is the statement my 103 00:05:36,040 --> 00:05:40,080 Speaker 1: life as I live it right now doesn't work without Amazon? 104 00:05:41,560 --> 00:05:44,040 Speaker 1: Um in terms of like how you get your groceeries. 105 00:05:44,080 --> 00:05:46,120 Speaker 1: How you get stuff for your job if you like work, 106 00:05:46,440 --> 00:05:48,520 Speaker 1: you know remotely, how you get things that you know 107 00:05:48,760 --> 00:05:50,920 Speaker 1: your employees need to you. If you run a business, 108 00:05:51,320 --> 00:05:53,479 Speaker 1: how you get or how you sell like things in 109 00:05:53,520 --> 00:05:56,800 Speaker 1: that business. Um Like, for my part, I've got a 110 00:05:56,920 --> 00:05:59,599 Speaker 1: huge amount of my workload is on Kindle, just because 111 00:05:59,640 --> 00:06:01,720 Speaker 1: the ease eiest way to get research off of a 112 00:06:01,760 --> 00:06:03,760 Speaker 1: book is to highlight it in any book and then 113 00:06:03,800 --> 00:06:05,520 Speaker 1: like you can kind of copy and paste the text 114 00:06:05,520 --> 00:06:07,760 Speaker 1: and do a research doc. It's much easier than just 115 00:06:07,800 --> 00:06:10,919 Speaker 1: like going through a paperback book. Um, which is I 116 00:06:10,920 --> 00:06:12,720 Speaker 1: guess a small example of it. But like I know, 117 00:06:13,120 --> 00:06:15,120 Speaker 1: I have a couple of friends who run small businesses. 118 00:06:15,160 --> 00:06:18,240 Speaker 1: I have a friend who's a teacher, Like they are everybody. 119 00:06:18,360 --> 00:06:21,360 Speaker 1: Everybody knows how fun up Amazon is, and everybody's also like, 120 00:06:21,720 --> 00:06:24,359 Speaker 1: well what else am I gotta do? And that is 121 00:06:24,760 --> 00:06:27,280 Speaker 1: wh you know we are? You know, you're right, It's 122 00:06:27,320 --> 00:06:29,560 Speaker 1: like I use Amazon still my book is on sale 123 00:06:29,560 --> 00:06:32,240 Speaker 1: through Amazone, you know what I mean. But it's like 124 00:06:32,279 --> 00:06:34,480 Speaker 1: I said, in like you know, episode kind of the 125 00:06:34,600 --> 00:06:37,360 Speaker 1: zero of the kind of prologue to Mega Corp. I'm 126 00:06:37,400 --> 00:06:40,240 Speaker 1: not telling people like don't use them. You're bad if 127 00:06:40,240 --> 00:06:43,240 Speaker 1: you use them, you know, be an activist, boycott them. 128 00:06:43,279 --> 00:06:45,160 Speaker 1: I'm not saying that. I'm just saying, if you are 129 00:06:45,240 --> 00:06:47,680 Speaker 1: using them, I think you the very least you should 130 00:06:47,680 --> 00:06:49,480 Speaker 1: know what they're doing to workers, you know what I mean. 131 00:06:49,720 --> 00:06:51,880 Speaker 1: And I think that's important. And I've had a lot 132 00:06:51,880 --> 00:06:54,279 Speaker 1: of workers and like people just message me already just 133 00:06:54,320 --> 00:06:57,279 Speaker 1: being like absolutely, like thanks for saying. You know. They're 134 00:06:57,279 --> 00:07:00,360 Speaker 1: not like let's burn Amazon. They're just like, we just 135 00:07:00,400 --> 00:07:03,000 Speaker 1: want fairer working conditions, you know what I mean. And 136 00:07:03,040 --> 00:07:06,240 Speaker 1: it's like it's really not. It's not that difficult, you know, 137 00:07:06,320 --> 00:07:09,240 Speaker 1: it's really not. But they still don't provide it for them. No, 138 00:07:09,400 --> 00:07:10,880 Speaker 1: And it is that thing that like, yeah, in a 139 00:07:10,960 --> 00:07:13,960 Speaker 1: world as connected as ours, with things that rely on 140 00:07:14,000 --> 00:07:17,080 Speaker 1: on technology as ours, and with a plague racing through 141 00:07:17,320 --> 00:07:21,840 Speaker 1: where our ability to go places to get things is disrupted, Yeah, 142 00:07:21,840 --> 00:07:26,680 Speaker 1: I mean, something like Amazon is going to be necessary. Um, 143 00:07:26,720 --> 00:07:29,000 Speaker 1: you know, and we can also talk about like consumption 144 00:07:29,120 --> 00:07:32,680 Speaker 1: patterns and whatnot, but like our in our current society, 145 00:07:32,880 --> 00:07:37,080 Speaker 1: something like it is necessary. Um, but is the suffering 146 00:07:37,280 --> 00:07:39,239 Speaker 1: is like all of the fucking ship that comes along 147 00:07:39,280 --> 00:07:44,200 Speaker 1: with it? Um? I would argue hopefully not. And I 148 00:07:44,240 --> 00:07:48,120 Speaker 1: think the reason that there's so much suffering associated with 149 00:07:48,160 --> 00:07:51,480 Speaker 1: it that like it's that the company has as many 150 00:07:51,600 --> 00:07:55,080 Speaker 1: horrible stories as you've been finding on a daily basis 151 00:07:55,480 --> 00:07:57,600 Speaker 1: is because of the guy who founded it. Because Amazon 152 00:07:57,680 --> 00:08:01,320 Speaker 1: is very much made in the image of its creator, 153 00:08:02,000 --> 00:08:10,760 Speaker 1: Jeffrey Preston Jourgensen. That's right, Jenson, Um not Bezos. He 154 00:08:10,800 --> 00:08:13,600 Speaker 1: was not born Jeffrey Bezos. He was, however, born on 155 00:08:13,720 --> 00:08:17,920 Speaker 1: January twelfth, nineteen sixty four, in Albuquerque, New Mexico. I 156 00:08:17,960 --> 00:08:20,960 Speaker 1: did not expect him to be an Albuquerque baby. Um, 157 00:08:21,000 --> 00:08:23,240 Speaker 1: but he's he's real. He's he's spent most of his 158 00:08:23,360 --> 00:08:25,920 Speaker 1: upbringing in like the south and Southwest, um, kind of 159 00:08:26,040 --> 00:08:29,880 Speaker 1: similar area that I did. His dad, Theodore Jorgenson, was 160 00:08:30,000 --> 00:08:34,520 Speaker 1: nineteen and his mother, Jacqueline Geese, was seventeen. UM. So 161 00:08:34,559 --> 00:08:38,760 Speaker 1: this is a little bit of like a ah uh, 162 00:08:38,840 --> 00:08:42,120 Speaker 1: a problematic union. You know. Um, Theodore is out of 163 00:08:42,200 --> 00:08:47,400 Speaker 1: high school. His his Jeffrey's mom is still in high school. 164 00:08:47,480 --> 00:08:49,480 Speaker 1: There's a two year age difference, which isn't huge, but 165 00:08:49,640 --> 00:08:51,760 Speaker 1: the fact that he's graduated she's in high school is 166 00:08:51,760 --> 00:08:53,880 Speaker 1: a little bit of especially for like the families kind 167 00:08:53,880 --> 00:08:57,440 Speaker 1: of like this, like type deal um and she struggles 168 00:08:57,480 --> 00:08:59,960 Speaker 1: to finish her high school degree while Jeff is growing 169 00:09:00,040 --> 00:09:03,960 Speaker 1: inside of her. Um. Theodore Jorgensen was not very good 170 00:09:03,960 --> 00:09:06,280 Speaker 1: at actually providing for the family. He was a high 171 00:09:06,320 --> 00:09:10,160 Speaker 1: wire unicyclist and a circus performer, and he was obsessed 172 00:09:10,200 --> 00:09:12,680 Speaker 1: with his dreams of unicycle greatness. At one point he 173 00:09:12,679 --> 00:09:14,400 Speaker 1: tried to get on the Ed Sullivan Show. This was 174 00:09:14,440 --> 00:09:17,120 Speaker 1: like the most important thing to him, and he neglected 175 00:09:17,160 --> 00:09:24,960 Speaker 1: his family for the unicycle. It's a dream. It's one 176 00:09:24,960 --> 00:09:29,160 Speaker 1: of those things. It's one of those things, like of 177 00:09:29,440 --> 00:09:32,679 Speaker 1: of the things I did not call and Jeff bezos backstory. 178 00:09:33,160 --> 00:09:36,079 Speaker 1: Dad abandoned them for the unicycle would not have been 179 00:09:38,800 --> 00:09:41,520 Speaker 1: um and and by the way, I should know we'll 180 00:09:41,559 --> 00:09:43,200 Speaker 1: talk a little bit more about this. But Jeff grows 181 00:09:43,280 --> 00:09:45,240 Speaker 1: up knowing none of this. He knows nothing. He doesn't 182 00:09:45,280 --> 00:09:48,480 Speaker 1: know his dad's name um as a as for decades. 183 00:09:49,040 --> 00:09:52,120 Speaker 1: So Theodore tries briefly to do like the family thing 184 00:09:52,120 --> 00:09:54,200 Speaker 1: with Jacqueline and Jeff. They move in together. They have 185 00:09:54,240 --> 00:09:56,160 Speaker 1: their kind of Brenda in any moment, but it turns 186 00:09:56,200 --> 00:09:58,719 Speaker 1: out it's hard to make a living as a unicyclist. 187 00:09:59,320 --> 00:10:01,360 Speaker 1: So Theodore is forced to make ends meet at an 188 00:10:01,400 --> 00:10:04,720 Speaker 1: apartment store. This makes him miserable because again his dream 189 00:10:05,040 --> 00:10:08,719 Speaker 1: is the open unicycle road. UM. So he takes to 190 00:10:08,800 --> 00:10:11,800 Speaker 1: drinking more and more. UM. I don't believe he's like abusive. 191 00:10:11,880 --> 00:10:14,040 Speaker 1: He's just like kind of not there. He's just like, 192 00:10:14,480 --> 00:10:18,080 Speaker 1: is incapable of really engaging as a father. UM. His 193 00:10:18,240 --> 00:10:21,280 Speaker 1: father in law, Jeff's father in law. UM, so his 194 00:10:21,280 --> 00:10:25,640 Speaker 1: his his his mom's dad tried to get him a job, 195 00:10:25,840 --> 00:10:28,440 Speaker 1: Theodore a job with the state police, but Theodore couldn't 196 00:10:28,480 --> 00:10:31,600 Speaker 1: be arsked to do that. Eventually, Jackie gave up on 197 00:10:31,679 --> 00:10:34,880 Speaker 1: him and took baby jeff aged seventeen months, and moved 198 00:10:34,920 --> 00:10:37,800 Speaker 1: back in with her parents. She filed for divorce and 199 00:10:37,880 --> 00:10:40,360 Speaker 1: got it. Theo continued to visit his kid on and 200 00:10:40,360 --> 00:10:42,720 Speaker 1: off for a little while, but he missed every child 201 00:10:42,840 --> 00:10:45,200 Speaker 1: support payment that he ever had to make because he 202 00:10:45,240 --> 00:10:49,280 Speaker 1: had absolutely no money. Now, this is not the best 203 00:10:49,320 --> 00:10:51,800 Speaker 1: case scenario for a new child coming into the world. 204 00:10:51,840 --> 00:10:54,360 Speaker 1: I think we can agree, but we widow Jeffy had 205 00:10:54,400 --> 00:10:56,520 Speaker 1: a few things in his corner to offset the fact 206 00:10:56,559 --> 00:10:59,320 Speaker 1: that his bio dad was sort of a deadbeat. For 207 00:10:59,400 --> 00:11:02,760 Speaker 1: one thing, both his maternal and paternal grandparents had a 208 00:11:02,800 --> 00:11:06,600 Speaker 1: lot of resources. So his his dad's dad, um was 209 00:11:06,640 --> 00:11:09,440 Speaker 1: a purchase agent for Sanda Military Base, which was the 210 00:11:09,520 --> 00:11:13,160 Speaker 1: largest nuclear weapons installation in the United States. UM, and 211 00:11:13,200 --> 00:11:15,960 Speaker 1: that's an important gig, right, He's handling all supply purchases 212 00:11:15,960 --> 00:11:19,160 Speaker 1: for the biggest nuke base in the US. Jacqueline. His 213 00:11:19,240 --> 00:11:21,080 Speaker 1: mom's dad, on the other hand, was a guy named 214 00:11:21,160 --> 00:11:25,080 Speaker 1: Lawrence Preston Geese and he ran the local US Atomic 215 00:11:25,200 --> 00:11:29,440 Speaker 1: Energy Commission office. UM. And he's running the Atomic Energy 216 00:11:29,440 --> 00:11:33,440 Speaker 1: Commission Office in like New Mexico, which is the big one, right, 217 00:11:33,480 --> 00:11:35,680 Speaker 1: that's where we figured it all out, right, that's gonna 218 00:11:35,720 --> 00:11:38,240 Speaker 1: be like your and he's a new he's a new 219 00:11:38,320 --> 00:11:40,600 Speaker 1: he's a rocket scientist, you know, he's like a nuclear 220 00:11:40,720 --> 00:11:43,920 Speaker 1: missile expert um and was very prominent in the field. 221 00:11:43,960 --> 00:11:46,360 Speaker 1: And that obviously you can make a good amount of 222 00:11:46,400 --> 00:11:49,679 Speaker 1: money doing that. So while THEO was not a great dad, 223 00:11:50,120 --> 00:11:53,160 Speaker 1: Jeff young Jeff had support from a family with a 224 00:11:53,160 --> 00:11:56,440 Speaker 1: lot of means. The earliest story I found that shows 225 00:11:56,520 --> 00:11:59,720 Speaker 1: any kind of personality from Jeffrey Jorgensen at this point 226 00:11:59,800 --> 00:12:02,680 Speaker 1: is for when he was three. His his mom was 227 00:12:02,760 --> 00:12:05,120 Speaker 1: really paranoid about his health, and she had him sleeping 228 00:12:05,120 --> 00:12:06,880 Speaker 1: in a crib long after the point of which he 229 00:12:07,160 --> 00:12:09,440 Speaker 1: should have stopped sleeping in a crib. And he kept 230 00:12:09,520 --> 00:12:11,160 Speaker 1: arguing with her that it was time for him to 231 00:12:11,200 --> 00:12:13,360 Speaker 1: get a real bed, and she would say no because 232 00:12:13,360 --> 00:12:15,040 Speaker 1: she was worried he was going to fall out, so 233 00:12:15,080 --> 00:12:18,160 Speaker 1: she refused him repeatedly, and one day Jeff got ahold 234 00:12:18,200 --> 00:12:20,719 Speaker 1: of a screwdriver and took the crib apart himself so 235 00:12:20,760 --> 00:12:23,120 Speaker 1: that it was just a bed um. And that's when 236 00:12:23,160 --> 00:12:25,200 Speaker 1: his mom decided to let him, like, okay, you can 237 00:12:25,280 --> 00:12:28,079 Speaker 1: have a yeah. And it's so we have some early 238 00:12:28,160 --> 00:12:30,920 Speaker 1: stories from him, um, and you hear that one a lot. 239 00:12:31,040 --> 00:12:33,880 Speaker 1: Most of them are like from him after he got 240 00:12:34,000 --> 00:12:37,120 Speaker 1: rich and famous. So as with any story like that, 241 00:12:37,200 --> 00:12:39,880 Speaker 1: a little bit of salt, you know. Yeah, I also 242 00:12:40,000 --> 00:12:42,520 Speaker 1: like being three years old, Like if someone gave me 243 00:12:42,520 --> 00:12:45,160 Speaker 1: a screwdriver, I would just like stop myself by accident, 244 00:12:45,200 --> 00:12:47,559 Speaker 1: you know what I mean. I don't know about that one. 245 00:12:47,600 --> 00:12:49,520 Speaker 1: It's a little bit, but you know, maybe he did, 246 00:12:49,520 --> 00:12:52,560 Speaker 1: if so, maybe, Yeah, it's yeah, I think you're right, 247 00:12:52,600 --> 00:12:55,120 Speaker 1: A little bit of salt with that one, little bit. Yeah. 248 00:12:55,360 --> 00:13:00,640 Speaker 1: So in nineteen his mom remarried to Miguel Angel Bezo Perez. 