1 00:00:02,720 --> 00:00:10,600 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. You're listening to the 2 00:00:10,600 --> 00:00:14,600 Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am 3 00:00:14,640 --> 00:00:17,880 Speaker 1: Eastern on Apple Coarclay, and Android Auto with the Bloomberg 4 00:00:17,920 --> 00:00:21,000 Speaker 1: Business App. Listen on demand wherever you get your podcasts, 5 00:00:21,360 --> 00:00:23,120 Speaker 1: or watch us live on YouTube. 6 00:00:23,520 --> 00:00:25,599 Speaker 2: I Paulus pointing out earlier that we got some of 7 00:00:25,600 --> 00:00:28,880 Speaker 2: that economic data. Factory orders lower for April. You also 8 00:00:29,320 --> 00:00:32,240 Speaker 2: had Jolt job openings coming in better than expected. The 9 00:00:32,320 --> 00:00:35,400 Speaker 2: quits rate also slightly better, although we are seeing more 10 00:00:35,440 --> 00:00:37,800 Speaker 2: people getting fired or laid off, So I want to 11 00:00:37,920 --> 00:00:40,720 Speaker 2: take that into account. With Ira Jersey, Boomberg Intelligence Senior 12 00:00:40,800 --> 00:00:43,680 Speaker 2: US interest rate strategist, you're seeing a little bit of 13 00:00:43,720 --> 00:00:45,600 Speaker 2: selling on that front end. Hey Iira, what's your. 14 00:00:45,520 --> 00:00:46,479 Speaker 3: Take on these numbers? 15 00:00:47,360 --> 00:00:47,560 Speaker 4: Yeah? 16 00:00:47,640 --> 00:00:51,000 Speaker 5: Yeah, I disagree with that take. Actually, the way that 17 00:00:51,040 --> 00:00:53,080 Speaker 5: I look at quits being down a little bit and 18 00:00:53,080 --> 00:00:54,959 Speaker 5: then layoffs being up a little bit is actually weak 19 00:00:55,000 --> 00:00:58,200 Speaker 5: for the economy because if you're if you're not thinking 20 00:00:58,240 --> 00:01:00,440 Speaker 5: you can find another job, that's the reason you don't. 21 00:01:00,800 --> 00:01:02,560 Speaker 5: You don't quit your job because you need to stay 22 00:01:02,560 --> 00:01:05,160 Speaker 5: where you are. And then obviously with layoffs up, that's 23 00:01:05,200 --> 00:01:08,119 Speaker 5: not particularly positive. Either, but it's it's all very incremental. 24 00:01:08,240 --> 00:01:11,759 Speaker 5: This isn't These aren't huge moves, and certainly job openings 25 00:01:11,800 --> 00:01:14,120 Speaker 5: being up a little bit in April, it's still. 26 00:01:13,920 --> 00:01:15,319 Speaker 3: Not back to February levels. 27 00:01:15,360 --> 00:01:18,720 Speaker 5: So again, like you know, we're talked about maybe you know, 28 00:01:18,800 --> 00:01:22,200 Speaker 5: lower highs here in terms of the downtrend. But but 29 00:01:22,240 --> 00:01:24,080 Speaker 5: some of the other data is also important to look at. 30 00:01:24,120 --> 00:01:28,120 Speaker 5: So you think about factory orders, those missed to the downside, 31 00:01:28,319 --> 00:01:31,280 Speaker 5: plus they were revised down as well the prior month. 32 00:01:31,680 --> 00:01:34,560 Speaker 5: So so overall this is you know a little bit 33 00:01:34,600 --> 00:01:37,080 Speaker 5: of a weak economy and you know the front end 34 00:01:37,200 --> 00:01:39,120 Speaker 5: selling off. So two year yields being a little bit 35 00:01:39,200 --> 00:01:41,840 Speaker 5: higher here is a little bit surprising because yes, the 36 00:01:41,920 --> 00:01:45,160 Speaker 5: data isn't terrible, but it's also not particularly good. 37 00:01:47,560 --> 00:01:49,680 Speaker 3: Kind of no matter how you slice it, IRA, it 38 00:01:49,680 --> 00:01:52,160 Speaker 3: seems like this fetter reserve is not really getting any 39 00:01:52,440 --> 00:01:55,560 Speaker 3: clear signals what to do next, which I guess for 40 00:01:55,680 --> 00:01:58,880 Speaker 3: a lot of folks means let's do nothing here. How 41 00:01:58,880 --> 00:02:00,440 Speaker 3: do you think they're going to interpret today data? 42 00:02:02,040 --> 00:02:03,640 Speaker 5: Yeah, well, well I think that they're going to not 43 00:02:03,680 --> 00:02:05,800 Speaker 5: be pleased with today's data, but at the same time 44 00:02:05,840 --> 00:02:08,600 Speaker 5: they're waiting for the next shoot to drop on inflation, right, 45 00:02:08,639 --> 00:02:11,520 Speaker 5: so next to so next week's inflation print is going 46 00:02:11,560 --> 00:02:13,440 Speaker 5: to be is going to be important as well as 47 00:02:13,440 --> 00:02:17,320 Speaker 5: this Friday's payrolls report. But you know that the likelihood 48 00:02:17,360 --> 00:02:19,520 Speaker 5: that the Federal Reserve does anything at the June meeting 49 00:02:19,560 --> 00:02:22,280 Speaker 5: is near zero because you're waiting to see what the 50 00:02:22,320 --> 00:02:25,760 Speaker 5: fallout is of all the tariffs. Will that increase prices? 51 00:02:25,800 --> 00:02:28,960 Speaker 5: Will how will that affect inflation and the inflation outlook. 52 00:02:29,520 --> 00:02:31,720 Speaker 5: So all of those things probably mean that they sit 53 00:02:31,760 --> 00:02:34,480 Speaker 5: on their hands. In fact, the minutes last week, according 54 00:02:34,520 --> 00:02:37,880 Speaker 5: to our natural language processing model, suggested that they were 55 00:02:37,919 --> 00:02:40,320 Speaker 5: as neutral as we want to be at the at. 56 00:02:40,200 --> 00:02:40,919 Speaker 3: The last meeting. 