1 00:00:02,720 --> 00:00:10,559 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the 2 00:00:10,600 --> 00:00:14,560 Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am 3 00:00:14,600 --> 00:00:17,840 Speaker 1: Eastern on Apple Coarcklay and Android Auto with the Bloomberg 4 00:00:17,920 --> 00:00:21,040 Speaker 1: Business App. Listen on demand wherever you get your podcasts, 5 00:00:21,360 --> 00:00:23,600 Speaker 1: or watch us live on YouTube. 6 00:00:24,040 --> 00:00:26,799 Speaker 2: In videos, working on a fresh round of AI infrastructure 7 00:00:26,880 --> 00:00:30,440 Speaker 2: deals potentially worth more than seven hundred and fifty billion dollars, 8 00:00:30,840 --> 00:00:33,720 Speaker 2: including an AI initiative with s k Heinez for almost 9 00:00:33,760 --> 00:00:36,880 Speaker 2: half a trillion dollars. The CEO of Nvidia, Jensen Hong, 10 00:00:36,920 --> 00:00:40,080 Speaker 2: spoke in an exclusive interview with Ed Ludlow about investing 11 00:00:40,200 --> 00:00:41,840 Speaker 2: in South Korea. Take a listen. 12 00:00:41,960 --> 00:00:44,040 Speaker 3: This is the golden ages for Korea, as you know, 13 00:00:44,440 --> 00:00:49,280 Speaker 3: their semiconductor businesses booming, their industrial businesses booming. You know, 14 00:00:49,320 --> 00:00:51,400 Speaker 3: this is a country that has the ability to help 15 00:00:51,440 --> 00:00:55,080 Speaker 3: the world build out the AI infrastructure that they're incredibly 16 00:00:55,400 --> 00:01:01,120 Speaker 3: adapt at adopting new technologies and as a real technologically 17 00:01:01,920 --> 00:01:03,200 Speaker 3: forward learning society. 18 00:01:03,800 --> 00:01:04,400 Speaker 4: All right, that was it. 19 00:01:04,440 --> 00:01:07,600 Speaker 5: In videos CEO Jensen one and joining us now is 20 00:01:07,680 --> 00:01:11,720 Speaker 5: Betech anchor Ed Ludlow ed. The numbers just keep coming, 21 00:01:11,760 --> 00:01:16,480 Speaker 5: the investments just keep coming. The circular deals keep coming. 22 00:01:17,040 --> 00:01:19,600 Speaker 5: What's the view of that type of structure as this 23 00:01:20,080 --> 00:01:23,480 Speaker 5: AI infrastructure evolves. What's it feeling out in Silicon Valley 24 00:01:23,640 --> 00:01:24,959 Speaker 5: about how this is evolving. 25 00:01:25,520 --> 00:01:27,640 Speaker 6: Part of the reason there is even a debate about 26 00:01:27,720 --> 00:01:29,920 Speaker 6: it being circular not is that there isn't a great 27 00:01:29,959 --> 00:01:33,440 Speaker 6: explanation of what the numbers actually represent. So you've done 28 00:01:33,480 --> 00:01:35,399 Speaker 6: the reporting about you know what Dean has written on 29 00:01:35,560 --> 00:01:37,720 Speaker 6: the terminal on open AI. If we just take SK 30 00:01:37,800 --> 00:01:40,560 Speaker 6: as a case study, it's five hundred billion dollars or 31 00:01:40,560 --> 00:01:43,360 Speaker 6: half a trillion dollars. And at some point in that 32 00:01:43,400 --> 00:01:45,640 Speaker 6: conversation with Jensen, I just said, could you just explain 33 00:01:45,640 --> 00:01:48,080 Speaker 6: what that number actually is and he was like, well, sure, 34 00:01:48,600 --> 00:01:51,080 Speaker 6: it's a number that represents what we and Vidia are 35 00:01:51,120 --> 00:01:53,960 Speaker 6: going to be buying from sk Heinex in memory chips, 36 00:01:54,200 --> 00:01:58,160 Speaker 6: but it also represents what SK as a group a conglomerate, 37 00:01:58,200 --> 00:02:00,880 Speaker 6: will be spending on their own AI infrastruc ructure and 38 00:02:00,920 --> 00:02:02,920 Speaker 6: then everything in between that we co invest in. And 39 00:02:02,960 --> 00:02:05,480 Speaker 6: there's like no real explanation of like, okay, so you're 40 00:02:05,520 --> 00:02:08,040 Speaker 6: giving them dollars or are they giving you dollars or 41 