1 00:00:02,960 --> 00:00:10,840 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the 2 00:00:10,880 --> 00:00:15,040 Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am 3 00:00:15,080 --> 00:00:18,040 Speaker 1: Eastern on Apple Card playing Android Auto with the Bloomberg 4 00:00:18,120 --> 00:00:21,440 Speaker 1: Business app. Listen on demand wherever you get your podcasts, 5 00:00:21,640 --> 00:00:24,520 Speaker 1: or watch us live on YouTube. 6 00:00:25,079 --> 00:00:27,280 Speaker 2: An m and a trade up you on the blocks 7 00:00:27,600 --> 00:00:28,280 Speaker 2: on the rocks here. 8 00:00:28,400 --> 00:00:33,000 Speaker 3: Anglo American rebuffed a sweetened takeover offer from BHP Group 9 00:00:33,000 --> 00:00:36,800 Speaker 3: divided at about forty three billion dollars US, leaving it 10 00:00:36,880 --> 00:00:39,080 Speaker 3: up to the Australia minor to make a better bid 11 00:00:39,479 --> 00:00:41,920 Speaker 3: or lose out of what could be the industry's biggest 12 00:00:41,960 --> 00:00:42,840 Speaker 3: deal in a decade. 13 00:00:42,880 --> 00:00:44,280 Speaker 2: Let's bring in an expert on this stuff. 14 00:00:44,280 --> 00:00:48,199 Speaker 3: Alon Ulsha, Bloomberg Intelligence senior Metals and mining analyst. 15 00:00:48,560 --> 00:00:50,760 Speaker 2: He joins us from London. I did not hire him. 16 00:00:50,800 --> 00:00:55,160 Speaker 3: He's a recent simple check him out here, Aylon, what 17 00:00:55,200 --> 00:00:55,760 Speaker 3: do you got for us? 18 00:00:55,760 --> 00:00:57,800 Speaker 2: What's going on in the global mining space? He says, 19 00:00:57,800 --> 00:00:58,600 Speaker 2: with these names. 20 00:01:00,960 --> 00:01:04,400 Speaker 4: Looking for Copper is the most coveted metal out there 21 00:01:04,400 --> 00:01:08,520 Speaker 4: in the mining industry. It's in short supply, it's expensive 22 00:01:08,560 --> 00:01:11,000 Speaker 4: to build new minds, it's risky to build new mines, 23 00:01:11,400 --> 00:01:15,040 Speaker 4: and demand is set to climb significantly because of applications 24 00:01:15,080 --> 00:01:18,880 Speaker 4: around the energy transition. So mineers are looking for opportunities 25 00:01:18,959 --> 00:01:22,120 Speaker 4: to grow exposure, and that's one of the main reasons 26 00:01:22,160 --> 00:01:25,520 Speaker 4: Beuchp's gone after Anglo American, which Anglo American has been 27 00:01:25,560 --> 00:01:27,560 Speaker 4: around for a while. They've got a bunch of assets 28 00:01:27,560 --> 00:01:29,880 Speaker 4: in they're not just copper. But it's always been a 29 00:01:29,920 --> 00:01:33,240 Speaker 4: difficult business to buy because there's some assets in there 30 00:01:33,280 --> 00:01:37,119 Speaker 4: that are problematic, that are unwanted. They've effectively served as 31 00:01:37,200 --> 00:01:41,880 Speaker 4: poison pulls for a takeover. But buchb has now come 32 00:01:41,920 --> 00:01:45,760 Speaker 4: with us, but it's structured in any interesting way, basically 33 00:01:45,840 --> 00:01:48,720 Speaker 4: saying we'll buy you, but you need to emerge some 34 00:01:48,800 --> 00:01:51,840 Speaker 4: of your South African assets, spin them off to your shareholders, 35 00:01:51,920 --> 00:01:52,960 Speaker 4: and we'll pick up the rest. 36 00:01:53,600 --> 00:01:56,160 Speaker 5: And that therein lies the problem. Like that's why no 37 00:01:56,200 --> 00:01:59,000 Speaker 5: one really wants that for Anglo American, right, because they're saying, 38 00:01:59,040 --> 00:02:01,280 Speaker 5: you go spin this off, then will buy the rest 39 00:02:01,280 --> 00:02:04,480 Speaker 5: of your company. Why don't they just buy the whole 40 00:02:04,520 --> 00:02:05,480 Speaker 5: company and then. 41 00:02:05,400 --> 00:02:06,000 Speaker 6: Sell it off. 42 00:02:07,280 --> 00:02:10,480 Speaker 4: Yeah, you're absolutely right. It's one of the reasons shareholders 43 00:02:10,800 --> 00:02:13,920 Speaker 4: don't seem to like it. It's a complicated structure. It 44 00:02:14,200 --> 00:02:17,400 Speaker 4: basically introduces a whole lot of risk for those shareholders 45 00:02:17,400 --> 00:02:21,720 Speaker 4: that will be inheriting these assets execution risk, value risk. 46 00:02:22,520 --> 00:02:24,639 Speaker 4: But for BHP, it's also a headache that they don't 47 00:02:24,639 --> 00:02:26,400 Speaker 4: want to absorb themselves. I don't want to take it 48 00:02:26,440 --> 00:02:30,239 Speaker 4: on there effectively would be taking on potentially the value 49 00:02:30,280 --> 00:02:34,200 Speaker 4: trap that has been plaguing Anglo American for so many years. 50 00:02:35,280 --> 00:02:38,359 Speaker 4: And remember BHP kind of has withdrawn from South Africa. 51 00:02:39,600 --> 00:02:44,119 Speaker 4: The BHP was until not so recently BHP Bulletin and 52 00:02:44,160 --> 00:02:47,600 Speaker 4: then and that was an acquisition of a business, a 53 00:02:47,680 --> 00:02:50,840 Speaker 4: merger with the business that had a huge South African presence, 54 00:02:50,919 --> 00:02:52,639 Speaker 4: and then they spun it off in South thirty two. 55 00:02:52,919 --> 00:02:54,880 Speaker 4: So there goes to South African assets. So they don't 56 00:02:54,919 --> 00:02:57,600 Speaker 4: really want to be there, and it's kind of understandable. 57 00:02:57,680 --> 00:02:59,960 Speaker 4: South Africa is a difficult place to operate at the moment. 58 00:03:01,240 --> 00:03:04,440 Speaker 2: So for them yourself, well, I. 59 00:03:04,480 --> 00:03:08,480 Speaker 4: Know, I do feel bad saying it, but it's it's 60 00:03:08,520 --> 00:03:10,920 Speaker 4: the truth. It's the truth, and we speak the truth. 61 00:03:10,760 --> 00:03:10,840 Speaker 3: And. 62 00:03:13,800 --> 00:03:17,160 Speaker 4: So it's yeah, I think it's just it's a tough 63 00:03:17,240 --> 00:03:19,400 Speaker 4: it's a tough one for them to get the hit around. 64 00:03:19,639 --> 00:03:20,799 Speaker 2: I think that in. 65 00:03:20,840 --> 00:03:24,680 Speaker 5: Essence, what this just also boils down to, is the 66 00:03:24,680 --> 00:03:29,280 Speaker 5: the money made in discovering new copper assets is minimal, 67 00:03:29,360 --> 00:03:31,560 Speaker 5: Like the hurdle rates really high. So the way to 68 00:03:31,600 --> 00:03:35,160 Speaker 5: do that is you have to buy competitors. It's very 69 00:03:35,200 --> 00:03:37,840 Speaker 5: difficult to get the kind of scale right. Building a 70 00:03:37,880 --> 00:03:40,200 Speaker 5: mind is really hard. There are a lot of different 71 00:03:40,240 --> 00:03:42,600 Speaker 5: labor laws in different countries. Look what happened in South 72 00:03:42,600 --> 00:03:45,000 Speaker 5: America all all of a sudden, like mining just was like, 73 00:03:45,080 --> 00:03:45,760 Speaker 5: know what, we're good? 74 00:03:45,800 --> 00:03:47,320 Speaker 6: Like you can't take any copper out of here? I 75 00:03:47,320 --> 00:03:50,160 Speaker 6: think that was in Peru. Does that just speak to 76 00:03:50,200 --> 00:03:53,080 Speaker 6: that problem? And there? What is? What's the other solution 77 00:03:53,200 --> 00:03:53,440 Speaker 6: to that? 78 00:03:53,480 --> 00:03:56,040 Speaker 5: The geopolitical risk premium for copper is a very different 79 00:03:56,080 --> 00:03:58,200 Speaker 5: proposition than I feel like it's been the last few decades. 80 00:03:59,320 --> 00:04:02,920 Speaker 4: Yeah, that that's absolutely right. I mean, geopolitics has made 81 00:04:02,920 --> 00:04:06,560 Speaker 4: things more difficult, but it's not really new. It's been 82 00:04:06,560 --> 00:04:09,720 Speaker 4: a bit of a slow burn issue. It's also the 83 00:04:09,800 --> 00:04:13,720 Speaker 4: reality is, you know, geologically, it's just it's tough to 84 00:04:13,760 --> 00:04:17,320 Speaker 4: find the deposits that are close to surface in really 85 00:04:17,360 --> 00:04:20,400 Speaker 4: high grades. And those deposits tend not to be in 86 00:04:20,440 --> 00:04:22,600 Speaker 4: the US or Australia. They tend to be in some 87 00:04:22,640 --> 00:04:29,279 Speaker 4: tougher jurisdictions like the Congo, the DRC or in Argentina, 88 00:04:29,480 --> 00:04:32,880 Speaker 4: which is politically seems to be always always in flex. 89 00:04:33,000 --> 00:04:34,480 Speaker 4: So if you want to go after these assets, you 90 00:04:34,480 --> 00:04:37,440 Speaker 4: would have built exposure and build these minds. You need 91 00:04:37,480 --> 00:04:39,120 Speaker 4: to take on that risk, and as you say, the 92 00:04:39,200 --> 00:04:42,880 Speaker 4: hurdle rate needs to be is very high, and at 93 00:04:42,920 --> 00:04:46,440 Speaker 4: current copper prices it probably starts to make sense. But 94 00:04:46,680 --> 00:04:49,600 Speaker 4: just a few months ago before kind of the resurgence 95 00:04:49,640 --> 00:04:53,000 Speaker 4: we'd seen when copper was trading, you know, eight and 96 00:04:53,000 --> 00:04:57,120 Speaker 4: a half thousand dollars a ton minusers couldn't make the 97 00:04:57,160 --> 00:05:00,960 Speaker 4: economics work. Now it's a little bit easy at close 98 00:05:01,000 --> 00:05:03,560 Speaker 4: to ten thousand dollars a ton, but even still there 99 00:05:03,560 --> 00:05:05,839 Speaker 4: in these questions as to how sustainable that price is 100 00:05:05,880 --> 00:05:06,560 Speaker 4: in the near term. 101 00:05:06,880 --> 00:05:09,080 Speaker 3: So Ellen, you know, I'm just putting on my investment 102 00:05:09,080 --> 00:05:12,599 Speaker 3: banker cap here. I think Anglo Americans got a great position. 103 00:05:12,680 --> 00:05:14,600 Speaker 3: They don't have to do anything, and they don't. If 104 00:05:14,640 --> 00:05:16,520 Speaker 3: I were them, I'd be like, no, if you guys 105 00:05:16,520 --> 00:05:19,200 Speaker 3: want us, buy the whole thing and then you deal 106 00:05:19,240 --> 00:05:22,440 Speaker 3: with it, you know, pay me a premium, or I'll 107 00:05:22,480 --> 00:05:25,520 Speaker 3: just stay with my copper. Here, I mean there's really 108 00:05:25,600 --> 00:05:27,680 Speaker 3: no reason for Anglo to cave in here, right. 109 00:05:28,760 --> 00:05:31,279 Speaker 4: Yeah, that seems to be the way they're going for 110 00:05:31,360 --> 00:05:33,000 Speaker 4: the time being. But there is a lot of pressure 111 00:05:33,000 --> 00:05:35,240 Speaker 4: on Anglo Americans. So at the end of last year 112 00:05:35,279 --> 00:05:38,520 Speaker 4: they came out with an investor update. You know, Anglo 113 00:05:38,520 --> 00:05:42,000 Speaker 4: American before the end of last year, before December, was 114 00:05:42,040 --> 00:05:44,640 Speaker 4: the go to play in the mining space for growth. 