1 00:00:02,759 --> 00:00:10,600 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the 2 00:00:10,640 --> 00:00:14,600 Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am 3 00:00:14,640 --> 00:00:17,880 Speaker 1: Eastern on Apple, Cocklay and Android Auto with the Bloomberg 4 00:00:17,960 --> 00:00:21,080 Speaker 1: Business App. Listen on demand wherever you get your podcasts, 5 00:00:21,400 --> 00:00:23,120 Speaker 1: or watch us live on YouTube. 6 00:00:24,040 --> 00:00:27,840 Speaker 2: Boeing shares have really been under pressure lately, investors waiting 7 00:00:27,840 --> 00:00:30,440 Speaker 2: detail to from flight data equipment. So here to tell 8 00:00:30,480 --> 00:00:32,040 Speaker 2: us more about it, break it all down for us. 9 00:00:32,080 --> 00:00:37,320 Speaker 2: George Ferguson Bloomberg Intelligence, Senior Aerospace Defense Airlines analyst, George, 10 00:00:37,360 --> 00:00:39,880 Speaker 2: I want to start with the flight data equipment to 11 00:00:39,920 --> 00:00:43,040 Speaker 2: start here. Have they found all of them? And what 12 00:00:43,240 --> 00:00:46,040 Speaker 2: particularly will they be looking for when it comes to 13 00:00:46,040 --> 00:00:46,600 Speaker 2: this equipment. 14 00:00:47,880 --> 00:00:51,279 Speaker 3: Yeah, so I'm not totally sure if they've found all 15 00:00:51,360 --> 00:00:54,920 Speaker 3: the equipment anything, had found some of the recorders. I 16 00:00:54,960 --> 00:00:57,080 Speaker 3: think once they get into it, I assume you're talking 17 00:00:57,080 --> 00:01:00,680 Speaker 3: about the Air India crash, right, correct, correct, When they 18 00:01:00,760 --> 00:01:03,840 Speaker 3: get into it, I think one of the items are 19 00:01:03,880 --> 00:01:06,560 Speaker 3: absolutely going to focus on is going to be what 20 00:01:06,600 --> 00:01:08,680 Speaker 3: was going on in the engines and that Air India 21 00:01:09,040 --> 00:01:12,839 Speaker 3: seven eighty seven. You know, look at Boeing seventy seven 22 00:01:12,920 --> 00:01:18,119 Speaker 3: can lose one engine and still take off and gain altitude. 23 00:01:18,200 --> 00:01:24,280 Speaker 3: So the videos we saw circumstances indicate that both engines 24 00:01:24,280 --> 00:01:28,440 Speaker 3: potentially were not producing enough lift. So my guess is 25 00:01:28,480 --> 00:01:30,839 Speaker 3: that's where the focus is going to be is on 26 00:01:31,360 --> 00:01:33,840 Speaker 3: what was going on in the engines. It was eleven 27 00:01:33,880 --> 00:01:37,720 Speaker 3: year old airplane, so we think that what it all 28 00:01:37,840 --> 00:01:40,560 Speaker 3: is said and done, probably not going to be a 29 00:01:40,560 --> 00:01:45,800 Speaker 3: manufacturing issue, either at Boeing or ge from original manufacturer, 30 00:01:46,600 --> 00:01:49,520 Speaker 3: and probably going to be some sort of engine issue, 31 00:01:50,040 --> 00:01:55,480 Speaker 3: which I think would probably you know, put Boeing not 32 00:01:55,560 --> 00:01:58,720 Speaker 3: in the responsible category, you know, for that that crash, 33 00:01:59,080 --> 00:02:02,840 Speaker 3: even though obviously something we're never happy to see and 34 00:02:02,880 --> 00:02:04,200 Speaker 3: you know, very sorry about that. 35 00:02:04,840 --> 00:02:09,200 Speaker 4: George, You've in your coverage of Boeing and your discussions 36 00:02:09,200 --> 00:02:10,760 Speaker 4: with us, you've always made sure that we focus on 37 00:02:10,800 --> 00:02:14,959 Speaker 4: what's important. That is the deliveries of aircraft. June was 38 00:02:15,000 --> 00:02:16,320 Speaker 4: a great month, wasn't. 39 00:02:16,120 --> 00:02:21,000 Speaker 3: It looked pretty good. So we see about US sixty deliveries. 