1 00:00:02,720 --> 00:00:10,559 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. You're listening to the 2 00:00:10,600 --> 00:00:14,560 Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am 3 00:00:14,600 --> 00:00:17,880 Speaker 1: Eastern on Apple, Cocklay and Android Auto with the Bloomberg 4 00:00:17,920 --> 00:00:21,040 Speaker 1: Business App. Listen on demand wherever you get your podcasts, 5 00:00:21,360 --> 00:00:23,080 Speaker 1: or watch us live on YouTube. 6 00:00:24,120 --> 00:00:26,439 Speaker 2: One of the stories, Paul, that's Scott, you and me 7 00:00:26,560 --> 00:00:31,440 Speaker 2: scratching our heads is eBay. Gamestop's bid for eBay, a 8 00:00:31,480 --> 00:00:34,800 Speaker 2: company that is four times its size. Ryan Cohen, of course, 9 00:00:34,840 --> 00:00:38,400 Speaker 2: making this very dramatic takeover offer for the company. eBay 10 00:00:38,440 --> 00:00:40,960 Speaker 2: has now rejected that fifty six billion dollar takeover offer, 11 00:00:41,040 --> 00:00:44,120 Speaker 2: calling it neither credible nor attractive. I think that pretty 12 00:00:44,159 --> 00:00:45,480 Speaker 2: much tells you what it thinks. 13 00:00:45,600 --> 00:00:47,240 Speaker 3: I did investment banking for a long time, and I 14 00:00:47,240 --> 00:00:49,559 Speaker 3: can get it very creative with capital structures, but I 15 00:00:49,560 --> 00:00:52,000 Speaker 3: have a hard time seeing how they're how they would 16 00:00:52,000 --> 00:00:52,680 Speaker 3: finance this thing. 17 00:00:52,880 --> 00:00:55,800 Speaker 2: All right, let's bring in Cecilia Denastasio. She is our 18 00:00:56,000 --> 00:00:57,680 Speaker 2: video game reporter here at Bloomberg. 19 00:00:57,760 --> 00:00:57,960 Speaker 4: Nice. 20 00:00:57,960 --> 00:00:59,000 Speaker 5: Great to see you, Cecilia. 21 00:00:59,080 --> 00:00:59,920 Speaker 6: Thank you for having me. 22 00:01:00,560 --> 00:01:03,880 Speaker 2: What was the thinking behind GameStop and making a bid 23 00:01:04,040 --> 00:01:04,760 Speaker 2: for eBay? 24 00:01:05,200 --> 00:01:06,440 Speaker 5: You know, we actually didn't get a. 25 00:01:06,400 --> 00:01:09,880 Speaker 6: Lot of insight into what CEO Ryan Cohen thought that 26 00:01:09,880 --> 00:01:12,200 Speaker 6: the proposed synergies could look like. He never really went 27 00:01:12,240 --> 00:01:15,520 Speaker 6: out and said, hey, we run a video game, you know, 28 00:01:15,600 --> 00:01:18,679 Speaker 6: retail company, but also we're doing really well and collectibles. 29 00:01:18,680 --> 00:01:22,040 Speaker 6: We're doing really well selling Pokemon cards. eBay also doing 30 00:01:22,080 --> 00:01:25,280 Speaker 6: really well selling Pokemon cards and baseball cards. We could 31 00:01:25,280 --> 00:01:27,960 Speaker 6: have heard that in any of the interviews that Ryan 32 00:01:27,959 --> 00:01:30,960 Speaker 6: Cohen gave, but he didn't actually really go out and 33 00:01:31,000 --> 00:01:33,959 Speaker 6: say that. In fact, when he was given questions about 34 00:01:33,959 --> 00:01:36,080 Speaker 6: what the combined company could look like, you often sort 35 00:01:36,120 --> 00:01:36,600 Speaker 6: of dodged. 36 00:01:37,280 --> 00:01:39,600 Speaker 3: So, I mean, what's the story with GameStop these days? 37 00:01:39,640 --> 00:01:43,240 Speaker 3: I mean, I'm not a gamer, thank goodness, most of 38 00:01:43,240 --> 00:01:47,039 Speaker 3: my kids are not gamers. But it's almost a meme stock. 39 00:01:47,400 --> 00:01:49,480 Speaker 3: But what's the real call in this company these days? 40 00:01:49,880 --> 00:01:53,320 Speaker 6: You know, it's interesting because GameStop could be doing a 41 00:01:53,360 --> 00:01:56,960 Speaker 6: lot worse than it is doing. You know, they shuttered, 42 00:01:57,120 --> 00:01:59,520 Speaker 6: you know, thousands of stores over the last couple of 43 00:01:59,560 --> 00:02:02,320 Speaker 6: years to be a big ubiquitous presence at your neighborhood 44 00:02:02,320 --> 00:02:06,120 Speaker 6: strip mall. And you know, gamers are increasingly purchasing their 45 00:02:06,160 --> 00:02:09,680 Speaker 6: wares in online marketplaces and not at these at these 46 00:02:09,720 --> 00:02:13,000 Speaker 6: physical storefronts, but GameStop is still hanging on. CEO Ryan 47 00:02:13,040 --> 00:02:15,679 Speaker 6: Cohen said, you know, we could be doing worse. It's 48 00:02:15,680 --> 00:02:17,519 Speaker 6: not a great business, but it's hanging in there. 49 00:02:18,200 --> 00:02:19,200 Speaker 5: We could be doing worse. 50 00:02:19,240 --> 00:02:21,000 Speaker 2: It's not a great line that you want to hear 51 00:02:21,040 --> 00:02:23,320 Speaker 2: from the CEO of a company stock that you happen 52 00:02:23,360 --> 00:02:26,720 Speaker 2: to own. So having said all of this, there's been 53 00:02:26,760 --> 00:02:30,800 Speaker 2: some reporting that perhaps Ryan Cohen just wanted to boost 54 00:02:30,800 --> 00:02:33,680 Speaker 2: the stock price of game Stop because there's some clause 55 00:02:33,720 --> 00:02:35,840 Speaker 2: I guess in one of his compensation packages that if 56 00:02:35,840 --> 00:02:38,240 Speaker 2: the share price reaches a certain level, he gets another 57 00:02:38,280 --> 00:02:38,880 Speaker 2: big payout. 