1 00:00:02,560 --> 00:00:07,040 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:09,000 --> 00:00:12,320 Speaker 2: Stacy, this is kind of a personal question, but what 3 00:00:12,520 --> 00:00:15,240 Speaker 2: is your relationship with Amazon Prime? 4 00:00:15,840 --> 00:00:19,200 Speaker 3: My relationship, Yeah, your relationship, it's complicated. 5 00:00:19,560 --> 00:00:20,320 Speaker 4: I see, it's funny. 6 00:00:20,320 --> 00:00:23,200 Speaker 2: I am on the outs with Amazon Prime, mostly because 7 00:00:23,200 --> 00:00:25,760 Speaker 2: my wife canceled it and I am trying to make 8 00:00:25,840 --> 00:00:29,640 Speaker 2: do with Walmart Plus or whatever it's called. Okay, very 9 00:00:29,640 --> 00:00:34,479 Speaker 2: similar service. And these services are everywhere. They have become 10 00:00:34,560 --> 00:00:39,519 Speaker 2: a defining part of pretty much or many people's lives, 11 00:00:39,560 --> 00:00:42,640 Speaker 2: at least in the developed world. And we sent our 12 00:00:42,680 --> 00:00:45,839 Speaker 2: producer Miles J. Hersenhorn out into New York where there 13 00:00:45,840 --> 00:00:49,839 Speaker 2: are a lot of Amazon Prime shoppers, just to understand 14 00:00:49,880 --> 00:00:51,199 Speaker 2: what their prime habits were. 15 00:00:51,240 --> 00:00:53,440 Speaker 4: This service has been around for something like twenty years. 16 00:00:53,640 --> 00:00:54,440 Speaker 4: How did they see it? 17 00:00:55,080 --> 00:01:00,279 Speaker 5: Do you have Amazon Prime? Yes? How often we say 18 00:01:00,320 --> 00:01:01,640 Speaker 5: you use Amazon. 19 00:01:02,200 --> 00:01:04,440 Speaker 4: Maybe like once every few weeks? 20 00:01:05,200 --> 00:01:06,600 Speaker 6: Yes, quite often. 21 00:01:06,760 --> 00:01:09,800 Speaker 5: Yeah, it's a couple of years. And what is the 22 00:01:09,840 --> 00:01:11,680 Speaker 5: last thing you purchased off of Amazon? 23 00:01:12,120 --> 00:01:12,600 Speaker 4: Literally? 24 00:01:12,640 --> 00:01:15,640 Speaker 6: We buy so many things every day, I mean every 25 00:01:15,680 --> 00:01:16,280 Speaker 6: single day. 26 00:01:18,120 --> 00:01:20,319 Speaker 4: I think just household essentials like. 27 00:01:20,319 --> 00:01:23,520 Speaker 7: Soap, small things like I had a cable that I 28 00:01:23,520 --> 00:01:23,960 Speaker 7: had to buy. 29 00:01:24,120 --> 00:01:26,440 Speaker 5: I think a phone case. Yeah, I'm pretty share a 30 00:01:26,440 --> 00:01:27,040 Speaker 5: phone case. 31 00:01:26,880 --> 00:01:30,600 Speaker 2: Yeah, maybe a dish rack, probably a pair of sneakers. 32 00:01:31,160 --> 00:01:33,440 Speaker 8: I bought my watch off Amazon. I think that's the 33 00:01:33,520 --> 00:01:34,360 Speaker 8: last thing I bought. 34 00:01:34,440 --> 00:01:37,640 Speaker 5: Yeah, what's your relationship to Amazon? I would say it's 35 00:01:37,640 --> 00:01:41,039 Speaker 5: a convenience, not dependent on it. Pretty good. 36 00:01:41,120 --> 00:01:45,040 Speaker 6: Yeah, think in my packages on time, pretty fast shipping, 37 00:01:47,360 --> 00:01:48,680 Speaker 6: and they have everything I need on there. 38 00:01:49,040 --> 00:01:51,360 Speaker 8: I mean, I like the convenience of the company itself, 39 00:01:51,600 --> 00:01:54,280 Speaker 8: and I've had Prime for a while, and I use 40 00:01:54,360 --> 00:01:58,560 Speaker 8: the video, but we'll troubled about the management. 41 00:01:58,600 --> 00:02:01,120 Speaker 9: To be honest, they provide a good service, but they're 42 00:02:01,120 --> 00:02:03,360 Speaker 9: also a massive corporation, so there's trade offs. 43 00:02:04,720 --> 00:02:05,960 Speaker 5: I'm unsure because. 44 00:02:07,200 --> 00:02:10,359 Speaker 7: Since COVID, people have been using its way too much, 45 00:02:10,400 --> 00:02:13,120 Speaker 7: I think, and it's making local business fail. 46 00:02:13,240 --> 00:02:13,760 Speaker 5: I think. 47 00:02:15,320 --> 00:02:19,880 Speaker 6: It saves time. Time is the scarcest resource. We can't 48 00:02:19,880 --> 00:02:24,120 Speaker 6: buy it with more money, and it saves time, so 49 00:02:24,240 --> 00:02:25,799 Speaker 6: we use it more and more. 50 00:02:27,120 --> 00:02:29,639 Speaker 4: As we moved forward here. 51 00:02:31,200 --> 00:02:36,560 Speaker 2: So a lot of usage of Amazon stacy, but missed feelings. 52 00:02:36,080 --> 00:02:39,120 Speaker 3: Well yeah, yeah, I mean, but most people seem to 53 00:02:39,120 --> 00:02:40,560 Speaker 3: be using it for all kinds of things. 54 00:02:40,600 --> 00:02:43,320 Speaker 2: What they're not talking about, but they are using it 55 00:02:43,400 --> 00:02:47,040 Speaker 2: for is AI and cloud computing, it's a huge part 56 00:02:47,160 --> 00:02:50,799 Speaker 2: of Amazon's business, and Bradstone will be joining us later. 57 00:02:50,880 --> 00:02:54,239 Speaker 2: We're a whole story for Bloomberg BusinessWeek about this kind 58 00:02:54,240 --> 00:02:57,359 Speaker 2: of new version of Amazon and where it's going. 59 00:02:57,720 --> 00:03:00,520 Speaker 9: How do you frame Amazon today? It's so many disc things, 60 00:03:00,840 --> 00:03:03,079 Speaker 9: and we write it's a corporate turduck. 61 00:03:03,160 --> 00:03:05,400 Speaker 4: In turnucans are always funny. 62 00:03:05,400 --> 00:03:08,480 Speaker 9: It's an ad business, a logistics company stuffed inside an 63 00:03:08,520 --> 00:03:13,000 Speaker 9: e commerce marketplace, trust to a cloud computing powerhouse, garnished 64 00:03:13,040 --> 00:03:15,120 Speaker 9: with Alexa, Whole Foods and Prime Video. 65 00:03:15,400 --> 00:03:18,440 Speaker 3: Amazon, of course, one of the big magnificent seven stocks, 66 00:03:18,440 --> 00:03:20,520 Speaker 3: has been having a pretty great year, had a great 67 00:03:20,600 --> 00:03:23,880 Speaker 3: last year, and in spite of all the things that 68 00:03:23,919 --> 00:03:27,359 Speaker 3: have happened, the markets have been going up. Amazon right 69 00:03:27,360 --> 00:03:29,720 Speaker 3: along with them. And we have Kyla Scanlon to talk 70 00:03:29,760 --> 00:03:35,200 Speaker 3: about why the markets keep relentlessly marching skyward even though 71 00:03:35,360 --> 00:03:37,720 Speaker 3: the economy has taken some major blows. 72 00:03:38,080 --> 00:03:40,080 Speaker 10: It's not a good or bad thing. It's just like 73 00:03:40,120 --> 00:03:43,760 Speaker 10: the worry that the market is not properly understanding what's happening. 74 00:03:43,760 --> 00:03:45,520 Speaker 10: And then that puts four to one k's at risk, 75 00:03:45,760 --> 00:03:49,160 Speaker 10: that puts retirese at risk, that puts the stability of 76 00:03:49,200 --> 00:03:52,360 Speaker 10: the entirely American experiment at risk. 77 00:03:52,680 --> 00:03:53,920 Speaker 3: So that's that's the concern. 78 00:03:59,080 --> 00:04:00,000 Speaker 4: This is everybody's business. 79 00:04:00,160 --> 00:04:02,800 Speaker 3: I'm a next chef and I'm Stacey Mannocksmith. 80 00:04:02,640 --> 00:04:04,960 Speaker 2: The behemoth that is Amazon coming up. 81 00:04:04,920 --> 00:04:05,480 Speaker 5: After the brink. 82 00:04:14,760 --> 00:04:17,400 Speaker 2: Stacy, I was given you a hard time earlier about 83 00:04:17,440 --> 00:04:18,880 Speaker 2: your Amazon Prime. 84 00:04:18,960 --> 00:04:21,560 Speaker 3: Are you giving me a hard time about my Amazon Prime? 85 00:04:21,680 --> 00:04:24,719 Speaker 4: But the truth is you are really in the majority. 86 00:04:24,720 --> 00:04:25,960 Speaker 3: I'm not going to a niche minority. 87 00:04:26,120 --> 00:04:26,240 Speaker 11: No. 88 00:04:26,480 --> 00:04:28,520 Speaker 4: Amazon Prime is very popular. 89 00:04:28,720 --> 00:04:32,200 Speaker 2: In fact, a third party survey published in December by 90 00:04:32,200 --> 00:04:36,160 Speaker 2: the Consumer Intelligence Research Partners of trying to estimate the 91 00:04:36,240 --> 00:04:39,040 Speaker 2: number of Prime subscribers put it at around two hundred million. 92 00:04:39,400 --> 00:04:43,640 Speaker 4: So lots of US, lots of us in the glories. 93 00:04:43,800 --> 00:04:46,159 Speaker 3: Well, I think it's become normalized. I feel like there's 94 00:04:46,200 --> 00:04:49,440 Speaker 3: an expectation that's developed of things getting shipped, of not 95 00:04:49,480 --> 00:04:52,040 Speaker 3: paying for shipping, and then things arriving in two days. 96 00:04:52,480 --> 00:04:54,840 Speaker 3: And when I do order things off of other websites, 97 00:04:55,440 --> 00:04:57,640 Speaker 3: even though on one level I feel better about myself, 98 00:04:57,680 --> 00:05:01,520 Speaker 3: sometimes on another level it is hard to pay a 99 00:05:01,600 --> 00:05:04,600 Speaker 3: twenty or ten or twenty dollars shipping fee. 100 00:05:05,000 --> 00:05:07,919 Speaker 2: And hard once we spend so much time with a 101 00:05:07,960 --> 00:05:10,560 Speaker 2: company like Amazon to quit it and we have someone 102 00:05:10,640 --> 00:05:13,280 Speaker 2: right here, right now in the studios who has been 103 00:05:13,320 --> 00:05:17,520 Speaker 2: thinking about this and other big questions. BusinessWeek editor Brad Stone. 