1 00:00:00,240 --> 00:00:11,480 Speaker 1: Bloomberg Audio, Studios, podcasts, radio news. This is Bloomberg Intelligence 2 00:00:11,560 --> 00:00:13,640 Speaker 1: with Scarletfoo and Paul Sweeney. 3 00:00:13,720 --> 00:00:15,800 Speaker 2: How do you think the FED is looking at tariffs? 4 00:00:15,960 --> 00:00:18,200 Speaker 2: The uncertainty of terriffs. Let's take a look at the 5 00:00:18,320 --> 00:00:20,120 Speaker 2: sectors and how they performed. 6 00:00:19,760 --> 00:00:22,239 Speaker 3: A lot of investors getting whip saled every day by news. 7 00:00:22,120 --> 00:00:24,520 Speaker 2: Events, breaking market headlines. 8 00:00:24,000 --> 00:00:26,160 Speaker 4: And corporate news from across the globe. 9 00:00:26,280 --> 00:00:29,040 Speaker 2: Could we see a market disruption of market events? So 10 00:00:29,120 --> 00:00:31,920 Speaker 2: people just too exuberant out there? You see some so 11 00:00:32,000 --> 00:00:34,520 Speaker 2: called low quality stocks driving this short term rally. 12 00:00:34,560 --> 00:00:39,360 Speaker 1: Bloomberg Intelligence with Scarletfoo and Paul Sweeney on Bloomberg Radio, 13 00:00:39,520 --> 00:00:41,440 Speaker 1: YouTube and Bloomberg Originals. 14 00:00:42,000 --> 00:00:45,760 Speaker 5: I'm Scarlettfoo and I'm Alexandra Semenova fill in for Paul Sweeney. 15 00:00:45,880 --> 00:00:48,600 Speaker 2: On today's Bloomberg Intelligence Show. We dig inside the big 16 00:00:48,640 --> 00:00:51,320 Speaker 2: business stories impacting Wall Street and the global markets. 17 00:00:51,479 --> 00:00:54,160 Speaker 5: Each and every week, we provide in depth research and 18 00:00:54,240 --> 00:00:56,360 Speaker 5: data on some of the two thousand companies and one 19 00:00:56,440 --> 00:00:59,280 Speaker 5: hundred and thirty industries are analysts cover worldwide. 20 00:00:59,320 --> 00:01:02,280 Speaker 2: Today, we'll look why the athletic apparel company Lulu Lemon 21 00:01:02,360 --> 00:01:04,560 Speaker 2: sees signs of a rebound in the fourth quarter. 22 00:01:04,640 --> 00:01:07,120 Speaker 5: Plus a look at why the tech giant Meta is 23 00:01:07,160 --> 00:01:10,040 Speaker 5: beginning to cut more jobs at its reality Labs division. 24 00:01:10,360 --> 00:01:12,679 Speaker 2: But first we begin with the aerospace industry. 25 00:01:13,080 --> 00:01:16,559 Speaker 5: This week, Delta Airlines reported fourth quarter earnings that missed 26 00:01:16,600 --> 00:01:19,840 Speaker 5: Wall Street's expectations. The company also said it will be 27 00:01:19,880 --> 00:01:22,399 Speaker 5: taking a more cautious view for twenty twenty six. 28 00:01:22,640 --> 00:01:24,960 Speaker 2: For more, Alex and I were joined by George ferguson 29 00:01:25,000 --> 00:01:28,360 Speaker 2: Bloomberg Intelligence Senior Aerospace, Defense and Airlines analyst. 30 00:01:28,720 --> 00:01:31,960 Speaker 5: We first asked George for his take on Delta's recent earnings. 31 00:01:32,440 --> 00:01:35,000 Speaker 6: Really, what we saw here from Delta was a continuation 32 00:01:35,240 --> 00:01:39,880 Speaker 6: of the you know, the premium and the loyalty revenue 33 00:01:39,959 --> 00:01:43,200 Speaker 6: are rolling in strongly, rolling in I think sort of 34 00:01:43,200 --> 00:01:45,920 Speaker 6: five to seven percent if I recall correctly, you know 35 00:01:46,280 --> 00:01:48,440 Speaker 6: above sort of GDP growth rates. 36 00:01:48,880 --> 00:01:51,520 Speaker 4: But the back of the airplane, you know. 37 00:01:51,560 --> 00:01:54,320 Speaker 6: They call it the main cabin or bas the economy 38 00:01:54,800 --> 00:01:57,240 Speaker 6: that's there was that revenue was down seven percent and 39 00:01:57,280 --> 00:02:00,320 Speaker 6: a quarter where Delta was growing a couple percent. And 40 00:02:00,400 --> 00:02:04,120 Speaker 6: so I think we still see this premium trend, and 41 00:02:04,200 --> 00:02:06,640 Speaker 6: I think we're going to see a lot of growth in. 42 00:02:06,600 --> 00:02:07,800 Speaker 4: Premium seats this year. 43 00:02:07,840 --> 00:02:11,200 Speaker 6: We've got Southwest converting to some you know, to more 44 00:02:11,240 --> 00:02:14,600 Speaker 6: of a full service carriers premium. Delta plans to go premium, 45 00:02:14,639 --> 00:02:18,360 Speaker 6: United plans to go premium. Alaska Jet Blue, everyone wants 46 00:02:18,360 --> 00:02:21,560 Speaker 6: to go premium. So I think there's probably some concern 47 00:02:22,320 --> 00:02:26,000 Speaker 6: in the marketplace that maybe premium gets crowded and pushes 48 00:02:26,520 --> 00:02:30,360 Speaker 6: closer to that main cabin kind of kind of fair 49 00:02:30,960 --> 00:02:32,600 Speaker 6: rather than main cabin coming up. 50 00:02:33,040 --> 00:02:36,239 Speaker 5: Delta said they're bracing for risk to their forecast, noting 51 00:02:36,360 --> 00:02:41,080 Speaker 5: the geopolitical environment, whether it's international or domestic POLICI ge 52 00:02:41,160 --> 00:02:43,680 Speaker 5: or is this unusual for them to issue this kind 53 00:02:43,720 --> 00:02:46,000 Speaker 5: of morning Well, I mean. 54 00:02:45,880 --> 00:02:47,480 Speaker 6: Again, I think we're at the beginning of the year, 55 00:02:47,720 --> 00:02:51,440 Speaker 6: and management teams like the caveat things right, and so 56 00:02:51,760 --> 00:02:54,880 Speaker 6: I guess what I would say is we had plenty 57 00:02:54,919 --> 00:02:58,680 Speaker 6: of geopolitical challenges in twenty twenty five. You know, we 58 00:02:58,720 --> 00:03:03,400 Speaker 6: were watching transit demand closely because you've got a war 59 00:03:03,480 --> 00:03:05,560 Speaker 6: raging in Ukraine's been doing that for a bunch of 60 00:03:05,600 --> 00:03:09,920 Speaker 6: years now. Didn't seem to slow down that demand. That 61 00:03:09,960 --> 00:03:12,880 Speaker 6: demand seemed to do okay some days. I wonder if 62 00:03:12,880 --> 00:03:15,679 Speaker 6: there maybe there's more risk domestically, right. One of the 63 00:03:15,720 --> 00:03:18,760 Speaker 6: other things we're watching is the President wants to cap 64 00:03:18,840 --> 00:03:21,080 Speaker 6: interest rates for the credit card companies at ten percent. 65 00:03:21,600 --> 00:03:24,320 Speaker 6: Loyalty through the MX program brings a lot of money 66 00:03:24,360 --> 00:03:29,880 Speaker 6: in for Delta, definitely a source of their competitive advantage. 67 00:03:30,040 --> 00:03:32,239 Speaker 6: And I think if you're capping interest rates, I think 68 00:03:32,600 --> 00:03:34,760 Speaker 6: some of those credit card programs have to change and 69 00:03:34,800 --> 00:03:37,360 Speaker 6: that would hurt some of those loyalty programs. So there's 70 00:03:37,480 --> 00:03:39,600 Speaker 6: I mean, there's risk all over the place all the time. Again, 71 00:03:39,640 --> 00:03:42,800 Speaker 6: the geopolitical didn't seem to hurt demand as much as 72 00:03:42,800 --> 00:03:44,480 Speaker 6: we would expect it in twenty twenty five. 73 00:03:44,680 --> 00:03:46,480 Speaker 2: Right, It kind of always threatens to hurt demand, but 74 00:03:46,480 --> 00:03:50,720 Speaker 2: in the end, people still prioritize traveling and paying for traveling. 75 00:03:51,040 --> 00:03:53,280 Speaker 2: One of the comments from Delta was that the airline 76 00:03:53,320 --> 00:03:58,720 Speaker 2: industry could see consolidation in twenty twenty six. Which players 77 00:03:58,760 --> 00:04:00,400 Speaker 2: would that most likely involved. 78 00:04:01,160 --> 00:04:03,240 Speaker 6: Yeah, so we're already starting to see some of that, right. 79 00:04:03,320 --> 00:04:07,320 Speaker 6: We had an announcement the other day that Allegiance and 80 00:04:07,520 --> 00:04:10,520 Speaker 6: Sun Country are going to put themselves together. They're two 81 00:04:10,600 --> 00:04:15,000 Speaker 6: smaller low cost airlines, some of them pretty quickly growing there, 82 00:04:15,080 --> 00:04:19,040 Speaker 6: quickly growing, so they'll consolidate. Uh, you know, we've heard 83 00:04:19,080 --> 00:04:24,720 Speaker 6: discussion about Frontier potentially buying Spirit Airlines. We still have 84 00:04:24,760 --> 00:04:26,760 Speaker 6: to see, you know, the ultimate outcome for Spirit yuring 85 00:04:27,560 --> 00:04:29,640 Speaker 6: Chapter eleven. We're trying to figure out what their structuring 86 00:04:29,720 --> 00:04:31,400 Speaker 6: looks like the second time they've been there. 87 00:04:31,920 --> 00:04:34,360 Speaker 4: So you can see on the edges. 88 00:04:34,800 --> 00:04:40,360 Speaker 6: Especially where in that low cost marketplace, you can see 89 00:04:40,960 --> 00:04:42,360 Speaker 6: consolidation beginning. 90 00:04:42,440 --> 00:04:43,680 Speaker 4: It takes a long. 91 00:04:43,520 --> 00:04:47,120 Speaker 6: Time and that's the challenge, but you can see some 92 00:04:47,160 --> 00:04:49,640 Speaker 6: of that consolidation already begin. We don't see the big 93 00:04:49,680 --> 00:04:52,120 Speaker 6: players getting consolidated, but you're not You're not going to 94 00:04:52,200 --> 00:04:54,840 Speaker 6: put together. I think you're not going to see sort 95 00:04:54,880 --> 00:04:58,040 Speaker 6: of a united to Delta and America and the Southwest. 96 00:04:58,520 --> 00:05:00,120 Speaker 6: They're not going to We're not gonna put an any 97 00:05:00,200 --> 00:05:03,320 Speaker 6: of those together. I think that's too much, but I 98 00:05:03,320 --> 00:05:05,120 Speaker 6: think a lot of other stuff is potentially in play. 99 00:05:05,640 --> 00:05:05,919 Speaker 4: George. 