1 00:00:00,280 --> 00:00:04,720 Speaker 1: This is Bloomberg Wall Street Week. The global push into infrastructure, 2 00:00:04,760 --> 00:00:08,480 Speaker 1: breaking the IPO logjam in text, the financial stories that 3 00:00:08,560 --> 00:00:11,680 Speaker 1: shape our work, cutting inflation without losing jobs. Do we 4 00:00:11,800 --> 00:00:14,440 Speaker 1: need rate cuts and if so? How many? Investing in 5 00:00:14,480 --> 00:00:16,400 Speaker 1: a time of geopolitical. 6 00:00:15,680 --> 00:00:18,560 Speaker 2: Turmoil Through the eyes of the most influential voices. 7 00:00:18,720 --> 00:00:22,480 Speaker 1: Ten Rogueff economists of Harvard, former FDIC had Shila bet 8 00:00:22,680 --> 00:00:27,360 Speaker 1: Ge CEO, Larry Culp, San Francisco fed President Mary Daily Bloomberg. 9 00:00:26,840 --> 00:00:30,480 Speaker 2: Wall Street Week with David Weston from Bloomberg Radio. 10 00:00:30,320 --> 00:00:33,880 Speaker 1: Market turmoil, Middle East threats, and the presidential race is 11 00:00:33,920 --> 00:00:37,080 Speaker 1: off and running. This is Bloomberg Wall Street Week. I'm 12 00:00:37,159 --> 00:00:41,800 Speaker 1: David Weston. This week, Harvard economist Greg Mank on facing 13 00:00:41,880 --> 00:00:43,239 Speaker 1: up to the federal deficit. 14 00:00:43,960 --> 00:00:47,279 Speaker 3: You can't leave your children a negative bequest unless you're 15 00:00:47,320 --> 00:00:48,080 Speaker 3: doing it through the government. 16 00:00:48,680 --> 00:00:52,080 Speaker 1: Former Pentagon Number two Michelle Flornoy on what's needed for 17 00:00:52,120 --> 00:00:54,520 Speaker 1: the US to face a more dangerous world. 18 00:00:55,160 --> 00:00:57,560 Speaker 4: We really have to take a fresh look at what 19 00:00:57,600 --> 00:00:59,840 Speaker 4: we're spending on and where we're investing. 20 00:01:00,560 --> 00:01:03,920 Speaker 1: And Jack Hittery of Sandbox AQ on what's real and 21 00:01:03,960 --> 00:01:06,679 Speaker 1: what's hype in the world of artificial intelligence. 22 00:01:07,360 --> 00:01:09,280 Speaker 5: This is a great moment for a I that takes 23 00:01:09,360 --> 00:01:14,840 Speaker 5: us beyond the chat GPT. 24 00:01:24,680 --> 00:01:27,520 Speaker 1: We start with a wild week in the markets, stocks 25 00:01:27,560 --> 00:01:30,280 Speaker 1: and bonds responded to weak jobs numbers, a shift in 26 00:01:30,360 --> 00:01:34,160 Speaker 1: Japanese monetary policy, and geopolitical uncertainty. To take us through 27 00:01:34,160 --> 00:01:36,440 Speaker 1: what it all means, we turn once again to our 28 00:01:36,480 --> 00:01:39,399 Speaker 1: special contributor, Larry Summers of Harvard. So, Larry a lot 29 00:01:39,440 --> 00:01:42,520 Speaker 1: to sort through here. What was your overall take on 30 00:01:42,600 --> 00:01:45,720 Speaker 1: the terminil the marketplace and the speculation, the speculation that 31 00:01:45,720 --> 00:01:47,840 Speaker 1: FED maybe should have an emergency rate cut. 32 00:01:48,760 --> 00:01:53,160 Speaker 6: I would shay on current facts, given that there has 33 00:01:53,240 --> 00:01:56,480 Speaker 6: been some recovery, given that volatility has. 34 00:01:56,440 --> 00:01:57,960 Speaker 7: Come way down. 35 00:01:58,840 --> 00:02:02,200 Speaker 6: It were not out of the woods. You can't be certain. 