1 00:00:00,080 --> 00:00:06,760 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. 2 00:00:11,960 --> 00:00:15,560 Speaker 2: This is the Bloomberg Surveillance Podcast. I'm Tom Keene along 3 00:00:15,600 --> 00:00:18,960 Speaker 2: with Paul Sweeney. Join us each day for insight from 4 00:00:18,960 --> 00:00:23,119 Speaker 2: the best in economics, finance, investment, and international relations. You 5 00:00:23,160 --> 00:00:26,520 Speaker 2: can also watch the show live on YouTube. Visit the 6 00:00:26,520 --> 00:00:31,280 Speaker 2: Bloomberg Podcast channel on YouTube to see the show weekday 7 00:00:31,280 --> 00:00:34,320 Speaker 2: mornings from seven to ten am Eastern from our global 8 00:00:34,360 --> 00:00:39,000 Speaker 2: headquarters in New York City. Subscribe to the podcast on Apple, Spotify, 9 00:00:39,360 --> 00:00:42,920 Speaker 2: or anywhere else you listen and always I'm Bloomberg Radio, 10 00:00:43,080 --> 00:00:46,720 Speaker 2: the Bloomberg Terminal, and the Bloomberg Business App. I'm not 11 00:00:46,800 --> 00:00:49,320 Speaker 2: so much in the camp of a certitude of a 12 00:00:49,360 --> 00:00:53,200 Speaker 2: September a rate Let's get wisdom on that with Nationwide 13 00:00:53,200 --> 00:00:56,400 Speaker 2: with a wonderful Ohio perspective. Kathleen bus Johnson joins US 14 00:00:56,440 --> 00:01:01,320 Speaker 2: now Chief Economists Nationwide because she's on our side. Kathy, 15 00:01:01,400 --> 00:01:04,440 Speaker 2: I look at where we are right now, and more 16 00:01:04,480 --> 00:01:06,880 Speaker 2: than anything, I would say there's a certitude about a 17 00:01:06,959 --> 00:01:09,800 Speaker 2: September rate cut. Why do we feel that way? 18 00:01:11,000 --> 00:01:15,680 Speaker 1: God, Good morning, Tom. Well the inflation data have you know, namely, 19 00:01:16,319 --> 00:01:20,720 Speaker 1: really cooled off significantly. It makes it seem as if 20 00:01:20,959 --> 00:01:23,160 Speaker 1: that the flare up we saw on the first quarter 21 00:01:23,400 --> 00:01:28,000 Speaker 1: was anomalous and that really inflation is back on a 22 00:01:28,000 --> 00:01:30,520 Speaker 1: good trend, you know, and eventually get back to two 23 00:01:30,600 --> 00:01:35,679 Speaker 1: percent case. And we've also seen some slowing or moderation 24 00:01:36,080 --> 00:01:41,760 Speaker 1: in employment and other key economic readings that you know, 25 00:01:41,800 --> 00:01:43,959 Speaker 1: as a FED was there says they're more balanced right 26 00:01:44,000 --> 00:01:47,680 Speaker 1: between the risks with inflation and employment, so it kind 27 00:01:47,680 --> 00:01:49,440 Speaker 1: of gives them the green light and the reasons to 28 00:01:49,480 --> 00:01:50,400 Speaker 1: start to cut rates. 29 00:01:50,480 --> 00:01:52,440 Speaker 2: Where are you on jab as claims when you say 30 00:01:52,440 --> 00:01:54,640 Speaker 2: that the labor market is breaking? I got a four 31 00:01:54,680 --> 00:01:57,800 Speaker 2: week moving average of the weekly data of two hundred 32 00:01:57,840 --> 00:02:00,840 Speaker 2: and thirty five thousand. It's buttressed right up against a 33 00:02:00,880 --> 00:02:04,040 Speaker 2: peak of twenty twenty three of about two hundred and 34 00:02:04,120 --> 00:02:07,840 Speaker 2: fifty one thousand. What's the statistic in your head where 35 00:02:07,920 --> 00:02:09,399 Speaker 2: jobless claim signals? 36 00:02:09,520 --> 00:02:14,040 Speaker 1: Let's go, yeah, so that you know, we've seen some 37 00:02:14,200 --> 00:02:18,480 Speaker 1: upward movement and jobless claims, but that's still quite low. 38 00:02:19,600 --> 00:02:22,520 Speaker 1: And I'm not suggesting or worry that we're about to 39 00:02:22,520 --> 00:02:25,359 Speaker 1: see a big roll over in the labor market. It's 40 00:02:25,360 --> 00:02:28,160 Speaker 1: not as if we're going to start seeing negative payroll 41 00:02:28,200 --> 00:02:31,760 Speaker 1: prints or the unemployment rate jumping sharply higher. But as 42 00:02:31,800 --> 00:02:34,480 Speaker 1: we creep higher, that's sick news. 43 00:02:34,480 --> 00:02:35,040 Speaker 3: Some slowing. 44 00:02:35,080 --> 00:02:38,200 Speaker 1: And you know, one statistic that I would say on payrolls, 45 00:02:38,200 --> 00:02:41,320 Speaker 1: besides all the other ones that will watch, is in 46 00:02:41,360 --> 00:02:45,160 Speaker 1: the household servey those who've been unemployed twenty six weeks 47 00:02:45,280 --> 00:02:47,480 Speaker 1: or longer. Interesting thing to see that pick up. 48 00:02:48,080 --> 00:02:50,040 Speaker 2: Gen know, that used to be the first statistic I 49 00:02:50,040 --> 00:02:53,880 Speaker 2: looked at back when we were eight nine, ten percent unemployment. 50 00:02:54,000 --> 00:02:56,440 Speaker 2: If that's If that's the case, Kathy, let me frame 51 00:02:56,520 --> 00:03:00,120 Speaker 2: this out as a dual mandate. The laureate Krugman over 52 00:03:00,200 --> 00:03:02,720 Speaker 2: at the New York Times has a tweet out yesterday 53 00:03:03,440 --> 00:03:07,080 Speaker 2: on this disinflation, and he says, now, now, now is 54 00:03:07,120 --> 00:03:09,600 Speaker 2: what he wants your own pole to do. I've got 55 00:03:09,639 --> 00:03:14,480 Speaker 2: a market economist plural maybe led by over at Steefel 56 00:03:16,360 --> 00:03:20,760 Speaker 2: the Lindsay Piagsa saying okay, maybe, but we need jobs 57 00:03:20,800 --> 00:03:24,640 Speaker 2: to finally collapse. I don't see jobs collapsing. And I 58 00:03:24,680 --> 00:03:27,679 Speaker 2: believe you just said edbing is more like what the 59 00:03:27,760 --> 00:03:29,440 Speaker 2: labor economy will do. 60 00:03:30,680 --> 00:03:34,839 Speaker 1: Yeah, that's right. I have sympathy for those who said 61 00:03:34,840 --> 00:03:37,920 Speaker 1: the face should consider cutting rates, you know tomorrow right, 62 00:03:37,960 --> 00:03:40,880 Speaker 1: getting the announcement, but they're not. They're not going to. 63 00:03:40,920 --> 00:03:43,200 Speaker 1: They haven't signaled that, and I do think they want 64 00:03:43,240 --> 00:03:46,960 Speaker 1: to see more data, particularly to be really convinced that 65 00:03:47,000 --> 00:03:49,960 Speaker 1: the Q one inflation data ship they're corobinists. But yeah, 66 00:03:50,040 --> 00:03:53,000 Speaker 1: back to the labor market moderation. Now, what we don't 67 00:03:53,040 --> 00:03:56,400 Speaker 1: know is this moderation a harbinger of something you know, 68 00:03:56,520 --> 00:03:58,920 Speaker 1: more dramatic as we go into twenty twenty five. We 69 00:03:59,000 --> 00:04:01,560 Speaker 1: just don't know yet as if had been too late 70 00:04:01,640 --> 00:04:03,560 Speaker 1: to cut rates starting in to cut rates again, we 71 00:04:03,600 --> 00:04:05,800 Speaker 1: don't know. But right now, I would say, in the 72 00:04:05,800 --> 00:04:08,360 Speaker 1: New York time, there's no signs of labor markets collapsing. 