57 00:02:43,120 --> 00:02:45,280 Speaker 2: Yeah, you know, he does this thing and it's really cool. 58 00:02:45,680 --> 00:02:48,639 Speaker 2: You put it in FED speak and then his model tells 59 00:02:48,680 --> 00:02:50,960 Speaker 2: them whether they're on the bullish or the bearish side 60 00:02:51,040 --> 00:02:53,040 Speaker 2: or I guess hawker dove side if we're putting it 61 00:02:53,240 --> 00:02:56,520 Speaker 2: in FED terms. So walk me through what you're expecting 62 00:02:56,560 --> 00:02:58,680 Speaker 2: then for Friday and how are positioned in the market, 63 00:02:58,680 --> 00:03:01,640 Speaker 2: because honestly, it's it's been pretty calm the last two days. 64 00:03:02,840 --> 00:03:04,880 Speaker 5: Well, so one of the one of the things that 65 00:03:04,880 --> 00:03:07,720 Speaker 5: we've noted, and I was talking to to our derivative 66 00:03:07,720 --> 00:03:11,280 Speaker 5: strategist Tander Sandu that just this morning, and he noted 67 00:03:11,360 --> 00:03:13,679 Speaker 5: that for the for long end, right, So we've had 68 00:03:13,680 --> 00:03:15,480 Speaker 5: this lot of angst with what's going on in the 69 00:03:15,480 --> 00:03:17,799 Speaker 5: thirty year debt and even then your debt because of 70 00:03:17,840 --> 00:03:20,440 Speaker 5: the fiscal situation and the fact that we're not really 71 00:03:20,840 --> 00:03:25,200 Speaker 5: removing paying down deficits very much. That that people are 72 00:03:25,560 --> 00:03:28,120 Speaker 5: long puts, right, So people have gotten out of the 73 00:03:28,160 --> 00:03:32,160 Speaker 5: money puts on long bonds. So so therefore it might 74 00:03:32,200 --> 00:03:34,639 Speaker 5: be more difficult for the long end to sell off 75 00:03:34,680 --> 00:03:38,200 Speaker 5: just because people are appropriately hedged. But I do think 76 00:03:38,240 --> 00:03:40,400 Speaker 5: that there's a lot of people who are, you know, 77 00:03:40,520 --> 00:03:43,720 Speaker 5: kind of waiting and seeing what the economic outcome is 78 00:03:43,760 --> 00:03:46,640 Speaker 5: going to be. But you know that being said, there 79 00:03:46,640 --> 00:03:48,880 Speaker 5: are people who are think are nibbling at the edges, 80 00:03:48,920 --> 00:03:51,640 Speaker 5: particularly when you get up to that five percent level 81 00:03:51,640 --> 00:03:55,880 Speaker 5: in thirty year bonds because liability driven investors, so insurance companies, 82 00:03:55,960 --> 00:03:59,480 Speaker 5: pension funds and a few others, they're really interested in 83 00:03:59,720 --> 00:04:02,680 Speaker 5: treashjuries at five percent. So I think that's another reason 84 00:04:02,680 --> 00:04:04,760 Speaker 5: why you keep on seeing a bit every time we 85 00:04:04,840 --> 00:04:06,920 Speaker 5: kind of get above that five percent level on the 86 00:04:06,960 --> 00:04:07,440 Speaker 5: long bond. 87 00:04:10,000 --> 00:04:12,960 Speaker 3: Long ago Lisa Bronwitz taught me to look at the 88 00:04:12,960 --> 00:04:16,440 Speaker 3: two tens spread there, and right now it's about fifty 89 00:04:16,480 --> 00:04:20,640 Speaker 3: basis points of steepening. That feels kind of normal to me. 90 00:04:20,680 --> 00:04:22,160 Speaker 3: Are kind of reasonable to me. How does it look 91 00:04:22,200 --> 00:04:22,440 Speaker 3: to you? 92 00:04:23,920 --> 00:04:26,280 Speaker 5: Yeah, so, actually on a historical basis, is actually still 93 00:04:26,360 --> 00:04:29,000 Speaker 5: kind of low. Normally, when you look at the twos 94 00:04:29,000 --> 00:04:31,920 Speaker 5: tens curve, it's somewhere closer to one hundred basis points 95 00:04:31,960 --> 00:04:35,600 Speaker 5: on average. We do think there's some structural reasons why. 96 00:04:35,640 --> 00:04:38,040 Speaker 5: I'm not sure that we'll get up to where it 97 00:04:38,120 --> 00:04:41,880 Speaker 5: has during other cycles, but I do think that steepening 98 00:04:41,960 --> 00:04:44,359 Speaker 5: is still probably the trade. Whether it's bull steepening or 99 00:04:44,360 --> 00:04:47,800 Speaker 5: bear steepening, We're more likely to see steepening than we 100 00:04:47,880 --> 00:04:50,760 Speaker 5: are significant flattening. I think over the next six months, 101 00:04:51,440 --> 00:04:56,080 Speaker 5: you know the fiscal situation and will likely keep the 102 00:04:56,120 --> 00:04:58,719 Speaker 5: long end where it is, or maybe even actually have 103 00:04:58,800 --> 00:05:01,280 Speaker 5: that cheapened, so we yields go a little bit higher. 104 00:05:01,480 --> 00:05:05,000 Speaker 5: But as the economy slows, which is our base case scenario, 105 00:05:05,560 --> 00:05:07,840 Speaker 5: over the course of this year, it will become more 106 00:05:07,880 --> 00:05:10,120 Speaker 5: obvious that the FED is going to cut rates, and 107 00:05:10,120 --> 00:05:13,200 Speaker 5: cut rates, maybe more aggressively than what's currently priced, and 108 00:05:13,240 --> 00:05:15,920 Speaker 5: that will cause two year yields and the front end 109 00:05:15,960 --> 00:05:19,280 Speaker 5: of the yield curve to rally and yields go lower, 110 00:05:19,480 --> 00:05:22,599 Speaker 5: So that will steep in the yield curve. So we 111 00:05:22,680 --> 00:05:24,600 Speaker 5: think that over the course of this year, one way 112 00:05:24,680 --> 00:05:27,200 Speaker 5: or the other, whether it's buller Bear, we'll see more 113 00:05:27,200 --> 00:05:28,240 Speaker 5: steepening of the guild. 114 00:05:28,040 --> 00:05:31,800 Speaker 2: Curve, right, buller Bear, Like, do you get it on 115 00:05:31,800 --> 00:05:33,320 Speaker 2: the front end and that's why the curve goes up? 116 00:05:33,400 --> 00:05:34,479 Speaker 2: Or do you get it on the back end and 117 00:05:34,480 --> 00:05:36,360 Speaker 2: that's selling in the long end, and do we get 118 00:05:36,360 --> 00:05:36,760 Speaker 2: that higher? 