115 00:05:44,640 --> 00:05:47,200 Speaker 4: They had a great growth profile, that had good projects, 116 00:05:47,200 --> 00:05:49,800 Speaker 4: that had been investing through the cycle, so growth was 117 00:05:49,800 --> 00:05:51,560 Speaker 4: coming in at the right point in the cycle when 118 00:05:51,560 --> 00:05:54,839 Speaker 4: most of their peers had pulled back and had a 119 00:05:54,839 --> 00:05:57,440 Speaker 4: bit of a growth gap. Then came December last year 120 00:05:57,560 --> 00:06:02,080 Speaker 4: investor update and they slashed guidance the board. Share price 121 00:06:02,240 --> 00:06:04,359 Speaker 4: was decimated, and that's why we're in the position that 122 00:06:04,360 --> 00:06:08,120 Speaker 4: we're in now, and it's it's it's shaken the credibility 123 00:06:08,600 --> 00:06:10,719 Speaker 4: or shake the confidence of investors in the in the 124 00:06:10,760 --> 00:06:15,920 Speaker 4: credibility of management. So yes, they can reject this, but 125 00:06:15,920 --> 00:06:18,039 Speaker 4: but the onuses will then to come up with a 126 00:06:18,160 --> 00:06:22,600 Speaker 4: credible strategy to unlock the value in the stock. And 127 00:06:22,640 --> 00:06:25,440 Speaker 4: that's probably actually going to happen tomorrow in some way, 128 00:06:25,440 --> 00:06:28,360 Speaker 4: shape or form. Where they said they are coming out 129 00:06:28,400 --> 00:06:31,840 Speaker 4: with their standalone strategy. So that'll be really interesting to watch. 130 00:06:31,880 --> 00:06:33,560 Speaker 3: All right, we'll circle back with you and see what 131 00:06:33,600 --> 00:06:35,920 Speaker 3: the see what the market thinks about that. Alon Ulsha 132 00:06:36,200 --> 00:06:40,520 Speaker 3: he's his senior Medals and mining analyst Bloomberg Intelligence in London, 133 00:06:40,560 --> 00:06:41,960 Speaker 3: the Pride of South Africa. 134 00:06:42,040 --> 00:06:42,919 Speaker 2: Although it's tough. 135 00:06:42,760 --> 00:06:44,080 Speaker 3: To be a miner, and so I don't know you 136 00:06:44,160 --> 00:06:45,960 Speaker 3: knew all about the mining stuff too, You're not. Oh, 137 00:06:46,320 --> 00:06:48,280 Speaker 3: you're just at the global energy space. 138 00:06:48,160 --> 00:06:48,560 Speaker 7: You know what. 139 00:06:48,760 --> 00:06:51,560 Speaker 5: No, I can all explain you that too, for sure. 140 00:06:51,760 --> 00:06:54,640 Speaker 5: I started in gold, you started that. I went to copper, 141 00:06:54,640 --> 00:06:55,479 Speaker 5: then I went to oil. 142 00:06:55,839 --> 00:06:57,880 Speaker 3: All right, very good stuff. You learn stuff every day. 143 00:06:57,880 --> 00:07:00,000 Speaker 2: Here. 144 00:06:59,680 --> 00:07:03,600 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 145 00:07:03,680 --> 00:07:07,200 Speaker 1: weekdays at ten am Eastern on applecar Play and Android 146 00:07:07,240 --> 00:07:10,040 Speaker 1: Auto with the Bloomberg Business Act. You can also listen 147 00:07:10,160 --> 00:07:13,240 Speaker 1: live on Amazon Alexa from our flagship New York station 148 00:07:13,600 --> 00:07:16,360 Speaker 1: Just Say Alexa playing Bloomberg eleven thirty. 149 00:07:18,280 --> 00:07:20,239 Speaker 5: The only big story that we're following is Paul alluded 150 00:07:20,240 --> 00:07:21,280 Speaker 5: to Happy Monday everybody. 151 00:07:21,280 --> 00:07:22,360 Speaker 6: By the way, is. 152 00:07:22,280 --> 00:07:26,000 Speaker 5: Bill Huang's trial on our Keego's capital management. The real issue, 153 00:07:26,040 --> 00:07:28,600 Speaker 5: of course, is that the accusation and what the prosecutors 154 00:07:28,600 --> 00:07:31,520 Speaker 5: are looking at is if he actively lied to the 155 00:07:31,560 --> 00:07:34,600 Speaker 5: banks that were helping him fund these swaps to bet 156 00:07:34,640 --> 00:07:38,680 Speaker 5: on basically ten stocks that came spectaclarly crashing down. 157 00:07:38,480 --> 00:07:40,480 Speaker 6: On him in the last few years. 158 00:07:40,720 --> 00:07:43,480 Speaker 5: Janey Basik is Bloomberg television anchor and she is joining 159 00:07:43,520 --> 00:07:46,280 Speaker 5: us outside of the New York City Federal District Court House. 160 00:07:46,320 --> 00:07:49,320 Speaker 6: Shanellie, what's at stake in this trial? 161 00:07:52,160 --> 00:07:54,360 Speaker 8: Well, there's a lot at stake for Bill Lang and 162 00:07:54,400 --> 00:07:58,280 Speaker 8: of course his chief financial officer, Patrick Halligan, who we're 163 00:07:58,320 --> 00:08:01,840 Speaker 8: being accused here by regulator and of course the Justice 164 00:08:01,880 --> 00:08:06,760 Speaker 8: Department of manipulating the markets of racketeering, wires fraud and 165 00:08:06,800 --> 00:08:09,760 Speaker 8: securities fraud are in particular the charges that they're facing. 166 00:08:10,080 --> 00:08:13,320 Speaker 8: They could face significant jail time depending on how this 167 00:08:13,560 --> 00:08:16,120 Speaker 8: trial goes. Of course they are planning to defend themselves 168 00:08:16,200 --> 00:08:20,320 Speaker 8: quite vigorously here. But there's also what happened, right, We 169 00:08:20,440 --> 00:08:22,960 Speaker 8: know a lot about what happened in the sense of 170 00:08:23,080 --> 00:08:26,000 Speaker 8: the banks had lost billions of dollars off the heels 171 00:08:26,040 --> 00:08:30,000 Speaker 8: of this alex. But remember, as a family office, there 172 00:08:30,480 --> 00:08:35,440 Speaker 8: was not required by means of disclosure for par Kagos 173 00:08:35,480 --> 00:08:38,880 Speaker 8: and Bill Wang in the initial indictment. You did see 174 00:08:39,120 --> 00:08:42,880 Speaker 8: prosecutors not to that fact, but said they did lie 175 00:08:42,920 --> 00:08:46,240 Speaker 8: to the banks and indeed attempt to manipulate the market. 176 00:08:46,360 --> 00:08:49,240 Speaker 8: So it will be a trial unlike we've really seen 177 00:08:49,280 --> 00:08:50,840 Speaker 8: before in the world of white collar crime. 178 00:08:51,440 --> 00:08:51,880 Speaker 2: Chanale. 179 00:08:51,960 --> 00:08:54,160 Speaker 3: The best I can tell from just reading into the 180 00:08:54,280 --> 00:08:57,760 Speaker 3: story is that mister Wang's defense is basically, hey, I'm 181 00:08:57,760 --> 00:08:59,360 Speaker 3: not responsible for the other. 182 00:08:59,200 --> 00:09:01,760 Speaker 2: Sides losses here. They're big boys too. 183 00:09:02,160 --> 00:09:06,160 Speaker 3: Does that does that have mary? 184 00:09:06,559 --> 00:09:09,400 Speaker 8: That is a fair assessment here if you think about 185 00:09:09,440 --> 00:09:11,360 Speaker 8: Credit Suis in its own right, this is not the 186 00:09:11,360 --> 00:09:14,160 Speaker 8: only place they've lost significant money tied. 187 00:09:13,920 --> 00:09:14,600 Speaker 2: To a client. 188 00:09:15,000 --> 00:09:17,080 Speaker 8: You have the banks saying that they were lied to, 189 00:09:17,440 --> 00:09:20,200 Speaker 8: but you have Bill Hung saying, well, okay, his defense 190 00:09:20,280 --> 00:09:23,080 Speaker 8: might bring up Credit Suise his own risk management report 191 00:09:23,360 --> 00:09:26,360 Speaker 8: outlining its own failures in risk management at this point 192 00:09:26,360 --> 00:09:29,760 Speaker 8: in time, So we can see that scuffle between the 193 00:09:29,800 --> 00:09:32,880 Speaker 8: banks and Bill Hung's defense come up into this course 194 00:09:32,920 --> 00:09:33,520 Speaker 8: of this child. 195 00:09:33,960 --> 00:09:34,160 Speaker 6: Yeah. 196 00:09:34,240 --> 00:09:36,800 Speaker 5: See, I think that's so interesting because it really is 197 00:09:36,840 --> 00:09:41,160 Speaker 5: about the banks helping buy a COMCBS issue the shares, right, 198 00:09:41,200 --> 00:09:44,480 Speaker 5: that's one part of investment banking that led to the selloff, 199 00:09:44,520 --> 00:09:46,200 Speaker 5: that led to the margin calls that led to all 200 00:09:46,240 --> 00:09:47,079 Speaker 5: of this unroundling. 201 00:09:47,160 --> 00:09:49,760 Speaker 6: So it's like the two sides shouldn't talk to each other. 202 00:09:49,720 --> 00:09:51,480 Speaker 5: The ones that are working with Bill Huang and the 203 00:09:51,520 --> 00:09:54,040 Speaker 5: ones that are helping buy ACOMCBS issue shares. But in 204 00:09:54,080 --> 00:09:56,400 Speaker 5: this case they probably should have been able to talk 205 00:09:56,440 --> 00:09:58,120 Speaker 5: to each other to see what each other was doing. 206 00:10:00,240 --> 00:10:03,120 Speaker 8: Yeah, let's go back and talk a little bit about 207 00:10:03,360 --> 00:10:04,320 Speaker 8: what prompted all of this. 208 00:10:04,440 --> 00:10:06,640 Speaker 6: It's the idea that he became. 209 00:10:06,400 --> 00:10:09,520 Speaker 8: A family office with an expected one point five billion 210 00:10:09,760 --> 00:10:13,400 Speaker 8: in capital to more than thirty five billion in capital 211 00:10:13,440 --> 00:10:15,920 Speaker 8: in a very short amount of time. And the reason 212 00:10:15,960 --> 00:10:19,560 Speaker 8: he was able to amass these massive positions was really 213 00:10:19,600 --> 00:10:23,160 Speaker 8: buying a lot of these shares on swap agreements. And 214 00:10:23,480 --> 00:10:28,000 Speaker 8: what happened was he had amassed size so sizable in 215 00:10:28,160 --> 00:10:31,559 Speaker 8: ViacomCBS that when the banks went on behalf of the 216 00:10:31,600 --> 00:10:34,680 Speaker 8: company to try to offload or sell more shares on 217 00:10:34,720 --> 00:10:38,439 Speaker 8: behalf of Viacom, that's when that created pressure on the shares. 218 00:10:38,600 --> 00:10:42,000 Speaker 8: That's when the banks started to really look at our chagos, 219 00:10:42,080 --> 00:10:44,679 Speaker 8: how much they were holding their exposures and the needs 220 00:10:44,679 --> 00:10:49,040 Speaker 8: for potential margin calls. Interestingly, on Monday, after those opening arguments, 221 00:10:49,120 --> 00:10:52,160 Speaker 8: we may hear from the first witness for this trial, 222 00:10:52,360 --> 00:10:56,800 Speaker 8: and it may be a counter party at UBS that 223 00:10:57,000 --> 00:11:00,960 Speaker 8: was behind one of those first early calls that really 224 00:11:01,240 --> 00:11:03,880 Speaker 8: talked through those margin calls and how quickly our Chagos 225 00:11:03,920 --> 00:11:05,880 Speaker 8: would be able to put those up. So what do 226 00:11:06,000 --> 00:11:07,720 Speaker 8: they know and not know? What do they need to 227 00:11:07,760 --> 00:11:09,520 Speaker 8: know and what do they need to know by law? 228 00:11:09,640 --> 00:11:12,600 Speaker 8: I think that that's the critical part of this discussion here. 229 00:11:12,600 --> 00:11:14,520 Speaker 3: He Shanan, just real quickly, what's the time you hear? 230 00:11:14,559 --> 00:11:16,199 Speaker 3: How long will this trial is? How long is it 231 00:11:16,280 --> 00:11:17,040 Speaker 3: expected to last? 232 00:11:18,960 --> 00:11:20,800 Speaker 8: It will be weeks. It will be a number of weeks. 