1 00:00:00,080 --> 00:00:02,320 Speaker 1: Guess what, Mango? What's that? Will? Did you know that 2 00:00:02,440 --> 00:00:05,040 Speaker 1: the early days of Amazon dot Com they actually had 3 00:00:05,080 --> 00:00:07,920 Speaker 1: this bell installed and they would ring it every time 4 00:00:07,960 --> 00:00:10,879 Speaker 1: a book was sold. The employees we then rush over 5 00:00:10,880 --> 00:00:12,719 Speaker 1: to see if they knew the name of the customer 6 00:00:12,720 --> 00:00:15,240 Speaker 1: who had made the purchase. I mean, that's crazy. I mean, 7 00:00:15,440 --> 00:00:18,400 Speaker 1: especially considering they now sell more than six items per 8 00:00:18,440 --> 00:00:21,080 Speaker 1: second on their biggest days. I'm guessing they don't ring 9 00:00:21,120 --> 00:00:24,119 Speaker 1: that bell anymore. Probably not, but actually it feels like 10 00:00:24,160 --> 00:00:26,000 Speaker 1: they should do this as a symbol, like maybe get 11 00:00:26,079 --> 00:00:28,639 Speaker 1: some intern that give them a bell and just be like, 12 00:00:28,720 --> 00:00:30,840 Speaker 1: just just do your best every time in order comes in? 13 00:00:31,360 --> 00:00:33,080 Speaker 1: All right, that that might be a little bit mean, 14 00:00:33,440 --> 00:00:35,519 Speaker 1: but it is crazy to think just how much Amazon 15 00:00:35,560 --> 00:00:37,720 Speaker 1: has grown in the past couple of decades, and how 16 00:00:37,720 --> 00:00:40,640 Speaker 1: many industries they're involved in, and and the crazy things 17 00:00:40,640 --> 00:00:42,880 Speaker 1: they may be up to next. So that's what today's 18 00:00:42,880 --> 00:01:03,160 Speaker 1: episode is all about. Let's get started, either podcast listeners, 19 00:01:03,200 --> 00:01:05,400 Speaker 1: Welcome to Part Time Genius. I'm Will Pearson and as 20 00:01:05,400 --> 00:01:07,639 Speaker 1: always I'm joined by my good friend Man guesh Ticketter 21 00:01:08,000 --> 00:01:09,440 Speaker 1: and the man on the other side of the sound 22 00:01:09,480 --> 00:01:12,800 Speaker 1: proof glass. What did he do? He checking his stocks 23 00:01:12,800 --> 00:01:17,440 Speaker 1: in the news anyway, whatever he's doing, that's our friend 24 00:01:17,440 --> 00:01:20,679 Speaker 1: and producer Tristan McNeil. That's that's been so long since 25 00:01:20,680 --> 00:01:23,240 Speaker 1: people check their stocks that way. But all right, well, 26 00:01:23,280 --> 00:01:25,959 Speaker 1: today we're talking about Amazon dot Com, the company that, 27 00:01:26,040 --> 00:01:29,160 Speaker 1: in twenty two short years has gone from this tiny 28 00:01:29,200 --> 00:01:32,960 Speaker 1: garage based startup in Washington to the largest and most 29 00:01:32,959 --> 00:01:36,480 Speaker 1: successful online purveyor of well, I mean, pretty much everything, 30 00:01:37,160 --> 00:01:39,399 Speaker 1: and likely it feels like every week brings this fresh 31 00:01:39,440 --> 00:01:42,640 Speaker 1: announcement from Amazon about some new product or venture that 32 00:01:42,680 --> 00:01:46,080 Speaker 1: the company's launching, or some milestone has reached on its 33 00:01:46,120 --> 00:01:49,880 Speaker 1: journey to total global domination. Right, and with all that 34 00:01:49,920 --> 00:01:51,800 Speaker 1: recent news in mind, we thought it might be interesting 35 00:01:51,840 --> 00:01:54,480 Speaker 1: to take a deep dive into Amazon's history and trying 36 00:01:54,480 --> 00:01:56,400 Speaker 1: to get a sense of how it got so big 37 00:01:56,440 --> 00:01:58,360 Speaker 1: and how much growing it could still do in the 38 00:01:58,440 --> 00:02:00,600 Speaker 1: years ahead. And along the way, of course, we'll get 39 00:02:00,600 --> 00:02:02,800 Speaker 1: a couple of PTG listeners on the line for a quiz. 40 00:02:03,120 --> 00:02:05,040 Speaker 1: All right, So let's start from the beginning on this one, 41 00:02:05,040 --> 00:02:08,399 Speaker 1: which which for Amazon was the your nineteen and that's 42 00:02:08,400 --> 00:02:10,920 Speaker 1: when the founder and CEO of the company, Jeff Bezos, 43 00:02:11,000 --> 00:02:13,720 Speaker 1: decided to quit his job at an investment firm and 44 00:02:13,760 --> 00:02:17,080 Speaker 1: take a gamble by starting his own internet company, which 45 00:02:17,120 --> 00:02:20,880 Speaker 1: is so risky. I mean this is remember the Internet 46 00:02:20,919 --> 00:02:23,360 Speaker 1: was a new phenomena for most people, and buying something 47 00:02:23,360 --> 00:02:26,239 Speaker 1: online was just about the shadiest, riskiest thing you could 48 00:02:26,240 --> 00:02:28,799 Speaker 1: think of. Yeah, and Bezos saw the potential though, and 49 00:02:28,840 --> 00:02:31,760 Speaker 1: when when he noticed the early rumblings of the impending 50 00:02:31,800 --> 00:02:36,000 Speaker 1: Internet revolution while working on Wall Street. And according to him, quote, 51 00:02:36,280 --> 00:02:39,320 Speaker 1: the wake up call was finding this startling statistic that 52 00:02:39,360 --> 00:02:44,519 Speaker 1: web usage in the spring of was growing at a year. 53 00:02:45,000 --> 00:02:48,040 Speaker 1: You know, things just don't grow that fast. It's highly unusual. 54 00:02:48,080 --> 00:02:51,000 Speaker 1: And that started me about thinking what kind of business 55 00:02:51,080 --> 00:02:53,880 Speaker 1: plan would make sense in the context of that growth. Well, 56 00:02:53,919 --> 00:02:56,400 Speaker 1: apparently the kind that involves starting an online bookstore out 57 00:02:56,440 --> 00:02:58,680 Speaker 1: of your garage, because that's what Jeff wound up doing 58 00:02:58,680 --> 00:03:01,320 Speaker 1: when he founded Amazon just a year later. And you know, 59 00:03:01,440 --> 00:03:03,639 Speaker 1: he wasn't sure what to name his company. The front 60 00:03:03,720 --> 00:03:07,240 Speaker 1: runner name I found was actually Cadabra, which is a 61 00:03:07,240 --> 00:03:10,840 Speaker 1: crib from that word abra cadabra, but that idea was 62 00:03:10,880 --> 00:03:13,000 Speaker 1: scrapped when jeffs lawyer pointed out that it sounded a 63 00:03:13,040 --> 00:03:16,200 Speaker 1: little too close to cadaver. That's kind of I could 64 00:03:16,200 --> 00:03:18,440 Speaker 1: see how that might be an issue cadaver. So the 65 00:03:18,520 --> 00:03:22,720 Speaker 1: name went from magician lingo to corpse, then to a river. 66 00:03:22,840 --> 00:03:26,600 Speaker 1: So how exactly did that progression happened? Well, supposedly went 67 00:03:26,639 --> 00:03:28,560 Speaker 1: with the river name for two reasons. The first was 68 00:03:28,600 --> 00:03:30,520 Speaker 1: that the Amazon River is one of the largest and 69 00:03:30,600 --> 00:03:32,959 Speaker 1: longest in the world, so the name has this built 70 00:03:33,000 --> 00:03:36,400 Speaker 1: in connotation of scale. Uh and and the second reason 71 00:03:36,520 --> 00:03:39,800 Speaker 1: was a bit more practical, like website listings for businesses 72 00:03:39,800 --> 00:03:42,600 Speaker 1: were usually alphabetical back then, and Jeff wanted a place 73 00:03:42,640 --> 00:03:44,320 Speaker 1: at the top of the list. So I love the 74 00:03:44,360 --> 00:03:47,320 Speaker 1: idea of the alphabetical listings like that. That that happened 75 00:03:47,360 --> 00:03:50,400 Speaker 1: at some point. Yeah, these have those directories, remember directory 76 00:03:50,400 --> 00:03:53,520 Speaker 1: of websites. But but by the way, another name Bezos 77 00:03:53,560 --> 00:03:57,240 Speaker 1: kicked around was Relentless, and while he eventually decided against it, 78 00:03:57,280 --> 00:03:59,480 Speaker 1: if you type relentless dot com and your search bar, 79 00:03:59,800 --> 00:04:04,160 Speaker 1: you actually get taken to Amazon. Cool. Yeah, all right, Well, 80 00:04:04,160 --> 00:04:08,000 Speaker 1: well back to the company's founding. In the whole venture 81 00:04:08,160 --> 00:04:10,920 Speaker 1: was Jeff's idea, but he definitely didn't get it off 82 00:04:10,920 --> 00:04:13,760 Speaker 1: the ground by himself, and the initial capital for the 83 00:04:13,800 --> 00:04:17,240 Speaker 1: site actually came from his parents and admittedly, they didn't 84 00:04:17,240 --> 00:04:20,280 Speaker 1: really understand what their money was going toward. In fact, 85 00:04:20,279 --> 00:04:23,000 Speaker 1: the very first question Jeff's dad asked after hearing the 86 00:04:23,040 --> 00:04:26,600 Speaker 1: pitch for this web based bookstore was what's the internet? 87 00:04:27,720 --> 00:04:30,159 Speaker 1: You know. Nonetheless, jeff parents ponied up a large portion 88 00:04:30,200 --> 00:04:32,400 Speaker 1: of their life savings. I mean, this was hundreds of 89 00:04:32,440 --> 00:04:35,680 Speaker 1: thousands of dollars all for their son's crazy dream of 90 00:04:35,720 --> 00:04:39,640 Speaker 1: opening this invisible bookstores. They saw it. Yeah, that's so serious, 91 00:04:39,720 --> 00:04:43,640 Speaker 1: unconditional love. But thankfully, Mom Potizos have done pretty well 92 00:04:43,640 --> 00:04:46,320 Speaker 1: on their investment. Uh. And that's to put it mildly. 