249 00:13:01,400 --> 00:13:03,840 Speaker 1: Miguel was Cuban and had fled the country on the 250 00:13:03,880 --> 00:13:06,720 Speaker 1: insistence of his mother after he was caught painting anti 251 00:13:06,800 --> 00:13:09,480 Speaker 1: castro graffiti. Um, he does get to take like a 252 00:13:09,520 --> 00:13:10,960 Speaker 1: plane out of there. He's not one of the people 253 00:13:10,960 --> 00:13:13,280 Speaker 1: who has to like hide on a smuggle himself out 254 00:13:13,280 --> 00:13:16,280 Speaker 1: on a raft. Um. But yeah, he gets out of 255 00:13:16,320 --> 00:13:18,760 Speaker 1: he has to leave his family behind in Cuba because 256 00:13:18,760 --> 00:13:21,960 Speaker 1: he's you know, to uh, too too much of a 257 00:13:22,000 --> 00:13:25,920 Speaker 1: little can't can't you know not rebel against the system. 258 00:13:25,960 --> 00:13:28,800 Speaker 1: I guess. Um, he's sixteen years old when he entered 259 00:13:28,800 --> 00:13:31,960 Speaker 1: the enters the country, and he speaks almost no English. Um. 260 00:13:32,080 --> 00:13:35,200 Speaker 1: Miguel eventually wound up doing his undergrad at the University 261 00:13:35,200 --> 00:13:39,920 Speaker 1: of Albuquerque, which offered free scholarships to Cuban refugees. He 262 00:13:39,960 --> 00:13:41,719 Speaker 1: worked as a clerk at a bank, and he met 263 00:13:41,800 --> 00:13:44,600 Speaker 1: Jacqueline while he was working there. He and Jackie married, 264 00:13:44,640 --> 00:13:46,480 Speaker 1: and most of what you really need to know about 265 00:13:46,520 --> 00:13:49,000 Speaker 1: Miguel is that he was enough of a father figure 266 00:13:49,040 --> 00:13:52,120 Speaker 1: to Jeff that Jacqueline reached out to Theodore before she 267 00:13:52,320 --> 00:13:55,000 Speaker 1: married Miguel and told him that their son was going 268 00:13:55,040 --> 00:13:58,800 Speaker 1: to be taking her new husband's last name. Um so 269 00:13:59,440 --> 00:14:02,120 Speaker 1: hard hit for Theodore, you know what I mean. Yeah, 270 00:14:02,320 --> 00:14:04,679 Speaker 1: you get the feeling. I mean, I think he from 271 00:14:04,720 --> 00:14:06,920 Speaker 1: what i've from what I can I've heard, he regrets 272 00:14:06,920 --> 00:14:08,480 Speaker 1: it now. I think in the time, it was like 273 00:14:08,520 --> 00:14:10,440 Speaker 1: you don't have to pay child support that you're not 274 00:14:10,480 --> 00:14:14,679 Speaker 1: paying already anymore, Like yeah, yeah, I mean life, life 275 00:14:14,720 --> 00:14:17,360 Speaker 1: is hard. Things can be hard and it's sad. Yeah, 276 00:14:17,400 --> 00:14:19,800 Speaker 1: but yeah, I get it. Yeah, And it's one of 277 00:14:19,800 --> 00:14:22,040 Speaker 1: those things like I can't say, like she's obviously not 278 00:14:22,120 --> 00:14:24,120 Speaker 1: in the wrong for that. It's like this your bio 279 00:14:24,240 --> 00:14:26,160 Speaker 1: dad won't do it. This guy comes in out of 280 00:14:26,200 --> 00:14:30,720 Speaker 1: nowhere and adopts your kid, like yeah, yeah, um anyway, 281 00:14:30,760 --> 00:14:34,520 Speaker 1: that's why he's Jeff Bezos and not Jeff Jorgensen. Um So, 282 00:14:34,760 --> 00:14:37,920 Speaker 1: Miguel eventually finished college and got a job working as 283 00:14:37,920 --> 00:14:42,360 Speaker 1: a petroleum engineer for x On. Um so his Jeff 284 00:14:42,640 --> 00:14:45,120 Speaker 1: as as a kid. Jeff's family is like either into 285 00:14:45,240 --> 00:14:48,200 Speaker 1: nuclear weapons or the oil and gas industry, which is 286 00:14:49,720 --> 00:14:53,960 Speaker 1: quite an upbreaking Yeah yeah, yeah, yeah, absolutely, man Like, yeah, 287 00:14:54,240 --> 00:14:57,680 Speaker 1: it's a very it's very like American industry as well. Yeah, 288 00:14:57,880 --> 00:15:01,080 Speaker 1: nukes and oil, nukes, and gas. Yeah, yeah, like the 289 00:15:01,120 --> 00:15:04,520 Speaker 1: favorite things. Yeah. And so he's you know, his family 290 00:15:04,640 --> 00:15:07,480 Speaker 1: is not They're not like even Bill Gates family rich, 291 00:15:07,600 --> 00:15:09,840 Speaker 1: I don't think, but they are well off. They're very 292 00:15:09,960 --> 00:15:12,720 Speaker 1: very like they're most people would consider them rich. I 293 00:15:12,760 --> 00:15:15,120 Speaker 1: don't think they're like multi millionaires. I'm sure by the 294 00:15:15,120 --> 00:15:17,000 Speaker 1: time they retired they had they had, you know, a 295 00:15:17,000 --> 00:15:20,040 Speaker 1: million or two in the bank. But they're very very comfortable, 296 00:15:20,160 --> 00:15:25,360 Speaker 1: you know. Um, So the family travels a bunch as 297 00:15:25,480 --> 00:15:29,120 Speaker 1: Miguel like gets all these different transfers across the world. 298 00:15:29,640 --> 00:15:32,560 Speaker 1: Um and along the way, you know, as their you know, 299 00:15:32,640 --> 00:15:36,160 Speaker 1: Miguel is starting his career and you know, Jeffrey's growing up. Um. 300 00:15:36,280 --> 00:15:40,160 Speaker 1: Miguel and Jacqueline have a daughter, Christina, and another son Mark. 301 00:15:40,760 --> 00:15:42,720 Speaker 1: We kind of know jeff was, I don't think happy 302 00:15:42,760 --> 00:15:45,120 Speaker 1: to have his dad tracked down. Um and his dad 303 00:15:45,160 --> 00:15:46,720 Speaker 1: like hadn't heard of him. I think it was two 304 00:15:46,760 --> 00:15:48,440 Speaker 1: thousand fifteen when he get tracked down. He was like, 305 00:15:48,440 --> 00:15:53,480 Speaker 1: who's jeff Bezos And it was like that your son, 306 00:15:54,280 --> 00:15:58,120 Speaker 1: and he's like a broke failed circus performer. It's it's 307 00:15:58,200 --> 00:16:00,880 Speaker 1: quite a thing. Um. So we don't really have a 308 00:16:00,920 --> 00:16:05,600 Speaker 1: whole lot of detail on how you know, uh, getting 309 00:16:05,600 --> 00:16:08,760 Speaker 1: abandoned by his biological dad may have influenced Jeff Bezos, 310 00:16:08,800 --> 00:16:10,280 Speaker 1: it may not have had much of an influence at all, 311 00:16:10,320 --> 00:16:12,720 Speaker 1: because again, he was four when Miguel comes into the picture, 312 00:16:13,000 --> 00:16:16,000 Speaker 1: so nearly all, if not all, of his early memories 313 00:16:16,000 --> 00:16:17,720 Speaker 1: are going to involve Miguel, who seems to have been 314 00:16:17,720 --> 00:16:20,080 Speaker 1: a pretty good dad. Um. And so when it comes 315 00:16:20,120 --> 00:16:24,280 Speaker 1: to how Miguel influenced his son's development, we have more meat, um, 316 00:16:24,320 --> 00:16:27,120 Speaker 1: and it's you know, he's a he's a Cuban refugee 317 00:16:27,120 --> 00:16:30,400 Speaker 1: into the United States. So obviously coming at it from 318 00:16:30,480 --> 00:16:36,440 Speaker 1: like kind of a very conservative, pro capitalist standpoint, not amunists, 319 00:16:37,920 --> 00:16:40,280 Speaker 1: not very surprising. But I'm going to read a passage 320 00:16:40,280 --> 00:16:42,080 Speaker 1: from the Everything Store that gives a little bit of 321 00:16:42,320 --> 00:16:45,880 Speaker 1: context for that. Jeff and his siblings grew up observing 322 00:16:45,920 --> 00:16:48,800 Speaker 1: their father's tireless work ethic and his frequent expressions of 323 00:16:48,840 --> 00:16:52,600 Speaker 1: love for America and its opportunities and freedoms. Miguel Bezos, 324 00:16:52,600 --> 00:16:55,320 Speaker 1: who later began going by the name Mike, acknowledges that 325 00:16:55,360 --> 00:16:58,000 Speaker 1: he may have also passed on a libertarian aversion to 326 00:16:58,080 --> 00:17:02,000 Speaker 1: government intrusion into the private live and enterprises of citizens. 327 00:17:02,040 --> 00:17:04,680 Speaker 1: Certainly it was something that permeated our home life, he says, 328 00:17:04,720 --> 00:17:07,600 Speaker 1: while noting that dinner time conversations were a political and 329 00:17:07,640 --> 00:17:10,600 Speaker 1: revolved around the kids. I cannot stand any kind of 330 00:17:10,640 --> 00:17:13,120 Speaker 1: totalitarian form of government from the right or the left 331 00:17:13,200 --> 00:17:15,840 Speaker 1: or anything in between. And maybe that had some impact. 332 00:17:16,640 --> 00:17:18,560 Speaker 1: Which is interesting because as we're going to talk about, 333 00:17:19,320 --> 00:17:22,199 Speaker 1: Jeff certainly like benefits from the lax kind of corporate 334 00:17:22,240 --> 00:17:25,439 Speaker 1: law in the United States, but he imposes something of 335 00:17:25,480 --> 00:17:28,000 Speaker 1: a dictatorship on the people who work for him. That's 336 00:17:28,000 --> 00:17:31,640 Speaker 1: his whole It's very totalitarian. Yeah, it's like the Stasi. 337 00:17:31,760 --> 00:17:33,560 Speaker 1: You know, you get searched every time you go in 338 00:17:33,600 --> 00:17:36,919 Speaker 1: and out just for lunch or to the toilet, constant monitoring, 339 00:17:37,440 --> 00:17:40,320 Speaker 1: Like yeah, no, it's it's you know, we like we 340 00:17:40,400 --> 00:17:42,840 Speaker 1: heard in the last episode ID for Megacourt, the the 341 00:17:42,880 --> 00:17:48,240 Speaker 1: internal training video was encouraging managers to spy on, you know, 342 00:17:48,400 --> 00:17:51,879 Speaker 1: quote the behavior of their workers to see if maybe 343 00:17:51,880 --> 00:17:55,119 Speaker 1: they're organizing the union. Like yeah, it's it's ridiculous to Okay, 344 00:17:55,160 --> 00:17:58,199 Speaker 1: maybe he thinks that, but then you know, as all dictators, 345 00:17:58,400 --> 00:18:02,280 Speaker 1: you know, he imposes his his Yeah, and it's it's 346 00:18:02,280 --> 00:18:04,959 Speaker 1: interesting to me I mean, this is something we've come 347 00:18:04,960 --> 00:18:06,879 Speaker 1: across a few times with some of these billionaires. But 348 00:18:06,960 --> 00:18:10,000 Speaker 1: like the things that as as well discuss, the things 349 00:18:10,040 --> 00:18:12,200 Speaker 1: that like make Jeff Bezos into the person who is 350 00:18:12,200 --> 00:18:14,040 Speaker 1: able to be as successful as he is, are all 351 00:18:14,080 --> 00:18:17,719 Speaker 1: things that he absolutely doesn't want other people to have. 352 00:18:17,880 --> 00:18:20,920 Speaker 1: He has a very permissive, open environment. He's he's very 353 00:18:20,920 --> 00:18:24,159 Speaker 1: well funded schools. You know, Amazon avoids paying taxes to 354 00:18:24,160 --> 00:18:27,199 Speaker 1: support the schools. A lot of his early jobs give 355 00:18:27,280 --> 00:18:29,400 Speaker 1: him a lot of freedom. And like, yeah, it's it's 356 00:18:29,400 --> 00:18:33,040 Speaker 1: this whole Yeah, you benefited from a system that you 357 00:18:33,080 --> 00:18:36,159 Speaker 1: have no desire in maintaining anyway. Well, that's that's what 358 00:18:36,200 --> 00:18:38,640 Speaker 1: we're getting too. So another thing that made an impact 359 00:18:38,680 --> 00:18:41,720 Speaker 1: on young Jeff was money. When he was four, he 360 00:18:41,840 --> 00:18:45,960 Speaker 1: first visited his maternal grandfather's cattle ranch in Texas. The 361 00:18:46,119 --> 00:18:49,520 Speaker 1: family ranch, the Lazy g was more than twenty five 362 00:18:49,560 --> 00:18:53,199 Speaker 1: tho acres um, which is a big ranch. Um. And 363 00:18:53,280 --> 00:18:57,080 Speaker 1: it's you he comes from old Texas money, um. His family. 364 00:18:57,080 --> 00:18:59,000 Speaker 1: The ranch has been in his family's hand since the 365 00:18:59,480 --> 00:19:02,520 Speaker 1: early teen hundreds. Um. And he had an ancestor who 366 00:19:02,520 --> 00:19:06,040 Speaker 1: took part in the early colonization of Texas by white people. Um, 367 00:19:06,080 --> 00:19:10,080 Speaker 1: which is I have a good friend who comes from 368 00:19:10,320 --> 00:19:12,600 Speaker 1: not nearly they don't have nearly as much land anymore, 369 00:19:12,640 --> 00:19:14,640 Speaker 1: but they used to have family land that was that big, 370 00:19:14,680 --> 00:19:16,120 Speaker 1: and then the family kind of fell on hard times. 