100 00:05:05,960 --> 00:05:08,960 Speaker 5: We also got news that Delta is ordering thirty Boeing 101 00:05:09,400 --> 00:05:13,800 Speaker 5: seven eighty seven Dreamliner jets, aiming to boost the company. 102 00:05:14,120 --> 00:05:17,440 Speaker 5: How much do you expect this purchase to impact its 103 00:05:17,440 --> 00:05:18,120 Speaker 5: bottom line? 104 00:05:19,279 --> 00:05:22,440 Speaker 6: Yeah, so they're not going to see these airplanes until 105 00:05:22,480 --> 00:05:25,400 Speaker 6: twenty thirty, so it's a bit of a ways away. 106 00:05:25,680 --> 00:05:30,320 Speaker 6: They're replacing seven six sevens, which are old airplanes. We 107 00:05:30,360 --> 00:05:32,720 Speaker 6: have an average age of them about twenty seven years 108 00:05:32,800 --> 00:05:35,600 Speaker 6: right now, you know the seven eight is going to 109 00:05:35,640 --> 00:05:38,640 Speaker 6: be a lot more efficient. Delta gave some numbers they 110 00:05:38,880 --> 00:05:42,520 Speaker 6: fifteen ish plus percent more efficient, so I think that 111 00:05:43,120 --> 00:05:46,240 Speaker 6: really helps the bottom line. Also very interesting to us 112 00:05:46,360 --> 00:05:50,080 Speaker 6: is that Delta is traditionally more of an air bus shop. 113 00:05:50,400 --> 00:05:53,719 Speaker 6: They buy a lot of Airbus product. The recent wide 114 00:05:53,720 --> 00:05:56,560 Speaker 6: bodies they've bought. Our airbus is a three fifty and 115 00:05:56,640 --> 00:05:59,839 Speaker 6: air buses a three thirty. Again, the Boeing wide bodies 116 00:06:00,160 --> 00:06:03,960 Speaker 6: told you twenty eight ish years old. Interesting to see 117 00:06:04,000 --> 00:06:07,159 Speaker 6: them come in for the seven eighty seven. That's really, 118 00:06:07,160 --> 00:06:10,560 Speaker 6: I think, been a category leader in that small narrow 119 00:06:10,600 --> 00:06:14,560 Speaker 6: body world. Boeing has over one thousand orders for that airplane. 120 00:06:14,600 --> 00:06:18,600 Speaker 6: We see rates continued to rise, which to improve profitability at. 121 00:06:18,520 --> 00:06:20,920 Speaker 4: Boeing as they put more through put in that seven eight. 122 00:06:21,279 --> 00:06:23,279 Speaker 6: I thought it was a bit of an endorsement for 123 00:06:23,360 --> 00:06:25,640 Speaker 6: seven eight, even though Delta is not going to say that. 124 00:06:25,680 --> 00:06:29,200 Speaker 2: Probably our thanks to George Ferguson, our senior Aerospace, Defense 125 00:06:29,240 --> 00:06:30,440 Speaker 2: and Airlines analyst. 126 00:06:30,839 --> 00:06:33,680 Speaker 5: This week we focused on a Bloomberg Big Take story 127 00:06:33,720 --> 00:06:37,360 Speaker 5: titled the CEO Playbook to Navigating Trump. You can find 128 00:06:37,400 --> 00:06:39,560 Speaker 5: it on Bloomberg dot Com and the Terminal. 129 00:06:39,680 --> 00:06:42,320 Speaker 2: So the story looks at how public feuds and protectionist 130 00:06:42,360 --> 00:06:45,360 Speaker 2: threats have turned CEO's dealings with the White House into 131 00:06:45,360 --> 00:06:48,039 Speaker 2: a high stakes game of loyalty and leverage. 132 00:06:48,200 --> 00:06:51,440 Speaker 5: And for the story, Bloomberg News recently spoke to experts 133 00:06:51,480 --> 00:06:55,120 Speaker 5: and business leaders on how CEOs should navigate Trump's second term. 134 00:06:55,400 --> 00:06:57,120 Speaker 2: For more on all of this, we were joined by 135 00:06:57,160 --> 00:06:59,960 Speaker 2: one of the Big Takes authors, Matthew Boyle, Bloomberg Senior 136 00:07:00,120 --> 00:07:00,960 Speaker 2: Management reporter. 137 00:07:01,360 --> 00:07:03,440 Speaker 5: We first asked Matt to break down some of the 138 00:07:03,520 --> 00:07:06,279 Speaker 5: challenges faced while interviewing CEOs for the story. 139 00:07:06,720 --> 00:07:10,280 Speaker 7: I mean the usual sort of voices of Corporate America, 140 00:07:10,360 --> 00:07:13,360 Speaker 7: the Chamber of Commerce, the Business Roundtable. We're just like, 141 00:07:13,520 --> 00:07:16,040 Speaker 7: you know, we're going to take a pass on this one, 142 00:07:16,400 --> 00:07:18,920 Speaker 7: and just about every CEO under the sun, you know, 143 00:07:19,080 --> 00:07:20,840 Speaker 7: just they just don't want to go there. It's like 144 00:07:20,880 --> 00:07:23,840 Speaker 7: the third rail. So it was challenging to report, but 145 00:07:23,880 --> 00:07:26,600 Speaker 7: we had a lot of great conversations, let's say, on background, 146 00:07:26,920 --> 00:07:29,840 Speaker 7: with those who are advising CEOs, and that led to 147 00:07:30,040 --> 00:07:32,520 Speaker 7: our playbook. Here the five rules for dealing with the 148 00:07:33,120 --> 00:07:33,920 Speaker 7: Trump Madness. 149 00:07:34,440 --> 00:07:37,160 Speaker 5: Matt We've seen a growing number of business leaders recently 150 00:07:37,280 --> 00:07:39,400 Speaker 5: kind of push back more than usual on some of 151 00:07:39,480 --> 00:07:43,160 Speaker 5: President Trump's policy proposals. We had City CFO this morning 152 00:07:43,200 --> 00:07:45,760 Speaker 5: pushing back on the credit card cap. X On Mobile 153 00:07:45,840 --> 00:07:49,280 Speaker 5: CEO calling Venezuela an investable. Is this unusual in what 154 00:07:49,400 --> 00:07:51,480 Speaker 5: might be the consequences for them? 155 00:07:51,680 --> 00:07:53,960 Speaker 7: Yeah, it takes a certain CEO to push back. It 156 00:07:54,000 --> 00:07:56,720 Speaker 7: takes a Jamie Diamond or the CEO of ex On Mobile, 157 00:07:56,720 --> 00:07:59,280 Speaker 7: who have the clout and the authority and the industry 158 00:07:59,320 --> 00:08:02,360 Speaker 7: backing to say no, you know, this is actually not 159 00:08:02,600 --> 00:08:05,000 Speaker 7: perhaps a good idea. But most of the time, as 160 00:08:05,040 --> 00:08:08,040 Speaker 7: we saw with tariffs, it was happening behind the scenes. 161 00:08:08,400 --> 00:08:11,440 Speaker 7: You remember, this is going back aways. But when Trump 162 00:08:11,520 --> 00:08:15,000 Speaker 7: told the Walmart CEO, Doug McMillan to eat the tariffs, 163 00:08:15,800 --> 00:08:18,440 Speaker 7: Walmart said nothing, and that was probably pretty wise. There 164 00:08:18,480 --> 00:08:20,400 Speaker 7: was no reason to get into a public spat on 165 00:08:20,400 --> 00:08:24,480 Speaker 7: true social with Trump. So but now maybe it's because 166 00:08:24,520 --> 00:08:27,200 Speaker 7: Trump is in a different position twelve months later, or 167 00:08:27,240 --> 00:08:30,440 Speaker 7: it's the issues involved. You know, banking CEOs are very 168 00:08:30,440 --> 00:08:33,880 Speaker 7: happy to go out against interest ratecaps, but we are 169 00:08:33,960 --> 00:08:36,640 Speaker 7: seeing in certain cases some CEOs pushed back. 170 00:08:36,520 --> 00:08:39,760 Speaker 2: Yes, and direct engagement does seem to pay off if 171 00:08:39,760 --> 00:08:43,080 Speaker 2: you can find your way to the President's mobile phone, 172 00:08:43,080 --> 00:08:46,040 Speaker 2: which apparently he pans out the number pretty willingly to 173 00:08:46,320 --> 00:08:50,360 Speaker 2: certain top CEOs. You talk about in videos Jensen Huang 174 00:08:50,720 --> 00:08:54,839 Speaker 2: having a direct line to the president. Also Lipboon tam 175 00:08:54,960 --> 00:08:56,920 Speaker 2: of Intel being able to do that as well. 176 00:08:57,040 --> 00:08:57,400 Speaker 4: Exactly. 177 00:08:57,480 --> 00:09:00,679 Speaker 7: I mean Jensen in video ceo one on Joe in 178 00:09:00,760 --> 00:09:04,280 Speaker 7: December and said, you know, Trump is extraordinarily accessible. The 179 00:09:04,360 --> 00:09:07,120 Speaker 7: United Airline ceo said the same thing to us. I mean, 180 00:09:07,120 --> 00:09:09,840 Speaker 7: you can call this man up. He does answer his 181 00:09:09,880 --> 00:09:12,000 Speaker 7: cell phone, as we've seen sometimes at four thirty in 182 00:09:12,040 --> 00:09:14,200 Speaker 7: the morning he will pick up his cell phone. But 183 00:09:14,240 --> 00:09:17,760 Speaker 7: not everybody has Trump on speed dials. So a point 184 00:09:17,800 --> 00:09:19,560 Speaker 7: of our story was that you have to find a 185 00:09:19,600 --> 00:09:22,200 Speaker 7: way in, whether that's Susie Wiles, a chief of staff, 186 00:09:22,640 --> 00:09:26,240 Speaker 7: whether it's Scott Besson, Treasury or Commerce, Howard Lutnick, or 187 00:09:26,280 --> 00:09:28,720 Speaker 7: one of the sort of lower level aids. Also, I 188 00:09:28,720 --> 00:09:31,040 Speaker 7: mean we found that, you know, the director of the 189 00:09:31,040 --> 00:09:34,360 Speaker 7: White House Office of Public Liaison is somebody you can 190 00:09:34,520 --> 00:09:36,640 Speaker 7: you can go to also, So the point is to 191 00:09:36,679 --> 00:09:38,600 Speaker 7: find a way in, no matter how you do it. 192 00:09:39,040 --> 00:09:41,320 Speaker 5: Matt, what are some of the differences in how President 193 00:09:41,360 --> 00:09:44,040 Speaker 5: and Trump deals with business leaders in his second term 194 00:09:44,080 --> 00:09:45,120 Speaker 5: from his first term. 195 00:09:45,360 --> 00:09:48,760 Speaker 7: Well, the second term, he's a little bit more unshackled. 