1 00:00:02,529 --> 00:00:04,210 Speaker 1: Bloomberg Audio Studios. 2 00:00:04,670 --> 00:00:05,380 Speaker 2: Podcasts. 3 00:00:05,700 --> 00:00:06,220 Speaker 1: Radio. 4 00:00:06,660 --> 00:00:07,040 Speaker 3: News. 5 00:00:12,520 --> 00:00:16,680 Speaker 1: This is the Bloomberg Surveillance Podcast. Catch us live weekdays 6 00:00:16,720 --> 00:00:20,220 Speaker 1: at 7 a.m. Eastern on Apple CarPlay or Android Auto 7 00:00:20,280 --> 00:00:23,860 Speaker 1: with the Bloomberg Business app. Listen on demand wherever you 8 00:00:23,910 --> 00:00:26,990 Speaker 1: get your podcasts or watch us live on YouTube. 9 00:00:27,730 --> 00:00:30,890 Speaker 3: We have been remiss. One of the interns at Macro 10 00:00:30,930 --> 00:00:33,340 Speaker 3: Policy Perspectives emailed me. 11 00:00:33,350 --> 00:00:33,510 Speaker 4: Okay. 12 00:00:33,770 --> 00:00:35,830 Speaker 3: I think out of Texas. You know, it's Julia. What 13 00:00:35,850 --> 00:00:38,870 Speaker 3: do you want? And she said, Julia's miffed. There's just 14 00:00:39,130 --> 00:00:44,530 Speaker 3: not enough Fed chat on Bloomberg surveillance. Joining us now 15 00:00:45,110 --> 00:00:51,570 Speaker 3: from the Texas sphere, Julia Coronado, president, founder, Macro Policy Perspectives. Julia, 16 00:00:51,750 --> 00:00:55,750 Speaker 3: are we talking less about the Fed because Chairman Warsh 17 00:00:55,850 --> 00:00:58,570 Speaker 3: wants us to talk less about the Fed? 18 00:01:01,300 --> 00:01:03,860 Speaker 4: I don't know if I would describe it that way, Tom. 19 00:01:03,940 --> 00:01:06,220 Speaker 5: I mean, right now we're talking less about the Fed 20 00:01:06,260 --> 00:01:09,520 Speaker 5: because it's the quiet period ahead of the FOMC meeting, 21 00:01:09,580 --> 00:01:12,060 Speaker 5: number one. And number two, we all know what they're 22 00:01:12,100 --> 00:01:14,600 Speaker 5: going to do. Why do we know what they're going 23 00:01:14,620 --> 00:01:19,819 Speaker 5: to do? Because we got good old-fashioned forward guidance from 24 00:01:20,100 --> 00:01:24,400 Speaker 5: leadership on the FOMC telling us that October was a 25 00:01:24,459 --> 00:01:27,720 Speaker 5: hold to gather more information. So I think that that's 26 00:01:28,080 --> 00:01:30,959 Speaker 5: the real answer is we already know what they're going 27 00:01:31,000 --> 00:01:33,810 Speaker 5: to do because we, in fact, did get forward guidance. 28 00:01:33,850 --> 00:01:37,890 Speaker 3: Can you glean, folks, full disclosure, Dr. Cornell has been 29 00:01:37,950 --> 00:01:42,069 Speaker 3: verbally shortlisted for governor and all that. Can you glean, Julia, 30 00:01:42,090 --> 00:01:48,280 Speaker 3: the governor and president's power or push against their newly 31 00:01:48,360 --> 00:01:49,240 Speaker 3: minted chairmen? 32 00:01:51,160 --> 00:01:51,540 Speaker 4: Yeah. 33 00:01:51,600 --> 00:01:55,740 Speaker 5: I mean, look, there was already a rethinking of the 34 00:01:55,820 --> 00:02:00,940 Speaker 5: inflationary dynamics we're seeing before Chair Warsh walked into the 35 00:02:00,980 --> 00:02:07,270 Speaker 5: door of the FOMC, of the Federal Reserve. And that 36 00:02:07,590 --> 00:02:11,290 Speaker 5: kept going. So he walked into a really challenging situation. 37 00:02:11,330 --> 00:02:13,970 Speaker 5: If your goal is going to be to cut rates 38 00:02:14,050 --> 00:02:17,190 Speaker 5: or even to hold them steady, you are confronting a 39 00:02:17,250 --> 00:02:23,460 Speaker 5: committee that was increasingly worried about stickiness and inflation, persistence 40 00:02:23,500 --> 00:02:26,000 Speaker 5: and inflation. And I think that that's been borne out 41 00:02:26,040 --> 00:02:28,500 Speaker 5: by the data. And so, yeah, I do think the 42 00:02:28,560 --> 00:02:35,050 Speaker 5: committee dynamic is one very powerful, constraining force on Warsh. 43 00:02:35,130 --> 00:02:37,929 Speaker 5: And he read the tea leaves and knows that his 44 00:02:38,010 --> 00:02:41,990 Speaker 5: job is to lead that committee. So he signed on 45 00:02:42,050 --> 00:02:43,690 Speaker 5: to basically a hiking cycle. 46 00:02:44,580 --> 00:02:47,330 Speaker 4: Joy, as we think about the stickiness of this inflation, 47 00:02:47,430 --> 00:02:49,329 Speaker 4: is there an argument to be made that maybe this 48 00:02:49,370 --> 00:02:52,549 Speaker 4: is kind of the new normal if we're trying to 49 00:02:52,690 --> 00:02:56,720 Speaker 4: reshore some manufacturing in this country? That suggests higher prices. 50 00:02:56,760 --> 00:03:01,340 Speaker 4: If we're talking about limiting or eliminating immigration, that might 51 00:03:01,419 --> 00:03:04,140 Speaker 4: push up wages for those that are here. Is there 52 00:03:04,520 --> 00:03:07,929 Speaker 4: some structural changes that might suggest, hey, this 2% number 53 00:03:07,970 --> 00:03:09,410 Speaker 4: is probably not the right target anymore? 54 00:03:11,169 --> 00:03:15,390 Speaker 5: Well, I hear that argument being made, and that sends 55 00:03:15,550 --> 00:03:21,660 Speaker 5: shivers down my spine as a monetary policy aficionado. Once 56 00:03:21,700 --> 00:03:26,060 Speaker 5: we start making excuses for inflation, price stability starts slipping 57 00:03:26,120 --> 00:03:28,820 Speaker 5: away from us. We've done it in the past, and 58 00:03:28,880 --> 00:03:31,780 Speaker 5: I do hear rumblings about that. Again, I hear, well, 59 00:03:31,919 --> 00:03:36,390 Speaker 5: AI isn't interest rate sensitive. So if it's one of 60 00:03:36,430 --> 00:03:39,869 Speaker 5: the drivers of inflation, the Fed can't do anything about that. 61 00:03:40,270 --> 00:03:42,610 Speaker 5: I think actually what we've seen in markets in the 62 00:03:42,650 --> 00:03:48,130 Speaker 5: last few weeks illustrates that AI is interest rate sensitive. 63 00:03:48,850 --> 00:03:51,950 Speaker 5: And of course it is because it's the most capital 64 00:03:52,090 --> 00:03:56,290 Speaker 5: hungry sector we've had in decades. So of course, the 65 00:03:56,330 --> 00:04:00,960 Speaker 5: cost of capital is going to impact investor sentiment and 66 00:04:01,000 --> 00:04:05,420 Speaker 5: sensitivity to the numbers that they're putting out. So I 67 00:04:05,460 --> 00:04:09,620 Speaker 5: don't like that. I don't subscribe to that line of thinking. 68 00:04:09,720 --> 00:04:12,790 Speaker 5: I think it's very important that we remember why the 69 00:04:12,850 --> 00:04:15,290 Speaker 5: Federal Reserve was created in the first place. 70 00:04:15,750 --> 00:04:18,710 Speaker 3: It was out of the ashes of the railroad boom 71 00:04:18,770 --> 00:04:21,490 Speaker 3: and bust. Let's not do that. 72 00:04:21,710 --> 00:04:25,820 Speaker 5: The Fed doesn't need to crush the economy. but it 73 00:04:26,000 --> 00:04:28,940 Speaker 5: does need to keep inflation in check and it set 74 00:04:29,000 --> 00:04:31,659 Speaker 5: a target and it needs to kind of try and 75 00:04:31,700 --> 00:04:32,520 Speaker 5: meet that target. 