58 00:02:39,440 --> 00:02:40,760 Speaker 5: Can you talk us through what's. 59 00:02:40,560 --> 00:02:43,440 Speaker 6: Going on there, It's really hard to say. If the 60 00:02:43,480 --> 00:02:46,359 Speaker 6: companies did end up combining, then it's possible that he 61 00:02:46,400 --> 00:02:48,639 Speaker 6: would have been able to receive that payout and he 62 00:02:48,680 --> 00:02:51,640 Speaker 6: would have been very happy. But also at the same time, 63 00:02:52,080 --> 00:02:53,840 Speaker 6: a lot of analysts pointed out that the way that 64 00:02:53,919 --> 00:02:59,920 Speaker 6: Ryan Cohen approached eBay wasn't exactly so friendly. You know, 65 00:03:00,000 --> 00:03:03,640 Speaker 6: so he it was an unsolicited bid, he didn't publicly 66 00:03:03,720 --> 00:03:06,840 Speaker 6: state what he thought the synergies could look like. When 67 00:03:06,919 --> 00:03:09,919 Speaker 6: eBay came back saying that the deal felt neither credible 68 00:03:09,960 --> 00:03:13,400 Speaker 6: nor attractive. I think that kind of speaks to this 69 00:03:13,560 --> 00:03:18,680 Speaker 6: idea that you know, maybe this wasn't so thoroughly thought out. 70 00:03:19,200 --> 00:03:21,560 Speaker 6: I think a lot of analysts are very very confused 71 00:03:21,560 --> 00:03:22,160 Speaker 6: about the tent. 72 00:03:22,680 --> 00:03:25,079 Speaker 4: You did a lot to improve that outlook there. Yeah, 73 00:03:25,120 --> 00:03:26,480 Speaker 4: I mean that what a disaster that was. 74 00:03:26,720 --> 00:03:30,600 Speaker 3: So do you think game Stop would pursue a proxy 75 00:03:30,600 --> 00:03:32,040 Speaker 3: fight and take it to that next step? 76 00:03:32,520 --> 00:03:33,280 Speaker 5: It's possible. 77 00:03:33,480 --> 00:03:36,360 Speaker 6: You know, Ryan is a very unpredictable executive, which is 78 00:03:36,400 --> 00:03:39,080 Speaker 6: part of what makes him really fun to cover. He 79 00:03:39,320 --> 00:03:42,520 Speaker 6: has He posted a manifesto on x formally known as 80 00:03:42,520 --> 00:03:46,800 Speaker 6: Twitter describing how he thinks American business people are often 81 00:03:46,960 --> 00:03:49,240 Speaker 6: taking a really risk averse approach to running their companies 82 00:03:49,280 --> 00:03:51,240 Speaker 6: and he's really trying to carve out a model for 83 00:03:51,280 --> 00:03:53,520 Speaker 6: something really different. So it's possible he would pursue the 84 00:03:53,520 --> 00:03:57,600 Speaker 6: proxy fight. It's possible also that this was a publicity stunt. 85 00:03:57,720 --> 00:04:01,279 Speaker 3: Do we know whose advisors are his investment banking advisors? 86 00:04:01,680 --> 00:04:03,880 Speaker 3: The only thing I saw was he's got a financing 87 00:04:03,960 --> 00:04:07,440 Speaker 3: letter from TD Securities, which again tells you that highly confident, 88 00:04:07,560 --> 00:04:10,200 Speaker 3: highly confident for twenty billion dollars forty years. 89 00:04:10,720 --> 00:04:13,200 Speaker 4: But we don't really know who other advisors are. 90 00:04:13,240 --> 00:04:14,920 Speaker 6: He's a highly autonomous individual. 91 00:04:15,800 --> 00:04:18,680 Speaker 5: Stay with us. More from Bloomberg Intelligence coming up after this. 92 00:04:22,320 --> 00:04:26,039 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 93 00:04:26,120 --> 00:04:29,200 Speaker 1: weekdays at ten am Easterned on apple Cockplay and Android 94 00:04:29,200 --> 00:04:32,520 Speaker 1: Auto with the Bloomberg Business App, Listen on demand wherever 95 00:04:32,560 --> 00:04:35,680 Speaker 1: you get your podcasts, or watch us live on YouTube. 96 00:04:37,000 --> 00:04:40,760 Speaker 3: Under Armour reported some numbers today. I didn't see them, 97 00:04:41,000 --> 00:04:44,200 Speaker 3: but they can't be good. The stock's down sixteen percent here. 98 00:04:45,120 --> 00:04:46,840 Speaker 3: But I know somebody who doesn't know what's going on here. 99 00:04:46,839 --> 00:04:49,560 Speaker 3: Lillly Myers, she's a retail reporter for Bloomberg News, joining 100 00:04:49,600 --> 00:04:52,160 Speaker 3: us live here in our Bloomberg Interactive Broker studio. What 101 00:04:52,279 --> 00:04:54,400 Speaker 3: did under Armour have to say that maybe the market 102 00:04:54,400 --> 00:04:55,160 Speaker 3: didn't go for. 103 00:04:55,520 --> 00:04:59,520 Speaker 7: Yeah, so I think under Armour disappointed investors. I think 104 00:04:59,560 --> 00:05:03,640 Speaker 7: that they're the revenue for next year or this current 105 00:05:03,720 --> 00:05:06,320 Speaker 7: year is not not as high as they expected, and 106 00:05:06,520 --> 00:05:08,279 Speaker 7: their earnings per share was also lower. 107 00:05:08,320 --> 00:05:11,920 Speaker 5: They also mentioned the Middle East conflict. 108 00:05:11,400 --> 00:05:14,440 Speaker 7: As impacting their sales and the split from the Steph 109 00:05:14,520 --> 00:05:16,640 Speaker 7: Curry brand hitting their bottom line. 110 00:05:17,240 --> 00:05:19,440 Speaker 4: My question with under Armour, I'm a Nike person, by 111 00:05:19,480 --> 00:05:19,720 Speaker 4: the way. 112 00:05:19,720 --> 00:05:25,120 Speaker 3: Full disclosure always happened, and I'm not switching, but it's competitive. 113 00:05:25,120 --> 00:05:28,000 Speaker 3: I mean, there's Nike out there body dust for those 114 00:05:28,000 --> 00:05:30,200 Speaker 3: of us in the know, we pronounce it that way, and. 115 00:05:30,320 --> 00:05:33,000 Speaker 4: I'm just not sure. And it's there's so many niche players. 