196 00:09:48,920 --> 00:09:51,080 Speaker 7: Let's say, in the first term, you had a few 197 00:09:51,120 --> 00:09:54,839 Speaker 7: more traditional Republican voices. You know, you had Rex Tillerson 198 00:09:55,200 --> 00:09:58,360 Speaker 7: in there and other folks who you know, were able 199 00:09:58,440 --> 00:10:00,720 Speaker 7: to maybe play a little defense. They were able to 200 00:10:00,840 --> 00:10:05,920 Speaker 7: slow walk some of Trump's more outrageous policy proposals. Now 201 00:10:05,920 --> 00:10:09,240 Speaker 7: it's just all true believers. So he's sort of unfettered. 202 00:10:09,840 --> 00:10:13,360 Speaker 7: He's unshackled, and there's really not many checks. So what 203 00:10:13,400 --> 00:10:15,720 Speaker 7: this means for CEOs is, yeah, they really can't hide. 204 00:10:15,760 --> 00:10:18,319 Speaker 7: They can't just say well, we'll let our industry association 205 00:10:18,440 --> 00:10:20,559 Speaker 7: take care of this or don't worry. I mean, look 206 00:10:20,600 --> 00:10:23,680 Speaker 7: at just the past week, what's been going on. CEOs 207 00:10:23,720 --> 00:10:24,880 Speaker 7: really need to be on watch. 208 00:10:25,040 --> 00:10:28,480 Speaker 5: Yeah, shackles is a fantastic word, a free range Trump right, 209 00:10:28,559 --> 00:10:29,640 Speaker 5: here we go, There we go. 210 00:10:30,040 --> 00:10:31,960 Speaker 2: The other thing that you could do, if you're trying 211 00:10:32,000 --> 00:10:34,200 Speaker 2: to get his attention or curry favor, is to give 212 00:10:34,280 --> 00:10:35,440 Speaker 2: him a made up award. 213 00:10:35,880 --> 00:10:40,920 Speaker 7: Yes, he does respond to these types of trinkets and trophies, 214 00:10:41,200 --> 00:10:44,040 Speaker 7: as we've seen with the Apple CEO, Tim Cook was 215 00:10:44,080 --> 00:10:46,319 Speaker 7: able to give him this sort of glass you know, 216 00:10:46,840 --> 00:10:49,520 Speaker 7: trophy with a twenty four carrot gold base or something, 217 00:10:49,520 --> 00:10:51,920 Speaker 7: you know, with the Apple logo on it. And you know, 218 00:10:51,960 --> 00:10:54,600 Speaker 7: guess what, you know, it was over tariffs and Cook 219 00:10:54,800 --> 00:10:58,800 Speaker 7: Apple we're looking for some relief on foreign microchip tariffs. 220 00:10:58,840 --> 00:11:01,280 Speaker 7: So and you know that helped. It also helped though 221 00:11:01,320 --> 00:11:03,480 Speaker 7: that Cook had really put in the work though, talking 222 00:11:03,480 --> 00:11:06,199 Speaker 7: to Trump for years now, going back to the first administration, 223 00:11:06,559 --> 00:11:08,120 Speaker 7: so you can't just you know, sort of throw a 224 00:11:08,160 --> 00:11:11,120 Speaker 7: trophy at him. But it does take some groundwork as well. 225 00:11:11,280 --> 00:11:13,800 Speaker 7: But look at the Swiss business executives also giving him 226 00:11:13,800 --> 00:11:16,040 Speaker 7: a you know, a gold rolex and you know, so 227 00:11:16,800 --> 00:11:19,439 Speaker 7: these things do tend to have an impact. As we say, 228 00:11:19,480 --> 00:11:21,920 Speaker 7: everything is a transaction with this president. 229 00:11:22,200 --> 00:11:25,199 Speaker 5: Matt, you mentioned it's been difficult getting people to speak 230 00:11:25,240 --> 00:11:27,400 Speaker 5: to you for this story. Did any of the business 231 00:11:27,480 --> 00:11:30,120 Speaker 5: leaders you spoke to give you reasoning for why they 232 00:11:30,240 --> 00:11:32,080 Speaker 5: might be afraid to talk or did they kind of 233 00:11:32,080 --> 00:11:32,959 Speaker 5: just brush you off? 234 00:11:33,200 --> 00:11:35,840 Speaker 7: It's I mean, many brush us off through their gatekeepers. 235 00:11:35,880 --> 00:11:38,280 Speaker 7: They just the thing is, they just don't see much upside. 236 00:11:38,320 --> 00:11:40,920 Speaker 7: They don't want to be the one CEO talking to 237 00:11:41,080 --> 00:11:43,959 Speaker 7: any reporter on the record. But as we've seen now, 238 00:11:44,000 --> 00:11:45,640 Speaker 7: maybe we might see some more come out of the 239 00:11:45,640 --> 00:11:49,080 Speaker 7: woodwork now that Diamond and others are talking about, you know, 240 00:11:49,200 --> 00:11:53,120 Speaker 7: the interest rates. The defense companies also might have something 241 00:11:53,120 --> 00:11:55,520 Speaker 7: to say about Trump pressuring them to, you know, to 242 00:11:55,640 --> 00:11:56,400 Speaker 7: up their game. 243 00:11:57,120 --> 00:11:58,040 Speaker 4: So we'll see. 244 00:11:58,040 --> 00:12:00,240 Speaker 7: But it's that you know, CEO is a yea, and 245 00:12:00,440 --> 00:12:02,360 Speaker 7: that they might be speaking out on certain issues, but 246 00:12:02,400 --> 00:12:05,680 Speaker 7: when it comes to Trump again, they just fail to 247 00:12:05,720 --> 00:12:08,480 Speaker 7: see the upside. But at this point, as we say, 248 00:12:08,600 --> 00:12:11,680 Speaker 7: or it was a Yale School of Management person wrote 249 00:12:11,720 --> 00:12:14,920 Speaker 7: in Bloomberg Opinion, the chaos is not just no longer 250 00:12:14,920 --> 00:12:17,320 Speaker 7: in the background. The chaos is baked in. It's in 251 00:12:17,360 --> 00:12:19,920 Speaker 7: the system. It's coming for them. So they might want 252 00:12:20,000 --> 00:12:20,320 Speaker 7: us talk. 253 00:12:20,760 --> 00:12:22,720 Speaker 2: Yeah, it's front and center, and it feels in many 254 00:12:22,760 --> 00:12:25,520 Speaker 2: ways like those who know how to navigate governments and 255 00:12:25,559 --> 00:12:27,480 Speaker 2: emerging markets might be better positioned. 256 00:12:27,559 --> 00:12:27,679 Speaker 8: Yea. 257 00:12:27,760 --> 00:12:29,959 Speaker 7: So you have that experience exactly, you know, of that 258 00:12:30,080 --> 00:12:33,679 Speaker 7: sort of slightly more chaotic, slightly more free wheeling environments, 259 00:12:33,920 --> 00:12:36,480 Speaker 7: and many of these big multinationals, of course, do operate 260 00:12:36,520 --> 00:12:37,200 Speaker 7: in those areas. 261 00:12:37,520 --> 00:12:41,000 Speaker 2: Our thanks to Matthew Boyle Bloomberg senior management reporter, coming 262 00:12:41,080 --> 00:12:43,400 Speaker 2: up a look at why the tech giant Nvidia and 263 00:12:43,520 --> 00:12:46,880 Speaker 2: the pharmaceutical company Eli Lilly are partnering up. You're listening 264 00:12:46,920 --> 00:12:50,080 Speaker 2: to Bloomberg Intelligence on Bloomberg Radio, providing in depth research 265 00:12:50,120 --> 00:12:53,040 Speaker 2: and data on two thousand companies and one hundred thirty industries. 266 00:12:53,360 --> 00:12:57,280 Speaker 5: You can access Bloomberg Intelligence via Big on the terminal. 267 00:12:57,320 --> 00:12:59,120 Speaker 5: I'm Alexandra Semenova. 268 00:12:58,720 --> 00:13:00,800 Speaker 2: And I'm Scarlett Foo is Bloomberg. 269 00:13:05,000 --> 00:13:09,520 Speaker 1: This is Bloomberg Intelligence with Scarlet Foo and Paul Sweeney 270 00:13:09,840 --> 00:13:11,120 Speaker 1: on Bloomberg Radio. 271 00:13:12,080 --> 00:13:15,840 Speaker 5: I'm Scarlettfou and I'm Alexandra Semenova filling in for Paul Sweeney. 272 00:13:15,880 --> 00:13:18,280 Speaker 2: We moved to some news in big tech this week. 273 00:13:18,360 --> 00:13:21,240 Speaker 5: Alphabet's Google confirmed that it has entered a multi year 274 00:13:21,280 --> 00:13:22,000 Speaker 5: deal with Apple. 275 00:13:22,440 --> 00:13:25,319 Speaker 2: This will allow Alphabet to power the iPhone maker's artificial 276 00:13:25,320 --> 00:13:28,560 Speaker 2: intelligence technology, including the Serie voice assistant. 277 00:13:28,720 --> 00:13:31,400 Speaker 5: We reported late last year that the two companies were 278 00:13:31,440 --> 00:13:34,160 Speaker 5: finalizing such a deal, with Apple planning to pay about 279 00:13:34,160 --> 00:13:35,320 Speaker 5: one billion dollars a year. 280 00:13:35,440 --> 00:13:37,880 Speaker 2: For more on this, we're joined by Ano Agrana, Bloomberg 281 00:13:37,880 --> 00:13:39,360 Speaker 2: Intelligence technology analyst. 282 00:13:39,720 --> 00:13:43,000 Speaker 5: We first asked Ana rog about the specific advantages Google 283 00:13:43,040 --> 00:13:45,080 Speaker 5: brings to Apple with its Gemini models. 