371 00:19:16,160 --> 00:19:18,520 Speaker 1: But they were like one of the families that was 372 00:19:18,600 --> 00:19:21,520 Speaker 1: part of the Texas Revolution and the establishment of Texas 373 00:19:21,560 --> 00:19:25,040 Speaker 1: as Estate. And like on her old family land there 374 00:19:25,119 --> 00:19:27,520 Speaker 1: is like a slave graveyard. Like that's all of them, right, 375 00:19:27,560 --> 00:19:30,800 Speaker 1: Like that's founded like that, that's the white people who 376 00:19:30,840 --> 00:19:33,760 Speaker 1: found in Texas. Um. So again that's kind of where 377 00:19:34,400 --> 00:19:36,840 Speaker 1: that that's the kind of old money that he's got 378 00:19:36,840 --> 00:19:42,440 Speaker 1: on that side. Huh yeah, Texas and Tea. Yeah yeah, 379 00:19:42,760 --> 00:19:46,400 Speaker 1: my family didn't own any land, but we didn't get 380 00:19:46,400 --> 00:19:50,080 Speaker 1: here from Italy until like the twenties. So uh yeah 381 00:19:50,160 --> 00:19:54,000 Speaker 1: when uh Jeff Bezos is like Grandpa Lawrence, Like even 382 00:19:54,040 --> 00:19:56,159 Speaker 1: though they have this family land, his grandfather was a 383 00:19:56,240 --> 00:19:58,760 Speaker 1: rocket scientist most of his career and when he retires, 384 00:19:58,880 --> 00:20:00,760 Speaker 1: he goes back to the family the ranch to be 385 00:20:00,840 --> 00:20:04,439 Speaker 1: like a hobby rancher, you know. Um, like I'm retired, 386 00:20:04,440 --> 00:20:06,720 Speaker 1: I don't have to do a grind anymore. I'm gonna 387 00:20:06,800 --> 00:20:10,159 Speaker 1: keep this ranch going just because it seems fun. The 388 00:20:10,240 --> 00:20:13,320 Speaker 1: Lazy G was a large, fully functional ranch, and Jeff 389 00:20:13,320 --> 00:20:16,000 Speaker 1: starts spending his summers there. So for about twelve straight years, 390 00:20:16,000 --> 00:20:19,120 Speaker 1: he spends every summer at the Lazy G doing ranch work, 391 00:20:19,200 --> 00:20:24,000 Speaker 1: cleaning stalls, gelding livestock, doing basic handyman stuff, and the 392 00:20:24,040 --> 00:20:26,560 Speaker 1: experience gave him a crash course and the kind of 393 00:20:26,560 --> 00:20:29,040 Speaker 1: practical engineering you have to do if you're going to 394 00:20:29,119 --> 00:20:33,200 Speaker 1: keep a ranch operational. One of his other biographers, Richard Brandt, 395 00:20:33,280 --> 00:20:36,120 Speaker 1: notes one particular event as an example of the formative 396 00:20:36,160 --> 00:20:39,040 Speaker 1: impact this had on Jeff. One day, his grandpa towed 397 00:20:39,040 --> 00:20:42,040 Speaker 1: in a busted old bulldozer with a stripped transmission. He 398 00:20:42,080 --> 00:20:43,680 Speaker 1: and Jeff set to work on it and had to 399 00:20:43,720 --> 00:20:46,160 Speaker 1: figure out a way to remove a five pound gear 400 00:20:46,240 --> 00:20:49,560 Speaker 1: from the engine. Grandpa Lawrence, built a crane to lift it, 401 00:20:49,600 --> 00:20:52,719 Speaker 1: and Jeff helped him. Experiences like this taught Jeffrey how 402 00:20:52,720 --> 00:20:55,800 Speaker 1: to be a pragmatic engineer and inured him to difficult labor. 403 00:20:56,040 --> 00:20:59,040 Speaker 1: He would later consider it an idyllic childhood, and I'd 404 00:20:59,040 --> 00:21:01,080 Speaker 1: be hard pressed to dis agree with him. There it 405 00:21:01,160 --> 00:21:04,120 Speaker 1: sounds like the perfect way to grow up, right, Like, Yeah, 406 00:21:04,160 --> 00:21:09,800 Speaker 1: you've got like this huge rant lessons, but you've got security. Yeah, yeah, yeah. 407 00:21:09,840 --> 00:21:12,760 Speaker 1: Who wouldn't want this in their childhood? He later told 408 00:21:12,800 --> 00:21:15,240 Speaker 1: an interviewer, One of the things you learn in a 409 00:21:15,320 --> 00:21:18,840 Speaker 1: rural area like that is self reliance. People do everything themselves. 410 00:21:19,200 --> 00:21:21,320 Speaker 1: That kind of self reliance is something you can learn, 411 00:21:21,400 --> 00:21:23,320 Speaker 1: and my grandfather was a huge role model for me. 412 00:21:23,359 --> 00:21:25,520 Speaker 1: If something is broken, let's fix it. To get something 413 00:21:25,520 --> 00:21:27,399 Speaker 1: new done, you have to be stubborn and focused to 414 00:21:27,440 --> 00:21:31,200 Speaker 1: the point that others might find unreasonable. And you might 415 00:21:31,240 --> 00:21:33,880 Speaker 1: find a certain dark humor in noting that Jeff's self 416 00:21:33,920 --> 00:21:37,000 Speaker 1: reliance today involves telling a lot, hundreds of thousands of 417 00:21:37,000 --> 00:21:39,480 Speaker 1: people what they have to do for him. One might 418 00:21:39,480 --> 00:21:42,600 Speaker 1: also note that Amazon has contributed to the absolute annihilation 419 00:21:42,640 --> 00:21:46,000 Speaker 1: of rural communities by destroying small businesses. And it is 420 00:21:46,080 --> 00:21:48,119 Speaker 1: kind of tempting to go down this road Jeff talking 421 00:21:48,160 --> 00:21:50,359 Speaker 1: about the value of like rural hard work, and then 422 00:21:50,400 --> 00:21:53,720 Speaker 1: how Amazon has actually impacted rural areas. But it's also 423 00:21:54,880 --> 00:21:58,280 Speaker 1: of the things to blame Amazon for not really fair, 424 00:21:59,080 --> 00:22:04,240 Speaker 1: because he's Amazon was kind of continuing a process that 425 00:22:04,320 --> 00:22:06,199 Speaker 1: got started a lot earlier. When it comes to that, 426 00:22:06,280 --> 00:22:08,560 Speaker 1: I actually found a local article from a paper in 427 00:22:08,640 --> 00:22:12,200 Speaker 1: Swift County, Minnesota, with the title Amazon's dominance not good 428 00:22:12,240 --> 00:22:15,960 Speaker 1: for small Towns, and obviously talks about what you'd expect Amazon, 429 00:22:16,119 --> 00:22:18,600 Speaker 1: the online retailers, destroying a bunch of local brick and 430 00:22:18,600 --> 00:22:21,919 Speaker 1: mortar businesses that give people in the area jobs. But 431 00:22:22,000 --> 00:22:24,679 Speaker 1: then that article gives a list of the local businesses 432 00:22:25,200 --> 00:22:28,120 Speaker 1: in this small town that might get wrecked by Amazon, 433 00:22:28,240 --> 00:22:33,719 Speaker 1: and those businesses include Advance Auto Parts, Auto Zone, O'Riley, Honda, Albertson's, Kroger, 434 00:22:33,760 --> 00:22:38,120 Speaker 1: Walmart Grocery, Barnes and Noble, Joebeth booksellers, Best Buy, Office Depot, Staples, 435 00:22:38,520 --> 00:22:41,680 Speaker 1: all of which are like giant corporations that previously came 436 00:22:41,720 --> 00:22:44,600 Speaker 1: in and destroyed small local businesses in rural communities. And 437 00:22:44,640 --> 00:22:47,360 Speaker 1: it's like, yeah, Amazon is a part of that tradition, 438 00:22:47,440 --> 00:22:50,280 Speaker 1: but it didn't, it didn't start with them, So I 439 00:22:50,280 --> 00:22:52,399 Speaker 1: don't know. It's like if you're if you're in like 440 00:22:52,440 --> 00:22:55,440 Speaker 1: a hyper corporate environment, that's just the circle of life, really, 441 00:22:55,480 --> 00:22:58,080 Speaker 1: you know what I mean. Yeah, it's one of those things. 442 00:22:58,480 --> 00:23:00,320 Speaker 1: It is, like I think there's a degree to which 443 00:23:00,680 --> 00:23:04,240 Speaker 1: it's worth acknowledging that Jeff benefited, as we've talked about, 444 00:23:04,280 --> 00:23:07,480 Speaker 1: like so much from this kind of rural ideal that 445 00:23:07,480 --> 00:23:10,800 Speaker 1: that does not exist anywhere anymore, um to the extent 446 00:23:10,840 --> 00:23:12,800 Speaker 1: that it existed many places when he was a kid. 447 00:23:13,840 --> 00:23:17,439 Speaker 1: But he's also not didn't start that process. So whatever 448 00:23:17,920 --> 00:23:20,919 Speaker 1: of of Amazon's crimes, I don't really I put that 449 00:23:20,960 --> 00:23:24,280 Speaker 1: more on the Walmart end of the Ledger. So when 450 00:23:24,359 --> 00:23:26,760 Speaker 1: he wasn't spending his summers at the ranch, Jeff spent 451 00:23:26,840 --> 00:23:29,639 Speaker 1: most of his childhood in Houston, Texas. He attended a 452 00:23:29,680 --> 00:23:32,040 Speaker 1: public school and was lucky enough to benefit from a 453 00:23:32,080 --> 00:23:35,280 Speaker 1: school district that had both money um and a devotion 454 00:23:35,320 --> 00:23:39,480 Speaker 1: to take in care of its gifted students. The Vanguard Program, 455 00:23:39,560 --> 00:23:42,119 Speaker 1: as it's called, was a gifted and talented program that 456 00:23:42,160 --> 00:23:44,600 Speaker 1: was meant to find bright kids and encourage them to 457 00:23:44,600 --> 00:23:47,439 Speaker 1: think outside the box and learn how to be independent minds. 458 00:23:48,040 --> 00:23:51,400 Speaker 1: The Vanguard Program was good and in the early nineteen seventies, 459 00:23:51,440 --> 00:23:54,560 Speaker 1: and ad executive named Julie Ray grew interested in it 460 00:23:54,600 --> 00:23:57,119 Speaker 1: after her son was admitted. Once he moved on to 461 00:23:57,280 --> 00:23:59,560 Speaker 1: Junior High, she decided to write a book about the 462 00:23:59,560 --> 00:24:02,360 Speaker 1: program him. When she went back to tour the program, 463 00:24:02,440 --> 00:24:05,840 Speaker 1: she met a sixth grader named Jeff Bezos. She used 464 00:24:05,840 --> 00:24:08,000 Speaker 1: the pseudonym Tim for him in her book at his 465 00:24:08,080 --> 00:24:10,840 Speaker 1: parents request. But from Julie's work, we have our first 466 00:24:10,880 --> 00:24:13,800 Speaker 1: early objective peak at young Mr Bezos. So this is 467 00:24:13,840 --> 00:24:16,760 Speaker 1: not coming out of his pr you know, Charing. This 468 00:24:16,840 --> 00:24:18,600 Speaker 1: is not coming from his family, This is not coming 469 00:24:18,600 --> 00:24:20,399 Speaker 1: from him. This is coming from someone who had no 470 00:24:20,440 --> 00:24:22,080 Speaker 1: idea what he was going to become, who was kind 471 00:24:22,080 --> 00:24:26,080 Speaker 1: of trying to analyze his intellectual growth objectively when he 472 00:24:26,119 --> 00:24:28,560 Speaker 1: was in the sixth grade. Um, so it's a pretty 473 00:24:28,560 --> 00:24:30,960 Speaker 1: interesting insight. We don't really have anything like this for 474 00:24:31,000 --> 00:24:33,920 Speaker 1: any of the other guys. So I find this fascinating. 475 00:24:33,920 --> 00:24:35,359 Speaker 1: I'm going to read a quote from the book The 476 00:24:35,440 --> 00:24:40,359 Speaker 1: Everything Store about this, uh, this early study. Jeff was 477 00:24:40,440 --> 00:24:44,359 Speaker 1: a student of general intellectual excellence, slight of build, friendly 478 00:24:44,400 --> 00:24:48,160 Speaker 1: but serious. He was not particularly gifted in leadership, according 479 00:24:48,200 --> 00:24:50,760 Speaker 1: to his teachers, but he moved confidently among his peers 480 00:24:50,800 --> 00:24:53,879 Speaker 1: and articulately extolled the virtues of the novel he was 481 00:24:53,920 --> 00:24:57,560 Speaker 1: reading at the time, j R. Tolkien's The Hobbit. Jeff 482 00:24:57,640 --> 00:25:00,440 Speaker 1: twelve was already competitive. He told Ray he was reading 483 00:25:00,480 --> 00:25:03,480 Speaker 1: a variety of books to qualify for a special Readers certificate, 484 00:25:03,520 --> 00:25:07,680 Speaker 1: but compared himself unfavorably to another classmate, who claimed improbably 485 00:25:07,920 --> 00:25:10,600 Speaker 1: that she was reading a dozen books a week. Jeff 486 00:25:10,640 --> 00:25:13,040 Speaker 1: also showed Ray a science project that he was working 487 00:25:13,119 --> 00:25:16,439 Speaker 1: on called an infinity cube, a battery powered contraption with 488 00:25:16,560 --> 00:25:19,840 Speaker 1: rotating mirrors that created the optical illusion of an endless tunnel. 489 00:25:20,320 --> 00:25:22,560 Speaker 1: Jeff modeled the device after one he had seen in 490 00:25:22,600 --> 00:25:26,000 Speaker 1: a store. That one cost twenty two dollars, but mine 491 00:25:26,119 --> 00:25:28,960 Speaker 1: was cheaper, he told Ray. Teachers said that three of 492 00:25:29,000 --> 00:25:31,880 Speaker 1: Jeff's projects were being entered in a local science competition 493 00:25:31,960 --> 00:25:34,240 Speaker 1: that drew most of its submissions from students in junior 494 00:25:34,240 --> 00:25:38,520 Speaker 1: and senior high schools. So you get a lot from that. 495 00:25:38,640 --> 00:25:41,119 Speaker 1: Number One, he's very advanced. People at the time recognize 496 00:25:41,160 --> 00:25:43,680 Speaker 1: him as brilliant. And number two, like, there's this toy 497 00:25:43,720 --> 00:25:46,199 Speaker 1: he wants. It's too expensive, his mom won't get it, 498 00:25:46,280 --> 00:25:48,560 Speaker 1: so he just builds it. You know, you get a 499 00:25:48,560 --> 00:25:52,160 Speaker 1: lot of like the future Jeff Bezos personality from from 500 00:25:52,160 --> 00:25:54,879 Speaker 1: that kind of decision. I just kept thinking of the 501 00:25:54,960 --> 00:26:00,479 Speaker 1: time cube. They Jeff built the time cube. Put that 502 00:26:00,560 --> 00:26:07,200 Speaker 1: on the internet. Yeah, yeah, that would be that would 503 00:26:07,240 --> 00:26:10,879 Speaker 1: be a fun twist. So from an early age, Jeff 504 00:26:10,920 --> 00:26:13,639 Speaker 1: seemed drawn to the idea of evaluating other people in 505 00:26:13,760 --> 00:26:17,560 Speaker 1: order to maximize their performance. While he was in sixth grade, 506 00:26:17,640 --> 00:26:20,879 Speaker 1: as practice for statistics class, he created a survey to 507 00:26:20,960 --> 00:26:23,880 Speaker 1: evaluate all the teachers in his grade. He claimed its 508 00:26:23,920 --> 00:26:26,639 Speaker 1: goal was to judge teachers on how they teach, not 509 00:26:26,760 --> 00:26:29,960 Speaker 1: as a popularity contest. When Julie met him, he was 510 00:26:30,000 --> 00:26:31,960 Speaker 1: working to lay out the results in a graph that 511 00:26:31,960 --> 00:26:34,640 Speaker 1: would compare all the teachers with each other. So he's 512 00:26:34,640 --> 00:26:37,840 Speaker 1: doing like this Amazon analytic ship as a sixth grader 513 00:26:37,880 --> 00:26:43,520 Speaker 1: to his teachers. Yeah, it's quite impressive. Yeah, and it 514 00:26:43,600 --> 00:26:46,840 Speaker 1: also I'd be scared of that kid, though, like fucking 515 00:26:46,960 --> 00:26:49,800 Speaker 1: Damien vibe, you know what I mean? Yeah, I think 516 00:26:49,840 --> 00:26:53,679 Speaker 1: I would like if a fucking sixth grader is like 517 00:26:53,760 --> 00:26:56,720 Speaker 1: graphing me and a bunch of other people like we're 518 00:26:56,720 --> 00:27:00,920 Speaker 1: their employees and like a char Yeah, maybe maybe i'd 519 00:27:00,920 --> 00:27:02,720 Speaker 1: spray that kid with some pepper water, you know, a 520 00:27:02,760 --> 00:27:05,200 Speaker 1: little bit of pepper water. Don't give him a ruler. 