76 00:04:32,779 --> 00:04:36,340 Speaker 3: Julia Carnato with us on Macro Policy Perspectives. Julia, do 77 00:04:36,360 --> 00:04:39,100 Speaker 3: you see evidence, and I'm looking at a 10-year where 78 00:04:39,140 --> 00:04:41,570 Speaker 3: Paul and I are starting to chat up a 499. 79 00:04:41,550 --> 00:04:44,830 Speaker 3: We're not there yet, but do you see before the 80 00:04:44,950 --> 00:04:49,370 Speaker 3: fact that the combination of inflation and real GDP could 81 00:04:49,510 --> 00:04:54,279 Speaker 3: shock and nominal GDP actually start to come down? Is 82 00:04:54,300 --> 00:04:56,160 Speaker 3: that a story we should be talking about? 83 00:04:58,320 --> 00:05:00,080 Speaker 5: Yeah, I mean, I think there's a number of things 84 00:05:00,120 --> 00:05:03,370 Speaker 5: that could slow down nominal GDP. I think what the 85 00:05:03,430 --> 00:05:07,590 Speaker 5: inflation pressures are telling us is that there is a 86 00:05:07,670 --> 00:05:12,330 Speaker 5: speed limit to the AI build out. And so and, 87 00:05:12,390 --> 00:05:15,490 Speaker 5: you know, consumer purchasing power is taking a hit and 88 00:05:15,570 --> 00:05:18,960 Speaker 5: really consumers are being held up by wealth effects. So 89 00:05:19,060 --> 00:05:22,200 Speaker 5: all of these things sort of tell us that yeah, 90 00:05:22,240 --> 00:05:27,000 Speaker 5: we could see some moderation in nominal GDP growth, you know, 91 00:05:27,020 --> 00:05:27,719 Speaker 5: in the year ahead. 92 00:05:27,760 --> 00:05:28,680 Speaker 4: And, you know, we've been on. 93 00:05:28,640 --> 00:05:34,410 Speaker 5: A hockey stick in terms of CapEx outlays and announced plans. 94 00:05:34,990 --> 00:05:38,990 Speaker 5: Even if that growth rate slows, you know, you're going 95 00:05:39,010 --> 00:05:41,789 Speaker 5: to see, you know, some more moderate numbers. So I 96 00:05:41,830 --> 00:05:45,780 Speaker 5: think some moderation in nominal GDP growth is probably a 97 00:05:45,800 --> 00:05:48,320 Speaker 5: baseline expectation for 2027. 98 00:05:48,320 --> 00:05:50,400 Speaker 3: What's the real wage doing right now? Yeah. 99 00:05:51,529 --> 00:05:56,050 Speaker 5: Well, real wages are not growing. The labor market is 100 00:05:56,310 --> 00:05:57,070 Speaker 5: not tight. 101 00:05:57,210 --> 00:05:58,370 Speaker 3: It is fine. 102 00:05:58,470 --> 00:06:02,120 Speaker 5: It is very balanced. But we do not see any 103 00:06:02,300 --> 00:06:06,780 Speaker 5: indications in the wage growth measure, any of the wage 104 00:06:06,839 --> 00:06:12,300 Speaker 5: growth measures, really, that wages are firming or accelerating. And 105 00:06:12,360 --> 00:06:15,050 Speaker 5: that means that this sticky inflation is taking a toll 106 00:06:15,170 --> 00:06:16,570 Speaker 5: on consumer purchasing power. 107 00:06:17,089 --> 00:06:17,630 Speaker 4: I mean, Paul. 108 00:06:18,150 --> 00:06:20,089 Speaker 3: I got to be honest, I didn't come up with 109 00:06:20,110 --> 00:06:23,130 Speaker 3: that question. No? That was from Adam Farstrup's deck. 110 00:06:23,490 --> 00:06:24,810 Speaker 4: Adam Farstrup's deck, exactly. 111 00:06:24,910 --> 00:06:26,950 Speaker 3: And Schroeder's came up with that question for me to 112 00:06:26,990 --> 00:06:28,770 Speaker 3: get me to Julia Cardano. 113 00:06:28,810 --> 00:06:32,029 Speaker 4: We love that. Hey, Julia, as we think about AI, 114 00:06:32,230 --> 00:06:35,090 Speaker 4: it just seems to be everywhere. It's in everyone's conversation, 115 00:06:35,130 --> 00:06:39,430 Speaker 4: it seems like. What are the economic impacts from AI, 116 00:06:39,490 --> 00:06:41,550 Speaker 4: all this spending? If you strip out AI, what does 117 00:06:41,589 --> 00:06:43,480 Speaker 4: it mean for GDP growth? What does it mean for 118 00:06:43,510 --> 00:06:47,230 Speaker 4: underlying inflation? We're just trying to figure out you know, 119 00:06:47,290 --> 00:06:49,690 Speaker 4: how this is impacting other parts of the economy. 120 00:06:50,370 --> 00:06:50,590 Speaker 3: Yeah. 121 00:06:50,870 --> 00:06:53,830 Speaker 5: I mean, it really is kind of drawing in all 122 00:06:53,890 --> 00:06:56,560 Speaker 5: heat and light, isn't it? I mean, it's, it is 123 00:06:56,680 --> 00:07:00,660 Speaker 5: holding up the economy. There's plenty of ripple effects from 124 00:07:01,120 --> 00:07:04,279 Speaker 5: the data center build out the AI build out into, 125 00:07:04,520 --> 00:07:07,760 Speaker 5: you know, the suppliers of the components. You can see 126 00:07:07,800 --> 00:07:12,030 Speaker 5: its footprint in the labor market. You're seeing construction hiring 127 00:07:12,370 --> 00:07:17,540 Speaker 5: and manufacturing hiring and transportation and warehousing and adding more jobs, 128 00:07:17,660 --> 00:07:21,820 Speaker 5: even as other sectors slow down. So you can see 129 00:07:21,840 --> 00:07:24,580 Speaker 5: that it really is the main driver. And then again, 130 00:07:24,640 --> 00:07:28,060 Speaker 5: if we turn back to the consumer, consumption's been running 131 00:07:28,140 --> 00:07:29,780 Speaker 5: ahead of real income growth. 132 00:07:29,860 --> 00:07:30,220 Speaker 3: Why? 133 00:07:30,300 --> 00:07:35,679 Speaker 5: Because of AI-led wealth effects. So It's not just the U.S. either, 134 00:07:35,760 --> 00:07:39,800 Speaker 5: because U.S. GDP for all of the capex boom we're 135 00:07:39,820 --> 00:07:43,460 Speaker 5: in the middle of is still only growing around 2%. 136 00:07:43,460 --> 00:07:43,720 Speaker 6: Why? 137 00:07:43,780 --> 00:07:46,760 Speaker 5: Because a lot of that demand is being exported. So 138 00:07:46,880 --> 00:07:51,370 Speaker 5: the chips and the components. So it's a global GDP driver, 139 00:07:51,430 --> 00:07:53,530 Speaker 5: not just a U.S. GDP driver. 140 00:07:53,930 --> 00:07:55,550 Speaker 3: But it's quite material. 141 00:07:55,610 --> 00:07:56,750 Speaker 4: It really is the story. Right. 142 00:07:58,300 --> 00:08:01,900 Speaker 3: I feel more at peace now after talking to Dr. Coronado. 143 00:08:02,200 --> 00:08:04,800 Speaker 3: We've done our obligatory Fed chat. It's just, you know, 144 00:08:05,560 --> 00:08:08,500 Speaker 3: it's like Dr. Julia. It's therapy. 145 00:08:08,560 --> 00:08:08,960 Speaker 4: Get it done. 146 00:08:09,160 --> 00:08:13,060 Speaker 3: Julian Coronado, thank you so much. Macro Policy Perspectives here. 147 00:08:14,870 --> 00:08:15,490 Speaker 3: Stay with us. 148 00:08:15,770 --> 00:08:19,010 Speaker 7: More from Bloomberg Surveillance coming up after this. 149 00:08:26,260 --> 00:08:29,860 Speaker 1: You're listening to the Bloomberg Surveillance Podcast. Catch us live 150 00:08:29,940 --> 00:08:33,120 Speaker 1: weekday afternoons from 7 to 10 a.m. Eastern. Listen on 151 00:08:33,220 --> 00:08:36,480 Speaker 1: Apple CarPlay and Android Auto with the Bloomberg Business app 152 00:08:36,679 --> 00:08:38,400 Speaker 1: or watch us live on YouTube. 