116 00:05:33,279 --> 00:05:35,240 Speaker 3: I mean, the kids here at Bloomberg they were in 117 00:05:35,360 --> 00:05:37,440 Speaker 3: all different kinds of sinkers that I've never seen before. 118 00:05:38,120 --> 00:05:40,040 Speaker 4: How does under Armour fit in there, do you think? 119 00:05:40,520 --> 00:05:40,760 Speaker 8: Yeah? 120 00:05:40,800 --> 00:05:43,159 Speaker 7: I mean I think it's a really competitive space, like 121 00:05:43,200 --> 00:05:44,200 Speaker 7: you described, both on. 122 00:05:44,120 --> 00:05:45,920 Speaker 5: The footwear side and the apparel side. 123 00:05:46,000 --> 00:05:48,760 Speaker 7: You know, under Armour is part of this restructuring plan 124 00:05:48,880 --> 00:05:51,919 Speaker 7: that they've been going through recently, has been trying to 125 00:05:51,920 --> 00:05:54,280 Speaker 7: really slim down the number of skews that they sell, 126 00:05:54,400 --> 00:05:57,560 Speaker 7: you know, kind of streamline the products and also cut 127 00:05:57,600 --> 00:05:59,279 Speaker 7: back on their discounts. 128 00:06:00,360 --> 00:06:04,200 Speaker 3: So is this you know, I understand that it's really 129 00:06:04,240 --> 00:06:06,880 Speaker 3: a product driven business. You can't just crank out the 130 00:06:06,920 --> 00:06:10,239 Speaker 3: same sneaker year after year after year. I mean every 131 00:06:10,320 --> 00:06:13,159 Speaker 3: year it's like fat it is fashion. Do they have 132 00:06:13,200 --> 00:06:16,279 Speaker 3: the resources, they have the capabilities to really compete against 133 00:06:16,320 --> 00:06:18,080 Speaker 3: the I guess the bigger players. 134 00:06:18,520 --> 00:06:20,800 Speaker 7: Yeah, I mean it'll be interesting to see. The hard 135 00:06:20,800 --> 00:06:23,120 Speaker 7: thing with some of the retail innovation cycle is that 136 00:06:23,200 --> 00:06:26,320 Speaker 7: it takes like eighteen months, you know to start seeing 137 00:06:26,480 --> 00:06:28,880 Speaker 7: some of that new product actually hit the shelves. So 138 00:06:28,960 --> 00:06:32,120 Speaker 7: I'll be interested to see what they come out with. 139 00:06:32,240 --> 00:06:36,200 Speaker 3: You mentioned they. 140 00:06:34,520 --> 00:06:36,479 Speaker 4: Lost Steph Curry. That's a big name. 141 00:06:38,000 --> 00:06:41,720 Speaker 3: How do they think about using athletes as part of 142 00:06:41,760 --> 00:06:42,720 Speaker 3: their marketing promotion? 143 00:06:42,920 --> 00:06:46,440 Speaker 4: Strategy is a core to them? And how big of 144 00:06:46,480 --> 00:06:47,640 Speaker 4: a loss was like Steph Curry? 145 00:06:47,920 --> 00:06:51,359 Speaker 7: Yeah, so for Steph Curry, I mean that was a big, 146 00:06:51,520 --> 00:06:53,719 Speaker 7: a big deal. They had worked with the Curry brand 147 00:06:53,839 --> 00:06:56,400 Speaker 7: for a long time. That was a really essential part 148 00:06:56,400 --> 00:06:59,560 Speaker 7: of their basketball business. So this quarter they said that 149 00:06:59,760 --> 00:07:04,040 Speaker 7: for this coming year, the Curry brand separation made it 150 00:07:04,080 --> 00:07:06,800 Speaker 7: so that their revenue was down slightly. If they had 151 00:07:06,800 --> 00:07:09,080 Speaker 7: stayed with Curry, it would have been flat for the year. 152 00:07:09,240 --> 00:07:12,160 Speaker 5: So it'll be interesting to see, you know, if they. 153 00:07:12,120 --> 00:07:14,920 Speaker 7: Try and side more big names or what comes next. 154 00:07:14,960 --> 00:07:17,360 Speaker 3: I mean, there's kids coming out of college every year. 155 00:07:18,640 --> 00:07:19,720 Speaker 3: Are they going to be aggressive? 156 00:07:19,920 --> 00:07:24,400 Speaker 4: What did they say? Their strategy is there because it's. 157 00:07:23,120 --> 00:07:26,800 Speaker 3: A tried and true strategy, particularly for athletic where Nike's 158 00:07:26,800 --> 00:07:29,440 Speaker 3: seen it. I mean, Michael Jordan, you know, that's the 159 00:07:29,800 --> 00:07:33,120 Speaker 3: great example. But are they committed to that part that strategy, 160 00:07:33,160 --> 00:07:33,800 Speaker 3: do you think. 161 00:07:34,080 --> 00:07:36,160 Speaker 5: Yeah, that's a good question. I mean, they definitely work 162 00:07:36,200 --> 00:07:36,880 Speaker 5: with athletes. 163 00:07:36,920 --> 00:07:40,640 Speaker 7: I'm curious to see how that will evolve moving forward, 164 00:07:40,720 --> 00:07:42,200 Speaker 7: especially without Curry. 165 00:07:43,040 --> 00:07:46,560 Speaker 3: What's the uh, what's the tariff situation with some of 166 00:07:46,600 --> 00:07:47,800 Speaker 3: these shoot companies these days? 167 00:07:47,840 --> 00:07:50,160 Speaker 4: I mean is it? Do they even talk about it anymore? 168 00:07:50,200 --> 00:07:52,280 Speaker 3: Because I know a lot of the retail companies they 169 00:07:52,360 --> 00:07:54,320 Speaker 3: used to others as well, used to just give you 170 00:07:54,360 --> 00:07:56,520 Speaker 3: a number. It costs US five hundred million this quarter 171 00:07:56,560 --> 00:07:59,360 Speaker 3: or forty million this this quarter. Is still an issue 172 00:07:59,400 --> 00:08:00,320 Speaker 3: for these apparel companies. 