119 00:05:36,839 --> 00:05:37,000 Speaker 3: Ira? 120 00:05:37,120 --> 00:05:41,000 Speaker 2: Thanks a lot, Ira Jersey, Bloomberg Intelligence TFUs interest rate strategist. 121 00:05:41,040 --> 00:05:42,560 Speaker 2: Joining us there. 122 00:05:43,320 --> 00:05:47,000 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 123 00:05:47,080 --> 00:05:50,160 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 124 00:05:50,200 --> 00:05:53,480 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 125 00:05:53,560 --> 00:05:56,680 Speaker 1: you get your podcasts, or watch us live on YouTube. 126 00:05:57,080 --> 00:06:00,920 Speaker 3: Today, the OECD, which is international trade orization based in Paris, 127 00:06:01,000 --> 00:06:04,280 Speaker 3: they slashed its government economic forecast due to Donald Trump's 128 00:06:04,320 --> 00:06:07,919 Speaker 3: trade policies, citing the impact of tariffs and uncertainty on 129 00:06:08,160 --> 00:06:11,240 Speaker 3: confidence and investments. Maybe we're starting to really see some 130 00:06:11,720 --> 00:06:16,240 Speaker 3: impacts there globally, Zoe Shay Shaney WECE joins this here 131 00:06:16,320 --> 00:06:20,600 Speaker 3: Western Europe Economy team leader. She's based in Frankfurt. Zoe, 132 00:06:20,720 --> 00:06:23,000 Speaker 3: thanks so much for joining us here. Specifically, what is 133 00:06:23,040 --> 00:06:26,560 Speaker 3: the OECD calling out for the US and for the 134 00:06:26,600 --> 00:06:27,440 Speaker 3: global economy? 135 00:06:28,720 --> 00:06:31,800 Speaker 6: So overall, the OECD, as you just mentioned, just cut 136 00:06:32,000 --> 00:06:35,360 Speaker 6: its forecasts across the board and look, for the world, 137 00:06:35,440 --> 00:06:38,560 Speaker 6: they were predicting for this year three point one percent growth, 138 00:06:38,760 --> 00:06:42,320 Speaker 6: now it's just two point nine. Similarly for the US 139 00:06:42,320 --> 00:06:45,040 Speaker 6: more drastically, actually, for the US they're pre predicting two 140 00:06:45,040 --> 00:06:47,159 Speaker 6: point two and now they's just say one point six. 141 00:06:47,640 --> 00:06:49,920 Speaker 6: And they're also next year for the US just present 142 00:06:50,160 --> 00:06:53,120 Speaker 6: predict one point five percent growth. So overall, these are 143 00:06:53,200 --> 00:06:58,719 Speaker 6: quite dramatic numbers and throughout the whole report. The reason 144 00:06:58,720 --> 00:07:02,360 Speaker 6: they give for this is Trump tariffs and that this overall, 145 00:07:02,400 --> 00:07:05,800 Speaker 6: this combative trade policy that's coming from the US just 146 00:07:05,880 --> 00:07:08,520 Speaker 6: just hurting the world all over but the US here 147 00:07:08,520 --> 00:07:09,159 Speaker 6: in particular. 148 00:07:11,400 --> 00:07:13,840 Speaker 2: But so I wonder what their baseline is for tariffs. 149 00:07:13,920 --> 00:07:16,120 Speaker 2: Is it that ten percent? Are they taking into account 150 00:07:16,160 --> 00:07:18,240 Speaker 2: the tariffs that are currently in place? Like what's the 151 00:07:18,400 --> 00:07:19,200 Speaker 2: tariff scenario? 152 00:07:20,600 --> 00:07:24,120 Speaker 6: The tariff scenario is just general uncertainty. So they are 153 00:07:24,240 --> 00:07:27,560 Speaker 6: very aware that because of the constant change and of 154 00:07:27,880 --> 00:07:31,440 Speaker 6: Trump policy that he says one tariff one day and 155 00:07:31,520 --> 00:07:33,680 Speaker 6: then changes again the next and then goes back and 156 00:07:33,720 --> 00:07:36,840 Speaker 6: forth all the time, that they're just this inherent uncertainty 157 00:07:36,880 --> 00:07:40,400 Speaker 6: here that will just hurt trade overall. If you look 158 00:07:40,400 --> 00:07:42,840 Speaker 6: at the charts that's say, what trade would do if 159 00:07:42,840 --> 00:07:45,160 Speaker 6: you compared with what they thought in December. What they're 160 00:07:45,200 --> 00:07:48,600 Speaker 6: saying now, the picture is just incredibly dramatic, and they 161 00:07:48,640 --> 00:07:50,880 Speaker 6: say that as long as this is there, as long 162 00:07:50,920 --> 00:07:54,720 Speaker 6: as this uncertainty and this constant what's it called there 163 00:07:54,760 --> 00:07:57,200 Speaker 6: and back again kind of trade policy is going on, 164 00:07:57,560 --> 00:08:01,040 Speaker 6: that this is something that will just hurt theonomy, almost 165 00:08:01,080 --> 00:08:04,120 Speaker 6: irrespective of what the trade to a trade, what the 166 00:08:04,120 --> 00:08:08,080 Speaker 6: tariffs are actually like, then just as uncertainty damages so much. 167 00:08:10,640 --> 00:08:13,720 Speaker 3: Zoe. You're based in Frankfort, you know, based upon my 168 00:08:13,840 --> 00:08:18,040 Speaker 3: travel through EUROPEBA was considered Frankfurt to obviously be the 169 00:08:18,080 --> 00:08:20,960 Speaker 3: you know, the corporate hub of Germany and certainly much 170 00:08:21,000 --> 00:08:23,760 Speaker 3: of Europe here. What are you hearing from the German 171 00:08:24,600 --> 00:08:27,360 Speaker 3: executives that you speak to about what we're seeing over 172 00:08:27,360 --> 00:08:28,240 Speaker 3: the last several months. 