153 00:08:39,160 --> 00:08:41,590 Speaker 3: Obviously, I think it's 13 days I heard away from 154 00:08:41,610 --> 00:08:45,310 Speaker 3: the election. Vote early, vote often. Jennifer Lawless with us, 155 00:08:45,370 --> 00:08:49,969 Speaker 3: professor at University of Virginia. Wonderful, wonderful. Jennifer, you have 156 00:08:49,990 --> 00:08:52,309 Speaker 3: it in your note. Thank you. I've got to bounce 157 00:08:52,370 --> 00:08:56,620 Speaker 3: off Jeff Green's tour de force. The first line of 158 00:08:56,980 --> 00:09:02,740 Speaker 3: young men blackpilling out. I've checked out. Professor Lawless, how 159 00:09:02,800 --> 00:09:05,720 Speaker 3: much of America would you guess is checked out of 160 00:09:05,760 --> 00:09:06,460 Speaker 3: this election? 161 00:09:07,950 --> 00:09:11,690 Speaker 6: Well, Ordinarily in midterm elections, the majority of the nation 162 00:09:11,770 --> 00:09:14,920 Speaker 6: is checked out. But we've learned over the last decade 163 00:09:15,300 --> 00:09:18,140 Speaker 6: that Donald Trump is a very motivating force for people 164 00:09:18,179 --> 00:09:21,520 Speaker 6: on both sides of the aisle. And midterm turnout has 165 00:09:21,620 --> 00:09:24,980 Speaker 6: increased in 2018 and 2022. It was higher than in 166 00:09:25,020 --> 00:09:28,020 Speaker 6: previous cycles. And my bet is that we'll see record 167 00:09:28,040 --> 00:09:30,610 Speaker 6: turnout this time. So at least a third of the 168 00:09:30,640 --> 00:09:33,209 Speaker 6: country will still be tuned out and, you know, very 169 00:09:33,230 --> 00:09:35,809 Speaker 6: turned off. But I do think that people will show 170 00:09:35,850 --> 00:09:37,610 Speaker 6: up at the polls and more than 10 million have 171 00:09:37,630 --> 00:09:38,690 Speaker 6: already cast ballots. 172 00:09:38,770 --> 00:09:45,130 Speaker 4: Interesting. Is the Senate in play, Professor, do you think? Oh, unquestionably. 173 00:09:46,059 --> 00:09:48,839 Speaker 6: I mean, depending on which predictions you look at and 174 00:09:48,880 --> 00:09:51,580 Speaker 6: depending on which polls you give more weight to, there 175 00:09:51,660 --> 00:09:54,510 Speaker 6: are up to a dozen seats that could potentially flip. 176 00:09:54,990 --> 00:09:57,569 Speaker 6: But the Democrats don't need a dozen seats. And I 177 00:09:58,030 --> 00:10:00,780 Speaker 6: think that if the election was held today. they would 178 00:10:00,800 --> 00:10:04,280 Speaker 6: likely take back the Senate, probably not by a huge margin, 179 00:10:04,360 --> 00:10:05,620 Speaker 6: but by one or two seats. 180 00:10:06,850 --> 00:10:10,400 Speaker 4: If you're the Republican National Committee chairperson, what are you 181 00:10:10,420 --> 00:10:12,559 Speaker 4: doing today? You've got a president who's got very low 182 00:10:12,600 --> 00:10:16,270 Speaker 4: approval ratings pursuing policies, whether it's tariffs or a war 183 00:10:16,290 --> 00:10:19,830 Speaker 4: in Iran that don't poll well in general. I mean, 184 00:10:19,970 --> 00:10:20,710 Speaker 4: what do you do here? 185 00:10:22,000 --> 00:10:24,800 Speaker 6: Well, I think we're seeing two strategies, and they're both 186 00:10:24,980 --> 00:10:28,699 Speaker 6: potentially smart, but regardless, they're the only options. The first 187 00:10:28,760 --> 00:10:32,410 Speaker 6: is for candidates in very close races to distance themselves 188 00:10:32,429 --> 00:10:35,130 Speaker 6: from Donald Trump. And we're seeing Trump allow that a 189 00:10:35,170 --> 00:10:38,500 Speaker 6: little bit more than we have in previous cycles. John 190 00:10:38,510 --> 00:10:41,180 Speaker 6: Sununu in New Hampshire, for example, has said, run an 191 00:10:41,420 --> 00:10:44,059 Speaker 6: ad where he basically says he's not Donald Trump and 192 00:10:44,140 --> 00:10:47,700 Speaker 6: the Democrats are mischaracterizing him that way. In a previous cycle, 193 00:10:47,740 --> 00:10:50,620 Speaker 6: Donald Trump would get very angry if a Republican was 194 00:10:50,640 --> 00:10:51,840 Speaker 6: trying to distance himself. 195 00:10:52,440 --> 00:10:53,220 Speaker 4: That's one option. 196 00:10:53,260 --> 00:10:56,200 Speaker 6: The other option is to really drive up voter turnout 197 00:10:56,400 --> 00:10:59,260 Speaker 6: and to make sure that those low propensity voters who 198 00:10:59,300 --> 00:11:01,400 Speaker 6: turned out when Trump was at the top of the ticket, 199 00:11:01,679 --> 00:11:04,010 Speaker 6: who are part of the MAGA base, turn out even 200 00:11:04,030 --> 00:11:06,190 Speaker 6: when Trump isn't on the ticket. So I think there's 201 00:11:06,210 --> 00:11:09,780 Speaker 6: this two-pronged strategy, but Basically, both are Hail Mary passes 202 00:11:09,800 --> 00:11:11,100 Speaker 6: for the Republicans at this point. 203 00:11:11,360 --> 00:11:14,460 Speaker 3: Jennifer Lawless, you are definitive in the study of women 204 00:11:15,230 --> 00:11:19,250 Speaker 3: in the American election history as well. I've heard almost 205 00:11:19,350 --> 00:11:22,770 Speaker 3: zero about women in this midterm election. 206 00:11:23,130 --> 00:11:23,770 Speaker 4: Is that odd? 207 00:11:25,559 --> 00:11:28,520 Speaker 6: Typically, we talk about every election as the year of 208 00:11:28,559 --> 00:11:29,079 Speaker 6: the woman. 209 00:11:29,380 --> 00:11:30,560 Speaker 4: Can women make gains? 210 00:11:30,720 --> 00:11:33,689 Speaker 6: Are women going to get a record number of seats 211 00:11:33,750 --> 00:11:37,050 Speaker 6: in the House or Senate. This time around, identity politics 212 00:11:37,130 --> 00:11:39,709 Speaker 6: just doesn't seem to be playing much of a role. 213 00:11:39,950 --> 00:11:45,410 Speaker 6: Women won several contested primaries. You know, there are women 214 00:11:45,500 --> 00:11:48,620 Speaker 6: who are obviously spending tons of money in this race. 215 00:11:48,679 --> 00:11:51,699 Speaker 6: Alexandria Ocasio-Cortez, for example, just put a million dollars of 216 00:11:51,740 --> 00:11:54,939 Speaker 6: her own money into the Michigan Senate race. But this 217 00:11:54,960 --> 00:11:57,580 Speaker 6: time around, it's really about partisanship all the way. 218 00:11:58,500 --> 00:12:01,490 Speaker 4: Professor, is it too early to think about 2028? Because 219 00:12:02,309 --> 00:12:04,250 Speaker 4: I don't read anything, hear anything about that. 220 00:12:04,270 --> 00:12:06,580 Speaker 3: I listen to you. Exactly. Get in front of the script. 