173 00:08:00,520 --> 00:08:02,360 Speaker 5: Yeah, it's interesting. So it's switched a little. 174 00:08:02,400 --> 00:08:05,480 Speaker 7: Now they're talking about what their expected tariff refund will be, 175 00:08:05,600 --> 00:08:06,080 Speaker 7: so they're. 176 00:08:05,920 --> 00:08:08,200 Speaker 5: Baking that in or on the positive side. 177 00:08:08,000 --> 00:08:09,880 Speaker 7: I think, you know, there's a lot we still don't 178 00:08:09,880 --> 00:08:13,200 Speaker 7: know about what companies will get out of tariff refund, 179 00:08:13,240 --> 00:08:16,040 Speaker 7: if they'll get the full amount, what that process will 180 00:08:16,080 --> 00:08:18,720 Speaker 7: actually look like, how long it will take. But that's 181 00:08:18,760 --> 00:08:21,600 Speaker 7: the discussion now, is you know the upside from that refund? 182 00:08:21,720 --> 00:08:24,320 Speaker 3: Now companies have actually you have to go like file 183 00:08:24,400 --> 00:08:26,360 Speaker 3: and say I want a refund right now? 184 00:08:26,680 --> 00:08:28,040 Speaker 4: Has the Nike done. 185 00:08:27,920 --> 00:08:30,560 Speaker 3: It something like I'm thinking of the big big name 186 00:08:30,760 --> 00:08:31,320 Speaker 3: in your retail. 187 00:08:31,440 --> 00:08:33,000 Speaker 4: Has Nike actually done that for example? 188 00:08:33,120 --> 00:08:35,959 Speaker 7: Yeah, so a lot of major retailers have have sued 189 00:08:36,040 --> 00:08:39,760 Speaker 7: for tariff refunds, and I think it'll be interesting to 190 00:08:39,800 --> 00:08:43,320 Speaker 7: see as as they become more of a reality, how 191 00:08:43,640 --> 00:08:44,920 Speaker 7: companies will go about it. 192 00:08:45,080 --> 00:08:49,400 Speaker 3: There still really isn't a stre There isn't I guess, 193 00:08:49,400 --> 00:08:51,320 Speaker 3: a policy framework to get the money right. 194 00:08:51,480 --> 00:08:52,439 Speaker 4: They're still working on that. 195 00:08:52,840 --> 00:08:56,640 Speaker 7: From my understanding, I think it's it's it's still in process. 196 00:08:56,760 --> 00:08:59,520 Speaker 4: How much is if Nike gets money they do with it? 197 00:08:59,559 --> 00:09:01,440 Speaker 4: Do they give it back to me? Who paid a 198 00:09:01,559 --> 00:09:03,240 Speaker 4: tariff on my kicks? 199 00:09:03,240 --> 00:09:03,400 Speaker 9: Here? 200 00:09:03,960 --> 00:09:04,520 Speaker 4: We don't know that. 201 00:09:04,840 --> 00:09:07,520 Speaker 7: Yeah, I mean, I think that will be really an 202 00:09:07,600 --> 00:09:10,800 Speaker 7: interesting thing to watch. I think consumer reaction will be interesting, 203 00:09:10,840 --> 00:09:14,640 Speaker 7: you know, consumers who have had to face those higher prices, 204 00:09:14,640 --> 00:09:16,920 Speaker 7: but also the company that's haed to shoulder a lot 205 00:09:16,960 --> 00:09:17,240 Speaker 7: of it. 206 00:09:18,040 --> 00:09:18,640 Speaker 10: Stay with us. 207 00:09:18,840 --> 00:09:21,160 Speaker 4: More from Bloomberg Intelligence coming up after this. 208 00:09:25,040 --> 00:09:28,720 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 209 00:09:28,840 --> 00:09:31,920 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 210 00:09:31,920 --> 00:09:35,240 Speaker 1: Auto with the Bloomberg Business app. Listen on demand wherever 211 00:09:35,280 --> 00:09:38,560 Speaker 1: you get your podcasts, or watch us live on YouTube. 212 00:09:39,679 --> 00:09:41,880 Speaker 3: All right, So President Trump leaving today for Chinese has 213 00:09:41,880 --> 00:09:43,880 Speaker 3: got a plane full of executives. 214 00:09:43,320 --> 00:09:46,920 Speaker 4: Including executive for Boeing, Yep and Boyden. 215 00:09:46,920 --> 00:09:49,880 Speaker 3: You think about a company that's leveraged to Chin it 216 00:09:50,240 --> 00:09:53,240 Speaker 3: a I always think of Apple, But Boeing's right there. 217 00:09:53,880 --> 00:09:55,400 Speaker 3: And we want to talk to Sid Philip about that, 218 00:09:55,400 --> 00:10:00,240 Speaker 3: Bloomberg Chief correspondent for Global Aviation, because this could be 219 00:10:00,720 --> 00:10:03,640 Speaker 3: one of those trips where somebody, you know, China announces 220 00:10:03,679 --> 00:10:05,880 Speaker 3: a gazillion plane deal and you. 221 00:10:05,800 --> 00:10:09,520 Speaker 2: Know, actually something that the CEO has kind of hinted 222 00:10:09,520 --> 00:10:13,280 Speaker 2: at already saying that you know, Trump's visit would be 223 00:10:13,400 --> 00:10:15,520 Speaker 2: quote a meaningful opportunity for us. 224 00:10:15,600 --> 00:10:18,400 Speaker 3: Yeah, Sid Philip, he does all this stuff for Blomberg News. 225 00:10:18,440 --> 00:10:20,480 Speaker 3: What are you expecting here on this trip for Boweing? 226 00:10:20,480 --> 00:10:22,040 Speaker 3: What do you think Boeing wants to get out of this? 227 00:10:22,360 --> 00:10:25,400 Speaker 8: So Boeing's looking to secure its first big order from 228 00:10:25,480 --> 00:10:29,920 Speaker 8: China in almost a decade, and so we understand it's 229 00:10:29,920 --> 00:10:32,800 Speaker 8: going to be five hundred seven three seven Maxes and 230 00:10:33,559 --> 00:10:36,240 Speaker 8: a large number of white body planes, and that would 231 00:10:36,280 --> 00:10:40,520 Speaker 8: be significant because the Chinese airlines haven't really ordered any 232 00:10:41,280 --> 00:10:42,800 Speaker 8: Boeing planes in a while and. 233 00:10:43,000 --> 00:10:44,160 Speaker 4: Just because they're mad at us. 234 00:10:44,559 --> 00:10:47,079 Speaker 8: They were mad at and there was COVID in the 235 00:10:47,080 --> 00:10:50,640 Speaker 8: middle of it. And so the Chinese airlines actually need 236 00:10:50,679 --> 00:10:53,960 Speaker 8: those planes because they don't really have a supply of Boeing. 