00:02:08,200 --> 00:02:10,360 Speaker 6: are you basically just tallying it up and calling it 42 00:02:10,400 --> 00:02:12,240 Speaker 6: even at the end of the day. And that's a 43 00:02:12,240 --> 00:02:14,360 Speaker 6: big part of it, right that people just don't know 44 00:02:14,400 --> 00:02:17,080 Speaker 6: the direction or travel of those dollars. In the open 45 00:02:17,080 --> 00:02:20,080 Speaker 6: AI case, what reporting is much more explicit, where you 46 00:02:20,080 --> 00:02:23,320 Speaker 6: know in videas both both guaranteeing and backstopping but also 47 00:02:23,360 --> 00:02:27,000 Speaker 6: providing financing for AI data centers that at the end 48 00:02:27,080 --> 00:02:29,720 Speaker 6: of the day they do use in Video's technology and 49 00:02:29,760 --> 00:02:31,560 Speaker 6: that is by definition circular. 50 00:02:32,200 --> 00:02:35,679 Speaker 2: Okay. So going back to that deal or agreement or 51 00:02:35,760 --> 00:02:39,440 Speaker 2: partnership with Skehiynix worth more than five hundred billion, do 52 00:02:39,440 --> 00:02:41,799 Speaker 2: we know if that ever actually happens. I mean, it's 53 00:02:41,800 --> 00:02:43,760 Speaker 2: one thing to announce that you have these grand plans, 54 00:02:43,800 --> 00:02:46,760 Speaker 2: it's another thing for the two companies to execute on it. 55 00:02:47,520 --> 00:02:50,520 Speaker 6: So it's over many years, it's over time, but there's 56 00:02:50,560 --> 00:02:54,160 Speaker 6: no set deadline or timeframe. It is literally bucket one 57 00:02:54,320 --> 00:02:57,400 Speaker 6: in video buying memory chips from sk Heinix. Sk Heinix 58 00:02:57,400 --> 00:03:00,680 Speaker 6: has sixty percent market share in high bandwidth memory, but 59 00:03:00,919 --> 00:03:04,880 Speaker 6: sk Group with SKA Telecom underneath it is a big 60 00:03:04,919 --> 00:03:08,600 Speaker 6: deployer of data center capacity. And where do they buy 61 00:03:08,680 --> 00:03:13,679 Speaker 6: their not just GPUs, but their their servers from now 62 00:03:13,720 --> 00:03:16,440 Speaker 6: from Nvidia. So that is it's a multi year thing. 63 00:03:16,840 --> 00:03:19,640 Speaker 6: It won't show up in any one company's one year financials. 64 00:03:20,520 --> 00:03:22,880 Speaker 6: But let me just be honest with you Scarlet about it. 65 00:03:23,800 --> 00:03:28,760 Speaker 6: Jensen and in video they like big round numbers, and 66 00:03:29,200 --> 00:03:31,720 Speaker 6: it's really normal for them to announce it that in 67 00:03:31,720 --> 00:03:32,119 Speaker 6: that way. 68 00:03:33,480 --> 00:03:36,120 Speaker 5: What do you think Silicon Valley is going to be 69 00:03:36,160 --> 00:03:40,280 Speaker 5: really listening for this week when we get some of 70 00:03:40,280 --> 00:03:42,520 Speaker 5: these big tech companies reporting on Wednesday and Thursday. 71 00:03:43,080 --> 00:03:43,360 Speaker 1: Yeah. 72 00:03:43,520 --> 00:03:46,480 Speaker 6: So it was a really simple arrangement between Wall Street 73 00:03:46,600 --> 00:03:50,600 Speaker 6: and the biggest technology companies this point, the biggest technology 74 00:03:50,600 --> 00:03:52,520 Speaker 6: companies had to say that they were going to spend 75 00:03:52,600 --> 00:03:55,520 Speaker 6: more every year, CAPEX would go up, and they needed 76 00:03:55,560 --> 00:03:57,480 Speaker 6: to show top line growth. And if they did that, 77 00:03:57,840 --> 00:03:59,240 Speaker 6: I would be on the show of View the next 78 00:03:59,240 --> 00:04:01,120 Speaker 6: morning and the big green on the screen and everything 79 00:04:01,160 --> 00:04:04,440 Speaker 6: would be rosy. Now there's a lot more scrutiny. There 80 00:04:04,480 --> 00:04:07,480 Speaker 6: needs to be much more evidence, not just top line growth, 81 00:04:07,800 --> 00:04:12,160 Speaker 6: that all of the rising CAPEX results in something material. 