93 00:04:47,080 --> 00:04:48,799 Speaker 1: I mean the site was a success from the start, 94 00:04:48,880 --> 00:04:51,960 Speaker 1: and that was with zero press promotion. I mean Amazon 95 00:04:52,040 --> 00:04:54,800 Speaker 1: sold books to customers in all fifty states and forty 96 00:04:54,839 --> 00:04:58,240 Speaker 1: five different countries within its first month of operation. It's 97 00:04:58,279 --> 00:05:00,840 Speaker 1: pretty incredible. Yeah, but it's second on. The site was 98 00:05:00,880 --> 00:05:04,200 Speaker 1: making more than twenty dollars in sales per week, and 99 00:05:04,240 --> 00:05:06,120 Speaker 1: that was way more than Jeff and his tiny startup 100 00:05:06,120 --> 00:05:08,719 Speaker 1: team ever anticipated. Yeah. You know, I still laugh thinking 101 00:05:08,720 --> 00:05:11,039 Speaker 1: about them ringing a bell with every sale in those 102 00:05:11,080 --> 00:05:13,760 Speaker 1: early days. Yeah, and especially thinking about that stat we 103 00:05:13,839 --> 00:05:17,920 Speaker 1: discussed earlier where peak times Amazon's annual prime day. For instance, 104 00:05:18,120 --> 00:05:23,920 Speaker 1: the company sells over six items per second. Never stopped ringing. Yeah, 105 00:05:23,920 --> 00:05:26,280 Speaker 1: I would be a total nightmare. Speaking of which, did 106 00:05:26,279 --> 00:05:29,239 Speaker 1: you hear about that early Amazon software bug? It basically 107 00:05:29,279 --> 00:05:31,880 Speaker 1: handed out free money to customers? Oh I did, I'm 108 00:05:31,880 --> 00:05:34,240 Speaker 1: sorry I missed out on this. What happened a fairly 109 00:05:34,240 --> 00:05:36,480 Speaker 1: There was a glitch in the program that allowed users 110 00:05:36,520 --> 00:05:39,159 Speaker 1: to order a negative quality of books, and the site 111 00:05:39,160 --> 00:05:40,960 Speaker 1: would end up crediting the cost of the books to 112 00:05:41,040 --> 00:05:43,800 Speaker 1: the customer's credit card. I mean, Amazon's team caught onto 113 00:05:43,800 --> 00:05:46,960 Speaker 1: it pretty quickly, but but it's still kind of amazing. Yeah, 114 00:05:47,000 --> 00:05:49,200 Speaker 1: you know. And Amazon has always been good about identifying 115 00:05:49,240 --> 00:05:53,200 Speaker 1: these hiccups in its service and quickly getting solutions in place. 116 00:05:53,279 --> 00:05:55,560 Speaker 1: And one great example of this is the decision to 117 00:05:55,640 --> 00:05:58,359 Speaker 1: branch out from book sales just three years after the 118 00:05:58,360 --> 00:06:01,000 Speaker 1: company's launch. And don't get me wrong, I mean, books 119 00:06:01,000 --> 00:06:03,679 Speaker 1: were a smart choice to start out with because they're sturdy, 120 00:06:03,839 --> 00:06:06,440 Speaker 1: they're fairly uniform in shape, and all of that makes 121 00:06:06,480 --> 00:06:09,520 Speaker 1: them easy to store and ship and bulk. But focusing 122 00:06:09,520 --> 00:06:11,720 Speaker 1: on just one product, you know, no matter how popular 123 00:06:11,920 --> 00:06:14,440 Speaker 1: or convenient it is, it puts a cap on the 124 00:06:14,440 --> 00:06:17,080 Speaker 1: company's growth potential. And it left a lot of money 125 00:06:17,160 --> 00:06:19,040 Speaker 1: on the table, as they saw it, and and that's 126 00:06:19,120 --> 00:06:22,000 Speaker 1: ultimately why Amazon started selling other forms of media. They 127 00:06:22,160 --> 00:06:26,360 Speaker 1: moved into CDs and DVDs, and they then moved into 128 00:06:26,440 --> 00:06:29,719 Speaker 1: things like clothing and toys and electronics, and since then 129 00:06:29,800 --> 00:06:33,400 Speaker 1: it's just about anything else. I Mean, what's baffling to 130 00:06:33,440 --> 00:06:35,880 Speaker 1: me is that Amazon's like one of the few expanding 131 00:06:35,880 --> 00:06:38,520 Speaker 1: e commerce sites that actually managed to survive that whole 132 00:06:38,640 --> 00:06:40,560 Speaker 1: dot bomb bust, you know that are the one in 133 00:06:40,560 --> 00:06:43,240 Speaker 1: the late nineties and early two thousands, and and I 134 00:06:43,240 --> 00:06:45,520 Speaker 1: don't know if you remember, but like, there are so many, 135 00:06:45,680 --> 00:06:48,200 Speaker 1: I think there are hundreds of online retail startups. I mean, 136 00:06:48,480 --> 00:06:51,400 Speaker 1: we remember things like pets dot com and Cosmo dot com. 137 00:06:51,480 --> 00:06:54,480 Speaker 1: But there was just so much hype. Yeah, and there's 138 00:06:54,480 --> 00:06:57,120 Speaker 1: been all this excitement about the burgeoning online market, and 139 00:06:57,440 --> 00:07:00,359 Speaker 1: investors were quick to back these fledgling companies that really 140 00:07:00,360 --> 00:07:03,839 Speaker 1: had no long term plan or business model really and 141 00:07:03,839 --> 00:07:06,479 Speaker 1: and then once the huge losses started mounting, they all 142 00:07:06,480 --> 00:07:09,040 Speaker 1: that funding dried up and the dot com bubble burst, 143 00:07:09,120 --> 00:07:11,400 Speaker 1: of course, So I'm curious how did Amazon manage to 144 00:07:11,400 --> 00:07:14,240 Speaker 1: come through all of that unscathed. Well, for one thing, 145 00:07:14,320 --> 00:07:15,960 Speaker 1: and this was something I really didn't know, but just 146 00:07:16,040 --> 00:07:19,080 Speaker 1: looking into this that Amazon was a lot more frugal 147 00:07:19,120 --> 00:07:21,400 Speaker 1: than some of the cash happy startups that were spending 148 00:07:21,720 --> 00:07:24,360 Speaker 1: so much money on things like lavish office space and 149 00:07:24,400 --> 00:07:27,160 Speaker 1: all these wild parties that you read about. And you know, 150 00:07:27,200 --> 00:07:30,280 Speaker 1: for example, Amazon's offices used to have these makeshift what 151 00:07:30,320 --> 00:07:33,360 Speaker 1: they called door desks for workers to use. According to 152 00:07:33,400 --> 00:07:37,040 Speaker 1: former employee Greg Lindon, the company would quote buy a 153 00:07:37,040 --> 00:07:40,000 Speaker 1: wooden door, preferably a hollow core wooden door with no 154 00:07:40,120 --> 00:07:42,960 Speaker 1: whole free drilled saw, a couple of four by four 155 00:07:43,040 --> 00:07:45,680 Speaker 1: by six pillars in half, bolt them to the door 156 00:07:45,760 --> 00:07:48,720 Speaker 1: with a couple of scary looking angle brackets, put them 157 00:07:48,720 --> 00:07:53,040 Speaker 1: in front of a programmer, and doordesk. That's what it is. 158 00:07:53,080 --> 00:07:55,480 Speaker 1: But I mean, there's no way Amazon survived just by 159 00:07:55,480 --> 00:07:58,120 Speaker 1: pitching pennies on a few door dusks. No, I mean, 160 00:07:58,160 --> 00:08:00,480 Speaker 1: but it's not like Amazon was completely she folded from 161 00:08:00,520 --> 00:08:02,720 Speaker 1: the dot com crisis either. The at one point in 162 00:08:02,760 --> 00:08:05,760 Speaker 1: two thousand one, the company's stock plummeted from a high 163 00:08:05,760 --> 00:08:07,920 Speaker 1: of about a hundred dollars per share to a low 164 00:08:08,000 --> 00:08:11,200 Speaker 1: of just six bucks. You know, Bezos was said to 165 00:08:11,200 --> 00:08:13,200 Speaker 1: have taken all of this in stride, though, I mean, 166 00:08:13,200 --> 00:08:15,600 Speaker 1: he said he preferred to focus on the ever increasing 167 00:08:15,680 --> 00:08:18,640 Speaker 1: cash flow that Amazon had to play with. He felt 168 00:08:18,640 --> 00:08:21,679 Speaker 1: the long term opportunities afforded by this capital would secure 169 00:08:21,720 --> 00:08:25,080 Speaker 1: the company's future, even if that meant profits would stay 170 00:08:25,080 --> 00:08:28,400 Speaker 1: at or you know, even below zero for the time being. Yeah. 171 00:08:28,480 --> 00:08:31,080 Speaker 1: Doing the research for this episode really highlighted how much 172 00:08:31,120 --> 00:08:34,880 Speaker 1: more Amazon values revenue over net income. But since most 173 00:08:34,920 --> 00:08:37,280 Speaker 1: of us tend to use those terms interchangeably, I just 174 00:08:37,320 --> 00:08:39,439 Speaker 1: want to give a quick note about the distinction between 175 00:08:39,480 --> 00:08:42,480 Speaker 1: the two. So, in a financial context, revenue is the 176 00:08:42,480 --> 00:08:45,800 Speaker 1: total amount of money generated through everything the company sells, right, 177 00:08:46,240 --> 00:08:49,800 Speaker 1: and net income refers to just the company's profits. So 178 00:08:50,200 --> 00:08:52,520 Speaker 1: that's the amount of money that it stays after you 179 00:08:52,600 --> 00:08:55,320 Speaker 1: take away you know, the company's operating costs and and 180 00:08:55,360 --> 00:08:58,760 Speaker 1: all their other expenses. Yeah. So for two thousand and sixteen, 181 00:08:58,840 --> 00:09:01,560 Speaker 1: just looking at the number here, Amazon earned about a 182 00:09:01,640 --> 00:09:04,920 Speaker 1: hundred and thirty six billion in revenue, but it only 183 00:09:04,960 --> 00:09:09,160 Speaker 1: reported two point three billion as actual profit only only time. 184 00:09:10,120 --> 00:09:12,200 Speaker 1: And how depressing would that be if our business only 185 00:09:12,240 --> 00:09:15,800 Speaker 1: generated two point three billion and actual profit. But you know, 186 00:09:15,840 --> 00:09:18,680 Speaker 1: sometimes you'll see reports that Amazon never turns a profit 187 00:09:18,760 --> 00:09:22,120 Speaker 1: despite being this huge sales juggernaut, but that's not really 188 00:09:22,120 --> 00:09:25,160 Speaker 1: the case at this point. Yeah. I mean, it's true 189 00:09:25,160 --> 00:09:27,760 Speaker 1: that the company didn't report a profit until six years 190 00:09:27,760 --> 00:09:32,040 Speaker 1: after it launched, and even today Amazon fluctuates between losses 191 00:09:32,120 --> 00:09:35,200 Speaker 1: and gains and breaking even all on a quarterly basis. 