233 00:11:20,800 --> 00:11:23,199 Speaker 8: So we're expected to hear not only from two of 234 00:11:23,240 --> 00:11:26,640 Speaker 8: his former employees, remember two of them also already pled guilty, 235 00:11:27,040 --> 00:11:29,560 Speaker 8: but there could be a list of more than two 236 00:11:29,800 --> 00:11:34,560 Speaker 8: dozen people from counterparties these banks that might step forward 237 00:11:34,559 --> 00:11:35,480 Speaker 8: in the wake of this trial. 238 00:11:35,760 --> 00:11:38,120 Speaker 3: In the course of this trial, Hi, shanaa Bassek, thank 239 00:11:38,160 --> 00:11:39,520 Speaker 3: you so much for joining a Shana on the basics. 240 00:11:39,559 --> 00:11:40,880 Speaker 2: She's downtown at the courthouse. 241 00:11:42,000 --> 00:11:46,040 Speaker 3: Our Chagos capital trial starting today could take several weeks. 242 00:11:46,440 --> 00:11:49,760 Speaker 3: Big dollars involved a lot of big important investment banks 243 00:11:49,880 --> 00:11:50,679 Speaker 3: in Vahome to here. 244 00:11:50,760 --> 00:11:52,760 Speaker 2: But this was certainly the news. 245 00:11:52,920 --> 00:11:55,959 Speaker 6: Oh my gosh, it was spectacular. I mean just I've 246 00:11:56,000 --> 00:11:57,120 Speaker 6: never seen anything like that before. 247 00:11:57,360 --> 00:12:00,199 Speaker 3: No, And of course, you know, the biggest manifestation is 248 00:12:00,240 --> 00:12:04,080 Speaker 3: it just contributed didn't cost, but it contributed to the 249 00:12:04,080 --> 00:12:06,320 Speaker 3: demise of Credit Swiss, my former employer. 250 00:12:08,280 --> 00:12:12,160 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 251 00:12:12,240 --> 00:12:15,319 Speaker 1: weekdays at ten am Eastern on Affo card playing Android 252 00:12:15,360 --> 00:12:18,440 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 253 00:12:18,520 --> 00:12:22,360 Speaker 1: you get your podcasts, or watch us live on YouTube. 254 00:12:23,520 --> 00:12:26,400 Speaker 5: Alex Steel alongside Paul Sweeney. It's a Bloomberg Intelligence Radio. 255 00:12:26,400 --> 00:12:28,920 Speaker 5: We cover all the top news in finance and economics 256 00:12:28,920 --> 00:12:31,719 Speaker 5: with our analysts worldwide. They cover two thousand companies and 257 00:12:31,720 --> 00:12:34,320 Speaker 5: one hundred and thirty industries. We also get the best 258 00:12:34,360 --> 00:12:36,880 Speaker 5: in the brightest to come into the Bloomberg studio on 259 00:12:36,960 --> 00:12:40,040 Speaker 5: a Monday and give us their take on the markets. 260 00:12:40,080 --> 00:12:42,360 Speaker 5: And for that we go to Dryden Pence see io 261 00:12:42,440 --> 00:12:46,600 Speaker 5: at Pence Wealth Management. Now typically Dryden, you're in California. 262 00:12:46,400 --> 00:12:47,000 Speaker 9: That's correct. 263 00:12:47,559 --> 00:12:50,240 Speaker 6: You know, why are you here? Isn't that fun for you? 264 00:12:50,600 --> 00:12:51,199 Speaker 6: Good for us? 265 00:12:51,480 --> 00:12:51,800 Speaker 2: Well? 266 00:12:51,880 --> 00:12:54,320 Speaker 9: Yeah, Now I come to New York about once a quarter. 267 00:12:54,360 --> 00:12:56,280 Speaker 10: We have a lot of you know folks in this area, 268 00:12:56,400 --> 00:12:58,600 Speaker 10: and so it's good to come here. So I'm really 269 00:12:58,600 --> 00:12:59,480 Speaker 10: happy to be here today. 270 00:12:59,520 --> 00:13:01,679 Speaker 5: Thank you for we welcome to you yesterday with rain 271 00:13:01,760 --> 00:13:03,640 Speaker 5: and coldness, so you're welcome on that point. 272 00:13:04,040 --> 00:13:05,840 Speaker 6: So there was a great piece in the Bloomberg. 273 00:13:05,880 --> 00:13:07,680 Speaker 5: They talked about how S and B five hundred companies 274 00:13:07,720 --> 00:13:09,760 Speaker 5: are on track to post their best quarterly earning this 275 00:13:09,880 --> 00:13:12,760 Speaker 5: relative to expectations in at least two years, because nothing 276 00:13:12,880 --> 00:13:17,319 Speaker 5: disastrous happened, we didn't get that recession. And this dovetails 277 00:13:17,320 --> 00:13:19,800 Speaker 5: with your note saying that this year has been much 278 00:13:19,800 --> 00:13:20,920 Speaker 5: ado about nothing. 279 00:13:21,400 --> 00:13:23,720 Speaker 10: It's it is. I mean, people kind of dance on 280 00:13:23,760 --> 00:13:25,440 Speaker 10: the head of the Fed's pen. But I mean the 281 00:13:25,440 --> 00:13:28,640 Speaker 10: point is is that if you look at basic earnings, 282 00:13:28,840 --> 00:13:31,120 Speaker 10: you know we're probably going to have record earnings across 283 00:13:31,120 --> 00:13:33,480 Speaker 10: the S and P five hundred. And we started off 284 00:13:33,520 --> 00:13:36,880 Speaker 10: with the Magnificent seven leading the market, but now the 285 00:13:36,880 --> 00:13:39,760 Speaker 10: rest of them are beginning to catch up. And and 286 00:13:39,800 --> 00:13:41,400 Speaker 10: I think that we're going to see this four to 287 00:13:41,480 --> 00:13:44,320 Speaker 10: ninety three continue to spread out and go, and that 288 00:13:44,360 --> 00:13:46,720 Speaker 10: earnings are going to It's basically the beat goes on 289 00:13:46,960 --> 00:13:49,000 Speaker 10: every time we come where the beat goes on. 290 00:13:49,559 --> 00:13:52,280 Speaker 3: What's the pin on your lapel? People on radio can't 291 00:13:52,280 --> 00:13:54,199 Speaker 3: see it, but what's the pin on your lapel? That's 292 00:13:54,240 --> 00:13:56,800 Speaker 3: a bronze star, Bronze star US Army. 293 00:13:57,000 --> 00:13:59,320 Speaker 2: That's correct. Thank you for your service, my friend, to. 294 00:13:59,440 --> 00:14:00,760 Speaker 9: Thank you for your taxes. 295 00:14:00,840 --> 00:14:03,480 Speaker 2: Yeah, exactly, that's what I'm good at. You don't want 296 00:14:03,480 --> 00:14:04,280 Speaker 2: me in a foxhole. 297 00:14:04,320 --> 00:14:05,400 Speaker 7: You want me writing check. 298 00:14:05,400 --> 00:14:09,320 Speaker 9: You write those checks the way you buy body armor. 299 00:14:09,320 --> 00:14:11,719 Speaker 9: And that says soldier's life. So thank you very much. 300 00:14:11,760 --> 00:14:14,719 Speaker 3: All right, total spent this earning cycle. We're just kind 301 00:14:14,720 --> 00:14:17,160 Speaker 3: of getting through. We'll here from some of the retailers here, 302 00:14:17,360 --> 00:14:19,320 Speaker 3: anything that kind of knocked you off your game one 303 00:14:19,320 --> 00:14:20,800 Speaker 3: way or the other from this earning cycle. 304 00:14:20,880 --> 00:14:23,000 Speaker 10: Well, in the earning cycle, of course, we've got Walmart 305 00:14:23,000 --> 00:14:25,040 Speaker 10: coming up at the at the end of the at 306 00:14:25,080 --> 00:14:25,800 Speaker 10: the end of the week here. 307 00:14:25,960 --> 00:14:30,720 Speaker 9: But I think that what we're seeing is that, you know, consumers. 308 00:14:30,120 --> 00:14:33,440 Speaker 10: Are continuing to spend uh and and that's beginning to 309 00:14:33,440 --> 00:14:37,040 Speaker 10: transfer into earnings. Everybody, you know, lowered their expectations and 310 00:14:37,120 --> 00:14:39,160 Speaker 10: kind of put them down, and so we're coming in 311 00:14:39,160 --> 00:14:41,400 Speaker 10: this thing. It's it's an easy beat cycle, and we 312 00:14:41,440 --> 00:14:44,200 Speaker 10: continue to do that. But consumer, you know, yeah, wages 313 00:14:44,240 --> 00:14:46,200 Speaker 10: are at an all time high. You know, earnings are 314 00:14:46,240 --> 00:14:48,560 Speaker 10: at all time high. We've got more people working than 315 00:14:48,600 --> 00:14:51,560 Speaker 10: ever before, and that drives consumers. And if you give 316 00:14:51,600 --> 00:14:54,560 Speaker 10: an American consumer money, they're. 317 00:14:54,400 --> 00:14:55,080 Speaker 7: Going to spend it. 318 00:14:55,360 --> 00:14:57,960 Speaker 10: So I think that we're continuing to see that across retail. 319 00:14:58,320 --> 00:14:59,360 Speaker 10: We'll continue to see that. 320 00:14:59,560 --> 00:15:02,280 Speaker 9: But it but people break break up. You have certain 321 00:15:02,320 --> 00:15:03,240 Speaker 9: amount of trade downs. 322 00:15:03,240 --> 00:15:05,920 Speaker 10: When we mentioned the Walmart piece of it is that 323 00:15:06,000 --> 00:15:10,280 Speaker 10: the largest fastest growing group of Walmart shoppers are folks 324 00:15:10,280 --> 00:15:12,280 Speaker 10: that make over one hundred thousand dollars a year really 325 00:15:12,320 --> 00:15:13,000 Speaker 10: and so oh. 326 00:15:12,960 --> 00:15:14,800 Speaker 9: Yeah, and then they get in there. 327 00:15:14,960 --> 00:15:17,400 Speaker 10: They get in there because you know, they come for groceries. 328 00:15:17,520 --> 00:15:19,560 Speaker 10: You know, inflation is kicked up groceries quite a bit. 329 00:15:19,600 --> 00:15:22,440 Speaker 10: People feel that they go in and then you know, 330 00:15:22,600 --> 00:15:25,640 Speaker 10: so there really is an opportunity for continued increase in 331 00:15:25,720 --> 00:15:28,280 Speaker 10: market share, and so you kind of get, you know, 332 00:15:28,280 --> 00:15:30,480 Speaker 10: then you get the aspirational side is you have you know, 333 00:15:30,760 --> 00:15:32,360 Speaker 10: the very high end is doing well. 334 00:15:32,440 --> 00:15:34,760 Speaker 9: I mean, gosh, I. 335 00:15:34,760 --> 00:15:37,160 Speaker 10: Mean forty percent of consumer spinning is done by the 336 00:15:37,280 --> 00:15:38,600 Speaker 10: top twenty percent of the population. 337 00:15:38,800 --> 00:15:41,640 Speaker 5: So yeah, and Torsten Slot had a great chart out 338 00:15:42,080 --> 00:15:43,040 Speaker 5: just and he kind of. 339 00:15:42,960 --> 00:15:45,480 Speaker 6: Recycles his chart every once in a while over at Apollo. 