82 00:04:12,480 --> 00:04:15,400 Speaker 6: And we learned that lesson through alphabet right, so with Amazon, 83 00:04:15,440 --> 00:04:18,360 Speaker 6: Microsoft in particular, but also Meta to a lesser extent. 84 00:04:18,839 --> 00:04:21,680 Speaker 6: People want to say, like, okay, I understand this metric 85 00:04:22,080 --> 00:04:25,160 Speaker 6: about how people using the AI that you've developed, not 86 00:04:25,240 --> 00:04:27,960 Speaker 6: just that your sales are growing. You know, Alphabet showed 87 00:04:27,960 --> 00:04:31,240 Speaker 6: really strong growth, but the market was still worried about 88 00:04:31,240 --> 00:04:32,440 Speaker 6: the rising CAPEX number. 89 00:04:33,440 --> 00:04:35,280 Speaker 2: Before we let you go very quickly, there was a 90 00:04:35,360 --> 00:04:38,480 Speaker 2: Chinese chip maker that made a debut and in its debut, 91 00:04:38,560 --> 00:04:41,120 Speaker 2: cx empty jumped. I'm looking at this, it looks like 92 00:04:41,120 --> 00:04:42,960 Speaker 2: a type of four hundred and sixty six percent. 93 00:04:43,320 --> 00:04:47,080 Speaker 6: Yeah, and now is Mainland China's most valuable company overnight. 94 00:04:47,560 --> 00:04:49,040 Speaker 6: What you need to know is cx empt is the 95 00:04:49,160 --> 00:04:52,160 Speaker 6: number four maker of memory chips, but it is the 96 00:04:52,160 --> 00:04:55,440 Speaker 6: case study for China wanting its own domestic industry. It's 97 00:04:55,480 --> 00:04:59,600 Speaker 6: the flagship for China finding a domestic hero. It has 98 00:04:59,600 --> 00:05:01,000 Speaker 6: a long way to go to catch up, but the 99 00:05:01,040 --> 00:05:04,279 Speaker 6: proceeds of this IPO will help it to establish memory 100 00:05:04,320 --> 00:05:09,000 Speaker 6: manufacturing in China. And it's also an international success, so 101 00:05:09,040 --> 00:05:11,120 Speaker 6: those other memory names will be looking over their shoulder. 102 00:05:11,600 --> 00:05:12,240 Speaker 1: Stay with us. 103 00:05:12,320 --> 00:05:14,520 Speaker 2: More from Bloomberg Intelligence coming up after this. 104 00:05:18,640 --> 00:05:22,359 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 105 00:05:22,440 --> 00:05:25,520 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 106 00:05:25,560 --> 00:05:28,840 Speaker 1: Auto with the Bloomberg Business app. Listen on demand wherever 107 00:05:28,920 --> 00:05:32,440 Speaker 1: you get your podcasts, or watch us live on YouTube. 108 00:05:33,160 --> 00:05:35,239 Speaker 5: Next one to bring in Laura Martin. She's a senior 109 00:05:35,240 --> 00:05:37,039 Speaker 5: analyst that need them a company. She's out there in 110 00:05:37,360 --> 00:05:39,599 Speaker 5: la I've known Laura for decades. She's covered the media 111 00:05:39,680 --> 00:05:42,840 Speaker 5: industry for decades, and what is characterized her work from 112 00:05:42,839 --> 00:05:46,000 Speaker 5: my perspective is just a level of innovation in her research. 113 00:05:46,040 --> 00:05:49,680 Speaker 5: She looks at companies and industries very differently than most 114 00:05:49,720 --> 00:05:51,880 Speaker 5: of the street. And as an example, I'm going to 115 00:05:51,920 --> 00:05:54,520 Speaker 5: say twenty five years ago, maybe even more, she really 116 00:05:54,520 --> 00:05:57,520 Speaker 5: put return on invested capital front and center for her 117 00:05:57,520 --> 00:05:59,600 Speaker 5: research and I didn't know anybody else doing that back 118 00:05:59,640 --> 00:06:02,320 Speaker 5: in the day. That was really unique and that drove 119 00:06:02,360 --> 00:06:04,680 Speaker 5: a lot of really cool research back in the day. Now, 120 00:06:05,720 --> 00:06:07,200 Speaker 5: I think she takes it one step further. 