192 00:09:35,480 --> 00:09:37,800 Speaker 1: But the thing to remember is that those sometimes less 193 00:09:37,800 --> 00:09:41,080 Speaker 1: in stellar profit margins are all part of visas plan. 194 00:09:41,600 --> 00:09:43,920 Speaker 1: Like Amazon makes a ton of money, it's just that 195 00:09:43,960 --> 00:09:46,520 Speaker 1: most of that money is immediately reinvested in the company's 196 00:09:46,520 --> 00:09:49,839 Speaker 1: growth and diversification, rather than being divvied up amongst its 197 00:09:49,840 --> 00:09:53,520 Speaker 1: employees and shareholders or set aside for a rainy day. Well, 198 00:09:53,520 --> 00:09:55,640 Speaker 1: and that's how we ended up with things like Prime 199 00:09:55,720 --> 00:09:59,439 Speaker 1: Shipping and the Kindle Alexa and all these other services 200 00:09:59,440 --> 00:10:01,680 Speaker 1: and gadgets that the company has developed and rolled out 201 00:10:01,679 --> 00:10:04,800 Speaker 1: over the years in lieu of banking these sky high profits. 202 00:10:05,280 --> 00:10:08,040 Speaker 1: So Bezos convinced the company to set aside short term 203 00:10:08,040 --> 00:10:11,720 Speaker 1: income really more in favor of long term expansion, And 204 00:10:11,800 --> 00:10:14,120 Speaker 1: just like the early move beyond books, the decision has 205 00:10:14,160 --> 00:10:17,280 Speaker 1: definitely paid off in a huge way. Yeah, and they're 206 00:10:17,320 --> 00:10:19,480 Speaker 1: all kinds of examples of that payoff. But I think 207 00:10:19,480 --> 00:10:23,120 Speaker 1: Amazon's Web services division, the a WS, is probably the 208 00:10:23,120 --> 00:10:26,319 Speaker 1: best one. That's the cloud computing business that Amazon started 209 00:10:26,320 --> 00:10:28,800 Speaker 1: in two thousand two, right after the dot com crash, 210 00:10:28,920 --> 00:10:33,120 Speaker 1: and it's really prescient these days. A WS is actually 211 00:10:33,120 --> 00:10:35,960 Speaker 1: the most profitable part of the company. And if you 212 00:10:35,960 --> 00:10:38,440 Speaker 1: look at it, like last year when Amazon reported the 213 00:10:38,440 --> 00:10:41,040 Speaker 1: two point three billion dollars in income that you mentioned 214 00:10:41,280 --> 00:10:44,360 Speaker 1: the profits from their web services that represented more than 215 00:10:44,440 --> 00:10:47,160 Speaker 1: half of that take. Yeah, so so really the company's 216 00:10:47,200 --> 00:10:49,440 Speaker 1: biggest money maker has nothing to do with selling or 217 00:10:49,440 --> 00:10:52,280 Speaker 1: shipping physical products at all, which is just amazing to 218 00:10:52,280 --> 00:10:54,760 Speaker 1: think of more than half of the company's profits coming 219 00:10:54,840 --> 00:10:57,320 Speaker 1: from a part of the company that most people really 220 00:10:57,360 --> 00:11:00,400 Speaker 1: don't even know exists or understanding exactly what it is. 221 00:11:00,480 --> 00:11:02,400 Speaker 1: And so for our listeners who might be going a 222 00:11:02,400 --> 00:11:04,840 Speaker 1: little cross side at the mention of web services and 223 00:11:04,960 --> 00:11:08,479 Speaker 1: cloud computing, you know, here's a little bit of an explanation. 224 00:11:08,520 --> 00:11:11,280 Speaker 1: So to just think about these companies that maybe startups 225 00:11:11,360 --> 00:11:14,120 Speaker 1: or even bigger companies, and they know that as they grow, 226 00:11:14,520 --> 00:11:16,600 Speaker 1: they're going to need new servers, they're going to need 227 00:11:16,679 --> 00:11:19,640 Speaker 1: to equipment all to be able to manage the you know, 228 00:11:19,679 --> 00:11:22,960 Speaker 1: a bigger online business. But it's hard to predict exactly 229 00:11:23,000 --> 00:11:24,959 Speaker 1: how quickly you're going to grow, and if you were 230 00:11:24,960 --> 00:11:27,720 Speaker 1: having to buy these servers in every piece of equipment 231 00:11:27,720 --> 00:11:31,280 Speaker 1: to predict that each time, it can get extraordinarily expensive. 232 00:11:31,280 --> 00:11:34,160 Speaker 1: And so to be able to just use Amazon Services 233 00:11:34,200 --> 00:11:37,040 Speaker 1: and Amazon servers saves them a ton of money and 234 00:11:37,080 --> 00:11:39,439 Speaker 1: allows them to grow at exactly the rate they need 235 00:11:39,480 --> 00:11:42,600 Speaker 1: to grow. Yeah, but what's interesting that isn't just like 236 00:11:42,679 --> 00:11:45,640 Speaker 1: small companies and startups that are taking advantage of this arrangement, 237 00:11:46,000 --> 00:11:48,840 Speaker 1: like big companies actually use it too. And if you 238 00:11:48,840 --> 00:11:52,920 Speaker 1: look at the client list, it's like Netflix, Instagram, Spotify, Airbnb. 239 00:11:53,360 --> 00:11:55,920 Speaker 1: They all rely on a w S servers for their power, 240 00:11:56,240 --> 00:11:59,240 Speaker 1: and even the government's partnered with a WS like did 241 00:11:59,280 --> 00:12:01,280 Speaker 1: you know the cia A uses them? I mean they 242 00:12:01,280 --> 00:12:03,720 Speaker 1: owe much of its computing power to a customize cloud 243 00:12:03,800 --> 00:12:06,800 Speaker 1: on the AWS servers that most of the division's one 244 00:12:06,840 --> 00:12:10,560 Speaker 1: million plus customers represent for profit businesses, but there are 245 00:12:10,600 --> 00:12:14,480 Speaker 1: also about eighteen thousand nonprofits, five thousand schools, and close 246 00:12:14,520 --> 00:12:17,640 Speaker 1: to two thousand government agencies to wow, and you know, 247 00:12:17,760 --> 00:12:20,640 Speaker 1: all that attention and profitability has caught the attention of 248 00:12:20,679 --> 00:12:22,960 Speaker 1: these other mammoth companies who now want in on this 249 00:12:23,040 --> 00:12:26,840 Speaker 1: cloud computing industry. You've got place like IBM and Microsoft 250 00:12:26,880 --> 00:12:30,240 Speaker 1: and Google. They've all started investing tons of money into 251 00:12:30,240 --> 00:12:33,800 Speaker 1: growing their competing services. Of course, a WS has poised 252 00:12:33,800 --> 00:12:37,280 Speaker 1: to make a record setting sixteen billion dollars in revenue 253 00:12:37,320 --> 00:12:39,560 Speaker 1: this year. The other guys have a long way to 254 00:12:39,600 --> 00:12:41,560 Speaker 1: go if they have any chance at trying to catch 255 00:12:41,640 --> 00:12:44,160 Speaker 1: up with Amazon, which is exactly how Amazon likes that, 256 00:12:44,200 --> 00:12:46,040 Speaker 1: I'm sure. I mean, they've made a habit of getting 257 00:12:46,040 --> 00:12:47,600 Speaker 1: out in front of the competition when it comes to 258 00:12:47,600 --> 00:12:50,720 Speaker 1: emerging tech and infrastructure, and that's to the chagrin of 259 00:12:50,800 --> 00:12:54,560 Speaker 1: all these rival companies and traditional brick and mortar stores. Yeah, 260 00:12:54,559 --> 00:12:56,320 Speaker 1: why don't we talk a little bit about some of 261 00:12:56,320 --> 00:12:58,839 Speaker 1: the other innovations that have given Amazon a leg up 262 00:12:58,880 --> 00:13:01,520 Speaker 1: over the years, and including a few on the horizon 263 00:13:01,559 --> 00:13:06,560 Speaker 1: that do raise some serious questions about things like consumer privacy. Yeah, definitely, 264 00:13:06,559 --> 00:13:08,000 Speaker 1: But why don't we get a listener on the line 265 00:13:08,000 --> 00:13:17,319 Speaker 1: for a quiz first? All right, so Ango, we've got 266 00:13:17,320 --> 00:13:19,960 Speaker 1: a brilliant listener on the line right now, and I 267 00:13:20,000 --> 00:13:22,520 Speaker 1: know we're going to be doing today's quiz about Amazon 268 00:13:22,640 --> 00:13:25,559 Speaker 1: because of the episode today. But the reason we've actually 269 00:13:25,559 --> 00:13:27,959 Speaker 1: got today's guest on the phone is because he called 270 00:13:27,960 --> 00:13:31,320 Speaker 1: into our Fact hotline with a terrific story related to 271 00:13:31,400 --> 00:13:35,520 Speaker 1: our Sugary Serials episode. So, Jeffrey Nichols, welcome to Part 272 00:13:35,520 --> 00:13:39,320 Speaker 1: Time Genius. Well, thank you for having me. All right, So, Jeffrey, 273 00:13:39,360 --> 00:13:41,440 Speaker 1: we we really need you to share with our listeners 274 00:13:41,440 --> 00:13:44,640 Speaker 1: what you told us and your voicemail. So it turns 275 00:13:44,640 --> 00:13:47,840 Speaker 1: out that you actually won a grand prize from a 276 00:13:47,920 --> 00:13:51,000 Speaker 1: box of wheaties almost thirty years ago. Is this right? 277 00:13:52,360 --> 00:13:56,920 Speaker 1: That that is correct? I had forgotten about it until 278 00:13:57,679 --> 00:14:00,520 Speaker 1: you got to the end of the Sugary Cerial episode 279 00:14:00,520 --> 00:14:03,000 Speaker 1: when you were discussing the prizes and how they had 280 00:14:03,040 --> 00:14:06,320 Speaker 1: to start putting. They couldn't put them in the actual 281 00:14:06,360 --> 00:14:08,720 Speaker 1: bag of cereal, and it was in the bag between 282 00:14:08,760 --> 00:14:10,920 Speaker 1: that and the box. And then I stopped in my 283 00:14:11,000 --> 00:14:14,280 Speaker 1: tracks while I was walking my dog and realized I 284 00:14:14,440 --> 00:14:17,720 Speaker 1: won one of those. And so, needless to say, I 285 00:14:17,760 --> 00:14:21,200 Speaker 1: had the Facebook you guys instantly and then lo and 286 00:14:21,240 --> 00:14:24,720 Speaker 1: behold you guys enjoyed by doing that and had me 287 00:14:24,800 --> 00:14:27,680 Speaker 1: leave the message on the fact. Yeah, but you got 288 00:14:27,800 --> 00:14:30,920 Speaker 1: you gotta tell everyone what you want because it's crazy. Um, 289 00:14:31,000 --> 00:14:34,440 Speaker 1: So there was there was a box of Walter Payton Wheatie's, 290 00:14:34,560 --> 00:14:42,040 Speaker 1: which I imagine was probably procured in the fall, because 291 00:14:42,200 --> 00:14:45,680 Speaker 1: that's that's when they're playing football, and Walter Payton was 292 00:14:45,720 --> 00:14:52,040 Speaker 1: still a demigogue at that time. And I months and 293 00:14:52,120 --> 00:14:55,360 Speaker 1: months later, I the box of cereal was still there. 