221 00:12:06,600 --> 00:12:08,699 Speaker 4: I mean, I don't even know who we should be 222 00:12:08,780 --> 00:12:10,020 Speaker 4: thinking about at this point in time. 223 00:12:11,059 --> 00:12:14,579 Speaker 6: My bet is that we'll start talking about 2028 about 224 00:12:14,580 --> 00:12:19,020 Speaker 6: 48 hours after the election. The problem, of course, for 225 00:12:19,140 --> 00:12:21,870 Speaker 6: candidates that are interested in running for president is that 226 00:12:21,920 --> 00:12:24,030 Speaker 6: many of them are up for reelection this time around. 227 00:12:24,070 --> 00:12:27,189 Speaker 6: So John Ossoff, for example, who would be a very 228 00:12:27,270 --> 00:12:31,329 Speaker 6: credible candidate and a strong contender for 2028, first has 229 00:12:31,370 --> 00:12:34,150 Speaker 6: to win back, win his Senate seat. And so it 230 00:12:34,230 --> 00:12:36,590 Speaker 6: would be gauche to discuss running for 2028 until he 231 00:12:36,630 --> 00:12:37,250 Speaker 6: gets that 2026 victory. 232 00:12:39,360 --> 00:12:43,160 Speaker 3: The view from Charlottesville, can the Democrats run to the center? 233 00:12:44,020 --> 00:12:47,790 Speaker 3: To Paul's good question on 28, is this a seismic 234 00:12:47,870 --> 00:12:51,630 Speaker 3: change where both parties leave the culture wars and the 235 00:12:51,690 --> 00:12:54,910 Speaker 3: rest of it, the polarity, and they run to the middle? 236 00:12:56,170 --> 00:12:58,800 Speaker 6: I don't think so. I think we've reached an era 237 00:12:58,860 --> 00:13:02,900 Speaker 6: of American politics where it's all about turnout and And 238 00:13:03,059 --> 00:13:07,190 Speaker 6: we are polarized, and we are generally split in terms 239 00:13:07,270 --> 00:13:09,690 Speaker 6: of who affiliates with the Democrats and the Republicans. And 240 00:13:09,730 --> 00:13:13,030 Speaker 6: keep in mind, by the way, that the electorate is 241 00:13:13,110 --> 00:13:15,850 Speaker 6: far more moderate than the candidates they elect. They're just 242 00:13:15,910 --> 00:13:19,150 Speaker 6: given these choices on the left and the right. But 243 00:13:19,710 --> 00:13:22,829 Speaker 6: because we're so polarized, we've gotten to the point where 244 00:13:22,890 --> 00:13:24,449 Speaker 6: whether there's a D or an R in front of 245 00:13:24,510 --> 00:13:27,150 Speaker 6: a candidate's name tells us almost everything we need to know. 246 00:13:27,290 --> 00:13:29,970 Speaker 6: So the goals for these candidates now is to drive 247 00:13:30,030 --> 00:13:32,970 Speaker 6: up turnout among their supporters and to win over independents. 248 00:13:33,030 --> 00:13:34,750 Speaker 4: It's not about running to the middle anymore. 249 00:13:34,990 --> 00:13:37,510 Speaker 3: Jennifer Lawless, thank you so much for wisdom this morning 250 00:13:37,570 --> 00:13:41,239 Speaker 3: at the University of Virginia. Professor Lawless, just definitive on 251 00:13:41,260 --> 00:13:46,420 Speaker 3: the study of our election system. Stay with us. 252 00:13:46,700 --> 00:13:49,980 Speaker 7: More from Bloomberg Surveillance coming up after this. 253 00:13:57,220 --> 00:14:00,829 Speaker 1: You're listening to the Bloomberg Surveillance Podcast. Catch us live 254 00:14:00,910 --> 00:14:04,090 Speaker 1: weekday afternoons from 7 to 10 a.m. Eastern. Listen on 255 00:14:04,190 --> 00:14:07,449 Speaker 1: Apple CarPlay and Android Auto with the Bloomberg Business app 256 00:14:07,650 --> 00:14:09,349 Speaker 1: or watch us live on YouTube. 257 00:14:09,910 --> 00:14:15,350 Speaker 3: Many talk, very few do. So you're a freshman and 258 00:14:15,410 --> 00:14:18,150 Speaker 3: mostly you're worried about which happy hour are we going to? 259 00:14:18,170 --> 00:14:19,550 Speaker 4: Exactly. That's how we rolled. 260 00:14:20,230 --> 00:14:22,570 Speaker 3: It was a little different for Sean Henry at Georgia 261 00:14:22,650 --> 00:14:25,470 Speaker 3: Tech as a freshman. He said, I just think I 262 00:14:25,510 --> 00:14:29,870 Speaker 3: want to be entrepreneurial and figure out prime Amazon prime 263 00:14:29,930 --> 00:14:33,840 Speaker 3: for people that are priming. And it is stored studio. 264 00:14:33,860 --> 00:14:38,100 Speaker 3: We're thrilled he could join us today after leaving Georgia Tech. 265 00:14:38,650 --> 00:14:40,930 Speaker 3: After they supported him and said, if it screws up, 266 00:14:40,950 --> 00:14:44,730 Speaker 3: you can come back. After taking beverages of their choices 267 00:14:44,770 --> 00:14:47,570 Speaker 3: with Kleiner Perkins and Founder Fund. 268 00:14:47,630 --> 00:14:47,950 Speaker 4: Not bad. 269 00:14:47,990 --> 00:14:51,150 Speaker 3: Not bad. Sean Henry's here after commencement. What was it 270 00:14:51,190 --> 00:14:54,370 Speaker 3: like your commencement speak when you came back at Georgia Tech? 271 00:14:54,870 --> 00:14:56,430 Speaker 8: You know, if I can tease them on air, I 272 00:14:56,490 --> 00:14:58,990 Speaker 8: really was hopeful for that, that honorary degree when I 273 00:14:59,130 --> 00:15:01,550 Speaker 8: gave the commencement. But no, more than anything, it's kind 274 00:15:01,570 --> 00:15:04,400 Speaker 8: of like the young entrepreneurial journey work. I stand up 275 00:15:04,420 --> 00:15:06,420 Speaker 8: there and say, I don't have all the answers, but 276 00:15:06,460 --> 00:15:07,890 Speaker 8: this is the best I have. 277 00:15:08,090 --> 00:15:11,530 Speaker 3: All the parents have a total cow. OMG, you're not 278 00:15:11,590 --> 00:15:15,720 Speaker 3: completing school. Was there pushback when you exited Georgia Tech 279 00:15:15,780 --> 00:15:18,100 Speaker 3: and said, I'm going to beat Amazon at Prime? 280 00:15:18,640 --> 00:15:21,070 Speaker 8: Yeah, I think with the scale and ambition of the mission, 281 00:15:21,130 --> 00:15:23,550 Speaker 8: it was quite daunting to anyone we said it to 282 00:15:23,610 --> 00:15:27,030 Speaker 8: back then. But that was really what enabled me to 283 00:15:27,050 --> 00:15:29,350 Speaker 8: go and found the business. I ended up at Teal Fellow, 284 00:15:29,410 --> 00:15:31,930 Speaker 8: and Georgia Tech was the most open door in the world, 285 00:15:32,290 --> 00:15:35,640 Speaker 8: telling my parents directly, if it doesn't work, we'll have 286 00:15:35,660 --> 00:15:36,220 Speaker 8: them back. 287 00:15:36,240 --> 00:15:40,300 Speaker 3: Very cool. What's the next step now? I'm not big 288 00:15:40,380 --> 00:15:43,500 Speaker 3: on what we're working at this valuation, but is the 289 00:15:43,560 --> 00:15:47,450 Speaker 3: next step Jassy and Amazon Bayou? Is the next step 290 00:15:47,910 --> 00:15:51,090 Speaker 3: Somebody else buys you. What's the next step for Stuart? 