173 00:08:29,480 --> 00:08:32,480 Speaker 6: So Germany is an interesting situation because we just had 174 00:08:32,559 --> 00:08:36,800 Speaker 6: elections here in February and there then a coalition was 175 00:08:36,800 --> 00:08:39,840 Speaker 6: formed quite quickly. And even before the coalition was formed, 176 00:08:40,160 --> 00:08:43,240 Speaker 6: because the fringe parties on the left on the right 177 00:08:43,640 --> 00:08:46,760 Speaker 6: one so much in parliament, they still in the lane duck, 178 00:08:46,800 --> 00:08:51,559 Speaker 6: Parliament still passed an incredibly generous fiscal package and also 179 00:08:51,559 --> 00:08:54,600 Speaker 6: this huge defense package. So the defence package, we know, it's 180 00:08:54,600 --> 00:08:56,840 Speaker 6: tricky how this will trickle through for the economy, but 181 00:08:56,920 --> 00:09:00,959 Speaker 6: the fiscal package is really massive, and so just generally 182 00:09:01,040 --> 00:09:05,400 Speaker 6: gave he gave executives here in Germany a whole boost 183 00:09:05,400 --> 00:09:09,360 Speaker 6: of confidence because Germany, as we know, red tape here 184 00:09:09,880 --> 00:09:13,520 Speaker 6: is it takes everything, takes agent's red tape is very prevalent. 185 00:09:14,040 --> 00:09:19,240 Speaker 6: And the lad during the entire shorts with the time 186 00:09:19,280 --> 00:09:22,920 Speaker 6: that Schorts was chancellor, the economy barely grew. There was 187 00:09:23,280 --> 00:09:26,400 Speaker 6: one quarter of contraction, one of growth. It just was 188 00:09:26,520 --> 00:09:29,120 Speaker 6: really very very sad, and so the fact that the 189 00:09:29,120 --> 00:09:31,600 Speaker 6: god that would be this huge fiscal package made everyone happy. 190 00:09:31,840 --> 00:09:32,840 Speaker 6: This was February. 191 00:09:33,000 --> 00:09:34,800 Speaker 3: Then we got the Liberation. 192 00:09:34,520 --> 00:09:36,320 Speaker 6: Day from the US and that has made them more 193 00:09:36,360 --> 00:09:40,559 Speaker 6: uncertain overall. Though given the uncertainty there where, this hurts investment, 194 00:09:40,600 --> 00:09:43,240 Speaker 6: they hurts or that, but the fact that there was 195 00:09:43,320 --> 00:09:46,719 Speaker 6: this huge fiscal commitment does make executives a lot more 196 00:09:46,760 --> 00:09:47,720 Speaker 6: confident than they were. 197 00:09:49,880 --> 00:09:52,839 Speaker 2: All right, Zoey, thanks a lot, really appreciated. Zoe snay 198 00:09:52,840 --> 00:09:55,880 Speaker 2: Wise Bloomberg Western European Economy team leader. She heads up 199 00:09:55,880 --> 00:09:58,040 Speaker 2: all the stuff over there in Frankfort. So she's in 200 00:09:58,080 --> 00:10:01,360 Speaker 2: Frankfort and Paul and I are here in a Maryland room. 201 00:10:02,920 --> 00:10:06,600 Speaker 1: You're listening to the Bloomberg Intelligence podcast. Catch us live 202 00:10:06,679 --> 00:10:09,760 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 203 00:10:09,800 --> 00:10:13,079 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 204 00:10:13,160 --> 00:10:16,280 Speaker 1: you get your podcasts, or watch us live on YouTube. 205 00:10:16,720 --> 00:10:19,120 Speaker 2: We are in Maryland right at the National Harbor at 206 00:10:19,160 --> 00:10:21,800 Speaker 2: the Gaylord Convention Center, as we have a lot of 207 00:10:21,840 --> 00:10:25,719 Speaker 2: financial advisors kind of coming together and discussing ideas and 208 00:10:25,760 --> 00:10:29,080 Speaker 2: how to manage asset and wealth management for three days 209 00:10:29,120 --> 00:10:32,520 Speaker 2: of learning experiences and networking solutions as well. Really been 210 00:10:32,559 --> 00:10:35,680 Speaker 2: a fascinating time so far. Keeping an eye on some 211 00:10:35,760 --> 00:10:38,640 Speaker 2: of the equity moves. One is Meta and Constellation. Constellation 212 00:10:38,760 --> 00:10:40,720 Speaker 2: Energy is up a little over half a percent, Metas 213 00:10:40,760 --> 00:10:43,240 Speaker 2: down by five tens to one percent, but a Constellation 214 00:10:43,360 --> 00:10:45,959 Speaker 2: Energy agree to sell power from an Illinois nuclear plant 215 00:10:45,960 --> 00:10:49,400 Speaker 2: to Meta Platforms. Is a twenty year contract starting in 216 00:10:49,440 --> 00:10:52,280 Speaker 2: mid twenty twenty seven. So Constellation's going to boost its 217 00:10:52,280 --> 00:10:56,080 Speaker 2: own investment into the Clinton plans output and maybe we'll 218 00:10:56,080 --> 00:10:59,200 Speaker 2: actually build another reactor at the site I did the 219 00:10:59,280 --> 00:11:01,000 Speaker 2: energy and go, let's at a take angle now with 220 00:11:01,120 --> 00:11:04,760 Speaker 2: man Deep saying Bloomberg intelligence in your technology analyst. Okay, 221 00:11:04,760 --> 00:11:06,280 Speaker 2: Man Deep, why does Meta need this? 222 00:11:07,600 --> 00:11:11,200 Speaker 7: Well, if they're raising their capex, which they did in 223 00:11:11,280 --> 00:11:15,240 Speaker 7: their last earnings, that capex is going towards, you know, 224 00:11:15,320 --> 00:11:19,360 Speaker 7: getting more AI chips, and you know, if you are 225 00:11:19,400 --> 00:11:22,480 Speaker 7: putting more AI chips, you need more power. So clearly 226 00:11:23,160 --> 00:11:27,199 Speaker 7: that is where the shortage is right now. And look, 227 00:11:27,320 --> 00:11:31,680 Speaker 7: if this is an AI supercycle that we all believe 228 00:11:31,840 --> 00:11:35,360 Speaker 7: it is going to be the case, and I