284 00:13:45,640 --> 00:13:49,720 Speaker 9: Now we go back in history. Apple doesn't have that 285 00:13:49,880 --> 00:13:53,680 Speaker 9: big of their own foundational model on which it's running 286 00:13:53,840 --> 00:13:56,559 Speaker 9: or it's supposed to run some of these technologies. But 287 00:13:56,679 --> 00:13:58,880 Speaker 9: Apple and Google go back long way. They have a 288 00:13:59,120 --> 00:14:04,080 Speaker 9: very very good agreement on search, which pays Apple billions 289 00:14:04,080 --> 00:14:07,000 Speaker 9: of dollars. So you know, there was some legal issue 290 00:14:07,040 --> 00:14:10,240 Speaker 9: around it. So we always knew that once that legal 291 00:14:10,240 --> 00:14:13,360 Speaker 9: issue goes away, Apple may be working closely with Google 292 00:14:13,440 --> 00:14:17,520 Speaker 9: to basically outsource a lot of their foundational models into 293 00:14:17,559 --> 00:14:18,640 Speaker 9: the technology that they have. 294 00:14:18,960 --> 00:14:19,240 Speaker 4: They have. 295 00:14:19,640 --> 00:14:22,720 Speaker 9: Now it's possible that the next CD upgrade, which is 296 00:14:22,760 --> 00:14:26,120 Speaker 9: most likely going to be in the March April timeframe, 297 00:14:26,400 --> 00:14:28,600 Speaker 9: could be that one big moment that we are all 298 00:14:28,640 --> 00:14:31,360 Speaker 9: looking for Apple to finally come out and say that, okay, 299 00:14:31,360 --> 00:14:33,480 Speaker 9: you know we are not a laggard in AI, that 300 00:14:33,560 --> 00:14:35,280 Speaker 9: they also have a product that's pretty good. 301 00:14:35,720 --> 00:14:38,920 Speaker 5: Ana rag I saw a tweet actually from the official 302 00:14:38,960 --> 00:14:42,520 Speaker 5: Google account announcing this deal, and Elon Musk replied. He 303 00:14:42,640 --> 00:14:46,480 Speaker 5: said that this seems like an unreasonable concentration of power 304 00:14:46,640 --> 00:14:51,000 Speaker 5: for Google obviously has a vested interest here, given you know, 305 00:14:51,080 --> 00:14:54,440 Speaker 5: his participation in XAI, but does he have a point, 306 00:14:54,480 --> 00:14:56,760 Speaker 5: what are the regulatory risks around this deal? 307 00:14:57,920 --> 00:14:59,520 Speaker 9: Yeah, I mean he won't have a point if his 308 00:14:59,560 --> 00:15:02,280 Speaker 9: model been used at that point. So I mean everybody 309 00:15:02,320 --> 00:15:05,400 Speaker 9: is trying to pitch Apple to use that model. But 310 00:15:05,800 --> 00:15:07,800 Speaker 9: you know, at the end of the day, Apple actually, 311 00:15:07,800 --> 00:15:10,080 Speaker 9: for the first time, if you go back a year 312 00:15:10,080 --> 00:15:12,160 Speaker 9: and a half ago, they used open ai. I mean 313 00:15:12,200 --> 00:15:14,040 Speaker 9: you can still use open ai when you go to 314 00:15:14,080 --> 00:15:16,480 Speaker 9: Syria and ask a question and it can take you 315 00:15:16,560 --> 00:15:19,000 Speaker 9: out of there. But for Apple, it has to be 316 00:15:19,080 --> 00:15:24,040 Speaker 9: integrated within the iOS, within the software, but they won't 317 00:15:24,080 --> 00:15:27,360 Speaker 9: do it to an outside vendor where they don't control 318 00:15:27,600 --> 00:15:30,440 Speaker 9: the data. So what Apple's going to do here, They're 319 00:15:30,440 --> 00:15:32,920 Speaker 9: going to use the foundational models from Google, but they're 320 00:15:32,920 --> 00:15:34,600 Speaker 9: going to run a lot of that either on the 321 00:15:34,680 --> 00:15:37,960 Speaker 9: device or their own private, private cloud data centers, which 322 00:15:38,000 --> 00:15:41,320 Speaker 9: means they will still protect the privacy of that question 323 00:15:42,000 --> 00:15:44,200 Speaker 9: and not let it have you know, access to other 324 00:15:44,480 --> 00:15:45,320 Speaker 9: players as well. 325 00:15:45,640 --> 00:15:47,760 Speaker 2: Yeah, the privacy angle is really big for Apple. It's 326 00:15:47,840 --> 00:15:50,320 Speaker 2: kind of its mark of distinction here among the big 327 00:15:50,360 --> 00:15:51,080 Speaker 2: tech players. 328 00:15:51,400 --> 00:15:51,840 Speaker 5: An rag. 329 00:15:52,160 --> 00:15:56,240 Speaker 2: Apple has seen this exodus of talent AI talent going 330 00:15:56,280 --> 00:15:59,480 Speaker 2: to other big tech companies in recent months. Does this 331 00:15:59,680 --> 00:16:04,160 Speaker 2: small year deal with alphabet, with Google change any of that. 332 00:16:04,200 --> 00:16:06,040 Speaker 2: Does that stanch the bleeding at all? 333 00:16:07,000 --> 00:16:07,200 Speaker 4: Yeah? 334 00:16:07,240 --> 00:16:10,400 Speaker 9: For me, it does only because I am now buying 335 00:16:10,440 --> 00:16:13,560 Speaker 9: the model outright from somebody who's doing all the hard work. 336 00:16:13,840 --> 00:16:16,640 Speaker 9: So I'm not in the business of model development. If 337 00:16:16,640 --> 00:16:18,760 Speaker 9: I can use the best model that's out there into 338 00:16:18,800 --> 00:16:21,000 Speaker 9: my product and I pay them some money, and the 339 00:16:21,600 --> 00:16:24,840 Speaker 9: humor amount is about a billion dollars a year, which is, 340 00:16:24,880 --> 00:16:27,760 Speaker 9: to be honest, nothing compared to how many billions these 341 00:16:27,760 --> 00:16:30,480 Speaker 9: companies are spending to come up with their own large 342 00:16:30,520 --> 00:16:33,240 Speaker 9: language model. And that's what actually Apple said in the 343 00:16:33,280 --> 00:16:35,920 Speaker 9: search case and says, I'm not in the search business. 344 00:16:36,000 --> 00:16:38,200 Speaker 9: If Google has the best search, I will outsource that 345 00:16:38,320 --> 00:16:40,640 Speaker 9: technology into my products from them. 346 00:16:40,560 --> 00:16:43,520 Speaker 2: Are thanks to Honor Agrana, bi's technology analyst. 347 00:16:43,960 --> 00:16:46,720 Speaker 5: We move next to news from the athletic apparel company 348 00:16:46,840 --> 00:16:48,320 Speaker 5: Lululemon Athletica. 349 00:16:48,360 --> 00:16:51,040 Speaker 2: This week, Lululemon said it's fourth quarter sales would land 350 00:16:51,040 --> 00:16:53,400 Speaker 2: at the higher end of its twenty twenty five guidance. 351 00:16:53,760 --> 00:16:56,320 Speaker 5: It's a sign that the yoga war company is regaining 352 00:16:56,360 --> 00:16:59,360 Speaker 5: some momentum following a series of disappointing results. 353 00:16:59,480 --> 00:17:01,720 Speaker 2: For more, Alex and I were joined by Punam Goyel, 354 00:17:01,920 --> 00:17:03,960 Speaker 2: senior US e Commerce and retail analyst. 355 00:17:04,359 --> 00:17:06,880 Speaker 5: We first asked Poonam to break down how the company's 356 00:17:06,920 --> 00:17:07,840 Speaker 5: turnaround is going. 357 00:17:08,440 --> 00:17:11,760 Speaker 10: So, the loyalty program is not something that Lulu Lemon 358 00:17:11,800 --> 00:17:15,280 Speaker 10: has had for a long long time, but it does help, 359 00:17:15,400 --> 00:17:18,159 Speaker 10: just like with any other loyalty program. You know, it 360 00:17:18,200 --> 00:17:22,000 Speaker 10: gives you perks. You have access to Lulu Lemon merchandise, 361 00:17:22,040 --> 00:17:25,479 Speaker 10: who have access to Lulu Lemon events, etc. And it 362 00:17:25,560 --> 00:17:29,600 Speaker 10: does bring the member who enjoys the lul Lemon experience 363 00:17:29,680 --> 00:17:33,359 Speaker 10: to keep coming back. It's an added reward for those 364 00:17:33,400 --> 00:17:37,520 Speaker 10: who have been loyal to the company. But make no mistakes, 365 00:17:37,560 --> 00:17:40,240 Speaker 10: that is not what's going to drive the turnaround. Their 366 00:17:40,240 --> 00:17:42,280 Speaker 10: turnaround needs a lot more than that. 367 00:17:42,800 --> 00:17:46,120 Speaker 2: Yeah, I mean, loyalty programs are something that most brands 368 00:17:46,160 --> 00:17:49,240 Speaker 2: have and Lula Lemon has some more structural issues when 369 00:17:49,240 --> 00:17:52,760 Speaker 2: it comes to Lululemon. Separate from the loyalty program, Lulu 370 00:17:52,800 --> 00:17:55,479 Speaker 2: has pre announced fourth quarter sales and it comes in 371 00:17:55,520 --> 00:17:57,600 Speaker 2: at the higher end of its guidance. This is kind 372 00:17:57,600 --> 00:18:00,600 Speaker 2: of surprising because Lulua Lemon has struggled to connect with 373 00:18:00,680 --> 00:18:02,520 Speaker 2: choppers the last couple of quarters. 374 00:18:03,359 --> 00:18:05,800 Speaker 10: Yes, you can say that it's surprising, but at the 375 00:18:05,880 --> 00:18:08,000 Speaker 10: end of the day, the sales are still down. The 376 00:18:08,119 --> 00:18:10,320 Speaker 10: estimate was for sales to be down negative one to 377 00:18:10,520 --> 00:18:13,880 Speaker 10: three percent, so maybe you're close to minus one. That's 378 00:18:13,920 --> 00:18:17,200 Speaker 10: still not where we expect Lemon to be. This doesn't 379 00:18:17,240 --> 00:18:21,200 Speaker 10: tell me that the turnaround is you know, has started 380 00:18:21,240 --> 00:18:24,200 Speaker 10: and is underway and things will begin to improve quarter 381 00:18:24,240 --> 00:18:27,560 Speaker 10: after quarter. I think it's good, but I need a 382 00:18:27,600 --> 00:18:30,320 Speaker 10: lot more to be confident that the turnaround will begin 383 00:18:30,400 --> 00:18:31,080 Speaker 10: to take place. 384 00:18:32,160 --> 00:18:35,600 Speaker 5: What are some of the macroeconomic trends that are driving 385 00:18:35,720 --> 00:18:39,080 Speaker 5: Lululemon's customer base. Is it inflation? Is it just an 386 00:18:39,119 --> 00:18:42,240 Speaker 5: overall change in discretionary spending patterns? 387 00:18:42,280 --> 00:18:42,760 Speaker 2: What is it? 388 00:18:43,680 --> 00:18:46,560 Speaker 10: Yeah, so the macro economy has been you know, good 389 00:18:46,600 --> 00:18:50,119 Speaker 10: and bad. The luxury consumer has been doing well Lemon. 