121 00:06:07,560 --> 00:06:08,320 Speaker 3: She uses the. 122 00:06:08,520 --> 00:06:12,360 Speaker 5: Rating some glass door which kind of looks at the 123 00:06:12,480 --> 00:06:16,359 Speaker 5: culture and values of its employees across her research coverage, 124 00:06:17,080 --> 00:06:20,480 Speaker 5: and she says, you know what if happy employees are 125 00:06:20,600 --> 00:06:23,200 Speaker 5: value employees and the create shareholder value, and that's one 126 00:06:23,240 --> 00:06:25,520 Speaker 5: of the metrics you should look at. Lauren, thanks so 127 00:06:25,600 --> 00:06:27,960 Speaker 5: much for joining us here, talk to us about this research, 128 00:06:28,000 --> 00:06:32,400 Speaker 5: and you guys highlighted Meta recently using this type of research. 129 00:06:33,680 --> 00:06:35,800 Speaker 7: Yeah, So what we're doing is we're looking at the 130 00:06:35,800 --> 00:06:38,080 Speaker 7: fact that out of the twenty three stocks we cover, 131 00:06:38,720 --> 00:06:43,440 Speaker 7: Meta pays the most in stock based compensation per employee. 132 00:06:43,480 --> 00:06:46,200 Speaker 7: Both are metrics you can pull out of their public financials. 133 00:06:46,520 --> 00:06:49,520 Speaker 7: So they're paying every single employee a mat at which 134 00:06:49,520 --> 00:06:52,880 Speaker 7: is like seventy thousand people three hundred thousand dollars each. 135 00:06:53,400 --> 00:06:56,920 Speaker 7: So presumably some people like receptionists, don't get stock, which 136 00:06:56,920 --> 00:06:59,160 Speaker 7: means they're probably playing people who do get stock five 137 00:06:59,200 --> 00:07:02,160 Speaker 7: hundred thousand dollars in stock based comp So you say 138 00:07:02,200 --> 00:07:04,880 Speaker 7: to yourself, why are they paying more a lot more 139 00:07:05,360 --> 00:07:08,440 Speaker 7: than everybody else in big tech and in big cap. 140 00:07:08,760 --> 00:07:10,560 Speaker 7: And when you look, you look at the answer. Out 141 00:07:10,600 --> 00:07:13,800 Speaker 7: of the twenty three companies we cover, their ratings by 142 00:07:13,880 --> 00:07:17,320 Speaker 7: employees today is a two point eight out of five, 143 00:07:17,480 --> 00:07:19,680 Speaker 7: which is the lowest of all the companies we cover 144 00:07:19,800 --> 00:07:24,760 Speaker 7: by a lot, Like Netflix is four point four, Google 145 00:07:24,840 --> 00:07:28,320 Speaker 7: four point two, and you know, Metas down at two 146 00:07:28,360 --> 00:07:28,760 Speaker 7: point eight. 147 00:07:29,040 --> 00:07:30,680 Speaker 2: So that's a huge different of. 148 00:07:30,600 --> 00:07:32,760 Speaker 7: The reasons they have to pay so much is because 149 00:07:32,800 --> 00:07:36,280 Speaker 7: their culture and values are really low and horrible internally. 150 00:07:36,800 --> 00:07:39,119 Speaker 2: Okay, say more about this. What do we know about 151 00:07:39,120 --> 00:07:41,320 Speaker 2: the culture and the values. I mean, you dug into 152 00:07:41,360 --> 00:07:42,600 Speaker 2: all of this, what did you find? 153 00:07:43,840 --> 00:07:46,200 Speaker 7: So I think the problem is Mark. You know, the 154 00:07:46,280 --> 00:07:50,080 Speaker 7: CEO is unimpeachable. He controls the company absolutely, and you've 155 00:07:50,120 --> 00:07:55,080 Speaker 7: seen him pivot really rapidly from the metaverse. Oh, change 156 00:07:55,120 --> 00:07:57,440 Speaker 7: the company name, but now we're firing all those people. 157 00:07:57,760 --> 00:08:00,640 Speaker 7: Quest Goggles were really in you know, Vogue for a 158 00:08:00,760 --> 00:08:03,560 Speaker 7: year and now they're sort of getting left by the wayside. 159 00:08:03,600 --> 00:08:06,160 Speaker 7: So now we're doing Now he's doing generative AI. But 160 00:08:06,520 --> 00:08:08,560 Speaker 7: three weeks ago he said we're getting into the chip 161 00:08:08,600 --> 00:08:11,080 Speaker 7: business and we're going to compete with Navidia. So I 162 00:08:11,080 --> 00:08:16,640 Speaker 7: think just this strategic pivoting really demoralizes people and confuses 163 00:08:16,680 --> 00:08:19,560 Speaker 7: them about what the strategic priorities are of the enterprise. 