36 00:02:02,280 --> 00:02:07,000 Speaker 6: The FED certainly needs to be watching carefully, but I 37 00:02:07,080 --> 00:02:15,600 Speaker 6: think an emergency response would be launching panicked, overheated and 38 00:02:16,880 --> 00:02:22,360 Speaker 6: counterproductive on current facts, Which doesn't mean that the FED 39 00:02:22,440 --> 00:02:28,519 Speaker 6: shouldn't be watching very closely. Before they have another decision 40 00:02:28,560 --> 00:02:31,639 Speaker 6: to make in September, there's going to be a lot 41 00:02:31,720 --> 00:02:35,680 Speaker 6: more data that it's going to come in, and I 42 00:02:35,720 --> 00:02:38,840 Speaker 6: think they should make clear that they're going to be 43 00:02:38,880 --> 00:02:41,959 Speaker 6: watching all that data and they're going to make a 44 00:02:42,040 --> 00:02:47,560 Speaker 6: decision reflecting the need to balance the concern of making 45 00:02:47,600 --> 00:02:51,960 Speaker 6: sure that we have stopped inflation with the concern of 46 00:02:52,320 --> 00:02:58,840 Speaker 6: maximizing employment, and I think they need to not be 47 00:02:59,200 --> 00:03:05,240 Speaker 6: pressured into specificity of a kind that is impossible. 48 00:03:05,840 --> 00:03:06,960 Speaker 1: Let me pick up on one of the things you 49 00:03:06,960 --> 00:03:09,760 Speaker 1: mentioned was volatility. The VIC really did spike up, I 50 00:03:09,800 --> 00:03:12,320 Speaker 1: think over sixty, maybe the highest it had been since 51 00:03:12,360 --> 00:03:16,200 Speaker 1: the pandemic first hit. How much attention should the FED 52 00:03:16,320 --> 00:03:19,359 Speaker 1: pay to that volatility index what it might be telling 53 00:03:19,360 --> 00:03:20,840 Speaker 1: you about the markets are functioning. 54 00:03:22,560 --> 00:03:29,200 Speaker 6: I actually think that the SEC and the relevant exchanges 55 00:03:29,840 --> 00:03:34,120 Speaker 6: may want to pay a bit of attention. My understanding 56 00:03:34,360 --> 00:03:38,200 Speaker 6: is that because there are some ill liquid instruments that 57 00:03:38,440 --> 00:03:43,800 Speaker 6: go into the calculation of the VIX, the VICS had 58 00:03:43,800 --> 00:03:49,800 Speaker 6: a somewhat artificial movement on Monday that if one looks 59 00:03:49,840 --> 00:03:53,560 Speaker 6: at the vic's futures, which are a somewhat different instrument, 60 00:03:54,240 --> 00:04:02,760 Speaker 6: they the movements were much much less drum. So I 61 00:04:02,800 --> 00:04:07,360 Speaker 6: certainly had my attention caught by the vics early in 62 00:04:07,400 --> 00:04:11,200 Speaker 6: the day on Monday. But as I have looked into it, 63 00:04:11,880 --> 00:04:16,520 Speaker 6: I think you were learning more about issues around liquidity 64 00:04:16,560 --> 00:04:21,479 Speaker 6: in the options markets than you were about some profound 65 00:04:21,560 --> 00:04:27,240 Speaker 6: reassessment of the American economy, and since that is so 66 00:04:27,400 --> 00:04:32,760 Speaker 6: widely watched an indicator issues of liquidity. Issues around how 67 00:04:32,800 --> 00:04:38,560 Speaker 6: it settles, I think should be studied by the relevant 68 00:04:38,600 --> 00:04:43,000 Speaker 6: parties in the industry and the regulator, the SEC. Larry. 