390 00:18:50,160 --> 00:18:53,879 Speaker 10: You could argue SIT's in the affluent space of active wear. 391 00:18:54,320 --> 00:18:57,360 Speaker 10: But that said, it's not that the customer is frushured 392 00:18:57,400 --> 00:19:00,199 Speaker 10: and that's why their sales have fallen. It is that 393 00:19:00,240 --> 00:19:03,920 Speaker 10: they have lacked innovation and execution has been weak. So 394 00:19:04,359 --> 00:19:08,120 Speaker 10: the key here is a company specific issue that they 395 00:19:08,160 --> 00:19:11,280 Speaker 10: need to resolve, and they are working towards it. We 396 00:19:11,359 --> 00:19:13,880 Speaker 10: will be getting a new CEO hopefully at some point 397 00:19:13,920 --> 00:19:17,000 Speaker 10: in twenty twenty six, and it'll really be then where 398 00:19:17,000 --> 00:19:20,159 Speaker 10: we can see who comes in, what's the strategy, and 399 00:19:20,200 --> 00:19:24,080 Speaker 10: how does lul Lemon re engage. It's for a customer 400 00:19:24,280 --> 00:19:27,600 Speaker 10: that probably still loves the brand but just hasn't found 401 00:19:27,840 --> 00:19:29,960 Speaker 10: enough new to keep going back and back. 402 00:19:30,680 --> 00:19:30,880 Speaker 4: Yeah. 403 00:19:30,880 --> 00:19:33,960 Speaker 2: Absolutely. I think about the competitors that Lulu Lemon faces 404 00:19:33,960 --> 00:19:38,199 Speaker 2: in the space. It ranges from Dorry to all the 405 00:19:38,240 --> 00:19:41,200 Speaker 2: other big names out there, including Nike, which offers perhaps 406 00:19:41,240 --> 00:19:43,800 Speaker 2: similar at leisure wear but at a lower price point too. 407 00:19:44,280 --> 00:19:46,720 Speaker 2: When it comes to this new CEO, Calvin MacDonald, the 408 00:19:46,760 --> 00:19:48,760 Speaker 2: current c is set to a step down, and I 409 00:19:48,800 --> 00:19:50,919 Speaker 2: know Elliott has been a big player in pushing for 410 00:19:51,000 --> 00:19:54,000 Speaker 2: Jan Nielsen, the former CFO at Ralph Lauren, to replace him. 411 00:19:55,119 --> 00:19:58,120 Speaker 2: What's the latest on that. Do we presume that Jane 412 00:19:58,160 --> 00:19:59,639 Speaker 2: Nielsen is going to be the next CEO? 413 00:20:01,000 --> 00:20:03,640 Speaker 10: I mean we don't know yet right so it's definitely 414 00:20:03,840 --> 00:20:06,680 Speaker 10: one of the contenders and could be. And I think 415 00:20:06,720 --> 00:20:09,400 Speaker 10: as long as they get a product led executive, which 416 00:20:09,400 --> 00:20:12,480 Speaker 10: he is, Little Lemon could be in good hands to 417 00:20:12,520 --> 00:20:13,679 Speaker 10: continue this turnaround. 418 00:20:14,040 --> 00:20:16,960 Speaker 2: Founder Chip Wilson is also kind of involved here, that's 419 00:20:17,000 --> 00:20:19,480 Speaker 2: another name that we haven't heard from in a while. 420 00:20:19,560 --> 00:20:22,720 Speaker 2: He founded the company. He no longer sits on the board, 421 00:20:22,760 --> 00:20:25,080 Speaker 2: but he still owns about nine percent of the stock. 422 00:20:25,400 --> 00:20:27,880 Speaker 2: How much do you pay attention to what he says? 423 00:20:28,200 --> 00:20:30,880 Speaker 10: We definitely look at it. Look what he said was important. 424 00:20:30,880 --> 00:20:34,040 Speaker 10: He founded the company. The company was grounded on innovation, 425 00:20:34,200 --> 00:20:38,080 Speaker 10: on products, and I think he makes fair points that 426 00:20:38,240 --> 00:20:42,760 Speaker 10: the company needs to kind of really re engage with 427 00:20:42,800 --> 00:20:45,240 Speaker 10: the customer and go back to its roots, which is, 428 00:20:45,520 --> 00:20:47,800 Speaker 10: are you still ahead of the competition? Like you know 429 00:20:47,840 --> 00:20:51,240 Speaker 10: you mentioned earlier, competition has increased, and it has, but 430 00:20:51,359 --> 00:20:54,880 Speaker 10: competition was always there. But where is Little Lemon ahead 431 00:20:54,880 --> 00:20:57,320 Speaker 10: of it? Can you get similar at leisure pants or 432 00:20:57,400 --> 00:21:01,760 Speaker 10: leggings at Vory or at Aloe or at Latta? How 433 00:21:01,800 --> 00:21:04,480 Speaker 10: does Little Lemons stand out today? And that's the big 434 00:21:04,560 --> 00:21:06,240 Speaker 10: question that they need to answer. 435 00:21:06,320 --> 00:21:08,680 Speaker 2: Our thanks to put Im Goyel, senior US e Commerce 436 00:21:08,720 --> 00:21:10,760 Speaker 2: and retail analysts at Bloomberg Intelligence. 437 00:21:10,920 --> 00:21:13,080 Speaker 5: We move next to the biotech space. 438 00:21:12,840 --> 00:21:15,320 Speaker 2: And this week tech Giant and Nvidia announced plans to 439 00:21:15,359 --> 00:21:18,120 Speaker 2: invest a billion dollars over five years in a new 440 00:21:18,200 --> 00:21:21,200 Speaker 2: AI lab. And that's with the pharmaceutical company Eli Lilly 441 00:21:21,640 --> 00:21:22,400 Speaker 2: for more on this. 442 00:21:22,640 --> 00:21:25,840 Speaker 5: We were joined by Sam Fazzelli, Bloomberg Intelligence, Director of 443 00:21:25,840 --> 00:21:28,960 Speaker 5: Research for Global Industries and senior pharmaceuticals analysts. 444 00:21:29,160 --> 00:21:31,240 Speaker 2: We started off by asking Sam to break down the 445 00:21:31,320 --> 00:21:32,600 Speaker 2: latest news at ELI Lilly. 446 00:21:33,080 --> 00:21:36,040 Speaker 11: Well, look, I think these are the brillion dollar club 447 00:21:36,119 --> 00:21:39,080 Speaker 11: getting together. A billion dollars is probably dropping the ocean 448 00:21:39,119 --> 00:21:43,640 Speaker 11: for both of them. It's over five years. And what 449 00:21:43,720 --> 00:21:45,399 Speaker 11: I think the aim of this, at the end of 450 00:21:45,400 --> 00:21:48,080 Speaker 11: the day is to try and automate and speed up 451 00:21:48,119 --> 00:21:52,800 Speaker 11: as much of the partly the drudgery of doing lab work, 452 00:21:52,800 --> 00:21:54,840 Speaker 11: because lab work just like as if you were a 453 00:21:54,920 --> 00:21:58,760 Speaker 11: doctor in a clinical setting, you need to take a 454 00:21:58,760 --> 00:22:00,679 Speaker 11: lot of notes, a lot of detail. But at the 455 00:22:00,680 --> 00:22:04,159 Speaker 11: same time, I think they're investing in what appears to 456 00:22:04,200 --> 00:22:08,840 Speaker 11: be automating twenty four to seven experiments, things that human 457 00:22:08,840 --> 00:22:12,600 Speaker 11: beings just can't do. And I know, having been a scientist, 458 00:22:12,800 --> 00:22:14,720 Speaker 11: that sometimes your life is on hold because you have 459 00:22:14,760 --> 00:22:18,399 Speaker 11: to go back and deal with your experiments, yoursells, or 460 00:22:18,400 --> 00:22:20,399 Speaker 11: whatever it is that you're running. So a lot of 461 00:22:20,440 --> 00:22:22,760 Speaker 11: these things I think could speed up and also make 462 00:22:22,800 --> 00:22:25,239 Speaker 11: the life of the scientists a little bit easier at 463 00:22:25,280 --> 00:22:27,000 Speaker 11: the end of the day, though I think the am 464 00:22:27,040 --> 00:22:31,000 Speaker 11: here is to try and get scientific discoveries translated to 465 00:22:31,080 --> 00:22:33,840 Speaker 11: drugs quicker and get them to market quicker. 466 00:22:34,280 --> 00:22:37,119 Speaker 5: Sam, What does this mean for Eli Lilly's rivals? Do 467 00:22:37,160 --> 00:22:39,760 Speaker 5: you expect that we'll see some of its competitors also 468 00:22:39,920 --> 00:22:42,840 Speaker 5: ramp up their AI endeavors given its partnership with the 469 00:22:42,880 --> 00:22:44,600 Speaker 5: company like Nvidia. 470 00:22:44,960 --> 00:22:47,200 Speaker 11: Pretty much all pharma companies. And I just want to 471 00:22:47,240 --> 00:22:50,720 Speaker 11: point you to a recent survey that EI Bloomberg Intelligence 472 00:22:50,720 --> 00:22:54,800 Speaker 11: has done that looked at ten different industries, one of 473 00:22:54,800 --> 00:22:58,679 Speaker 11: which being the pharmaceutical industry. Ask executives, what are you 474 00:22:58,760 --> 00:23:00,639 Speaker 11: doing with this? What are you trying to get to? 475 00:23:00,720 --> 00:23:03,720 Speaker 11: What is the aim? At the end? Everybody is at this. 476 00:23:03,720 --> 00:23:07,479 Speaker 11: This is not something specific to Lily. And you know, Google, 477 00:23:07,520 --> 00:23:11,560 Speaker 11: for example, has has through deep Mind, has created an 478 00:23:11,560 --> 00:23:15,120 Speaker 11: agentic KI called Google Scientists. It's like, I think four 479 00:23:15,160 --> 00:23:17,639 Speaker 11: agents or five agents who interact with each other and 480 00:23:17,680 --> 00:23:20,560 Speaker 11: check each other's hypothesis. So a lot of this is 481 00:23:20,640 --> 00:23:22,800 Speaker 11: going on in all companies, but of course here we 482 00:23:22,880 --> 00:23:26,320 Speaker 11: have Lily, one of the richest farmer companies around in 483 00:23:26,440 --> 00:23:28,440 Speaker 11: terms of the amount of cash blow that is got 484 00:23:28,800 --> 00:23:33,760 Speaker 11: really being able to spend the money without really impacting it. 485 00:23:33,760 --> 00:23:36,400 Speaker 11: It's balance sheet and cash flow, and I'm pretty sure 486 00:23:36,600 --> 00:23:40,240 Speaker 11: everybody's at this, but clearly this is the news, doujou if. 487 00:23:40,080 --> 00:23:43,119 Speaker 2: You like, absolutely, Like you said, it's a drop in 488 00:23:43,160 --> 00:23:44,800 Speaker 2: the bucket for both of these companies. But when you 489 00:23:44,800 --> 00:23:47,000 Speaker 2: have a number like one billion dollars and these big 490 00:23:47,560 --> 00:23:51,800 Speaker 2: brand names, it gets people's attention. What also gets my attention, Sam, 491 00:23:51,920 --> 00:23:55,080 Speaker 2: is Maderna. Maderna obviously one of the vaccine makers during 492 00:23:55,680 --> 00:23:58,919 Speaker 2: the pandemic, but it's had a rough go at it 493 00:23:59,000 --> 00:24:03,280 Speaker 2: recently because it's so dependent on these vaccines and we 494 00:24:03,359 --> 00:24:06,400 Speaker 2: have an administration that is kind of anti vaccine right now. 495 00:24:06,560 --> 00:24:09,520 Speaker 2: Yet madernal pre announced at the JP Morgan Healthcare conference 496 00:24:09,520 --> 00:24:12,400 Speaker 2: that is the US COVID business did better than expected. 