164 00:08:20,360 --> 00:08:23,560 Speaker 5: So the net result is to I guess, attract and retain, 165 00:08:23,800 --> 00:08:27,520 Speaker 5: they have to pay more. And in Silicon Valley that 166 00:08:27,600 --> 00:08:29,800 Speaker 5: means stock. That's not like Laura and I grew up 167 00:08:30,160 --> 00:08:32,280 Speaker 5: in Wall Street where it was just cash cash cash out. 168 00:08:32,280 --> 00:08:36,400 Speaker 5: There it's all stock. What's the long term impact do 169 00:08:36,400 --> 00:08:39,240 Speaker 5: you think on their equity value their stock performance? 170 00:08:40,640 --> 00:08:43,960 Speaker 7: I think it means you have higher employee churn, and 171 00:08:44,080 --> 00:08:45,840 Speaker 7: the people who churn are the people who can get 172 00:08:45,880 --> 00:08:48,040 Speaker 7: the next job, which means they're good people. So they're 173 00:08:48,120 --> 00:08:52,440 Speaker 7: leaving for Open AI or Anthropic or Google, and so 174 00:08:52,600 --> 00:08:55,320 Speaker 7: that makes to get the new person to replace that 175 00:08:55,440 --> 00:08:58,760 Speaker 7: churned out person is just more and more expensive. And 176 00:08:59,040 --> 00:09:01,920 Speaker 7: that's bad for erald because it's deluded to shareholders if 177 00:09:01,960 --> 00:09:04,280 Speaker 7: you have to pay people more to attract them and 178 00:09:04,320 --> 00:09:07,200 Speaker 7: retain them. So I think it's bad for it. It's bad 179 00:09:07,200 --> 00:09:10,960 Speaker 7: for both. I mean, both employees and Wall Street need 180 00:09:11,040 --> 00:09:14,560 Speaker 7: stock prices to rise, so it's bad for both employees 181 00:09:14,640 --> 00:09:17,840 Speaker 7: and Wall Street to have them have to give so 182 00:09:17,960 --> 00:09:20,280 Speaker 7: much equity to attract and retain people. 183 00:09:20,320 --> 00:09:23,079 Speaker 2: I think, what does it mean for the company's ability 184 00:09:23,120 --> 00:09:25,760 Speaker 2: to deliver on these new strategic initiatives? I mean, if 185 00:09:25,880 --> 00:09:29,319 Speaker 2: Mark Zuckerberg is pivoting the company every six months every 186 00:09:29,360 --> 00:09:32,040 Speaker 2: year to something new, he's paying up for the talent, 187 00:09:32,120 --> 00:09:35,400 Speaker 2: But can they execute if the I mean, if there 188 00:09:35,440 --> 00:09:38,199 Speaker 2: isn't that allegiance between company and employee the way you 189 00:09:38,240 --> 00:09:39,559 Speaker 2: might find elsewhere. 190 00:09:40,360 --> 00:09:42,560 Speaker 7: Right exactly. So one of the things he just said 191 00:09:42,559 --> 00:09:45,240 Speaker 7: in a town hall that he's going to lease capacity. 192 00:09:45,280 --> 00:09:47,520 Speaker 7: He just did a ten billion dollar leasing deal where 193 00:09:47,520 --> 00:09:50,760 Speaker 7: he has built too much capacity, because he said his 194 00:09:50,880 --> 00:09:55,679 Speaker 7: AI initiatives aren't going as quickly is he had hoped. Well, 195 00:09:55,679 --> 00:09:58,600 Speaker 7: that's exactly the answer to your question that the employees 196 00:09:58,640 --> 00:10:01,839 Speaker 7: are either infighting or it's clear what priorities are. Things 197 00:10:01,840 --> 00:10:06,000 Speaker 7: are happening slower in it, regardless of despite the amount 198 00:10:06,000 --> 00:10:07,600 Speaker 7: of money he's spending on talent. 