73 00:04:08,400 --> 00:04:11,080 Speaker 4: Hey, Kathy, give me an idea. Just how restrictive are 74 00:04:11,160 --> 00:04:12,720 Speaker 4: we at this point? 75 00:04:13,720 --> 00:04:17,200 Speaker 1: Well, we're restricted when you look at certain pockets of 76 00:04:17,240 --> 00:04:20,240 Speaker 1: the economy. Right, So, housing we know has been paralyzed 77 00:04:20,320 --> 00:04:23,480 Speaker 1: for you know, for months, right well over a year. 78 00:04:24,440 --> 00:04:26,920 Speaker 1: People are locked into low mortgages, aren't even putting their 79 00:04:26,920 --> 00:04:29,640 Speaker 1: homes up for sale or moving. Housing starts are still 80 00:04:29,680 --> 00:04:33,520 Speaker 1: really low. And then we know the lower income households 81 00:04:33,560 --> 00:04:37,000 Speaker 1: are really facing headwinds that they're increasingly maxed out on 82 00:04:37,040 --> 00:04:39,120 Speaker 1: their credit card. The New York Fed data shows us 83 00:04:39,160 --> 00:04:43,640 Speaker 1: a fully fed data shows us that they're delinquent. And 84 00:04:43,800 --> 00:04:46,280 Speaker 1: even though inflation is cooled, as we all know, price 85 00:04:46,360 --> 00:04:49,320 Speaker 1: levels are still high relative to pre pandemic, So they're 86 00:04:49,360 --> 00:04:53,000 Speaker 1: feeling the pressure there. I think that and the pandemic 87 00:04:53,040 --> 00:04:55,320 Speaker 1: savings are topped out, So we have to remember that 88 00:04:55,880 --> 00:04:59,280 Speaker 1: the tail was that god ess as resilient economy are 89 00:04:59,279 --> 00:05:01,880 Speaker 1: really fading and the headwinds are still there and maybe 90 00:05:01,880 --> 00:05:03,040 Speaker 1: even get a little stronger. 91 00:05:03,120 --> 00:05:05,680 Speaker 4: And that other part of the dual mandate with employment. 92 00:05:05,680 --> 00:05:11,719 Speaker 4: When employment goes, what does it take with it consumer spending? Obviously, right, Yeah. 93 00:05:11,440 --> 00:05:17,720 Speaker 1: That's rights. As employment slows, aggregate income declines, and then 94 00:05:17,960 --> 00:05:22,200 Speaker 1: that starts to restrain consumer spending. And the consumers don't 95 00:05:22,240 --> 00:05:24,160 Speaker 1: have that, you know, they can't really, they don't have 96 00:05:24,200 --> 00:05:27,080 Speaker 1: a lot of savings to rely on. If income starts 97 00:05:27,040 --> 00:05:29,520 Speaker 1: as slow, you've got an unemployment a savings rate that's 98 00:05:29,560 --> 00:05:33,240 Speaker 1: three point four mark that against pre pandemic savings rate 99 00:05:33,240 --> 00:05:37,479 Speaker 1: which was seven point seven. And we also know again 100 00:05:37,800 --> 00:05:40,440 Speaker 1: that you're starting to see delinquencies when the unemployment rate's 101 00:05:40,480 --> 00:05:44,600 Speaker 1: only four point one, delinquencies on credit card and loan payments. 102 00:05:45,000 --> 00:05:47,200 Speaker 1: What will that look like if we do start to 103 00:05:47,240 --> 00:05:49,520 Speaker 1: see some deeper cracks in the labor market. 104 00:05:49,680 --> 00:05:52,919 Speaker 2: Kathy, Thank you so much, Kathleen in Wes johnsick the nationwide. 105 00:05:52,960 --> 00:06:13,800 Speaker 2: You get us started towards a FED day. 106 00:06:05,200 --> 00:06:05,680 Speaker 3: Joining us. 107 00:06:05,720 --> 00:06:08,640 Speaker 2: Now he's been a huge value to us. Terry Haynes 108 00:06:08,640 --> 00:06:13,400 Speaker 2: at PENGEEA Policy here on the next step forward, I 109 00:06:13,480 --> 00:06:17,560 Speaker 2: get a plan of exhilarance, exuberants, Terry Haynes from Harris. 110 00:06:18,240 --> 00:06:20,159 Speaker 2: What's the plan from the Trump camp? 111 00:06:21,560 --> 00:06:23,919 Speaker 5: The plan from the Trump campaign Tom is basically to 112 00:06:23,960 --> 00:06:27,560 Speaker 5: try to define Harris before she can define herself effectively. 113 00:06:28,000 --> 00:06:30,440 Speaker 5: They see a window of about three weeks before the 114 00:06:30,520 --> 00:06:35,120 Speaker 5: convention where they can present Harris not only kind of 115 00:06:35,160 --> 00:06:39,960 Speaker 5: the word salad that everybody knows about, but also defining 116 00:06:40,000 --> 00:06:42,400 Speaker 5: I forget what Trump's exact words are, but you know 117 00:06:42,440 --> 00:06:45,160 Speaker 5: it's extreme, most liberal ever, you know. So on and 118 00:06:45,160 --> 00:06:50,400 Speaker 5: so forth, in an attempt, frankly, to blunt momentum and 119 00:06:50,480 --> 00:06:53,520 Speaker 5: blunt the potential ceiling that she has, which is, you know, 120 00:06:53,600 --> 00:06:54,400 Speaker 5: quite substantial. 121 00:06:55,480 --> 00:06:57,600 Speaker 2: I look at this and just as a vignette, and 122 00:06:57,600 --> 00:07:01,400 Speaker 2: I have not seen the video, folks, Neil Cavudo over 123 00:07:01,440 --> 00:07:04,600 Speaker 2: at Fox last night went after the senator from Louisiana. 124 00:07:05,160 --> 00:07:09,520 Speaker 2: Mister Kennedy on the verbiage that seems to be used 125 00:07:09,600 --> 00:07:14,480 Speaker 2: by the Trump campaign and Trump supporters against the Vice president. 