104 00:05:17,760 --> 00:05:20,640 Speaker 2: He's also the author of two books on Amazon, The 105 00:05:20,680 --> 00:05:24,960 Speaker 2: Everything Store and Amazon Unbound. What even is Amazon at 106 00:05:24,960 --> 00:05:27,320 Speaker 2: this point? I mean, I think most people think of 107 00:05:27,360 --> 00:05:30,200 Speaker 2: it as a store, an everything store, if. 108 00:05:30,000 --> 00:05:31,040 Speaker 4: You will, Max. 109 00:05:31,040 --> 00:05:33,720 Speaker 9: There's one line from the story I wrote with Matt 110 00:05:33,839 --> 00:05:36,719 Speaker 9: Day that I'm particularly proud of because we thought about this, 111 00:05:37,120 --> 00:05:39,920 Speaker 9: how do you frame Amazon today? It's so many disparate things, 112 00:05:40,200 --> 00:05:42,479 Speaker 9: and we write it's a corporate urduck. 113 00:05:42,520 --> 00:05:44,400 Speaker 4: In turnucins are always funny. 114 00:05:44,440 --> 00:05:47,560 Speaker 9: It's an ad business, a logistics company stuffed inside an 115 00:05:47,560 --> 00:05:52,040 Speaker 9: e commerce marketplace, trust to a cloud, computing powerhosts, garnished 116 00:05:52,040 --> 00:05:55,159 Speaker 9: with Alexa, Whole Foods and Prime Video. So in a way, 117 00:05:55,240 --> 00:05:58,000 Speaker 9: it's not as easily described as an everything store. 118 00:05:58,160 --> 00:06:01,040 Speaker 3: You talk about in your article that they're making this 119 00:06:01,080 --> 00:06:04,080 Speaker 3: big move into AI, that they've made just this huge bet, 120 00:06:04,240 --> 00:06:06,960 Speaker 3: and that their goal with AI is not just AI. 121 00:06:07,040 --> 00:06:09,000 Speaker 3: I feel like a lot of companies are everybody's even 122 00:06:09,040 --> 00:06:12,040 Speaker 3: shoe companies are getting into AI. But you make the 123 00:06:12,080 --> 00:06:14,559 Speaker 3: point that they want to be the Amazon of AI. 124 00:06:14,839 --> 00:06:16,160 Speaker 3: What does that mean, well. 125 00:06:16,080 --> 00:06:18,520 Speaker 9: First of all, Amazon has a lot to lose, right 126 00:06:18,560 --> 00:06:22,360 Speaker 9: AWS is the market leader in cloud computing. It's got 127 00:06:22,400 --> 00:06:25,280 Speaker 9: something like what thirty five or forty percent market share. 128 00:06:25,480 --> 00:06:26,320 Speaker 4: Explain what that is. 129 00:06:26,360 --> 00:06:28,400 Speaker 2: I think people sort of know what cloud computing is. 130 00:06:28,440 --> 00:06:31,359 Speaker 2: But when you say AWS, Amazon Web Services. 131 00:06:31,880 --> 00:06:38,719 Speaker 9: This is companies, organizations, governments renting their computing capacity, running 132 00:06:38,760 --> 00:06:42,440 Speaker 9: their applications on the servers of what we call in 133 00:06:42,480 --> 00:06:46,000 Speaker 9: the industry like the big hyperscalers. That's Amazon primarily with 134 00:06:46,320 --> 00:06:49,760 Speaker 9: the majority of market share, but also Google and Microsoft 135 00:06:50,520 --> 00:06:54,000 Speaker 9: in this new wave of AI, where the potential is 136 00:06:54,040 --> 00:06:58,280 Speaker 9: to kind of supercharge your operations with AI, Microsoft and 137 00:06:58,360 --> 00:07:00,839 Speaker 9: Google have started to make up a lot. They have 138 00:07:00,920 --> 00:07:04,360 Speaker 9: booked a lot more future business. They each early on 139 00:07:04,560 --> 00:07:07,680 Speaker 9: invested in the leading AI companies, Microsoft in open AI, 140 00:07:08,160 --> 00:07:10,920 Speaker 9: Google very early on in Anthropic And there was the 141 00:07:10,960 --> 00:07:14,760 Speaker 9: perception internally and externally that Amazon was caught flat footed. 142 00:07:15,120 --> 00:07:18,000 Speaker 9: And here on the fifth anniversary of Andy Jasse's tenure 143 00:07:18,280 --> 00:07:22,480 Speaker 9: as CEO, taking over from Jeff Bezos, we are looking 144 00:07:22,800 --> 00:07:25,080 Speaker 9: at his playbook and how he's. 145 00:07:24,960 --> 00:07:27,760 Speaker 4: Been trying to catch up, and he has largely caught up. 146 00:07:28,480 --> 00:07:31,320 Speaker 2: In a lot of ways, Amazon was behind on AI 147 00:07:31,560 --> 00:07:35,160 Speaker 2: like this trend caught them by surprise in a sense 148 00:07:35,240 --> 00:07:38,600 Speaker 2: because they do not have one of the leading large 149 00:07:38,640 --> 00:07:41,240 Speaker 2: language models. On the other hand, they were very early 150 00:07:41,280 --> 00:07:43,360 Speaker 2: in a bunch of things that are kind of AI adjacent, 151 00:07:43,560 --> 00:07:46,560 Speaker 2: data centers being one of them, Alexa this kind of 152 00:07:46,560 --> 00:07:50,200 Speaker 2: a proto chatbot being another, and even like the Amazon 153 00:07:50,440 --> 00:07:53,600 Speaker 2: ghost store that has now been shut down. Like they 154 00:07:54,160 --> 00:07:56,920 Speaker 2: had some sense that this was going to be a thing, 155 00:07:56,960 --> 00:07:58,440 Speaker 2: but they didn't quite play it right. 156 00:07:58,520 --> 00:08:01,040 Speaker 4: How have they turned it around? No, I think it's true. 157 00:08:01,080 --> 00:08:03,360 Speaker 9: I mean, Jeff was pushing machine learning tools inside the 158 00:08:03,400 --> 00:08:06,480 Speaker 9: company for more than a decade. I think that there 159 00:08:06,520 --> 00:08:09,080 Speaker 9: was an opportunity for them to invest earlier than they 160 00:08:09,120 --> 00:08:13,720 Speaker 9: did in anthropic maybe even open AI. But they've turned 161 00:08:13,760 --> 00:08:17,680 Speaker 9: it around by running the Amazon playbook. So they produce 162 00:08:18,160 --> 00:08:21,520 Speaker 9: chips to compete with in videos AI processors, and the 163 00:08:21,560 --> 00:08:23,520 Speaker 9: selling point for those is that they're a little bit 164 00:08:23,520 --> 00:08:27,360 Speaker 9: more cost effective. Come to Amazon, run the models, save 165 00:08:27,400 --> 00:08:29,280 Speaker 9: a little bit of money, or do it more efficiently. 166 00:08:29,560 --> 00:08:31,960 Speaker 9: The other thing is you go to Amazon's Bedrock service, 167 00:08:32,000 --> 00:08:34,679 Speaker 9: which is how you'd run your AI applications if you're 168 00:08:34,679 --> 00:08:37,040 Speaker 9: an AWS customer, and I kind of compare it to 169 00:08:37,040 --> 00:08:40,640 Speaker 9: a sort of a diner's menu of options. People want 170 00:08:40,640 --> 00:08:42,960 Speaker 9: to run their AI where their data is and where 171 00:08:43,000 --> 00:08:46,000 Speaker 9: their applications are, so depending on their customers and their 172 00:08:46,040 --> 00:08:49,480 Speaker 9: market share and the hassle or the inconvenience of moving 173 00:08:49,520 --> 00:08:52,320 Speaker 9: off that to basically solidify their position. 174 00:08:53,200 --> 00:08:57,240 Speaker 3: So is Amazon's AI basically just a business to business? 175 00:08:57,320 --> 00:08:59,680 Speaker 3: Is it like a B to b AI? Or will 176 00:08:59,679 --> 00:09:02,959 Speaker 3: costs like Prime members experience it too? 177 00:09:03,080 --> 00:09:05,560 Speaker 4: I mean, this is Amazon, right, so it's always everything. 178 00:09:05,640 --> 00:09:06,760 Speaker 4: So they've done a couple of things. 179 00:09:06,800 --> 00:09:07,560 Speaker 3: The answer is yes. 180 00:09:07,760 --> 00:09:09,120 Speaker 4: The answer is always yes. 181 00:09:09,240 --> 00:09:11,840 Speaker 2: This is maybe a good time to broad the conversation 182 00:09:12,320 --> 00:09:14,960 Speaker 2: to AI in general. This is the kind of cover 183 00:09:15,120 --> 00:09:17,520 Speaker 2: story of a series of stories that are in the 184 00:09:17,520 --> 00:09:21,960 Speaker 2: same issue, all about AI and kind of what it's 185 00:09:22,000 --> 00:09:24,640 Speaker 2: going to take to take AI to the next level. 186 00:09:24,840 --> 00:09:26,640 Speaker 2: Stacy has a story, and I've got a story in it. 187 00:09:26,720 --> 00:09:29,959 Speaker 2: And I think, Stacey, I thought of your story, which 188 00:09:30,000 --> 00:09:32,760 Speaker 2: is about AI and productivity and the question of what 189 00:09:32,800 --> 00:09:34,800 Speaker 2: it means for productivity, and it made me wonder, And 190 00:09:34,840 --> 00:09:36,559 Speaker 2: I think there is a big question hanging over this 191 00:09:37,080 --> 00:09:39,840 Speaker 2: whole AI revolution, which is like when. 192 00:09:39,720 --> 00:09:42,360 Speaker 4: Are the productivity gains going to come? And are they 193 00:09:42,360 --> 00:09:44,160 Speaker 4: going to come. Are are we sure they're going to come? 194 00:09:44,800 --> 00:09:48,080 Speaker 3: I mean to me, that's a really interesting question about 195 00:09:48,120 --> 00:09:51,480 Speaker 3: AI is that there's all this promise, all this excitement. 