237 00:10:54,000 --> 00:10:56,520 Speaker 8: I mean, they operate both Airbus and Boeing planes, and 238 00:10:56,559 --> 00:10:58,839 Speaker 8: they don't really have a supply of planes beyond the 239 00:10:59,200 --> 00:11:02,080 Speaker 8: twenty thirties. And so if they want to keep expanding, 240 00:11:02,120 --> 00:11:04,040 Speaker 8: if they want to keep adding more service, they need 241 00:11:04,080 --> 00:11:07,600 Speaker 8: those planes, and disorder is a way to get that. 242 00:11:08,280 --> 00:11:09,959 Speaker 5: Okay, so this order is a way to get that. 243 00:11:10,320 --> 00:11:11,360 Speaker 5: I guess if you're. 244 00:11:13,000 --> 00:11:15,480 Speaker 2: An airliner, you're thinking, what's the next thing Boeing is 245 00:11:15,520 --> 00:11:17,760 Speaker 2: selling that I want to get my hands on. I mean, 246 00:11:17,960 --> 00:11:20,199 Speaker 2: you know, not tomorrow, not next month, but down the 247 00:11:20,280 --> 00:11:21,640 Speaker 2: road as I plan my fleet. 248 00:11:22,360 --> 00:11:23,400 Speaker 5: Does boe have an answer to that? 249 00:11:23,440 --> 00:11:25,120 Speaker 2: Because it feels like the company has spent so much 250 00:11:25,160 --> 00:11:28,320 Speaker 2: time writing itself that it hasn't really put as much, 251 00:11:29,640 --> 00:11:32,839 Speaker 2: at least outward energy into innovating the next jet. 252 00:11:33,480 --> 00:11:35,800 Speaker 8: So that's that's the really big question that the CEO, 253 00:11:35,840 --> 00:11:39,000 Speaker 8: Kelly Ottberg's really looking at. So while obviously you have 254 00:11:39,080 --> 00:11:42,000 Speaker 8: the short term and the immediate issues that they have 255 00:11:42,040 --> 00:11:44,079 Speaker 8: to ramp up production, they have to ramp up cash 256 00:11:44,320 --> 00:11:47,440 Speaker 8: like cash generation reduce that debt pile. 257 00:11:47,800 --> 00:11:49,680 Speaker 10: The bigger question for Boeing is where do they go 258 00:11:49,720 --> 00:11:50,160 Speaker 10: from here? 259 00:11:50,600 --> 00:11:55,640 Speaker 8: And the seventry seven is basically a long derivative of 260 00:11:55,920 --> 00:11:59,160 Speaker 8: a nineteen sixties jet, and so for Boeing they need 261 00:11:59,200 --> 00:12:01,240 Speaker 8: to decide what the do next, and that will be 262 00:12:01,320 --> 00:12:04,240 Speaker 8: crucial in the sort of deeoperly that they have with Airbus, 263 00:12:04,800 --> 00:12:07,520 Speaker 8: and Airbus has been talking about how they are working 264 00:12:07,520 --> 00:12:10,200 Speaker 8: on their next generation of product that they're aiming to 265 00:12:10,240 --> 00:12:13,400 Speaker 8: get out in the middle of next decade, and that 266 00:12:13,480 --> 00:12:16,839 Speaker 8: would basically allow airlines to get something that's more fuel 267 00:12:16,840 --> 00:12:20,559 Speaker 8: efficient and allows them to fly greater distances with more 268 00:12:20,600 --> 00:12:24,199 Speaker 8: passengers on them. And so what we understand is that 269 00:12:24,320 --> 00:12:28,640 Speaker 8: Boeing is working on something that's more evolutionary rather than revolutionary, 270 00:12:29,160 --> 00:12:33,360 Speaker 8: because the last thing airlines need is uncertainty. And when 271 00:12:33,400 --> 00:12:37,120 Speaker 8: you have a product that's revolutionary, it always comes with 272 00:12:37,280 --> 00:12:40,439 Speaker 8: deathing issues. It comes with all sorts of complications with 273 00:12:40,840 --> 00:12:45,120 Speaker 8: new technology and how that whole measures together with existing infrastructure. 274 00:12:45,400 --> 00:12:47,160 Speaker 5: Yeah, we've seen that play UPFT exactly. 275 00:12:47,200 --> 00:12:49,400 Speaker 8: And so for the airlines, they want something that stable 276 00:12:49,440 --> 00:12:52,200 Speaker 8: allows them to sort of keep going as usual and 277 00:12:52,240 --> 00:12:53,840 Speaker 8: not sort of rocking the boat too much. 278 00:12:53,880 --> 00:12:55,760 Speaker 10: And that's really key for Boeing. 279 00:12:55,840 --> 00:13:01,920 Speaker 8: So how do you derive more fuel efficiency using evolutionary technology? 280 00:13:02,400 --> 00:13:05,040 Speaker 8: And that's where we see Boeing sort of going in 281 00:13:05,080 --> 00:13:06,640 Speaker 8: terms of their next products. 282 00:13:08,080 --> 00:13:10,559 Speaker 3: Ten seconds, if I get a big order today from China, 283 00:13:10,880 --> 00:13:12,559 Speaker 3: they can't even make the planes bowing. 