294 00:14:55,680 --> 00:14:59,280 Speaker 1: I poured it in in this plastic envelope, her plunked 295 00:14:59,320 --> 00:15:03,000 Speaker 1: in the bowl and you know, we've all had that 296 00:15:03,040 --> 00:15:06,480 Speaker 1: happened a million times. But for whatever reason, I opened 297 00:15:06,520 --> 00:15:11,800 Speaker 1: it up and it said you won, and I uh. 298 00:15:11,880 --> 00:15:14,120 Speaker 1: I was still living at home because I was a teenager, 299 00:15:14,200 --> 00:15:16,240 Speaker 1: and I and I yelled up to my mom and 300 00:15:16,600 --> 00:15:19,560 Speaker 1: you're never gonna believe this, but we won the grand prize, 301 00:15:19,560 --> 00:15:23,720 Speaker 1: which turned out to be a thousand dollars cash and 302 00:15:23,840 --> 00:15:27,760 Speaker 1: a week in Aspen slash Snowmac with the Phil and 303 00:15:27,960 --> 00:15:32,360 Speaker 1: Steve Mayer, who were the greatest Olympic American skiers of 304 00:15:32,400 --> 00:15:37,280 Speaker 1: all time at that point. And so it just it 305 00:15:37,400 --> 00:15:40,720 Speaker 1: was happening. It was falling at the end of what 306 00:15:40,960 --> 00:15:43,960 Speaker 1: was my spring break of I guess would have been 307 00:15:44,000 --> 00:15:47,000 Speaker 1: the spring of eighty nine, my senior year, And so 308 00:15:47,080 --> 00:15:52,080 Speaker 1: my mom flew and I flew up to Colorado and 309 00:15:52,160 --> 00:15:57,680 Speaker 1: the most bizarre aspect of this whole uh contest was 310 00:15:57,760 --> 00:16:01,480 Speaker 1: that I was the only person who won the contest 311 00:16:01,640 --> 00:16:05,440 Speaker 1: by accident. And what do you mean? Well, I didn't 312 00:16:05,440 --> 00:16:08,640 Speaker 1: realize that, because why would I I was only teen 313 00:16:08,720 --> 00:16:11,720 Speaker 1: and a half years old. But there's a whole subculture 314 00:16:11,720 --> 00:16:16,760 Speaker 1: in America apparently who everything that they eat and consume 315 00:16:17,160 --> 00:16:24,640 Speaker 1: and where and do is because of no purchase necessary. Um. 316 00:16:24,680 --> 00:16:28,880 Speaker 1: They have these big ledgers filled um from front to 317 00:16:28,920 --> 00:16:32,520 Speaker 1: back and top to bottom with every contest that they 318 00:16:32,560 --> 00:16:37,120 Speaker 1: are currently uh entered in, and and that's how they live. 319 00:16:37,360 --> 00:16:40,240 Speaker 1: It was the bizarre I mean, of all of the winners, 320 00:16:40,280 --> 00:16:43,480 Speaker 1: they were all looking at each other's ledgers to see 321 00:16:43,480 --> 00:16:48,080 Speaker 1: where they were in proximity to everybody else's occupations. But 322 00:16:48,320 --> 00:16:51,480 Speaker 1: so I was the only person who skied, and so 323 00:16:51,640 --> 00:16:54,600 Speaker 1: Phil Mere and I just skied all day every day 324 00:16:54,640 --> 00:16:57,160 Speaker 1: and ask them because all of the other Grand Prize 325 00:16:57,200 --> 00:17:00,840 Speaker 1: players they were all back in their hotel room working. 326 00:17:01,640 --> 00:17:03,400 Speaker 1: I do love that you actually one of the Grand Prize. 327 00:17:03,400 --> 00:17:06,680 Speaker 1: That sort of restores some faith in these serial prizes 328 00:17:06,680 --> 00:17:08,639 Speaker 1: for me, because you know, you always see these massive 329 00:17:08,640 --> 00:17:10,720 Speaker 1: prizes and you don't think anyone's actually winning them. So 330 00:17:10,760 --> 00:17:14,040 Speaker 1: I'm glad to know you want it. Yeah, it was, 331 00:17:14,240 --> 00:17:16,879 Speaker 1: I mean it was. It was very cool and in 332 00:17:17,080 --> 00:17:18,800 Speaker 1: just so look, I mean it was as cool to 333 00:17:18,920 --> 00:17:21,200 Speaker 1: have one as it was to find out that there 334 00:17:21,359 --> 00:17:24,879 Speaker 1: is a subculture. Um he was to say it was funny. 335 00:17:24,880 --> 00:17:27,959 Speaker 1: When the topic of the thousand dollars cash came up, 336 00:17:28,119 --> 00:17:32,040 Speaker 1: my mother quickly reminded me as to who bought the cereal, 337 00:17:32,600 --> 00:17:37,280 Speaker 1: So that's who was also pocketing the thousand dollars cads. Well, 338 00:17:37,320 --> 00:17:39,280 Speaker 1: that is a great story and actually gives us an 339 00:17:39,320 --> 00:17:42,679 Speaker 1: idea for a future episode on that subculture. But for 340 00:17:42,760 --> 00:17:46,560 Speaker 1: today's episode, since we're focused on Amazon, we're going to 341 00:17:46,640 --> 00:17:49,199 Speaker 1: have a related quiz. Now, angle what what quiz are 342 00:17:49,200 --> 00:17:51,680 Speaker 1: we having Jeffrey play today? So we're playing a game 343 00:17:51,720 --> 00:17:54,560 Speaker 1: called one star movie Reviews and this all comes from 344 00:17:54,600 --> 00:17:58,160 Speaker 1: the great Twitter account Amazon movie Reviews, which is which 345 00:17:58,200 --> 00:18:00,720 Speaker 1: is hilarious. And so there's actually a review on there 346 00:18:00,760 --> 00:18:03,080 Speaker 1: for Tooth Fairy Too that I was looking at starring 347 00:18:03,160 --> 00:18:06,560 Speaker 1: Larry the Cable Guy, I think, and so the review says, 348 00:18:07,119 --> 00:18:10,480 Speaker 1: I have kids, they wanted to watch it. Sometimes I 349 00:18:10,520 --> 00:18:16,480 Speaker 1: wish I didn't have kids. One star my favorite on 350 00:18:16,520 --> 00:18:18,600 Speaker 1: there's actually this five star review on the site. It's 351 00:18:18,640 --> 00:18:22,200 Speaker 1: for boss Baby, and it just reads, quote, this movie 352 00:18:22,200 --> 00:18:26,160 Speaker 1: saved my marriage five stars. That's pretty incredible. All right, So, Jeffrey, 353 00:18:26,160 --> 00:18:28,880 Speaker 1: we're going to read you some reviews from the site there, 354 00:18:28,920 --> 00:18:30,560 Speaker 1: and all you have to do is tell us what 355 00:18:30,680 --> 00:18:35,119 Speaker 1: movie they're referring to you. Ready to play? Alright? Review 356 00:18:35,200 --> 00:18:39,240 Speaker 1: number one, there were no wolves in this movie? One star. 357 00:18:39,960 --> 00:18:43,760 Speaker 1: So what two thousand thirteen movie featuring Leonardo DiCaprio are 358 00:18:43,800 --> 00:18:47,879 Speaker 1: we talking about. I would have to say The Wolf 359 00:18:47,920 --> 00:18:50,800 Speaker 1: of Wall Street. Yeah, that's right. So they're actually a 360 00:18:50,880 --> 00:18:53,919 Speaker 1: similar one star reviews for The Accountant, which says not 361 00:18:54,040 --> 00:18:57,639 Speaker 1: enough accounting in this movie, and Cars, which simply says, 362 00:18:58,000 --> 00:19:02,320 Speaker 1: I see enough cars in real life. All right, all right, 363 00:19:02,400 --> 00:19:04,639 Speaker 1: you're one for one, Jeffrey, here we go. Number two. 364 00:19:05,320 --> 00:19:09,080 Speaker 1: What Nickelodeon cartoon about an intrepid Mexican girl who loves 365 00:19:09,119 --> 00:19:12,840 Speaker 1: to travel got the following review? Hate it? Who lets 366 00:19:12,880 --> 00:19:15,080 Speaker 1: their three year old go on adventures with just a 367 00:19:15,080 --> 00:19:21,560 Speaker 1: monkey for supervision? I would have to say Dora the Explorer. 368 00:19:22,119 --> 00:19:25,840 Speaker 1: Absolutely your two for two? All right too for two? 369 00:19:25,920 --> 00:19:30,159 Speaker 1: Question number three, This classic Judy Garland movie features some 370 00:19:30,240 --> 00:19:33,399 Speaker 1: monkey talk as well. There's a one star review that reads, 371 00:19:33,480 --> 00:19:37,560 Speaker 1: flying monkeys, that's too far fetched. I would certainly hope 372 00:19:37,600 --> 00:19:42,800 Speaker 1: they were referring to the Yeah, of course, and I 373 00:19:43,160 --> 00:19:45,359 Speaker 1: love that, like evil witches and a tin man and 374 00:19:45,400 --> 00:19:47,879 Speaker 1: a talking line are all within the realm of possibility. 375 00:19:47,960 --> 00:19:51,720 Speaker 1: But somehow flying monkeys are a step too far. Alright. 