521 00:27:05,240 --> 00:27:09,399 Speaker 1: Who's your head, you know? Yeah, exactly, it's in that room, 522 00:27:09,440 --> 00:27:13,159 Speaker 1: you know what I mean. Yeah, Yeah, it's it's a 523 00:27:13,240 --> 00:27:16,320 Speaker 1: little bit like unsettling. And his own family was not 524 00:27:16,359 --> 00:27:18,760 Speaker 1: immune to this sort of behavior. When he was tinned, 525 00:27:18,800 --> 00:27:21,560 Speaker 1: Jeff grew concerned about his grandmother, who was a smoker. 526 00:27:22,400 --> 00:27:24,679 Speaker 1: His first attempt at convincing her to stop wasn't to 527 00:27:24,720 --> 00:27:27,159 Speaker 1: just say, like, grandma, could you stop smoking? Grandma? I 528 00:27:27,200 --> 00:27:29,920 Speaker 1: don't like it with you know, he calculated how many 529 00:27:30,000 --> 00:27:33,680 Speaker 1: minutes of life she lost per cigarette and then extrapolated 530 00:27:33,720 --> 00:27:35,760 Speaker 1: that based on the total amount of time that she'd 531 00:27:35,800 --> 00:27:39,000 Speaker 1: been smoking, and then told her you've taken nine years 532 00:27:39,040 --> 00:27:45,280 Speaker 1: off your life, and she burst into tears. That's the 533 00:27:45,359 --> 00:27:48,399 Speaker 1: kind of mind this kid, again, always driven by like 534 00:27:48,560 --> 00:27:54,080 Speaker 1: data and always like white people to have. Yeah, but 535 00:27:54,160 --> 00:27:57,199 Speaker 1: don't hurt granny's feelings, Like that's just no, No one 536 00:27:57,240 --> 00:28:00,080 Speaker 1: should do that, you know what I mean? I mean it. 537 00:28:00,320 --> 00:28:02,440 Speaker 1: I think you get this feeling from him with both 538 00:28:02,440 --> 00:28:05,159 Speaker 1: of those anecdotes that as a kid he um and 539 00:28:05,240 --> 00:28:06,919 Speaker 1: as a kid and as an adult he has this 540 00:28:06,960 --> 00:28:10,080 Speaker 1: attitude of like, well, if there's good data to analyze 541 00:28:10,080 --> 00:28:13,440 Speaker 1: exactly how you're performing, why wouldn't you want it? And 542 00:28:13,880 --> 00:28:16,320 Speaker 1: you know most of us are like, well, number one, 543 00:28:16,400 --> 00:28:19,359 Speaker 1: raw data doesn't tell you everything it leaves out. And 544 00:28:19,480 --> 00:28:22,080 Speaker 1: number two, it's kind of exhausting to live your whole 545 00:28:22,080 --> 00:28:24,800 Speaker 1: life that way, Yeah, no it is. And then do 546 00:28:24,840 --> 00:28:27,160 Speaker 1: you know what it reminds me of like the problem 547 00:28:27,280 --> 00:28:30,199 Speaker 1: like particularly like in our profession when you have like 548 00:28:30,240 --> 00:28:33,760 Speaker 1: analysts versus on the ground reporters, and it's like, yeah, 549 00:28:33,920 --> 00:28:36,359 Speaker 1: data is great, and it's a part of the process, 550 00:28:36,640 --> 00:28:39,360 Speaker 1: but it removes the soul of everything if you just 551 00:28:39,440 --> 00:28:40,840 Speaker 1: rely on it, you know what I mean? And that's 552 00:28:40,840 --> 00:28:44,480 Speaker 1: so much problem and it's it's not like it's also 553 00:28:44,560 --> 00:28:47,120 Speaker 1: it also leaves out inherently a lot like you can 554 00:28:47,120 --> 00:28:48,800 Speaker 1: look at there's all there's all these kind of like 555 00:28:49,400 --> 00:28:52,840 Speaker 1: the this wing of scholars who will make the claim 556 00:28:52,880 --> 00:28:54,600 Speaker 1: that like this is the best life has ever been, 557 00:28:54,680 --> 00:28:56,920 Speaker 1: and they'll they always bring up that like because of 558 00:28:57,000 --> 00:28:59,200 Speaker 1: this economic wature, this measure and it's like, well, but 559 00:29:00,040 --> 00:29:01,880 Speaker 1: there's a lot of ways in which like those are 560 00:29:01,960 --> 00:29:04,160 Speaker 1: jigged and you look at like yeah, it looks like 561 00:29:04,200 --> 00:29:06,239 Speaker 1: these indexes have been moving up, but you adjust them 562 00:29:06,280 --> 00:29:08,920 Speaker 1: every couple of years, so that like the you know, 563 00:29:09,440 --> 00:29:12,000 Speaker 1: the you're measuring different things that aren't going up in 564 00:29:12,040 --> 00:29:14,560 Speaker 1: price as much to show that people are are gaining more. 565 00:29:14,600 --> 00:29:17,280 Speaker 1: And it's like yeah, and that's why a lot of 566 00:29:17,280 --> 00:29:20,200 Speaker 1: these like statistics on how great a time it is 567 00:29:20,240 --> 00:29:22,040 Speaker 1: to live in now. Don't take into account the fact 568 00:29:22,040 --> 00:29:25,240 Speaker 1: that like people in the United States in particular are 569 00:29:25,280 --> 00:29:28,160 Speaker 1: fucking miserable and self report being miserable. And it's like, yeah, 570 00:29:28,720 --> 00:29:32,400 Speaker 1: you you can't just reduce everything to data. It doesn't 571 00:29:33,560 --> 00:29:36,920 Speaker 1: like that's a lot Yeah, oh no, no one's dropping 572 00:29:36,960 --> 00:29:38,960 Speaker 1: a rock on your head anymore. Like that was happening 573 00:29:39,000 --> 00:29:41,360 Speaker 1: every week and you know, five years ago, so so 574 00:29:41,440 --> 00:29:43,000 Speaker 1: you'll find it's like, no, there's there's a lot of 575 00:29:43,040 --> 00:29:46,240 Speaker 1: other things. I think without without context, statistics are just 576 00:29:46,440 --> 00:29:49,480 Speaker 1: like just ways for people to argue online, you know 577 00:29:49,480 --> 00:29:52,040 Speaker 1: what I mean. It's like these kind of you know, 578 00:29:52,160 --> 00:29:55,360 Speaker 1: like oh, Ben Shapiro Slaughter as some like twelve year olds. 579 00:29:55,400 --> 00:29:57,800 Speaker 1: He's like very much into all that, like, you know, 580 00:29:57,880 --> 00:30:01,400 Speaker 1: statistics without context, and it's they're just digits without any contexts. 581 00:30:01,440 --> 00:30:03,240 Speaker 1: You know, it's not it's not helpful, I don't think, 582 00:30:03,720 --> 00:30:06,320 Speaker 1: but he is, Jeff is in love with statistics and 583 00:30:06,400 --> 00:30:09,000 Speaker 1: digits and yeah, he's he's a he's a data driven 584 00:30:09,080 --> 00:30:13,200 Speaker 1: little boy. Uh. He also lived what sounds to me 585 00:30:13,280 --> 00:30:16,320 Speaker 1: like an exhausting life. He started school at seven am 586 00:30:16,480 --> 00:30:19,040 Speaker 1: every day, um, and he had like the special program 587 00:30:19,040 --> 00:30:21,600 Speaker 1: he was in was just this dizzying mix of classes, 588 00:30:21,960 --> 00:30:27,479 Speaker 1: individual projects in small group discussion time. Julie documented one 589 00:30:27,520 --> 00:30:29,880 Speaker 1: of the group discussions Jeff was a part of UH. 590 00:30:29,920 --> 00:30:32,120 Speaker 1: He and six other students were sitting with the principal 591 00:30:32,240 --> 00:30:35,640 Speaker 1: reading short stories and then discussing the short stories. And 592 00:30:35,680 --> 00:30:38,200 Speaker 1: the first story they read was about an archaeologist who 593 00:30:38,200 --> 00:30:41,680 Speaker 1: faked a cash of artifacts, and Julie recorded what Jeff 594 00:30:41,720 --> 00:30:43,600 Speaker 1: had to say about the story. So these are just 595 00:30:43,640 --> 00:30:46,080 Speaker 1: like some comments that she noted him making about the 596 00:30:46,160 --> 00:30:49,720 Speaker 1: archaeologists in the story. They probably wanted to become famous. 597 00:30:49,760 --> 00:30:51,840 Speaker 1: They wished away the things they didn't want to face. 598 00:30:52,400 --> 00:30:55,120 Speaker 1: Some people go throughrough life thinking like they always have. 599 00:30:55,640 --> 00:30:58,160 Speaker 1: You should be patient, analyze what you've have to work 600 00:30:58,200 --> 00:31:00,840 Speaker 1: with UM. So he is, you know, this is kind 601 00:31:00,840 --> 00:31:03,760 Speaker 1: of the way he's he's thinking about people. I especially 602 00:31:04,120 --> 00:31:06,320 Speaker 1: I think the line some people go through life thinking 603 00:31:06,360 --> 00:31:08,760 Speaker 1: like they always have, like he's. He's always very obsessed 604 00:31:08,840 --> 00:31:14,080 Speaker 1: with this idea of UM like treating things like it's 605 00:31:14,120 --> 00:31:16,320 Speaker 1: the first time anyone's done them, so you can try 606 00:31:16,360 --> 00:31:18,440 Speaker 1: to look at them from a new perspective, which is 607 00:31:19,200 --> 00:31:21,800 Speaker 1: very successful way to think in business. Like it's interesting 608 00:31:21,840 --> 00:31:24,360 Speaker 1: to me some of his his thought processes. He doesn't 609 00:31:24,360 --> 00:31:26,479 Speaker 1: really change, you know, as he grows up. He's kind 610 00:31:26,480 --> 00:31:31,360 Speaker 1: of always the same person who becomes the CEO of Amazon. UM. 611 00:31:31,520 --> 00:31:35,120 Speaker 1: Jeff told Julie Ray that he loved these exercises. Quote 612 00:31:35,160 --> 00:31:37,080 Speaker 1: the way the world is. You know, someone could tell 613 00:31:37,120 --> 00:31:39,320 Speaker 1: you to press the button. You have to think what 614 00:31:39,360 --> 00:31:40,920 Speaker 1: you're doing for you. You have to be able to 615 00:31:40,920 --> 00:31:43,400 Speaker 1: think about what you're doing for yourself, which is again 616 00:31:43,480 --> 00:31:45,920 Speaker 1: interesting because that's the opposite of what he wants from 617 00:31:45,920 --> 00:31:48,080 Speaker 1: his employees, but he recognizes it as the key to 618 00:31:48,120 --> 00:31:53,040 Speaker 1: his own success. UM. Julie's book was not a big hit. 619 00:31:53,160 --> 00:31:56,040 Speaker 1: This book about this special program Jefferson as a kid. 620 00:31:56,240 --> 00:31:58,280 Speaker 1: She had to self publish it, and as far as 621 00:31:58,280 --> 00:32:01,400 Speaker 1: I can tell, we know mostly about it because of 622 00:32:01,640 --> 00:32:05,600 Speaker 1: journalists like brad Stone Um who found this out, like 623 00:32:05,680 --> 00:32:07,920 Speaker 1: found copies of the book in the library after it 624 00:32:07,960 --> 00:32:10,480 Speaker 1: had been published, and like grabbed this early look at 625 00:32:10,560 --> 00:32:13,840 Speaker 1: Jeff Bezos for us UM. Brad Stone caught up with 626 00:32:13,920 --> 00:32:16,680 Speaker 1: Julie decades later when Jeff was a billionaire, and he 627 00:32:16,720 --> 00:32:19,240 Speaker 1: asked her what she felt about the things Jeff Bezos 628 00:32:19,240 --> 00:32:22,600 Speaker 1: had accomplished in the decades since sixth grade. She said 629 00:32:22,680 --> 00:32:25,040 Speaker 1: she had watched Tim's rise to fame and fortune over 630 00:32:25,040 --> 00:32:27,800 Speaker 1: the past two decades with admiration and amazement, but without 631 00:32:27,920 --> 00:32:30,240 Speaker 1: much surprise. When I met him as a young boy, 632 00:32:30,280 --> 00:32:32,520 Speaker 1: his ability was obvious and it was being nurtured and 633 00:32:32,640 --> 00:32:35,600 Speaker 1: encouraged by the new program. She says. The program also 634 00:32:35,680 --> 00:32:39,120 Speaker 1: benefited by his responsiveness and enthusiasm for learning. It was 635 00:32:39,160 --> 00:32:42,560 Speaker 1: a total validation of the concept. And it's certainly, i 636 00:32:42,560 --> 00:32:45,200 Speaker 1: would say, a validation of the concept of funding public 637 00:32:45,240 --> 00:32:48,920 Speaker 1: schools well enough that they can experiment. UM. In this regard, 638 00:32:49,040 --> 00:32:51,880 Speaker 1: Jeff isn't entirely dissimilar to Bill Gates, although Bill didn't 639 00:32:51,920 --> 00:32:54,640 Speaker 1: go to a public school, but both men benefited as 640 00:32:54,760 --> 00:32:57,720 Speaker 1: kids from communities that put a lot of resources into 641 00:32:57,840 --> 00:33:01,080 Speaker 1: educating them. A lot of a lot of went into 642 00:33:01,440 --> 00:33:04,320 Speaker 1: training up both of these children and giving them opportunities 643 00:33:04,360 --> 00:33:06,560 Speaker 1: when they were little kids, and a lot of that 644 00:33:06,640 --> 00:33:09,400 Speaker 1: came from, like in the fact that the schools they 645 00:33:09,400 --> 00:33:13,400 Speaker 1: were in valued their intellectual freedom and valued their personal 646 00:33:13,440 --> 00:33:16,760 Speaker 1: freedom and considered that an important part of them being 647 00:33:16,800 --> 00:33:19,000 Speaker 1: able to grow into the kind of people that they 648 00:33:19,000 --> 00:33:22,240 Speaker 1: were capable of becoming. UM. And again, it's a total 649 00:33:22,320 --> 00:33:25,480 Speaker 1: You look at what Amazon pays and taxes. Uh, it's 650 00:33:25,480 --> 00:33:27,560 