199 00:10:08,520 --> 00:10:10,880 Speaker 5: Lauri, you're one of there's seventy two buy ratings on 200 00:10:10,920 --> 00:10:13,880 Speaker 5: his company, only seven holds you're one of them. Why 201 00:10:13,960 --> 00:10:14,160 Speaker 5: is that? 202 00:10:15,480 --> 00:10:18,920 Speaker 7: Because I think he is destroying value by not having 203 00:10:19,000 --> 00:10:23,679 Speaker 7: strategic focus that I think he's got. Quest was about 204 00:10:23,760 --> 00:10:26,560 Speaker 7: trying to go into competition with Apple. He's in the 205 00:10:26,559 --> 00:10:29,800 Speaker 7: shopping business because he's trying to displace Amazon. Of course 206 00:10:29,840 --> 00:10:32,280 Speaker 7: he does search because right so that tries to he 207 00:10:32,320 --> 00:10:35,200 Speaker 7: really is next to search, social and search. He is 208 00:10:35,240 --> 00:10:38,160 Speaker 7: the number one competitor to Google. And now he's going 209 00:10:38,200 --> 00:10:40,800 Speaker 7: in the chip business with competes with Navidia. So I 210 00:10:40,800 --> 00:10:46,560 Speaker 7: feel like his strategy is reactionary and keeps going into business, 211 00:10:46,600 --> 00:10:48,920 Speaker 7: you know, sort of trying to compete with the incumbent 212 00:10:49,120 --> 00:10:52,319 Speaker 7: winner take all you know, participant, and I just think 213 00:10:52,360 --> 00:10:54,840 Speaker 7: that won't work in the end. He's much smaller many 214 00:10:54,920 --> 00:10:57,880 Speaker 7: of these companies he's going into competition against. I just 215 00:10:57,920 --> 00:10:59,640 Speaker 7: think it doesn't work in the end. 216 00:10:59,640 --> 00:11:02,760 Speaker 2: I think stay with us. More from Bloomberg Intelligence coming 217 00:11:02,800 --> 00:11:03,480 Speaker 2: up after this. 218 00:11:07,800 --> 00:11:11,480 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 219 00:11:11,559 --> 00:11:14,640 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 220 00:11:14,679 --> 00:11:17,960 Speaker 1: Auto with the Bloomberg Business app. Listen on demand wherever 221 00:11:18,040 --> 00:11:21,160 Speaker 1: you get your podcasts, or watch us live on YouTube. 222 00:11:21,800 --> 00:11:24,440 Speaker 5: All right, let's get away from the AI talk. Let's 223 00:11:24,440 --> 00:11:27,600 Speaker 5: go to the big take story out there. And it's 224 00:11:27,640 --> 00:11:30,120 Speaker 5: a good one because it's a non AI big take 225 00:11:30,480 --> 00:11:34,160 Speaker 5: story here. It goes about the tin can industry here, 226 00:11:34,760 --> 00:11:39,640 Speaker 5: and it goes to tariffs and protectionism, the industry's struggle. 227 00:11:39,679 --> 00:11:42,439 Speaker 5: That's the tin can industry struggle is a cautionary tale 228 00:11:42,800 --> 00:11:46,920 Speaker 5: as the US President tries to rebuild his protectionist resume. 229 00:11:47,080 --> 00:11:51,160 Speaker 5: This story when deeply diving into the tin can business, 230 00:11:51,160 --> 00:11:53,120 Speaker 5: which I don't even think about the tin can business, 231 00:11:53,120 --> 00:11:55,480 Speaker 5: but apparently we make a lot of tin cans every year. 232 00:11:55,600 --> 00:11:55,920 Speaker 1: Yeah. 233 00:11:55,920 --> 00:11:58,080 Speaker 5: Sean Donna Joins is here Bloomberg News. He's a senior 234 00:11:58,080 --> 00:12:01,960 Speaker 5: economics writer. Sean talk to us about the tin can 235 00:12:02,400 --> 00:12:04,600 Speaker 5: business in this country and how is it being impacted 236 00:12:04,640 --> 00:12:05,920 Speaker 5: by terrffs. 237 00:12:06,800 --> 00:12:09,880 Speaker 4: Sure, well, thanks for having me. Look, the tin can 238 00:12:09,960 --> 00:12:14,160 Speaker 4: is this wonderful little case study that we can take 239 00:12:14,400 --> 00:12:18,640 Speaker 4: into what has happened actually with tariffs and the results. 