69 00:04:43,000 --> 00:04:44,840 Speaker 1: We spent much of the week with various people getting 70 00:04:44,839 --> 00:04:47,599 Speaker 1: advice to the FAT. It included our former President Donald 71 00:04:47,640 --> 00:04:50,520 Speaker 1: Trump on Thursday and a long news conference and which 72 00:04:50,560 --> 00:04:53,920 Speaker 1: he addressed it, reiterating he thinks the president should have 73 00:04:53,960 --> 00:04:57,680 Speaker 1: some say over monetary policy and rate setting, including saying, boy, 74 00:04:57,720 --> 00:04:59,279 Speaker 1: he'd made an awful lot of money and he thinks 75 00:04:59,279 --> 00:05:01,960 Speaker 1: he knows better than the chair of the FED. You 76 00:05:02,000 --> 00:05:04,680 Speaker 1: had a reaction to that. I know you posted on 77 00:05:04,720 --> 00:05:08,320 Speaker 1: that question. Give us your stance about how dangerous that 78 00:05:08,440 --> 00:05:09,040 Speaker 1: could be. 79 00:05:10,520 --> 00:05:15,080 Speaker 6: I guess I can't say I was surprised by ex 80 00:05:15,200 --> 00:05:20,159 Speaker 6: President Trump because he had said things like that before, 81 00:05:20,839 --> 00:05:24,719 Speaker 6: but I sure was appalled at. 82 00:05:24,600 --> 00:05:27,200 Speaker 8: How bad an idea it was. 83 00:05:28,240 --> 00:05:35,400 Speaker 6: I mean, start with the preposterous arrogance. The Central Bank 84 00:05:35,600 --> 00:05:41,720 Speaker 6: has nineteen members of the FOMC who spend more or 85 00:05:41,800 --> 00:05:48,240 Speaker 6: less all their time scrutinizing every economic statistic. President's got 86 00:05:48,279 --> 00:05:52,400 Speaker 6: a lot of things to do at any given moment, 87 00:05:52,480 --> 00:05:59,080 Speaker 6: and he's actually much less close to the economy. God knows, 88 00:06:00,400 --> 00:06:06,560 Speaker 6: the skills associated with being an economic forecaster and the 89 00:06:06,640 --> 00:06:11,880 Speaker 6: skills associated with being a successful real estate operator are very, 90 00:06:12,000 --> 00:06:15,840 Speaker 6: very different ones. So I don't think there's any particular 91 00:06:15,920 --> 00:06:18,920 Speaker 6: reason to think that a president of the United States 92 00:06:19,000 --> 00:06:26,080 Speaker 6: would have a intellectual contribution to make. And the reason 93 00:06:26,200 --> 00:06:29,640 Speaker 6: why countries all over the world this isn't just. 94 00:06:29,600 --> 00:06:32,279 Speaker 7: An American thing, but countries essentially. 95 00:06:31,960 --> 00:06:37,760 Speaker 6: All over the world have moved to independent central banks 96 00:06:38,440 --> 00:06:42,920 Speaker 6: is that they recognize a profound conflict of interest. 97 00:06:43,160 --> 00:06:44,960 Speaker 7: Who's ever an. 98 00:06:44,360 --> 00:06:49,039 Speaker 6: Elective or political office always is tempted to put more 99 00:06:49,080 --> 00:06:54,479 Speaker 6: money lower Intereststrates hit the accelerator hard to get a 100 00:06:54,839 --> 00:06:58,560 Speaker 6: boost to the economy to make people feel good, But 101 00:06:58,720 --> 00:07:02,800 Speaker 6: when everybody sees that coming, it doesn't actually make people 102 00:07:02,839 --> 00:07:08,359 Speaker 6: feel good. It just raises the expectation they have for inflation, 103 00:07:09,040 --> 00:07:11,680 Speaker 6: so you get more inflation and you don't get any 104 00:07:11,720 --> 00:07:13,240 Speaker 6: substantial output gain. 105 00:07:13,800 --> 00:07:15,960 Speaker 1: Okay, Larry, thank you so very much once again for 106 00:07:16,000 --> 00:07:18,840 Speaker 1: being with us. That is our special Wall Street Week contributor. 