196 00:09:51,640 --> 00:09:53,280 Speaker 3: Our economy in a lot of ways is kind of 197 00:09:53,360 --> 00:09:56,520 Speaker 3: counting on it. But there is always a gap with 198 00:09:56,640 --> 00:09:59,960 Speaker 3: new technology between when the technology is introduced and adopt 199 00:10:00,760 --> 00:10:04,200 Speaker 3: and when it actually causes the economy to grow. And 200 00:10:04,240 --> 00:10:06,719 Speaker 3: there's also a gap with jobs, which I think is 201 00:10:06,760 --> 00:10:09,680 Speaker 3: scaring everybody where. It's a lot of jobs get lost 202 00:10:09,760 --> 00:10:12,320 Speaker 3: sometimes because of a new technology, and then eventually economists 203 00:10:12,320 --> 00:10:14,560 Speaker 3: will always say, but new jobs come, But there's a 204 00:10:14,559 --> 00:10:18,840 Speaker 3: gap there too. But one interesting thing about Amazon is 205 00:10:19,120 --> 00:10:21,040 Speaker 3: a lot of companies are grappling with this, like they 206 00:10:21,080 --> 00:10:23,120 Speaker 3: have to make a huge investment. A is not cheap, 207 00:10:23,640 --> 00:10:25,840 Speaker 3: so they have to make a big investment and the 208 00:10:25,880 --> 00:10:28,800 Speaker 3: payoff might not come for a while. But I'm wondering 209 00:10:28,840 --> 00:10:32,040 Speaker 3: if that matters to Amazon or if Amazon can just 210 00:10:32,080 --> 00:10:34,520 Speaker 3: weather the storm, whereas a lot of smaller companies might 211 00:10:34,559 --> 00:10:36,920 Speaker 3: be a little more locked out. 212 00:10:37,280 --> 00:10:40,120 Speaker 9: There is almost no way for these large tech companies 213 00:10:40,160 --> 00:10:41,360 Speaker 9: to lose, right. 214 00:10:41,400 --> 00:10:44,880 Speaker 4: They are the beneficiaries of so much so chilling. 215 00:10:45,120 --> 00:10:49,520 Speaker 9: Yeah, there's so much experimentation that's happening such an industry wide, 216 00:10:49,640 --> 00:10:53,679 Speaker 9: business wide commitment to trying out these AI tools. Amazon's 217 00:10:53,720 --> 00:10:57,360 Speaker 9: also building these data centers. That's two hundred billion dollar 218 00:10:57,480 --> 00:11:01,160 Speaker 9: investment in cap access here. That momentary alarmed investors, and 219 00:11:01,200 --> 00:11:03,440 Speaker 9: then they kind of came back and they fund most 220 00:11:03,440 --> 00:11:05,400 Speaker 9: of that with their own balance sheet, So they're not 221 00:11:05,559 --> 00:11:07,680 Speaker 9: doing the kind of risky lending that maybe some of 222 00:11:07,720 --> 00:11:11,760 Speaker 9: the smaller competitors are. The big risk for Amazon is 223 00:11:11,800 --> 00:11:14,440 Speaker 9: that for the original part of its business, for still 224 00:11:14,440 --> 00:11:17,040 Speaker 9: the largest part of its business, the e commerce company, 225 00:11:17,360 --> 00:11:21,240 Speaker 9: that people start shopping within chat ept or clawd, that 226 00:11:21,400 --> 00:11:25,280 Speaker 9: top of the funnel changes, and these chatbots seem to 227 00:11:25,360 --> 00:11:28,080 Speaker 9: customers to be like a far superior experience and the 228 00:11:28,120 --> 00:11:31,520 Speaker 9: currently ad riddled search results you would get in searching 229 00:11:31,679 --> 00:11:35,120 Speaker 9: on Amazon. And so in some ways rufus maybe you know, 230 00:11:35,200 --> 00:11:37,680 Speaker 9: doesn't have to solve all of our problems as shoppers, 231 00:11:37,679 --> 00:11:39,520 Speaker 9: but it does have to be kind of part of 232 00:11:39,559 --> 00:11:42,680 Speaker 9: the Amazon moat and a reason if these shopping agents 233 00:11:42,720 --> 00:11:45,520 Speaker 9: do take off, that people are compelled to stick with Amazon. 234 00:11:45,760 --> 00:11:47,720 Speaker 4: So much of this just feels very slippery to me. 235 00:11:47,960 --> 00:11:49,720 Speaker 4: I mean, we don't. 236 00:11:49,960 --> 00:11:53,959 Speaker 2: The like prousness wise or politically everything. The productivity gains 237 00:11:54,120 --> 00:11:57,560 Speaker 2: have not come. The question of job losses is very 238 00:11:57,640 --> 00:12:00,680 Speaker 2: much open. I mean, you have companies saying they're laying 239 00:12:00,679 --> 00:12:04,400 Speaker 2: people off with AI because of AI, but it's not 240 00:12:04,559 --> 00:12:07,760 Speaker 2: totally clear. That's the reason. You know, the concept that 241 00:12:07,800 --> 00:12:11,040 Speaker 2: we've talked about AI washing or whatever. And you also 242 00:12:11,080 --> 00:12:14,959 Speaker 2: have including some of these technologists saying now being aware 243 00:12:15,280 --> 00:12:18,079 Speaker 2: that it's maybe a bad look to talk about laying 244 00:12:18,080 --> 00:12:20,960 Speaker 2: people off with because of technology, are now saying, oh no, no, 245 00:12:21,000 --> 00:12:22,800 Speaker 2: AI is actually going to create lots of jobs. So 246 00:12:22,880 --> 00:12:25,520 Speaker 2: like there's all this the messaging is kind of confused. 247 00:12:25,800 --> 00:12:28,480 Speaker 2: I do think there's risk to Amazon. The story that 248 00:12:28,559 --> 00:12:32,280 Speaker 2: I wrote in this issue is about ro Kanna, who's 249 00:12:32,320 --> 00:12:35,160 Speaker 2: a congressman from Silicon Valley. I thought of him as 250 00:12:35,200 --> 00:12:38,800 Speaker 2: basically the most pro tech member of Congress there was. 251 00:12:39,120 --> 00:12:41,640 Speaker 2: He was out there talking about how great Crypto was 252 00:12:41,679 --> 00:12:42,600 Speaker 2: when Crypto. 253 00:12:42,280 --> 00:12:45,000 Speaker 3: Was controversially a lot of pain money. 254 00:12:46,080 --> 00:12:50,120 Speaker 2: Constituents are technologists and he is, And what my story 255 00:12:50,200 --> 00:12:53,640 Speaker 2: is about is how he's basically turned against the industry. 256 00:12:54,160 --> 00:12:56,559 Speaker 2: And I think he's doing it because he's trying to 257 00:12:56,600 --> 00:13:00,160 Speaker 2: run for president, and also because many people in the 258 00:13:00,240 --> 00:13:03,040 Speaker 2: United States are turning against this stuff, these data centers, 259 00:13:03,040 --> 00:13:04,280 Speaker 2: and I think this is going to be a risk 260 00:13:04,320 --> 00:13:07,439 Speaker 2: for Amazon. I think Amazon has a risk of going 261 00:13:07,440 --> 00:13:09,480 Speaker 2: through something similar to what Tesla went through, where maybe 262 00:13:09,760 --> 00:13:12,560 Speaker 2: in a less pronounced way, just because Jeff Bezos and 263 00:13:12,600 --> 00:13:15,839 Speaker 2: the company is not seen as polarizing as Elon Musk is. 264 00:13:15,880 --> 00:13:17,360 Speaker 2: But I don't know, like you even hear it in 265 00:13:17,440 --> 00:13:19,880 Speaker 2: the tape we played. People have ambivalent feelings. 266 00:13:20,160 --> 00:13:22,040 Speaker 9: I agree with that, but I also think we should 267 00:13:22,040 --> 00:13:25,640 Speaker 9: note that a billion people are using chat GBT every 268 00:13:25,720 --> 00:13:31,480 Speaker 9: WEEKAWS just hit its fastest growth rate in like several years. 269 00:13:31,600 --> 00:13:36,040 Speaker 9: People are understandably nervous and maybe ambivalent about AI at 270 00:13:36,040 --> 00:13:39,120 Speaker 9: the same time as another set of numbers showed that 271 00:13:39,120 --> 00:13:43,280 Speaker 9: they're doing everything they can preferences yeah, to experiment with it, 272 00:13:43,320 --> 00:13:45,440 Speaker 9: to integrate it into their own lives. And I'm sure 273 00:13:45,800 --> 00:13:48,280 Speaker 9: Andy jasse is looking at the numbers. He told us 274 00:13:48,320 --> 00:13:51,320 Speaker 9: that Alexa use has doubled since they rolled out Alexa plus. 275 00:13:51,640 --> 00:13:54,679 Speaker 9: They seem very optimistic about rufus that, yeah, maybe despite 276 00:13:54,720 --> 00:13:58,439 Speaker 9: the political sentiment that the nervousness, people are embracing these tools. 277 00:13:58,720 --> 00:14:01,320 Speaker 2: It'll be interesting watching this out over the next couple 278 00:14:01,360 --> 00:14:04,360 Speaker 2: of years. We get an election in twenty eight and 279 00:14:04,600 --> 00:14:07,680 Speaker 2: there's going to be opportunities for customers to make their voices. 280 00:14:07,760 --> 00:14:09,680 Speaker 3: I mean, it's a huge issue. There are tons of protests, 281 00:14:09,720 --> 00:14:12,320 Speaker 3: like in the West Utah there, you know, there's a 282 00:14:12,440 --> 00:14:16,840 Speaker 3: forty thousand acre data center going in and like just 283 00:14:17,360 --> 00:14:21,680 Speaker 3: protests have erupted. People in places with lots of space 284 00:14:21,720 --> 00:14:25,360 Speaker 3: and not a lot of political power are really upset. 285 00:14:25,520 --> 00:14:28,880 Speaker 3: But there may be some victories. I just I guess 286 00:14:28,920 --> 00:14:32,400 Speaker 3: I'm skeptical because of the juggernaut that is AI, that 287 00:14:33,200 --> 00:14:34,280 Speaker 3: it will stop. 288 00:14:35,120 --> 00:14:37,800 Speaker 2: We will continue covering this, We will continue watching this. 289 00:14:37,920 --> 00:14:40,800 Speaker 2: Brad'll be back to update us on the next turn 290 00:14:40,840 --> 00:14:43,600 Speaker 2: of the Yes, please the screw in tech and in 291 00:14:43,640 --> 00:14:46,160 Speaker 2: all the quarters of power, Bradstone, thanks for being here. 292 00:14:46,200 --> 00:14:51,560 Speaker 5: Thank you guys. 293 00:14:52,720 --> 00:14:56,520 Speaker 3: Of course, Max companies like Amazon have been a major 294 00:14:56,600 --> 00:14:59,800 Speaker 3: part of the economic boom that we have been seeing, 295 00:15:00,280 --> 00:15:02,640 Speaker 3: a lot of that economic boom being fueled by AI, 296 00:15:02,960 --> 00:15:07,160 Speaker 3: or at least hopes about AI, and the stock market 297 00:15:07,320 --> 00:15:09,480 Speaker 3: for the last couple of years has just been on 298 00:15:09,560 --> 00:15:11,400 Speaker 3: a tear, going gangbusters. 