126 00:07:15,320 --> 00:07:19,040 Speaker 2: Is everybody on the same page within conservative America? Or 127 00:07:19,120 --> 00:07:23,160 Speaker 2: do you see the divides Terry even greater as you've 128 00:07:23,240 --> 00:07:26,160 Speaker 2: laid out for the last two years? Oh? 129 00:07:26,200 --> 00:07:29,440 Speaker 5: I see the divides in terms of the message. The 130 00:07:29,480 --> 00:07:32,520 Speaker 5: message is all over the place right now, and and 131 00:07:32,560 --> 00:07:35,800 Speaker 5: there isn't a clear signal from the Trump side about 132 00:07:35,800 --> 00:07:39,160 Speaker 5: what they you know, what they prefer. So therefore, you know, 133 00:07:39,200 --> 00:07:42,680 Speaker 5: you're getting very scattershot kind of kind of words. And 134 00:07:42,800 --> 00:07:46,200 Speaker 5: you know, by comparison, the Harris folks have been have 135 00:07:46,280 --> 00:07:47,360 Speaker 5: been very disciplined on that. 136 00:07:47,760 --> 00:07:50,120 Speaker 2: Terry John Tucker here the question, are you going to 137 00:07:50,160 --> 00:07:51,800 Speaker 2: ask him about property taxes? 138 00:07:53,400 --> 00:08:01,600 Speaker 4: Oh? Yeah, it's saltcoming in the hunt you or Vice President? 139 00:08:02,120 --> 00:08:05,040 Speaker 4: I'm hearing now the name of the Minnesota Governor Tim Waltz. 140 00:08:05,320 --> 00:08:09,760 Speaker 4: Who is he and does he counterbalance the idea of 141 00:08:08,960 --> 00:08:11,920 Speaker 4: the liberal San Francisco Democrat? 142 00:08:13,040 --> 00:08:16,960 Speaker 5: Governor Wall seems to be beloved of progressives, and for 143 00:08:16,960 --> 00:08:20,680 Speaker 5: that reason alone, I think I discount his chances. 144 00:08:21,720 --> 00:08:21,920 Speaker 2: You know. 145 00:08:22,000 --> 00:08:25,840 Speaker 5: The you know, what Harris needs is is balance on 146 00:08:25,880 --> 00:08:28,800 Speaker 5: the ticket. She needs it both geographically. She needs it 147 00:08:28,800 --> 00:08:30,880 Speaker 5: in terms of swing states, and she needs it in 148 00:08:30,960 --> 00:08:36,120 Speaker 5: terms of you know, the centrism, and I think Walts 149 00:08:36,160 --> 00:08:38,240 Speaker 5: doesn't really fill much. 150 00:08:38,120 --> 00:08:39,000 Speaker 2: Of those buckets. 151 00:08:39,040 --> 00:08:43,960 Speaker 5: So I think that he's less likely than somebody like 152 00:08:43,960 --> 00:08:46,160 Speaker 5: like Shapiro for example, like Whitmer. 153 00:08:46,240 --> 00:08:46,720 Speaker 3: Maybe. 154 00:08:46,840 --> 00:08:51,840 Speaker 4: Okay, so as the favorite son of Pittsburgh, Terry, you're 155 00:08:51,840 --> 00:08:56,080 Speaker 4: going for Pennsylvania's governor with his nineteen electoral college votes. 156 00:08:57,040 --> 00:09:00,280 Speaker 5: Well, now, favorite sonism doesn't havehing to do with it, really, John, 157 00:09:00,360 --> 00:09:01,719 Speaker 5: the it's much more that. 158 00:09:03,440 --> 00:09:03,679 Speaker 2: You know. 159 00:09:04,320 --> 00:09:06,960 Speaker 5: I think last time I saw you all I called 160 00:09:07,000 --> 00:09:10,880 Speaker 5: this the captain obvious analysis, and that is that he 161 00:09:10,960 --> 00:09:14,480 Speaker 5: fills the biggest electoral college need, and he also checks 162 00:09:14,480 --> 00:09:17,960 Speaker 5: all of those other boxes as well. It's interesting to 163 00:09:18,000 --> 00:09:20,680 Speaker 5: me that there's been a somewhat of a backlash within 164 00:09:20,720 --> 00:09:26,080 Speaker 5: Democratic circles about his unabashed support for Israel. You know, 165 00:09:26,120 --> 00:09:31,120 Speaker 5: I think that's plus for him. Frankly, that's ticket back Terry. 166 00:09:31,200 --> 00:09:35,320 Speaker 2: John Tucker has a one item candidate voter. Rather, all 167 00:09:35,320 --> 00:09:38,440 Speaker 2: he cares about is property texts in New Jersey and 168 00:09:38,480 --> 00:09:40,920 Speaker 2: a repeal of the assault. Are there a lot of 169 00:09:41,040 --> 00:09:43,920 Speaker 2: John Tucker's out there and issues when you look at 170 00:09:43,920 --> 00:09:48,559 Speaker 2: the electorate of America. How much of us has one item? 171 00:09:49,080 --> 00:09:51,400 Speaker 2: This is the way we want to be. Are we 172 00:09:51,520 --> 00:09:54,000 Speaker 2: that focused? Are we more sophisticated? 173 00:09:55,200 --> 00:09:58,320 Speaker 5: Ah, We're more scattershot. I think there's I'd say most 174 00:09:58,400 --> 00:10:01,440 Speaker 5: voters care about lots of different issues. And you see 175 00:10:01,559 --> 00:10:04,319 Speaker 5: you see that, you know, in the usual surveys that happen, 176 00:10:04,840 --> 00:10:07,400 Speaker 5: there aren't that many to think only one thing is important. 177 00:10:07,960 --> 00:10:11,480 Speaker 5: I will say that I've been hearing uh, you know, 178 00:10:11,920 --> 00:10:15,640 Speaker 5: hugely from New York New Jersey area folks for a 179 00:10:15,720 --> 00:10:19,920 Speaker 5: decade now about salt and whether it might come back, 180 00:10:20,000 --> 00:10:22,880 Speaker 5: and so that seems to be a myopic focus of 181 00:10:23,160 --> 00:10:24,360 Speaker 5: your particular area. 182 00:10:24,480 --> 00:10:27,000 Speaker 2: It seems to be very myopic, particularly to my right 183 00:10:27,040 --> 00:10:31,680 Speaker 2: here at Bloomberg Studio, Terry. Let's go then to the border, 184 00:10:32,080 --> 00:10:34,680 Speaker 2: and does a gentleman from Arizona have a strength for 185 00:10:34,760 --> 00:10:39,120 Speaker 2: Vice President Harris because he's in Tucson, some hundred miles 186 00:10:39,120 --> 00:10:39,760 Speaker 2: from the border. 187 00:10:40,640 --> 00:10:42,959 Speaker 5: I think it's less I think it's less likely that 188 00:10:43,080 --> 00:10:45,640 Speaker 5: Senator Kelly gets it because of the because of the 189 00:10:46,559 --> 00:10:50,119 Speaker 5: UH and UH, and also less likely because of Arizona. 190 00:10:50,160 --> 00:10:52,400 Speaker 5: If you look at the swing States, Arizona seems a 191 00:10:52,440 --> 00:10:55,160 Speaker 5: little bit more out of hand than uh and and 192 00:10:55,280 --> 00:10:58,160 Speaker 5: less gettable, I would say than some of the others, 193 00:10:58,160 --> 00:11:03,080 Speaker 5: although I think it very much in play. But comparatively speaking, 194 00:11:03,160 --> 00:11:05,559 Speaker 5: the Harris people seem to be emphasizing what they call 195 00:11:05,640 --> 00:11:10,760 Speaker 5: the blue Wall of Michigan, Wisconsin, and Pennsylvania. That seems 196 00:11:10,760 --> 00:11:13,600 Speaker 5: to be where their focus is. And you know, although 197 00:11:13,960 --> 00:11:16,240 Speaker 5: Senator Kelly I think would be a fine candidate. 198 00:11:16,360 --> 00:11:18,080 Speaker 4: When do I get the polls that are really going 199 00:11:18,120 --> 00:11:20,720 Speaker 4: to tell me about how this new lineup is faring? 