Speaker 1: a bit of a run poll that they kind of 651 00:33:27,600 --> 00:33:29,600 Speaker 1: do as soon as they get up to where they 652 00:33:29,680 --> 00:33:32,320 Speaker 1: where they are. Um. I always find that interesting because 653 00:33:32,320 --> 00:33:37,200 Speaker 1: they benefit from societies that invest a ton of resources 654 00:33:37,200 --> 00:33:41,160 Speaker 1: into them, but they have no interest in investing back. Yeah, 655 00:33:41,200 --> 00:33:42,760 Speaker 1: I get do you know what? I really get the 656 00:33:42,880 --> 00:33:46,239 Speaker 1: vibe from Bezos kind of a Nixon vibe. You know, 657 00:33:46,400 --> 00:33:48,440 Speaker 1: it's not illegal when I do it, you know that 658 00:33:48,600 --> 00:33:50,880 Speaker 1: kind of thing. It's like I get that vibe from 659 00:33:50,960 --> 00:33:54,080 Speaker 1: him a lot, like I'll take everything and then you know, 660 00:33:54,360 --> 00:33:56,280 Speaker 1: but whatever I do is okay. You know what I mean. 661 00:33:56,360 --> 00:33:59,280 Speaker 1: It's like, yeah, it's not really I mean, all right, fine, 662 00:33:59,400 --> 00:34:02,560 Speaker 1: everybody has to give back, but he's the richest. I 663 00:34:02,560 --> 00:34:07,960 Speaker 1: don't enough, Like yeah yeah, yeah, I mean I'm sure 664 00:34:07,960 --> 00:34:10,719 Speaker 1: he talk about how space travel is giving back, but 665 00:34:10,760 --> 00:34:15,120 Speaker 1: that's a discussion for another day. Um. Yeah. So Jeff 666 00:34:15,200 --> 00:34:18,040 Speaker 1: was prone to such intense focus as a kid that 667 00:34:18,360 --> 00:34:20,680 Speaker 1: his teachers would sometimes have to pick him up while 668 00:34:20,719 --> 00:34:23,040 Speaker 1: he was still in his chair and move him to 669 00:34:23,080 --> 00:34:25,000 Speaker 1: different tables when it was time for him to switch 670 00:34:25,040 --> 00:34:28,080 Speaker 1: tasks because he would be so invested in whatever he 671 00:34:28,120 --> 00:34:31,080 Speaker 1: was doing. Um, and he never seemed to like turn off. 672 00:34:31,080 --> 00:34:32,719 Speaker 1: When he was not at school, he would spend his 673 00:34:32,760 --> 00:34:36,200 Speaker 1: time rebuilding radios, building robots from kits, and experimenting with 674 00:34:36,239 --> 00:34:40,239 Speaker 1: electronics purchased from radio shack. His most telling invention was 675 00:34:40,280 --> 00:34:43,000 Speaker 1: an electric alarm, which he wired to alert him if 676 00:34:43,040 --> 00:34:45,279 Speaker 1: his younger brother or sister tried to get into his 677 00:34:45,360 --> 00:34:50,120 Speaker 1: room because he was obsessed with his personal privacy. Um yeah, 678 00:34:50,760 --> 00:34:54,320 Speaker 1: like Bill, Yeah, that's cute. Like Bill Gates, Jeff benefited 679 00:34:54,320 --> 00:34:56,160 Speaker 1: from the fact that he had access to a computer 680 00:34:56,280 --> 00:34:59,280 Speaker 1: earlier than any other kids pretty much in the United States. 681 00:34:59,560 --> 00:35:03,080 Speaker 1: A huge based company provided his school with a terminal 682 00:35:03,160 --> 00:35:06,239 Speaker 1: and loan them extra time on the company mainframe. And 683 00:35:06,320 --> 00:35:08,160 Speaker 1: this was during a period when kind of in the 684 00:35:08,200 --> 00:35:11,520 Speaker 1: same time, Bill Gates's rich parents at like a fancy 685 00:35:11,560 --> 00:35:14,279 Speaker 1: private school raised money via a bake sale to buy 686 00:35:14,320 --> 00:35:17,080 Speaker 1: a terminal for the kids. Um. So this is again 687 00:35:17,120 --> 00:35:20,600 Speaker 1: another thing that kind of all these guys, these early 688 00:35:20,640 --> 00:35:24,200 Speaker 1: tech billionaires have in common, is that adults around them 689 00:35:24,400 --> 00:35:26,480 Speaker 1: make the choice to put a lot of money into 690 00:35:26,560 --> 00:35:29,640 Speaker 1: giving them access to a computer in like the seventies, 691 00:35:29,680 --> 00:35:33,160 Speaker 1: you know, when very few other people have access to computers. Yeah, 692 00:35:33,360 --> 00:35:36,319 Speaker 1: the massive like very very privileged position to be as 693 00:35:36,360 --> 00:35:38,799 Speaker 1: a kid, especially but then you know what I mean. Yeah, 694 00:35:38,840 --> 00:35:42,440 Speaker 1: and again it's if if, if, if, like either of 695 00:35:42,440 --> 00:35:44,680 Speaker 1: them were to really learn the lesson of their success 696 00:35:44,719 --> 00:35:47,200 Speaker 1: would be like, yeah, you should, you should invest a 697 00:35:47,200 --> 00:35:51,480 Speaker 1: lot in children. Um, But neither of them really seem 698 00:35:51,520 --> 00:35:54,680 Speaker 1: to get that from the book one click quote. The 699 00:35:54,719 --> 00:35:57,000 Speaker 1: setup came with a stack of manuals, but no one 700 00:35:57,040 --> 00:35:59,120 Speaker 1: at the school knew how to use it. Jeff and 701 00:35:59,160 --> 00:36:01,239 Speaker 1: a couple of other stud stayed after class to go 702 00:36:01,280 --> 00:36:03,320 Speaker 1: through the manuals and figure out how to program it, 703 00:36:03,600 --> 00:36:06,200 Speaker 1: but that novelty only lasted about a week. Then they 704 00:36:06,239 --> 00:36:09,359 Speaker 1: discovered that the main frame contained a primitive starf Trek game. 705 00:36:09,600 --> 00:36:11,640 Speaker 1: From then on, all they used the computer for was 706 00:36:11,680 --> 00:36:14,000 Speaker 1: playing Star Trek, each taking on a role of one 707 00:36:14,000 --> 00:36:16,440 Speaker 1: of the characters in the TV program. Like all of 708 00:36:16,480 --> 00:36:18,799 Speaker 1: his other nerdy friends, he considered the choice role in 709 00:36:18,840 --> 00:36:21,440 Speaker 1: the game to be that of Mr Spock. Captain Kirk 710 00:36:21,520 --> 00:36:23,880 Speaker 1: was the backup choice. If Jeff couldn't get either of 711 00:36:23,880 --> 00:36:26,040 Speaker 1: those roles, he preferred to take on the persona of 712 00:36:26,080 --> 00:36:32,520 Speaker 1: the starship's computer, which also says a lot Um, Yeah, 713 00:36:31,600 --> 00:36:35,480 Speaker 1: I get like Spock is rad, but wanting to be 714 00:36:35,560 --> 00:36:39,400 Speaker 1: the computer is a little weird. Um. The career that 715 00:36:39,480 --> 00:36:41,880 Speaker 1: most of I mean, bones is right there, Jeff, but whatever. 716 00:36:42,360 --> 00:36:46,080 Speaker 1: The career that most appealed to child Jeff Bezos was archaeology, 717 00:36:46,239 --> 00:36:49,719 Speaker 1: but his heroes were all businessmen, particularly Walt Disney and 718 00:36:49,800 --> 00:36:53,360 Speaker 1: Thomas Edison, both of whom were very successful at monetizing 719 00:36:53,400 --> 00:36:56,480 Speaker 1: the achievements of their employees and utterly ruthless at crushing 720 00:36:56,520 --> 00:37:00,279 Speaker 1: competition to their dominance. He actually admired Disney more than 721 00:37:00,400 --> 00:37:02,959 Speaker 1: Edison because he saw Walt Disney is having been better 722 00:37:03,000 --> 00:37:05,800 Speaker 1: at putting a team together to work in a concerted fashion. 723 00:37:05,880 --> 00:37:08,680 Speaker 1: So like, again, Disney is kind of the guy he's 724 00:37:08,680 --> 00:37:12,799 Speaker 1: worshiping as a little kid. Yeah, but not even for 725 00:37:13,000 --> 00:37:15,520 Speaker 1: like not even like wow, I love his cartoons. It's 726 00:37:15,520 --> 00:37:19,080 Speaker 1: just like the lamest version of like him, you know, Yeah, 727 00:37:19,400 --> 00:37:23,560 Speaker 1: it really is now. As you probably guessed, Jeff Bezos 728 00:37:23,640 --> 00:37:25,799 Speaker 1: did not get in trouble often as a kid. He 729 00:37:25,920 --> 00:37:29,440 Speaker 1: lost his library privileges once for laughing too loud. Um, 730 00:37:29,480 --> 00:37:31,480 Speaker 1: but that's kind of like most of what you hear 731 00:37:31,480 --> 00:37:33,080 Speaker 1: about it, Like, he's not and we'll talk about his 732 00:37:33,160 --> 00:37:35,160 Speaker 1: laugh a little bit later, but he's he does not 733 00:37:36,000 --> 00:37:38,520 Speaker 1: run into a lot of hot water based on all 734 00:37:38,520 --> 00:37:40,200 Speaker 1: of this, and you might be surprised to know that 735 00:37:40,280 --> 00:37:43,720 Speaker 1: Jeff joined a youth football league, um and was actually 736 00:37:43,800 --> 00:37:46,319 Speaker 1: pretty good at it, which is why there's a number 737 00:37:46,320 --> 00:37:48,640 Speaker 1: of reasons this is weird. For one, football is a 738 00:37:48,680 --> 00:37:51,799 Speaker 1: fucking huge deal in Texas. It's like a religion there. 739 00:37:51,840 --> 00:37:54,400 Speaker 1: There were like schools like the school I went to, 740 00:37:54,520 --> 00:37:57,720 Speaker 1: paid millions and millions of dollars for their football stadium, 741 00:37:57,760 --> 00:38:00,680 Speaker 1: Like schools where I live now cost less than just 742 00:38:00,760 --> 00:38:03,480 Speaker 1: the football stadium from my high school did. It's it's 743 00:38:03,480 --> 00:38:07,560 Speaker 1: a Texas thing. We put ridiculous resources into football. So 744 00:38:07,640 --> 00:38:09,799 Speaker 1: the fact that he was good at football in a 745 00:38:09,880 --> 00:38:13,560 Speaker 1: Texas youth league means something. Um, he was not a 746 00:38:13,560 --> 00:38:15,959 Speaker 1: big kid. His mom didn't want him to join because 747 00:38:16,000 --> 00:38:18,239 Speaker 1: she was worried he'd get beaten badly. But he was 748 00:38:18,320 --> 00:38:21,480 Speaker 1: so good at memorizing plays and understanding like the rule 749 00:38:21,560 --> 00:38:23,799 Speaker 1: mechanics of the games that his coach made him a 750 00:38:23,840 --> 00:38:26,719 Speaker 1: defensive captain. So you're kind of he's he's he's more 751 00:38:26,760 --> 00:38:28,880 Speaker 1: flexible than you might expect, And I think that a 752 00:38:28,920 --> 00:38:30,640 Speaker 1: lot of that goes down to the kind of education 753 00:38:30,680 --> 00:38:32,920 Speaker 1: he has that they really put a lot of value 754 00:38:32,920 --> 00:38:35,760 Speaker 1: in trying to like make these kids, give them space 755 00:38:35,760 --> 00:38:38,440 Speaker 1: to experiments so they grow up well rounded, because clearly 756 00:38:39,120 --> 00:38:42,160 Speaker 1: he does. The fact that he's he's he's building robots, 757 00:38:42,200 --> 00:38:46,200 Speaker 1: and he's playing football. UM is a kind of is 758 00:38:46,239 --> 00:38:48,440 Speaker 1: sort of an example of the success of the school system. 759 00:38:48,480 --> 00:38:52,440 Speaker 1: He grows up in UM. He spent his high school years. 760 00:38:52,600 --> 00:38:53,840 Speaker 1: By the time he moved in the high school he 761 00:38:53,880 --> 00:38:56,760 Speaker 1: got to high school, his family moves to Florida because 762 00:38:56,840 --> 00:38:59,399 Speaker 1: for ex On mobile kind of ship UM. And once 763 00:38:59,440 --> 00:39:02,040 Speaker 1: he gets to Florida, he immediately informs his new public 764 00:39:02,080 --> 00:39:04,880 Speaker 1: school classmates that he was going to be the valedictorian. 765 00:39:05,880 --> 00:39:08,120 Speaker 1: His first summer job in high school was as a 766 00:39:08,120 --> 00:39:10,600 Speaker 1: fry cook at McDonald's, and he passed the time by 767 00:39:10,600 --> 00:39:14,440 Speaker 1: studying the different ways that McDonald's had automated their production process. 768 00:39:14,920 --> 00:39:17,440 Speaker 1: However interested he was in this, he decided early on 769 00:39:17,520 --> 00:39:19,759 Speaker 1: that he did not like working for other people and 770 00:39:19,760 --> 00:39:23,200 Speaker 1: he wanted to own his own business. Someday. He succeeded 771 00:39:23,239 --> 00:39:26,200 Speaker 1: in becoming valedictorian of his high school, and in his speech, 772 00:39:26,239 --> 00:39:29,239 Speaker 1: he called for humankind to colonize space so that they 773 00:39:29,239 --> 00:39:31,800 Speaker 1: could preserve the Earth and turn it into a giant 774 00:39:31,800 --> 00:39:34,800 Speaker 1: park once all the human beings were gone, which is 775 00:39:35,080 --> 00:39:37,520 Speaker 1: a story that story like came out recently Jeff Bezos 776 00:39:37,560 --> 00:39:39,160 Speaker 1: saying that, like, yeah, I think most people will move 777 00:39:39,160 --> 00:39:41,360 Speaker 1: into space and we'll keep a few people behind on 778 00:39:41,400 --> 00:39:44,160 Speaker 1: the Earth to to maintain it as like a park. 779 00:39:48,120 --> 00:39:51,880 Speaker 1: I'm not happy, man, No, it is interesting to me again, 780 00:39:51,920 --> 00:39:54,399 Speaker 1: how consistent all this is. Like, I don't think he's 781 00:39:54,400 --> 00:39:58,560 Speaker 1: ever changed as a person. Uh, it's the same thing. 