376 00:19:51,800 --> 00:19:54,439 Speaker 1: Question number four, You're doing great so far. This review 377 00:19:54,480 --> 00:19:59,080 Speaker 1: starts quote, I've never seen penguins dance. This propaganda film 378 00:19:59,160 --> 00:20:02,040 Speaker 1: is meant to deprive of us of tuna. What movie 379 00:20:02,040 --> 00:20:04,879 Speaker 1: about dancing penguins which shares its name with an old 380 00:20:04,920 --> 00:20:08,439 Speaker 1: cab callaway song? Are we talking about? That would be 381 00:20:08,520 --> 00:20:13,359 Speaker 1: happy feats? Yeah, you're right, a four for four. Nice job. 382 00:20:13,440 --> 00:20:15,000 Speaker 1: Let's see if you can bring it home with the 383 00:20:15,080 --> 00:20:17,920 Speaker 1: last one and go five for five. Question number five, 384 00:20:18,520 --> 00:20:22,440 Speaker 1: This review disappointedly sums up the movie quote one star, 385 00:20:22,800 --> 00:20:28,399 Speaker 1: the boat sinks. What movie are we talking about? That 386 00:20:28,440 --> 00:20:32,120 Speaker 1: would be tight hand? That's amazing, Jeffrey. So you went 387 00:20:32,200 --> 00:20:34,800 Speaker 1: five for five, which gets you an official part Time 388 00:20:34,840 --> 00:20:37,760 Speaker 1: Genius Certificate of Genius along with the part Time Genius 389 00:20:37,800 --> 00:20:54,520 Speaker 1: t shirts. So congratulations, thank you. You're listening to part 390 00:20:54,520 --> 00:20:56,840 Speaker 1: Time Genius and we're talking about the innovative thinking and 391 00:20:56,920 --> 00:20:59,960 Speaker 1: breakthrough technologies that have made Amazon one of the fastest 392 00:21:00,040 --> 00:21:04,200 Speaker 1: growing and most ubiquitous companies in history. And just how 393 00:21:04,200 --> 00:21:07,560 Speaker 1: prevalent is Amazon exactly? According to a report out earlier 394 00:21:07,600 --> 00:21:11,440 Speaker 1: this year from Walker Sands Communication, an astounding eighty four 395 00:21:11,560 --> 00:21:14,760 Speaker 1: percent of US consumers have bought something from Amazon in 396 00:21:14,800 --> 00:21:19,119 Speaker 1: the past. Unbelievable and as far as consumer basis go, 397 00:21:19,240 --> 00:21:21,800 Speaker 1: that's just about as good as you could possibly get. 398 00:21:21,840 --> 00:21:24,199 Speaker 1: It's pretty much everyone. Yeah, And and part of the 399 00:21:24,200 --> 00:21:26,200 Speaker 1: reasons so many people buy from Amazon is that the 400 00:21:26,200 --> 00:21:28,640 Speaker 1: company has gotten so good at selling what you want, 401 00:21:28,960 --> 00:21:31,640 Speaker 1: I mean sometimes even before you know it. Yeah, it's true. 402 00:21:31,680 --> 00:21:34,000 Speaker 1: And they've really set the bar in terms of predictive 403 00:21:34,040 --> 00:21:37,679 Speaker 1: analytics and targeted marketing. For example, they have this super 404 00:21:37,720 --> 00:21:41,320 Speaker 1: comprehensive filtering engine that can analyze everything from what you've 405 00:21:41,320 --> 00:21:44,320 Speaker 1: purchased in the past to what's currently in your online 406 00:21:44,320 --> 00:21:47,720 Speaker 1: shopping car to things like which items you've searched for 407 00:21:47,760 --> 00:21:50,879 Speaker 1: the most. And your information then gets cross reference with 408 00:21:50,920 --> 00:21:53,239 Speaker 1: that of other users. And as we've talked about, this 409 00:21:53,320 --> 00:21:55,560 Speaker 1: is eighty four percent of America, so it's a ton 410 00:21:55,600 --> 00:21:58,680 Speaker 1: of people. So the company can recommend products that other 411 00:21:58,760 --> 00:22:01,200 Speaker 1: people have bought when shot upping for the same things 412 00:22:01,240 --> 00:22:04,200 Speaker 1: that you have. And just like with impulse buys near 413 00:22:04,200 --> 00:22:07,439 Speaker 1: registers at grocery stores, that power of suggestion goes a 414 00:22:07,480 --> 00:22:11,320 Speaker 1: long way toward making you spend more on Amazon, especially 415 00:22:11,320 --> 00:22:15,639 Speaker 1: when it's customized to this degree. Investipdia reports that Amazon's 416 00:22:15,680 --> 00:22:19,240 Speaker 1: personalized recommendation system snags the company as much as an 417 00:22:19,240 --> 00:22:22,680 Speaker 1: extra thirty percent and revenue every single year. Oh man, 418 00:22:22,800 --> 00:22:25,320 Speaker 1: we're such cheap, but but you kind of have to 419 00:22:25,320 --> 00:22:27,600 Speaker 1: admire how much thought and effort goes into the design 420 00:22:27,640 --> 00:22:30,080 Speaker 1: of the platform. I mean, even if it is optimized 421 00:22:30,119 --> 00:22:32,639 Speaker 1: to suck every dollar out of your wallet. Like you 422 00:22:32,680 --> 00:22:35,359 Speaker 1: know that one click ordering button that Amazon has. Not 423 00:22:35,400 --> 00:22:38,360 Speaker 1: only is that this in house invention meant to increase 424 00:22:38,400 --> 00:22:41,560 Speaker 1: impulse buys and limit the lag between purchases and shipments, 425 00:22:41,560 --> 00:22:45,199 Speaker 1: but it's also a patented feature that Amazon licenses to 426 00:22:45,280 --> 00:22:48,159 Speaker 1: other companies. So you know, for for instance, anytime you 427 00:22:48,200 --> 00:22:50,719 Speaker 1: buy a song or movie or whatever on iTunes with 428 00:22:50,760 --> 00:22:53,720 Speaker 1: one click ordering, not only does Apple make money, but 429 00:22:53,920 --> 00:22:56,840 Speaker 1: Amazon sharing in that profit too. Yeah, it really is 430 00:22:56,840 --> 00:22:59,000 Speaker 1: amazing to think about this. And of course they're cloud 431 00:22:59,040 --> 00:23:01,879 Speaker 1: computing business that we talked about, where you know, another 432 00:23:01,880 --> 00:23:04,960 Speaker 1: company's ability to function and and even make money, it 433 00:23:05,280 --> 00:23:08,560 Speaker 1: becomes dependent on giving Amazon a cut of these profits. 434 00:23:09,040 --> 00:23:11,280 Speaker 1: It's really clever how they've worked out all of these 435 00:23:11,320 --> 00:23:14,879 Speaker 1: ways of making money from other companies sales. Yeah, and 436 00:23:14,920 --> 00:23:17,960 Speaker 1: Amazon's not shy about capitalizing on another company's money making 437 00:23:18,000 --> 00:23:21,320 Speaker 1: schemes either. Just last month, Walmart announced I would start 438 00:23:21,320 --> 00:23:24,080 Speaker 1: testing a delivery service in which it's workers would enter 439 00:23:24,160 --> 00:23:26,760 Speaker 1: customers homes by way of a smartlock, and then they 440 00:23:26,840 --> 00:23:30,520 Speaker 1: drop off the packages or even unload grocery items directly 441 00:23:30,520 --> 00:23:33,040 Speaker 1: into the fridge or freezer. And now just a few 442 00:23:33,040 --> 00:23:36,040 Speaker 1: weeks later, Amazon's one up them by launching their own 443 00:23:36,160 --> 00:23:39,640 Speaker 1: in home delivery service in thirties seven cities. I mean, 444 00:23:39,680 --> 00:23:42,920 Speaker 1: should should we actually feel bad for Walmart year for once? 445 00:23:43,600 --> 00:23:44,840 Speaker 1: I don't know if I'm going to go that far 446 00:23:44,920 --> 00:23:47,240 Speaker 1: with I mean, it's the one time they were actually 447 00:23:47,280 --> 00:23:49,520 Speaker 1: ahead of the curve, even if by only a month. 448 00:23:49,560 --> 00:23:53,080 Speaker 1: And then along comes Amazon and one ups though. But yeah, 449 00:23:53,280 --> 00:23:55,119 Speaker 1: not that I'm really rooting for either of them in 450 00:23:55,119 --> 00:23:57,840 Speaker 1: this case. That there's something about granting a giant company 451 00:23:57,880 --> 00:24:00,600 Speaker 1: this unfettered access to my home that I'm not sure 452 00:24:00,640 --> 00:24:03,280 Speaker 1: I'm ready to buy into. Just yet I totally get that, 453 00:24:03,359 --> 00:24:06,040 Speaker 1: and to be fair, it's not a meritless idea. So 454 00:24:06,359 --> 00:24:09,080 Speaker 1: this company called August Home, Inc. Did a study last 455 00:24:09,119 --> 00:24:12,680 Speaker 1: year and found that approximately eleven million Americans had packages 456 00:24:12,680 --> 00:24:15,639 Speaker 1: stolen off their doorsteps in two thousand and sixteen. But 457 00:24:15,720 --> 00:24:18,119 Speaker 1: you're right, this new kind of delivery service has already 458 00:24:18,200 --> 00:24:21,320 Speaker 1: raised a lot of eyebrows due to privacy concerns, even 459 00:24:21,320 --> 00:24:24,520 Speaker 1: with the promise of cameras that livestream the whole delivery process. 460 00:24:24,880 --> 00:24:26,720 Speaker 1: I mean, the service isn't guaranteed to take off for 461 00:24:26,800 --> 00:24:29,560 Speaker 1: either company, but I'd say Amazon has a better chance 462 00:24:29,560 --> 00:24:32,000 Speaker 1: of making it all work. Yeah, and this seems especially 463 00:24:32,040 --> 00:24:34,840 Speaker 1: true here. I mean, Amazon could theoretically take the idea 464 00:24:34,960 --> 00:24:38,399 Speaker 1: beyond simple delivery of packages. I mean, they already have 465 00:24:38,520 --> 00:24:41,080 Speaker 1: this home and business services section of the site and 466 00:24:41,440 --> 00:24:43,879 Speaker 1: it kind of works like task Grabbit, except all the 467 00:24:43,920 --> 00:24:47,800 Speaker 1: services you can buy and schedule are handled by real professionals. 468 00:24:48,000 --> 00:24:50,200 Speaker 1: You know, things like house cleaning and lawn care or 469 00:24:50,280 --> 00:24:54,119 Speaker 1: even furniture assembly can all be arranged through Amazon. So 470 00:24:54,160 --> 00:24:56,280 Speaker 1: with this new smart log program, you know, that kind 471 00:24:56,280 --> 00:24:59,080 Speaker 1: of stuff could be set up through Amazon without you 472 00:24:59,119 --> 00:25:02,439 Speaker 1: needing to be there. The service is performed. I do 473 00:25:02,520 --> 00:25:04,320 Speaker 1: have to men, it's all a little bit creepy and 474 00:25:04,359 --> 00:25:06,439 Speaker 1: still a little too new for me to get behind 475 00:25:06,440 --> 00:25:09,120 Speaker 1: this completely. But but you're right, if anyone can sell 476 00:25:09,160 --> 00:25:12,720 Speaker 1: the concept to the public, it would definitely be Amazon. Yeah, 477 00:25:12,840 --> 00:25:15,120 Speaker 1: and they managed to float some pretty off putting ideas 478 00:25:15,440 --> 00:25:17,679 Speaker 1: so far, but just look at what they've managed with 479 00:25:17,720 --> 00:25:20,720 Speaker 1: Alexa and Amazon Echo. Right twenty years ago, when the 480 00:25:20,720 --> 00:25:22,680 Speaker 1: company came on the scene, it would have been this 481 00:25:22,840 --> 00:25:26,840 Speaker 1: unthinkable prospect that people would live within ever present virtual 482 00:25:26,880 --> 00:25:30,280 Speaker 1: assistant that kept this constant thread of like what you 483 00:25:30,320 --> 00:25:32,880 Speaker 1: were listening to and every word you said. But now 484 00:25:32,920 --> 00:25:35,520 Speaker 1: about three million different people use the voice control of 485 00:25:35,520 --> 00:25:38,600 Speaker 1: Alexa service every month, and that numbers only set to 486 00:25:38,720 --> 00:25:41,919 Speaker 1: rise as the company expands the Echo line. Yeah, and 487 00:25:41,960 --> 00:25:45,160 Speaker 1: as you remember that that Alexa was originally this standalone 488 00:25:45,160 --> 00:25:48,359 Speaker 1: company that did web analysis and web analytics, if you 489 00:25:48,400 --> 00:25:50,639 Speaker 1: remember that, and Amazon bought it for two d and 490 00:25:50,680 --> 00:25:54,600 Speaker 1: fifty million dollars back in and they ended up sitting 491 00:25:54,600 --> 00:25:57,560 Speaker 1: on this software for about sixteen years, not knowing exactly 492 00:25:57,640 --> 00:25:59,440 Speaker 1: what they were going to do about it, and then 493 00:25:59,440 --> 00:26:02,040 Speaker 1: they finally rolled it out as this very different than 494 00:26:02,320 --> 00:26:05,639 Speaker 1: this home assistant and entertainment center. That's so crazy. I 495 00:26:05,680 --> 00:26:08,240 Speaker 1: actually forgotten that that Alexa was where you go to 496 00:26:08,400 --> 00:26:11,520 Speaker 1: UM to check other websites and what traffic they were getting. 