200 00:11:22,120 --> 00:11:24,120 Speaker 5: I think not until after Labor Day, John, And the 201 00:11:24,160 --> 00:11:27,040 Speaker 5: reason why is that you know what you see here 202 00:11:27,120 --> 00:11:30,679 Speaker 5: in something of hammer for a while. But you know, 203 00:11:30,800 --> 00:11:33,120 Speaker 5: registered voter poles, I think, don't tell you very much. 204 00:11:33,120 --> 00:11:35,240 Speaker 5: And that's pretty much all you've got right now. I mean, 205 00:11:35,240 --> 00:11:38,959 Speaker 5: I've seen likely voter polls occasionally, but you know, registered 206 00:11:39,040 --> 00:11:41,559 Speaker 5: voter poles are like, you know, I'm thinking about maybe 207 00:11:41,559 --> 00:11:44,040 Speaker 5: going to see that movie over the weekend. As opposed 208 00:11:44,080 --> 00:11:45,800 Speaker 5: to the people who are actually going to go see 209 00:11:45,800 --> 00:11:49,920 Speaker 5: the movie, it is less of less interest to you know, 210 00:11:50,000 --> 00:11:53,280 Speaker 5: theater owners and movie studios who might see as opposed 211 00:11:53,320 --> 00:11:55,480 Speaker 5: to who's likely to see. So I think it gets 212 00:11:55,520 --> 00:11:58,920 Speaker 5: the stilled down starting after Labor Day before then not 213 00:11:58,960 --> 00:12:00,000 Speaker 5: really running those poles. 214 00:12:00,160 --> 00:12:02,719 Speaker 2: Just quickly here, Terry Haines, is as simple as this 215 00:12:02,920 --> 00:12:05,760 Speaker 2: old style. I'll use Reagan, but I could use any 216 00:12:05,760 --> 00:12:10,840 Speaker 2: other candidate as well. You entrenched the base through August, 217 00:12:11,160 --> 00:12:15,240 Speaker 2: get into September, and as a miraculous amendment the second 218 00:12:15,240 --> 00:12:18,199 Speaker 2: week of October where you move to the middle. Are 219 00:12:18,240 --> 00:12:19,600 Speaker 2: we that old school? 220 00:12:20,600 --> 00:12:23,439 Speaker 5: Well yeah, I mean that that both of these campaigns 221 00:12:23,520 --> 00:12:27,000 Speaker 5: are a start from the premise that their bases are 222 00:12:27,040 --> 00:12:29,800 Speaker 5: not secure, which is interesting because they're always talked about 223 00:12:29,800 --> 00:12:32,160 Speaker 5: as if they are, but they're not. They're constantly doing 224 00:12:32,200 --> 00:12:35,400 Speaker 5: things to try to to lock down their base. But 225 00:12:35,440 --> 00:12:37,319 Speaker 5: then they you know, they kind of move to the middle, 226 00:12:37,360 --> 00:12:39,240 Speaker 5: and you'll see Harris start to move to the middle 227 00:12:39,240 --> 00:12:42,640 Speaker 5: with the vice presidential pick. I think Trump, in contrast, 228 00:12:42,679 --> 00:12:45,800 Speaker 5: I think has given up his chance to bridge by 229 00:12:46,679 --> 00:12:49,040 Speaker 5: picking Vance and I said so an hour after we 230 00:12:49,120 --> 00:12:52,880 Speaker 5: got picked. You know, he doubled down on I think 231 00:12:53,280 --> 00:12:55,480 Speaker 5: more small ball, and it's going to come back to 232 00:12:55,480 --> 00:12:55,920 Speaker 5: bite him. 233 00:12:56,120 --> 00:12:58,840 Speaker 2: Terry Haynes, thank you so much for Pangaea. An update 234 00:12:58,840 --> 00:13:03,360 Speaker 2: there and really careful advice. I think to maintain the 235 00:13:03,440 --> 00:13:11,000 Speaker 2: certitude until we get into September. In October, ed Ludlow. 236 00:13:11,400 --> 00:13:14,720 Speaker 2: In the last two three four months, he's done a 237 00:13:14,840 --> 00:13:21,080 Speaker 2: national value add But you bought a Tesla, right, Retto didn't. 238 00:13:21,280 --> 00:13:23,000 Speaker 2: Retto didn't buy it for your keeper. 239 00:13:22,800 --> 00:13:25,680 Speaker 6: Coming straight out of my paycheck. Rehtto doesn't pay for it. 240 00:13:25,760 --> 00:13:28,760 Speaker 2: You bought a Tesla, and they had a Bloomberg technology 241 00:13:28,760 --> 00:13:34,599 Speaker 2: and all our San Francisco efforts. His chronicled self driving 242 00:13:35,000 --> 00:13:39,080 Speaker 2: on a Tesla like, literally, it's a public good. When 243 00:13:39,280 --> 00:13:42,760 Speaker 2: are we going to have confidence for a Tesla or 244 00:13:42,800 --> 00:13:45,880 Speaker 2: any other car to be quote unquote self driving. 245 00:13:46,120 --> 00:13:49,240 Speaker 6: I don't know when we collectively will have confidence. I 246 00:13:49,320 --> 00:13:53,600 Speaker 6: don't have confidence. So I leased a Tesla. Little why 247 00:13:53,760 --> 00:13:55,600 Speaker 6: I think I've discussed it with you on the show. 248 00:13:55,679 --> 00:13:55,800 Speaker 1: Right. 249 00:13:55,800 --> 00:13:57,480 Speaker 6: I did it because it was the most affordable option 250 00:13:57,559 --> 00:13:58,760 Speaker 6: at the time when I needed a car. 251 00:13:58,880 --> 00:14:01,360 Speaker 2: I was in one yesterday. Actually it's very. 252 00:14:01,160 --> 00:14:03,840 Speaker 6: Comfortable, Yeah, very comfortable, And the lease is like, it's 253 00:14:04,040 --> 00:14:06,360 Speaker 6: very cheap for you know, and we can afford it. 254 00:14:06,559 --> 00:14:09,280 Speaker 6: But I love technology, so my thinking was get hands 255 00:14:09,320 --> 00:14:11,560 Speaker 6: on with it. We write about and talk about full 256 00:14:11,559 --> 00:14:15,000 Speaker 6: self driving supervised software on air all the time. I 257 00:14:15,040 --> 00:14:16,880 Speaker 6: don't think it's fair for a journist to do that 258 00:14:16,960 --> 00:14:18,720 Speaker 6: and not use it. So I've been using it a lot, 259 00:14:18,800 --> 00:14:21,600 Speaker 6: and I have the latest version, twelve point five point 260 00:14:21,640 --> 00:14:25,640 Speaker 6: one that was released limited release Friday. It started to 261 00:14:25,720 --> 00:14:28,880 Speaker 6: roll out widespread today last twenty four hours, and I 262 00:14:28,880 --> 00:14:31,000 Speaker 6: took it for a ride Saturday, thirty miles from my 263 00:14:31,040 --> 00:14:34,240 Speaker 6: home to San Francisco. There were no incidents bar one, 264 00:14:34,640 --> 00:14:36,120 Speaker 6: so Golden gate Bridge iconic. 265 00:14:36,280 --> 00:14:37,600 Speaker 4: All it takes is one incident. 