240 00:12:18,640 --> 00:12:21,400 Speaker 4: I think when we talk about tariffs nowadays, we often 241 00:12:21,480 --> 00:12:24,480 Speaker 4: talk about the promise that this will mean a return 242 00:12:24,559 --> 00:12:28,520 Speaker 4: in manufacturing, the promise that lots of jobs will come 243 00:12:28,559 --> 00:12:30,640 Speaker 4: with it. Eventually that will be a huge amount of 244 00:12:30,640 --> 00:12:35,240 Speaker 4: investment in the United States. And the tin cans are 245 00:12:37,080 --> 00:12:38,600 Speaker 4: an example that you kind of give us a bit 246 00:12:38,600 --> 00:12:41,800 Speaker 4: of pause when we hear some of those promises. And 247 00:12:41,920 --> 00:12:45,319 Speaker 4: I dove into this because in March of twenty eighteen, 248 00:12:45,960 --> 00:12:49,440 Speaker 4: Wilbur Ross went on TV and this was right as 249 00:12:49,440 --> 00:12:52,480 Speaker 4: they announced really the first Trump tariffs, which were the 250 00:12:52,520 --> 00:12:57,679 Speaker 4: tariffs on steel, and the markets were going down. Investors 251 00:12:57,679 --> 00:13:01,720 Speaker 4: were nervous, and Wilbur Ross went on and he held 252 00:13:01,800 --> 00:13:04,319 Speaker 4: up a Campbell's soup can and he said, you know what, 253 00:13:05,080 --> 00:13:07,840 Speaker 4: there's only two point six cents worth of steel in this, 254 00:13:08,280 --> 00:13:12,280 Speaker 4: and these tariffs aren't really gonna affect it and the 255 00:13:12,320 --> 00:13:15,480 Speaker 4: price of this, and moreover, we're going to get tens 256 00:13:15,520 --> 00:13:18,760 Speaker 4: of thousands of jobs and hundreds of millions of dollars 257 00:13:18,880 --> 00:13:23,040 Speaker 4: in investment. Well, he went out that day and he 258 00:13:23,080 --> 00:13:26,319 Speaker 4: bought a can of Campbell's. It was chicken soup, chicken 259 00:13:26,320 --> 00:13:28,840 Speaker 4: noodle soup, and he said he went as Local seven 260 00:13:28,920 --> 00:13:30,800 Speaker 4: eleven bought it for a dollar ninety nine. So I thought, 261 00:13:30,800 --> 00:13:32,520 Speaker 4: I did the same thing as Wilbur Ross, and I 262 00:13:32,559 --> 00:13:34,800 Speaker 4: went out to my local seven to eleven and I 263 00:13:34,840 --> 00:13:36,680 Speaker 4: bought that can of chicken noodle soup and it would 264 00:13:36,720 --> 00:13:38,760 Speaker 4: cost me three forty nine. Right, So that's not a 265 00:13:38,800 --> 00:13:42,040 Speaker 4: surprise that in this kind of era of inflation, price 266 00:13:42,080 --> 00:13:44,760 Speaker 4: has gone up. But it's why the price has gone up. 267 00:13:45,200 --> 00:13:47,199 Speaker 4: And part of the reason for that is there is 268 00:13:47,240 --> 00:13:51,120 Speaker 4: a fifty percent tariff on the steel that is used 269 00:13:51,160 --> 00:13:54,320 Speaker 4: to make tin cans. And then we started looking at okay, 270 00:13:54,360 --> 00:13:57,960 Speaker 4: has there been investment in making that steel that is 271 00:13:58,040 --> 00:14:01,000 Speaker 4: used in tin cans? Well, the answer is no, Actually 272 00:14:01,040 --> 00:14:05,600 Speaker 4: it's gone the opposite way. There were a dozen mills 273 00:14:05,640 --> 00:14:08,800 Speaker 4: that made ten plate steel when will Barross held up 274 00:14:08,840 --> 00:14:11,920 Speaker 4: that can. There's now three running in the United States. 275 00:14:11,960 --> 00:14:16,160 Speaker 4: In twenty eighteen, the US imported fifty percent of the 276 00:14:16,200 --> 00:14:19,080 Speaker 4: steel that was used in to make ten cans. Now 277 00:14:19,120 --> 00:14:22,160 Speaker 4: it imports eighty percent of that stike just because domestic 278 00:14:22,200 --> 00:14:25,640 Speaker 4: production has not gone anyway, and we just haven't seen 279 00:14:26,160 --> 00:14:28,320 Speaker 4: the jobs they're now in all of the steel mills 280 00:14:28,320 --> 00:14:31,080 Speaker 4: in the United States. They are now only about thirteen 281 00:14:31,160 --> 00:14:34,720 Speaker 4: hundred more people working in those mills than there were 282 