291 00:15:51,450 --> 00:15:55,370 Speaker 8: Yeah, we completed a $ 250 million Series F at $ 3 292 00:15:55,010 --> 00:15:57,490 Speaker 8: billion earlier this year. And to your point, that's still 293 00:15:57,670 --> 00:16:02,460 Speaker 8: roughly one one-thousandth of the Amazon market cap, which is 36% 294 00:16:02,460 --> 00:16:03,000 Speaker 8: of U.S. 295 00:16:03,080 --> 00:16:03,640 Speaker 4: E-commerce. 296 00:16:03,760 --> 00:16:06,680 Speaker 8: And we really exist to serve the rest of that market, 297 00:16:06,720 --> 00:16:10,320 Speaker 8: enable all independent merchants with prime level speeds and costs. 298 00:16:10,740 --> 00:16:12,720 Speaker 4: But we're not there yet, to be honest. We are 299 00:16:12,800 --> 00:16:14,640 Speaker 4: better than the industry. The average U.S. 300 00:16:14,700 --> 00:16:18,470 Speaker 8: E-commerce business, 86% of them don't offer free delivery. The 301 00:16:18,550 --> 00:16:21,330 Speaker 8: average delivery speed is between five and six days. It 302 00:16:21,510 --> 00:16:25,780 Speaker 8: costs that business 12 plus dollars. Meanwhile, Amazon's delivering tomorrow 303 00:16:25,860 --> 00:16:28,480 Speaker 8: for four to five dollars with free returns and free 304 00:16:28,540 --> 00:16:31,720 Speaker 8: upfront promise and more. So we're closing that gap where 305 00:16:31,760 --> 00:16:36,040 Speaker 8: today stored offers two day delivery across the US, across Canada, 306 00:16:36,100 --> 00:16:39,450 Speaker 8: across Europe. But we're still in the pursuit of scale. 307 00:16:39,550 --> 00:16:42,610 Speaker 8: Scale is what enables us to place units close to consumers, 308 00:16:42,970 --> 00:16:46,150 Speaker 8: get that last mile density, get that speed and cost 309 00:16:46,210 --> 00:16:47,210 Speaker 8: and trust of Prime. 310 00:16:47,270 --> 00:16:50,660 Speaker 3: Sounds like a CEO. He's got a dumb cold, Paul. 311 00:16:50,990 --> 00:16:53,950 Speaker 4: So you guys made an acquisition today that helps you 312 00:16:53,970 --> 00:16:56,690 Speaker 4: get there. Talk to us about what Steward did today. 313 00:16:56,870 --> 00:16:57,290 Speaker 3: Exactly. 314 00:16:57,310 --> 00:16:59,150 Speaker 8: And that's part of that added scale I was hitting at. 315 00:16:59,190 --> 00:17:02,310 Speaker 8: So we just closed the acquisition of AMS Fulfillment, a 316 00:17:02,310 --> 00:17:06,820 Speaker 8: 20 plus year operator, over 10 million annual e-commerce orders 317 00:17:06,880 --> 00:17:10,920 Speaker 8: and packages, over 600 employees across over a million square 318 00:17:10,980 --> 00:17:13,980 Speaker 8: feet of fulfillment infrastructure. And as I said, what we've 319 00:17:14,040 --> 00:17:17,100 Speaker 8: really found is that more scale spins our flywheel of 320 00:17:17,240 --> 00:17:20,070 Speaker 8: speed and cost. Earlier this year as part of the 321 00:17:20,109 --> 00:17:22,530 Speaker 8: Series F, we also announced Stored Labs, which is a 322 00:17:22,630 --> 00:17:26,639 Speaker 8: very dedicated investment behind robotic deployment. We're partnering with some 323 00:17:26,660 --> 00:17:29,780 Speaker 8: of the most advanced agentic robotic companies out there, as 324 00:17:29,800 --> 00:17:31,580 Speaker 8: well as a data foundry. And so now as we 325 00:17:31,680 --> 00:17:34,359 Speaker 8: add more scale, we're not only adding 20 years of 326 00:17:34,600 --> 00:17:38,290 Speaker 8: operational learnings, we're adding all that volume onto our intelligence 327 00:17:38,350 --> 00:17:41,050 Speaker 8: and scale flywheel in the business. And so really what 328 00:17:41,090 --> 00:17:45,450 Speaker 8: happens is packages get faster and cheaper for every Stored customer, 329 00:17:45,470 --> 00:17:48,470 Speaker 8: not just the new customers. And to the AMS team, 330 00:17:48,530 --> 00:17:52,220 Speaker 8: they get the extended network of stored. All their customers 331 00:17:52,240 --> 00:17:55,800 Speaker 8: get our technology, and they also benefit from cheaper and faster. 332 00:17:55,820 --> 00:17:57,840 Speaker 3: How do you move the boxes? How do you deal 333 00:17:57,880 --> 00:18:03,920 Speaker 3: with the huge challenge that FedEx and others had of airlines, railroads, trucks? 334 00:18:04,220 --> 00:18:06,310 Speaker 3: How do you deal with old technology? 335 00:18:06,600 --> 00:18:09,850 Speaker 8: Yeah, we spent a decade building all of the technology in-house, 336 00:18:09,869 --> 00:18:10,870 Speaker 8: and that was the hardest part. 337 00:18:10,910 --> 00:18:12,270 Speaker 4: You nailed it from day one. 338 00:18:12,710 --> 00:18:15,170 Speaker 8: We looked out, and today it's kind of popular right 339 00:18:15,210 --> 00:18:18,690 Speaker 8: now in 2026 to say, I'm a physical intelligence and 340 00:18:18,790 --> 00:18:22,540 Speaker 8: physical AI business. But when we started, physical was... kind 341 00:18:22,560 --> 00:18:24,780 Speaker 8: of a bad word in venture capital. It was the 342 00:18:24,960 --> 00:18:27,700 Speaker 8: WeWork days and all these other problems that had occurred. 343 00:18:27,720 --> 00:18:30,020 Speaker 8: And we said, no, you have to build all the 344 00:18:30,060 --> 00:18:32,980 Speaker 8: software and all the physical network in one. That's the 345 00:18:33,180 --> 00:18:36,440 Speaker 8: only way you get prime level delivery. So exactly to 346 00:18:36,480 --> 00:18:39,280 Speaker 8: your point, we actually spent the first six, seven years 347 00:18:39,320 --> 00:18:42,460 Speaker 8: of the decade history of Stored really just building that 348 00:18:42,520 --> 00:18:46,700 Speaker 8: top to bottom, all the WMS, warehouse management, transportation management, 349 00:18:46,760 --> 00:18:51,470 Speaker 8: labor management, billing management, inventory and demand planning, order orchestration, 350 00:18:51,590 --> 00:18:55,110 Speaker 8: front-end checkout technology. And really at year seven or eight, 351 00:18:55,250 --> 00:18:57,890 Speaker 8: once we had built this vertically integrated stack, we reached 352 00:18:57,930 --> 00:19:00,649 Speaker 8: that point of, ah, it's working. Now we can really 353 00:19:00,830 --> 00:19:03,990 Speaker 8: add scale to this system and keep closing that gap farther. 