266 00:14:37,640 --> 00:14:39,240 Speaker 6: By the way, well exactly, So I get onto the 267 00:14:39,240 --> 00:14:41,480 Speaker 6: Golden gate Bridge, I'm in the correct lane. I actually 268 00:14:41,560 --> 00:14:44,240 Speaker 6: change the software changes lanes for me, and it spits 269 00:14:44,280 --> 00:14:46,240 Speaker 6: me out the other side, on the San Francisco side, 270 00:14:46,280 --> 00:14:48,560 Speaker 6: and I'm heading toward the toll booth. There are six 271 00:14:48,600 --> 00:14:50,840 Speaker 6: toll booths, only two of them are open. Gets me 272 00:14:50,880 --> 00:14:52,560 Speaker 6: in one of them that's open with a green arrow. 273 00:14:52,760 --> 00:14:56,040 Speaker 6: Good start, but it says very clearly, do not stop. 274 00:14:56,200 --> 00:14:58,680 Speaker 6: Maintain speed twenty five miles an hour. I get to 275 00:14:58,720 --> 00:15:02,280 Speaker 6: the bollards and the self driving system slams the brakes 276 00:15:02,480 --> 00:15:04,680 Speaker 6: and I go down to six miles an hour. What's 277 00:15:04,720 --> 00:15:08,640 Speaker 6: behind me dozens of cars shooting off the bridge, not 278 00:15:08,680 --> 00:15:10,360 Speaker 6: at twenty five miles or forty miles an hour. I 279 00:15:10,480 --> 00:15:12,440 Speaker 6: made the choice in the moment not to intervene. 280 00:15:12,440 --> 00:15:13,040 Speaker 3: I should have done. 281 00:15:13,120 --> 00:15:16,000 Speaker 2: Were you listening to Jefferson Airplane and Grateful Dead? 282 00:15:18,400 --> 00:15:25,040 Speaker 6: I was listening to Kenny Gail. Okay, just classic for 283 00:15:25,080 --> 00:15:26,480 Speaker 6: the Woodwind movement, John. 284 00:15:26,280 --> 00:15:29,240 Speaker 2: Lives and the Garden State Parkway. There's someone doing in 285 00:15:29,320 --> 00:15:30,400 Speaker 2: Ned Ludlow experience. 286 00:15:30,400 --> 00:15:33,720 Speaker 4: Well, no, it's working, that's right. It doesn't take into 287 00:15:33,760 --> 00:15:37,080 Speaker 4: account the other nuts who are behind you on the roadway. 288 00:15:37,160 --> 00:15:39,320 Speaker 6: Yes, yes, and no. It's a purely camera based system, right, 289 00:15:39,360 --> 00:15:41,760 Speaker 6: and there are cameras around the perimeter of the car, 290 00:15:42,200 --> 00:15:45,880 Speaker 6: and so it literally does take into account the nuts 291 00:15:45,920 --> 00:15:49,240 Speaker 6: around you based on what the cameras can detect envision 292 00:15:49,240 --> 00:15:51,400 Speaker 6: in the speed they're moving at. I mean, I saw 293 00:15:51,440 --> 00:15:53,520 Speaker 6: something on the Bloomberg terminal yesterday that I don't think 294 00:15:53,520 --> 00:15:56,440 Speaker 6: I've seen in my career, which is Bloomberg's trick created 295 00:15:56,520 --> 00:15:59,560 Speaker 6: Now writing up that truest analyst note, he reflected on 296 00:15:59,600 --> 00:16:02,080 Speaker 6: his own experience where he almost had an incident, right, 297 00:16:02,080 --> 00:16:05,200 Speaker 6: he almost had crash. But what's the most amazing about 298 00:16:05,200 --> 00:16:07,080 Speaker 6: the story is not just the details of his note 299 00:16:07,200 --> 00:16:10,600 Speaker 6: and his thesis around Tesla's that that story got almost 300 00:16:10,600 --> 00:16:13,520 Speaker 6: fourteen thousand terminal reads in a single kill that day. 301 00:16:13,920 --> 00:16:15,960 Speaker 6: So people care, and I think that's why it's important 302 00:16:16,000 --> 00:16:16,600 Speaker 6: we talk about it. 303 00:16:16,960 --> 00:16:20,400 Speaker 4: Peter Thiel tells a great story about getting in Elon 304 00:16:20,520 --> 00:16:23,600 Speaker 4: Musk when he got his supercar way back when, and 305 00:16:23,640 --> 00:16:26,320 Speaker 4: he crashed it with him in the car. Didn't have 306 00:16:26,400 --> 00:16:27,880 Speaker 4: insurance either, by the way. 307 00:16:27,760 --> 00:16:31,240 Speaker 2: So what's the relationship of Tesla, California to Texas. We 308 00:16:31,280 --> 00:16:35,160 Speaker 2: all know the SOBA right and say, the California Department 309 00:16:35,320 --> 00:16:40,200 Speaker 2: of Driver's Licenses. Is there a government watching of Ed 310 00:16:40,280 --> 00:16:43,560 Speaker 2: Ludlow almost coming to death on the Golden gate Bridge? 311 00:16:43,600 --> 00:16:44,120 Speaker 2: Do they care? 312 00:16:44,360 --> 00:16:45,840 Speaker 6: That's a really excellent question. 313 00:16:46,040 --> 00:16:47,480 Speaker 2: So I only good one of the days to go 314 00:16:47,560 --> 00:16:47,960 Speaker 2: with a day. 315 00:16:48,080 --> 00:16:50,560 Speaker 6: The answer is that the federal level, it's NITZA, the 316 00:16:50,680 --> 00:16:55,800 Speaker 6: National Highway in Traffic Safety Administration, who essentially policed this 317 00:16:56,480 --> 00:16:59,800 Speaker 6: and different to other companies that you may or may 318 00:16:59,800 --> 00:17:01,680 Speaker 6: not have heard of, for example Zooks which is owned 319 00:17:01,680 --> 00:17:04,840 Speaker 6: by Amazon, or Waimo, which is owned by Alphabet, the 320 00:17:04,840 --> 00:17:08,439 Speaker 6: parent of Google. What Tester is doing is developing this 321 00:17:08,560 --> 00:17:11,520 Speaker 6: technology through the consumer in the real world with a 322 00:17:11,600 --> 00:17:16,600 Speaker 6: view long term to operating so called robotaxi fleet. And 323 00:17:16,680 --> 00:17:19,119 Speaker 6: what others are doing are testing vehicles where there is 324 00:17:19,200 --> 00:17:21,639 Speaker 6: no driver in the front seat and the consumer sits 325 00:17:21,640 --> 00:17:24,760 Speaker 6: in the back a genuine robotaxi, and that answers the 326 00:17:24,840 --> 00:17:27,800 Speaker 6: jurisdiction question that because this is going through the consumer, 327 00:17:28,200 --> 00:17:32,240 Speaker 6: it's largely NITZA that is adjudicating the development of it 328 00:17:32,320 --> 00:17:35,360 Speaker 6: in the risk in real time in this country only, 329 00:17:35,400 --> 00:17:35,920 Speaker 6: I should. 