340 00:15:45,240 --> 00:15:47,920 Speaker 5: And it's basically those that have either no mortgage on 341 00:15:47,960 --> 00:15:50,880 Speaker 5: their home or have a mortgage below four percent, which 342 00:15:50,920 --> 00:15:54,480 Speaker 5: to that point just freeze up all that cash. 343 00:15:54,760 --> 00:15:57,040 Speaker 10: Exactly if your if your mortgage is at four if 344 00:15:57,040 --> 00:15:58,920 Speaker 10: you have a thirty year mortgage at four percent, you 345 00:15:58,960 --> 00:16:02,320 Speaker 10: don't really work worry too much about mortgages going to 346 00:16:02,360 --> 00:16:03,120 Speaker 10: seven or eight percent. 347 00:16:03,120 --> 00:16:04,640 Speaker 9: Now, if you're a millennial trying to be a first 348 00:16:04,680 --> 00:16:07,600 Speaker 9: time home buyer, your brain's going to explode. But for 349 00:16:07,680 --> 00:16:10,560 Speaker 9: everybody else it's really not as big an issue. 350 00:16:10,600 --> 00:16:12,520 Speaker 3: One of the names on your list is really interesting 351 00:16:12,520 --> 00:16:15,000 Speaker 3: to me, which is Apple, because for the first time 352 00:16:15,160 --> 00:16:18,880 Speaker 3: in a collective memory of a lot of investors, there's 353 00:16:19,040 --> 00:16:20,760 Speaker 3: serious and fundamental headwinds for Apple. 354 00:16:20,800 --> 00:16:23,240 Speaker 2: And if you bull it down to one word's China. 355 00:16:23,440 --> 00:16:25,800 Speaker 2: How do you guys get comfortable with that? We get comforted. 356 00:16:25,960 --> 00:16:30,000 Speaker 10: But I think people underestimate the massive size of Apple. 357 00:16:30,120 --> 00:16:33,080 Speaker 10: If you take a look at the app Store, that's 358 00:16:33,120 --> 00:16:36,280 Speaker 10: an environment of two point two billion active users, and 359 00:16:36,360 --> 00:16:39,640 Speaker 10: they've got two billion active devices that are out there, 360 00:16:39,720 --> 00:16:41,920 Speaker 10: So that's nine hundred million people more than the entire 361 00:16:41,920 --> 00:16:45,400 Speaker 10: population of China. Why so sometimes when you think, okay, 362 00:16:45,440 --> 00:16:47,720 Speaker 10: it's a China story, now it's a global story. 363 00:16:48,000 --> 00:16:50,200 Speaker 9: You know, if you take a look at the app Store, the. 364 00:16:50,200 --> 00:16:53,200 Speaker 10: Total merchandise value that's traded on that is over one 365 00:16:53,200 --> 00:16:58,600 Speaker 10: point one trillion dollars. It's bigger than the Netherlands. 366 00:16:58,440 --> 00:17:00,320 Speaker 9: It's amazing how big it really is. 367 00:17:00,840 --> 00:17:02,400 Speaker 3: So, I mean, I guess the issue there for for 368 00:17:02,480 --> 00:17:05,399 Speaker 3: Apple is, you know, it's can they manage around China? 369 00:17:05,920 --> 00:17:08,119 Speaker 3: Number one? And I think there are a lot of 370 00:17:08,119 --> 00:17:10,560 Speaker 3: people that are on both sides. And then the number two, 371 00:17:10,640 --> 00:17:12,640 Speaker 3: what is their AI strategy? Again, do you think they're 372 00:17:12,680 --> 00:17:15,119 Speaker 3: a tech company that has to have an AI strategy 373 00:17:15,160 --> 00:17:17,560 Speaker 3: because really, at the moment, I don't know. 374 00:17:17,800 --> 00:17:21,160 Speaker 10: I think they have the luxury a because they're so big, 375 00:17:21,400 --> 00:17:23,439 Speaker 10: they have the luxury to take a little time to 376 00:17:23,520 --> 00:17:26,280 Speaker 10: make sure it's right. I mean, Apple doesn't make many mistakes, 377 00:17:26,560 --> 00:17:29,080 Speaker 10: and I think that that you know, they really they're 378 00:17:29,119 --> 00:17:30,960 Speaker 10: really to use of military parliance. 379 00:17:31,080 --> 00:17:32,520 Speaker 9: They're really about you. 380 00:17:32,480 --> 00:17:37,199 Speaker 10: Know, ready aim fire, and so I think rather than 381 00:17:37,280 --> 00:17:40,280 Speaker 10: jump into just anything AI, they want to get it right. 382 00:17:40,680 --> 00:17:43,200 Speaker 10: And I think that's why people trust that brand, They 383 00:17:43,200 --> 00:17:46,040 Speaker 10: trust equipment, they trust all those things. So you know 384 00:17:46,080 --> 00:17:47,919 Speaker 10: the fact that they're not rushing in to be the 385 00:17:47,960 --> 00:17:49,960 Speaker 10: exact leader on integration. 386 00:17:49,600 --> 00:17:51,360 Speaker 9: Of AI means that they're going to try to get 387 00:17:51,359 --> 00:17:51,680 Speaker 9: it right. 388 00:17:51,920 --> 00:17:53,280 Speaker 10: And I think that that's going to be important. 389 00:17:53,320 --> 00:17:55,840 Speaker 5: Well, you mentioned Walmart earlier. We talked about that. What 390 00:17:55,960 --> 00:17:56,720 Speaker 5: about Amazon? 391 00:17:57,000 --> 00:17:59,880 Speaker 6: What why do you own a stock like Amazon? 392 00:18:00,960 --> 00:18:04,640 Speaker 10: You know, Amazon gets about you know, forty cents out 393 00:18:04,680 --> 00:18:07,720 Speaker 10: of every dollar, four out of ten, you know, of 394 00:18:08,040 --> 00:18:12,119 Speaker 10: everything online. And so when you think about how massively 395 00:18:12,240 --> 00:18:15,159 Speaker 10: large that is, and we have a consumer that's moving 396 00:18:15,320 --> 00:18:18,240 Speaker 10: more and more online. If you move from right now, 397 00:18:18,240 --> 00:18:20,680 Speaker 10: we're going like fifteen percent of retail is online. 398 00:18:20,720 --> 00:18:21,760 Speaker 9: If they just move up to. 399 00:18:21,800 --> 00:18:24,800 Speaker 10: If they move to twenty five, which is kind of 400 00:18:24,800 --> 00:18:26,480 Speaker 10: where the most of the world is going, that's a 401 00:18:26,520 --> 00:18:30,639 Speaker 10: three hundred billion dollar opportunity that Amazon has. And so 402 00:18:31,080 --> 00:18:35,359 Speaker 10: as behavior changes and behavior moves more and more online, 403 00:18:35,840 --> 00:18:38,560 Speaker 10: you're going to see greater use of Amazon. And one 404 00:18:38,560 --> 00:18:42,200 Speaker 10: thing we have to think about is on this inflation cycle. 405 00:18:42,359 --> 00:18:44,760 Speaker 10: And even if you if you look at inflation, people 406 00:18:44,760 --> 00:18:47,840 Speaker 10: being price conscious and things like that, the most efficient 407 00:18:47,920 --> 00:18:53,760 Speaker 10: way to bargainshop is online. Oh you're preaching the fire, please, 408 00:18:53,800 --> 00:18:55,840 Speaker 10: and there you go, and there you go. So it's 409 00:18:55,880 --> 00:18:59,840 Speaker 10: just going to increase utilization of that platform. They're so dominant, 410 00:19:00,080 --> 00:19:02,320 Speaker 10: and so I think that we find that that, you know, 411 00:19:02,440 --> 00:19:04,800 Speaker 10: long term, if we're thinking about this, that they're going 412 00:19:04,840 --> 00:19:07,199 Speaker 10: to overcome whatever headwinds people are trying to imagine that 413 00:19:07,200 --> 00:19:07,480 Speaker 10: they have. 414 00:19:07,760 --> 00:19:09,960 Speaker 3: You know, one of the many reasons we're like hearing 415 00:19:10,000 --> 00:19:12,159 Speaker 3: from Walmart and we will this week is just it 416 00:19:12,200 --> 00:19:14,359 Speaker 3: gives us a good read on the consumer. It feels 417 00:19:14,359 --> 00:19:17,399 Speaker 3: like a broad based read on the consumer. How do 418 00:19:17,440 --> 00:19:20,040 Speaker 3: you view the consumer here, because there's a there's definitely 419 00:19:20,080 --> 00:19:21,879 Speaker 3: some cracks out there for a lot of folks. 420 00:19:22,280 --> 00:19:25,200 Speaker 10: I think that then when we talked about early on 421 00:19:25,320 --> 00:19:28,119 Speaker 10: this trade down, right, if you break the consumer up 422 00:19:28,160 --> 00:19:30,280 Speaker 10: into three groups, you have the the top tier, the 423 00:19:30,280 --> 00:19:32,919 Speaker 10: middle tier, and then the lower tier. You know, the 424 00:19:32,960 --> 00:19:35,920 Speaker 10: lower tier, inflation is showing up and it's and it's 425 00:19:35,960 --> 00:19:38,199 Speaker 10: beginning to change some of their behavior. People having to 426 00:19:38,240 --> 00:19:40,800 Speaker 10: pay more attention to pricing and things like that. They're 427 00:19:40,800 --> 00:19:44,600 Speaker 10: going to move into more frequency of a Walmart or 428 00:19:44,800 --> 00:19:47,240 Speaker 10: any of the discounters. But then when you take a 429 00:19:47,240 --> 00:19:49,239 Speaker 10: look at that middle group, like I mentioned before, that 430 00:19:49,320 --> 00:19:53,040 Speaker 10: trade down, where now they're because grocery prices are so high. 431 00:19:53,160 --> 00:19:54,760 Speaker 10: People are going to move in there for grocery and 432 00:19:54,800 --> 00:19:56,480 Speaker 10: the next thing, you know, they look at the rest 433 00:19:56,520 --> 00:19:58,199 Speaker 10: of the sore and when you and you hear it 434 00:19:58,200 --> 00:20:00,680 Speaker 10: all the time, I can't find it anywhere else. 435 00:20:00,800 --> 00:20:02,679 Speaker 9: So I went to Walmart. And so I think that 436 00:20:02,720 --> 00:20:05,000 Speaker 9: we're going to continue to see increased. 437 00:20:04,640 --> 00:20:08,200 Speaker 10: Traffic and that's going to translate, uh to increase increased 438 00:20:08,240 --> 00:20:09,040 Speaker 10: growth in utilization. 439 00:20:09,480 --> 00:20:11,679 Speaker 5: But to the Amazon story, you're definitely buying it as 440 00:20:11,680 --> 00:20:13,800 Speaker 5: a retail story, not as like a cloud play or 441 00:20:13,840 --> 00:20:17,360 Speaker 5: an AI play, where you know, is that just like 442 00:20:17,359 --> 00:20:18,359 Speaker 5: like a pony on a cake? 443 00:20:18,400 --> 00:20:19,080 Speaker 2: What is that? 444 00:20:20,119 --> 00:20:21,680 Speaker 6: I don't want my cake? 445 00:20:21,760 --> 00:20:23,439 Speaker 9: You want a pony on your cake? That could be 446 00:20:23,440 --> 00:20:27,239 Speaker 9: a problem, but I think I think that it is. 447 00:20:27,520 --> 00:20:31,480 Speaker 10: It is you know, Amazon is It's got the cloud 448 00:20:31,560 --> 00:20:35,200 Speaker 10: of course, and that's just going to continue to grow 449 00:20:35,359 --> 00:20:37,840 Speaker 10: as we go through all this demand and AI and 450 00:20:37,880 --> 00:20:40,399 Speaker 10: those things. But when we really kind of think about 451 00:20:40,440 --> 00:20:43,160 Speaker 10: the consumer, because that's you know, what are people doing. 