284 00:13:12,679 --> 00:13:14,480 Speaker 4: I mean they have to build another factory or two. 285 00:13:14,840 --> 00:13:17,360 Speaker 8: So Boeing is in the process of opening a new 286 00:13:17,400 --> 00:13:20,640 Speaker 8: factory for the seven three seven, and they are looking 287 00:13:20,640 --> 00:13:22,720 Speaker 8: at ramping up production and they've been talking about how 288 00:13:22,760 --> 00:13:26,120 Speaker 8: they've got these short term goals. So they're currently looking 289 00:13:26,160 --> 00:13:28,080 Speaker 8: at forty two a month. They're going to ramp up 290 00:13:28,080 --> 00:13:30,120 Speaker 8: to forty seven a month, then fifty two a month 291 00:13:30,120 --> 00:13:34,040 Speaker 8: and sixty So essentially for Boeing that they have a 292 00:13:34,160 --> 00:13:37,280 Speaker 8: sort of stepped up plan to ramp up production depending 293 00:13:37,320 --> 00:13:39,160 Speaker 8: on how the FA sort of signs. 294 00:13:38,840 --> 00:13:40,760 Speaker 5: Off on it, and product will be in the US. 295 00:13:41,000 --> 00:13:42,960 Speaker 10: They will be in the US, so Boying produces all 296 00:13:42,960 --> 00:13:45,640 Speaker 10: its planes in the US. Stay with us. 297 00:13:45,840 --> 00:13:48,199 Speaker 4: More from Bloomberg Intelligence coming up after this. 298 00:13:52,080 --> 00:13:55,800 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us Live 299 00:13:55,880 --> 00:13:58,920 Speaker 1: weekdays at ten am Eastern on Apple Corplay and Android 300 00:13:58,960 --> 00:14:01,840 Speaker 1: Auto with the bloom Work Business Up listen on demand 301 00:14:01,880 --> 00:14:04,959 Speaker 1: wherever you get your podcasts, or watch us live on 302 00:14:05,000 --> 00:14:06,640 Speaker 1: YouTube AI. 303 00:14:06,880 --> 00:14:10,560 Speaker 3: It's impacting seemingly all parts of our life, all parts 304 00:14:10,679 --> 00:14:14,800 Speaker 3: of business and the economy, and that includes weather forecasting. 305 00:14:14,840 --> 00:14:17,320 Speaker 3: But maybe some shortcomings there. Our next guest has some 306 00:14:17,360 --> 00:14:19,600 Speaker 3: thoughts there at Caatham Kunda. He's a lecturer at the 307 00:14:19,680 --> 00:14:23,120 Speaker 3: Yale School of Management and he is a Bloomberg Opinion contributor. 308 00:14:23,680 --> 00:14:27,600 Speaker 3: You say AI isn't built for bad weather's Black Swan era? 309 00:14:27,680 --> 00:14:29,320 Speaker 4: What do you mean by that? Gotham? 310 00:14:29,520 --> 00:14:30,560 Speaker 10: Hey, Paul, good to be back. 311 00:14:30,680 --> 00:14:33,800 Speaker 9: So it's about weather forecasting, but it's not just about 312 00:14:33,800 --> 00:14:36,920 Speaker 9: weather forecasting. It's about the fact that we're using AI 313 00:14:37,160 --> 00:14:40,440 Speaker 9: all over the place as a tool to use the 314 00:14:40,480 --> 00:14:43,520 Speaker 9: past to predict the future, which is maybe the most 315 00:14:43,600 --> 00:14:47,600 Speaker 9: fundamental of all human activities, and it's incredibly important. And 316 00:14:47,640 --> 00:14:49,480 Speaker 9: it turns out that if as long as you have 317 00:14:49,600 --> 00:14:53,000 Speaker 9: enough and rich enough data about the past, AI can 318 00:14:53,040 --> 00:14:55,680 Speaker 9: be incredibly good at it, even better than the tools 319 00:14:55,680 --> 00:14:57,480 Speaker 9: we've used in the past, which in the case of 320 00:14:57,520 --> 00:15:02,480 Speaker 9: weather forecasting, involve planes and physics models and supercomputers and 321 00:15:02,520 --> 00:15:06,640 Speaker 9: weather satellites. So all of that is great. The problem 322 00:15:06,800 --> 00:15:09,960 Speaker 9: is that the weather is changing, so the weather of 323 00:15:10,000 --> 00:15:12,600 Speaker 9: the past may not be a good guide to the 324 00:15:12,600 --> 00:15:16,640 Speaker 9: weather of the future. And AI is incredibly good at 325 00:15:16,760 --> 00:15:19,800 Speaker 9: understanding stuff that happened within its training data set, and 326 00:15:19,840 --> 00:15:23,360 Speaker 9: incredibly bad about understanding stuff that's happening outside its training 327 00:15:23,440 --> 00:15:26,440 Speaker 9: data set. And as I said, that is about weather forecasting, 328 00:15:26,440 --> 00:15:29,680 Speaker 9: but it's not just about weather forecasting. It's about anywhere 329 00:15:29,880 --> 00:15:31,960 Speaker 9: where we're facing this problem of using the past to 330 00:15:31,960 --> 00:15:34,960 Speaker 9: predict the future. But the past is not like the future. 331 00:15:35,640 --> 00:15:38,440 Speaker 2: And in your column you write it will work right 332 00:15:38,520 --> 00:15:40,920 Speaker 2: up until it doesn't. AI models have a tendency to 333 00:15:40,960 --> 00:15:44,280 Speaker 2: fail in the biggest moments. And one example you give 334 00:15:44,280 --> 00:15:47,240 Speaker 2: that I think anyone who's over the age of eighteen 335 00:15:47,280 --> 00:15:50,640 Speaker 2: can relate to is the value risk models that banks 336 00:15:50,720 --> 00:15:53,640 Speaker 2: use during the Great Financial Crisis, where they relied on 337 00:15:54,480 --> 00:15:57,240 Speaker 2: past data to predict how much risk they can each 338 00:15:57,280 --> 00:15:57,600 Speaker 2: take on. 339 00:15:58,720 --> 00:16:00,800 Speaker 9: That's right, And that's another case of a tool that 340 00:16:00,840 --> 00:16:04,120 Speaker 9: works really, really well under normal circumstances, so well that 341 00:16:04,200 --> 00:16:06,720 Speaker 9: it gives you confidence, right, it makes you start to 342 00:16:06,720 --> 00:16:09,040 Speaker 9: rely on the tool. You might even enshrine it in 343 00:16:09,080 --> 00:16:12,560 Speaker 9: regulations so that when it fails, it's not just a 344 00:16:12,600 --> 00:16:14,680 Speaker 9: normal failure, it's a catastrophic failure. 