299 00:15:12,200 --> 00:15:15,240 Speaker 2: Yeah, And it's kind of strange because we've been talking 300 00:15:15,280 --> 00:15:18,160 Speaker 2: on this show all along about the kind of difficult 301 00:15:18,160 --> 00:15:22,360 Speaker 2: news that the economy is confronting. Obviously, there's potential questions 302 00:15:22,400 --> 00:15:25,240 Speaker 2: about AI. There's also the war in Iran oil. The 303 00:15:25,280 --> 00:15:29,520 Speaker 2: price of oil has doubled consumer sentiment. It's not great 304 00:15:29,600 --> 00:15:32,480 Speaker 2: stacy and the Fed, even the Fed, the interest rates 305 00:15:32,560 --> 00:15:33,400 Speaker 2: are not getting cut. 306 00:15:33,600 --> 00:15:36,920 Speaker 3: Yeah, we got some not awesome inflation numbers out this week. 307 00:15:37,040 --> 00:15:39,240 Speaker 3: And the price of everything has been going up pretty 308 00:15:39,280 --> 00:15:42,280 Speaker 3: fast and doesn't look to be slowing down anytime soon. 309 00:15:43,200 --> 00:15:45,280 Speaker 3: And there was a really interesting article that we came 310 00:15:45,320 --> 00:15:48,480 Speaker 3: across from our friend of the show, Kyla Scanlon, about 311 00:15:49,120 --> 00:15:52,720 Speaker 3: why this might be and what we should think about it. 312 00:15:53,040 --> 00:15:55,960 Speaker 3: Kyla is the best selling author of In This Economy. 313 00:15:56,080 --> 00:15:59,320 Speaker 3: She joins us Now, welcome Kyla, I thanks for having me. Okay, 314 00:15:59,400 --> 00:16:01,800 Speaker 3: So the question, there are a lot of reasons why 315 00:16:01,840 --> 00:16:03,280 Speaker 3: it seems like the market should not be on the 316 00:16:03,320 --> 00:16:05,680 Speaker 3: tear they're on. What is going on? Why are they 317 00:16:05,800 --> 00:16:09,000 Speaker 3: so exuberant? I mean, it's a it's a good question. 318 00:16:09,120 --> 00:16:10,240 Speaker 3: Like part of it is earning. 319 00:16:10,440 --> 00:16:12,120 Speaker 10: So like a lot of what's happening with the stock 320 00:16:12,160 --> 00:16:14,280 Speaker 10: market right now is the companies are making a lot 321 00:16:14,280 --> 00:16:17,040 Speaker 10: of money, Like the AI trade is really working out. 322 00:16:17,120 --> 00:16:20,360 Speaker 10: But like what you said about energy prices also really 323 00:16:20,400 --> 00:16:23,160 Speaker 10: matters because that puts a lot of pressure on in 324 00:16:23,280 --> 00:16:27,360 Speaker 10: companies historically. I mean, sometimes it doesn't, but the companies 325 00:16:27,560 --> 00:16:31,280 Speaker 10: in the stocks are kind of shrugging everything off, it seems. 326 00:16:31,560 --> 00:16:35,440 Speaker 10: And there's this implicit assumption that AI will continue to 327 00:16:35,480 --> 00:16:38,400 Speaker 10: carry the economy forward, that the data center build out 328 00:16:38,400 --> 00:16:41,120 Speaker 10: will continue to happen, that the grid can support all 329 00:16:41,160 --> 00:16:43,240 Speaker 10: of the data centers that are being built, the companies 330 00:16:43,280 --> 00:16:46,120 Speaker 10: are going to adopt all of the AI, and that 331 00:16:46,160 --> 00:16:48,600 Speaker 10: will carry the stock market because the AI companies are 332 00:16:48,640 --> 00:16:50,600 Speaker 10: such a big component of the S and P five 333 00:16:50,680 --> 00:16:53,760 Speaker 10: hundred that, Yeah, it just seems like nothing can slow 334 00:16:53,800 --> 00:16:54,200 Speaker 10: it down. 335 00:16:54,760 --> 00:16:57,480 Speaker 2: The challenges that you're referring to, like we should just 336 00:16:57,520 --> 00:17:00,520 Speaker 2: spell them out. So I guess the big one is 337 00:17:00,560 --> 00:17:04,440 Speaker 2: that Trump started a war with Ran and the trade 338 00:17:04,480 --> 00:17:07,480 Speaker 2: of horror moves has been closed as we're recording this 339 00:17:07,640 --> 00:17:08,639 Speaker 2: for six weeks. 340 00:17:08,640 --> 00:17:09,960 Speaker 4: I guess something like that more than. 341 00:17:10,600 --> 00:17:16,600 Speaker 2: Yeah, and probably like more importantly shows no sign of reopening. 342 00:17:17,040 --> 00:17:21,560 Speaker 2: And and that is causing fuel crisis crises and countries 343 00:17:21,600 --> 00:17:22,240 Speaker 2: around the world. 344 00:17:22,280 --> 00:17:25,240 Speaker 4: There's like and on top of that, it creates inflation. House. 345 00:17:25,480 --> 00:17:27,080 Speaker 4: There's that that's happening. 346 00:17:27,359 --> 00:17:32,360 Speaker 2: I mean, what are the other big economic challenges that that, 347 00:17:32,440 --> 00:17:34,800 Speaker 2: in your mind, should be the stock mark should be 348 00:17:34,800 --> 00:17:36,400 Speaker 2: responding to, besides fuel prices. 349 00:17:36,760 --> 00:17:38,600 Speaker 3: I mean, I think it's just the uncertainty of it all. 350 00:17:38,680 --> 00:17:41,320 Speaker 10: Like I for businesses right now, like we don't even 351 00:17:41,320 --> 00:17:43,480 Speaker 10: talk about tariffs anymore, but like that's still a thing 352 00:17:43,960 --> 00:17:45,960 Speaker 10: that companies are having to deal with. The higher health 353 00:17:45,960 --> 00:17:48,480 Speaker 10: insurance costs have led a lot of companies to pull 354 00:17:48,600 --> 00:17:51,200 Speaker 10: back on hiring spin So I think it's just kind 355 00:17:51,240 --> 00:17:55,080 Speaker 10: of like the general environment you would think would put 356 00:17:55,160 --> 00:17:57,800 Speaker 10: a lot of pressure on companies. You would think that 357 00:17:57,840 --> 00:18:00,200 Speaker 10: what's happening with the federal reserves, sort of the question 358 00:18:00,320 --> 00:18:03,159 Speaker 10: around independence would cause the stock market to sell off 359 00:18:03,200 --> 00:18:05,120 Speaker 10: a little bit, But it doesn't seem to care at all. 360 00:18:05,520 --> 00:18:08,679 Speaker 10: You would think that the political path that we're on, 361 00:18:08,800 --> 00:18:11,720 Speaker 10: where we're you know, sort of objectively kind of fighting 362 00:18:11,720 --> 00:18:14,879 Speaker 10: with our allies would upset the stock market a little bit, 363 00:18:14,920 --> 00:18:16,680 Speaker 10: but it doesn't seem to care. Like, it doesn't seem 364 00:18:16,720 --> 00:18:20,120 Speaker 10: to worry about the fact that a lot of what 365 00:18:20,480 --> 00:18:23,960 Speaker 10: our trade relationships are are you know, requiring our friends 366 00:18:24,000 --> 00:18:26,560 Speaker 10: to continue to exist, to have those friendships continue to 367 00:18:26,600 --> 00:18:30,040 Speaker 10: be fruitful, not putting tariffs on other countries. So I mean, 368 00:18:30,080 --> 00:18:32,080 Speaker 10: it's just it's kind of everything. You would expect the 369 00:18:32,119 --> 00:18:35,280 Speaker 10: stock market to go down, but maybe it's because everything 370 00:18:35,320 --> 00:18:37,040 Speaker 10: is so crazy that it just goes up. 371 00:18:37,400 --> 00:18:40,240 Speaker 3: Well, also, interest rates, that's normally something the market really 372 00:18:40,280 --> 00:18:42,919 Speaker 3: responds to. With interest rates aren't cut, you know, that 373 00:18:42,960 --> 00:18:45,440 Speaker 3: can really slow the economy down. It makes borrowing more expensive, 374 00:18:45,480 --> 00:18:49,200 Speaker 3: which can mean businesses and people borrow less and spend less. 375 00:18:49,359 --> 00:18:54,320 Speaker 3: But that even that hasn't dampened the markets. Nothing, nothing else, 376 00:18:54,960 --> 00:18:57,520 Speaker 3: So what what do you think is going on? 377 00:18:57,720 --> 00:18:59,959 Speaker 10: So in this New York Times opinion piece that I wrote, 378 00:19:00,440 --> 00:19:06,120 Speaker 10: I theorized that the markets expect to get rescued. So 379 00:19:06,200 --> 00:19:08,920 Speaker 10: like every time something goes bad, you know, the stock 380 00:19:09,000 --> 00:19:11,840 Speaker 10: market gets rescued by the Federal Reserve, and this is 381 00:19:11,840 --> 00:19:13,800 Speaker 10: something the Fed has to do. I think it makes 382 00:19:13,840 --> 00:19:16,679 Speaker 10: a lot of sense, it's very rational. But Greenspan did it, 383 00:19:16,760 --> 00:19:19,600 Speaker 10: or Ninki did it. Powell did it during the pandemic. 384 00:19:19,680 --> 00:19:22,560 Speaker 10: Because when you raise interest rates, as you were saying, 385 00:19:22,640 --> 00:19:26,160 Speaker 10: like people spend less money, you know, the money's more expensive, 386 00:19:26,480 --> 00:19:29,800 Speaker 10: people slow the economy down. So then ultimately hopefully inflation 387 00:19:29,960 --> 00:19:32,000 Speaker 10: goes down. As sort of like the mechanism that interest 388 00:19:32,040 --> 00:19:35,280 Speaker 10: rates move throughout the economy. And so if the stock 389 00:19:35,320 --> 00:19:38,719 Speaker 10: market or the economy rather it needs more support, the 390 00:19:38,760 --> 00:19:42,040 Speaker 10: Federal Reserve will lower interest rates, money gets more free, 391 00:19:42,119 --> 00:19:45,400 Speaker 10: everything's a little easier. But the stock market now expects 392 00:19:45,400 --> 00:19:49,240 Speaker 10: that rescue mechanism to take place, like it's been saved 393 00:19:49,359 --> 00:19:52,360 Speaker 10: for you know, I guess is that like forty years, right. 