497 00:26:11,600 --> 00:26:14,560 Speaker 1: But but you know, as cool and sci fi concept 498 00:26:14,600 --> 00:26:17,800 Speaker 1: as like this AI assistant is, it's actually got nothing 499 00:26:17,880 --> 00:26:21,280 Speaker 1: on the actual robots that Amazon uses. And that's why 500 00:26:21,280 --> 00:26:24,479 Speaker 1: I have to mention another Amazon acquisition that's only recently 501 00:26:24,520 --> 00:26:27,080 Speaker 1: started to pay off for the company. In two twelve, 502 00:26:27,119 --> 00:26:31,040 Speaker 1: Amazon bought a robotics company called Kiva for three quarters 503 00:26:31,040 --> 00:26:33,800 Speaker 1: of a billion dollars, and today the Kiva robots are 504 00:26:33,840 --> 00:26:35,919 Speaker 1: a big part of the picking and packing process that 505 00:26:35,960 --> 00:26:40,359 Speaker 1: goes on at Amazon's enormous fulfillment centers. So these robots, like, 506 00:26:40,440 --> 00:26:42,879 Speaker 1: what what do they do? Exactly? Well, the way it 507 00:26:42,960 --> 00:26:45,399 Speaker 1: usually works is that whenever an order comes through an 508 00:26:45,440 --> 00:26:48,280 Speaker 1: Amazon a human worker called a picker has to navigate 509 00:26:48,320 --> 00:26:51,639 Speaker 1: those endless aisles of merchandise to find the specific bins 510 00:26:51,720 --> 00:26:54,800 Speaker 1: that contain the ordered items. And now, of course, the 511 00:26:54,800 --> 00:26:57,200 Speaker 1: computer tells the picker which aisles and bins to check. 512 00:26:57,320 --> 00:26:59,760 Speaker 1: But with so many orders coming in each day and 513 00:26:59,800 --> 00:27:02,960 Speaker 1: so many different items to choose from, filling orders can 514 00:27:03,000 --> 00:27:06,680 Speaker 1: still be this tiring, time consuming task. In fact, most 515 00:27:06,680 --> 00:27:09,399 Speaker 1: pickers walk as many as like fifteen miles during a 516 00:27:09,440 --> 00:27:12,560 Speaker 1: single shift. Wow. Yeah, I know. Those Amazon warehouses are 517 00:27:12,640 --> 00:27:14,960 Speaker 1: just huge. I remember seeing this fact that you know, 518 00:27:15,119 --> 00:27:17,520 Speaker 1: if you if you added up all the square footage 519 00:27:17,520 --> 00:27:21,119 Speaker 1: of these Amazon warehouses, it makes up something like seventeen 520 00:27:21,200 --> 00:27:24,159 Speaker 1: New Hampshires Orthoning. Imagine how many delawares. That is, so 521 00:27:24,200 --> 00:27:27,800 Speaker 1: many delawares. But but all right, so I just want 522 00:27:27,800 --> 00:27:29,720 Speaker 1: to make sure I understand this. So they so, actually 523 00:27:29,720 --> 00:27:32,679 Speaker 1: they have robots that are dexperous enough to pick the 524 00:27:32,800 --> 00:27:36,000 Speaker 1: items out of bins. That seems remarkable. No they don't. 525 00:27:36,080 --> 00:27:38,680 Speaker 1: So that so the Kiva robots don't even have arms. 526 00:27:38,720 --> 00:27:41,720 Speaker 1: So pictures something more like an enormous roomba that scoots 527 00:27:41,760 --> 00:27:44,119 Speaker 1: around really close to the ground at about five miles 528 00:27:44,119 --> 00:27:47,320 Speaker 1: per hour. So, rather than sending human pickers on this 529 00:27:47,480 --> 00:27:51,679 Speaker 1: like massive route through these labyrinthine warehouses, a kiva root 530 00:27:51,760 --> 00:27:54,520 Speaker 1: brings all the needed items right to them. But the 531 00:27:54,600 --> 00:27:58,000 Speaker 1: kivas don't carry the items individually. Instead, they rolled beneath 532 00:27:58,040 --> 00:28:01,919 Speaker 1: these vertical columns that hold merchandise spins and then extend 533 00:28:02,000 --> 00:28:05,760 Speaker 1: themselves upwards using hydraulics like keebas can actually towed up 534 00:28:05,760 --> 00:28:09,000 Speaker 1: to seven pounds on their backs. So once they've located 535 00:28:09,040 --> 00:28:11,639 Speaker 1: the column that contains the correct item, they just simply 536 00:28:11,720 --> 00:28:14,080 Speaker 1: carry it back to the human worker, who can then 537 00:28:14,119 --> 00:28:16,560 Speaker 1: pick out the item needed to fill the order. Wow 538 00:28:16,960 --> 00:28:22,040 Speaker 1: like that you keep saying human worker anyway? You know? Actually, 539 00:28:22,080 --> 00:28:24,640 Speaker 1: as a as a side note, speaking of human workers, 540 00:28:24,640 --> 00:28:27,600 Speaker 1: there was one program totally unrelated to this, but I 541 00:28:27,640 --> 00:28:30,120 Speaker 1: read about this program called camper Force. Have you heard 542 00:28:30,119 --> 00:28:33,200 Speaker 1: about this before? The Amazon uses it to staff its 543 00:28:33,200 --> 00:28:36,719 Speaker 1: warehouses during the holiday season because it's so much busier 544 00:28:36,800 --> 00:28:39,960 Speaker 1: during that time, they just need temporary workers. And so basically, 545 00:28:40,000 --> 00:28:42,280 Speaker 1: the company invites these nomadic r V e rs to 546 00:28:42,360 --> 00:28:45,400 Speaker 1: set up shop near their facilities and come work as 547 00:28:45,440 --> 00:28:48,640 Speaker 1: seasonal hires there. The program has its own logo, which 548 00:28:48,680 --> 00:28:51,480 Speaker 1: is a silhouette of an RV speeding along with amazon 549 00:28:51,640 --> 00:28:54,560 Speaker 1: smile logo on its side. I kind of love that 550 00:28:54,600 --> 00:28:56,600 Speaker 1: it's it's it's a little strange, but yeah, it's kind 551 00:28:56,600 --> 00:28:59,000 Speaker 1: of an interesting model. So I thought we could take 552 00:28:59,040 --> 00:29:01,160 Speaker 1: a look at where Amazon is headed next and how 553 00:29:01,200 --> 00:29:03,080 Speaker 1: they might convince us to let them wedge a little 554 00:29:03,080 --> 00:29:15,040 Speaker 1: deeper into our personal lives. Okay, so what's on the 555 00:29:15,080 --> 00:29:18,800 Speaker 1: horizon for Amazon. Well, in the immediate future, there's the 556 00:29:18,800 --> 00:29:21,320 Speaker 1: new second headquarters that the company has been talking about 557 00:29:21,320 --> 00:29:24,320 Speaker 1: building somewhere outside of its home base of Seattle. And 558 00:29:24,400 --> 00:29:26,560 Speaker 1: while we could talk about what the five billion dollar 559 00:29:26,600 --> 00:29:29,760 Speaker 1: investment could mean for the company or what the jobs 560 00:29:29,800 --> 00:29:32,480 Speaker 1: will do for the local economy, I think it would 561 00:29:32,520 --> 00:29:34,160 Speaker 1: be way more fun to talk about some of the 562 00:29:34,280 --> 00:29:37,200 Speaker 1: ridiculous things cities have done to get Amazon to look 563 00:29:37,240 --> 00:29:40,600 Speaker 1: their way. Yeah. I do love thinking about these kinds 564 00:29:40,600 --> 00:29:42,840 Speaker 1: of things. In fact, one of the silliest promotional tactics 565 00:29:42,880 --> 00:29:45,920 Speaker 1: I've read about, or maybe least effective, Okay, from my 566 00:29:46,000 --> 00:29:49,240 Speaker 1: own hometown of Birmingham, and apparently part of what was 567 00:29:49,360 --> 00:29:52,520 Speaker 1: dubbed this bring a to b campaign, and the city 568 00:29:52,600 --> 00:29:56,120 Speaker 1: set up two giant replicas of Amazon's dash buttons. Those 569 00:29:56,160 --> 00:29:58,760 Speaker 1: are those, you know, doorbell looking gadgets that let you 570 00:29:58,840 --> 00:30:01,640 Speaker 1: reorder a certain produ with a single press of the button, 571 00:30:02,040 --> 00:30:04,680 Speaker 1: except in this case, whenever residents pressed the big prop 572 00:30:04,720 --> 00:30:08,320 Speaker 1: buttons instead of ordering more detergent or whatever, it would 573 00:30:08,320 --> 00:30:12,640 Speaker 1: send one of six hundred different pregenerated tweets to Amazon's Twitter. 