330 00:17:35,720 --> 00:17:39,440 Speaker 4: Say, crossing fifty seventh Street this morning at three am, 331 00:17:39,680 --> 00:17:44,800 Speaker 4: they've milled the roadway preparing it for removing. There are 332 00:17:44,800 --> 00:17:46,919 Speaker 4: no lines on the roadway and this has been this 333 00:17:46,960 --> 00:17:49,159 Speaker 4: way for three week typical in New York City. You 334 00:17:49,200 --> 00:17:52,520 Speaker 4: get prepared to pave a roadway, it takes about three 335 00:17:52,560 --> 00:17:56,520 Speaker 4: weeks after you jam. Well, no, it's the truth. AI 336 00:17:56,680 --> 00:17:58,119 Speaker 4: is going to solve that problem for me. 337 00:17:58,320 --> 00:18:00,040 Speaker 6: Finance, isn't you go across. 338 00:17:59,720 --> 00:18:01,880 Speaker 4: Fifty seven streets lines the road? 339 00:18:02,040 --> 00:18:05,000 Speaker 6: So this is another excellent question. In the truest note, 340 00:18:05,119 --> 00:18:08,160 Speaker 6: the analyst recalls three incidents where he had to intervene 341 00:18:08,200 --> 00:18:11,680 Speaker 6: because he felt unsafe. What the camera system is supposed 342 00:18:11,720 --> 00:18:14,800 Speaker 6: to do is detect all elements of the road around you, 343 00:18:14,880 --> 00:18:17,680 Speaker 6: including a white solid line now on a freeway or 344 00:18:17,680 --> 00:18:20,400 Speaker 6: a highway environment, the rules of the road in any 345 00:18:20,440 --> 00:18:22,840 Speaker 6: state are that you must not change lane with a 346 00:18:22,880 --> 00:18:26,520 Speaker 6: white solid line. In the analyst's note, the car chose 347 00:18:26,560 --> 00:18:29,760 Speaker 6: to change lane of its own decision making, breaking that 348 00:18:29,880 --> 00:18:32,000 Speaker 6: rule of the road. Now, in the incident or the 349 00:18:32,320 --> 00:18:34,960 Speaker 6: environment you've described, there are no lines, and so that's 350 00:18:35,000 --> 00:18:37,119 Speaker 6: one of the things the camera based system is supposed 351 00:18:37,160 --> 00:18:39,800 Speaker 6: to use to help it navigate the world around it. 352 00:18:39,800 --> 00:18:44,280 Speaker 6: It's another great question. It would use proximity to basically 353 00:18:44,320 --> 00:18:46,600 Speaker 6: maintain a course, but of course it's not ideal. 354 00:18:46,760 --> 00:18:52,000 Speaker 2: Do you assume at some point that Tesla will continue this, 355 00:18:52,160 --> 00:18:55,040 Speaker 2: reinvigorate it, or will they step aside and say we 356 00:18:55,200 --> 00:18:56,040 Speaker 2: just can't get it done. 357 00:18:56,320 --> 00:18:56,800 Speaker 5: They're all in. 358 00:18:56,880 --> 00:18:59,440 Speaker 6: I mean, my sourcing that company is that this is it, 359 00:18:59,680 --> 00:19:01,880 Speaker 6: and you're either with it or you're not. I'll say 360 00:19:01,880 --> 00:19:03,719 Speaker 6: something I've said to you many times Tom, which is 361 00:19:04,000 --> 00:19:07,600 Speaker 6: Elon Musk is often late, always late, but he gets 362 00:19:07,680 --> 00:19:11,080 Speaker 6: there in the end, and to his credit, you know, 363 00:19:11,560 --> 00:19:16,080 Speaker 6: it's not a perfect safe experience. It's pretty astonishing the technology. 364 00:19:16,359 --> 00:19:19,760 Speaker 6: The difficulty for investors many listening to this program is 365 00:19:19,960 --> 00:19:22,160 Speaker 6: I don't get the jump. How do I go from 366 00:19:22,200 --> 00:19:24,960 Speaker 6: sitting in my own car where I partially allow it 367 00:19:25,000 --> 00:19:28,480 Speaker 6: to dry itself drive itself to a future where Tesla 368 00:19:28,520 --> 00:19:31,000 Speaker 6: operates a fleet like Uber of robotaxing. 369 00:19:31,040 --> 00:19:33,760 Speaker 2: We've been unfair, We've been Thank you seriously ed for 370 00:19:34,000 --> 00:19:36,879 Speaker 2: just this incredible real life coverage. I read it voraciously 371 00:19:36,920 --> 00:19:40,240 Speaker 2: on the weekends. Ludlow is all I read anything else kind? 372 00:19:40,400 --> 00:19:44,600 Speaker 2: Microsoft this afternoon, what's your nuance at Bloomberg Technology? Yeah? 373 00:19:44,720 --> 00:19:47,520 Speaker 6: I call it this earning season. Do you understand the 374 00:19:47,560 --> 00:19:50,000 Speaker 6: money machine? For every dollar you're putting in the machine, 375 00:19:50,000 --> 00:19:52,760 Speaker 6: how many dollars are coming out? And Microsoft gave us 376 00:19:52,760 --> 00:19:55,800 Speaker 6: a sense of that last quarter. Basically, they gave us 377 00:19:55,800 --> 00:19:58,480 Speaker 6: a CAPEX figure and they said we had thirty percent 378 00:19:58,520 --> 00:20:01,280 Speaker 6: top line growth on Cloud. Seven percent of that came 379 00:20:01,320 --> 00:20:04,359 Speaker 6: from AI and for the street, that was kind of enough. 380 00:20:04,600 --> 00:20:07,199 Speaker 6: But the concern is, you're spending all this money, what 381 00:20:07,280 --> 00:20:08,760 Speaker 6: do you have to show for it in terms of 382 00:20:08,760 --> 00:20:11,359 Speaker 6: top line growth. You guys probably have a longer standing 383 00:20:11,400 --> 00:20:13,919 Speaker 6: relationship with Microsoft than I do. With respect. It's been 384 00:20:13,960 --> 00:20:16,800 Speaker 6: around since the mid seventies. But what it's always been 385 00:20:16,840 --> 00:20:19,520 Speaker 6: good at is selling software you could otherwise get for free, 386 00:20:19,840 --> 00:20:21,880 Speaker 6: and that gives the market some confidence in that name. 387 00:20:21,880 --> 00:20:24,880 Speaker 6: It's one of the most widely held stocks among institutionals 388 00:20:24,920 --> 00:20:25,919 Speaker 6: and retailers on the planet. 389 00:20:26,119 --> 00:20:27,919 Speaker 2: We're doing it. We have a baseball player of the 390 00:20:28,040 --> 00:20:32,040 Speaker 2: New York Yankees. It's a baseball team, ED and they 391 00:20:32,040 --> 00:20:35,199 Speaker 2: have a new player that's wonderful called Jazz Chisholm. So 392 00:20:35,720 --> 00:20:38,439 Speaker 2: I talk to your people and I said, what does 393 00:20:38,640 --> 00:20:41,320 Speaker 2: what does Ed Lovelow want in terms of jazz And 394 00:20:41,359 --> 00:20:45,920 Speaker 2: they said, adds just like all Kenny g is Kenny 395 00:20:46,000 --> 00:20:46,800 Speaker 2: g js Jan. 396 00:20:47,840 --> 00:20:51,440 Speaker 4: Well, if that's open to debate, I would say no, 397 00:20:51,720 --> 00:20:53,520 Speaker 4: But yeah. 398 00:20:53,160 --> 00:21:04,479 Speaker 3: Ed Ludlow wember respect. 