782 00:39:59,000 --> 00:40:02,200 Speaker 1: You know, who does want to turn the earth into 783 00:40:02,239 --> 00:40:05,839 Speaker 1: a giant park free of human beings. You can't verify that. 784 00:40:06,719 --> 00:40:12,000 Speaker 1: I mean unless it is like audible or something. That's 785 00:40:12,000 --> 00:40:16,680 Speaker 1: my point. You can't verify that. Yeah, well here's some 786 00:40:16,800 --> 00:40:26,200 Speaker 1: ads we are back. Oh my gosh, I do love 787 00:40:26,520 --> 00:40:30,240 Speaker 1: I do love o our good solid ads for products 788 00:40:30,280 --> 00:40:33,120 Speaker 1: and services. So, and one thing I want to add, 789 00:40:33,160 --> 00:40:36,360 Speaker 1: I I really love everyone telling me how many ads 790 00:40:36,360 --> 00:40:38,719 Speaker 1: there are in Megacort. Thanks. I love that. Yeah, that's 791 00:40:38,800 --> 00:40:43,839 Speaker 1: quite true. Let me know every every episode, let me know, yeah, 792 00:40:43,920 --> 00:40:47,840 Speaker 1: that's literally every message. If they want to pay for it, 793 00:40:48,080 --> 00:40:49,719 Speaker 1: you know, yeah exactly, or if you're gonna pay for 794 00:40:49,800 --> 00:40:51,920 Speaker 1: you know, put put food in my family's table, Like 795 00:40:52,120 --> 00:40:55,560 Speaker 1: I gotta paper out there. You know, it's it's all good. Look, man, 796 00:40:55,640 --> 00:40:58,440 Speaker 1: you can skip you can skip ads. You can go 797 00:40:58,480 --> 00:41:00,480 Speaker 1: in and cut them out and put the episode up 798 00:41:00,520 --> 00:41:06,160 Speaker 1: as a torrent. Nobody's going to complain. There's options, there's options. 799 00:41:06,680 --> 00:41:10,239 Speaker 1: As soon as he graduated, Jeff Um, he and a 800 00:41:10,280 --> 00:41:13,200 Speaker 1: school friend started a company of their own. UM. So 801 00:41:13,239 --> 00:41:15,080 Speaker 1: he gets out of high school and like the summer 802 00:41:15,120 --> 00:41:18,719 Speaker 1: after high school, he starts a business with a classmate. 803 00:41:19,000 --> 00:41:22,360 Speaker 1: Their business is called the Dream Institute, and Dream is 804 00:41:22,400 --> 00:41:26,200 Speaker 1: an acronym, and it's a terrible acronym because dream stands 805 00:41:26,239 --> 00:41:30,359 Speaker 1: for directed. There's the D reasoning, there's the R e 806 00:41:30,480 --> 00:41:36,520 Speaker 1: A methods that that's not, that's not, that's not an acronym, 807 00:41:36,520 --> 00:41:38,880 Speaker 1: that's not it's and it's fine, but like it's not 808 00:41:38,960 --> 00:41:44,480 Speaker 1: an active Sorry, young Jeff bezos Um. The Dream Institute 809 00:41:44,480 --> 00:41:46,840 Speaker 1: was a two week summer camp program for fifth graders 810 00:41:46,880 --> 00:41:49,760 Speaker 1: where Jeff and his friend would teach kids about engines, fission, 811 00:41:49,840 --> 00:41:53,960 Speaker 1: interstellar travel, and space phenomena, along with stuff like advertising 812 00:41:54,000 --> 00:41:56,719 Speaker 1: and like the history of advertising. It was an eclectic 813 00:41:56,760 --> 00:41:58,719 Speaker 1: program to say the least. You can see it as 814 00:41:58,760 --> 00:42:01,399 Speaker 1: Jeff trying to kind of give act to kids, some 815 00:42:01,520 --> 00:42:04,240 Speaker 1: element of what he had gotten um as a child. 816 00:42:04,600 --> 00:42:07,600 Speaker 1: A local news report on the Dream Institute makes it 817 00:42:07,640 --> 00:42:10,840 Speaker 1: seem like Jeff's primary interest as an educator was teaching 818 00:42:10,920 --> 00:42:14,279 Speaker 1: kids about space. This passage from the book One Click, 819 00:42:14,400 --> 00:42:17,359 Speaker 1: based on interviews with Jeff's early business partner, makes it 820 00:42:17,400 --> 00:42:20,279 Speaker 1: clear where his head was at the time. Quote he said, 821 00:42:20,320 --> 00:42:22,799 Speaker 1: the future of mankind is not on this planet because 822 00:42:22,800 --> 00:42:24,799 Speaker 1: we might be struck by something and we better have 823 00:42:24,840 --> 00:42:28,320 Speaker 1: a spaceship out there. So, I don't know, just interesting 824 00:42:28,320 --> 00:42:31,840 Speaker 1: to me, very consistent. Yeah, he's yeah, he's had that 825 00:42:32,040 --> 00:42:34,840 Speaker 1: that germ like in his you know, the germ of 826 00:42:34,840 --> 00:42:37,879 Speaker 1: an idea in his brain from very young. Clearly. Yeah. Now, 827 00:42:37,960 --> 00:42:41,120 Speaker 1: the Dream Institute didn't last long, not because it failed, 828 00:42:41,160 --> 00:42:43,560 Speaker 1: but because Jeff was off to Princeton, where he got 829 00:42:43,560 --> 00:42:46,799 Speaker 1: a degree in computer science and electrical engineering. He did 830 00:42:46,880 --> 00:42:49,560 Speaker 1: very well at school, although he was not the valedictorian. 831 00:42:49,640 --> 00:42:51,600 Speaker 1: And in fact, this is kind of a humbling moment 832 00:42:51,640 --> 00:42:54,160 Speaker 1: for him because at Princeton, for the first time, he's 833 00:42:54,160 --> 00:42:57,480 Speaker 1: surrounded by a bunch of other like young, rich, overachievers, UM, 834 00:42:57,520 --> 00:43:00,760 Speaker 1: and he's not the best anymore. Uh, he doesn't really 835 00:43:00,840 --> 00:43:03,680 Speaker 1: stand out at Princeton, and in fact, one of the 836 00:43:03,760 --> 00:43:06,399 Speaker 1: things when when journalists go back to people who were 837 00:43:06,440 --> 00:43:08,799 Speaker 1: in college with Jeff Bezos, one of the things they 838 00:43:08,840 --> 00:43:11,279 Speaker 1: note is that basically no one who was with him 839 00:43:11,280 --> 00:43:14,240 Speaker 1: in school really noticed him or remembered him Like he didn't. 840 00:43:14,239 --> 00:43:16,960 Speaker 1: He doesn't stand out at all. Uh. One of the 841 00:43:16,960 --> 00:43:19,240 Speaker 1: few anecdotes we get about him from a college friend 842 00:43:19,280 --> 00:43:21,520 Speaker 1: is that he liked to play beer pong, which is 843 00:43:21,560 --> 00:43:25,359 Speaker 1: like the blandest personality trait you can have in college, Like, yeah, 844 00:43:25,400 --> 00:43:28,120 Speaker 1: everybody plays beer pog into college, Like is that? What 845 00:43:28,600 --> 00:43:33,000 Speaker 1: is sorry? What is beer pong? Again? It's so you 846 00:43:33,040 --> 00:43:36,040 Speaker 1: have you have like a fucking a table tennis court, 847 00:43:36,080 --> 00:43:39,680 Speaker 1: and we're not going to laugh about that. What did 848 00:43:40,640 --> 00:43:45,319 Speaker 1: Like Jacob didn't play beer poll I don't know doing 849 00:43:45,360 --> 00:43:46,960 Speaker 1: in England, you know, I didn't. I didn't go to 850 00:43:47,040 --> 00:43:49,239 Speaker 1: university though, so I don't know, man, Like, I don't 851 00:43:49,280 --> 00:43:51,399 Speaker 1: think people do it here, you know, I dropped out 852 00:43:51,440 --> 00:43:56,080 Speaker 1: of university. But I definitely, yeah, I can imagine. But 853 00:43:56,360 --> 00:43:58,360 Speaker 1: I think people who just they just get smashed, you know, 854 00:43:58,520 --> 00:44:01,120 Speaker 1: I don't. Yeah, I mean that's mostly what you do. 855 00:44:01,200 --> 00:44:03,279 Speaker 1: The basic ideas you have like a table tennis or 856 00:44:03,440 --> 00:44:05,080 Speaker 1: you could just set up like a card table and 857 00:44:05,120 --> 00:44:08,400 Speaker 1: you stack a bunch of like like quarter full cups 858 00:44:08,400 --> 00:44:11,760 Speaker 1: of beer on it, and you have like table tennis rackets, 859 00:44:11,800 --> 00:44:14,400 Speaker 1: and you have like little clear white plastic balls and 860 00:44:14,440 --> 00:44:16,439 Speaker 1: you hit them to each other. And if you knock, 861 00:44:16,560 --> 00:44:18,000 Speaker 1: if you knock a ball in the one of the 862 00:44:18,000 --> 00:44:21,799 Speaker 1: cups the table tennis racket, there's a number of ways, 863 00:44:21,840 --> 00:44:24,080 Speaker 1: but you're you're you're knocking these balls back and forth. 864 00:44:24,120 --> 00:44:26,000 Speaker 1: And if you knock a ball into a drink, your 865 00:44:26,000 --> 00:44:28,359 Speaker 1: opponent has to drink it. If they knock a ball 866 00:44:28,400 --> 00:44:30,239 Speaker 1: into a drink, you have to drink it. And you 867 00:44:30,280 --> 00:44:35,080 Speaker 1: repeat this until everybody's drunk. Right, that's the basic idea. Yeah, 868 00:44:35,080 --> 00:44:37,400 Speaker 1: it's not I have nothing against beer pong. It's just 869 00:44:37,480 --> 00:44:42,440 Speaker 1: it's like everybody in college like liking I remember he 870 00:44:42,560 --> 00:44:45,280 Speaker 1: liked beer pong. It's like I remember he ate food. 871 00:44:45,800 --> 00:44:50,479 Speaker 1: Yeah yeah, okay, if you say he refused to play 872 00:44:50,480 --> 00:44:52,920 Speaker 1: beer pong, that's a personality trait. But like you know, 873 00:44:53,040 --> 00:44:55,759 Speaker 1: although just kind of the idea in my head of 874 00:44:55,800 --> 00:44:58,320 Speaker 1: like a guy that just really loves beer pong is 875 00:44:58,400 --> 00:45:03,840 Speaker 1: quite funny. Like every he just like start Jeff calmed 876 00:45:03,920 --> 00:45:06,960 Speaker 1: down like Jesus Christ. I mean, if he liked it 877 00:45:07,000 --> 00:45:10,000 Speaker 1: for the like One of the things I found positive 878 00:45:10,040 --> 00:45:12,080 Speaker 1: about beer pong is that, like I was not a 879 00:45:12,160 --> 00:45:14,880 Speaker 1: very social kid, I was not good Like when I 880 00:45:14,920 --> 00:45:16,959 Speaker 1: was nineteen, I was not good at talking to people 881 00:45:17,000 --> 00:45:19,319 Speaker 1: at parties. But I could drink a lot and if 882 00:45:19,360 --> 00:45:22,240 Speaker 1: I could just play, and beer pong was very simple, 883 00:45:22,560 --> 00:45:25,840 Speaker 1: so you could participate in the party without actually engaging 884 00:45:25,880 --> 00:45:28,560 Speaker 1: with anybody. If you were, you know, an anxious kid 885 00:45:28,560 --> 00:45:30,840 Speaker 1: who didn't really understand how to hang out around people 886 00:45:31,200 --> 00:45:34,080 Speaker 1: until I found mushrooms. I was not very good at 887 00:45:33,640 --> 00:45:36,680 Speaker 1: at being in social situations. So I'm wondering if maybe 888 00:45:36,760 --> 00:45:38,480 Speaker 1: it was something like that for Jeff, where it's like, oh, 889 00:45:38,520 --> 00:45:40,800 Speaker 1: I understand the rules of this, you know what. It 890 00:45:40,880 --> 00:45:43,560 Speaker 1: might be funnier that I can just imagine him being like, 891 00:45:44,360 --> 00:45:47,120 Speaker 1: I want to be the best beer pong you know, 892 00:45:48,760 --> 00:45:50,520 Speaker 1: you don't even care about He's like, I need to win. 893 00:45:50,760 --> 00:45:52,879 Speaker 1: I don't know. I could just say yeah, that that 894 00:45:52,880 --> 00:45:56,520 Speaker 1: that that would not surprise me. Um. Yeah, I'm gonna 895 00:45:56,600 --> 00:45:59,120 Speaker 1: read a quote from the book One Click about Jeff 896 00:45:59,160 --> 00:46:03,760 Speaker 1: in uh college from some interviews with his classmates. Quote, 897 00:46:04,320 --> 00:46:07,160 Speaker 1: Jeff wasn't much of a ladies man, perhaps Princeton women 898 00:46:07,200 --> 00:46:10,440 Speaker 1: aren't into beer pong. He explained his own romantic shortcomings 899 00:46:10,440 --> 00:46:13,440 Speaker 1: this way. I'm not the kind of person where women say, oh, look, 900 00:46:13,480 --> 00:46:15,560 Speaker 1: how great he is a half hour after meeting me. 901 00:46:15,800 --> 00:46:17,560 Speaker 1: I'm kind of goofy, and I'm not. It's not the 902 00:46:17,600 --> 00:46:19,440 Speaker 1: kind of thing where people are going to say about me, 903 00:46:19,480 --> 00:46:21,560 Speaker 1: Oh my god, this is what we've been looking for. 904 00:46:21,960 --> 00:46:26,480 Speaker 1: Another classmate, e J. Chichilnisky, would bump into him occasionally, 905 00:46:26,560 --> 00:46:28,719 Speaker 1: but today can recall nothing more than that he was 906 00:46:28,920 --> 00:46:32,200 Speaker 1: bright and motivated and organized. So he doesn't really like 907 00:46:32,640 --> 00:46:35,600 Speaker 1: he doesn't stand out at all in in college, which 908 00:46:35,640 --> 00:46:37,680 Speaker 1: is interesting to me, Like he becomes the richest man 909 00:46:37,719 --> 00:46:39,600 Speaker 1: in the world, and all his classmates are like, yeah, 910 00:46:39,640 --> 00:46:43,360 Speaker 1: he was just kind of a kid, you know. Um, 911 00:46:43,400 --> 00:46:47,040 Speaker 1: it's probably worth noting here in terms of weird aspects 912 00:46:47,040 --> 00:46:50,000 Speaker 1: of Jeff Bezos' personality, that he does not like music, 913 00:46:50,600 --> 00:46:55,880 Speaker 1: like period, he doesn't does not get music. That's the 914 00:46:55,920 --> 00:46:58,839 Speaker 1: biggest red flag, right, Like, I don't care what kind 915 00:46:58,880 --> 00:47:01,840 Speaker 1: of music you like. It's like it's the it's the 916 00:47:01,960 --> 00:47:05,080 Speaker 1: chief miracle of human existence. If you don't get it 917 00:47:05,200 --> 00:47:08,959 Speaker 1: at all, that's kind of odd. Yeah, that's really strange. Yeah, 918 00:47:09,000 --> 00:47:12,640 Speaker 1: that's that's so nerving. Actually, yeah, it is weird, like 919 00:47:13,360 --> 00:47:16,800 Speaker 1: force your favorite music. I don't like music, like in general, Like, no, 920 00:47:17,040 --> 00:47:19,880 Speaker 1: that's odd man. Yeah, and he has to um. As 921 00:47:19,960 --> 00:47:22,759 Speaker 1: a kid, he recognizes that this is off putting, and 922 00:47:22,800 --> 00:47:25,399 Speaker 1: so while he's in high school, he memorizes the call 923 00:47:25,480 --> 00:47:28,480 Speaker 1: letters for every local radio station so he can pretend 924 00:47:28,480 --> 00:47:30,960 Speaker 1: to like music if the subject comes up in conversations 925 00:47:30,960 --> 00:47:34,960 Speaker 1: with other kids. It shows the disconnect too, because it's like, 926 00:47:35,080 --> 00:47:36,759 Speaker 1: I don't like music, but I need to be able 927 00:47:36,760 --> 00:47:39,760 Speaker 1: to pretend it. I'll memorize the names of radio stations. 