think 229 00:11:35,480 --> 00:11:38,520 Speaker 7: you will see a power supercycle as well. And in 230 00:11:38,559 --> 00:11:42,600 Speaker 7: this case, all the hyperscalers will be looking to procure 231 00:11:42,720 --> 00:11:45,960 Speaker 7: power for their data centers. I mean, Meta serves over 232 00:11:46,080 --> 00:11:50,920 Speaker 7: three billion monthly active users across their family of apps. Now, granted, 233 00:11:51,400 --> 00:11:55,080 Speaker 7: those users right now are using more CPU compute, but 234 00:11:55,160 --> 00:11:59,520 Speaker 7: if you're rolling out AI tools and LLM searches. You 235 00:11:59,640 --> 00:12:02,280 Speaker 7: need more power for the AI chips, and I think 236 00:12:02,320 --> 00:12:03,600 Speaker 7: that's the intent here. 237 00:12:05,880 --> 00:12:07,720 Speaker 3: And Mandy, let's be honest here. I mean, all you 238 00:12:07,840 --> 00:12:10,280 Speaker 3: tech guys, you Silicon Valley guys, you always think you're 239 00:12:10,280 --> 00:12:13,080 Speaker 3: the smartest guys in the room. But let's be honest. 240 00:12:13,120 --> 00:12:15,520 Speaker 3: You guys don't know anything about the oil and gas 241 00:12:15,559 --> 00:12:19,640 Speaker 3: business and the nuclear business. Why is Meta? And I'm 242 00:12:19,679 --> 00:12:21,760 Speaker 3: guessing some others are going to kind of get more 243 00:12:21,840 --> 00:12:25,840 Speaker 3: deeply and strategically involved in the energy side of their business. 244 00:12:25,880 --> 00:12:26,720 Speaker 3: Is this a trend here? 245 00:12:27,640 --> 00:12:31,000 Speaker 7: I mean, just look at how far they have come 246 00:12:31,040 --> 00:12:34,640 Speaker 7: in terms of you know, infrastructure, Like a company like Google. 247 00:12:35,080 --> 00:12:38,559 Speaker 7: Not only are they designing their own data centers and 248 00:12:38,840 --> 00:12:43,600 Speaker 7: you know, creating their own chips, and now they are 249 00:12:43,920 --> 00:12:47,440 Speaker 7: you know, procuring power to they extend that. They want 250 00:12:47,480 --> 00:12:51,160 Speaker 7: to make sure that these AI chips, which can have 251 00:12:51,280 --> 00:12:55,640 Speaker 7: volatile power needs, are most optimized when it comes to 252 00:12:55,679 --> 00:13:00,600 Speaker 7: the efficiency of their infrastructure. And that's why these hyperscalar 253 00:13:00,720 --> 00:13:05,040 Speaker 7: companies really have got so many different aspects to their mode. 254 00:13:05,080 --> 00:13:08,720 Speaker 7: So it's not just about the search algorithm or in 255 00:13:08,760 --> 00:13:11,960 Speaker 7: the case of Meta, you know, their social media knowledge graph. 256 00:13:12,040 --> 00:13:15,559 Speaker 7: It is about the entirety of how they run their infrastructure, 257 00:13:15,600 --> 00:13:18,080 Speaker 7: and that is what is a long term mode, because 258 00:13:18,120 --> 00:13:22,400 Speaker 7: nobody else can operate at the scale at which these 259 00:13:22,480 --> 00:13:24,200 Speaker 7: hyperscalers are operating right now. 260 00:13:26,240 --> 00:13:27,839 Speaker 2: This is a fun fact in the article by Will 261 00:13:27,920 --> 00:13:30,160 Speaker 2: Raid from Bloomberg. So Meta first of all, has a 262 00:13:30,200 --> 00:13:31,160 Speaker 2: global energy head. 263 00:13:31,840 --> 00:13:35,080 Speaker 3: They have a global energy head. Okay, all right, so 264 00:13:35,120 --> 00:13:35,400 Speaker 3: they do. 265 00:13:35,520 --> 00:13:37,559 Speaker 2: They're really kind of ramping that out, and they were 266 00:13:37,600 --> 00:13:40,440 Speaker 2: looking at proposals the announcing December. They were looking at 267 00:13:40,480 --> 00:13:42,960 Speaker 2: proposals for as much as four gigawatts of new US 268 00:13:43,040 --> 00:13:47,479 Speaker 2: reactors and received fifty proposals from a range of companies, 269 00:13:47,800 --> 00:13:53,440 Speaker 2: including Constellation. To that point, I'm also wondering Mande the 270 00:13:53,480 --> 00:13:56,880 Speaker 2: significance of when they're building data centers and hyperscalers for 271 00:13:57,120 --> 00:14:00,120 Speaker 2: lllm's a large language model and that training versus and 272 00:14:00,160 --> 00:14:02,520 Speaker 2: they have to build stuff for inference which could be 273 00:14:02,559 --> 00:14:05,600 Speaker 2: smaller and more co located, so closer to the cities, 274 00:14:05,640 --> 00:14:07,880 Speaker 2: closer to our Paul and I have our phone. Does 275 00:14:07,880 --> 00:14:10,079 Speaker 2: that tend to look different than in terms of their 276 00:14:10,120 --> 00:14:10,760 Speaker 2: power needs? 277 00:14:11,160 --> 00:14:14,760 Speaker 7: Absolutely, And I think so far the market was concentrated 278 00:14:14,800 --> 00:14:19,280 Speaker 7: towards training. Every year you would have a ten times 279 00:14:19,400 --> 00:14:23,560 Speaker 7: larger model, which was more intelligent and it could do 280 00:14:23,680 --> 00:14:26,280 Speaker 7: more in terms of you know, answering your queries. But 281 00:14:26,720 --> 00:14:30,480 Speaker 7: with inferencing, you're right, I mean, the traffic patterns may 282 00:14:30,560 --> 00:14:34,440 Speaker 7: vary depending on the population, and so if you're serving 283 00:14:34,480 --> 00:14:38,080 Speaker 7: traffic in New York, it's very different from serving traffic 284 00:14:38,160 --> 00:14:41,160 Speaker 7: somewhere else that may not have the same concentration. And 285 00:14:41,960 --> 00:14:45,240 Speaker 