107 00:07:18,880 --> 00:07:22,360 Speaker 1: He is Larry Summers of Harvard Markets went on a 108 00:07:22,480 --> 00:07:24,880 Speaker 1: wild ride this week in the wake of the boj 109 00:07:25,120 --> 00:07:27,840 Speaker 1: moved to titan and the week US jobs numbers from 110 00:07:27,920 --> 00:07:31,160 Speaker 1: last Friday, but after gapping down when trading began the 111 00:07:31,200 --> 00:07:34,120 Speaker 1: week with the VIC shooting up to over sixty, things 112 00:07:34,200 --> 00:07:36,520 Speaker 1: settled down a bit. The S and P five hundred 113 00:07:36,600 --> 00:07:39,040 Speaker 1: ended the week at fifty three forty four, just about 114 00:07:39,040 --> 00:07:41,160 Speaker 1: where it was at the end of last week and 115 00:07:41,200 --> 00:07:44,040 Speaker 1: only slightly below the median year end number for our 116 00:07:44,040 --> 00:07:47,840 Speaker 1: Bloomberg elves of fifty five sixty eight. The Nasdaq hit 117 00:07:47,880 --> 00:07:51,320 Speaker 1: a similar air pocket Monday morning, but ended the week 118 00:07:51,560 --> 00:07:55,040 Speaker 1: up two point six percent from Monday's lows, again just 119 00:07:55,200 --> 00:07:57,480 Speaker 1: under where it ended last week. The yield on the 120 00:07:57,480 --> 00:08:00,600 Speaker 1: tenure in the meantime, added about fourteen bases points over 121 00:08:00,640 --> 00:08:02,560 Speaker 1: the course of the week to end at three point 122 00:08:02,680 --> 00:08:04,880 Speaker 1: nine to four percent to take us through the week. 123 00:08:04,880 --> 00:08:07,560 Speaker 1: In the markets, we welcome back now, David Bianco, DWS 124 00:08:07,720 --> 00:08:11,120 Speaker 1: America's Chief investment Officer. Always great to have you here, David, 125 00:08:11,360 --> 00:08:12,880 Speaker 1: and this is a perfect week to have you because 126 00:08:12,880 --> 00:08:14,480 Speaker 1: where there was a lot of action in the market, 127 00:08:14,560 --> 00:08:15,440 Speaker 1: what did you make of it? 128 00:08:15,760 --> 00:08:18,400 Speaker 9: It was wild indeed for in August, and even though 129 00:08:18,400 --> 00:08:20,040 Speaker 9: the S and P is back to where we were 130 00:08:20,120 --> 00:08:23,240 Speaker 9: last Friday, let's just for a moment talk a little 131 00:08:23,240 --> 00:08:25,480 Speaker 9: bit about what the causes were for the volatility around 132 00:08:25,520 --> 00:08:29,000 Speaker 9: the world. As said, a lot of conversation around the 133 00:08:29,000 --> 00:08:33,319 Speaker 9: Bank of Japan hiking, poor timing around the hiking, and 134 00:08:33,760 --> 00:08:36,880 Speaker 9: the unwind of the end carry trade, this legendary en 135 00:08:36,960 --> 00:08:39,480 Speaker 9: carriage trade. It's real, always difficult to know just how 136 00:08:39,520 --> 00:08:43,440 Speaker 9: big it is. But what happened last week was that 137 00:08:43,640 --> 00:08:47,920 Speaker 9: not only did the BOJ hike, the FED put on 138 00:08:48,040 --> 00:08:52,840 Speaker 9: the table the possibility of a cut in September. But then, 139 00:08:52,880 --> 00:08:55,960 Speaker 9: don't forget, we had that jobs report on Friday, and 140 00:08:56,040 --> 00:08:59,160 Speaker 9: the jobs report had a jump of further climb in 141 00:08:59,160 --> 00:09:02,480 Speaker 9: the unemployment eight to four point three percent, and it 142 00:09:02,520 --> 00:09:04,959 Speaker 9: triggered what a lot of people are calling the somb rules, 