394 00:19:52,960 --> 00:19:56,000 Speaker 10: It seems like that is part of the reason that 395 00:19:56,080 --> 00:19:59,439 Speaker 10: it refuses to go down is because they know that 396 00:19:59,800 --> 00:20:00,760 Speaker 10: it doesn't have to. 397 00:20:01,160 --> 00:20:04,280 Speaker 2: Well, and we have a new kind of mechanism, which 398 00:20:04,320 --> 00:20:09,480 Speaker 2: is taco or just this sense. Yeah, Donald Trump cares 399 00:20:10,040 --> 00:20:11,560 Speaker 2: a lot about the stock. 400 00:20:11,280 --> 00:20:14,480 Speaker 10: Market, but yeah, the market, just the taco stuff only 401 00:20:14,480 --> 00:20:17,520 Speaker 10: works to a certain extent. Like you can only move 402 00:20:17,560 --> 00:20:20,600 Speaker 10: things forward by narrative so much. And like with the tariffs, 403 00:20:20,600 --> 00:20:23,199 Speaker 10: it was kind of fine because you could pretend that 404 00:20:23,720 --> 00:20:25,720 Speaker 10: you were taking them on and taking them off and 405 00:20:25,760 --> 00:20:28,520 Speaker 10: you could push it out. But when you enter a war, 406 00:20:29,080 --> 00:20:31,439 Speaker 10: you can't really be like never mind. And so I 407 00:20:31,440 --> 00:20:35,400 Speaker 10: think that's the issue that markets are wrapping their head around. 408 00:20:35,880 --> 00:20:39,160 Speaker 10: So with this expectation of rescue. But yeah, it doesn't 409 00:20:39,160 --> 00:20:42,000 Speaker 10: seem like the street's going to open anytime soon. It 410 00:20:42,000 --> 00:20:44,960 Speaker 10: seems like it opens potentially right before Future is open 411 00:20:45,160 --> 00:20:47,360 Speaker 10: on Sunday nights. I think we get like a little 412 00:20:47,440 --> 00:20:50,720 Speaker 10: hint to that it could open, and then it closes 413 00:20:50,760 --> 00:20:51,960 Speaker 10: again Monday morning. 414 00:20:52,520 --> 00:20:55,800 Speaker 3: So let's say you know the markets think okay, well, 415 00:20:55,960 --> 00:20:58,159 Speaker 3: Trump is watching the markets. He's going to respond to this. 416 00:20:58,280 --> 00:20:59,720 Speaker 3: He's not going to let things get really bad and 417 00:21:00,119 --> 00:21:03,200 Speaker 3: if they do, we'll get bailed out. What is bad 418 00:21:03,240 --> 00:21:06,360 Speaker 3: about that? Because, as Max said, like having the markets 419 00:21:06,400 --> 00:21:10,040 Speaker 3: be high is a good thing. It companies make more money, 420 00:21:10,040 --> 00:21:13,160 Speaker 3: they tend to expand higher. Like this seems not so bad. 421 00:21:13,720 --> 00:21:16,640 Speaker 10: I mean, I guess it just depends on your thoughts 422 00:21:16,680 --> 00:21:20,080 Speaker 10: around if it's real, like a lot of the company's 423 00:21:20,200 --> 00:21:23,560 Speaker 10: earnings are profitability is up. But you don't want a 424 00:21:23,560 --> 00:21:27,800 Speaker 10: market that is so detached from fundamentals when it comes 425 00:21:27,800 --> 00:21:30,800 Speaker 10: time for like fundamentals to really matter. So I think 426 00:21:30,840 --> 00:21:34,800 Speaker 10: the worry is that the market is not pricing risk properly, 427 00:21:35,200 --> 00:21:37,560 Speaker 10: Like it doesn't seem very aware that there's this like 428 00:21:37,720 --> 00:21:40,320 Speaker 10: very big thing happening that it should have more of 429 00:21:40,359 --> 00:21:43,160 Speaker 10: a concept around rather than betting that this AI trade, 430 00:21:43,160 --> 00:21:45,520 Speaker 10: which is reliant on the strait of Hormuz being open, 431 00:21:46,240 --> 00:21:48,160 Speaker 10: that that'll just carry us all through. So I think 432 00:21:48,200 --> 00:21:50,800 Speaker 10: it's like it's not a good or bad thing. It's 433 00:21:50,840 --> 00:21:52,800 Speaker 10: just like the worry that the market is not properly 434 00:21:52,880 --> 00:21:56,040 Speaker 10: understanding what's happening. And then that puts four one k's 435 00:21:56,040 --> 00:21:59,080 Speaker 10: at risk, that puts retirees at risk, That puts the 436 00:21:59,080 --> 00:22:04,320 Speaker 10: stability of the entire American experiment at risk. So that's 437 00:22:04,359 --> 00:22:08,640 Speaker 10: the concern. Yeah, that's very dramatic. But like, we socialize 438 00:22:08,640 --> 00:22:11,719 Speaker 10: retirement in the United States through the stock market, and 439 00:22:11,800 --> 00:22:14,359 Speaker 10: we have an aging population. One in five Americans are 440 00:22:14,359 --> 00:22:15,879 Speaker 10: going to be over the age of sixty five by 441 00:22:15,880 --> 00:22:18,280 Speaker 10: twenty thirty, and we don't really have a plan for 442 00:22:18,359 --> 00:22:20,080 Speaker 10: like what is going to happen, And so that's my 443 00:22:20,280 --> 00:22:22,760 Speaker 10: concern is that we're going to have an aging population 444 00:22:22,880 --> 00:22:24,560 Speaker 10: that is not going to be able to retire because 445 00:22:24,600 --> 00:22:27,280 Speaker 10: the stock market is not properly understanding the risk that 446 00:22:27,280 --> 00:22:28,040 Speaker 10: it is before it. 447 00:22:28,359 --> 00:22:31,000 Speaker 3: What about a bailout. I mean, let's say the stock 448 00:22:31,000 --> 00:22:34,120 Speaker 3: market does not understand the risk properly, Like, why can't 449 00:22:34,119 --> 00:22:35,040 Speaker 3: a bailout happen? 450 00:22:36,000 --> 00:22:39,399 Speaker 10: That's like the rescue mechanism, right, Yeah, so it could happen, 451 00:22:39,600 --> 00:22:42,480 Speaker 10: but then that puts the US government even more extreme 452 00:22:42,600 --> 00:22:45,440 Speaker 10: in the debt situation. I think debt to GDP just 453 00:22:45,480 --> 00:22:47,399 Speaker 10: passed like one hundred percent. There's a chart from the 454 00:22:47,400 --> 00:22:50,520 Speaker 10: Wallsty Journal. You don't want a situation where you're always 455 00:22:50,560 --> 00:22:54,159 Speaker 10: having to finance a way out of a crisis, because 456 00:22:54,280 --> 00:22:56,520 Speaker 10: that puts more pressure on the US government. That puts 457 00:22:56,520 --> 00:22:58,760 Speaker 10: more pressure on Social Security and Medicare. Like, the government 458 00:22:58,760 --> 00:23:01,399 Speaker 10: can only spend money on some things. So if all 459 00:23:01,400 --> 00:23:03,400 Speaker 10: of a sudden it has you know, both the Federal 460 00:23:03,400 --> 00:23:05,879 Speaker 10: Reserve and the US government. If fiscal policy came in 461 00:23:05,880 --> 00:23:07,800 Speaker 10: and saved the stock market, a lot of pressure on 462 00:23:07,840 --> 00:23:10,159 Speaker 10: the government. If montey policy has to come in and 463 00:23:10,200 --> 00:23:13,480 Speaker 10: save the stock market, a lot of pressure on indust rates. 464 00:23:13,520 --> 00:23:17,760 Speaker 2: This this idea that AI is the last hope for 465 00:23:17,800 --> 00:23:20,119 Speaker 2: this economy, I find this very concerning. 466 00:23:20,400 --> 00:23:22,160 Speaker 4: As somebody's followed the AI. 467 00:23:22,880 --> 00:23:24,280 Speaker 3: Very serious AI skeptic. 468 00:23:24,320 --> 00:23:27,000 Speaker 2: Well, I mean, I do hope that the AI can 469 00:23:27,040 --> 00:23:29,480 Speaker 2: save us all and will continue. But you know, we 470 00:23:29,520 --> 00:23:33,439 Speaker 2: have yet to see big productivity gains from any of 471 00:23:33,440 --> 00:23:37,520 Speaker 2: this stuff. The history of productivity gains from other technology 472 00:23:37,520 --> 00:23:39,360 Speaker 2: suggests it could take a really long time for it. 473 00:23:39,320 --> 00:23:39,960 Speaker 4: To show up. 474 00:23:40,320 --> 00:23:44,160 Speaker 2: So it feels like there is going to be at 475 00:23:44,200 --> 00:23:49,879 Speaker 2: some point a sort of reckoning around AI, and just 476 00:23:49,880 --> 00:23:53,000 Speaker 2: because asset prices are so high, and because we have 477 00:23:53,119 --> 00:23:56,480 Speaker 2: yet to see a lot of like a lot to 478 00:23:56,520 --> 00:23:59,320 Speaker 2: show for all this investment, and because there's a big 479 00:23:59,359 --> 00:24:02,040 Speaker 2: political backlash that it appears to be. 480 00:24:02,000 --> 00:24:03,600 Speaker 4: Brewing that the data center. 481 00:24:03,960 --> 00:24:06,199 Speaker 2: Yeah, that would also put and so what would an 482 00:24:06,240 --> 00:24:08,679 Speaker 2: AI bailout look like to your mind? I mean, you 483 00:24:08,720 --> 00:24:10,920 Speaker 2: brought up some of the things in this Times op 484 00:24:11,080 --> 00:24:12,880 Speaker 2: ed some of the things that the Trump administraty sort 485 00:24:12,880 --> 00:24:13,720 Speaker 2: of already doing. 486 00:24:14,280 --> 00:24:16,760 Speaker 4: Yeah, what what would that look like an AI bailout? 