497 00:24:12,960 --> 00:24:17,080 Speaker 2: Is Madernas starting to stabilize, Well. 498 00:24:16,960 --> 00:24:20,840 Speaker 11: One would hope. So they've stuck with their aim of 499 00:24:21,000 --> 00:24:24,199 Speaker 11: growing twenty twenty six point ten percent. But you know, 500 00:24:24,400 --> 00:24:26,600 Speaker 11: if you look back at the beginning of the year, 501 00:24:26,640 --> 00:24:30,520 Speaker 11: where the company in twenty twenty five started with guidance 502 00:24:31,000 --> 00:24:33,600 Speaker 11: and where we ended up, we're a good five six 503 00:24:33,680 --> 00:24:36,240 Speaker 11: hundred million dollars short of what the hope was at 504 00:24:36,240 --> 00:24:39,359 Speaker 11: the beginning of the year. So everybody, I thinking knows 505 00:24:39,560 --> 00:24:41,639 Speaker 11: that this is a very difficult market to call for 506 00:24:41,720 --> 00:24:44,720 Speaker 11: exactly the reasons you just highlighted. There is a constant 507 00:24:44,840 --> 00:24:47,359 Speaker 11: change in the way that the administration in the US, 508 00:24:47,400 --> 00:24:51,040 Speaker 11: and not just the US elsewhere is also dealing with vaccination, 509 00:24:51,960 --> 00:24:55,360 Speaker 11: particularly in the COVID side of things. Other vaccinations are 510 00:24:55,359 --> 00:24:59,520 Speaker 11: still pretty much well settled and at least outside the US. 511 00:25:00,000 --> 00:25:03,880 Speaker 11: Problem also that MODERNA is going to face or has 512 00:25:03,920 --> 00:25:05,840 Speaker 11: been facing, is that there are lots of people. There's 513 00:25:06,000 --> 00:25:08,480 Speaker 11: I think my patent colleagues say that at least twenty 514 00:25:09,119 --> 00:25:13,800 Speaker 11: various directions of legal cases or trials going on different 515 00:25:13,920 --> 00:25:16,600 Speaker 11: groups saying you're infringing my patents, so you've fringe my 516 00:25:16,680 --> 00:25:18,639 Speaker 11: patents and you need to because there was billions and 517 00:25:18,720 --> 00:25:22,760 Speaker 11: billions of dollars of revenues. So some of that is 518 00:25:22,800 --> 00:25:28,320 Speaker 11: coming potentially in the March timeframe, very lightly against a 519 00:25:28,359 --> 00:25:31,120 Speaker 11: trial coming up against our Bridges, another company that's listed 520 00:25:31,160 --> 00:25:33,360 Speaker 11: in the US. So some of that is I think 521 00:25:33,440 --> 00:25:36,520 Speaker 11: something that might keep people from getting too excited. 522 00:25:36,640 --> 00:25:40,000 Speaker 5: Our Thanks to Sam Fazzelli, Bloomberg Intelligence Director of Research 523 00:25:40,000 --> 00:25:43,679 Speaker 5: for Global Industries and senior pharmaceuticals analyst, coming up all 524 00:25:43,680 --> 00:25:46,400 Speaker 5: look at how artificial intelligence is changing shopping. 525 00:25:46,640 --> 00:25:49,760 Speaker 2: You're listening to Bloomberg Intelligence on Bloomberg Radio, providing in 526 00:25:49,800 --> 00:25:52,479 Speaker 2: depth research and data on two thousand companies and one 527 00:25:52,560 --> 00:25:53,680 Speaker 2: hundred and thirty industries. 528 00:25:54,040 --> 00:25:57,720 Speaker 5: You can access Bloomberg Intelligence via big on the terminal. 529 00:25:57,800 --> 00:26:01,240 Speaker 5: I'm Alexandra Semenova and I'm Scarlett Foo. This is Bloomberg. 530 00:26:09,320 --> 00:26:13,840 Speaker 1: This is Bloomberg Intelligence with Scarlet Foo and Paul Sweeney 531 00:26:14,160 --> 00:26:15,480 Speaker 1: on Bloomberg Radio. 532 00:26:16,240 --> 00:26:17,280 Speaker 2: I'm Scarlet Foo. 533 00:26:17,320 --> 00:26:20,080 Speaker 5: And I'm Alexandra Semenova filling in for Paul Sweeney. 534 00:26:20,160 --> 00:26:22,159 Speaker 2: We move now to some news in the tech space. 535 00:26:22,640 --> 00:26:25,240 Speaker 5: This week, we heard that the tech giant Meta Platforms 536 00:26:25,320 --> 00:26:27,359 Speaker 5: is beginning to cut more than a thousand jobs from 537 00:26:27,400 --> 00:26:28,800 Speaker 5: its Reality Labs division. 538 00:26:29,160 --> 00:26:31,919 Speaker 2: It's part of a plan to redirect resources from virtual 539 00:26:31,920 --> 00:26:35,720 Speaker 2: reality and metaverse products towards AI, wearables and phone features. 540 00:26:36,040 --> 00:26:37,959 Speaker 5: For more on this and the latest tech news, we 541 00:26:37,960 --> 00:26:40,760 Speaker 5: were joined by Mandeep Saying, global tech research head at 542 00:26:40,760 --> 00:26:41,840 Speaker 5: Bloomberg Intelligence. 543 00:26:41,920 --> 00:26:44,199 Speaker 2: We first as Ban Deep what these job cuts mean 544 00:26:44,240 --> 00:26:45,480 Speaker 2: for Meta's bottom line. 545 00:26:45,920 --> 00:26:50,080 Speaker 8: I mean reality Lab segment is almost losing twenty billion 546 00:26:50,119 --> 00:26:54,639 Speaker 8: dollars a year and cumulatively they've lost about seventy billion 547 00:26:54,720 --> 00:26:57,800 Speaker 8: dollars over the past three years, so a lot of 548 00:26:57,840 --> 00:27:02,439 Speaker 8: investors questioned, you know, how long they were expected to 549 00:27:03,200 --> 00:27:06,960 Speaker 8: remain patient on those kind of losses. And I think 550 00:27:06,960 --> 00:27:10,280 Speaker 8: the initial rumors were about a thirty percent cut in 551 00:27:10,320 --> 00:27:14,600 Speaker 8: that unit. So this is somewhat below expectations in terms 552 00:27:14,600 --> 00:27:17,840 Speaker 8: of ten percent job cut, but it just goes to 553 00:27:17,960 --> 00:27:22,160 Speaker 8: show that right now, the companies obviously focus more on 554 00:27:22,200 --> 00:27:25,399 Speaker 8: the AI side in terms of building the infrastructure, building 555 00:27:25,400 --> 00:27:28,399 Speaker 8: their own large anguid model, and it may take a 556 00:27:28,440 --> 00:27:33,840 Speaker 8: while to you know, really bring it, bring that LLLM 557 00:27:33,920 --> 00:27:37,600 Speaker 8: concept in that variables or the whether it's a VR 558 00:27:37,640 --> 00:27:43,080 Speaker 8: headsets or the glasses. And I mean when you compare 559 00:27:43,560 --> 00:27:47,440 Speaker 8: VR headsets or the glasses that they're selling to let's 560 00:27:47,440 --> 00:27:51,480 Speaker 8: say AirPods, the number of units pale in comparison. We're talking, 561 00:27:51,560 --> 00:27:53,960 Speaker 8: you know, ten million, maybe if they do twenty million. 562 00:27:54,680 --> 00:27:57,400 Speaker 8: Apple does more than one hundred million AirPods a year, 563 00:27:57,960 --> 00:28:01,639 Speaker 8: So what's the opportunity here? And I think that's where 564 00:28:01,720 --> 00:28:04,760 Speaker 8: you probably will see more cuts in that business. 565 00:28:04,840 --> 00:28:07,520 Speaker 2: That is a really really important contrast to make there 566 00:28:07,840 --> 00:28:09,840 Speaker 2: in terms of what Meta is trying to do, because 567 00:28:09,840 --> 00:28:12,040 Speaker 2: they want to be part of the mass market here, 568 00:28:12,080 --> 00:28:16,399 Speaker 2: but it's not quite there yet. Mindy. As Meta pivots 569 00:28:16,440 --> 00:28:18,640 Speaker 2: away from VR and the metaverse to AI, what does 570 00:28:18,640 --> 00:28:20,720 Speaker 2: that mean for spending? I mean, they obviously had to 571 00:28:20,720 --> 00:28:23,439 Speaker 2: spend a lot to build out the metaverse, to build 572 00:28:23,440 --> 00:28:27,639 Speaker 2: out their VR offerings, and now they're going to shift 573 00:28:27,680 --> 00:28:31,000 Speaker 2: everything to building out the AI offerings of large language models, 574 00:28:31,040 --> 00:28:35,280 Speaker 2: these AI glasses. What do you think that the pace 575 00:28:35,320 --> 00:28:37,399 Speaker 2: of spending will just kind of continue, It won't really 576 00:28:37,520 --> 00:28:38,360 Speaker 2: shift all that much. 577 00:28:39,120 --> 00:28:42,280 Speaker 8: Yes, On the AI side, the opportunity is huge. What 578 00:28:42,400 --> 00:28:46,040 Speaker 8: everyone is chasing right now is an AI agent that 579 00:28:46,160 --> 00:28:50,200 Speaker 8: can book your travel, your Uber trip, order food, you know, 580 00:28:50,480 --> 00:28:53,680 Speaker 8: do shopping for you. That's the vision Google is chasing. 581 00:28:53,720 --> 00:28:58,000 Speaker 8: That's what Amazon alexaplus launch was all about. So Meta 582 00:28:58,120 --> 00:29:01,400 Speaker 8: has that surface area with you know, Instagram and WhatsApp, 583 00:29:01,520 --> 00:29:05,480 Speaker 8: and you could argue they could in theory develop such 584 00:29:05,520 --> 00:29:09,920 Speaker 8: an agent, but the hard part is integration and getting 585 00:29:09,960 --> 00:29:13,400 Speaker 8: the AI to where it's you know, reliable and predictable. 586 00:29:13,440 --> 00:29:17,800 Speaker 8: And that's where I mean, it's anybody's guests who is 587 00:29:17,880 --> 00:29:21,920 Speaker 8: best positioned. I think the Apple Google partnership that we 588 00:29:22,000 --> 00:29:25,280 Speaker 8: saw this week is probably a negative for Meta in 589 00:29:25,320 --> 00:29:29,080 Speaker 8: this sense. Like, if Apple is setting up defaults in 590 00:29:29,120 --> 00:29:32,240 Speaker 8: their phone, then that makes it hard for an external 591 00:29:33,280 --> 00:29:36,880 Speaker 8: kind of agent to do these kind of things. So 592 00:29:37,320 --> 00:29:42,240 Speaker 8: from that perspective, distribution really matters and operating system control 593 00:29:42,400 --> 00:29:46,920 Speaker 8: really matters. So Meta is somewhat at a disadvantage when 594 00:29:46,920 --> 00:29:49,840 Speaker 8: it comes to, you know, their distribution on Apple and 595 00:29:49,840 --> 00:29:50,800 Speaker 8: Android devices. 