7: with that, I think the challenge these companies have had 286 00:14:45,320 --> 00:14:49,160 Speaker 7: is the transmission lines can be upgraded. I mean you need, 287 00:14:49,840 --> 00:14:53,600 Speaker 7: you know, a pretty lengthy review process and the whole 288 00:14:53,640 --> 00:14:57,040 Speaker 7: thing may take years. So right now they are trying 289 00:14:57,080 --> 00:15:01,920 Speaker 7: to procure power wherever they can to offset the increased consumption, 290 00:15:02,080 --> 00:15:04,680 Speaker 7: and some of it may come at the expense of 291 00:15:04,920 --> 00:15:10,560 Speaker 7: power usage for some other purpose. Then you know why 292 00:15:10,640 --> 00:15:14,880 Speaker 7: data center is being prioritized here, and that is what 293 00:15:15,000 --> 00:15:18,880 Speaker 7: will be interesting because with inferencing, the power needs can 294 00:15:18,920 --> 00:15:21,120 Speaker 7: be variable. I mean, one trend we have seen is 295 00:15:21,400 --> 00:15:25,760 Speaker 7: with reasoning, you need more compute, so you know, the 296 00:15:25,840 --> 00:15:28,360 Speaker 7: model is thinking a lot more at the time you 297 00:15:28,400 --> 00:15:31,400 Speaker 7: are putting the prompt in, as opposed to training, where 298 00:15:31,560 --> 00:15:34,600 Speaker 7: all of the training is happening in background over a 299 00:15:34,640 --> 00:15:37,720 Speaker 7: period of a few weeks. So reasoning is per query, 300 00:15:38,240 --> 00:15:41,480 Speaker 7: and depending on how complex your query is, the model 301 00:15:41,560 --> 00:15:43,440 Speaker 7: is doing more work and that's why it may need 302 00:15:43,480 --> 00:15:44,040 Speaker 7: more power. 303 00:15:46,400 --> 00:15:49,720 Speaker 3: Another data point from this Bloomberg News article, Meta is 304 00:15:49,760 --> 00:15:53,480 Speaker 3: contracting for more power after the company's total electricity consumption 305 00:15:54,080 --> 00:15:57,720 Speaker 3: nearly tripled from twenty nineteen to twenty twenty three. So yeah, 306 00:15:57,760 --> 00:16:00,960 Speaker 3: I guess they need some more energy. Mandy position the 307 00:16:00,960 --> 00:16:04,520 Speaker 3: CAPEX numbers for Meta and for these other tech companies, 308 00:16:04,720 --> 00:16:06,920 Speaker 3: they need to take those CAPEX numbers up even more. 309 00:16:07,000 --> 00:16:08,920 Speaker 3: They're going to get deeper into the energy space. 310 00:16:09,520 --> 00:16:13,119 Speaker 7: I mean, all signs are the CAPEX numbers may continue 311 00:16:13,200 --> 00:16:17,400 Speaker 7: to go up, but right now, the biggest component of 312 00:16:17,440 --> 00:16:22,360 Speaker 7: the CAPEX is still the spend on the GPU chips. Obviously, 313 00:16:22,400 --> 00:16:25,880 Speaker 7: the power needs will take some portion of the CAPEX, 314 00:16:25,920 --> 00:16:28,480 Speaker 7: but it's still not as big as the chips. And 315 00:16:28,600 --> 00:16:30,680 Speaker 7: think of it this way. If you're getting you know, 316 00:16:31,200 --> 00:16:34,520 Speaker 7: twenty thousand in Nvidia chips, the cost of keeping them 317 00:16:34,600 --> 00:16:37,080 Speaker 7: idle or not being able to utilize them one hundred 318 00:16:37,080 --> 00:16:40,080 Speaker 7: percent is way higher than anything you are spending in 319 00:16:40,160 --> 00:16:43,480 Speaker 7: terms of you know, the energy deal that they had, 320 00:16:43,560 --> 00:16:47,040 Speaker 7: So clearly they are focusing on utilizing the chips that 321 00:16:47,040 --> 00:16:51,000 Speaker 7: they have already procured and that's why they are proactive 322 00:16:51,080 --> 00:16:52,560 Speaker 7: with you know, these power deals. 323 00:16:54,600 --> 00:16:56,440 Speaker 2: All right, Mandy, thanks a lot, really appreciate and man 324 00:16:56,480 --> 00:16:59,840 Speaker 2: deep saying joining us when we're intelligence senior Technology and. 325 00:17:01,800 --> 00:17:05,480 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 326 00:17:05,560 --> 00:17:08,680 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 327 00:17:08,680 --> 00:17:12,000 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 328 00:17:12,040 --> 00:17:15,160 Speaker 1: you get your podcasts, or watch us live on YouTube. 329 00:17:15,440 --> 00:17:18,320 Speaker 2: Happy Tuesday, everybody, Alex Stealer alongside Paul Sweeny IM You're 330 00:17:18,400 --> 00:17:21,440 Speaker 2: live from B and Y Insight twenty twenty five. We 331 00:17:21,800 --> 00:17:25,159 Speaker 2: are in National Harbor Meryl app National Harbor, Maryland at 332 00:17:25,160 --> 00:17:29,280 Speaker 2: the Gaylord Convention Center for this tremendous of men. Over 333 00:17:29,320 --> 00:17:31,840 Speaker 2: two thousand people are here. They talk about ideas, they 334 00:17:31,880 --> 00:17:34,760 Speaker 2: exchange thoughts about the markets and wealth management and private 335 00:17:34,800 --> 00:17:35,800 Speaker 2: wealth and alternatives. 336 00:17:35,920 --> 00:17:37,200 Speaker 3: It's a pretty exciting. 337 00:17:36,920 --> 00:17:39,000 Speaker 2: Dynamic place to be and joining us is one of 338 00:17:39,040 --> 00:17:42,280 Speaker 2: our favorites. Phil Orlando, chief equity market strategist and head 339 00:17:42,280 --> 00:17:45,120 Speaker 2: of client portfolio Management at FREEDERA to Hermes. 340 00:17:45,119 --> 00:17:46,639 Speaker 3: Phil, always good to see you, Alex. 341 00:17:46,680 --> 00:17:48,000 Speaker 4: Thank you so much for having me back. 342 00:17:48,000 --> 00:17:48,600 Speaker 3: It's a thrill. 