452 00:20:43,480 --> 00:20:45,720 Speaker 9: That's a very solid growth. 453 00:20:45,440 --> 00:20:49,000 Speaker 10: Story, and it's a behavior story of the American consumer, 454 00:20:49,240 --> 00:20:50,960 Speaker 10: and I think that it's one that that we really 455 00:20:51,240 --> 00:20:54,280 Speaker 10: need to pay attention to because remember too that that 456 00:20:54,480 --> 00:20:59,280 Speaker 10: pricing online has kind of gone down over over the 457 00:20:59,359 --> 00:21:02,240 Speaker 10: you know, past ten years, whereas CPI and everywhere else 458 00:21:02,280 --> 00:21:05,680 Speaker 10: has gone up. So you get better competitive pricing online, 459 00:21:05,920 --> 00:21:10,080 Speaker 10: better competitive pricing in an inflationary environment. 460 00:21:10,119 --> 00:21:11,680 Speaker 9: And that's just going to continue to. 461 00:21:11,760 --> 00:21:15,040 Speaker 10: Play right into that ecosystem that is part of I've 462 00:21:15,080 --> 00:21:17,600 Speaker 10: yet to find I very rarely find somebody who hasn't 463 00:21:17,600 --> 00:21:18,600 Speaker 10: bought something on Amazon. 464 00:21:18,760 --> 00:21:21,160 Speaker 9: Yeah, and it just continues. 465 00:21:21,280 --> 00:21:21,480 Speaker 2: Yeah. 466 00:21:21,480 --> 00:21:26,159 Speaker 3: Absolutely, We'll hear from Amazon Thursday, Port with earnings. I'm sorry, 467 00:21:26,520 --> 00:21:29,359 Speaker 3: Walmart Thursday. Dreda Pence, thank you so much for joining us. 468 00:21:29,440 --> 00:21:32,760 Speaker 3: Dreda Penci is the chief investment officer Pence Wealth Management, 469 00:21:33,320 --> 00:21:35,640 Speaker 3: based out there in Newport Beach, California. 470 00:21:35,720 --> 00:21:38,120 Speaker 2: Joining us here in a Bloomberg Interactive Broker studio. 471 00:21:39,359 --> 00:21:43,240 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 472 00:21:43,320 --> 00:21:46,840 Speaker 1: weekdays at ten am Eastern on applecar Play and Android 473 00:21:46,880 --> 00:21:49,639 Speaker 1: Auto with the Bloomberg Business App. You can also listen 474 00:21:49,760 --> 00:21:52,879 Speaker 1: live on Amazon Alexa from our flagship New York station, 475 00:21:53,240 --> 00:21:56,000 Speaker 1: Just Say Alexa, playing Bloomberg eleven thirty. 476 00:21:58,040 --> 00:22:01,919 Speaker 5: I'm Alex the alongside Paul Sweeney is Bloomberg Intelligence Radio. 477 00:22:01,960 --> 00:22:04,399 Speaker 5: We bring you all the top financial and economic news 478 00:22:04,480 --> 00:22:07,440 Speaker 5: through the lens of our Bloomberg Intelligence analysts. They cover 479 00:22:07,520 --> 00:22:10,760 Speaker 5: two thousand companies and one hundred and thirty industries worldwide, 480 00:22:10,920 --> 00:22:13,000 Speaker 5: and every Monday at this time we dip into our 481 00:22:13,040 --> 00:22:17,680 Speaker 5: Bloomberg NIF research team. So this is basically a research 482 00:22:17,800 --> 00:22:21,320 Speaker 5: team that has the best data sets and models built 483 00:22:21,320 --> 00:22:23,639 Speaker 5: by analysts and industry experts all around the world, and 484 00:22:23,640 --> 00:22:28,639 Speaker 5: they cover everything commodity, power, transport, industry, buildings, AGG related 485 00:22:29,240 --> 00:22:29,960 Speaker 5: and it's really all. 486 00:22:29,960 --> 00:22:31,560 Speaker 6: The cutting issues of our. 487 00:22:31,480 --> 00:22:33,639 Speaker 5: Time in my personal opinion, because you know how I 488 00:22:33,720 --> 00:22:36,280 Speaker 5: like the whole green stuff, energy transition stuff. If you 489 00:22:36,280 --> 00:22:39,399 Speaker 5: look at any report, they all quote the EV data 490 00:22:39,440 --> 00:22:41,760 Speaker 5: that comes from Bloomberg NIF. And we have one of 491 00:22:41,760 --> 00:22:44,800 Speaker 5: those individual analysts here with us now, Corey Kanter, is 492 00:22:44,840 --> 00:22:49,320 Speaker 5: being at Bloomberg NAF lead US electric Vehicle analyst. It's 493 00:22:49,359 --> 00:22:51,400 Speaker 5: great to have you here at Corey, thanks for joining us. 494 00:22:51,720 --> 00:22:54,160 Speaker 11: Great to be here, guys. Happy Monday, Happy Monday. 495 00:22:54,200 --> 00:22:56,160 Speaker 6: We made it so you told. 496 00:22:56,160 --> 00:23:01,280 Speaker 5: Your latest note is about how Tesla's there's recent stumbles, 497 00:23:01,640 --> 00:23:03,960 Speaker 5: not necessarily government policy or anything like that, but Tesla's 498 00:23:03,960 --> 00:23:07,800 Speaker 5: recent stumbles creates some adoor opening for the other EV 499 00:23:07,920 --> 00:23:09,800 Speaker 5: makers like a GM and a Ribbean. 500 00:23:09,880 --> 00:23:12,879 Speaker 6: Can you talk us through what your latest research says. 501 00:23:13,320 --> 00:23:13,520 Speaker 11: Yeah. 502 00:23:13,560 --> 00:23:16,000 Speaker 12: So, if you look at last quarter, both globally and 503 00:23:16,040 --> 00:23:19,359 Speaker 12: within the US, Tesla hit a bit of a rough patch. 504 00:23:19,480 --> 00:23:22,480 Speaker 12: You saw globally sales down about nine percent year on year. 505 00:23:22,960 --> 00:23:25,200 Speaker 12: And when you're looking at the EV space and the 506 00:23:25,240 --> 00:23:27,399 Speaker 12: automotive space, year onyar is usually the best way to 507 00:23:27,440 --> 00:23:28,679 Speaker 12: take a look at this market. 508 00:23:28,440 --> 00:23:30,000 Speaker 11: To see how things are changing and growing. 509 00:23:30,640 --> 00:23:33,000 Speaker 12: And so when it comes to GM, when it comes 510 00:23:33,040 --> 00:23:36,919 Speaker 12: to Rivian, Yuendi and even Lucid, there's just an opening 511 00:23:36,960 --> 00:23:38,960 Speaker 12: here for them to expand their market share. Each of 512 00:23:39,000 --> 00:23:41,080 Speaker 12: them has a different strategy as it pertains to twenty 513 00:23:41,119 --> 00:23:43,520 Speaker 12: twenty four. And this doesn't mean that either of them 514 00:23:43,520 --> 00:23:44,800 Speaker 12: are going to or any of them are going to 515 00:23:44,800 --> 00:23:47,560 Speaker 12: pass Tesla in this year. But from yours working in 516 00:23:47,560 --> 00:23:49,720 Speaker 12: the EV space, all you would hear is we're always 517 00:23:49,720 --> 00:23:53,199 Speaker 12: competing for number two. We can never surpass Tesla. And 518 00:23:53,359 --> 00:23:56,080 Speaker 12: whether you're looking at the kind of EV car space 519 00:23:56,240 --> 00:23:59,119 Speaker 12: or even EV charging, the fact that Tesla's taking a 520 00:23:59,119 --> 00:24:01,760 Speaker 12: step back birth both in terms of where they're focusing 521 00:24:01,760 --> 00:24:05,160 Speaker 12: their sales as well as their EV charging network efforts, 522 00:24:05,200 --> 00:24:06,919 Speaker 12: it means that other people, if they are able to 523 00:24:06,920 --> 00:24:08,440 Speaker 12: step up can make in roads. 524 00:24:08,840 --> 00:24:11,919 Speaker 3: It feels like after an initial surge over the last five, six, 525 00:24:12,000 --> 00:24:16,320 Speaker 3: seven years that there's been a lull in demand for evs, 526 00:24:16,400 --> 00:24:18,240 Speaker 3: And I don't know a do you believe that's the 527 00:24:18,280 --> 00:24:20,800 Speaker 3: case in b Why is that? Do you think? 528 00:24:21,200 --> 00:24:21,280 Speaker 5: So? 529 00:24:21,480 --> 00:24:24,040 Speaker 12: Last year we saw in the US about fifty percent 530 00:24:24,119 --> 00:24:27,879 Speaker 12: year on year growth for electric vehicles from twenty twenty two, So, 531 00:24:27,960 --> 00:24:30,200 Speaker 12: if anything, that was a really strong and successful year. 532 00:24:30,359 --> 00:24:32,840 Speaker 12: We did begin to see some of the challenges around 533 00:24:33,520 --> 00:24:36,000 Speaker 12: adoption as you move from that kind of early adopter's 534 00:24:36,040 --> 00:24:38,400 Speaker 12: phase to more of the mass market, and that's where 535 00:24:38,400 --> 00:24:40,639 Speaker 12: consumers are going to be less patient with the technology 536 00:24:41,080 --> 00:24:43,720 Speaker 12: and with the upfront cost as well as charging infrastructure. 537 00:24:43,760 --> 00:24:45,720 Speaker 12: And I think what makes the US different than other 538 00:24:45,760 --> 00:24:48,840 Speaker 12: regions like Europe and China is that charging really hasn't 539 00:24:48,880 --> 00:24:51,480 Speaker 12: been figured out to the same extent, and the heavy 540 00:24:51,520 --> 00:24:54,280 Speaker 12: reliance on Tesla means that when Tesla steps back from 541 00:24:54,359 --> 00:24:58,360 Speaker 12: charging investments, you know, lays off many of the supercharging 542 00:24:58,400 --> 00:25:00,879 Speaker 12: network team, it means it's to be more of a 543 00:25:01,440 --> 00:25:05,560 Speaker 12: uncertainty for the consumer here. So data in the first quarter, 544 00:25:05,640 --> 00:25:08,240 Speaker 12: you know, we're finalizing We've seen a lot of data 545 00:25:08,280 --> 00:25:13,760 Speaker 12: come up outside of Tesla. Other automakers like Ford, Hyendai, 546 00:25:14,040 --> 00:25:17,920 Speaker 12: Rivian all did quite well, but GM, Volkswagen and Tesla 547 00:25:18,200 --> 00:25:19,919 Speaker 12: took a step back and that has a big impact 548 00:25:19,920 --> 00:25:20,320 Speaker 12: on the market. 549 00:25:20,400 --> 00:25:22,359 Speaker 11: In the first quarter. We'll see how Q two goes. 550 00:25:22,720 --> 00:25:25,280 Speaker 5: We are looking down the barrel here this week of 551 00:25:25,400 --> 00:25:28,600 Speaker 5: some tariffs that will be imposed on China EV's imports 552 00:25:28,640 --> 00:25:30,959 Speaker 5: into the US, just knowing what we know about how 553 00:25:30,960 --> 00:25:34,040 Speaker 5: these car makers operate and look at losses per vehicle 554 00:25:34,040 --> 00:25:36,439 Speaker 5: when it comes to EV's, what would be better for 555 00:25:36,600 --> 00:25:39,600 Speaker 5: Rivian and GM, like more competition to kind of light 556 00:25:39,640 --> 00:25:43,239 Speaker 5: a fire or less that gives them more time to 557 00:25:43,320 --> 00:25:43,920 Speaker 5: do stuff. 558 00:25:44,280 --> 00:25:45,639 Speaker 11: Yeah, that's a really good question, Alex. 559 00:25:45,680 --> 00:25:48,360 Speaker 12: I think everyone that you speak to in the automotive 560 00:25:48,400 --> 00:25:51,080 Speaker 12: space knows that the Chinese EV makers are out there, 561 00:25:51,119 --> 00:25:54,360 Speaker 12: your bid and other brands. I mean, we even saw 562 00:25:54,480 --> 00:25:58,240 Speaker 12: Zeker last week a public right, Hio public and. 563 00:25:58,240 --> 00:26:00,640 Speaker 11: So the pressure should be there. 