345 00:16:14,720 --> 00:16:16,040 Speaker 10: So that's one layer of problem. 346 00:16:16,520 --> 00:16:18,680 Speaker 9: The second layer is that often these tools, it can 347 00:16:18,680 --> 00:16:20,520 Speaker 9: start to get used by people who aren't they people 348 00:16:20,560 --> 00:16:22,760 Speaker 9: who built them, and so who don't necessarily understand them 349 00:16:22,840 --> 00:16:25,320 Speaker 9: very well. I was at MIT during the to the 350 00:16:25,360 --> 00:16:28,400 Speaker 9: financial crisis, and I remember a very senior risk management 351 00:16:28,440 --> 00:16:31,200 Speaker 9: person at one of the banks at MIT saying to 352 00:16:31,240 --> 00:16:34,080 Speaker 9: a group of us, you know, I saw six sigma 353 00:16:34,120 --> 00:16:37,920 Speaker 9: events on three consecutive days, so the markets are weird. 354 00:16:38,200 --> 00:16:40,320 Speaker 9: And I just stared at him and I said, that 355 00:16:40,680 --> 00:16:43,360 Speaker 9: doesn't mean that the markets are weird. It means that 356 00:16:43,400 --> 00:16:47,560 Speaker 9: your tool has failed, because six sigma events three days 357 00:16:47,560 --> 00:16:50,960 Speaker 9: in a row will not happen in the entire lifespan 358 00:16:51,040 --> 00:16:54,640 Speaker 9: of the universe. Right, That's how rare we're. 359 00:16:54,440 --> 00:16:55,160 Speaker 10: Talking about is. 360 00:16:55,440 --> 00:16:57,320 Speaker 9: And this was a person who, you know, they were 361 00:16:57,400 --> 00:16:59,960 Speaker 9: very smart, but they didn't understand what this meant. 362 00:17:01,560 --> 00:17:04,560 Speaker 3: So, you know, I think one of the AI question 363 00:17:04,680 --> 00:17:08,000 Speaker 3: marks Gotham right now, is is AI going to create 364 00:17:08,119 --> 00:17:11,320 Speaker 3: jobs in this economy or destroy jobs in this economy? 365 00:17:11,440 --> 00:17:13,640 Speaker 3: I'm not sure we know that, do you have any 366 00:17:13,640 --> 00:17:14,160 Speaker 3: thoughts on that. 367 00:17:14,800 --> 00:17:16,760 Speaker 9: Well, I think we know that the answer is both, 368 00:17:17,760 --> 00:17:20,119 Speaker 9: it's going to create it's going to destroy some jobs, 369 00:17:20,119 --> 00:17:23,439 Speaker 9: it's going to create others. But the data on AI 370 00:17:23,600 --> 00:17:27,399 Speaker 9: job described destruction, the actual empirical data is pretty weak. 371 00:17:27,800 --> 00:17:28,760 Speaker 10: It looks more. 372 00:17:28,600 --> 00:17:30,919 Speaker 9: Like companies that over hired or that are looking to 373 00:17:30,920 --> 00:17:33,159 Speaker 9: cut costs and are kind of blaming AI for what 374 00:17:33,200 --> 00:17:37,040 Speaker 9: they did. But at the same time, there are going 375 00:17:37,119 --> 00:17:39,800 Speaker 9: to be some big transitions, and you know, all of 376 00:17:39,840 --> 00:17:42,159 Speaker 9: the standard models that we have, they're all about like 377 00:17:42,200 --> 00:17:44,480 Speaker 9: you know, bank tellers or things like that that tell 378 00:17:44,560 --> 00:17:47,879 Speaker 9: us new technologies don't create jobs, don't don't destroy jobs, 379 00:17:48,080 --> 00:17:50,840 Speaker 9: they don't destroy jobs and aggregate, but they destroy some jobs, 380 00:17:50,880 --> 00:17:53,000 Speaker 9: and the transition can be really hard for the people 381 00:17:53,119 --> 00:17:54,320 Speaker 9: who are undergoing through it. 382 00:17:55,600 --> 00:17:58,560 Speaker 2: So you are lecturer at the Yale School of Management, 383 00:17:58,680 --> 00:18:04,800 Speaker 2: How does education prepare young people? Graduates, those who have 384 00:18:04,880 --> 00:18:07,280 Speaker 2: some working experience, are going back to school and want 385 00:18:07,280 --> 00:18:09,000 Speaker 2: to come out, you know, with a better skill set 386 00:18:09,119 --> 00:18:10,159 Speaker 2: to prepare for this world. 387 00:18:10,880 --> 00:18:12,439 Speaker 9: So I think there are two things we need to 388 00:18:12,440 --> 00:18:14,639 Speaker 9: think about really hard here. One is that one of 389 00:18:14,640 --> 00:18:17,000 Speaker 9: the really interesting things about AI, at least as it's 390 00:18:17,000 --> 00:18:20,560 Speaker 9: currently structured, is it seems to be a magnifier, not 391 00:18:20,600 --> 00:18:23,960 Speaker 9: a compressor. Right, So if you think about like power tools, 392 00:18:24,040 --> 00:18:28,640 Speaker 9: power tools and construction equipment made brute physical strength less 393 00:18:28,680 --> 00:18:31,680 Speaker 9: important if you were working in manual labor. So they 394 00:18:31,800 --> 00:18:35,320 Speaker 9: compressed the differential and productivity between people who were physically 395 00:18:35,440 --> 00:18:38,000 Speaker 9: very strong and people who were less strong. But there 396 00:18:38,040 --> 00:18:41,000 Speaker 9: are other sorts of technologies that they increase the differences 397 00:18:41,000 --> 00:18:43,720 Speaker 9: in capability, so someone who normally would be twice as 398 00:18:43,720 --> 00:18:47,840 Speaker 9: capable becomes ten times as capable. AI looks more like 399 00:18:47,920 --> 00:18:50,199 Speaker 