00:14:34,880 --> 00:14:37,280 Speaker 4: in twenty eighteen. So it's you know, it's one of 283 00:14:37,320 --> 00:14:39,320 Speaker 4: the stories of I think of it as a story 284 00:14:39,320 --> 00:14:47,240 Speaker 4: of kind of unfulfilled promises, unintended consequences, and that, you know, 285 00:14:47,320 --> 00:14:50,560 Speaker 4: the promise of tariffs. What what actually happens when they 286 00:14:50,560 --> 00:14:52,680 Speaker 4: got Now this is just ten cans, right, this is 287 00:14:52,720 --> 00:14:54,080 Speaker 4: just ten cans part of the can. 288 00:14:54,680 --> 00:14:56,800 Speaker 2: What is happening to those ten cans because they're made 289 00:14:56,800 --> 00:14:59,840 Speaker 2: somewhat differently now than they were before, aren't they? 290 00:15:00,040 --> 00:15:01,440 Speaker 4: Yeah, this is really interesting. So I went up to 291 00:15:01,480 --> 00:15:05,760 Speaker 4: the Can Corporation of America, which is just outside Allentown, Pennsylvania, 292 00:15:05,760 --> 00:15:07,600 Speaker 4: and if you go there on the production line, they 293 00:15:07,640 --> 00:15:09,880 Speaker 4: will tell you that they use roughly the same amount 294 00:15:09,920 --> 00:15:15,360 Speaker 4: of steel that they did in twenty eighteen, and they 295 00:15:15,360 --> 00:15:18,320 Speaker 4: turn out more cans. Why because that tin can that 296 00:15:18,360 --> 00:15:21,160 Speaker 4: you're buying in the store is actually thinner than it 297 00:15:21,240 --> 00:15:24,160 Speaker 4: used to be. They're actually reducing the amount of steel 298 00:15:24,200 --> 00:15:27,160 Speaker 4: in each tin can to try and save on costs. 299 00:15:27,240 --> 00:15:31,320 Speaker 4: So we've had some kind of innovation there. That steel 300 00:15:31,640 --> 00:15:35,440 Speaker 4: is also, as I said before, it's more likely to 301 00:15:35,480 --> 00:15:36,960 Speaker 4: be coming from overseas. 302 00:15:37,400 --> 00:15:40,080 Speaker 5: And I love this stet here. Each year Can Corporation 303 00:15:40,320 --> 00:15:42,680 Speaker 5: and it's three hundred and fifty employees turn out just 304 00:15:42,720 --> 00:15:46,600 Speaker 5: shy of one billion in cans and two hundred different 305 00:15:46,600 --> 00:15:48,960 Speaker 5: sizes that are destined to be filled with everything from 306 00:15:49,000 --> 00:15:50,480 Speaker 5: coffee to industrial adhesives. 307 00:15:50,880 --> 00:15:52,360 Speaker 2: You know, I think about this and like, I can't 308 00:15:52,400 --> 00:15:54,280 Speaker 2: remember the last time I used a can opener because 309 00:15:54,320 --> 00:15:56,560 Speaker 2: so much soup, for instance, broth is always in the 310 00:15:56,560 --> 00:15:59,680 Speaker 2: paper packaging. Now instead of cannon, I go Campbell's Soup, 311 00:15:59,680 --> 00:16:03,239 Speaker 2: big time New Jersey Push. That's why. 312 00:16:04,080 --> 00:16:07,480 Speaker 4: Sorry. And if you talk to people in the can industry, 313 00:16:07,880 --> 00:16:11,040 Speaker 4: they will kind of look at you very skeptically and say, 314 00:16:11,080 --> 00:16:13,840 Speaker 4: there's nothing quite like a tin can in terms of 315 00:16:14,880 --> 00:16:17,640 Speaker 4: holding up and the ability to stack it and and 316 00:16:18,920 --> 00:16:20,840 Speaker 4: so on. But yeah, that has been one of the 317 00:16:20,880 --> 00:16:24,040 Speaker 4: threats to the can industry has been kind of alternative 318 00:16:24,080 --> 00:16:26,560 Speaker 4: packaging and some of that which has been prompted by 319 00:16:27,280 --> 00:16:29,040 Speaker 4: the higher costs of cans. 320 00:16:29,080 --> 00:16:35,200 Speaker 1: And this is the Bloomberg Intelligence podcast, available on Apple, Spotify, 321 00:16:35,400 --> 00:16:39,360 Speaker 1: and anywhere else you get your podcasts. 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