596 00:29:51,000 --> 00:29:54,480 Speaker 5: Mandy. We also got new Airbnb hired Meta's head of 597 00:29:54,600 --> 00:29:58,240 Speaker 5: General AI, which is really interesting because wasn't it not 598 00:29:58,360 --> 00:30:01,160 Speaker 5: too long ago that they declined work with open AI. 599 00:30:01,240 --> 00:30:04,320 Speaker 5: What does this all mean for its artificial intelligence endeavors. 600 00:30:04,520 --> 00:30:08,240 Speaker 8: Yeah, I think Brian Chesky has been quite vocal about 601 00:30:08,480 --> 00:30:12,960 Speaker 8: using open source llms as opposed to you know, proprietary 602 00:30:13,240 --> 00:30:17,520 Speaker 8: llms like open AI and Gemini, and so his thing is, 603 00:30:17,920 --> 00:30:21,320 Speaker 8: I've got a direct customer traffic coming to my website. 604 00:30:21,840 --> 00:30:26,040 Speaker 8: If I give away you know, my bookings interface to 605 00:30:26,160 --> 00:30:30,080 Speaker 8: these llms, then I'm losing that customer direct customer touch. 606 00:30:30,720 --> 00:30:33,080 Speaker 8: And he doesn't want to do that. He instead wants 607 00:30:33,120 --> 00:30:35,880 Speaker 8: to build his own LLM based on an open source 608 00:30:36,320 --> 00:30:38,520 Speaker 8: model that's already out there, and that's where you know, 609 00:30:39,320 --> 00:30:42,440 Speaker 8: Meta has open source their model in the past, so 610 00:30:42,520 --> 00:30:45,400 Speaker 8: it makes sense to have somebody from Meta come in 611 00:30:45,520 --> 00:30:49,280 Speaker 8: and do something along those lines. AB and B has 612 00:30:49,320 --> 00:30:52,400 Speaker 8: been open to using Chinese open source models and building 613 00:30:52,440 --> 00:30:54,600 Speaker 8: on top of that. So from that perspective, it's an 614 00:30:54,640 --> 00:30:58,120 Speaker 8: interesting strategy that they are going ahead with in terms 615 00:30:58,160 --> 00:31:01,120 Speaker 8: of using all kinds of open source and not just 616 00:31:01,360 --> 00:31:02,600 Speaker 8: you know, the US based model. 617 00:31:02,760 --> 00:31:04,680 Speaker 2: Who in the tech world is winning the talent war 618 00:31:04,760 --> 00:31:08,000 Speaker 2: because it felt like for a long time open AI 619 00:31:08,120 --> 00:31:10,240 Speaker 2: and traffic they were, you know, picking up a lot 620 00:31:10,240 --> 00:31:12,080 Speaker 2: of talent. Is that still the case? 621 00:31:13,160 --> 00:31:15,480 Speaker 8: I mean right now, the talent is going to where 622 00:31:15,520 --> 00:31:18,760 Speaker 8: the compute is. If you don't have the compute, you 623 00:31:19,000 --> 00:31:22,280 Speaker 8: just cannot attract the talent because these models need a 624 00:31:22,320 --> 00:31:25,480 Speaker 8: lot of compute for training. And you may be the 625 00:31:25,520 --> 00:31:28,080 Speaker 8: smartest person, but if you don't have the computer, you 626 00:31:28,120 --> 00:31:32,080 Speaker 8: can't test your idea. So from that perspective, infrastructure build 627 00:31:32,200 --> 00:31:34,880 Speaker 8: really matters, which is why in Nvidia, even though they 628 00:31:34,880 --> 00:31:38,160 Speaker 8: are the chip provider, now they launch their own foundational 629 00:31:38,240 --> 00:31:42,360 Speaker 8: model in autos self driving that just goes to show 630 00:31:42,440 --> 00:31:45,720 Speaker 8: what compute can do, you know, and Vidia is definitely 631 00:31:45,720 --> 00:31:47,840 Speaker 8: moving up the stax. It will be interesting to see 632 00:31:47,840 --> 00:31:50,880 Speaker 8: how many areas where they compete in with their own 633 00:31:50,880 --> 00:31:52,600 Speaker 8: foundational model man Deep. 634 00:31:52,600 --> 00:31:55,440 Speaker 5: We're early into the earning season, but big tech results 635 00:31:55,480 --> 00:31:57,520 Speaker 5: will be here before we know it. And obviously the 636 00:31:57,520 --> 00:31:59,680 Speaker 5: bar is really high when it comes to what these 637 00:31:59,680 --> 00:32:03,760 Speaker 5: companies are saying about how they're monetizing their heavy AI investments. 638 00:32:03,760 --> 00:32:06,120 Speaker 5: Do you think that they'll live up to the expectations. 639 00:32:06,600 --> 00:32:08,800 Speaker 8: I think right now you have to focus on where 640 00:32:08,840 --> 00:32:13,880 Speaker 8: you will see positive earnings revisions, and given the kapex 641 00:32:13,920 --> 00:32:16,880 Speaker 8: investments are going up for this year, it's going to 642 00:32:16,920 --> 00:32:19,920 Speaker 8: be hard to show, you know, positive revisions when it 643 00:32:19,920 --> 00:32:24,360 Speaker 8: comes to earnings, except for someone like Alphabet that really 644 00:32:24,400 --> 00:32:28,640 Speaker 8: has seen a big shift in sentiment because one everyone 645 00:32:28,760 --> 00:32:31,600 Speaker 8: realizes that their models have caught up, and they also 646 00:32:31,600 --> 00:32:34,160 Speaker 8: are the most efficient when it comes to their stack, 647 00:32:34,240 --> 00:32:37,600 Speaker 8: the use of TPUs and low cost inferencing. So from 648 00:32:37,600 --> 00:32:41,200 Speaker 8: that perspective, I think Alphabet clearly is best position to 649 00:32:41,280 --> 00:32:44,760 Speaker 8: deliver positive surprises. But for someone like Meta, I mean, 650 00:32:44,800 --> 00:32:48,240 Speaker 8: if you're hearing job cuts, then you know probably it's 651 00:32:48,280 --> 00:32:49,920 Speaker 8: going to be hard for them to you know, show 652 00:32:49,960 --> 00:32:51,280 Speaker 8: positive revisions this year. 653 00:32:51,640 --> 00:32:54,600 Speaker 5: Our thanks to man Deep sing Global Tech Research had 654 00:32:54,640 --> 00:32:56,440 Speaker 5: at Bloomberg Intelligence this week. 655 00:32:56,520 --> 00:32:59,280 Speaker 2: We also looked at the e commerce platform Commerce which 656 00:32:59,320 --> 00:33:02,959 Speaker 2: provides a driven tools for brands and retaillers. This company 657 00:33:03,000 --> 00:33:05,640 Speaker 2: trades on the NAZAC under the ticker CMRC. 658 00:33:06,120 --> 00:33:09,080 Speaker 5: We were joined by the company's CEO, Travis Hess, who 659 00:33:09,160 --> 00:33:12,400 Speaker 5: discussed how AI is changing shopping and this came on 660 00:33:12,440 --> 00:33:16,720 Speaker 5: the heels of Google launching its Universal Commerce Protocol or UCP. 661 00:33:17,240 --> 00:33:20,800 Speaker 2: Commerce is an active partner in UCP, which enables buying 662 00:33:20,840 --> 00:33:24,720 Speaker 2: directly across Google's AI surfaces. We first asked Travis to 663 00:33:24,840 --> 00:33:28,360 Speaker 2: break down Google's UCP and just how exactly this makes 664 00:33:28,360 --> 00:33:29,200 Speaker 2: shopping easier. 665 00:33:30,000 --> 00:33:32,840 Speaker 3: Given the extent of which Google plays in all of 666 00:33:32,880 --> 00:33:36,120 Speaker 3: our lives and how people think of them, It's essentially 667 00:33:36,120 --> 00:33:40,040 Speaker 3: setting the foundation and the standard for brands and retailers 668 00:33:40,040 --> 00:33:42,720 Speaker 3: and other organizations to be able to scale agent to 669 00:33:42,760 --> 00:33:46,680 Speaker 3: commerce across all of the surfaces that Google AI would hit. 670 00:33:46,720 --> 00:33:50,480 Speaker 3: So think of the contextualized conversations everyone's having through answer 671 00:33:50,520 --> 00:33:54,960 Speaker 3: engines today. Those conversations are then dynamically recommending not only 672 00:33:55,040 --> 00:33:58,400 Speaker 3: experiences and facts, but certainly shopping and brands based on 673 00:33:58,520 --> 00:34:01,440 Speaker 3: the enrichment of data against those surfaces, and Google is 674 00:34:01,520 --> 00:34:04,560 Speaker 3: enabling a standard by which consumers would be able to 675 00:34:04,600 --> 00:34:08,720 Speaker 3: buy in a standardized, safe secure way. As it serves 676 00:34:08,840 --> 00:34:12,440 Speaker 3: up discovery, you'll be able to buy seamlessly across those surfaces. 677 00:34:12,239 --> 00:34:14,800 Speaker 5: Travis, can you walk us through how AI will change 678 00:34:14,840 --> 00:34:18,160 Speaker 5: the shopping experience in practical terms? Let's say I'm looking 679 00:34:18,239 --> 00:34:20,839 Speaker 5: for a bag? How do I go about using your platform? 