928 00:47:39,800 --> 00:47:41,640 Speaker 1: That way people will think I like music, And it's 929 00:47:41,680 --> 00:47:46,120 Speaker 1: like that's not how people talk about music. Ye, that 930 00:47:46,239 --> 00:47:50,239 Speaker 1: behavior this new radio station, I know the phone number 931 00:47:50,280 --> 00:47:55,360 Speaker 1: to it. No one's gonna be like, oh, he loves music. Yeah, okay, Jeff, 932 00:47:57,160 --> 00:47:59,000 Speaker 1: Like I mean, I don't. I don't. I don't want 933 00:47:59,000 --> 00:48:00,919 Speaker 1: to like second get it. But I've read a lot 934 00:48:00,960 --> 00:48:04,200 Speaker 1: about like, like you know, sociopaths, and they say that 935 00:48:04,200 --> 00:48:06,319 Speaker 1: they're like, you know, there's certain things that they don't 936 00:48:06,400 --> 00:48:08,520 Speaker 1: understand it, but they know how to emulate it to 937 00:48:08,600 --> 00:48:10,960 Speaker 1: fit in. And that sounds like that, you know, I 938 00:48:11,040 --> 00:48:13,160 Speaker 1: was saying that he emulated it pretty badly. So yeah, 939 00:48:13,160 --> 00:48:15,200 Speaker 1: and it's one of those things I don't know, because 940 00:48:15,200 --> 00:48:17,120 Speaker 1: like I had my version of that, Like I never 941 00:48:17,239 --> 00:48:19,800 Speaker 1: liked football, but I played it, and I like learned 942 00:48:19,800 --> 00:48:21,520 Speaker 1: to feign interest in it because you have to be 943 00:48:21,560 --> 00:48:24,400 Speaker 1: able to pretend to like football in Texas. But yeah, 944 00:48:24,760 --> 00:48:28,200 Speaker 1: that's less basic than pretending to understand the appeal of 945 00:48:28,360 --> 00:48:34,560 Speaker 1: music as a concept. Dogs like music, dogs like plants 946 00:48:34,600 --> 00:48:37,480 Speaker 1: grow from it. Like, no, it's very weird that yeah, 947 00:48:37,520 --> 00:48:40,680 Speaker 1: that's it's that's a bit peculiar. A lot of people 948 00:48:40,960 --> 00:48:43,040 Speaker 1: this is relevant to Amazon in part because a lot 949 00:48:43,080 --> 00:48:45,839 Speaker 1: of people claim the reason Apple got the iPod out 950 00:48:45,880 --> 00:48:49,520 Speaker 1: and iTunes that Apple because Amazon and Apple kind of 951 00:48:49,520 --> 00:48:51,040 Speaker 1: had a fight to see who was going to be 952 00:48:51,080 --> 00:48:54,319 Speaker 1: the kings of digital music, and Apple wins um and 953 00:48:54,400 --> 00:48:56,520 Speaker 1: a lot of people will say that it's probably because 954 00:48:56,520 --> 00:49:00,279 Speaker 1: Steve Jobs famously loved music. Jobs was like obsessed with with, 955 00:49:00,600 --> 00:49:03,920 Speaker 1: uh like music, and from a pretty early stage in 956 00:49:04,000 --> 00:49:06,960 Speaker 1: his business, wanted to deliver something like the iPod because 957 00:49:06,960 --> 00:49:10,080 Speaker 1: he cared about people being able to listen to music. 958 00:49:10,360 --> 00:49:13,680 Speaker 1: And Jeff Bezos misses this trade, which is kind of 959 00:49:13,680 --> 00:49:16,680 Speaker 1: an obvious money train right, especially if you're doing Amazon 960 00:49:16,680 --> 00:49:18,799 Speaker 1: ship in the nineties, you should probably be able to 961 00:49:18,800 --> 00:49:20,600 Speaker 1: see that, like, oh yeah, digital music is going to 962 00:49:20,640 --> 00:49:22,839 Speaker 1: be a big deal financially, we should get into that. 963 00:49:23,160 --> 00:49:25,759 Speaker 1: But Jeff doesn't, because like he does not understand the 964 00:49:25,800 --> 00:49:29,120 Speaker 1: appeal of music on a fundamental level. Yeah, that's such 965 00:49:29,239 --> 00:49:32,000 Speaker 1: like a hiccup there. He's just it's like he's just 966 00:49:32,040 --> 00:49:34,279 Speaker 1: like no, I don't. I don't why, like why It's 967 00:49:34,320 --> 00:49:37,320 Speaker 1: like that's that's kind of nuts. Yeah, there's another story 968 00:49:37,320 --> 00:49:39,960 Speaker 1: about him that, like in the early days of the company, 969 00:49:40,040 --> 00:49:43,080 Speaker 1: like two one, Uh, he's like on a work trip 970 00:49:43,160 --> 00:49:45,000 Speaker 1: with a bunch of employees and they have to drive 971 00:49:45,040 --> 00:49:48,640 Speaker 1: back and nine eleven happens, and everybody's like bummed out 972 00:49:48,680 --> 00:49:50,680 Speaker 1: and like freaked out and has to they have to 973 00:49:50,760 --> 00:49:53,640 Speaker 1: drive across the country like right after nine eleven, and 974 00:49:53,680 --> 00:49:55,440 Speaker 1: to try to cheer everyone up, he goes into a 975 00:49:55,440 --> 00:49:58,120 Speaker 1: truck stop and just gets a random assortment of CDs 976 00:49:58,520 --> 00:50:03,800 Speaker 1: just like randomly picks mus off the shelf to play. Okay, 977 00:50:03,920 --> 00:50:08,080 Speaker 1: Jeff to see him put it on, like like you know, 978 00:50:10,760 --> 00:50:15,239 Speaker 1: everyone's like, no, not that song just yet. I can 979 00:50:15,360 --> 00:50:19,320 Speaker 1: only imagine what young Jeffrey bais this is like dating 980 00:50:19,400 --> 00:50:23,759 Speaker 1: profile would look like. I don't think he did. I mean, yeah, 981 00:50:23,760 --> 00:50:26,560 Speaker 1: he's got other things on his mind dating like what 982 00:50:26,640 --> 00:50:30,880 Speaker 1: he would mock up on there. Oh yeah, you just 983 00:50:30,920 --> 00:50:33,560 Speaker 1: look at the top forty and list all of the bands, 984 00:50:35,440 --> 00:50:37,600 Speaker 1: just like I can calculate how long it's going to 985 00:50:37,640 --> 00:50:40,160 Speaker 1: be before your grandma dies, you know what I mean? Yeah, yeah, 986 00:50:41,880 --> 00:50:44,600 Speaker 1: that's his entire Okay, Cupid, I can tell you when 987 00:50:44,640 --> 00:50:49,400 Speaker 1: your grandparents will die. The women will love this, you 988 00:50:49,440 --> 00:50:52,880 Speaker 1: know what. That might do better than you'd expect to 989 00:50:52,920 --> 00:50:59,799 Speaker 1: be fair worse. During his summer vacation in nineteen four, 990 00:51:00,120 --> 00:51:02,239 Speaker 1: he's in college, Jeff goes to visit his mom and 991 00:51:02,320 --> 00:51:05,440 Speaker 1: dad in Norway, where Mike was working again for x On. 992 00:51:06,000 --> 00:51:08,920 Speaker 1: Jeff got a summer job programming for the oil giant, 993 00:51:09,040 --> 00:51:12,800 Speaker 1: mainly coding a computer model to calculate royalties for the company. 994 00:51:13,120 --> 00:51:15,360 Speaker 1: He gets some work for IBM the next year, and 995 00:51:15,360 --> 00:51:17,560 Speaker 1: when he graduates, he manages to get a job with 996 00:51:17,600 --> 00:51:21,839 Speaker 1: a company called Fittell, off the recommendation of his classmate Chichilnitsky, 997 00:51:22,040 --> 00:51:25,520 Speaker 1: who now claims to barely remember him. Fittell provided computer 998 00:51:25,640 --> 00:51:29,200 Speaker 1: solutions for investment banks and brokerages. When Jeff joined. They 999 00:51:29,200 --> 00:51:31,360 Speaker 1: were in the process of building a sort of internet 1000 00:51:31,440 --> 00:51:34,320 Speaker 1: to link computers from a bunch of different financial firms 1001 00:51:34,360 --> 00:51:38,680 Speaker 1: together in order to trade more effectively. And the descendant 1002 00:51:38,800 --> 00:51:40,560 Speaker 1: of this thing that Jeff was working on is how 1003 00:51:40,600 --> 00:51:42,640 Speaker 1: all trading has done today. Right. It's all banks of 1004 00:51:42,680 --> 00:51:46,200 Speaker 1: networked machines talking to each other making split second financial 1005 00:51:46,239 --> 00:51:49,080 Speaker 1: decisions that no human would have the time to make. Uh. 1006 00:51:49,120 --> 00:51:52,160 Speaker 1: And Jeff is not the driving force behind this change, right, 1007 00:51:52,200 --> 00:51:54,640 Speaker 1: He's He's one of the people who's coding the early 1008 00:51:54,760 --> 00:51:56,600 Speaker 1: stages of this change that I think you could argue 1009 00:51:56,640 --> 00:51:59,120 Speaker 1: has been disastrous. But he's not. It's not like this 1010 00:51:59,160 --> 00:52:01,759 Speaker 1: is he doesn't start this trend um, but he is 1011 00:52:01,800 --> 00:52:04,640 Speaker 1: a part of it. Yeah. Um. He was good at 1012 00:52:04,640 --> 00:52:06,359 Speaker 1: the job, and inside of a year he had been 1013 00:52:06,400 --> 00:52:10,400 Speaker 1: promoted to manage a dozen programmers. He bounced around different 1014 00:52:10,440 --> 00:52:14,200 Speaker 1: early financial tech companies for a while, mainly programming different 1015 00:52:14,239 --> 00:52:17,279 Speaker 1: software solutions to allow bankers to communicate with each other. 1016 00:52:17,640 --> 00:52:20,080 Speaker 1: In nine nine, he got to talking with a banking 1017 00:52:20,120 --> 00:52:23,320 Speaker 1: analyst named Halsey, who also wanted to be an entrepreneur. 1018 00:52:23,640 --> 00:52:26,400 Speaker 1: Halsey and Bezos hit on an idea to expand the 1019 00:52:26,440 --> 00:52:30,680 Speaker 1: limited intranets, they've been building into something separate, a network 1020 00:52:30,719 --> 00:52:33,920 Speaker 1: that anyone could use to connect subscribers to news stories 1021 00:52:34,200 --> 00:52:37,759 Speaker 1: curated algorithmically by the person's interests. So they kind of 1022 00:52:37,840 --> 00:52:40,759 Speaker 1: hit upon in the in the eighties this idea of like, 1023 00:52:41,280 --> 00:52:44,919 Speaker 1: we should build a news feed that like reads, pays 1024 00:52:44,960 --> 00:52:47,359 Speaker 1: attention to what people are reading and gives them more 1025 00:52:47,400 --> 00:52:49,840 Speaker 1: of that, which is they never make this, but it 1026 00:52:49,960 --> 00:52:51,960 Speaker 1: is interesting that he like this is like how the 1027 00:52:52,000 --> 00:52:53,960 Speaker 1: whole internet works now, and they kind of get that 1028 00:52:54,040 --> 00:52:57,239 Speaker 1: in the eighties. Like he's clearly understands the promise of 1029 00:52:57,280 --> 00:53:00,640 Speaker 1: a lot of this technology earlier than most people. Yeah, definitely, 1030 00:53:00,840 --> 00:53:03,560 Speaker 1: I think you've definitely heard of his time in some ways, 1031 00:53:03,640 --> 00:53:06,080 Speaker 1: you know what I mean. Yeah, especially from a data 1032 00:53:06,120 --> 00:53:09,160 Speaker 1: perspective line, I think that actually helped him a lot. Yeah, 1033 00:53:09,200 --> 00:53:11,960 Speaker 1: I think it absolutely does. Um And for a while 1034 00:53:12,000 --> 00:53:14,279 Speaker 1: Bezos and Halsey like try to get Mary Lynch to 1035 00:53:14,360 --> 00:53:16,319 Speaker 1: invest in this idea, but Marylynch is like, I don't 1036 00:53:16,360 --> 00:53:18,640 Speaker 1: think there's any financial future in this, and they don't 1037 00:53:18,640 --> 00:53:21,160 Speaker 1: put down the money, which is very funny that like 1038 00:53:21,200 --> 00:53:25,520 Speaker 1: Mary Lynch could have owned basically Facebook in the late eighties. 1039 00:53:26,840 --> 00:53:30,320 Speaker 1: But maybe the Yeah, I mean, obviously it would have 1040 00:53:30,360 --> 00:53:34,439 Speaker 1: been a disaster. Probably it didn't happen. Yeah, Jeff moves 1041 00:53:34,440 --> 00:53:37,480 Speaker 1: on to a company called D E. Shaw. Now D E. 1042 00:53:37,600 --> 00:53:40,279 Speaker 1: Shaw was another investment kind of firm. They manage a 1043 00:53:40,320 --> 00:53:42,800 Speaker 1: hedge fund, and it would be the last place Jeff 1044 00:53:42,840 --> 00:53:46,400 Speaker 1: Bezos ever worked for anyone else. Um. The firm is 1045 00:53:46,480 --> 00:53:48,640 Speaker 1: kind of where he gets finished and turned into the 1046 00:53:48,640 --> 00:53:50,560 Speaker 1: man who would start Amazon. And a big part of 1047 00:53:50,560 --> 00:53:52,960 Speaker 1: that goes to his boss, the founder of the company, 1048 00:53:53,040 --> 00:53:57,719 Speaker 1: David Shaw. David is like, he's a brilliant financial technology guy. 1049 00:53:57,760 --> 00:54:01,360 Speaker 1: He's he's a really innovative thinker within that field. His company, 1050 00:54:01,440 --> 00:54:03,400 Speaker 1: they're kind of they're kind of seen as within this 1051 00:54:03,440 --> 00:54:07,160 Speaker 1: industry being mavericks and being like super creative and ahead 1052 00:54:07,160 --> 00:54:11,000 Speaker 1: of the curve on everything. And Jeff really admires David Shaw, 1053 00:54:11,040 --> 00:54:14,200 Speaker 1: who's analytical. But Jeff like the thing Jeff praises him 1054 00:54:14,200 --> 00:54:17,799 Speaker 1: for is having a perfectly developed left and right brain. Um, 1055 00:54:17,840 --> 00:54:20,080 Speaker 1: and you kind of get the impression he's one of 1056 00:54:20,120 --> 00:54:22,560 Speaker 1: the very few people Jeff ever saw as not just 1057 00:54:22,600 --> 00:54:26,040 Speaker 1: a mentor but like an equal. Um. That doesn't happen 1058 00:54:26,080 --> 00:54:28,640 Speaker 1: a lot with Bezos, but he really admires this guy. 1059 00:54:29,080 --> 00:54:31,319 Speaker 1: So while he's working at D. E. Shaw, Jeff was 1060 00:54:31,360 --> 00:54:34,120 Speaker 1: also working to get himself a girlfriend. And I'm going 1061 00:54:34,160 --> 00:54:36,800 Speaker 1: to read a quote now from the book The Everything Store. 