40 00:02:23,000 --> 00:02:27,600 Speaker 3: Of that, I think we had maybe forty two or 41 00:02:27,800 --> 00:02:31,080 Speaker 3: so seven thirty seven deliveries. Seven thirty seven is the 42 00:02:31,080 --> 00:02:35,000 Speaker 3: money maker, so that's super important. Of those forty two, 43 00:02:35,040 --> 00:02:39,440 Speaker 3: we're focusing very closely on how many were first flown 44 00:02:40,040 --> 00:02:42,400 Speaker 3: in twenty twenty five. And the reason we're doing that 45 00:02:42,560 --> 00:02:45,400 Speaker 3: is we know Boeing has a lot of inventory airplanes, 46 00:02:45,960 --> 00:02:49,640 Speaker 3: so if they're boosting deliveries through inventory airplanes, that's indicating 47 00:02:49,639 --> 00:02:53,360 Speaker 3: to us that the factory isn't as strong as we 48 00:02:53,360 --> 00:02:56,079 Speaker 3: would hope. What we're seeing right now when we look 49 00:02:56,120 --> 00:02:59,239 Speaker 3: at Syrium data is five of those forty two airplanes 50 00:02:59,680 --> 00:03:03,840 Speaker 3: are inventory deliveries airplanes that were flown previous to this year, 51 00:03:04,320 --> 00:03:09,000 Speaker 3: So that indicates it something around thirty seven Max's came 52 00:03:09,040 --> 00:03:13,000 Speaker 3: through the rentin factory in June. That's a pretty nice number. 53 00:03:13,400 --> 00:03:19,240 Speaker 3: That corresponds pretty well with Boeing CEO's indication that they 54 00:03:19,280 --> 00:03:22,800 Speaker 3: were producing at a thirty eight ish through put in 55 00:03:22,880 --> 00:03:25,200 Speaker 3: the factory and that they were going to be breaking 56 00:03:25,200 --> 00:03:28,679 Speaker 3: into forty two's levels sort of in the back half 57 00:03:28,680 --> 00:03:33,000 Speaker 3: of the year. And so this absolutely confirms I think 58 00:03:33,080 --> 00:03:37,240 Speaker 3: the improving health of the Boeing production system and that 59 00:03:37,280 --> 00:03:38,120 Speaker 3: retin factory. 60 00:03:38,440 --> 00:03:41,080 Speaker 2: Now, George, you mentioned CEO Kelly Ordberg. What's the war 61 00:03:41,160 --> 00:03:43,360 Speaker 2: on the street. How has he been handling this new role? 62 00:03:43,360 --> 00:03:44,680 Speaker 2: I mean, a lot of pressures on him. 63 00:03:45,400 --> 00:03:48,240 Speaker 3: Yeah, I mean, I think when it's all done, it's 64 00:03:48,240 --> 00:03:51,440 Speaker 3: going to be a function of did he get quality improved, 65 00:03:51,880 --> 00:03:55,200 Speaker 3: did he improve throughput? Did he generate cash? Did he 66 00:03:55,280 --> 00:03:58,120 Speaker 3: improve the balance sheet of Boeing, you know, sort of 67 00:03:58,120 --> 00:04:01,480 Speaker 3: stave off any downgrades? And right now I'd say the 68 00:04:01,560 --> 00:04:04,040 Speaker 3: report card would be pretty good for what Kelly Orbrick 69 00:04:04,160 --> 00:04:04,480 Speaker 3: is doing. 70 00:04:04,760 --> 00:04:07,800 Speaker 4: And George, you also told us that, you know, tooling 71 00:04:07,880 --> 00:04:10,720 Speaker 4: up these factors to crank a production, it's not as 72 00:04:10,760 --> 00:04:12,400 Speaker 4: easy as it sounds. It's a little bit more difficult 73 00:04:12,440 --> 00:04:15,360 Speaker 4: in manufacturing and automobile coming down the line here, and 74 00:04:15,400 --> 00:04:18,200 Speaker 4: that goes to the labor issue. You need some pretty 75 00:04:18,760 --> 00:04:21,640 Speaker 4: highly trained labor and coming out of the pandemic, that 76 00:04:21,720 --> 00:04:24,520 Speaker 4: was a challenge. How is Boeing doing these days on 77 00:04:24,520 --> 00:04:24,960 Speaker 4: that front? 78 00:04:25,920 --> 00:04:28,320 Speaker 3: Yeah, So, you know, if we measure it from the 79 00:04:28,320 --> 00:04:31,200 Speaker 3: throughput in the factory, it looks like it's it's doing better. 80 00:04:31,279 --> 00:04:36,680 Speaker 3: We're hearing, you know, better sort of noises from the 81 00:04:36,760 --> 00:04:39,800 Speaker 3: supply chain that labor is stabilizing. I think we see 82 00:04:39,880 --> 00:04:44,160 Speaker 3: in the country right the labor market isn't as sort 83 00:04:44,160 --> 00:04:46,599 Speaker 3: of white hot as it was coming right out of 84 00:04:46,600 --> 00:04:50,760 Speaker 3: the pandemic, especially for you know, some of this manual 85 00:04:50,839 --> 00:04:54,240 Speaker 3: labor that they're looking for and so I think that 86 00:04:54,320 --> 00:04:57,720 Speaker 3: helps stability. That stability allows you to go ahead and 87 00:04:57,880 --> 00:05:02,960 Speaker 3: train people and through