399 00:21:06,320 --> 00:21:09,600 Speaker 2: Mike Green joins its chief strategies to simplify asset management, 400 00:21:10,080 --> 00:21:13,680 Speaker 2: always thinking about the market for various and sundry firms, 401 00:21:13,720 --> 00:21:17,960 Speaker 2: including Canyon. Over the years you have and you know, Mike, 402 00:21:18,000 --> 00:21:21,800 Speaker 2: how I feel about this. I hate the modern parlor 403 00:21:21,880 --> 00:21:26,520 Speaker 2: game of the FED. And the parlor game exists because 404 00:21:26,560 --> 00:21:29,000 Speaker 2: we have data and we could go to our Bloomberg 405 00:21:29,040 --> 00:21:31,880 Speaker 2: and look at the data, and we're more certain ourselves 406 00:21:31,920 --> 00:21:35,040 Speaker 2: because we've got the data. And now we're data dependent. 407 00:21:35,359 --> 00:21:38,359 Speaker 2: And you liken it to two drunks, the dual mandates 408 00:21:38,800 --> 00:21:41,520 Speaker 2: at the ex post bar of the FED. 409 00:21:42,119 --> 00:21:45,760 Speaker 7: Yeah, we're definitely staggering home trying to solve both unemployment, 410 00:21:45,800 --> 00:21:47,679 Speaker 7: which we're not entirely sure what it is and how 411 00:21:47,720 --> 00:21:49,800 Speaker 7: it's caused. And we're trying to solve inflation, which we 412 00:21:49,840 --> 00:21:51,960 Speaker 7: freely admit we're not entirely sure how to fix it 413 00:21:52,080 --> 00:21:52,600 Speaker 7: or solve it. 414 00:21:52,640 --> 00:21:54,639 Speaker 2: And you mentioned the Blinder op ed here in the 415 00:21:54,680 --> 00:21:58,919 Speaker 2: Western Journal is the example. So how does that fold 416 00:21:58,960 --> 00:22:02,359 Speaker 2: into the July seven September guessing game of those in 417 00:22:02,400 --> 00:22:02,960 Speaker 2: the parlor? 418 00:22:03,200 --> 00:22:05,040 Speaker 7: So I think we're now at a point of decision 419 00:22:05,040 --> 00:22:07,040 Speaker 7: where the FED is likely to go ahead barring some 420 00:22:07,240 --> 00:22:10,119 Speaker 7: catastrophic rating that comes through. But the important point the 421 00:22:10,160 --> 00:22:13,560 Speaker 7: Blinder is making is that we've changed the seasonality. Just 422 00:22:13,600 --> 00:22:16,680 Speaker 7: the methodology with which we produce the data on inflation 423 00:22:17,440 --> 00:22:21,399 Speaker 7: is influenced by the prior behavior of prices. The last 424 00:22:21,480 --> 00:22:25,200 Speaker 7: revision basically adjusted for the time period from twenty seventeen 425 00:22:25,280 --> 00:22:28,280 Speaker 7: to twenty twenty two, which includes the nonsense around COVID. 426 00:22:28,320 --> 00:22:33,000 Speaker 7: This has shifted seasonality dramatically. It raises prices or raises 427 00:22:33,040 --> 00:22:37,000 Speaker 7: inflation numbers that we're getting in the fall and early spring, 428 00:22:37,400 --> 00:22:40,720 Speaker 7: and it's lowering them in the summer. That's creating conditions 429 00:22:40,760 --> 00:22:43,520 Speaker 7: where we could come into the fall, even as underlying 430 00:22:43,560 --> 00:22:46,680 Speaker 7: inflation continues to fall, and suddenly we see a hot 431 00:22:46,680 --> 00:22:49,400 Speaker 7: CPI print again, derailing the question of is the FED 432 00:22:49,480 --> 00:22:52,400 Speaker 7: going to cut despite the very obvious need to do 433 00:22:52,480 --> 00:22:53,160 Speaker 7: so and. 434 00:22:53,160 --> 00:22:57,360 Speaker 4: The inflation fight. It comes at the expanse of jobs. 435 00:22:57,680 --> 00:23:00,159 Speaker 7: It can right. It becomes a question of what we 436 00:23:00,280 --> 00:23:02,600 Speaker 7: actually trying to diagnose. And this is one of these 437 00:23:02,640 --> 00:23:06,560 Speaker 7: really weird situations in which the map becomes the territory. 438 00:23:07,080 --> 00:23:10,399 Speaker 7: We're not actually looking at what's happening in the economy. Instead, 439 00:23:10,400 --> 00:23:13,040 Speaker 7: we're turning around and relying on the data, which was 440 00:23:13,080 --> 00:23:15,879 Speaker 7: the point that Tom was making, that we confidently receive 441 00:23:15,920 --> 00:23:18,640 Speaker 7: from government agencies that are printing this. Now most people 442 00:23:18,680 --> 00:23:22,679 Speaker 7: think there's giant conspiracies around this. It's really just mechanical inputs. 443 00:23:23,000 --> 00:23:23,080 Speaker 2: Right. 444 00:23:23,119 --> 00:23:25,439 Speaker 7: When you have thousands and thousands of employees who are 445 00:23:25,440 --> 00:23:27,800 Speaker 7: doing this, you have to have rules around how you 446 00:23:27,840 --> 00:23:31,200 Speaker 7: construct it. Those rules are built for time periods of stability, 447 00:23:31,240 --> 00:23:32,840 Speaker 7: and the last five years have been anything. 448 00:23:32,880 --> 00:23:37,560 Speaker 4: But if we're not data dependent, then we're what anecdotally depended. 449 00:23:37,800 --> 00:23:40,639 Speaker 7: Well, anecdotes are data in one form or another, right, 450 00:23:40,680 --> 00:23:42,919 Speaker 7: but we need to actually have a theoretical model and 451 00:23:42,920 --> 00:23:46,400 Speaker 7: an understanding of what's really going on. We are looking 452 00:23:46,480 --> 00:23:49,879 Speaker 7: at components of inflation that are deeply lagging, things like 453 00:23:50,000 --> 00:23:52,919 Speaker 7: insurance that ensures the value of the car as of 454 00:23:53,040 --> 00:23:56,520 Speaker 7: last year. Right, that it relates to behaviors of consumers 455 00:23:56,560 --> 00:24:00,760 Speaker 7: who are increasingly stressed and deciding to, you know, sue 456 00:24:00,840 --> 00:24:03,720 Speaker 7: people for compensation in a variety of ways. We're seeing 457 00:24:03,720 --> 00:24:07,320 Speaker 7: this insurance fraud is rising. Those are not inflationary. Those 458 00:24:07,359 --> 00:24:10,040 Speaker 7: are market pressures and market powers, And the answer to them, 459 00:24:10,040 --> 00:24:13,520 Speaker 7: of course, is fewer people drive cars. That's gonna end 460 00:24:13,600 --> 00:24:15,560 Speaker 7: up playing out even if the FED reacts. 461 00:24:15,640 --> 00:24:18,680 Speaker 2: What does it do to assets? I mean, what we're 462 00:24:18,680 --> 00:24:21,920 Speaker 2: talking here about, folks, is a difference between risk where 463 00:24:21,920 --> 00:24:25,760 Speaker 2: you can model what you've got, and outright uncertainty where 464 00:24:25,800 --> 00:24:29,359 Speaker 2: you can't model it. What does that do to asset prices? 