343 00:17:48,760 --> 00:17:50,760 Speaker 2: You've been coming to these for a long time, right, 344 00:17:50,840 --> 00:17:53,480 Speaker 2: So what's the different vibe here this year? 345 00:17:53,640 --> 00:17:53,920 Speaker 3: Is there? 346 00:17:53,920 --> 00:17:54,360 Speaker 2: One? 347 00:17:55,480 --> 00:17:59,199 Speaker 4: The vibe is very optimistic. Really, there is more of 348 00:17:59,240 --> 00:18:03,359 Speaker 4: an emphasis on technology today and AI than there ever 349 00:18:03,480 --> 00:18:07,399 Speaker 4: has been. But that probably it's not surprising given the 350 00:18:07,760 --> 00:18:12,199 Speaker 4: evolution of the business. I've always found these conferences to 351 00:18:12,240 --> 00:18:14,960 Speaker 4: be very cutting edge in terms of what are the 352 00:18:15,640 --> 00:18:17,639 Speaker 4: key issues that are going to be driving us in 353 00:18:17,720 --> 00:18:21,240 Speaker 4: the months and years forward, and so far I haven't 354 00:18:21,240 --> 00:18:24,080 Speaker 4: been disappointed here today. 355 00:18:23,160 --> 00:18:25,359 Speaker 3: Phil, I'm just looking at the S and P five hundred. 356 00:18:25,400 --> 00:18:28,560 Speaker 3: We've almost completely round trip peaked to trough back up 357 00:18:28,600 --> 00:18:32,840 Speaker 3: to peak here. What do you make of that? I mean, 358 00:18:32,840 --> 00:18:34,600 Speaker 3: that was a short period of time, you saw big 359 00:18:34,640 --> 00:18:37,000 Speaker 3: moves in the marketplace. What do you do looking forward 360 00:18:37,000 --> 00:18:39,040 Speaker 3: for the next six months? Oh more to go? 361 00:18:39,640 --> 00:18:41,720 Speaker 4: I mean remember our full year target for the S 362 00:18:41,760 --> 00:18:43,920 Speaker 4: and P this year sixty five hundred. We've got a 363 00:18:44,000 --> 00:18:47,920 Speaker 4: seven thousand next year. So yes, we've had a really 364 00:18:48,040 --> 00:18:50,160 Speaker 4: nice bounce off of that support level. I think we're 365 00:18:50,240 --> 00:18:52,720 Speaker 4: up about twenty three percent of the S and P 366 00:18:52,840 --> 00:18:56,800 Speaker 4: five hundred. But again not surprising to us because that 367 00:18:57,720 --> 00:19:01,640 Speaker 4: waterfall decline was largely a function of the uncertainty relating 368 00:19:01,680 --> 00:19:05,399 Speaker 4: to the tariff situation, and to some degree, how is 369 00:19:05,440 --> 00:19:08,280 Speaker 4: the tax legislation going to work. We're starting to get 370 00:19:08,359 --> 00:19:12,760 Speaker 4: some early resolution of exactly, at least directionally, how that's 371 00:19:12,800 --> 00:19:14,920 Speaker 4: going to play out, and the market at that point 372 00:19:14,920 --> 00:19:18,000 Speaker 4: should begin to think about, Okay, what are the underlying fundamentals, 373 00:19:18,359 --> 00:19:22,120 Speaker 4: not the chaos associated with what caused the decline back 374 00:19:22,160 --> 00:19:23,359 Speaker 4: in early April. 375 00:19:23,480 --> 00:19:25,280 Speaker 2: All right, let's pretend that we can put aside the kaas. 376 00:19:25,320 --> 00:19:27,280 Speaker 2: Then for a second, does that mean that tech continues 377 00:19:27,280 --> 00:19:29,280 Speaker 2: to lead or do we get the broadening out? 378 00:19:29,720 --> 00:19:33,639 Speaker 3: Well, yes, they can both be true. 379 00:19:33,920 --> 00:19:38,359 Speaker 4: So so our feeling going into the collapse was that 380 00:19:38,640 --> 00:19:41,639 Speaker 4: Bill Rally did need to broaden out. We were de 381 00:19:41,760 --> 00:19:44,720 Speaker 4: emphasizing sort of the technology and the Mag seven names 382 00:19:44,760 --> 00:19:47,080 Speaker 4: because we felt that they were ahead of themselves. So 383 00:19:47,160 --> 00:19:50,680 Speaker 4: the area of focus for us was the forgotten four 384 00:19:50,760 --> 00:19:53,960 Speaker 4: ninety three, the value names, the international names, the smaller 385 00:19:54,000 --> 00:19:57,720 Speaker 4: cap names. But as the market you came down twenty 386 00:19:57,760 --> 00:20:00,159 Speaker 4: one percent, the Max seven names were down forty five 387 00:20:00,000 --> 00:20:04,080 Speaker 4: five percent. So as we started to put additional money 388 00:20:04,080 --> 00:20:07,080 Speaker 4: to work, we increased our equity allocation to a five 389 00:20:07,080 --> 00:20:10,800 Speaker 4: percent overweight near the bottom that incremental ad for US 390 00:20:11,000 --> 00:20:14,040 Speaker 4: was in domestic large cap growth. So we recognized that 391 00:20:14,080 --> 00:20:18,199 Speaker 4: forty five percent down was actually an attractive point to 392 00:20:18,200 --> 00:20:19,600 Speaker 4: start to put some money back to work. 393 00:20:20,000 --> 00:20:21,560 Speaker 3: You know, with a little bit of hindsight, As we 394 00:20:21,640 --> 00:20:23,720 Speaker 3: think back to that sell off in April, a couple 395 00:20:23,760 --> 00:20:25,960 Speaker 3: of things kind of come to mind. Number One, we 396 00:20:26,040 --> 00:20:28,639 Speaker 3: had people during the sell off telling us, you know, 397 00:20:28,920 --> 00:20:31,760 Speaker 3: people aren't panicking out there. That's number one. And number two, 398 00:20:32,600 --> 00:20:35,240 Speaker 3: retail is buying. What is that? What are those two 399 00:20:35,240 --> 00:20:36,560 Speaker 3: things to kind of tell you that kind of I 400 00:20:36,600 --> 00:20:38,359 Speaker 3: guess in hindsight I was said, boy, I should have 401 00:20:38,359 --> 00:20:40,639 Speaker 3: been buying there because the retail aganis are buying. I 402 00:20:40,640 --> 00:20:42,840 Speaker 3: don't think I really saw one panic selling. 