564 00:26:01,640 --> 00:26:05,040 Speaker 12: Tariffs, you're not seeing really too many Chinese evs really 565 00:26:05,080 --> 00:26:08,119 Speaker 12: imported into the US. Volvo and Pulstor might be the exception, 566 00:26:08,640 --> 00:26:10,679 Speaker 12: but really what those tariffs looks like. I want to 567 00:26:10,720 --> 00:26:12,640 Speaker 12: keep an eye on the battery portion because I think 568 00:26:12,640 --> 00:26:15,240 Speaker 12: that could have more of an impact than the couple 569 00:26:15,400 --> 00:26:17,880 Speaker 12: or you know, the thousands of evs from China, specifically, 570 00:26:18,359 --> 00:26:21,560 Speaker 12: given that the battery supply chain is still very heavily 571 00:26:21,600 --> 00:26:25,480 Speaker 12: reliant on China, particularly for those lithyan iron phosphate batteries LFP, 572 00:26:25,880 --> 00:26:27,800 Speaker 12: which is used to help bring down costs and electric 573 00:26:27,920 --> 00:26:31,440 Speaker 12: vehicles for GM. You know, GM is competing in China already, 574 00:26:31,480 --> 00:26:34,000 Speaker 12: so they have a good sense of how competitive that 575 00:26:34,080 --> 00:26:36,600 Speaker 12: space is. And really there you're seeing great technology and 576 00:26:36,760 --> 00:26:40,879 Speaker 12: upfront cost at a low level, which eventually you'll see here. 577 00:26:41,040 --> 00:26:43,160 Speaker 12: I mean, if anything, you know, maybe the tariffs help 578 00:26:43,440 --> 00:26:45,879 Speaker 12: stem the gap a little bit, but if you fall 579 00:26:45,920 --> 00:26:48,480 Speaker 12: behind and don't invest in electric vehicles now, you know, 580 00:26:48,480 --> 00:26:50,399 Speaker 12: we could be having the same conversation in two to 581 00:26:50,400 --> 00:26:52,639 Speaker 12: three years with those Chinese automakers aiming to come to 582 00:26:52,680 --> 00:26:53,159 Speaker 12: the US. 583 00:26:53,560 --> 00:26:56,960 Speaker 3: So do we have a sense that the US OEMs forwards, 584 00:26:56,960 --> 00:26:59,280 Speaker 3: the GM, Stilantis or the world have pulled back on 585 00:26:59,320 --> 00:27:02,879 Speaker 3: their commitment to EV's or maybe they're timing of investments 586 00:27:02,920 --> 00:27:03,560 Speaker 3: on EV's. 587 00:27:03,880 --> 00:27:04,760 Speaker 2: You think that's happening. 588 00:27:05,240 --> 00:27:08,160 Speaker 12: I think the timing on investment in evs is happening 589 00:27:08,320 --> 00:27:11,600 Speaker 12: right in terms of their overall strategy. They've been I 590 00:27:11,680 --> 00:27:14,200 Speaker 12: think approaching the market in a different way. Ford has 591 00:27:14,240 --> 00:27:17,400 Speaker 12: been pushing towards this new EV platform that they're beginning 592 00:27:17,440 --> 00:27:20,080 Speaker 12: to kind of produce in next year. GM of course 593 00:27:20,080 --> 00:27:23,040 Speaker 12: has its Ultium platform, And for Stilantis, they don't actually 594 00:27:23,080 --> 00:27:25,359 Speaker 12: have any fully electric vehicles here yet, although they have 595 00:27:25,400 --> 00:27:27,440 Speaker 12: the highest selling plug in hybrid electric. 596 00:27:27,119 --> 00:27:28,480 Speaker 11: Vehicle, the Jeep Wrangler. 597 00:27:29,200 --> 00:27:31,200 Speaker 12: A lot of this is un timing, but what I 598 00:27:31,280 --> 00:27:35,359 Speaker 12: always like to say is that timing and the investment 599 00:27:36,080 --> 00:27:38,959 Speaker 12: is one thing, but you have to produce ultimately, and 600 00:27:39,040 --> 00:27:41,560 Speaker 12: so if it's a delay of six months a year, 601 00:27:41,640 --> 00:27:44,359 Speaker 12: that's okay, but the cost of inaction gets higher. And 602 00:27:44,400 --> 00:27:47,159 Speaker 12: if you really say, not just to the Chinese automakers, 603 00:27:47,160 --> 00:27:49,880 Speaker 12: but other automakers like Handai and Kia, really could make 604 00:27:50,240 --> 00:27:51,720 Speaker 12: up a lot of ground here, and that's why there's 605 00:27:51,720 --> 00:27:55,000 Speaker 12: such a big opportunity for Rivian to kind of step in. 606 00:27:55,440 --> 00:27:58,720 Speaker 12: Ford and Tesla remain the number one and two EV 607 00:27:58,840 --> 00:28:02,080 Speaker 12: makers here in the US, but they don't really have 608 00:28:02,119 --> 00:28:04,280 Speaker 12: any near term goals for twenty twenty four, and so 609 00:28:04,440 --> 00:28:06,560 Speaker 12: when analysts are taking a look trying to see what 610 00:28:06,560 --> 00:28:09,199 Speaker 12: they're doing next, having a clear product roadmap could kind 611 00:28:09,200 --> 00:28:10,840 Speaker 12: of clear up a lot of that uncertainty, and we 612 00:28:10,880 --> 00:28:11,800 Speaker 12: don't have it right now. 613 00:28:12,240 --> 00:28:13,920 Speaker 5: When am I gonna be able to buy a sixteen 614 00:28:13,920 --> 00:28:16,840 Speaker 5: thousand dollars EV that helps me go from here to 615 00:28:16,880 --> 00:28:18,680 Speaker 5: the Berkshires without a charge? 616 00:28:18,920 --> 00:28:19,119 Speaker 11: You know? 617 00:28:19,160 --> 00:28:20,960 Speaker 12: And I think there's a lot of interesting EV startups 618 00:28:20,960 --> 00:28:23,600 Speaker 12: out there, and you seem pretty well attuned. I think 619 00:28:23,680 --> 00:28:26,240 Speaker 12: we're going to see starting next year, more EV's kind 620 00:28:26,240 --> 00:28:28,480 Speaker 12: of coming down to the low thirty thousand dollars price point. 621 00:28:28,760 --> 00:28:30,199 Speaker 12: The big question is how many of them are going 622 00:28:30,240 --> 00:28:32,199 Speaker 12: to have that seventy five hundred dollars tax credit. And 623 00:28:32,240 --> 00:28:34,320 Speaker 12: of course there's a lot of policy questions, and then 624 00:28:34,320 --> 00:28:36,560 Speaker 12: an election this year that'll have an impact on that too. 625 00:28:36,640 --> 00:28:40,120 Speaker 6: And takeaways is not yet colple of years. Yeah, of course, I. 626 00:28:40,120 --> 00:28:42,960 Speaker 5: Think the seagull is tw thousand books. 627 00:28:43,000 --> 00:28:44,840 Speaker 12: I'm going to look at this thing, all right, Yeah, right, 628 00:28:44,960 --> 00:28:46,840 Speaker 12: it's the BYD's, but. 629 00:28:46,720 --> 00:28:48,280 Speaker 6: That's the whole point that's byd. 630 00:28:49,040 --> 00:28:52,600 Speaker 12: Okay well tiny yeah, in that car, it's about twenty 631 00:28:52,600 --> 00:28:53,719 Speaker 12: thousand dollars in Mexico. 632 00:28:53,800 --> 00:28:55,120 Speaker 11: So the ten thousand, I think is. 633 00:28:55,880 --> 00:28:58,120 Speaker 6: Maybe not the price that I would be paying. Only 634 00:28:58,200 --> 00:28:59,280 Speaker 6: one Winshield wiper. 635 00:29:00,560 --> 00:29:01,320 Speaker 2: How many do you need? 636 00:29:01,400 --> 00:29:01,960 Speaker 6: How does that work? 637 00:29:02,680 --> 00:29:03,959 Speaker 5: I mean to be fair, did the two really make 638 00:29:04,000 --> 00:29:07,400 Speaker 5: a difference, Corey, Thanks a lot, Corey Kanter Bloomberg any 639 00:29:08,360 --> 00:29:10,480 Speaker 5: US electric vehicle analysts. 640 00:29:10,560 --> 00:29:12,560 Speaker 6: But if you put one hundred percent tariff on that, 641 00:29:13,440 --> 00:29:14,480 Speaker 6: how much is that going to cost me? 642 00:29:14,720 --> 00:29:15,280 Speaker 2: That's not going to help. 643 00:29:15,280 --> 00:29:16,120 Speaker 6: But if I could get a ten. 644 00:29:16,080 --> 00:29:19,600 Speaker 5: Thousand dollars one that can get me from here to 645 00:29:19,640 --> 00:29:22,000 Speaker 5: the Berg heres with one charge, I would definitely consider 646 00:29:22,040 --> 00:29:22,560 Speaker 5: something like that. 647 00:29:22,800 --> 00:29:26,440 Speaker 6: This is why, Oh that's. 648 00:29:26,400 --> 00:29:28,880 Speaker 5: Right, the real cold, all of a sudden you have to, like, 649 00:29:29,040 --> 00:29:32,520 Speaker 5: you know, walk home, take your seventeen days. It's just 650 00:29:32,560 --> 00:29:34,960 Speaker 5: basically the batteries don't work that well in the cold. 651 00:29:34,960 --> 00:29:36,880 Speaker 5: They got to figure that one out, all. 652 00:29:36,840 --> 00:29:38,479 Speaker 6: Right, Thanks a lot, Corey. Really appreciate that. 653 00:29:38,520 --> 00:29:40,120 Speaker 5: This is all the pressing stuff that we get you 654 00:29:40,120 --> 00:29:42,640 Speaker 5: here on Bloomberg Intelligence and Bloomberg an EF. 655 00:29:45,160 --> 00:29:49,080 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 656 00:29:49,160 --> 00:29:52,200 Speaker 1: weekdays at ten am Eastern on Apple car Play and 657 00:29:52,200 --> 00:29:55,120 Speaker 1: Android Auto with the Bloomberg Business app. You can also 658 00:29:55,200 --> 00:29:58,680 Speaker 1: listen live on Amazon Alexa from our flagship New York station. 659 00:29:59,040 --> 00:30:01,800 Speaker 1: Just say Alexa play Bloomberg eleven thirty. 660 00:30:03,120 --> 00:30:06,160 Speaker 3: All right, last week I was visiting our good friends 661 00:30:06,560 --> 00:30:10,840 Speaker 3: at Boston Consulting Group BCG UP in Boston, I tended 662 00:30:10,880 --> 00:30:13,600 Speaker 3: the Boston Consulting Group Edge Expo in Boston. Had a 663 00:30:13,640 --> 00:30:16,520 Speaker 3: lot of great conversations with executives at BCG. One was 664 00:30:16,560 --> 00:30:19,200 Speaker 3: with Sessha iiro North American co Chare. He focuses on 665 00:30:19,600 --> 00:30:22,400 Speaker 3: helping clients think through the levels of value for new 666 00:30:22,520 --> 00:30:25,440 Speaker 3: artificial intelligence and the role of ai'm moving forward. I 667 00:30:25,440 --> 00:30:27,840 Speaker 3: began by asking him to tell us what he thinks 668 00:30:27,880 --> 00:30:29,120 Speaker 3: AI is today. 669 00:30:29,160 --> 00:30:34,360 Speaker 7: Let's listen, Sylvia, really looking at how we can really 670 00:30:34,440 --> 00:30:39,480 Speaker 7: leverage technology and when we say intelligence, being able to 671 00:30:39,520 --> 00:30:43,120 Speaker 7: really interpret data, apply data to really drive change, to 672 00:30:43,280 --> 00:30:48,120 Speaker 7: create value inside of organizations. It's how do you take 673 00:30:48,200 --> 00:30:51,680 Speaker 7: the data apply it against processes to really get rounds 674 00:30:51,680 --> 00:30:56,760 Speaker 7: of value around either productivity, around cost, around top line, 675 00:30:56,960 --> 00:31:01,479 Speaker 7: around customer impact, around customer intimate, see and delight. So 676 00:31:01,520 --> 00:31:05,480 Speaker 7: how do you essentially use data to generate that insight. 