574 00:30:14,000 --> 00:30:16,440 Speaker 1: So what kind of stuff were they're tweeting Amazon? Well, 575 00:30:16,560 --> 00:30:18,800 Speaker 1: some of the messages were just little factoys about the 576 00:30:18,800 --> 00:30:21,880 Speaker 1: city's quality of life, and others had bits of trivia, 577 00:30:22,000 --> 00:30:24,040 Speaker 1: you know, like how Michael Jordan briefly played for the 578 00:30:24,080 --> 00:30:27,000 Speaker 1: Birmingham Barons during his stint as a baseball player, And 579 00:30:27,560 --> 00:30:29,640 Speaker 1: I don't know. Most of them were these corny, kind 580 00:30:29,720 --> 00:30:34,040 Speaker 1: of flirty messages, like one of them says, um, Amazon, 581 00:30:34,120 --> 00:30:36,240 Speaker 1: we got a hundred percent match on bumble, want to 582 00:30:36,240 --> 00:30:40,600 Speaker 1: go on a date? Or I'd retreat that. Or here's 583 00:30:40,640 --> 00:30:42,920 Speaker 1: one that I actually find kind of offensive. It says, uh, 584 00:30:43,320 --> 00:30:47,200 Speaker 1: we are Chipotle and those other cities are Taco Bell Amazon. Yeah, 585 00:30:47,280 --> 00:30:50,920 Speaker 1: I don't like that one thing. All right. Well, I'm 586 00:30:50,920 --> 00:30:54,120 Speaker 1: not sure why the city thought bombarding Amazon with annoying 587 00:30:54,120 --> 00:30:56,080 Speaker 1: tweets would be the way to lock them down, but 588 00:30:56,640 --> 00:31:00,320 Speaker 1: either way, it's my hometown. So I'm pulling for you, Birmingham. Well, 589 00:31:00,360 --> 00:31:03,280 Speaker 1: with two d thirty eight cities and regions across both 590 00:31:03,320 --> 00:31:05,840 Speaker 1: the US and Canada vying for the chance to be 591 00:31:05,920 --> 00:31:10,200 Speaker 1: Amazon's new hometown, I mean, there's some steep competition. So 592 00:31:10,200 --> 00:31:12,200 Speaker 1: so one of the things Calgary is doing and I 593 00:31:12,200 --> 00:31:14,760 Speaker 1: didn't even realize Calgary was up for this, but they've 594 00:31:14,840 --> 00:31:18,719 Speaker 1: hung this two under foot banner outside Amazon's current Seattle headquarters. 595 00:31:18,760 --> 00:31:22,200 Speaker 1: That says, quote, hey, Amazon, not saying we'd fight a 596 00:31:22,200 --> 00:31:25,960 Speaker 1: bear for you, but we totally would. That's actually kind 597 00:31:25,960 --> 00:31:28,480 Speaker 1: of awesome. All Right, I'm changing the team Calgary here, 598 00:31:28,800 --> 00:31:31,120 Speaker 1: but you know, Mago, I thought we could switch gears 599 00:31:31,160 --> 00:31:33,520 Speaker 1: and and talk for a minute about Whole Foods. Obviously, 600 00:31:33,560 --> 00:31:36,800 Speaker 1: this was a big, big acquisition for them recently, and 601 00:31:36,800 --> 00:31:40,400 Speaker 1: and particularly because Amazon now has the ability to do 602 00:31:40,520 --> 00:31:43,040 Speaker 1: something that they didn't have before, and that was to 603 00:31:43,120 --> 00:31:46,440 Speaker 1: access this brick and mortar world. Yeah, and and where 604 00:31:46,480 --> 00:31:50,160 Speaker 1: a few months past Amazon's thirteen point seven billion dollar buyout. 605 00:31:50,240 --> 00:31:53,040 Speaker 1: But there's still a lot of unanswered questions about what 606 00:31:53,120 --> 00:31:56,160 Speaker 1: shape the new partnership will take. I mean, prime Member 607 00:31:56,200 --> 00:31:58,280 Speaker 1: is now going to discount a Whole Foods and some 608 00:31:58,360 --> 00:32:00,200 Speaker 1: of the stores have a stack of Amazon at goes 609 00:32:00,240 --> 00:32:03,160 Speaker 1: for sale by the entrance. But obviously there's potential there 610 00:32:03,200 --> 00:32:05,719 Speaker 1: for something much more interesting, right, And there's been all 611 00:32:05,800 --> 00:32:08,480 Speaker 1: kinds of speculation about whether Amazon will just gut the 612 00:32:08,520 --> 00:32:12,160 Speaker 1: stores and use them as these urban or suburban distribution hubs, 613 00:32:12,280 --> 00:32:14,959 Speaker 1: or maybe force Whole Foods to adopt the model of 614 00:32:15,000 --> 00:32:18,440 Speaker 1: the Amazon Go prototype grocery stores. These are the ones 615 00:32:18,480 --> 00:32:20,480 Speaker 1: where you know the company has been testing out the 616 00:32:20,480 --> 00:32:25,320 Speaker 1: possibility of minimizing human contact in their stores, and sensors 617 00:32:25,400 --> 00:32:27,480 Speaker 1: just keep track of the items that you grab and 618 00:32:27,520 --> 00:32:31,160 Speaker 1: automatically charge the total. It goes directly to your Amazon 619 00:32:31,200 --> 00:32:34,080 Speaker 1: account once you head for the exits there. Yeah, and 620 00:32:34,120 --> 00:32:36,320 Speaker 1: that could definitely happen down the line. But I'm looking 621 00:32:36,320 --> 00:32:38,560 Speaker 1: forward to pressing a button at my desk, watching a 622 00:32:38,640 --> 00:32:41,160 Speaker 1: drone pick up an avocado for me, marring it on 623 00:32:41,200 --> 00:32:43,480 Speaker 1: my phone, and then having a helicopter up to my 624 00:32:43,560 --> 00:32:45,400 Speaker 1: office where I can reach out the window and plug 625 00:32:45,400 --> 00:32:47,240 Speaker 1: it from the sky. I'm sure that's going to happen 626 00:32:47,320 --> 00:32:50,320 Speaker 1: one day. Actually, I did come across this thought experiment 627 00:32:50,320 --> 00:32:53,360 Speaker 1: from a Texas design firm called Argo Design, and some 628 00:32:53,440 --> 00:32:55,400 Speaker 1: of the concepts weren't too far off from what you 629 00:32:55,520 --> 00:32:58,400 Speaker 1: just describe. So they try to imagine what a potential 630 00:32:58,480 --> 00:33:01,280 Speaker 1: Amazon Foods might look like when paired with new and 631 00:33:01,280 --> 00:33:04,800 Speaker 1: emerging technology. So one of the ideas was that these 632 00:33:04,840 --> 00:33:07,960 Speaker 1: new echo fridges could be built with these exterior facing 633 00:33:07,960 --> 00:33:11,000 Speaker 1: doors that a delivery driver or maybe a flying drone 634 00:33:11,040 --> 00:33:14,200 Speaker 1: like you dreamed of, could access from the outside. And 635 00:33:14,200 --> 00:33:16,120 Speaker 1: the idea was that one side of the fridge would 636 00:33:16,160 --> 00:33:18,640 Speaker 1: belong to the consumer and would store all of his 637 00:33:18,760 --> 00:33:21,320 Speaker 1: or her food products, while the other side would house 638 00:33:21,320 --> 00:33:25,360 Speaker 1: a rotating assortment of suggested products that Amazon swaps out 639 00:33:25,840 --> 00:33:28,320 Speaker 1: once or twice a day. So if you wanted to 640 00:33:28,320 --> 00:33:31,120 Speaker 1: buy that bag of grapes or those heirloom tomatoes, all 641 00:33:31,160 --> 00:33:33,080 Speaker 1: you'd have to do is slide them over to your 642 00:33:33,160 --> 00:33:36,240 Speaker 1: side of the fridge and they'd be purchased automatically, And 643 00:33:36,280 --> 00:33:38,480 Speaker 1: if there was nothing on sale that you wanted, you 644 00:33:38,600 --> 00:33:40,800 Speaker 1: just leave it all being Amazon would haul it over 645 00:33:40,800 --> 00:33:42,520 Speaker 1: to your neighbor or something to see if he was 646 00:33:43,840 --> 00:33:46,640 Speaker 1: That sounds equal parts amazing and horrifying, And it's almost 647 00:33:46,680 --> 00:33:49,720 Speaker 1: like the mini bar you don't want. It's tear tempting, 648 00:33:50,040 --> 00:33:53,400 Speaker 1: and the communians is obviously great, but getting the jar 649 00:33:53,480 --> 00:33:55,560 Speaker 1: of all is that my neighbors have rejected. That kind 650 00:33:55,560 --> 00:33:58,120 Speaker 1: of turns me off just a little bit. Yeah, well, 651 00:33:58,160 --> 00:34:01,040 Speaker 1: it is a little unsettling to think about. But with 652 00:34:01,120 --> 00:34:04,840 Speaker 1: drones and in home deliveries already on the table, you know, 653 00:34:04,880 --> 00:34:07,520 Speaker 1: sharing a fridge with Amazon may not be outside the 654 00:34:07,560 --> 00:34:11,040 Speaker 1: realm of reason. There was actually one idea from Argo 655 00:34:11,080 --> 00:34:13,600 Speaker 1: Design that I really thought was very very interesting, and 656 00:34:14,000 --> 00:34:16,000 Speaker 1: you know, it's kind of their take on the sharing 657 00:34:16,040 --> 00:34:19,960 Speaker 1: economy model that's been popularized by companies like Uber and Airbnb. 658 00:34:20,200 --> 00:34:23,640 Speaker 1: And so basically, talented home cooks could prepare meals to 659 00:34:23,719 --> 00:34:26,360 Speaker 1: sell in their community. So how does Amazon play a 660 00:34:26,440 --> 00:34:28,439 Speaker 1: role here? So they would provide them with some high 661 00:34:28,480 --> 00:34:31,920 Speaker 1: tech tupperware containers to pack the food in, and then 662 00:34:32,000 --> 00:34:35,440 Speaker 1: each of these special containers would include this mass spectrometer 663 00:34:35,840 --> 00:34:38,799 Speaker 1: to do things like detecting allergens and pathogens and the 664 00:34:38,840 --> 00:34:42,200 Speaker 1: prepared meals. And this way Amazon and the aspiring chefs 665 00:34:42,200 --> 00:34:44,800 Speaker 1: could sidestep the f d A and the whole packaged 666 00:34:44,800 --> 00:34:47,960 Speaker 1: food industry, yeah, while still ensuring that their home cooked 667 00:34:47,960 --> 00:34:50,760 Speaker 1: meals were safety eat. I mean, that's an awesome idea, 668 00:34:50,840 --> 00:34:53,600 Speaker 1: but that one actually might be outside the realm of reason. 