403 00:20:43,480 --> 00:20:46,080 Speaker 4: Yeah, you know, we've done some work looking at the 404 00:20:46,119 --> 00:20:48,360 Speaker 4: performance of the market literally over. 405 00:20:48,160 --> 00:20:49,880 Speaker 3: The last half a century, okay. 406 00:20:49,760 --> 00:20:51,840 Speaker 4: And over that period of time there have been ten 407 00:20:52,760 --> 00:20:56,399 Speaker 4: massive declines twenty percent or more twenty thirty, forty to fifty, 408 00:20:56,840 --> 00:21:00,400 Speaker 4: and in every instance that was a great buying opernity 409 00:21:00,480 --> 00:21:03,960 Speaker 4: that over the long cycle, the ingenuity and hard work 410 00:21:04,000 --> 00:21:07,000 Speaker 4: of the American people stocks up into the right. So 411 00:21:07,160 --> 00:21:10,920 Speaker 4: if you look at those big declines down and think 412 00:21:10,960 --> 00:21:14,880 Speaker 4: you understand what the underlying fundamentals are that to us 413 00:21:15,080 --> 00:21:17,400 Speaker 4: was a reasonably and an attractive buying point. 414 00:21:18,160 --> 00:21:19,560 Speaker 2: So how do you look in I will talk about 415 00:21:19,560 --> 00:21:20,760 Speaker 2: this before, but how do you look at it the 416 00:21:20,760 --> 00:21:21,879 Speaker 2: next six to eight months? 417 00:21:22,200 --> 00:21:22,960 Speaker 3: As you manage the. 418 00:21:23,000 --> 00:21:26,040 Speaker 2: Uncertainty, you just be really strategic. You have your buying 419 00:21:26,040 --> 00:21:27,399 Speaker 2: list and you think of it that way and you 420 00:21:27,480 --> 00:21:29,480 Speaker 2: kind of ignore the actual headlines. 421 00:21:29,720 --> 00:21:33,720 Speaker 4: Well, we take a longer term perspective that we're looking 422 00:21:33,880 --> 00:21:36,680 Speaker 4: at six to twelve to eighteen to twenty four months, 423 00:21:36,720 --> 00:21:40,000 Speaker 4: and as we look at that perspective, the noise and 424 00:21:40,040 --> 00:21:42,600 Speaker 4: the nonsense and the chaos associated with the day to 425 00:21:42,680 --> 00:21:44,160 Speaker 4: day we sort of screen out. 426 00:21:44,240 --> 00:21:45,680 Speaker 2: But some of those things could be structural. 427 00:21:46,359 --> 00:21:48,880 Speaker 4: They could be and if they were structural, we would 428 00:21:48,920 --> 00:21:53,920 Speaker 4: change our view. But we've got a view that for example, 429 00:21:54,000 --> 00:21:57,080 Speaker 4: our GDP forecast for next year is two point seven percent. 430 00:21:57,200 --> 00:21:59,879 Speaker 4: All right, that doesn't sound like much, but consensus has 431 00:22:00,080 --> 00:22:02,800 Speaker 4: one three. The highest estimate on the street other than 432 00:22:02,840 --> 00:22:05,600 Speaker 4: us is two percent. So we think that things are 433 00:22:05,600 --> 00:22:07,960 Speaker 4: going to work out over the course of the next 434 00:22:08,000 --> 00:22:11,000 Speaker 4: eighteen months, and if we're right, we want to be 435 00:22:11,040 --> 00:22:12,560 Speaker 4: buyers of stocks in that environment. 436 00:22:12,920 --> 00:22:14,959 Speaker 3: Do we still have earnings risk. I know earning testaments 437 00:22:15,000 --> 00:22:16,680 Speaker 3: have come down, but do we still have earnings risk? 438 00:22:16,760 --> 00:22:19,160 Speaker 3: Do you take out there because we still have tariffs 439 00:22:19,160 --> 00:22:21,480 Speaker 3: that are going to be gradually rolling into the economy, 440 00:22:21,720 --> 00:22:23,040 Speaker 3: there's always earnings risk. 441 00:22:23,080 --> 00:22:26,040 Speaker 4: But we just got through the first quarter and revenues 442 00:22:26,119 --> 00:22:28,800 Speaker 4: ro up about five percent, earnings roup about twelve percent. 443 00:22:28,840 --> 00:22:32,359 Speaker 4: Those numbers are better than expected. Uh, you know, we've 444 00:22:32,400 --> 00:22:35,520 Speaker 4: sort of adjusted our full year numbers based upon the 445 00:22:35,560 --> 00:22:39,240 Speaker 4: implementation of tariffs, but that trajectory is still positive. 446 00:22:39,680 --> 00:22:41,280 Speaker 3: All right, Phil, Really great to chat with you. Good 447 00:22:41,280 --> 00:22:41,960 Speaker 3: to see you in person. 448 00:22:42,000 --> 00:22:43,879 Speaker 2: It's so weird but you just not ask me how 449 00:22:43,920 --> 00:22:45,199 Speaker 2: fill Orlando was chief equity. 450 00:22:45,440 --> 00:22:46,560 Speaker 3: Thank you very much for having me on. 451 00:22:46,640 --> 00:22:48,879 Speaker 2: I bet a chief ecuty market strategist and head of 452 00:22:48,960 --> 00:22:53,240 Speaker 2: client portfolio Management at Betra Hermes, joining us on the market. 453 00:22:53,640 --> 00:22:58,320 Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apple, of Spotify, 454 00:22:58,520 --> 00:23:02,000 Speaker 1: and anywhere else you get your podcasts. Listen live each 455 00:23:02,040 --> 00:23:05,800 Speaker 1: weekday ten am to noon Eastern on Bloomberg dot com, 456 00:23:05,920 --> 00:23:09,440 Speaker 1: the iHeartRadio app, tune In, and the Bloomberg Business app. 457 00:23:09,840 --> 00:23:12,800 Speaker 1: You can also watch us live every weekday on YouTube 458 00:23:13,160 --> 00:23:15,400 Speaker 1: and always on the Bloomberg terminal