put. So it appears to us 88 00:05:02,960 --> 00:05:08,120 Speaker 3: all all those indicators that you know, labor stabilizing qualities improving, 89 00:05:08,160 --> 00:05:11,840 Speaker 3: and that's definitely having Boweing benefiting from it. 90 00:05:12,160 --> 00:05:15,599 Speaker 2: George, you mentioned deliveries earlier. The Paris Air Show is 91 00:05:15,760 --> 00:05:19,200 Speaker 2: a huge event for these airlines. Yes, yes, how did 92 00:05:19,400 --> 00:05:21,160 Speaker 2: how did Boeing fair coming out of that? 93 00:05:22,279 --> 00:05:24,640 Speaker 3: Yeah, So we haven't seen a lot of orders for 94 00:05:24,720 --> 00:05:27,000 Speaker 3: Boeing recently, and we you know, we didn't see any 95 00:05:27,160 --> 00:05:30,800 Speaker 3: Paris air Show right they had pulled out. I think 96 00:05:30,880 --> 00:05:34,719 Speaker 3: just as they're monitoring the developments of that Air India crash. 97 00:05:34,800 --> 00:05:37,640 Speaker 3: But we really haven't seen a lot of orders recently 98 00:05:37,640 --> 00:05:42,200 Speaker 3: for Boeing. I suspect that given the tariff backdrop, you're 99 00:05:42,240 --> 00:05:43,880 Speaker 3: just not going to see a lot of orders for 100 00:05:43,960 --> 00:05:47,200 Speaker 3: Boeing until that tariff backdrop gets cleared right. Boeing has 101 00:05:47,279 --> 00:05:50,360 Speaker 3: one factory they build seven thirty sevens at it's in 102 00:05:50,400 --> 00:05:53,040 Speaker 3: the US, So terraffs between US and the rest of 103 00:05:53,040 --> 00:05:55,120 Speaker 3: the world always get in the way. That has to 104 00:05:55,120 --> 00:05:55,880 Speaker 3: get cleared. 105 00:05:55,640 --> 00:05:58,520 Speaker 4: Up, George. Great stuff has always George Ferguson's senior airspace 106 00:05:58,560 --> 00:05:59,679 Speaker 4: depends in airlines unels. 107 00:06:01,520 --> 00:06:05,159 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 108 00:06:05,320 --> 00:06:08,679 Speaker 1: weekdays at ten am Eastern on Applecarplay and Android Auto 109 00:06:08,800 --> 00:06:11,880 Speaker 1: with the Bloomberg Business app. Listen on demand wherever you 110 00:06:11,880 --> 00:06:15,000 Speaker 1: get your podcasts, or watch us live on YouTube. 111 00:06:15,600 --> 00:06:17,440 Speaker 2: We're going to keep it here kind of in that 112 00:06:17,520 --> 00:06:20,119 Speaker 2: AI tech space. I want to go to Man Deep thing. 113 00:06:20,160 --> 00:06:23,680 Speaker 2: He's Bloomberg Intelligence senior tech industry analysts. So Man Deep, 114 00:06:23,720 --> 00:06:27,000 Speaker 2: we have this sign of just how competitive the AI 115 00:06:27,320 --> 00:06:30,839 Speaker 2: space is for talent. So you have Meta possibly poaching 116 00:06:30,880 --> 00:06:34,719 Speaker 2: Apple's top AI executive, offering this big, big, big payout. 117 00:06:35,560 --> 00:06:38,479 Speaker 2: Explain to us first of all, who this guy is 118 00:06:38,560 --> 00:06:40,159 Speaker 2: and how much are they offering him. 119 00:06:40,920 --> 00:06:43,919 Speaker 5: Well, I don't know the exact dollar amount what they're offering, 120 00:06:43,960 --> 00:06:48,360 Speaker 5: but clearly Meta is going big in terms of poaching 121 00:06:48,640 --> 00:06:52,479 Speaker 5: the top AI researchers. We saw that with the scale 122 00:06:52,600 --> 00:06:59,479 Speaker 5: AI aquahars, they pretty much post the founder and look, 123 00:06:59,560 --> 00:07:03,280 Speaker 5: I think they want to assemble the top fifty AI 124 00:07:03,400 --> 00:07:07,080 Speaker 5: researchers that are out there who have got published papers. 