9: something in the second pool, right. It looks like it 400 00:18:50,240 --> 00:18:52,200 Speaker 9: takes people who are good and it makes them great, 401 00:18:52,480 --> 00:18:54,600 Speaker 9: but it's not that useful if you're not already an 402 00:18:54,640 --> 00:18:58,159 Speaker 9: expert in the field, and so someone has to create 403 00:18:58,240 --> 00:19:00,680 Speaker 9: those experts. That's the second That's what the one thing 404 00:19:00,720 --> 00:19:03,320 Speaker 9: that schools can do. The second thing is that schools 405 00:19:03,320 --> 00:19:05,560 Speaker 9: are you know, like at their best, and not all 406 00:19:05,600 --> 00:19:07,879 Speaker 9: schools do this all the time. I'm not defending that 407 00:19:08,160 --> 00:19:10,359 Speaker 9: saying that at all, but at their best, schools can 408 00:19:10,560 --> 00:19:14,040 Speaker 9: learn teach people to think independently of these skill sets. 409 00:19:14,040 --> 00:19:16,480 Speaker 9: And you're even seeing AI companies coming out and saying 410 00:19:16,720 --> 00:19:20,119 Speaker 9: that like humanity skills are really really important, that's what. 411 00:19:21,160 --> 00:19:21,360 Speaker 10: Yeah. 412 00:19:21,400 --> 00:19:23,639 Speaker 9: So, so something I've been I've been quoting at all 413 00:19:23,680 --> 00:19:26,639 Speaker 9: my programmer friends with great joy is when there was 414 00:19:26,680 --> 00:19:29,320 Speaker 9: all this turmoil before, people were saying they would you 415 00:19:29,359 --> 00:19:31,920 Speaker 9: know the responsibilities, but you guys need to learn to code, right, 416 00:19:31,960 --> 00:19:33,840 Speaker 9: And so now the AI is doing the coding, and 417 00:19:33,920 --> 00:19:36,960 Speaker 9: I said, you guys need to learn to ode. 418 00:19:38,920 --> 00:19:40,919 Speaker 3: I mean that kind of goes to one of the 419 00:19:40,920 --> 00:19:44,040 Speaker 3: things I'm learning from the young folks is it's the 420 00:19:44,080 --> 00:19:48,200 Speaker 3: ability to ask AI the right question, the prompts, yes, 421 00:19:48,240 --> 00:19:51,080 Speaker 3: the prompts, and in your world and your day to 422 00:19:51,119 --> 00:19:55,919 Speaker 3: day at a university environment, gotam, How's AI impacting your world? 423 00:19:56,680 --> 00:19:59,520 Speaker 9: So a lot in multiple different ways. So it's but 424 00:19:59,560 --> 00:20:02,880 Speaker 9: I'll it's not just learn the prompts, it's you have 425 00:20:02,960 --> 00:20:05,439 Speaker 9: to know enough to know when the AI is wrong. 426 00:20:06,240 --> 00:20:08,600 Speaker 9: That is actually I found the single most important skill. 427 00:20:08,720 --> 00:20:11,640 Speaker 9: So I used AI to help me create the syllabus 428 00:20:11,680 --> 00:20:12,160 Speaker 9: for a course. 429 00:20:12,520 --> 00:20:13,520 Speaker 10: It was amazing. 430 00:20:13,760 --> 00:20:15,720 Speaker 9: It took what would have been a week of work 431 00:20:15,720 --> 00:20:17,080 Speaker 9: and let me do it in a day. I was 432 00:20:17,080 --> 00:20:20,000 Speaker 9: blown away. But several of the things that it gave 433 00:20:20,080 --> 00:20:22,800 Speaker 9: me were wrong in ways that you had to be 434 00:20:22,920 --> 00:20:26,840 Speaker 9: an expert to understand. We're wrong, but another expert we've 435 00:20:26,840 --> 00:20:28,879 Speaker 9: seen this and thought I was need it right. So 436 00:20:30,200 --> 00:20:34,200 Speaker 9: that was a really important distinction. And what you're seeing 437 00:20:34,200 --> 00:20:36,480 Speaker 9: first is, you know, look, cheating is a huge problem. 438 00:20:36,640 --> 00:20:41,360 Speaker 9: Like we've had to crack down on that, like written exams, 439 00:20:41,480 --> 00:20:44,359 Speaker 9: take home exams. I think, like, I'm really sorry to 440 00:20:44,400 --> 00:20:47,399 Speaker 9: hear that, see that happening, But it's really happening. And 441 00:20:47,480 --> 00:20:49,760 Speaker 9: I tell my students on the first day, I'm like, 442 00:20:50,359 --> 00:20:52,960 Speaker 9: you probably can cheat. And you know, look, honestly, I 443 00:20:53,200 --> 00:20:55,000 Speaker 9: can't tell you if I'm going to catch you or not, right, 444 00:20:55,080 --> 00:20:57,040 Speaker 9: Like the tools to catch you are not that great, 445 00:20:57,600 --> 00:21:01,360 Speaker 9: but you're gonna cheat yourself. Like what you want here 446 00:21:01,400 --> 00:21:04,119 Speaker 9: isn't the grade. Nobody cares about your grades. What you 447 00:21:04,200 --> 00:21:06,880 Speaker 9: want is the learning, and you get the learning from 448 00:21:06,920 --> 00:21:09,679 Speaker 9: doing the work, not by asking trat ept to do 449 00:21:09,720 --> 00:21:10,200 Speaker 9: it for you. 450 00:21:12,359 --> 00:21:17,040 Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apple, Spotify, 451 00:21:17,200 --> 00:21:20,680 Speaker 1: and anywhere else you get your podcasts. Listen live each 452 00:21:20,720 --> 00:21:24,440 Speaker 1: weekday ten am to noon Eastern on Bloomberg dot com, 453 00:21:24,600 --> 00:21:28,119 Speaker 1: the iHeartRadio app, tune In, and the Bloomberg Business app. 454 00:21:28,560 --> 00:21:31,480 Speaker 1: You can also watch us live every weekday on YouTube 455 00:21:31,880 --> 00:21:34,120 Speaker 1: and always on the Bloomberg terminal