677 00:31:05,560 --> 00:31:07,480 Speaker 7: So in a simple way, that's what he is about. 678 00:31:07,720 --> 00:31:10,400 Speaker 7: A lot that we are clearly observing right now. Is 679 00:31:10,480 --> 00:31:14,040 Speaker 7: also not just predictive AI, which has been around for 680 00:31:14,160 --> 00:31:18,200 Speaker 7: a long time, but the emergence of generative AI. That 681 00:31:18,320 --> 00:31:23,800 Speaker 7: then adds four specific things. The first is the synthesis 682 00:31:23,880 --> 00:31:27,000 Speaker 7: of information and large reams of information. How do you 683 00:31:27,040 --> 00:31:30,440 Speaker 7: synthesize and get the right information in the right construct 684 00:31:30,480 --> 00:31:33,600 Speaker 7: to the right person. There's a lot around generation. We 685 00:31:33,720 --> 00:31:37,840 Speaker 7: actually can generate not only text, but we can generate voice, 686 00:31:37,840 --> 00:31:42,120 Speaker 7: we can generate videos. The third is it's very conversational, right, 687 00:31:42,160 --> 00:31:45,520 Speaker 7: so you can actually engage with very low friction. And 688 00:31:45,560 --> 00:31:48,240 Speaker 7: then the last is it has the ability to reason, 689 00:31:48,760 --> 00:31:51,440 Speaker 7: so it can actually have a good dialogue with you. 690 00:31:51,840 --> 00:31:54,640 Speaker 7: So we are starting to see these additional attributes in 691 00:31:54,680 --> 00:31:57,320 Speaker 7: gen AI that are actually bringing a lot more value 692 00:31:57,360 --> 00:31:57,960 Speaker 7: front inside. 693 00:31:58,400 --> 00:31:59,040 Speaker 2: It's interesting. 694 00:31:59,600 --> 00:32:02,200 Speaker 3: I think there is Bloomberg ran a story a quarter 695 00:32:02,320 --> 00:32:04,440 Speaker 3: or two ago. Every company in the S and P. 696 00:32:04,520 --> 00:32:06,560 Speaker 3: Five hundred on their earnings conference. 697 00:32:06,200 --> 00:32:09,120 Speaker 2: Call mentioned AI. It has come out of nowhere. 698 00:32:09,400 --> 00:32:13,520 Speaker 3: How receptive are your clients to discussing AI, making the 699 00:32:13,600 --> 00:32:18,480 Speaker 3: investments in AI, whether that's upping their technology budget, reallocating 700 00:32:18,520 --> 00:32:19,560 Speaker 3: their technology budget. 701 00:32:19,560 --> 00:32:20,600 Speaker 2: How do those discussions go? 702 00:32:21,160 --> 00:32:24,280 Speaker 7: So we did a survey earlier this year. We are 703 00:32:24,280 --> 00:32:28,680 Speaker 7: clearly seeing seventy percent of execs increasing the payment okay, 704 00:32:28,880 --> 00:32:32,520 Speaker 7: and then within that that seventy percent. We are seeing, 705 00:32:32,880 --> 00:32:35,800 Speaker 7: you know, fifty percent of execs say that JENNYI is 706 00:32:35,840 --> 00:32:39,479 Speaker 7: going to be a top priority. So we're clearly seeing 707 00:32:40,360 --> 00:32:44,280 Speaker 7: more investments going against leveraging this to deliver to value. 708 00:32:44,400 --> 00:32:46,480 Speaker 7: Now that being said, the other thing that we're also 709 00:32:46,560 --> 00:32:50,120 Speaker 7: observing is there's only one in ten executives who's are 710 00:32:50,320 --> 00:32:53,479 Speaker 7: companies that are truly pioneers. Okay, right, the other nine 711 00:32:53,560 --> 00:32:55,760 Speaker 7: and ten are I would say doing a bit and watch. 712 00:32:56,440 --> 00:33:00,160 Speaker 7: But what we're really observing that are amazing characteristics in 713 00:33:00,200 --> 00:33:04,080 Speaker 7: these pioneers. One is they are massively ambitious. They truly 714 00:33:04,120 --> 00:33:07,040 Speaker 7: see the potential of the technology. They want to get 715 00:33:07,080 --> 00:33:10,040 Speaker 7: the value from it, they're investing in it. They're very 716 00:33:10,120 --> 00:33:13,960 Speaker 7: business value driven. That's one. And their ambition, what we 717 00:33:14,000 --> 00:33:16,440 Speaker 7: observe is anyway one point three to one point five 718 00:33:16,480 --> 00:33:18,720 Speaker 7: times the ambition of the folks who are not investing. 719 00:33:19,400 --> 00:33:23,920 Speaker 7: The second is we clearly see upskilling and reskilling to 720 00:33:24,000 --> 00:33:29,840 Speaker 7: be center of their focus. Forty six percent of people 721 00:33:30,080 --> 00:33:33,560 Speaker 7: will need to be reskilled. It's bigger, it's a big number. 722 00:33:33,760 --> 00:33:36,200 Speaker 7: It's a big number in organizations. You think about organizations 723 00:33:36,200 --> 00:33:38,360 Speaker 7: that are you know, fifty hundred thousand people, you're drunging 724 00:33:38,360 --> 00:33:40,680 Speaker 7: about thousands and thousands of people that need to be reskilled. 725 00:33:41,400 --> 00:33:44,680 Speaker 7: Also related to that are senior executive Sixty percent of 726 00:33:44,760 --> 00:33:48,000 Speaker 7: senior executives need to be upskilled so they know how 727 00:33:48,040 --> 00:33:51,040 Speaker 7: to be good sponsors to actually apply the technology. The 728 00:33:51,120 --> 00:33:55,040 Speaker 7: third thing is intimately understanding costs. I mean, many folks 729 00:33:55,120 --> 00:33:58,160 Speaker 7: understand derect costs. Indrect costs are still not fully in 730 00:33:58,200 --> 00:34:01,440 Speaker 7: the equations understanding the cost of what the stakes not 731 00:34:01,480 --> 00:34:03,880 Speaker 7: only on the build but also once you deploy to 732 00:34:04,000 --> 00:34:08,040 Speaker 7: operate it. The fourth is partnerships. Are you truly engaging 733 00:34:08,040 --> 00:34:10,279 Speaker 7: the right partners in your ecosystem because this is not 734 00:34:10,360 --> 00:34:13,799 Speaker 7: a one partner solved, this is a multi partner solve. 735 00:34:14,280 --> 00:34:17,080 Speaker 7: And then the last thing is which is very important 736 00:34:17,080 --> 00:34:20,640 Speaker 7: to us at VCG, is leading with responsibile AI. You 737 00:34:20,680 --> 00:34:22,960 Speaker 7: can apply the technology, there are a lot of downsides 738 00:34:23,000 --> 00:34:24,360 Speaker 7: to it. How do you put the right controls in? 739 00:34:24,800 --> 00:34:26,400 Speaker 3: If you come into my office or when you come 740 00:34:26,400 --> 00:34:28,920 Speaker 3: into my office and really make the case that I 741 00:34:29,000 --> 00:34:32,880 Speaker 3: need to invest in AI, I'm going to ask you, okay, 742 00:34:32,920 --> 00:34:34,200 Speaker 3: but is it going to make me better? 743 00:34:34,320 --> 00:34:35,480 Speaker 2: Is it going to make me more efficient? 744 00:34:35,520 --> 00:34:37,240 Speaker 3: Because I'm gonna have to up my tech budget. 745 00:34:37,280 --> 00:34:39,160 Speaker 2: You just explain to me how I'm probably gonna need 746 00:34:39,200 --> 00:34:39,600 Speaker 2: to up my. 747 00:34:39,520 --> 00:34:41,359 Speaker 3: Tech budget over a long period of time. 748 00:34:41,760 --> 00:34:44,200 Speaker 2: What's the payoff for me from a P and L perspective? 749 00:34:44,200 --> 00:34:45,759 Speaker 2: Can you make that ROI case? 750 00:34:45,920 --> 00:34:49,280 Speaker 7: Yeah, So there are three pathways to value that we observe. 751 00:34:49,920 --> 00:34:54,560 Speaker 7: The first is what we call deploy, where existing solutions 752 00:34:54,600 --> 00:34:57,720 Speaker 7: already have GENII features. So if you think about Office 753 00:34:57,719 --> 00:35:00,560 Speaker 7: three sixty five pilot, or you think about giub copilot, 754 00:35:00,680 --> 00:35:04,000 Speaker 7: or you think about Zoom, the video application that can 755 00:35:04,520 --> 00:35:07,680 Speaker 7: generate a transcript from a call, it's turning on those 756 00:35:07,680 --> 00:35:10,640 Speaker 7: features and you can start to see value that's tend 757 00:35:10,680 --> 00:35:13,480 Speaker 7: to twenty percent there. The second pathway is what we 758 00:35:13,520 --> 00:35:17,200 Speaker 7: call reshape, which is driving end to end transformations of 759 00:35:17,239 --> 00:35:19,759 Speaker 7: specific functions. You think about the marketing function, you think 760 00:35:19,880 --> 00:35:22,040 Speaker 7: about the finance function, or you think about the customer 761 00:35:22,040 --> 00:35:25,439 Speaker 7: service function, or let's say the underwriting process in an 762 00:35:25,440 --> 00:35:29,040 Speaker 7: insurance company. That's thirty to fifty percent in terms of 763 00:35:29,040 --> 00:35:32,640 Speaker 7: not just productivity unlock, but in terms of speed of performance, 764 00:35:32,760 --> 00:35:36,280 Speaker 7: in terms of value delivery that happens to the customer 765 00:35:36,280 --> 00:35:38,239 Speaker 7: that is at the end of that process. And then 766 00:35:38,280 --> 00:35:39,960 Speaker 7: the third thing that we're observing is what we call 767 00:35:40,000 --> 00:35:42,680 Speaker 7: invent where it's completely new business models. So you think 768 00:35:42,680 --> 00:35:45,440 Speaker 7: about creating a new beauty assistant? Do you think about 769 00:35:45,480 --> 00:35:49,520 Speaker 7: creating new molecules? V Recently and on the invent side, 770 00:35:49,800 --> 00:35:55,040 Speaker 7: we have now done a longitudinal study where AI generated 771 00:35:55,080 --> 00:36:00,640 Speaker 7: molecules have two times the success coming out of clinical 772 00:36:00,760 --> 00:36:04,880 Speaker 7: phase one trials compared to molecules that are created in 773 00:36:04,920 --> 00:36:07,600 Speaker 7: the latter. Look, that's amaze, it is right, And you 774 00:36:07,600 --> 00:36:09,880 Speaker 7: think about ROI there, I mean, the r I is 775 00:36:10,040 --> 00:36:14,600 Speaker 7: so obvious, so we are seeing specific sectors and you know, 776 00:36:14,680 --> 00:36:18,000 Speaker 7: specific functions really go through massive change. 777 00:36:18,320 --> 00:36:20,600 Speaker 3: I think most people as they've come up the AI 778 00:36:20,680 --> 00:36:25,480 Speaker 3: curve and just understanding, are concerned about responsiblity, this responsible 779 00:36:25,600 --> 00:36:27,480 Speaker 3: use of AI, the risk to AI. 780 00:36:27,560 --> 00:36:29,879 Speaker 2: Will AI take over the world? War, the machines take 781 00:36:29,880 --> 00:36:30,440 Speaker 2: over the world? 