125 00:07:07,120 --> 00:07:13,080 Speaker 5: And they are paying up in this case because they've 126 00:07:13,080 --> 00:07:15,800 Speaker 5: made all the big investments in terms of compute, in 127 00:07:15,880 --> 00:07:20,280 Speaker 5: terms of infrastructure, but they don't have a cloud business 128 00:07:20,440 --> 00:07:24,520 Speaker 5: or some other form of monetization that you know Google 129 00:07:24,640 --> 00:07:29,120 Speaker 5: has or a Microsoft has. So from that perspective, I 130 00:07:29,280 --> 00:07:32,320 Speaker 5: think it's crunched time for them to really show that, 131 00:07:32,600 --> 00:07:35,480 Speaker 5: you know, they can develop an ecosystem with their large 132 00:07:35,520 --> 00:07:41,000 Speaker 5: aguage model, which has been underperforming relative to OpenAI, Google 133 00:07:41,080 --> 00:07:45,760 Speaker 5: and Cloud. And I think it's the founder Mark Zuckerberg 134 00:07:45,880 --> 00:07:49,040 Speaker 5: who really is going all out. I mean, you can't 135 00:07:49,120 --> 00:07:53,120 Speaker 5: expect any other CEO to be that aggressive with paying 136 00:07:53,240 --> 00:07:57,720 Speaker 5: five billion dollars for fifty people. Literally, that's what the 137 00:07:58,320 --> 00:08:01,320 Speaker 5: expense that we are talking about Facebook. A Meta as 138 00:08:01,320 --> 00:08:06,000 Speaker 5: a company has over seventy six thousand employees with an 139 00:08:06,000 --> 00:08:10,160 Speaker 5: expense space of one hundred and fifteen billion. They're adding 140 00:08:10,280 --> 00:08:14,000 Speaker 5: five more billion for fifty people. That's all we're talking about. 141 00:08:14,040 --> 00:08:17,480 Speaker 4: Wow, that's an order magnifade. Yeah, all right, so it's 142 00:08:17,520 --> 00:08:22,040 Speaker 4: clear that metas all in. What does this mean for Apple? 143 00:08:22,160 --> 00:08:22,240 Speaker 3: This? 144 00:08:22,440 --> 00:08:25,120 Speaker 4: If I were an Apple Cheryl, I'd be concerned here A, 145 00:08:25,280 --> 00:08:29,400 Speaker 4: I'm losing talent, but B I've already it's coming from 146 00:08:29,440 --> 00:08:31,840 Speaker 4: an area where I feel like I'm under invested already 147 00:08:31,880 --> 00:08:34,920 Speaker 4: perhaps I'm already behind. What do you think this means 148 00:08:34,920 --> 00:08:35,320 Speaker 4: for Apple? 149 00:08:35,480 --> 00:08:40,320 Speaker 5: I mean, clearly everyone on the research site realizes that 150 00:08:40,520 --> 00:08:44,640 Speaker 5: Apple doesn't have the big AI cluster that these companies have, 151 00:08:44,840 --> 00:08:49,040 Speaker 5: and you know, generative AI is all about having a 152 00:08:49,120 --> 00:08:52,400 Speaker 5: large cluster, training your model on that cluster, and then 153 00:08:52,800 --> 00:08:56,080 Speaker 5: really building from there. So from that perspective, Apple has 154 00:08:56,200 --> 00:08:59,440 Speaker 5: lagged behind. And if you're a top AI researcher, there 155 00:08:59,480 --> 00:09:02,920 Speaker 5: are better companies to work for right now, so I'm 156 00:09:02,920 --> 00:09:06,120 Speaker 5: not surprised, you know, a top AI researcher has ended 157 00:09:06,200 --> 00:09:09,160 Speaker 5: up taking up the offer. But for Apple, look, they're 158 00:09:09,160 --> 00:09:13,320 Speaker 5: going to use a combination of partnerships and some on 159 00:09:13,400 --> 00:09:16,080 Speaker 5: device AI investment, which is what they have done in 160 00:09:16,160 --> 00:09:19,200 Speaker 5: terms of, you know, their own model efforts. They're going 161 00:09:19,240 --> 00:09:23,160 Speaker 5: to partner with cloud open AI, probably Google as well 162 00:09:23,480 --> 00:09:27,680 Speaker 5: once the anti trust issues are over, and that's how 163 00:09:27,720 --> 00:09:30,280 Speaker 5: they're going to provide the functionality. As long as it's 164 00:09:30,320 --> 00:09:34,280 Speaker 5: cloud based. They have the app infrastructure to offer AI 165 00:09:34,880 --> 00:09:38,080 Speaker 5: for on device AI, they have to do it natively 166 00:09:38,280 --> 00:09:42,040 Speaker 5: at the operating system level. You can't really leverage the 167 00:09:42,160 --> 00:09:45,640 Speaker 5: large acreage model from open AI on device because you 168 00:09:45,760 --> 00:09:48,960 Speaker 5: have to really open up your operating system, which Apple 169 00:09:49,040 --> 00:09:49,360 Speaker 5: won't do. 170 00:09:50,000 --> 00:09:51,880 Speaker 2: I have to add some kind of AI in my resume. 171 00:09:52,000 --> 00:09:52,400 Speaker 6: I think. 172 00:09:53,880 --> 00:09:57,960 Speaker 2: On this, Maddie, what kind of tone does this set 173 00:09:58,000 --> 00:10:00,440 Speaker 2: for the industry, what kind of messages sending? 