465 00:24:29,840 --> 00:24:33,560 Speaker 7: So the uncertainty around the Fed's reaction function, or the 466 00:24:33,760 --> 00:24:37,879 Speaker 7: uncertainty around do we have anything approximating an optimal path 467 00:24:37,920 --> 00:24:41,439 Speaker 7: of interest rates is ultimately just like sand in the 468 00:24:41,480 --> 00:24:44,119 Speaker 7: gears of the system. Right, it slows it down, It 469 00:24:44,200 --> 00:24:47,000 Speaker 7: creates increased risks. You can err on the side of 470 00:24:47,000 --> 00:24:49,680 Speaker 7: too liberal and too generous, as we may have done 471 00:24:49,880 --> 00:24:52,560 Speaker 7: ten years ago. Now we're on the flip side of that, 472 00:24:52,680 --> 00:24:56,119 Speaker 7: and we're watching segments of the economy slowly wither away. 473 00:24:56,560 --> 00:24:59,159 Speaker 4: How restrictive are we right now? 474 00:24:59,320 --> 00:25:01,520 Speaker 7: I think it really depends on who you ask. Right 475 00:25:01,560 --> 00:25:04,280 Speaker 7: there's no restriction for a company that is incredibly cash 476 00:25:04,359 --> 00:25:08,399 Speaker 7: rich like Microsoft. There was a fantastic headline that Apple, 477 00:25:08,640 --> 00:25:10,879 Speaker 7: you know, made over a billion dollars on its cash 478 00:25:10,920 --> 00:25:14,480 Speaker 7: balance last quarter. They're not thinking about interest rates at all. 479 00:25:14,520 --> 00:25:17,480 Speaker 7: They don't care. But there are many companies that are 480 00:25:17,520 --> 00:25:20,400 Speaker 7: deeply distressed in terms of their access to credit, particularly 481 00:25:20,440 --> 00:25:21,160 Speaker 7: at the small side. 482 00:25:21,320 --> 00:25:24,600 Speaker 2: So who's your study of who the Fed should listen to? 483 00:25:25,080 --> 00:25:28,600 Speaker 2: The to to borrow a word from pure research, the 484 00:25:28,640 --> 00:25:33,440 Speaker 2: topology of America, your own Powell has to choose who 485 00:25:33,480 --> 00:25:36,200 Speaker 2: to adapt policy to. Obviously it's not going to be 486 00:25:36,280 --> 00:25:39,560 Speaker 2: Tim Cook, I get that, But they who do they 487 00:25:40,119 --> 00:25:41,280 Speaker 2: adapt for? 488 00:25:42,680 --> 00:25:45,760 Speaker 7: Ultimately, what we should be doing is adapting for the 489 00:25:45,800 --> 00:25:49,720 Speaker 7: maximum ability to change, right so, the ability to accommodate 490 00:25:49,720 --> 00:25:53,000 Speaker 7: the shifts that are occurring in our economy right right now. 491 00:25:53,040 --> 00:25:55,480 Speaker 7: What we're doing is we're basically saying the answer is 492 00:25:55,520 --> 00:25:58,600 Speaker 7: get prices down, where the answer is get inflation or 493 00:25:58,640 --> 00:26:01,960 Speaker 7: get unemployment up. Neither of those are answers. Those are 494 00:26:02,000 --> 00:26:03,119 Speaker 7: actually just data. 495 00:26:03,160 --> 00:26:05,080 Speaker 2: Again, well, I could go on for this for hours 496 00:26:05,080 --> 00:26:08,359 Speaker 2: because full disclosure, folks. I agree with everything Mike Green's saying. 497 00:26:08,760 --> 00:26:13,359 Speaker 2: But to be direct about it, are we victims of 498 00:26:13,520 --> 00:26:18,680 Speaker 2: green spans measured? To me? The dampening folks, I'm not 499 00:26:18,680 --> 00:26:20,000 Speaker 2: going to go to the math of this, but there's 500 00:26:20,040 --> 00:26:22,920 Speaker 2: a browning function and a drift function, and the answer 501 00:26:22,960 --> 00:26:25,960 Speaker 2: is you dampen out. And that's a good thing. Basically, 502 00:26:26,040 --> 00:26:29,720 Speaker 2: with a measured approach, we've dampened out our outcomes. That's 503 00:26:29,720 --> 00:26:30,280 Speaker 2: the hope. 504 00:26:30,560 --> 00:26:33,520 Speaker 7: That is the objective is to minimize the disruption that 505 00:26:33,520 --> 00:26:37,040 Speaker 7: occurs because when people become unemployed, you lose years of 506 00:26:37,119 --> 00:26:40,280 Speaker 7: human capital. Right, It's just that simple. That's really the 507 00:26:40,280 --> 00:26:42,239 Speaker 7: issue that we're dealing with. On the flip side of that, 508 00:26:42,640 --> 00:26:46,240 Speaker 7: as Stan Druckenmiller pointed out many years ago, you can overdampen. 509 00:26:46,520 --> 00:26:49,880 Speaker 7: Over damping encourages excess leverage. And so it is a 510 00:26:50,000 --> 00:26:51,920 Speaker 7: very tough job that the FED has And the simple 511 00:26:51,920 --> 00:26:54,680 Speaker 7: answer is we're asking it to do way too much. 512 00:26:54,680 --> 00:26:57,880 Speaker 7: We got a dysfunctional Congress, We've outsourced much of our 513 00:26:57,920 --> 00:26:59,760 Speaker 7: policy making to monitor. 514 00:26:59,600 --> 00:27:02,400 Speaker 2: They are are they affected by Laurence Summer and Olivia 515 00:27:02,440 --> 00:27:06,680 Speaker 2: Blanchard's great work on hysteresis the long term effect of unemployment, 516 00:27:06,960 --> 00:27:09,800 Speaker 2: or they just simply slaves to the Dallas trim mean 517 00:27:10,440 --> 00:27:12,080 Speaker 2: or the PCE deflator. 518 00:27:12,200 --> 00:27:14,679 Speaker 7: I think unfortunately this is where it gets confused with 519 00:27:14,840 --> 00:27:18,080 Speaker 7: political influence, right. It really depends on what people are 520 00:27:18,080 --> 00:27:18,600 Speaker 7: worried about. 521 00:27:18,800 --> 00:27:18,919 Speaker 2: Right. 522 00:27:19,000 --> 00:27:21,720 Speaker 7: So in a low unemployment environment, they're very focused on 523 00:27:21,760 --> 00:27:25,199 Speaker 7: the inflation number. That's what people are angry about. In 524 00:27:25,240 --> 00:27:27,480 Speaker 7: a high unemployment environment, you're gonna have to flip that. 525 00:27:27,560 --> 00:27:29,800 Speaker 7: And unfortunately it feels very much like the risks that 526 00:27:29,920 --> 00:27:31,920 Speaker 7: is flipping in the opposite direction are quite high. 527 00:27:32,000 --> 00:27:34,159 Speaker 2: Now, bottle of Mike Green and a little bit out 528 00:27:34,200 --> 00:27:38,600 Speaker 2: on Bloomberg Digital. Here today Mike Green is to simplify 529 00:27:39,080 --> 00:27:43,040 Speaker 2: asset management. 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