782 00:36:30,719 --> 00:36:33,880 Speaker 3: How do you frame that risk out and what do 783 00:36:33,920 --> 00:36:35,200 Speaker 3: you suggest to your clients. 784 00:36:35,640 --> 00:36:37,760 Speaker 7: So one of the things we tell our clients is clearly, 785 00:36:37,760 --> 00:36:39,840 Speaker 7: there's so much of value when you adopt this technology, 786 00:36:39,880 --> 00:36:42,200 Speaker 7: but you have to do it in a really responsible way. 787 00:36:42,680 --> 00:36:45,680 Speaker 7: So what is your responsible AI framework to put against it? 788 00:36:46,560 --> 00:36:49,399 Speaker 7: What is your responsibili strategy? How do you really think 789 00:36:49,440 --> 00:36:52,200 Speaker 7: about responsible II governance. What are your lines of defense? 790 00:36:52,200 --> 00:36:54,520 Speaker 7: How do you actually implement the right structures in place? 791 00:36:55,400 --> 00:36:58,239 Speaker 7: When you think about processes, your product development process or 792 00:36:58,239 --> 00:37:03,120 Speaker 7: your portfolio monitoring process, how you really including responsible AI 793 00:37:03,160 --> 00:37:06,160 Speaker 7: in it? For example, when you're building GENI solutions that 794 00:37:06,200 --> 00:37:08,680 Speaker 7: have lms in the middle of it, are you really 795 00:37:08,719 --> 00:37:12,160 Speaker 7: doing proper verification and validation to ensure that llms perform 796 00:37:12,239 --> 00:37:14,520 Speaker 7: to your objective function and they don't start doing something 797 00:37:14,520 --> 00:37:16,439 Speaker 7: you don't want it to do. Do you have things 798 00:37:16,440 --> 00:37:18,840 Speaker 7: in place for that? So there are a number of 799 00:37:18,880 --> 00:37:22,480 Speaker 7: these different pieces of the entire responsible AI framework. So 800 00:37:22,520 --> 00:37:25,960 Speaker 7: we have a comprehensive framework. We'll lead with that at 801 00:37:25,960 --> 00:37:29,080 Speaker 7: our clients, and it is a capability built So just 802 00:37:29,120 --> 00:37:31,759 Speaker 7: like you have a capability built around risk and compliance, 803 00:37:32,120 --> 00:37:34,200 Speaker 7: there is now a capability built around responsibily. 804 00:37:34,239 --> 00:37:34,960 Speaker 2: Am interesting? 805 00:37:35,320 --> 00:37:38,919 Speaker 3: I know at BCG you have the ten twenty seventy U. 806 00:37:39,040 --> 00:37:39,719 Speaker 2: Yeah, what is that? 807 00:37:41,160 --> 00:37:46,319 Speaker 7: So when we think about truly driving transformative change with technology, 808 00:37:46,360 --> 00:37:50,560 Speaker 7: at the core, our ten twenty seventy is ten percent 809 00:37:50,719 --> 00:37:54,239 Speaker 7: is the algorithm, twenty percent is that data and the 810 00:37:54,320 --> 00:37:59,920 Speaker 7: technology components, and seventy percent is the change management processor engineering, 811 00:38:00,080 --> 00:38:04,320 Speaker 7: the adoption of the technology. So to drive real value 812 00:38:04,400 --> 00:38:07,640 Speaker 7: you need all three. Right, were many organizations I would 813 00:38:07,640 --> 00:38:09,799 Speaker 7: say focus on the ten and the twenty yep, but 814 00:38:09,880 --> 00:38:13,480 Speaker 7: not fully focus on the seventy. And we lead with 815 00:38:13,800 --> 00:38:16,920 Speaker 7: the ten twenty seventy that really drives, so we deliver 816 00:38:17,520 --> 00:38:20,759 Speaker 7: I would say impact and outcomes, not systems. 817 00:38:21,320 --> 00:38:21,640 Speaker 2: Right. 818 00:38:21,880 --> 00:38:24,040 Speaker 3: So I mean it sounds like in order for a 819 00:38:24,080 --> 00:38:28,600 Speaker 3: company to really champion AI within your business, the commitment 820 00:38:28,640 --> 00:38:32,280 Speaker 3: has to be from the board to the c suite. 821 00:38:32,040 --> 00:38:33,799 Speaker 2: Down all the way through the organization. 822 00:38:33,840 --> 00:38:37,120 Speaker 3: Because it sounds like, A it's a big financial investment, 823 00:38:37,160 --> 00:38:38,520 Speaker 3: but B it's a little bit of a different way 824 00:38:38,560 --> 00:38:40,560 Speaker 3: of thinking about your business. 825 00:38:40,840 --> 00:38:41,960 Speaker 2: How do you achieve that? 826 00:38:43,640 --> 00:38:47,560 Speaker 7: I think it goes back to if you think about 827 00:38:47,600 --> 00:38:50,240 Speaker 7: the unbroken chain of why, why does the company exist? 828 00:38:50,360 --> 00:38:52,760 Speaker 7: What value does it add to its customers? What value 829 00:38:52,760 --> 00:38:55,040 Speaker 7: does it add to it employees, how does it contribute 830 00:38:55,040 --> 00:38:58,920 Speaker 7: to society? If you really unpack the why, I think 831 00:38:59,160 --> 00:39:01,600 Speaker 7: this does need to start at the top. So what 832 00:39:01,800 --> 00:39:07,560 Speaker 7: is the competitive differentiation that technology or GENI can drive 833 00:39:07,600 --> 00:39:10,120 Speaker 7: for an organization? It starts from there, and that is 834 00:39:10,160 --> 00:39:13,080 Speaker 7: a CEO level point, and then it migrates into what 835 00:39:13,120 --> 00:39:14,759 Speaker 7: does it do for my customer, What does it do 836 00:39:14,840 --> 00:39:16,960 Speaker 7: for my product? What does it do for my processes? 837 00:39:17,000 --> 00:39:19,759 Speaker 7: What does it do for my employees? All of that 838 00:39:19,880 --> 00:39:25,600 Speaker 7: then follows through. So if you think about value, customer intimacy, value. 839 00:39:26,239 --> 00:39:29,000 Speaker 7: We have a car manufacturer that has integrated GENI into 840 00:39:29,000 --> 00:39:31,680 Speaker 7: their cars. They sell two million cars a year. That 841 00:39:31,960 --> 00:39:35,360 Speaker 7: is changing the way in which consumers experience their product. 842 00:39:35,680 --> 00:39:39,239 Speaker 7: You think about product lines. Google recently came out with 843 00:39:39,280 --> 00:39:41,960 Speaker 7: Google Wids, where you can actually now create videos on demand. 844 00:39:41,960 --> 00:39:45,759 Speaker 7: The marginal cost of creativity has dropped substantially. You can 845 00:39:45,760 --> 00:39:48,480 Speaker 7: build not only images, but you can build presentations, you 846 00:39:48,480 --> 00:39:51,000 Speaker 7: can build videos. It's so easy to do it. You 847 00:39:51,040 --> 00:39:54,760 Speaker 7: think about process excellence. We already talked about our software development. 848 00:39:54,840 --> 00:39:59,440 Speaker 7: You have a large financial global bank that is looking 849 00:39:59,520 --> 00:40:02,600 Speaker 7: to imp productivity by thirty to forty percent. I mean, 850 00:40:02,640 --> 00:40:06,080 Speaker 7: these things are real, and I think the CEOs clearly 851 00:40:06,120 --> 00:40:09,279 Speaker 7: know that they can drive differential competitive advantage across all 852 00:40:09,320 --> 00:40:11,360 Speaker 7: of these different dimensions. 853 00:40:11,960 --> 00:40:15,160 Speaker 3: What's the biggest pushback you get from your clients about this? 854 00:40:15,400 --> 00:40:16,200 Speaker 7: Is it the cost? 855 00:40:16,360 --> 00:40:20,480 Speaker 3: Is it the I don't know, I'm just technology concerned 856 00:40:20,480 --> 00:40:21,240 Speaker 3: about technology. 857 00:40:21,280 --> 00:40:22,319 Speaker 2: What's the pushback you get? 858 00:40:24,320 --> 00:40:27,560 Speaker 7: I think the biggest thing that we see. If I 859 00:40:27,560 --> 00:40:33,319 Speaker 7: think about CEOs, their entire focus is on how can 860 00:40:33,360 --> 00:40:37,839 Speaker 7: I get to this value fast? So is it going 861 00:40:37,920 --> 00:40:43,279 Speaker 7: to really change the trajectory of my business? Talk to 862 00:40:43,320 --> 00:40:46,480 Speaker 7: me about outcomes that I can deliver too. So when 863 00:40:46,520 --> 00:40:49,239 Speaker 7: we think about the three dimensions of you know what 864 00:40:49,400 --> 00:40:52,240 Speaker 7: really matters to these folks is can I deliver outcomes 865 00:40:52,280 --> 00:40:54,880 Speaker 7: at scale? Don't talk to me about an experiment to 866 00:40:54,880 --> 00:40:58,040 Speaker 7: talk about it at scale? Can I combine JENNYI with 867 00:40:58,120 --> 00:41:02,000 Speaker 7: predictive AI and other technologies? Automation? Show me the end 868 00:41:02,000 --> 00:41:04,880 Speaker 7: to end, not just the once sliver of this. And 869 00:41:04,920 --> 00:41:07,040 Speaker 7: then I think the third is the ten twenty seventy. 870 00:41:07,400 --> 00:41:08,960 Speaker 7: A lot of folks need with oh this is such 871 00:41:09,000 --> 00:41:11,960 Speaker 7: cool technology, but can you really show me the impact? 872 00:41:12,000 --> 00:41:16,600 Speaker 7: Will my people be better off when they adopt the technology? 873 00:41:17,239 --> 00:41:18,719 Speaker 7: That's a big part of our PC. So I think 874 00:41:18,719 --> 00:41:21,640 Speaker 7: those are the three things outcome at scale, predictive plus 875 00:41:21,680 --> 00:41:25,480 Speaker 7: plus generative AI plus automation and then the third pieces 876 00:41:25,480 --> 00:41:26,520 Speaker 7: of ten twenty seventy. 877 00:41:26,800 --> 00:41:30,040 Speaker 3: All right, that was my conversation with Sesshire, he's a 878 00:41:30,040 --> 00:41:32,600 Speaker 3: North American co chair of the Boston Consulting Group. Talking 879 00:41:32,600 --> 00:41:34,960 Speaker 3: about AI. We were up at their Expo condors up 880 00:41:34,960 --> 00:41:38,000 Speaker 3: in Boston last week. Can't say AI. It was front 881 00:41:38,000 --> 00:41:41,279 Speaker 3: and center as BCG talks to their clients. That's what 882 00:41:41,320 --> 00:41:43,960 Speaker 3: the CEOs want to talk about in one strategy, about 883 00:41:43,960 --> 00:41:46,399 Speaker 3: how to invest and when to invest and how much 884 00:41:46,480 --> 00:41:48,520 Speaker 3: to invest. So it was good to check in with 885 00:41:48,560 --> 00:41:50,000 Speaker 3: some of the smart folks at BCG. 886 00:41:50,440 --> 00:41:54,600 Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apple, Spot, 887 00:41:54,680 --> 00:41:57,680 Speaker 1: of Pye, and anywhere else you get your podcasts. Listen 888 00:41:57,800 --> 00:42:01,040 Speaker 1: live each weekday ten am to noon East on Bloomberg 889 00:42:01,080 --> 00:42:04,560 Speaker 1: dot com, the iHeartRadio app, tune In, and the Bloomberg 890 00:42:04,600 --> 00:42:07,720 Speaker 1: Business app. You can also watch us live every weekday 891 00:42:07,760 --> 00:42:10,440 Speaker 1: on YouTube and always on the Bloomberg terminal