354 00:19:04,430 --> 00:19:06,670 Speaker 8: And that's really what we've been doing. The last four years, 355 00:19:06,750 --> 00:19:08,229 Speaker 8: we've now grown over 10X. 356 00:19:08,790 --> 00:19:12,030 Speaker 4: What's the marketplace? I think of delivery. I just think 357 00:19:12,050 --> 00:19:17,450 Speaker 4: of Amazon. What percentage of that e-commerce market does Amazon 358 00:19:17,510 --> 00:19:20,899 Speaker 4: control and And then the rest of you guys, what 359 00:19:20,920 --> 00:19:21,530 Speaker 4: do you guys make up? 360 00:19:21,740 --> 00:19:25,609 Speaker 8: Yeah, so Amazon spends about $ 212 billion a year in 361 00:19:25,609 --> 00:19:29,970 Speaker 8: 2025 on delivery just for themselves. And they're 36% of 362 00:19:30,010 --> 00:19:33,490 Speaker 8: the nearly $ 2 trillion US e-commerce market. And so if 363 00:19:33,510 --> 00:19:35,910 Speaker 8: you trend that out to the other two thirds, they're 364 00:19:35,950 --> 00:19:40,680 Speaker 8: largely left behind. Think about your average online experience. It's slow, 365 00:19:40,740 --> 00:19:43,880 Speaker 8: it's bad tracking, it's unclear returns. And what we really 366 00:19:43,960 --> 00:19:47,649 Speaker 8: preach to our customers is today, that delivery infrastructure is 367 00:19:47,770 --> 00:19:51,240 Speaker 8: actually a marketing cost. It's actually a customer acquisition and 368 00:19:51,300 --> 00:19:54,720 Speaker 8: loyalty cost. And that's what Prime has realized. And others 369 00:19:54,740 --> 00:19:57,800 Speaker 8: just don't have the infrastructure or scale to achieve that 370 00:19:57,859 --> 00:19:58,260 Speaker 8: on their own. 371 00:19:58,480 --> 00:20:01,570 Speaker 3: Interesting. We don't care. OK, I'm looking. You beat Duke 372 00:20:01,609 --> 00:20:04,570 Speaker 3: by three points. Duke, Georgia Tech. I mean, it was ugly. 373 00:20:05,090 --> 00:20:06,970 Speaker 3: Then Virginia Tech lost. What are you going to do 374 00:20:06,990 --> 00:20:09,790 Speaker 3: with Boston College this weekend? You going down there to 375 00:20:09,830 --> 00:20:10,330 Speaker 3: see the game? 376 00:20:10,670 --> 00:20:14,490 Speaker 8: I won't be there myself, but as a proud rambling 377 00:20:14,550 --> 00:20:16,790 Speaker 8: wreck and as a board member, as we were talking about, 378 00:20:16,830 --> 00:20:19,150 Speaker 8: I do root for every game, and I hope we 379 00:20:19,330 --> 00:20:21,150 Speaker 8: give it to Boston College this weekend ahead. 380 00:20:22,109 --> 00:20:26,050 Speaker 3: That was for you, 92.9 FM Boston. Good morning. Sean Henry, 381 00:20:26,090 --> 00:20:29,930 Speaker 3: thank you so much. Chief Executive Officer, Stored Studio from Georgia. 382 00:20:29,970 --> 00:20:34,880 Speaker 3: It's just a hugely wonderful place. a wonderful story about 383 00:20:34,920 --> 00:20:38,340 Speaker 3: a freshman in school saying, I have an idea. Peter Thiel, 384 00:20:38,380 --> 00:20:41,940 Speaker 3: thank you so much. He is a Thiel scholar. Sean Henry, 385 00:20:42,080 --> 00:20:45,359 Speaker 3: I should say. Stay with us. 386 00:20:45,660 --> 00:20:48,900 Speaker 7: More from Bloomberg Surveillance coming up after this. 387 00:20:56,170 --> 00:20:59,750 Speaker 1: You're listening to the Bloomberg Surveillance Podcast. Catch us live 388 00:20:59,830 --> 00:21:03,010 Speaker 1: weekday afternoons from 7 to 10 a.m. Eastern. Listen on 389 00:21:03,130 --> 00:21:06,380 Speaker 1: Apple CarPlay and Android Auto with the Bloomberg Business app. 390 00:21:06,600 --> 00:21:08,280 Speaker 1: Or watch us live on YouTube. 391 00:21:08,300 --> 00:21:12,700 Speaker 3: I mean, Damien's here, so we can talk about, you know, 392 00:21:12,820 --> 00:21:18,280 Speaker 3: Peru's peso curve. But Michael Dean's here. Introduce him. This 393 00:21:18,359 --> 00:21:19,360 Speaker 3: is just glorious. 394 00:21:19,380 --> 00:21:23,860 Speaker 4: Michael Dean's the rock star of auto analysts over there. Yeah, 395 00:21:23,880 --> 00:21:25,780 Speaker 4: in London. For some reason, he's here in New York. 396 00:21:25,800 --> 00:21:26,940 Speaker 4: I don't know what. 397 00:21:27,020 --> 00:21:28,560 Speaker 2: I had to steal him and bring him down, Paul. 398 00:21:28,600 --> 00:21:31,090 Speaker 2: I mean, he is the coolest guy in our city, 399 00:21:31,119 --> 00:21:33,570 Speaker 2: James Long. I mean, dude, he goes to the paddock 400 00:21:33,650 --> 00:21:35,990 Speaker 2: and all. He covers Ferrari and Porsche, covers all the 401 00:21:36,050 --> 00:21:37,869 Speaker 2: great auto companies out of Europe. 402 00:21:38,230 --> 00:21:42,690 Speaker 4: So I want to ask Michael Dean, if I'm Volkswagen, 403 00:21:43,130 --> 00:21:46,190 Speaker 4: what am I doing here? I mean, am I into EVs? 404 00:21:46,310 --> 00:21:48,740 Speaker 4: Am I not into EVs? Do I cut my investment? 405 00:21:49,080 --> 00:21:52,020 Speaker 4: I'm cutting workforce. I don't know what your European auto 406 00:21:52,060 --> 00:21:55,640 Speaker 4: company's what the focus is here because the US seems 407 00:21:55,660 --> 00:21:57,480 Speaker 4: like they backed off from the EVs. We're just going 408 00:21:57,500 --> 00:21:59,639 Speaker 4: to build the big trucks. That's what sells here in 409 00:21:59,660 --> 00:22:01,540 Speaker 4: the US. What are the European auto companies doing? 410 00:22:01,940 --> 00:22:04,780 Speaker 9: You definitely have to be in EVs. So they're certainly 411 00:22:05,380 --> 00:22:08,740 Speaker 9: going down that road, but they have to restructure. And 412 00:22:09,040 --> 00:22:12,120 Speaker 9: they just announced the new strategy 2030. And they're going 413 00:22:12,540 --> 00:22:15,220 Speaker 9: to have to lose another 50,000 jobs. They're going to 414 00:22:15,240 --> 00:22:17,700 Speaker 9: have to cut capacity. They're going to have to reduce 415 00:22:17,740 --> 00:22:20,140 Speaker 9: capacity in China as well. So things are really tough 416 00:22:20,160 --> 00:22:20,830 Speaker 9: for the Europeans. 417 00:22:21,050 --> 00:22:22,870 Speaker 4: Well, I think the issue is, and this goes, I'm 418 00:22:22,890 --> 00:22:24,210 Speaker 4: not going to blame Damien on this one. 419 00:22:24,490 --> 00:22:25,210 Speaker 3: It's the Chinese. 420 00:22:25,490 --> 00:22:28,270 Speaker 4: I was in Paris for 12 days in the past 421 00:22:28,290 --> 00:22:32,350 Speaker 4: couple weeks. I probably was in four or five BYD taxis. 422 00:22:32,450 --> 00:22:34,330 Speaker 4: They're beautiful, aren't they? They're phenomenal. 423 00:22:34,390 --> 00:22:34,730 Speaker 3: I know. 424 00:22:34,910 --> 00:22:35,870 Speaker 4: I was in Paris as well. 425 00:22:35,910 --> 00:22:36,490 Speaker 10: I didn't see you. 426 00:22:36,609 --> 00:22:38,929 Speaker 4: Oh, well, we don't go to the same spots, my friend. 427 00:22:38,990 --> 00:22:41,669 Speaker 9: So they also show 50% of the show now is 428 00:22:41,710 --> 00:22:42,490 Speaker 9: Chinese brands. 429 00:22:42,570 --> 00:22:43,010 Speaker 4: Is that right? 430 00:22:43,270 --> 00:22:46,200 Speaker 3: Michael, where Damien lives, I mean, Damien pops out of 431 00:22:46,220 --> 00:22:49,380 Speaker 3: the garage in the Maserati Gran Turismo, just like Jon Hamm. 432 00:22:49,990 --> 00:22:53,290 Speaker 3: in the Apple show. So that's like super expensive car. 433 00:22:53,750 --> 00:22:58,510 Speaker 3: Help us with Europe's luxury cars that so many Americans drive. 