174 00:10:01,600 --> 00:10:04,360 Speaker 5: I mean, to my mind, this is a very high 175 00:10:04,440 --> 00:10:08,600 Speaker 5: risk play right now. The market sentiment is Meta can 176 00:10:08,679 --> 00:10:11,360 Speaker 5: do no wrong. They are making all the right moves 177 00:10:11,400 --> 00:10:15,160 Speaker 5: with getting these you know, big AI researchers, but at 178 00:10:15,160 --> 00:10:17,840 Speaker 5: the end of the day, it is you know, they're 179 00:10:18,280 --> 00:10:22,640 Speaker 5: doubling down on capex, adding more opics. So from a 180 00:10:22,679 --> 00:10:27,200 Speaker 5: spending perspective, Meta is going all in, and so you know, 181 00:10:27,360 --> 00:10:29,880 Speaker 5: once it starts to weigh on free cash flow and 182 00:10:29,960 --> 00:10:32,240 Speaker 5: the returns are not there, which is why I said, 183 00:10:32,280 --> 00:10:35,440 Speaker 5: you know, with everyone else, you see the AI monetization. 184 00:10:36,040 --> 00:10:39,080 Speaker 5: You know, if you have a coding agent from Microsoft 185 00:10:39,200 --> 00:10:42,839 Speaker 5: or Google, they are monetizing it with Meta. All these 186 00:10:42,880 --> 00:10:47,040 Speaker 5: are upfront investments with the hope that if we'll add 187 00:10:47,080 --> 00:10:50,440 Speaker 5: more engagement time across their family of apps, they will 188 00:10:50,480 --> 00:10:53,800 Speaker 5: probably have a killer AI product that they'll be able 189 00:10:53,840 --> 00:10:57,520 Speaker 5: to monetize with in addition to their recommendation systems. So 190 00:10:57,600 --> 00:11:01,000 Speaker 5: the stakes are getting higher and higher, but clearly they 191 00:11:01,000 --> 00:11:03,319 Speaker 5: are doubling down in terms of their investment, and at 192 00:11:03,360 --> 00:11:07,240 Speaker 5: some point the monetization question, kay King, Probably not this 193 00:11:07,400 --> 00:11:09,880 Speaker 5: earning season, but maybe a couple of quarters from now. 194 00:11:10,000 --> 00:11:14,160 Speaker 4: All right, Man deep Seeing, big, big numbers. That's his companies, folks. 195 00:11:14,240 --> 00:11:18,199 Speaker 4: They just they traffic in huge numbers, huge investments, huge revenue, huge, 196 00:11:18,280 --> 00:11:21,520 Speaker 4: huge free cash flow. That is the state of global 197 00:11:21,559 --> 00:11:25,520 Speaker 4: technology these days, centered in the United States. Men Deep 198 00:11:25,520 --> 00:11:29,280 Speaker 4: Seeing senior techannels for Bloomberg Intelligence. We appreciate that, and 199 00:11:29,320 --> 00:11:32,600 Speaker 4: again Meta going all in, not that they weren't before, 200 00:11:32,640 --> 00:11:34,679 Speaker 4: but it's just kind of another example the type of 201 00:11:34,760 --> 00:11:37,839 Speaker 4: investments they are making there. 202 00:11:38,520 --> 00:11:42,200 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 203 00:11:42,280 --> 00:11:45,760 Speaker 1: weekdays at ten am Eastern on Applecarclay, and Android Auto 204 00:11:45,800 --> 00:11:48,840 Speaker 1: with the Bloomberg Business app. Listen on demand wherever you 205 00:11:48,880 --> 00:11:51,880 Speaker 1: get your podcasts, or watch us live on YouTube. 206 00:11:52,320 --> 00:11:55,360 Speaker 4: I think from the pandemic, we all became familiar with 207 00:11:55,400 --> 00:11:58,400 Speaker 4: this concept of supply chains and global supply chains and 208 00:11:58,440 --> 00:12:02,080 Speaker 4: where stuff comes from. And boy, when the ship stops sailing, 209 00:12:02,640 --> 00:12:04,280 Speaker 4: that's a problem. And do we need to bring some 210 00:12:04,320 --> 00:12:05,839 Speaker 4: of that stuff closer to home? And that's one of 211 00:12:05,880 --> 00:12:08,560 Speaker 4: the reasons I think for President Trump and his focus 212 00:12:08,600 --> 00:12:10,520 Speaker 4: on tariffs here. But let's get a sense of what 213 00:12:10,559 --> 00:12:13,800 Speaker 4: the global supply chains are looking at now and how 214 00:12:14,320 --> 00:12:16,880 Speaker 4: they may react in a world where tariffs are higher. 