487 00:24:16,840 --> 00:24:20,639 Speaker 10: Yeah, So my colleague got the Vanderbilt Policy Accelerator. He 488 00:24:20,680 --> 00:24:23,640 Speaker 10: wrote this incredible paper called After the AI Crash where 489 00:24:23,680 --> 00:24:26,919 Speaker 10: he goes into this and great detail. Is an excellent paper, 490 00:24:27,280 --> 00:24:29,840 Speaker 10: Highly recommend people read it. But he has all of 491 00:24:29,880 --> 00:24:32,560 Speaker 10: these policy ideas prepared for, like what it could happen, 492 00:24:32,640 --> 00:24:36,240 Speaker 10: what could we do to respond to an AI crash happening? 493 00:24:36,240 --> 00:24:38,200 Speaker 10: And a lot of it is sort of separating things, 494 00:24:38,240 --> 00:24:40,800 Speaker 10: so like separating the al labs and the data centers 495 00:24:41,320 --> 00:24:44,760 Speaker 10: Glass stekl for AI having maybe a public data center 496 00:24:44,840 --> 00:24:45,680 Speaker 10: so the public. 497 00:24:45,400 --> 00:24:48,760 Speaker 12: Can regulation government regular so much regulation because we're doing 498 00:24:48,760 --> 00:24:50,440 Speaker 12: the same thing that we did with social media where 499 00:24:50,440 --> 00:24:53,400 Speaker 12: we're like let it ride and like let's see what happens, 500 00:24:53,400 --> 00:24:55,600 Speaker 12: and it's a crazy, crazy experiment. 501 00:24:55,800 --> 00:24:57,240 Speaker 4: I have one other question. 502 00:24:57,760 --> 00:24:59,680 Speaker 2: Maybe the market is just right, like a lot of 503 00:24:59,720 --> 00:25:04,520 Speaker 2: smart people and you know, corporate profits are high, Like hey, 504 00:25:04,720 --> 00:25:07,600 Speaker 2: like you know this is you guys are just doom saying, 505 00:25:08,119 --> 00:25:10,400 Speaker 2: what's your kind of response, Like, maybe the market's right. 506 00:25:10,880 --> 00:25:11,399 Speaker 3: It might be. 507 00:25:11,600 --> 00:25:14,520 Speaker 10: I mean, yeah, sure, but I think that if you 508 00:25:14,640 --> 00:25:18,719 Speaker 10: just sort of look at the reality of what is 509 00:25:18,760 --> 00:25:22,280 Speaker 10: happening in terms of the underpricing of the risk, like 510 00:25:23,040 --> 00:25:25,040 Speaker 10: a lot of it doesn't make sense. And so I 511 00:25:25,080 --> 00:25:26,600 Speaker 10: think if you look at the metrics and you look 512 00:25:26,640 --> 00:25:28,960 Speaker 10: at the numbers, you can certainly point to the companies 513 00:25:28,960 --> 00:25:30,679 Speaker 10: making a bunch of money and like the AI company 514 00:25:30,720 --> 00:25:32,520 Speaker 10: is spending a ton of money, and like that's obviously 515 00:25:32,560 --> 00:25:34,440 Speaker 10: you're going to bring some money in. But I think 516 00:25:34,480 --> 00:25:36,640 Speaker 10: if you like dive just a little bit deeper into 517 00:25:36,680 --> 00:25:38,840 Speaker 10: the other companies and that's some pop five hundred that 518 00:25:38,840 --> 00:25:41,040 Speaker 10: are not in the AI trade, you started to see 519 00:25:41,040 --> 00:25:42,400 Speaker 10: where some of the cracks are forming. 520 00:25:42,880 --> 00:25:45,240 Speaker 2: Is Trump going to be like it's your patriotic duty 521 00:25:45,280 --> 00:25:47,480 Speaker 2: to use chat apt for like eight hours a day. 522 00:25:47,640 --> 00:25:50,520 Speaker 10: I'm just trying to imagine he'll have his own AI model. 523 00:25:51,320 --> 00:25:53,919 Speaker 10: I do think there will be a Trump branded AI model. 524 00:25:54,640 --> 00:25:57,240 Speaker 2: It is Honestly, the more we talk about this is 525 00:25:57,240 --> 00:26:02,439 Speaker 2: the more shockdand that there isn't Trump companies and family 526 00:26:02,480 --> 00:26:05,480 Speaker 2: business very good at like at sniffing out where the 527 00:26:05,520 --> 00:26:06,440 Speaker 2: hot trend is. 528 00:26:06,720 --> 00:26:08,440 Speaker 3: It's a big deal, you would. 529 00:26:08,240 --> 00:26:11,640 Speaker 2: Think, Donald Junior, write us, let us know. We could 530 00:26:11,640 --> 00:26:14,840 Speaker 2: probably send you some others. Right, everybody's a bloomber done net. 531 00:26:15,080 --> 00:26:15,439 Speaker 5: That's right. 532 00:26:15,680 --> 00:26:19,160 Speaker 3: Yes, indeed, so Kyler, like what are you watching right now? 533 00:26:19,240 --> 00:26:21,920 Speaker 3: What are the tells that you're watching? Kyla? I don't 534 00:26:21,960 --> 00:26:25,800 Speaker 3: even know anymore. I mean, like there are no Canaris. 535 00:26:27,240 --> 00:26:29,359 Speaker 1: It's good, Like I'm happy that I don't want to 536 00:26:30,240 --> 00:26:33,159 Speaker 1: tell yes, like I write, like, I just think it's 537 00:26:33,200 --> 00:26:35,280 Speaker 1: important to be like, hey, there's a huge risk thing 538 00:26:35,600 --> 00:26:37,879 Speaker 1: that like maybe the stock market isn't pricing in and 539 00:26:37,920 --> 00:26:41,200 Speaker 1: considering our demographic issue in terms of an aging population, 540 00:26:41,600 --> 00:26:43,440 Speaker 1: we should really be paying attention to, like how the 541 00:26:43,480 --> 00:26:46,120 Speaker 1: stock market is pricing risk to make sure that people 542 00:26:46,160 --> 00:26:48,480 Speaker 1: can retire properly, because that's the way we retire people. 543 00:26:48,560 --> 00:26:51,400 Speaker 1: So like that's why I keep harpening on this this problem. 544 00:26:51,720 --> 00:26:55,400 Speaker 1: But I like the economy is astoundingly resilient. 545 00:26:55,760 --> 00:26:58,600 Speaker 10: It is. Yeah, I mean that's interesting though, because like 546 00:26:58,640 --> 00:27:02,160 Speaker 10: the little treat economy is fascinating where there's a really 547 00:27:02,240 --> 00:27:04,680 Speaker 10: interesting research peaper that came out in November twenty twenty 548 00:27:04,680 --> 00:27:07,879 Speaker 10: five talking about the perception of housing affordability and if 549 00:27:07,920 --> 00:27:10,479 Speaker 10: people feel like they can't afford a home, they're more 550 00:27:10,680 --> 00:27:13,560 Speaker 10: likely to take riskier investment decisions and more likely to 551 00:27:13,560 --> 00:27:16,320 Speaker 10: gamble in prediction markets or sports betting, and then they're 552 00:27:16,359 --> 00:27:19,280 Speaker 10: more likely to indulge in kind of like these little 553 00:27:19,440 --> 00:27:21,800 Speaker 10: one off things, like spend a bit more money on 554 00:27:21,840 --> 00:27:23,520 Speaker 10: the little treats because. 555 00:27:23,280 --> 00:27:24,200 Speaker 3: They're not saving for anything. 556 00:27:24,560 --> 00:27:26,560 Speaker 10: Yeah, because they're like, I'm never going to afford a house, 557 00:27:26,600 --> 00:27:28,800 Speaker 10: and that's kind of where I'm at, Like, I don't 558 00:27:28,840 --> 00:27:31,320 Speaker 10: really know how I like it, just to feel so 559 00:27:31,480 --> 00:27:33,560 Speaker 10: out of reach, and I think a lot of people 560 00:27:33,600 --> 00:27:34,240 Speaker 10: are in that boat. 561 00:27:34,280 --> 00:27:35,880 Speaker 3: And you're just like, Okay, sure, I'll get the nines 562 00:27:35,960 --> 00:27:37,560 Speaker 3: like the Yolo economy. 563 00:27:37,160 --> 00:27:38,920 Speaker 10: Right, totally, totally. 564 00:27:39,000 --> 00:27:41,679 Speaker 3: Well, Kylo, you will have to come back as this 565 00:27:41,760 --> 00:27:45,200 Speaker 3: thing shakes out and help us navigate through these crazy times. 566 00:27:45,240 --> 00:27:47,280 Speaker 3: But thank you for joining us, Thanks for having me. 567 00:27:55,640 --> 00:27:59,000 Speaker 2: All right, Stacey. We have a special guest for today's 568 00:27:59,080 --> 00:28:02,200 Speaker 2: Underrated Story. But first I need to share this email 569 00:28:02,280 --> 00:28:08,119 Speaker 2: that we got in the Everybody's business mailbox Everybody's. 570 00:28:06,440 --> 00:28:09,040 Speaker 4: From Kyle about tickets. Remember we had asked. 571 00:28:09,080 --> 00:28:11,679 Speaker 2: This was a couple of weeks ago, and we've been 572 00:28:11,680 --> 00:28:14,600 Speaker 2: sitting on this because things got busy or whatever. Sorry Kyle, 573 00:28:14,920 --> 00:28:17,040 Speaker 2: but the email is so funny because we had asked, 574 00:28:17,080 --> 00:28:19,280 Speaker 2: what's the most you've ever spent on tickets? You know, 575 00:28:19,359 --> 00:28:21,800 Speaker 2: we talked about I forget what I said, a few 576 00:28:21,840 --> 00:28:25,600 Speaker 2: hundred bucks, and Kyle took us on a journey from 577 00:28:25,920 --> 00:28:28,000 Speaker 2: the most he had ever spent had been twenty five 578 00:28:28,080 --> 00:28:30,879 Speaker 2: dollars to go to the Van's Warped tour in the 579 00:28:30,920 --> 00:28:34,760 Speaker 2: early two thousands to then he went and he bought 580 00:28:34,760 --> 00:28:38,240 Speaker 2: tickets on the early side to an Era's tour show 581 00:28:38,400 --> 00:28:41,280 Speaker 2: in Arizona to Taylor Swich dollars. 582 00:28:41,360 --> 00:28:42,160 Speaker 4: Yeah, so that was the. 583 00:28:42,480 --> 00:28:45,120 Speaker 3: Like a pretty good deal, Kyle. 584 00:28:45,160 --> 00:28:46,920 Speaker 4: I would agree with you because you will hear what 585 00:28:46,960 --> 00:28:47,640 Speaker 4: happens next. 586 00:28:47,880 --> 00:28:50,160 Speaker 2: Then he was so into it that they went to 587 00:28:50,240 --> 00:28:54,320 Speaker 2: another show one thousand dollars each in southern California, then 588 00:28:55,120 --> 00:28:57,920 Speaker 2: Taylor also a Taylor Swish show. Then at the end 589 00:28:57,960 --> 00:29:01,000 Speaker 2: of the US leg they went again thirteen hundred dollars 590 00:29:01,040 --> 00:29:01,480 Speaker 2: a ticket. 