1062 00:54:36,960 --> 00:54:39,680 Speaker 1: There's always one of these for Bezos, for Zuckerberg, for 1063 00:54:39,719 --> 00:54:44,040 Speaker 1: fucking uh um Gates. There's always a weirdo quote about 1064 00:54:44,080 --> 00:54:48,240 Speaker 1: this sort of thing. Bezos thought analytically about everything, including 1065 00:54:48,280 --> 00:54:51,719 Speaker 1: social situations. Single at the time, he started taking ballroom 1066 00:54:51,800 --> 00:54:54,840 Speaker 1: dance classes, calculating that it would increase his exposure to 1067 00:54:54,880 --> 00:54:58,560 Speaker 1: what he called in plus women. He later famously admitted 1068 00:54:58,560 --> 00:55:01,200 Speaker 1: to thinking about how to increase his as women flow, 1069 00:55:01,480 --> 00:55:04,400 Speaker 1: a Wall Street corollary to deal flow the number of 1070 00:55:04,440 --> 00:55:10,200 Speaker 1: new opportunities a banker can access. So it's it's one 1071 00:55:10,239 --> 00:55:12,040 Speaker 1: of those things. It's like if he just said he 1072 00:55:12,080 --> 00:55:14,279 Speaker 1: wanted to meet women, so he learned a ballroom dance, 1073 00:55:14,320 --> 00:55:16,120 Speaker 1: I'd say, well, that's great. That's what you should do. 1074 00:55:16,160 --> 00:55:18,160 Speaker 1: If you want to meet people, go learn a new thing. 1075 00:55:18,280 --> 00:55:20,359 Speaker 1: You expose yourself to more people. The fact that he's 1076 00:55:20,400 --> 00:55:21,840 Speaker 1: thinking about it like, well, I have to increase the 1077 00:55:21,840 --> 00:55:23,839 Speaker 1: flow of women that I communicate with on a daily 1078 00:55:23,880 --> 00:55:26,799 Speaker 1: basis in order, Like that's there's implus coefficient will raise 1079 00:55:26,840 --> 00:55:28,920 Speaker 1: and then I'll have a higher chance of it. Is like, 1080 00:55:29,520 --> 00:55:33,319 Speaker 1: it's not how flow go up? Yeah, women flow up. 1081 00:55:34,680 --> 00:55:37,640 Speaker 1: It's so creepy man. Yeah, it's like everything's a transaction, 1082 00:55:37,800 --> 00:55:41,600 Speaker 1: you know. Yeah, he's a way. Sometimes it is, but 1083 00:55:41,680 --> 00:55:44,960 Speaker 1: that's not a positive way, you know. And I don't 1084 00:55:45,000 --> 00:55:46,880 Speaker 1: think you tend to get the best results if you 1085 00:55:46,920 --> 00:55:50,879 Speaker 1: go out to meet people thinking about it like a transaction. Yeah. Yeah, 1086 00:55:50,960 --> 00:55:53,680 Speaker 1: definitely definitely not worrying about your m flow or whatever 1087 00:55:53,719 --> 00:55:57,680 Speaker 1: it was. Yeah. So, Jeff's colleagues knew him as a 1088 00:55:57,719 --> 00:56:00,000 Speaker 1: hard worker who also felt the need to brag about 1089 00:56:00,040 --> 00:56:02,320 Speaker 1: how hard he worked. He kept a rolled up sleeping 1090 00:56:02,320 --> 00:56:04,920 Speaker 1: bag in his office in ostensibly in case he needed 1091 00:56:04,960 --> 00:56:07,440 Speaker 1: to work overnight, but his colleagues say it was mostly 1092 00:56:07,480 --> 00:56:11,320 Speaker 1: a prop right. Um. The bag and the phone padding 1093 00:56:11,360 --> 00:56:14,959 Speaker 1: for it were always in view, like he I'm sure 1094 00:56:14,960 --> 00:56:17,400 Speaker 1: he does sleep in his office occasionally, but more than anything, 1095 00:56:17,440 --> 00:56:19,720 Speaker 1: it's so that other people know he's willing to sleep 1096 00:56:19,719 --> 00:56:23,680 Speaker 1: in his office, you know. Um yeah. Uh. In nine, 1097 00:56:24,560 --> 00:56:26,919 Speaker 1: the hedge fund that he worked for hired a woman 1098 00:56:27,040 --> 00:56:30,919 Speaker 1: named mckenzy Tuttle. She'd graduated from Princeton, where she'd gotten 1099 00:56:30,920 --> 00:56:33,919 Speaker 1: a degree in English and studied with Tony Morrison. She'd 1100 00:56:33,920 --> 00:56:37,160 Speaker 1: published a novel prior to starting work as an administrative assistant. 1101 00:56:37,360 --> 00:56:40,319 Speaker 1: In short order, she worked directly for Jeff Bezos, who 1102 00:56:40,360 --> 00:56:43,080 Speaker 1: had a huge crush on her. One of his then 1103 00:56:43,080 --> 00:56:46,239 Speaker 1: colleagues remembers a night out when Bezos hired a limousine 1104 00:56:46,280 --> 00:56:49,319 Speaker 1: to take several co workers to a nightclub. Quote, he 1105 00:56:49,400 --> 00:56:52,320 Speaker 1: was treating the whole group, but he was clearly focused 1106 00:56:52,360 --> 00:56:57,000 Speaker 1: on mackenzie. So I mean, that's whatever. And I should 1107 00:56:57,040 --> 00:56:59,560 Speaker 1: note here that, like, because we talked about Bill Gates, 1108 00:56:59,640 --> 00:57:01,880 Speaker 1: kind of it on coworkers in a way that was 1109 00:57:02,040 --> 00:57:05,480 Speaker 1: more unsettling. McKenzie herself claims that Jeff did not hit 1110 00:57:05,560 --> 00:57:08,520 Speaker 1: on her. She disagrees with the version of events put 1111 00:57:08,520 --> 00:57:11,040 Speaker 1: forward by her colleagues and claims that she fell in 1112 00:57:11,080 --> 00:57:13,520 Speaker 1: love with him. Um and and was the person who 1113 00:57:13,520 --> 00:57:16,880 Speaker 1: instigated the relationship. Um and I certainly am not second 1114 00:57:16,920 --> 00:57:19,000 Speaker 1: guessing her on this, Like, I don't know he Wasn't 1115 00:57:19,040 --> 00:57:22,080 Speaker 1: he a creepy She does not. She she doesn't think 1116 00:57:22,080 --> 00:57:26,120 Speaker 1: it's like a creepy office thing. Yeah. She claims that 1117 00:57:26,160 --> 00:57:28,280 Speaker 1: she had an office next to his and she fell 1118 00:57:28,320 --> 00:57:36,680 Speaker 1: in love with his. Laugh, um laugh, It's like Yeah, 1119 00:57:36,720 --> 00:57:40,080 Speaker 1: it's it's bizarre. That's a that's a fascinating thing to say, 1120 00:57:40,640 --> 00:57:42,840 Speaker 1: because I think we should break in to discuss Jeff 1121 00:57:42,880 --> 00:57:44,840 Speaker 1: Bezos's laugh here, and I want to read a quote 1122 00:57:44,840 --> 00:57:49,880 Speaker 1: from Brad Stone, his best biographer, discussing Jeff's laugh. Much 1123 00:57:49,920 --> 00:57:52,400 Speaker 1: has been made over the years of Bezos's famous laugh. 1124 00:57:52,560 --> 00:57:55,680 Speaker 1: It's a startling, pulse pounding bray that he leans into 1125 00:57:55,680 --> 00:57:58,800 Speaker 1: while craning his neck back, closing his eyes, and letting 1126 00:57:58,800 --> 00:58:01,240 Speaker 1: loose with a guttural or that sounds like a cross 1127 00:58:01,280 --> 00:58:04,680 Speaker 1: between a mating elephant, seal and a power tool. Often 1128 00:58:04,680 --> 00:58:07,520 Speaker 1: it comes when nothing is obviously funny to anyone else. 1129 00:58:07,800 --> 00:58:10,040 Speaker 1: In a way, bezos His laugh is a mystery that 1130 00:58:10,080 --> 00:58:13,200 Speaker 1: has never been solved. One doesn't expect someone so intense 1131 00:58:13,240 --> 00:58:15,720 Speaker 1: and focused to have a raucous laugh like that, and 1132 00:58:15,760 --> 00:58:18,560 Speaker 1: no one in his family seems to share it. Employees 1133 00:58:18,640 --> 00:58:21,440 Speaker 1: know the laugh primarily as a heart stabbing sound that 1134 00:58:21,520 --> 00:58:24,920 Speaker 1: slices through conversation and rocks its targets back on their heels. 1135 00:58:25,240 --> 00:58:27,160 Speaker 1: More than a few of his colleagues suggests that on 1136 00:58:27,200 --> 00:58:30,280 Speaker 1: some level this is intentional. That Bezos wields his laugh 1137 00:58:30,400 --> 00:58:33,440 Speaker 1: like a weapon. You can't understand it, says Rick Dalzel, 1138 00:58:33,840 --> 00:58:38,280 Speaker 1: Amazon's former chief information officer. It's disarming and punishing. He's 1139 00:58:38,280 --> 00:58:44,920 Speaker 1: punishing you. Yeah, he's Gargamo's amazing. Yeah, he's fucking garga Bell. Yeah. 1140 00:58:45,080 --> 00:58:49,000 Speaker 1: McKinsey falls over him because if his punishment laugh Jesus, 1141 00:58:49,120 --> 00:58:51,320 Speaker 1: yeah she. I don't know. Some people are into like said, 1142 00:58:51,360 --> 00:58:54,040 Speaker 1: oh mesochistics. Ship. I don't know, but fucking know, that's 1143 00:58:54,080 --> 00:58:56,760 Speaker 1: a weird one if I mean, you know, I don't 1144 00:58:56,760 --> 00:58:58,240 Speaker 1: mean I don't want to be like pick on his carrot. 1145 00:58:59,120 --> 00:59:02,360 Speaker 1: The laugh is like really unsettling. I think, Yeah, we 1146 00:59:02,360 --> 00:59:06,800 Speaker 1: should actually probably in bed audio of that. Yeah, let 1147 00:59:06,840 --> 00:59:16,280 Speaker 1: exact me find it. Okay, here's a compilation of Jeff 1148 00:59:16,320 --> 00:59:20,400 Speaker 1: Bezos laugh. You want to be a chef, maybe you 1149 00:59:20,440 --> 00:59:22,000 Speaker 1: want to be a dancer. I don't know what you 1150 00:59:22,040 --> 00:59:27,640 Speaker 1: want to do is going to like, but you do 1151 00:59:27,760 --> 00:59:30,400 Speaker 1: need to follow your passion. I would love for it 1152 00:59:30,400 --> 00:59:36,560 Speaker 1: to be after I'm dead forty minutes with you. It's 1153 00:59:36,560 --> 00:59:39,880 Speaker 1: already clicking down or to use everyone, So no droning 1154 00:59:39,920 --> 00:59:43,200 Speaker 1: on I see no, no, that's coming up too. But 1155 00:59:43,240 --> 00:59:44,760 Speaker 1: that was actually not supposed to be a bad pun 1156 00:59:45,000 --> 00:59:48,040 Speaker 1: And then disclosure you are an investor in business decidety. 1157 00:59:48,040 --> 00:59:50,840 Speaker 1: I just want to happy, thank you. It is great 1158 00:59:51,240 --> 00:59:55,520 Speaker 1: best midnight snack while your brain stormy. I'm asleep at midnight. 1159 00:59:56,280 --> 00:59:59,959 Speaker 1: That's important. That's really good. Guy like you actually gets 1160 01:00:00,000 --> 01:00:02,520 Speaker 1: it is it's it's weird. It is a little bit 1161 01:00:02,560 --> 01:00:07,120 Speaker 1: of a weird laugh. Look, I don't like it. It's 1162 01:00:07,160 --> 01:00:13,960 Speaker 1: not nothing good. It is like weirdly the same a 1163 01:00:14,000 --> 01:00:16,480 Speaker 1: lot of the time, Like he's practiced it. I don't know, 1164 01:00:16,960 --> 01:00:22,400 Speaker 1: has like villain undertone. He's one of those rich people 1165 01:00:22,440 --> 01:00:25,240 Speaker 1: who runs the world that gives credence to the lizard 1166 01:00:25,240 --> 01:00:28,120 Speaker 1: man conspiracy theories because like you look at you look 1167 01:00:28,120 --> 01:00:29,440 Speaker 1: at and listen to Jeff and you're like, yeah, he 1168 01:00:29,480 --> 01:00:31,640 Speaker 1: could be a lizard, right, Like he could be a lizard. 1169 01:00:31,960 --> 01:00:35,240 Speaker 1: There could be a lizard under that skin, no doubt, 1170 01:00:35,320 --> 01:00:38,720 Speaker 1: no doubt. Yeah, yeah, yeah. But it's like, um, it's 1171 01:00:38,760 --> 01:00:40,680 Speaker 1: just I think it just feels forced. I think that's 1172 01:00:40,720 --> 01:00:42,480 Speaker 1: the thing, and it's so over the top, you know 1173 01:00:42,560 --> 01:00:45,080 Speaker 1: what I mean, When there's no need that, and it 1174 01:00:45,160 --> 01:00:48,120 Speaker 1: feels it feels like such an effort to like not 1175 01:00:49,560 --> 01:00:53,000 Speaker 1: react genuinely. Um, I don't feel like we're hearing whatever 1176 01:00:53,160 --> 01:00:55,240 Speaker 1: is the same thing in the laugh that McKinsey heard, 1177 01:00:55,280 --> 01:00:57,440 Speaker 1: probably because he's changed since then. It had to put 1178 01:00:57,480 --> 01:01:00,440 Speaker 1: on armor. But it is like Jeff could be a man, 1179 01:01:00,440 --> 01:01:03,200 Speaker 1: Elon Musk could only be a human like he's, he's, 1180 01:01:03,280 --> 01:01:07,920 Speaker 1: he's his flaws are too obvious and evident, you know, Um, 1181 01:01:08,040 --> 01:01:13,480 Speaker 1: Jeff Bezos, Yeah, he's, he's, he's very He's polished himself 1182 01:01:13,520 --> 01:01:15,880 Speaker 1: to like a higher extent than pretty much any of 1183 01:01:15,920 --> 01:01:19,560 Speaker 1: these other any of the other people in his position, um, 1184 01:01:19,600 --> 01:01:22,720 Speaker 1: which is probably why he's he's built something that's so 1185 01:01:22,840 --> 01:01:27,439 Speaker 1: much more integral to daily life, and why he's he's 1186 01:01:27,560 --> 01:01:29,280 Speaker 1: been so much more successful than most of them. Is 1187 01:01:29,320 --> 01:01:35,760 Speaker 1: he's he's he's really good at polishing an image. Yeah, hey, everybody, 1188 01:01:35,840 --> 01:01:37,680 Speaker 1: Robert Evans here. This is actually going to be a 1189 01:01:37,680 --> 01:01:40,560 Speaker 1: two parter and wound up running very long, so we 1190 01:01:40,640 --> 01:01:43,600 Speaker 1: decided to split it here. If you want to find 1191 01:01:43,760 --> 01:01:46,920 Speaker 1: Jake Hanrahan on the internet, you can find him at 1192 01:01:46,960 --> 01:01:49,880 Speaker 1: popular front. Um. You can support Popular front you can 1193 01:01:49,880 --> 01:01:52,680 Speaker 1: listen to or watch their great content, and of course 1194 01:01:52,720 --> 01:01:55,320 Speaker 1: you can find his new show Megacorp on the cool 1195 01:01:55,400 --> 01:01:58,880 Speaker 1: Zone Media network. Uh. You can also find this show 1196 01:01:59,040 --> 01:02:01,080 Speaker 1: wherever shows are found own, which is wherever you are 1197 01:02:01,160 --> 01:02:05,280 Speaker 1: right now, because you found it. M mhm