215 00:12:16,920 --> 00:12:20,400 Speaker 4: Brandon Daniels joins the CEO of EXEG. He joins us 216 00:12:20,440 --> 00:12:24,679 Speaker 4: from Chicago. Brandon, how do you put into context all 217 00:12:24,760 --> 00:12:27,720 Speaker 4: this talk about tariffs and kind of what it means 218 00:12:27,800 --> 00:12:31,200 Speaker 4: for the global supply chain and how we get stuff, 219 00:12:31,240 --> 00:12:33,280 Speaker 4: where we make stuff, where we import stuff from. How 220 00:12:33,320 --> 00:12:34,240 Speaker 4: do you guys think about that? 221 00:12:35,440 --> 00:12:38,240 Speaker 6: Absolutely? Well, thank you for having me on. It's good 222 00:12:38,240 --> 00:12:41,120 Speaker 6: to talk to you guys today. I think when we 223 00:12:41,160 --> 00:12:48,520 Speaker 6: think about it, it is a reflection of a major 224 00:12:48,559 --> 00:12:52,840 Speaker 6: shift in priorities across across all countries from a global 225 00:12:52,880 --> 00:12:56,120 Speaker 6: commerce perspective. Right, Like, when you think about what the 226 00:12:56,200 --> 00:12:59,280 Speaker 6: United States is doing and what the administration is doing, 227 00:12:59,679 --> 00:13:02,720 Speaker 6: is there they're trying to prioritize three things, right. The 228 00:13:02,760 --> 00:13:06,079 Speaker 6: first thing that we've in our discussions and in our 229 00:13:06,120 --> 00:13:10,320 Speaker 6: talks with the administration, they want to bring back manufacturing 230 00:13:10,679 --> 00:13:14,120 Speaker 6: where the United States can be competitive. And that's that's 231 00:13:14,120 --> 00:13:17,600 Speaker 6: what our customers are utilizing our AI tools today and 232 00:13:17,679 --> 00:13:22,760 Speaker 6: our multi tier supply chain visibility to understand where can 233 00:13:22,880 --> 00:13:25,839 Speaker 6: they actually source in the United States in a way 234 00:13:26,480 --> 00:13:29,800 Speaker 6: that is at parity with their global sourcing requirements. And 235 00:13:29,880 --> 00:13:33,959 Speaker 6: so that is mostly focused on where labor arbitrage has 236 00:13:34,000 --> 00:13:36,680 Speaker 6: been taken out of the equation. So think things like 237 00:13:36,800 --> 00:13:43,240 Speaker 6: additive manufacturing, Think things like you know, largely automated production floors. 238 00:13:44,120 --> 00:13:47,120 Speaker 6: You know, those are the areas where you're starting to 239 00:13:47,160 --> 00:13:54,520 Speaker 6: see major cost collapse in between emerging markets and more 240 00:13:54,520 --> 00:13:58,199 Speaker 6: sophisticated markets, because the cost of the manufacturing equipment it's 241 00:13:58,200 --> 00:14:02,640 Speaker 6: the same across the globe, the cast of the land. 242 00:14:02,960 --> 00:14:05,600 Speaker 6: I mean, there's there's parts of North Texas that are 243 00:14:05,679 --> 00:14:08,960 Speaker 6: cheaper than parts of heav A, China. And with the 244 00:14:09,040 --> 00:14:13,720 Speaker 6: volume of operators necessary in these factories going down and 245 00:14:13,760 --> 00:14:18,640 Speaker 6: them having to be also more higher skilled, uh, the 246 00:14:18,400 --> 00:14:23,480 Speaker 6: the major labor arbitrage effect kind of dwindles, and so 247 00:14:23,680 --> 00:14:27,200 Speaker 6: they want to move those areas of automated manufacturing back 248 00:14:27,280 --> 00:14:29,920 Speaker 6: to the United States. And so I see our customers 249 00:14:30,800 --> 00:14:35,200 Speaker 6: focused on those commodity areas as well as you know, 250 00:14:35,240 --> 00:14:39,920 Speaker 6: some of the more specialized areas that require automated manufacturing floors. Uh, 251 00:14:40,080 --> 00:14:42,800 Speaker 6: that stuff coming back to the US. The second thing 252 00:14:42,880 --> 00:14:46,480 Speaker 6: is national security and economic security. So from a national 253 00:14:46,560 --> 00:14:49,720 Speaker 6: security perspective, you know, there's a there's a core focus 254 00:14:49,840 --> 00:14:55,000 Speaker 6: on semiconductors because AI is the future of our national 255 00:14:55,040 --> 00:14:59,280 Speaker 6: security capacity, whether it's in managing you know, UA s 256 00:14:59,360 --> 00:15:02,520 Speaker 6: and drones, or it's in you know, fighting cyber attacks. 