591 00:29:02,120 --> 00:29:04,840 Speaker 4: Then how big is the family European leg? 592 00:29:04,920 --> 00:29:06,720 Speaker 2: This is mostly him and his wife, although there's some 593 00:29:06,840 --> 00:29:09,680 Speaker 2: friends and family thrown in there as well. Then they 594 00:29:09,720 --> 00:29:13,320 Speaker 2: went on the European leg of the tour twenty four 595 00:29:13,400 --> 00:29:14,800 Speaker 2: hundred dollars per ticket. 596 00:29:14,800 --> 00:29:17,320 Speaker 3: And still tweeler swift. They were like still they were 597 00:29:17,360 --> 00:29:19,720 Speaker 3: like deadheads, but they were following. 598 00:29:19,480 --> 00:29:22,360 Speaker 2: Thirty six one hundred dollars. That's the most he has 599 00:29:22,400 --> 00:29:26,240 Speaker 2: spent on a concert ticket. Now, that was the Vancouver 600 00:29:26,560 --> 00:29:29,480 Speaker 2: Eras tour. That was the end of the RAS tour. 601 00:29:29,520 --> 00:29:33,080 Speaker 2: He wanted to see the ending. Kyle, I, first of all, 602 00:29:33,200 --> 00:29:35,560 Speaker 2: I thank you for this email. It is an awesome email. 603 00:29:35,560 --> 00:29:39,360 Speaker 2: It really shows you how concert tickets have gotten more expensive, certainly, 604 00:29:39,480 --> 00:29:41,880 Speaker 2: but also the way that that fandom can just draw 605 00:29:41,920 --> 00:29:42,200 Speaker 2: you in. 606 00:29:42,880 --> 00:29:46,640 Speaker 3: It does show I mean, one of the big things 607 00:29:46,720 --> 00:29:50,239 Speaker 3: that is very important to millennials, especially post pandemic. One 608 00:29:50,280 --> 00:29:52,560 Speaker 3: of the things that's been the big shift in spending 609 00:29:52,600 --> 00:29:56,000 Speaker 3: has been people buying fewer goods and buying more services. 610 00:29:56,040 --> 00:30:01,960 Speaker 3: People want experiences. Younger people don't want stuff, they want experiences, 611 00:30:02,080 --> 00:30:05,800 Speaker 3: and I do feel like this speaks to the fact 612 00:30:05,960 --> 00:30:09,680 Speaker 3: that the experience of seeing Taylor Swift and concert apparently 613 00:30:10,040 --> 00:30:13,880 Speaker 3: so powerful. You travel the country and the other world 614 00:30:13,920 --> 00:30:18,400 Speaker 3: and shell out thousands to see the same songs and 615 00:30:18,520 --> 00:30:22,320 Speaker 3: outfit changes because there is something so powerful in the experience. 616 00:30:22,440 --> 00:30:25,480 Speaker 2: All right, Kyle, thank you for the email. Listeners, please 617 00:30:25,480 --> 00:30:27,360 Speaker 2: write to us. Everybody's at Bloomberg dot net. 618 00:30:28,040 --> 00:30:35,480 Speaker 4: Shake it off, Stacy. Normally the underrated portion of the 619 00:30:35,480 --> 00:30:36,760 Speaker 4: show when we get here, it's just you and me. 620 00:30:36,880 --> 00:30:37,360 Speaker 3: This is true. 621 00:30:37,560 --> 00:30:40,160 Speaker 2: We have a special guest with us right here, Sean WN, 622 00:30:40,280 --> 00:30:43,840 Speaker 2: Bloomberg reporter, joining us to help with the underrated segment. 623 00:30:43,920 --> 00:30:46,880 Speaker 4: Hey Sean, thank you, pleasure to be here. Now. Sean 624 00:30:47,080 --> 00:30:47,960 Speaker 4: has a new show. 625 00:30:48,040 --> 00:30:52,280 Speaker 2: She is the host the reporter of Foundering The Killing 626 00:30:52,280 --> 00:30:55,920 Speaker 2: of Bob Lee, which is an excellent, excellent show about 627 00:30:56,320 --> 00:30:59,240 Speaker 2: this murder in San Francisco. It gets to all these 628 00:30:59,280 --> 00:31:01,760 Speaker 2: issues on what kind of drew you did this story. 629 00:31:02,720 --> 00:31:05,360 Speaker 13: I think I was drawn to it because I liked 630 00:31:05,400 --> 00:31:07,480 Speaker 13: that there were a lot of different things going on. 631 00:31:07,880 --> 00:31:08,360 Speaker 3: It was a. 632 00:31:08,280 --> 00:31:12,320 Speaker 13: Story where every time you thought it was about one thing, 633 00:31:13,120 --> 00:31:14,800 Speaker 13: it ended up being about something else. 634 00:31:15,680 --> 00:31:18,440 Speaker 3: And describe. I feel like everybody saw the headlines, but 635 00:31:18,560 --> 00:31:21,360 Speaker 3: it's there have been a lot of headlines. So briefly 636 00:31:21,400 --> 00:31:23,560 Speaker 3: sum up the story because I think most people will 637 00:31:23,800 --> 00:31:25,560 Speaker 3: remember it. Sure. 638 00:31:25,760 --> 00:31:28,560 Speaker 13: So, three years ago in San Francisco, which is where 639 00:31:28,600 --> 00:31:31,560 Speaker 13: I live, a tech executive was found stab to death 640 00:31:31,680 --> 00:31:35,440 Speaker 13: on the street. And this was during a super sensitive 641 00:31:35,480 --> 00:31:38,560 Speaker 13: time for the city. San Francisco was slow to recover 642 00:31:38,600 --> 00:31:41,960 Speaker 13: from the pandemic. Downtown was hollowed out, and there was 643 00:31:41,960 --> 00:31:45,320 Speaker 13: a widespread fear of crime, which led to the recall 644 00:31:45,480 --> 00:31:49,880 Speaker 13: of our progressive district attorney. And so then you had 645 00:31:49,880 --> 00:31:55,040 Speaker 13: this very grisly, very mysterious seeming death, and it set 646 00:31:55,080 --> 00:31:58,400 Speaker 13: off a wave of online fury. There were rumors, there 647 00:31:58,400 --> 00:32:02,400 Speaker 13: were speculation, and some big names in the tech industry 648 00:32:02,480 --> 00:32:06,040 Speaker 13: weighed in. You had David Sachs who was saying that 649 00:32:06,120 --> 00:32:09,440 Speaker 13: he bet dollars to dimes that Bob Lee was stabbed 650 00:32:09,520 --> 00:32:12,800 Speaker 13: by a psychotic, homeless person. And then you had Ela 651 00:32:12,880 --> 00:32:15,520 Speaker 13: Musk disparaging the New DA. 652 00:32:15,040 --> 00:32:17,080 Speaker 3: So, Sean, we have a clip of your show to 653 00:32:17,120 --> 00:32:19,600 Speaker 3: play and set us up a little bit. What are 654 00:32:19,640 --> 00:32:20,280 Speaker 3: we about to hear? 655 00:32:20,480 --> 00:32:23,920 Speaker 13: So the irony of all of the rumors and speculation 656 00:32:24,880 --> 00:32:29,320 Speaker 13: was that a Boble's actual killer was someone he knew 657 00:32:29,840 --> 00:32:33,400 Speaker 13: and b that the police had their suspect almost immediately. 658 00:32:33,920 --> 00:32:37,720 Speaker 13: They waited nine days to Restnema MOMENTI because they wanted 659 00:32:37,760 --> 00:32:41,320 Speaker 13: to build a stronger case. But in those nine days 660 00:32:41,520 --> 00:32:44,880 Speaker 13: they were actually following him around and they got this 661 00:32:45,080 --> 00:32:49,960 Speaker 13: incredible piece of footage. The prosecution wanted the jury to 662 00:32:49,960 --> 00:32:54,000 Speaker 13: pay attention to Nima's behavior after the stabbing, and for 663 00:32:54,080 --> 00:32:58,160 Speaker 13: this they introduced new evidence, a video of Nima made 664 00:32:58,200 --> 00:33:01,240 Speaker 13: six days after Bob was killed, but before Nima was 665 00:33:01,320 --> 00:33:05,440 Speaker 13: arrested Ohmed to lie. The lead prosecutor was at the 666 00:33:05,440 --> 00:33:07,600 Speaker 13: police station when he saw it for the first time. 667 00:33:08,080 --> 00:33:14,120 Speaker 7: I remember Sergeant Goff calling saying, Hey, I'm headed back 668 00:33:14,160 --> 00:33:15,680 Speaker 7: from South Bay. You gotta see this. 669 00:33:17,800 --> 00:33:20,600 Speaker 13: Sergeant David Goff is an undercover cop who had been 670 00:33:20,640 --> 00:33:24,400 Speaker 13: following Nima since the day after Bob was killed. GoF 671 00:33:24,440 --> 00:33:27,920 Speaker 13: had recorded something he wanted to show the prosecutors, and. 672 00:33:27,840 --> 00:33:30,480 Speaker 7: He wouldn't even really explain it to us. He just 673 00:33:30,560 --> 00:33:35,880 Speaker 7: wanted us to see it. The homicide inspectors do not 674 00:33:36,040 --> 00:33:38,440 Speaker 7: have these fancy offices. They're all kind of in cubes, 675 00:33:38,520 --> 00:33:41,360 Speaker 7: cubicles next to each other. So we were standing and 676 00:33:42,280 --> 00:33:46,480 Speaker 7: Sergeant Goff put in the video and we just press 677 00:33:46,560 --> 00:33:48,560 Speaker 7: play and you kind of I think a couple times 678 00:33:48,600 --> 00:33:51,400 Speaker 7: says he said, just wait, just wait, like just just 679 00:33:51,400 --> 00:33:52,360 Speaker 7: wait for it. Wait for it. 680 00:33:55,160 --> 00:33:58,560 Speaker 4: Also, Stacey, there's a little easter egg. People should check 681 00:33:58,600 --> 00:33:59,040 Speaker 4: this out. 682 00:33:59,080 --> 00:34:02,920 Speaker 2: Foundering and Killing a subscribe listen to it is the usering. 683 00:34:02,600 --> 00:34:07,680 Speaker 3: That you're in with. Unbelievable. 