434 00:22:58,550 --> 00:23:02,250 Speaker 3: Like they're in a BMW X5 and they aspire to 435 00:23:02,310 --> 00:23:05,810 Speaker 3: an X7 or someday I'm going to own a Porsche. 436 00:23:05,970 --> 00:23:07,950 Speaker 3: It seems like it's just been lost. 437 00:23:08,930 --> 00:23:11,730 Speaker 9: Yeah, I wouldn't call BMW luxury, but certainly for the 438 00:23:11,770 --> 00:23:12,850 Speaker 9: pure luxury. 439 00:23:13,350 --> 00:23:14,670 Speaker 4: That's about as luxury as I go. 440 00:23:14,930 --> 00:23:18,750 Speaker 9: So Ferrari is selling less cars, making more money. The 441 00:23:18,770 --> 00:23:20,409 Speaker 9: same with Lamborghini, same with Bentley. 442 00:23:20,450 --> 00:23:25,040 Speaker 3: But what the price point, Damien's always talking Maserati and Lamborghini, 443 00:23:25,060 --> 00:23:27,980 Speaker 3: because that's the way he rolls. The price point for 444 00:23:28,040 --> 00:23:32,380 Speaker 3: mere mortals down there, I mean, it's really threatened. Right? 445 00:23:32,619 --> 00:23:33,220 Speaker 4: Yeah, it's tough. 446 00:23:33,300 --> 00:23:36,480 Speaker 9: Prices are going up. So that's not going to change. 447 00:23:37,140 --> 00:23:39,920 Speaker 2: Well, I think it's those price insensitive buyers out there 448 00:23:39,980 --> 00:23:42,590 Speaker 2: that are really driving up the price for a luxury vehicle. No, 449 00:23:42,630 --> 00:23:45,510 Speaker 2: but seriously, I mean, isn't Porsche going, they're trying to 450 00:23:45,730 --> 00:23:47,410 Speaker 2: eat a little bit of the cake that Ferrari built, right? 451 00:23:47,450 --> 00:23:49,030 Speaker 4: They're going to that super luxury. 452 00:23:49,109 --> 00:23:51,369 Speaker 3: I saw Matt Miller had the, yeah. Oh, my word. 453 00:23:51,470 --> 00:23:54,310 Speaker 9: So the Mission S is their concept, which is going 454 00:23:54,330 --> 00:23:55,910 Speaker 9: to be launched in a couple of years. It's above 455 00:23:55,930 --> 00:23:59,290 Speaker 9: the 911, probably priced about half a million dollars. What 456 00:23:59,310 --> 00:24:03,600 Speaker 9: a fantastic car. But Tesla stole its thunder. So they 457 00:24:03,640 --> 00:24:07,020 Speaker 9: launched the Tesla Roadster on exactly the same day. And 458 00:24:07,080 --> 00:24:09,580 Speaker 9: Porsche can compete with a flying car. 459 00:24:09,780 --> 00:24:13,500 Speaker 4: All right. Let's talk about Tesla. What is this Tesla 460 00:24:13,520 --> 00:24:14,900 Speaker 4: story in the marketplace these days? 461 00:24:15,910 --> 00:24:18,760 Speaker 10: Well, it's flying cars, for one. 462 00:24:19,359 --> 00:24:21,200 Speaker 4: Well, you're talking to somebody who has a pilot's license 463 00:24:21,240 --> 00:24:21,880 Speaker 4: over here, right? 464 00:24:21,900 --> 00:24:24,000 Speaker 2: So Mike Dean, you know, David Dwyer, we had a 465 00:24:24,020 --> 00:24:26,139 Speaker 2: lot of pilots up there in BI, very smart people. 466 00:24:26,480 --> 00:24:28,100 Speaker 10: Yeah, so I can certainly fly one. 467 00:24:28,340 --> 00:24:32,340 Speaker 9: But, you know, it's Optimus robots, it's FSD, full-service driving. 468 00:24:32,480 --> 00:24:33,960 Speaker 4: Are people still buying that argument? 469 00:24:34,560 --> 00:24:35,159 Speaker 10: I think so. 470 00:24:35,180 --> 00:24:36,050 Speaker 3: Look at the valuation. 471 00:24:36,070 --> 00:24:36,979 Speaker 4: They have to. You're right. 472 00:24:37,200 --> 00:24:39,990 Speaker 3: Michael Dean, Damien Sassar was two of our brightest at 473 00:24:40,030 --> 00:24:43,070 Speaker 3: Bloomberg Intelligence. So, Damien, take this. I'm going to take 474 00:24:43,190 --> 00:24:48,750 Speaker 3: a prosperous country to Indonesia. Yeah. They're buying like 114% 475 00:24:48,750 --> 00:24:50,350 Speaker 3: Chinese cars, right? 476 00:24:52,030 --> 00:24:54,340 Speaker 2: I don't know if Indonesia is the real end market, 477 00:24:54,359 --> 00:24:55,679 Speaker 2: but I think what you have to look at when 478 00:24:55,720 --> 00:24:57,780 Speaker 2: it comes to autos and it comes to Indonesia is 479 00:24:57,800 --> 00:25:00,080 Speaker 2: they are flooding the markets with aluminum right now. And 480 00:25:00,119 --> 00:25:01,720 Speaker 2: I think there's a wedge going on in the base 481 00:25:01,760 --> 00:25:02,440 Speaker 2: metals complex. 482 00:25:02,480 --> 00:25:03,250 Speaker 4: Here we go. Here we go. 483 00:25:03,660 --> 00:25:06,359 Speaker 2: Where you're basically looking at copper supply really falling off 484 00:25:06,400 --> 00:25:07,660 Speaker 2: a cliff. And this is going to be a real 485 00:25:07,740 --> 00:25:10,480 Speaker 2: Q127 story, in my opinion. So, you know, there's just 486 00:25:10,550 --> 00:25:12,869 Speaker 2: not a lot of copper production. We have 14,000 plus 487 00:25:12,920 --> 00:25:15,290 Speaker 2: on LME copper. It's going higher, in my opinion. And 488 00:25:15,310 --> 00:25:17,310 Speaker 2: that's what I'm pricing it to my models. But then 489 00:25:17,350 --> 00:25:19,750 Speaker 2: if you look at aluminium, which, again, another huge input. 490 00:25:20,470 --> 00:25:22,510 Speaker 2: it's going in the other direction. It might break below 3,000. 491 00:25:22,490 --> 00:25:27,090 Speaker 2: The technicals don't look good. And so Indonesia is obviously 492 00:25:27,170 --> 00:25:29,230 Speaker 2: a huge smelter. They're a huge copper producer, a huge 493 00:25:29,290 --> 00:25:32,889 Speaker 2: aluminum producer. It's really their incremental supply that's driving down 494 00:25:32,910 --> 00:25:33,730 Speaker 2: the aluminum price. 495 00:25:33,970 --> 00:25:37,210 Speaker 3: Mike Dean, when I look at Europe and I look 496 00:25:37,430 --> 00:25:41,510 Speaker 3: at the political response to the auto realities you're truly 497 00:25:41,670 --> 00:25:44,830 Speaker 3: expert in, what do you preserve? I mean, Francine talking 498 00:25:44,850 --> 00:25:48,629 Speaker 3: to Merz yesterday of Germany. What do the politicians actually 499 00:25:48,760 --> 00:25:51,359 Speaker 3: do about these Chinese realities. 500 00:25:51,680 --> 00:25:54,010 Speaker 9: Well, that's a good point. But finally, the EU has 501 00:25:54,070 --> 00:25:58,250 Speaker 9: done something. So they're putting curbs on hybrids. They're extending 502 00:25:59,170 --> 00:26:02,290 Speaker 9: the emission legislation so you can have combustion cars beyond 2035. 503 00:26:02,290 --> 00:26:06,120 Speaker 9: So these are small things, but finally, they're doing something. 504 00:26:06,490 --> 00:26:09,480 Speaker 3: So do you have a winner in all this in Europe, 505 00:26:09,540 --> 00:26:12,600 Speaker 3: in the US? Does your team have somebody who seems 506 00:26:12,640 --> 00:26:17,470 Speaker 3: to be GM yesterday with a $ 20 gazillion share buyback? 507 00:26:17,530 --> 00:26:20,030 Speaker 3: I mean, is there somebody out there that survives? 