257 00:15:02,800 --> 00:15:05,920 Speaker 6: AI is where it's at. And then the other area 258 00:15:06,080 --> 00:15:10,560 Speaker 6: is in pharmaceuticals. It's actually keeping our you know, medical 259 00:15:10,560 --> 00:15:15,680 Speaker 6: supply chains clean UH and independent. And you know, obviously 260 00:15:15,720 --> 00:15:18,440 Speaker 6: we pulled back the veil on that in COVID Exeger 261 00:15:18,640 --> 00:15:21,600 Speaker 6: was the technology utilized by the federal government to purchase 262 00:15:21,920 --> 00:15:24,960 Speaker 6: seven billion dollars of goods and to do it in 263 00:15:25,000 --> 00:15:27,840 Speaker 6: a way that didn't in a way that we could 264 00:15:27,880 --> 00:15:30,600 Speaker 6: get it to the healthcare frontlines quickly and in a 265 00:15:30,600 --> 00:15:34,160 Speaker 6: way that didn't allow for fraud, waste, abuse, and adversarial investment. 266 00:15:34,640 --> 00:15:37,440 Speaker 6: And I think they're looking to try to expand that 267 00:15:38,120 --> 00:15:42,440 Speaker 6: and trying to bring back pharmaceutical manufacturing, medical device manufacturing 268 00:15:42,480 --> 00:15:45,600 Speaker 6: to the United States. And so when when I look 269 00:15:45,640 --> 00:15:50,040 Speaker 6: at this, I see those two first strategies getting you know, 270 00:15:50,080 --> 00:15:54,000 Speaker 6: sort of being prioritized. The last one is honestly to 271 00:15:54,480 --> 00:16:00,160 Speaker 6: ramp up our actual external UH tariff collection. You know, 272 00:16:00,280 --> 00:16:03,680 Speaker 6: there have been estimates between thirty and one hundred billion 273 00:16:03,800 --> 00:16:06,960 Speaker 6: dollars of transhipment per year. You saw this in the 274 00:16:07,000 --> 00:16:10,680 Speaker 6: deal with Vietnam, where it was twenty percent tariffs and 275 00:16:10,720 --> 00:16:17,280 Speaker 6: then forty percent tariffs on expected or potential transshipment goods. Right. 276 00:16:17,720 --> 00:16:20,120 Speaker 6: So one of the things that we've seen is China 277 00:16:20,160 --> 00:16:24,600 Speaker 6: has used this sort of global economic coercion to create 278 00:16:24,760 --> 00:16:28,240 Speaker 6: veneer centers of manufacturing across the globe, and the US 279 00:16:28,320 --> 00:16:31,200 Speaker 6: is trying to crack down on that to level the 280 00:16:31,200 --> 00:16:31,840 Speaker 6: playing field. 281 00:16:32,200 --> 00:16:33,800 Speaker 2: All right, bern I hear you got to talk about 282 00:16:33,800 --> 00:16:37,080 Speaker 2: a couple of things, so AI pharmaceuticals. One of the 283 00:16:37,080 --> 00:16:39,640 Speaker 2: things you mentioned in your notes is how auto is 284 00:16:39,680 --> 00:16:41,120 Speaker 2: there going to be the best case study and how 285 00:16:41,200 --> 00:16:44,120 Speaker 2: nuanced this policy really is? Can you dig into that 286 00:16:44,200 --> 00:16:44,760 Speaker 2: for us? 287 00:16:45,560 --> 00:16:45,880 Speaker 5: Yeah? 288 00:16:45,920 --> 00:16:49,960 Speaker 6: Absolutely, so auto manufacturing is a great case study of 289 00:16:50,000 --> 00:16:52,600 Speaker 6: where all of this might be going. I don't think 290 00:16:52,640 --> 00:16:59,440 Speaker 6: we'll end up with just blanket country level policies. And 291 00:16:59,480 --> 00:17:03,960 Speaker 6: I think those blanket country level policies will get nuanced 292 00:17:04,119 --> 00:17:07,840 Speaker 6: down to the HTS code or to the actual sort 293 00:17:07,840 --> 00:17:11,760 Speaker 6: of segment or sector of goods, right. And I think 294 00:17:11,800 --> 00:17:15,480 Speaker 6: that they will get nuanced down to a place where 295 00:17:15,760 --> 00:17:19,919 Speaker 6: you know, the recognized dependencies we have on critical minerals 296 00:17:20,040 --> 00:17:24,160 Speaker 6: or specialty alloys, or on specific goods that are indigenous 297 00:17:24,200 --> 00:17:27,280 Speaker 6: to some of our allies, you know, where those are 298 00:17:27,400 --> 00:17:30,440 Speaker 6: necessary for us to do the manufacturing in the United States. 