684 00:34:07,760 --> 00:34:11,640 Speaker 2: Yeah, so you know it'll it's but anyway, it's really 685 00:34:11,680 --> 00:34:13,680 Speaker 2: worth your time and you should check it out and 686 00:34:13,760 --> 00:34:14,640 Speaker 2: let us know what you think. 687 00:34:15,000 --> 00:34:16,920 Speaker 4: Sean, you have an underrated story. 688 00:34:16,920 --> 00:34:20,080 Speaker 2: That podcast, i'd say is fairly rated, rated, highly busy, 689 00:34:20,200 --> 00:34:21,000 Speaker 2: especially one of. 690 00:34:20,920 --> 00:34:22,920 Speaker 3: The episodes you hear is quite excellent. 691 00:34:23,200 --> 00:34:27,080 Speaker 4: But we have an underrated story as well. Sean Grace 692 00:34:27,160 --> 00:34:28,320 Speaker 4: us with the underrated story. 693 00:34:28,480 --> 00:34:32,080 Speaker 13: Okay, my underrated story of the week is the University 694 00:34:32,120 --> 00:34:36,319 Speaker 13: of Central Florida students who are graduating twenty twenty six 695 00:34:36,880 --> 00:34:40,360 Speaker 13: booing their commencement speaker, a woman named Gloria Caulfield. 696 00:34:41,040 --> 00:34:47,560 Speaker 11: Change is exciting, very exciting, and let's face it, change 697 00:34:48,120 --> 00:34:55,440 Speaker 11: can be daunting. The rise of artificial intelligence is the 698 00:34:55,480 --> 00:35:10,880 Speaker 11: next industrial revolution? WHOA what happened? Okay? I struck a chord? 699 00:35:12,160 --> 00:35:23,960 Speaker 11: May I finish only a few years ago? AI was 700 00:35:24,120 --> 00:35:37,840 Speaker 11: not a factor in our lives. Okay, all right, Okay, 701 00:35:38,600 --> 00:35:41,800 Speaker 11: we've got a bipolar topic here. I see. 702 00:35:42,120 --> 00:35:44,840 Speaker 4: Okay, Sean, such a good choice. 703 00:35:44,880 --> 00:35:48,400 Speaker 2: We were just talking about on the segment before you 704 00:35:48,520 --> 00:35:53,040 Speaker 2: joined us with Bradstone, about the backlash to AI, and wow, 705 00:35:53,080 --> 00:35:54,400 Speaker 2: you really hear it in that audio. 706 00:35:54,440 --> 00:35:56,279 Speaker 4: I mean to me, the thing that's most surprising about 707 00:35:56,280 --> 00:35:57,000 Speaker 4: this is it's. 708 00:35:56,880 --> 00:36:02,000 Speaker 2: Not Berkeley, it's not the New School or something. It's 709 00:36:02,000 --> 00:36:06,040 Speaker 2: the University of Central Florida. It's a big state college 710 00:36:06,239 --> 00:36:07,640 Speaker 2: in the middle of Florida. 711 00:36:07,920 --> 00:36:11,840 Speaker 13: Yes, and also just the very idea of a commencement address. 712 00:36:11,960 --> 00:36:15,560 Speaker 13: Right that you're graduating school. You've been schooled your whole life, 713 00:36:15,560 --> 00:36:18,280 Speaker 13: and you're about to go into the world. Your university 714 00:36:18,360 --> 00:36:22,719 Speaker 13: has invited someone who's older, wiser and successful to give 715 00:36:22,719 --> 00:36:27,040 Speaker 13: you life advice. But the thing is, there are some 716 00:36:27,120 --> 00:36:31,839 Speaker 13: big generational changes that have really upset the natural order 717 00:36:31,840 --> 00:36:36,840 Speaker 13: of this. So I'm a millennial, I'm a geriatric millennial. 718 00:36:36,920 --> 00:36:40,880 Speaker 13: And one of the defining things about our generation is 719 00:36:40,920 --> 00:36:43,399 Speaker 13: that a lot of the advice that we've received from 720 00:36:43,440 --> 00:36:47,960 Speaker 13: our parents' generation baby Boomers were a lot of advice 721 00:36:48,040 --> 00:36:51,319 Speaker 13: was irrelevant as we were coming of age they grew 722 00:36:51,400 --> 00:36:56,280 Speaker 13: up during a time without crushing student loans, when owning 723 00:36:56,280 --> 00:37:01,680 Speaker 13: a house was more achievable, when upwards social mobility was assumed. 724 00:37:02,600 --> 00:37:04,440 Speaker 3: For this generation of kids. 725 00:37:04,360 --> 00:37:07,920 Speaker 13: You have a whole generation that was sold on the 726 00:37:08,000 --> 00:37:11,120 Speaker 13: idea that learning to code would be their golden ticket. 727 00:37:11,760 --> 00:37:14,520 Speaker 13: And one of the first changes that we see from 728 00:37:14,600 --> 00:37:18,760 Speaker 13: AI is the decimation of entry level computer programming jobs. 729 00:37:19,600 --> 00:37:23,440 Speaker 13: And so graduating students are contending already with the effects 730 00:37:23,480 --> 00:37:25,960 Speaker 13: of AI in a very real way, and in a 731 00:37:26,080 --> 00:37:26,959 Speaker 13: very negative way. 732 00:37:27,400 --> 00:37:31,080 Speaker 3: I would say that I saw that a little differently 733 00:37:31,120 --> 00:37:35,000 Speaker 3: because she calls it an industrial revolution, and I don't 734 00:37:35,000 --> 00:37:37,799 Speaker 3: think of the industrial revolution as this totally positive thing, 735 00:37:37,880 --> 00:37:39,040 Speaker 3: like it was pretty brutal. 736 00:37:39,239 --> 00:37:41,080 Speaker 4: But that's what's so crazy about the clip. 737 00:37:41,160 --> 00:37:44,200 Speaker 2: She's saying what basically what every business person in the 738 00:37:44,320 --> 00:37:47,920 Speaker 2: world thinks is conventional wisdom, and then is being booed, 739 00:37:48,160 --> 00:37:52,160 Speaker 2: and then is surprised she didn't realize that these kids 740 00:37:52,280 --> 00:37:53,920 Speaker 2: might not like AI. 741 00:37:53,960 --> 00:37:56,839 Speaker 3: And I looked at the unemployment numbers for people under 742 00:37:56,920 --> 00:37:57,680 Speaker 3: twenty five. 743 00:37:57,560 --> 00:38:00,160 Speaker 2: Totally, and I read these and I read like the 744 00:38:00,200 --> 00:38:03,240 Speaker 2: story that Brad just wrote about Andy Chassi, and read 745 00:38:03,800 --> 00:38:08,480 Speaker 2: lines from executives basically saying very similar things, and it 746 00:38:08,520 --> 00:38:11,279 Speaker 2: does feel like there's a whole, big part of the 747 00:38:11,320 --> 00:38:13,040 Speaker 2: world that they may not be listening to. 748 00:38:13,360 --> 00:38:16,360 Speaker 13: And I also think that what's satisfying about the clip 749 00:38:16,560 --> 00:38:18,520 Speaker 13: is that the way the AI is presented to us 750 00:38:18,680 --> 00:38:22,680 Speaker 13: is so top down right. Big tech companies are talking 751 00:38:22,680 --> 00:38:27,000 Speaker 13: about AGI, a superhuman, super powerful intelligence that's going to 752 00:38:27,120 --> 00:38:29,080 Speaker 13: transform our society. 753 00:38:29,320 --> 00:38:33,200 Speaker 3: And steal our banking passwords exactly exactly. 754 00:38:33,800 --> 00:38:39,160 Speaker 13: And to see young people, a groundswell of young people booing, 755 00:38:40,080 --> 00:38:41,880 Speaker 13: that's something that AI can't replace. 756 00:38:42,239 --> 00:38:43,080 Speaker 3: We can still boo. 757 00:38:43,400 --> 00:38:46,799 Speaker 2: As a Mets fan, booing is very satisfying. Let me 758 00:38:46,840 --> 00:38:49,280 Speaker 2: tell you anything. There is nothing like a good boo 759 00:38:49,480 --> 00:38:52,160 Speaker 2: to get your feelings out in a public place. So 760 00:38:52,239 --> 00:38:54,840 Speaker 2: I really relate to those University of Central Florida students. 761 00:38:54,920 --> 00:38:59,240 Speaker 2: Although I've never bowed AI, I've only booed underperforming members. 762 00:38:59,360 --> 00:39:04,439 Speaker 3: Only is New York Mets. Wait, you boo your people. 763 00:39:05,160 --> 00:39:06,640 Speaker 13: I assume you would boo the other team. 764 00:39:06,719 --> 00:39:08,799 Speaker 2: Yeah, poo the other team as well, But sometimes you 765 00:39:08,800 --> 00:39:10,759 Speaker 2: have no choice but to boo your ow team. The 766 00:39:10,840 --> 00:39:13,880 Speaker 2: show is called Foundering The Killing of Bob Lee. It 767 00:39:13,960 --> 00:39:16,560 Speaker 2: is available on Bloomberg dot Com. We'll put a link 768 00:39:16,640 --> 00:39:19,240 Speaker 2: in the show notes and wherever you get podcast. 769 00:39:19,320 --> 00:39:21,000 Speaker 4: Sean Wen, thanks for being here, Thanks so much. 770 00:39:20,920 --> 00:39:21,480 Speaker 5: For having me. 771 00:39:21,760 --> 00:39:28,800 Speaker 3: Thanks Im. 772 00:39:28,880 --> 00:39:31,439 Speaker 2: This show is produced by Jasmine J. T. Green, Stacey Wong, 773 00:39:31,520 --> 00:39:35,040 Speaker 2: and Miles J. Hersenhorn. Mangus Hendrickson is our supervising producer. 774 00:39:35,320 --> 00:39:39,160 Speaker 2: Sam Rogich handles engineering, and They Purcell fact checks. Special 775 00:39:39,160 --> 00:39:42,319 Speaker 2: thanks to Jeff Muscus, Julia Rubin and Maria Lingk. If 776 00:39:42,320 --> 00:39:44,120 Speaker 2: you have a minute, please rate and review the show. 777 00:39:44,160 --> 00:39:45,480 Speaker 2: It means a lot to us. And if you have 778 00:39:45,520 --> 00:39:47,440 Speaker 2: a story that should be our business. If you want 779 00:39:47,440 --> 00:39:50,879 Speaker 2: to confess to some out of control spending, email us 780 00:39:50,920 --> 00:39:53,719 Speaker 2: at Everybody's at Bloomberg dot net. That's everybody with an 781 00:39:53,719 --> 00:39:56,239 Speaker 2: ass at Bloomberg dot net. Thank you for listening and 782 00:39:56,280 --> 00:39:57,479 Speaker 2: we'll see you next week.