508 00:26:20,170 --> 00:26:23,280 Speaker 9: Yeah, if you're not exposed to Europe and the Chinese invasion, 509 00:26:23,320 --> 00:26:26,060 Speaker 9: then sure. So GM for one. 510 00:26:27,480 --> 00:26:30,359 Speaker 2: Mike, let me ask you this. What are gas prices 511 00:26:30,400 --> 00:26:32,700 Speaker 2: like there? We hear about diesel. We hear about TTF. 512 00:26:32,760 --> 00:26:35,620 Speaker 2: I mean, TTF, not gas, is up, what, 200% now? 513 00:26:36,000 --> 00:26:36,680 Speaker 4: I mean, year-to-date. 514 00:26:36,760 --> 00:26:40,870 Speaker 2: Talk to us a little bit about, from your perspective, 515 00:26:40,880 --> 00:26:43,370 Speaker 2: are you seeing gas prices being prohibitive? Are people not 516 00:26:44,050 --> 00:26:46,590 Speaker 2: willing to traverse that much? I mean, what are you thinking? 517 00:26:46,850 --> 00:26:48,670 Speaker 9: Yeah, so funny enough, I was driving into the office 518 00:26:48,710 --> 00:26:54,510 Speaker 9: this morning. So, diesel, $ 6. 00. that's still pretty cheap compared 519 00:26:54,710 --> 00:26:57,540 Speaker 9: to Europe. So it's starting to have an impact and 520 00:26:57,580 --> 00:27:02,190 Speaker 9: you're seeing higher take-up of battery electric vehicles. But that's 521 00:27:02,250 --> 00:27:05,020 Speaker 9: morally because the Chinese imports are so cheap rather than 522 00:27:05,040 --> 00:27:06,430 Speaker 9: it's the actual fuel price. 523 00:27:06,890 --> 00:27:09,479 Speaker 3: Michael, in England, like if Arsenal wins, do they get 524 00:27:09,520 --> 00:27:12,859 Speaker 3: a ring? Do they get some big, ginormous ring? 525 00:27:13,160 --> 00:27:16,160 Speaker 9: It's funny you mention Arsenal because Brighton, my team, beat 526 00:27:16,200 --> 00:27:18,280 Speaker 9: them 3-0 a couple of years ago. 527 00:27:18,500 --> 00:27:22,200 Speaker 3: A couple of years ago. We turned to Dan Sassauer, 528 00:27:22,740 --> 00:27:26,179 Speaker 3: surveillance jeweler, this just out from front office sports. Let 529 00:27:26,200 --> 00:27:29,420 Speaker 3: me hear it. Knick fans can buy one of 200 530 00:27:29,420 --> 00:27:32,730 Speaker 3: replica championship rings for $ 19, 905. 531 00:27:32,290 --> 00:27:32,740 Speaker 4: Is that all? 532 00:27:32,750 --> 00:27:37,790 Speaker 3: Is that all? I don't know if this is legitimate. 533 00:27:37,950 --> 00:27:38,970 Speaker 3: I mean, it looks. 534 00:27:39,470 --> 00:27:40,330 Speaker 10: It's got to be a replica. 535 00:27:40,670 --> 00:27:44,669 Speaker 3: It's front office sports. I need to be careful with this, folks. 536 00:27:45,080 --> 00:27:46,379 Speaker 3: But there's your pricing, Paul. 537 00:27:46,430 --> 00:27:47,840 Speaker 4: How much do you think those rings cost? 538 00:27:47,880 --> 00:27:49,660 Speaker 2: Did we get a number on what they actually cost? 539 00:27:49,760 --> 00:27:52,500 Speaker 4: No, I haven't seen that. They were massive. 540 00:27:52,619 --> 00:27:53,240 Speaker 3: Massive. Yeah. 541 00:27:53,320 --> 00:27:55,840 Speaker 4: But those things, Tom, I think that price point will 542 00:27:55,880 --> 00:27:57,040 Speaker 4: sell out in a day. 543 00:27:57,240 --> 00:27:59,639 Speaker 3: In a day. I wonder if this tweet, I mean, 544 00:27:59,660 --> 00:28:02,830 Speaker 3: there's so much. spam out there. I'm hesitant to say it, 545 00:28:02,890 --> 00:28:05,450 Speaker 3: but it fits with Damien. I mean, Damien would be first. 546 00:28:05,490 --> 00:28:07,250 Speaker 2: Were you guys crying when the banner went up in 547 00:28:07,290 --> 00:28:08,510 Speaker 2: Madison square garden last night? 548 00:28:08,570 --> 00:28:11,490 Speaker 4: I was right next to the one I remember 73. 549 00:28:11,490 --> 00:28:13,680 Speaker 4: I was nine years old. It was awesome. 550 00:28:14,040 --> 00:28:14,180 Speaker 5: Yep. 551 00:28:14,480 --> 00:28:16,320 Speaker 4: So I'm surprised you weren't there. Cause all the sleds 552 00:28:16,340 --> 00:28:16,620 Speaker 4: were there. 553 00:28:16,640 --> 00:28:17,840 Speaker 3: I'm surprised you weren't there. 554 00:28:17,880 --> 00:28:21,260 Speaker 4: Well, maybe I was. Nah, it was a beautiful night. 555 00:28:21,520 --> 00:28:25,780 Speaker 3: Side coming in here with some knowledge, unlike American sport leagues, 556 00:28:26,270 --> 00:28:31,630 Speaker 3: The English Premier League does not issue official championship rings. Instead, 557 00:28:31,690 --> 00:28:38,880 Speaker 3: winners receive a trophy and 40 silver medals distributed to players, coaches, 558 00:28:38,950 --> 00:28:42,600 Speaker 3: and staff who make five league appearances. 559 00:28:42,840 --> 00:28:43,760 Speaker 4: Okay, all right. 560 00:28:43,860 --> 00:28:45,260 Speaker 3: That's good stuff. Much more equitable. 561 00:28:45,500 --> 00:28:47,400 Speaker 9: But as I say, watch out for Brighton this season. 562 00:28:47,780 --> 00:28:51,550 Speaker 9: Third in the table. We're definitely a contender. And I 563 00:28:51,570 --> 00:28:52,460 Speaker 9: haven't said that before. 564 00:28:52,810 --> 00:28:55,890 Speaker 4: And Breitner, the mighty fighting, what are they? What's their mascot? 565 00:28:55,950 --> 00:28:56,890 Speaker 3: Seagulls. The seagulls. 566 00:28:57,090 --> 00:28:59,350 Speaker 4: The seagulls. You got that now? 567 00:28:59,370 --> 00:29:01,210 Speaker 3: And they're doing better than Tottenham, right? 568 00:29:01,470 --> 00:29:03,610 Speaker 10: Well, everybody's doing better than Tottenham. 569 00:29:03,710 --> 00:29:07,469 Speaker 3: I needed that. Mike Dean, go away. The tots are 570 00:29:07,550 --> 00:29:10,410 Speaker 3: ascended and they will do fine. This is a real treat, folks. 571 00:29:10,450 --> 00:29:12,650 Speaker 3: We never, I mean, he's too, he can't come on 572 00:29:12,670 --> 00:29:12,930 Speaker 3: with us. 573 00:29:13,010 --> 00:29:14,510 Speaker 4: No, no, he's too busy. 574 00:29:14,810 --> 00:29:16,630 Speaker 3: He's too busy. I mean, he doesn't speak to us. 575 00:29:16,670 --> 00:29:17,330 Speaker 10: No, his people. 576 00:29:17,430 --> 00:29:22,130 Speaker 3: Davian Sassauer, Michael Dean of Bloomberg Intelligence. truly read and 577 00:29:22,190 --> 00:29:25,460 Speaker 3: listened to across all of global wall. 578 00:29:25,260 --> 00:29:29,860 Speaker 1: Street this is the bloomberg surveillance podcast available on apple 579 00:29:30,020 --> 00:29:34,220 Speaker 1: spotify and anywhere else you get your podcasts listen live 580 00:29:34,330 --> 00:29:38,630 Speaker 1: each weekday 7 to 10 a.m eastern on bloomberg.com the 581 00:29:38,750 --> 00:29:42,880 Speaker 1: iheart radio app and the Bloomberg Business app. You can 582 00:29:42,960 --> 00:29:46,680 Speaker 1: also watch us live every weekday on YouTube and always 583 00:29:46,760 --> 00:29:47,900 Speaker 1: on the Bloomberg Terminal.