669 00:34:54,200 --> 00:34:56,560 Speaker 1: That's true, I mean, at least at this point. But 670 00:34:56,640 --> 00:34:59,400 Speaker 1: I do appreciate the positivity behind it. And you know, 671 00:34:59,480 --> 00:35:01,440 Speaker 1: sometimes if it was, like all the tech that's aimed 672 00:35:01,440 --> 00:35:04,880 Speaker 1: at convenience does make us feel a little more isolated again, 673 00:35:05,040 --> 00:35:07,080 Speaker 1: So there was something about this that was just nice 674 00:35:07,120 --> 00:35:09,439 Speaker 1: to see an application that would encourage people to eat 675 00:35:09,520 --> 00:35:12,880 Speaker 1: locally and also maybe meet their neighbors. I thought the 676 00:35:12,920 --> 00:35:15,520 Speaker 1: founder of the design firm, expressed it pretty well. And 677 00:35:15,760 --> 00:35:18,759 Speaker 1: a quote that he said, it says, maybe, rather than 678 00:35:18,840 --> 00:35:21,680 Speaker 1: keeping us away from each other, stronger data systems could 679 00:35:21,680 --> 00:35:24,440 Speaker 1: help us get closer to each other over time. Well, 680 00:35:24,480 --> 00:35:26,640 Speaker 1: I like that heartwarming sentiment. But you know what else 681 00:35:26,640 --> 00:35:29,680 Speaker 1: brings us all together, a good old fashioned fact off, 682 00:35:30,480 --> 00:35:41,879 Speaker 1: let's do it all right, I'll start us off here. 683 00:35:42,280 --> 00:35:43,640 Speaker 1: So you want to know a good way to get 684 00:35:43,640 --> 00:35:46,680 Speaker 1: a building named after you on the Amazon campus be 685 00:35:46,800 --> 00:35:49,600 Speaker 1: the first person to have purchased a book from Amazon. 686 00:35:49,680 --> 00:35:54,080 Speaker 1: It sounds it's hard to do that again, Yeah, it 687 00:35:54,120 --> 00:35:56,520 Speaker 1: sounds like a riveting book too. It was purchased by 688 00:35:56,520 --> 00:35:59,400 Speaker 1: software engineer John Waynewright, and the name of the book 689 00:35:59,520 --> 00:36:02,840 Speaker 1: was flew with concepts and creative analogies and I'm not 690 00:36:02,880 --> 00:36:05,480 Speaker 1: done with the title and then goes on computer models 691 00:36:05,520 --> 00:36:09,040 Speaker 1: of the fundamental mechanisms of thought and uh so after 692 00:36:09,080 --> 00:36:11,560 Speaker 1: buying that years later, Jeff Bezos decided to name a 693 00:36:11,600 --> 00:36:14,560 Speaker 1: building after him, And that title just feels like clickbait. Yes, 694 00:36:15,360 --> 00:36:17,400 Speaker 1: speaking of Jeff Bezos, did you know that if you 695 00:36:17,440 --> 00:36:19,880 Speaker 1: call the Amazon service desk you might actually get Ahold 696 00:36:19,920 --> 00:36:23,799 Speaker 1: of Visos himself. So every Amazon employees required to spend 697 00:36:23,840 --> 00:36:26,680 Speaker 1: a couple of days every two years answering customer calls, 698 00:36:26,680 --> 00:36:29,760 Speaker 1: and apparently he really enjoys it. It kind of reminds 699 00:36:29,760 --> 00:36:31,840 Speaker 1: you of Steve Jobs answering customer email, That's what I 700 00:36:31,880 --> 00:36:33,319 Speaker 1: was gonna say. It reminded me of that as well. 701 00:36:33,360 --> 00:36:36,160 Speaker 1: I remember seeing seeing that story about that. Well, another 702 00:36:36,280 --> 00:36:38,680 Speaker 1: Visos fact here. You know, he's got this problem of 703 00:36:38,680 --> 00:36:40,960 Speaker 1: trying to figure out what to do with his billions 704 00:36:40,960 --> 00:36:43,279 Speaker 1: and billions of dollars. Poor guy, I mean, trying to 705 00:36:43,280 --> 00:36:46,160 Speaker 1: figure this out. Well, he is finding some interesting ways 706 00:36:46,200 --> 00:36:48,439 Speaker 1: to spend some of his money, and one of those 707 00:36:48,480 --> 00:36:52,000 Speaker 1: ways is building this ten thousand year clock. So the 708 00:36:52,040 --> 00:36:55,680 Speaker 1: clock apparently ticks once a year, and the century hand 709 00:36:55,719 --> 00:36:59,120 Speaker 1: moves ahead once every one hundred years. And because you 710 00:36:59,160 --> 00:37:01,880 Speaker 1: know every cool hawk needs a cuckoo, that that happens 711 00:37:01,920 --> 00:37:06,040 Speaker 1: every thousand years, and apparently the clock is supposed to 712 00:37:06,040 --> 00:37:09,000 Speaker 1: be like a symbol of long term thinking. I guess 713 00:37:09,120 --> 00:37:10,880 Speaker 1: I like that. I mean, I hope it's just not 714 00:37:11,000 --> 00:37:15,560 Speaker 1: a like traditional tiny cuckoo, but something spectacular. Um, did 715 00:37:15,560 --> 00:37:18,399 Speaker 1: you know that a book about Lin apparently helped keep 716 00:37:18,680 --> 00:37:21,279 Speaker 1: Amazon in business in the early days, So, as we 717 00:37:21,320 --> 00:37:23,359 Speaker 1: both know, from our e commerce herees. But when you're 718 00:37:23,360 --> 00:37:26,280 Speaker 1: a bookseller, you buy books from distributors and you typically 719 00:37:26,320 --> 00:37:28,640 Speaker 1: can't buy a single copy. You have to buy them 720 00:37:28,640 --> 00:37:30,600 Speaker 1: in bulk, like ten or so books at a time. 721 00:37:31,000 --> 00:37:33,640 Speaker 1: But in the earliest days, Amazon didn't need ten copies 722 00:37:33,680 --> 00:37:36,680 Speaker 1: of every book. So Amazon figured out a loophole. When 723 00:37:36,680 --> 00:37:38,600 Speaker 1: they needed a book, they'd order one copy of the 724 00:37:38,600 --> 00:37:40,640 Speaker 1: book that they wanted to have on hand, and then 725 00:37:40,760 --> 00:37:43,600 Speaker 1: nine copies of this really obscure book on liking that 726 00:37:43,719 --> 00:37:46,840 Speaker 1: was always out of thoughts, pretty hilarious. So I do 727 00:37:46,880 --> 00:37:48,839 Speaker 1: wish they had realized that and be like, there's such 728 00:37:48,880 --> 00:37:51,360 Speaker 1: demand for this liking book, we need to bring it back. 729 00:37:51,480 --> 00:37:54,280 Speaker 1: That's awesome. Well that, you know, that may have prevented 730 00:37:54,320 --> 00:37:56,280 Speaker 1: them from losing a lot of money at that point, 731 00:37:56,280 --> 00:37:59,440 Speaker 1: But many years later, in two thousand thirteen, Amazon showed 732 00:37:59,480 --> 00:38:02,080 Speaker 1: just how much money it could lose in less than 733 00:38:02,160 --> 00:38:04,959 Speaker 1: an hour. When the Amazon dot Com site went down 734 00:38:05,000 --> 00:38:08,120 Speaker 1: for only forty minutes. The company apparently lost close to 735 00:38:08,239 --> 00:38:11,040 Speaker 1: five million dollars in that brief period. That's about a 736 00:38:11,160 --> 00:38:14,759 Speaker 1: hundred and twenty thousand dollars each minute. That's insane. So, 737 00:38:14,960 --> 00:38:17,160 Speaker 1: as you know, there have been stories over the years 738 00:38:17,160 --> 00:38:20,960 Speaker 1: of Amazon employees and fulfillment centers not always being the happiest. 739 00:38:21,400 --> 00:38:23,600 Speaker 1: And there are actually some funny stories about how various 740 00:38:23,600 --> 00:38:26,840 Speaker 1: employees have decided to protest or communicate their unhappiness. So 741 00:38:27,280 --> 00:38:29,600 Speaker 1: one funny one involves an employee back in two thousand 742 00:38:29,640 --> 00:38:32,919 Speaker 1: and six in the Kansas fulfillment Center. So he'd arrived 743 00:38:32,960 --> 00:38:35,279 Speaker 1: every day for work, but then wasn't clocked as doing 744 00:38:35,320 --> 00:38:37,840 Speaker 1: any work. It turns out he'd kind of built himself 745 00:38:37,840 --> 00:38:40,280 Speaker 1: this fourt in the middle of a bunch of empty palates. 746 00:38:40,600 --> 00:38:42,640 Speaker 1: He was then able to use products that were for 747 00:38:42,680 --> 00:38:44,920 Speaker 1: sale to make himself right at home, so he made 748 00:38:45,000 --> 00:38:47,600 Speaker 1: himself bed, He tore out pictures from books to decorate 749 00:38:47,680 --> 00:38:49,920 Speaker 1: the walls he'd built around him, and he had food 750 00:38:49,960 --> 00:38:52,640 Speaker 1: to snack on. And get this, when they figured out 751 00:38:52,719 --> 00:38:54,879 Speaker 1: what the guy was doing, can you believe they fired him? 752 00:38:55,000 --> 00:38:58,719 Speaker 1: What shocking? All right, well, I have to admit of 753 00:38:58,719 --> 00:39:01,960 Speaker 1: fact that is that of heard is probably worthy of 754 00:39:02,000 --> 00:39:04,720 Speaker 1: today's trophy. So I'm going to give it to you. Congratulations, Mango, 755 00:39:05,000 --> 00:39:06,880 Speaker 1: Thank you so much, and thank you all for listening. 756 00:39:07,080 --> 00:39:09,320 Speaker 1: Don't forget if you have any great facts about Amazon 757 00:39:09,400 --> 00:39:11,560 Speaker 1: that we missed. Let us know at one eight four 758 00:39:11,640 --> 00:39:14,759 Speaker 1: four pt Genius or emails at part Time Genius at 759 00:39:14,760 --> 00:39:21,200 Speaker 1: how stuff works dot com. Thanks so much for listening, Kay, 760 00:39:29,760 --> 00:39:32,280 Speaker 1: thanks again for listening. Part Time Genius is a production 761 00:39:32,320 --> 00:39:34,760 Speaker 1: of How Stuff Works and wouldn't be possible without several 762 00:39:34,800 --> 00:39:37,319 Speaker 1: brilliant people who do the important things we couldn't even 763 00:39:37,320 --> 00:39:40,640 Speaker 1: begin to understand. Tristan McNeil does the editing thing. Noel 764 00:39:40,680 --> 00:39:42,720 Speaker 1: Brown made the theme song and does the MIXI mixy 765 00:39:42,800 --> 00:39:46,279 Speaker 1: sound thing. Jerry Rowland does the exact producer thing. Gay 766 00:39:46,320 --> 00:39:48,920 Speaker 1: Bluesier is our lead researcher, with support from the Research 767 00:39:49,000 --> 00:39:51,960 Speaker 1: Army including Austin Thompson, Nolan Brown and Lucas Adams and 768 00:39:52,000 --> 00:39:54,640 Speaker 1: Eve Jeff Cook gets the show to your ears. Good job, Eves. 769 00:39:54,880 --> 00:39:56,800 Speaker 1: If you like what you heard, we hope you'll subscribe, 770 00:39:56,800 --> 00:39:58,719 Speaker 1: And if you really really like what you've heard, maybe 771 00:39:58,719 --> 00:40:00,799 Speaker 1: you could leave a good review for us. Did we did? 772 00:40:00,800 --> 00:40:02,480 Speaker 1: We forget Jason Jason who