299 00:17:30,720 --> 00:17:34,840 Speaker 6: I think those goods will become subject to exceptions or 300 00:17:34,880 --> 00:17:38,520 Speaker 6: exemptions very similar to USMCA so. So automotive is a 301 00:17:38,560 --> 00:17:42,000 Speaker 6: great example of this because basically, you can have fifteen 302 00:17:42,160 --> 00:17:46,920 Speaker 6: percent of your auto that's being manufactured in the United States. 303 00:17:47,160 --> 00:17:51,080 Speaker 6: You can have fifteen percent of it be parts, components, 304 00:17:51,080 --> 00:17:55,240 Speaker 6: and goods from other places, and your entire tariff burden 305 00:17:55,280 --> 00:17:58,880 Speaker 6: can be offset by the three point seventy five percent 306 00:17:59,720 --> 00:18:04,000 Speaker 6: tear credit you get on the vehicle's MSRP, right, and 307 00:18:04,040 --> 00:18:08,920 Speaker 6: so it's almost like you have your tariff free if 308 00:18:09,000 --> 00:18:11,680 Speaker 6: you have fifteen percent of the goods coming from everywhere else. 309 00:18:11,960 --> 00:18:15,359 Speaker 6: And that's because we realize that in many cases there 310 00:18:15,359 --> 00:18:17,800 Speaker 6: are things that the US doesn't make yet, and it's 311 00:18:17,840 --> 00:18:20,360 Speaker 6: going to take a while for the US or allies 312 00:18:20,400 --> 00:18:22,840 Speaker 6: to build those things right, and we want to give 313 00:18:23,080 --> 00:18:26,720 Speaker 6: you know, people the ability to have some leniency there. 314 00:18:27,040 --> 00:18:29,760 Speaker 4: Red headline crossing the Bloomberg terminal, Trump says August one, 315 00:18:29,840 --> 00:18:34,320 Speaker 4: tariff deadline won't be extended. We'll have more reporting on that. 316 00:18:34,359 --> 00:18:36,359 Speaker 4: Brandom got thirty seconds left here. When you talk to 317 00:18:36,359 --> 00:18:41,159 Speaker 4: your customers, who's going to pay tariff costs? Is there 318 00:18:41,160 --> 00:18:43,760 Speaker 4: going to be the importer or the manufacturer, the distributor 319 00:18:43,960 --> 00:18:46,280 Speaker 4: or the consumer or how are these tariffs going to 320 00:18:46,280 --> 00:18:47,480 Speaker 4: be worn by the economy. 321 00:18:48,280 --> 00:18:50,359 Speaker 6: I think the consumer is going to be the last 322 00:18:50,359 --> 00:18:53,959 Speaker 6: to feel it. And I think, you know, the fact 323 00:18:54,080 --> 00:18:56,919 Speaker 6: is this is going to be absorbed somewhere in the 324 00:18:56,920 --> 00:19:00,359 Speaker 6: supply chain. In twenty eighteen, when we had the regional 325 00:19:00,840 --> 00:19:05,919 Speaker 6: Trump tariffs on steel, you know, the entire defense industry, 326 00:19:06,040 --> 00:19:09,960 Speaker 6: the entire automotive industry, airline industry, they all ate it, 327 00:19:10,080 --> 00:19:14,480 Speaker 6: right because you just needed the steel, yeah, and you 328 00:19:14,520 --> 00:19:16,399 Speaker 6: didn't and you didn't want to affect the end market. 329 00:19:16,520 --> 00:19:18,159 Speaker 6: I think that the consumer is going to be the 330 00:19:18,200 --> 00:19:19,480 Speaker 6: last the feeling on this fr I. 331 00:19:19,440 --> 00:19:21,680 Speaker 4: Hopefully that is the case. Brandon Daniels, Thank you so much. 332 00:19:21,880 --> 00:19:25,320 Speaker 4: Brandon Daniels. He's the CEO of Exeger, joining us from Chicago, 333 00:19:25,600 --> 00:19:28,879 Speaker 4: Vias zoom talking about tariff talk about supply chains, fascinating 334 00:19:29,119 --> 00:19:30,120 Speaker 4: and fluid situation. 335 00:19:30,520 --> 00:19:35,200 Speaker 1: This is the Bloomberg Intelligence Podcast available on Apple, Spotify, 336 00:19:35,400 --> 00:19:39,360 Speaker 1: and anywhere else you get your podcasts. Listen live each weekday, 337 00:19:39,600 --> 00:19:42,879 Speaker 1: ten am to noon Eastern on Bloomberg dot Com, the 338 00:19:42,960 --> 00:19:46,840 Speaker 1: iHeartRadio app, tune In, and the Bloomberg Business app. You 339 00:19:46,840 --> 00:19:50,159 Speaker 1: can also watch us live every weekday on YouTube and 340 00:19:50,359 --> 00:19:52,280 Speaker 1: always on the Bloomberg terminal