680 00:34:21,080 --> 00:34:24,080 Speaker 3: The consumer behavior has changed more than anything, So the 681 00:34:24,160 --> 00:34:27,120 Speaker 3: amount of eyeballs and behavior going to the answer engines 682 00:34:27,200 --> 00:34:30,440 Speaker 3: is probably the fastest adopting technology we've seen. So because 683 00:34:30,520 --> 00:34:33,120 Speaker 3: the consumers are going there and they're having these conversations, 684 00:34:33,800 --> 00:34:36,200 Speaker 3: brands and retailers are having to respond in kind, and 685 00:34:36,480 --> 00:34:38,640 Speaker 3: they tend to have a fair amount of brand ethos, 686 00:34:39,120 --> 00:34:41,279 Speaker 3: so they're very concerned about where they show up, how 687 00:34:41,280 --> 00:34:42,600 Speaker 3: they show up, and who they show up next to. 688 00:34:42,920 --> 00:34:44,840 Speaker 3: So trust is at the cornerstone of this. So a 689 00:34:44,840 --> 00:34:47,120 Speaker 3: lot of the foundational stuff you're hearing about now is 690 00:34:47,160 --> 00:34:48,719 Speaker 3: to set up that trust. So when you're shopping for 691 00:34:48,760 --> 00:34:51,960 Speaker 3: that bag, it's giving you a response that you can 692 00:34:52,000 --> 00:34:55,200 Speaker 3: objectively trust. It's not serving up who paid the most 693 00:34:55,360 --> 00:34:57,960 Speaker 3: to be listed or injected in that conversation. The thesis 694 00:34:58,000 --> 00:35:01,560 Speaker 3: behind this is based on your own behavior within those 695 00:35:01,600 --> 00:35:04,919 Speaker 3: answer engines, and of course other behavior within Google. Whether 696 00:35:04,920 --> 00:35:07,480 Speaker 3: that's through Chrome or through other Google products. They have 697 00:35:07,520 --> 00:35:10,640 Speaker 3: a unique advantage there because of the amount of surfaces 698 00:35:10,640 --> 00:35:14,400 Speaker 3: an individual might use to synthesize that in a hyper 699 00:35:14,440 --> 00:35:17,640 Speaker 3: personalized way. So think of it as not only shopping either, 700 00:35:17,640 --> 00:35:20,080 Speaker 3: but think of going into your favorite store and someone 701 00:35:20,160 --> 00:35:22,520 Speaker 3: as you walk in can scan a code and immediately 702 00:35:22,560 --> 00:35:25,080 Speaker 3: knows what you own, what you like, where you're going, 703 00:35:25,120 --> 00:35:27,160 Speaker 3: what you're looking for, so they could curate something for 704 00:35:27,200 --> 00:35:30,680 Speaker 3: you immediately. Where that friction is removed through the process. 705 00:35:30,800 --> 00:35:33,440 Speaker 3: Think of that virtually in this particular capacity. That's the 706 00:35:33,440 --> 00:35:33,959 Speaker 3: future state. 707 00:35:34,200 --> 00:35:36,399 Speaker 2: So all the information that Google has on me, they 708 00:35:36,400 --> 00:35:38,279 Speaker 2: are also selling who are they selling it to? 709 00:35:38,440 --> 00:35:41,080 Speaker 3: Oh that's not for me, that's that's top. 710 00:35:42,520 --> 00:35:44,440 Speaker 2: I mean. The reason I ask is because it depends 711 00:35:44,480 --> 00:35:46,680 Speaker 2: on me logging into Google for them to give me 712 00:35:46,719 --> 00:35:49,759 Speaker 2: those personalized recommendations. If I don't choose to log in, 713 00:35:49,880 --> 00:35:51,760 Speaker 2: then I end up with the equivalent of a Google 714 00:35:51,800 --> 00:35:53,040 Speaker 2: search on a public computer. 715 00:35:53,280 --> 00:35:53,560 Speaker 8: You do. 716 00:35:53,640 --> 00:35:56,200 Speaker 3: I think the models are changing. Certainly the old model 717 00:35:56,320 --> 00:36:00,160 Speaker 3: was you're searching for terms, and certainly organizations are optimizing 718 00:36:00,440 --> 00:36:02,759 Speaker 3: through SEO to be listed organically at the top, or 719 00:36:02,840 --> 00:36:06,120 Speaker 3: they're through paid search, but listed that still exists today 720 00:36:06,160 --> 00:36:08,239 Speaker 3: certainly in this behavior that drives that, but where this 721 00:36:08,320 --> 00:36:12,279 Speaker 3: is evolving to is called GEO, so generative optimization, which 722 00:36:12,320 --> 00:36:15,200 Speaker 3: is really going to be a combination of structured data, 723 00:36:15,239 --> 00:36:18,239 Speaker 3: which is like product catalog data, skew, color size, all 724 00:36:18,239 --> 00:36:23,319 Speaker 3: those dynamics dimensions coupled with unstructured data, which are brand guidelines, 725 00:36:23,680 --> 00:36:27,839 Speaker 3: call center, transcripts, blogs, articles, all the things that would 726 00:36:27,840 --> 00:36:32,000 Speaker 3: contextualize that brand and that product, if you will. That's 727 00:36:32,040 --> 00:36:34,960 Speaker 3: being enriched oftentimes by us on the petonomic side, which 728 00:36:35,000 --> 00:36:37,719 Speaker 3: is part of our portfolio, and then syndicated across these 729 00:36:37,760 --> 00:36:41,839 Speaker 3: different surfaces, be that Google or Open AI or Microsoft 730 00:36:42,160 --> 00:36:44,640 Speaker 3: or whomever. And it's going to continue to expand as 731 00:36:44,680 --> 00:36:46,520 Speaker 3: the channels have expanded over the last several years. 732 00:36:46,680 --> 00:36:49,239 Speaker 5: What kind of AI trends and retail trends have you 733 00:36:49,320 --> 00:36:50,000 Speaker 5: been monitoring. 734 00:36:50,160 --> 00:36:53,440 Speaker 3: It's all about AI this year. That's what's hot exactly 735 00:36:53,480 --> 00:36:55,840 Speaker 3: this right, I think there's two sides of the spectrum. 736 00:36:55,880 --> 00:36:58,080 Speaker 3: There's a lot of buzz, which is exciting, but at 737 00:36:58,120 --> 00:37:00,279 Speaker 3: the same time, it's confusing a lot of people. Is 738 00:37:00,640 --> 00:37:03,920 Speaker 3: everyone is doing everything and it's hard to kind of reconcile, 739 00:37:04,040 --> 00:37:06,880 Speaker 3: like what's meaningful to the business I think for larger 740 00:37:06,960 --> 00:37:10,920 Speaker 3: organizations that we work with, brand manufacturers and retailers, trust, 741 00:37:11,040 --> 00:37:14,279 Speaker 3: brand ethos, and security are top of mind. So for them, 742 00:37:14,600 --> 00:37:16,840 Speaker 3: it's less about the sizzle, it's more about the stake 743 00:37:16,920 --> 00:37:19,160 Speaker 3: and the foundational elements. I think the importance of the 744 00:37:19,200 --> 00:37:22,480 Speaker 3: Google announcement is they are taking a very pragmatic approach 745 00:37:22,520 --> 00:37:25,120 Speaker 3: to this to lay the tracks so this can scale 746 00:37:25,160 --> 00:37:28,920 Speaker 3: and scale properly with trust, with security, and most importantly 747 00:37:28,960 --> 00:37:32,040 Speaker 3: with a frictionless experience for customers. Because the brands don't 748 00:37:32,080 --> 00:37:33,840 Speaker 3: want to lose that customer data. They still want to 749 00:37:33,880 --> 00:37:37,319 Speaker 3: maintain that experience, even though they're not fully controlling the 750 00:37:37,360 --> 00:37:39,640 Speaker 3: surface by which they're showing up against. It's not like 751 00:37:39,680 --> 00:37:42,520 Speaker 3: they're showing up against their own websites, so they're in 752 00:37:42,560 --> 00:37:45,080 Speaker 3: a surface they don't completely control. They're controlling part of 753 00:37:45,120 --> 00:37:47,880 Speaker 3: it through the data enrichment in syndication, but they're not 754 00:37:47,880 --> 00:37:51,919 Speaker 3: controlling all of it. So they're very, very concerned that 755 00:37:51,920 --> 00:37:54,080 Speaker 3: that experience is a positive one and a frictional list one. 756 00:37:54,120 --> 00:37:57,120 Speaker 3: Otherwise it has ramifications, It drives cost up and call centers, 757 00:37:57,120 --> 00:38:00,000 Speaker 3: it drives maybe negative customer experience, it drives back things 758 00:38:00,520 --> 00:38:03,040 Speaker 3: exactly we're in turns bad things on the back end. 759 00:38:03,160 --> 00:38:06,040 Speaker 3: So that's the reconciliation people are trying to have. But yes, 760 00:38:06,320 --> 00:38:09,000 Speaker 3: that EI has sucked all the oxygen out of the 761 00:38:09,040 --> 00:38:09,640 Speaker 3: room at Javis. 762 00:38:09,680 --> 00:38:10,319 Speaker 4: And it's a big rule. 763 00:38:10,680 --> 00:38:12,960 Speaker 2: What will AI not change for shoppers? 764 00:38:15,840 --> 00:38:20,399 Speaker 3: But will it not change for shoppers? I think the 765 00:38:20,480 --> 00:38:23,600 Speaker 3: behavior and the surfaces by which they're going to they're 766 00:38:23,640 --> 00:38:25,719 Speaker 3: still going to go to. Right. I think new ones 767 00:38:25,760 --> 00:38:29,920 Speaker 3: are showing up. I think the expectation personalization has been 768 00:38:29,920 --> 00:38:33,600 Speaker 3: promised for a long time. I think there's some hesitancy 769 00:38:33,640 --> 00:38:36,560 Speaker 3: there around the trust, So I think overcoming the trust 770 00:38:36,640 --> 00:38:40,160 Speaker 3: aspect of this and having that reinforced through continued behavior. 771 00:38:40,280 --> 00:38:42,040 Speaker 3: The shopping is very new. There was a big rush 772 00:38:42,040 --> 00:38:45,320 Speaker 3: to do this in holiday and the experiences weren't great, 773 00:38:45,760 --> 00:38:47,520 Speaker 3: so I think there's a little bit of hesitation. I 774 00:38:47,520 --> 00:38:50,160 Speaker 3: think you'll see a much more robust offering this holiday season. 775 00:38:50,600 --> 00:38:52,960 Speaker 5: Our thanks to Commerce CEO Travis Hess. 776 00:38:53,200 --> 00:38:56,439 Speaker 2: That is this week's edition of Bloomberg Intelligence on Bloomberg Radio, 777 00:38:56,560 --> 00:38:59,280 Speaker 2: providing in depth research and data on two thousand companies 778 00:38:59,320 --> 00:39:00,719 Speaker 2: and one hundred the industries. 779 00:39:01,120 --> 00:39:04,279 Speaker 5: And remember you can access Bloomberg Intelligence via bi go 780 00:39:04,560 --> 00:39:06,839 Speaker 5: on the terminal I'm Alexandra Samonova and. 781 00:39:06,800 --> 00:39:09,239 Speaker 2: I'm Scarlet Foo. Stay with us. Today's top stories and 782 00:39:09,280 --> 00:39:12,400 Speaker 2: global business headlines are coming up right now.