143 00:09:04,960 --> 00:09:09,559 Speaker 9: some indication of a recession in progress. The job creation 144 00:09:09,640 --> 00:09:11,800 Speaker 9: numbers were still good one hundred and fourteen thousand on 145 00:09:11,800 --> 00:09:15,040 Speaker 9: the payroll survey, but a lot of panic broke out, 146 00:09:15,080 --> 00:09:17,720 Speaker 9: particularly in the bond market, that maybe the Fed's way 147 00:09:17,760 --> 00:09:21,120 Speaker 9: behind and maybe emergency cuts are necessary. So just as 148 00:09:21,440 --> 00:09:23,720 Speaker 9: the BOJ what I think they could have done this 149 00:09:23,760 --> 00:09:27,000 Speaker 9: earlier in the year, decided to do a hike, last week, 150 00:09:27,240 --> 00:09:29,840 Speaker 9: you have the FED and then data suggesting the FED 151 00:09:29,880 --> 00:09:33,439 Speaker 9: maybe cutting a lot. It was kind of crossing the streams. 152 00:09:33,559 --> 00:09:36,640 Speaker 9: It's really dangerous across the streams to major central banks 153 00:09:36,640 --> 00:09:42,040 Speaker 9: moving in different directions, and that sparked ye appreciation people 154 00:09:42,280 --> 00:09:45,079 Speaker 9: trying to probably pay back some of their yen loans. 155 00:09:45,840 --> 00:09:48,800 Speaker 9: That's part of what created all the volativity and made markets, 156 00:09:48,840 --> 00:09:53,359 Speaker 9: particularly Japan, fall out of bed on Sunday night, Yet appreciated. 157 00:09:53,640 --> 00:09:56,360 Speaker 9: Japanese stocks down more than twenty percent on one point, 158 00:09:56,440 --> 00:10:00,760 Speaker 9: led by the big banks. It hurt other global equity 159 00:10:00,760 --> 00:10:03,600 Speaker 9: markets on Monday. But we've recovered and there's more going 160 00:10:03,640 --> 00:10:06,040 Speaker 9: on though than Japan during the week and last week. 161 00:10:06,080 --> 00:10:07,920 Speaker 1: But David, how much did you think was the markets 162 00:10:07,920 --> 00:10:09,680 Speaker 1: and how much was the economy? I mean, you referred 163 00:10:09,720 --> 00:10:12,360 Speaker 1: to recession possibility, and some of the banks started saying, well, 164 00:10:12,360 --> 00:10:15,439 Speaker 1: we're taking up our probability of recession, something that last 165 00:10:15,520 --> 00:10:18,760 Speaker 1: year we thought was a real probability, Now not so much. 166 00:10:18,880 --> 00:10:20,640 Speaker 1: Where are you on the probability of recession? 167 00:10:21,080 --> 00:10:24,880 Speaker 9: I think we're still a safe distance from recession. We 168 00:10:25,040 --> 00:10:29,400 Speaker 9: do dbs. We expect the economy to slow further, but 169 00:10:29,440 --> 00:10:32,000 Speaker 9: I don't think we're slipping into a recession. Not right 170 00:10:32,040 --> 00:10:36,079 Speaker 9: now what happened this week late last week, I think 171 00:10:36,160 --> 00:10:42,160 Speaker 9: is more about investors overreacting, sometimes putting words into policymaker's 172 00:10:42,240 --> 00:10:44,680 Speaker 9: mouths about what they may be doing, or maybe just 173 00:10:44,760 --> 00:10:48,360 Speaker 9: overreacting to step in one direction how much further you're 174 00:10:48,400 --> 00:10:49,719 Speaker 9: going to go, whether it be. 175 00:10:49,760 --> 00:10:50,560 Speaker 4: Hikes or cuts. 176 00:10:52,000 --> 00:10:54,840 Speaker 9: But there was this background of other concerns. We've had 177 00:10:54,880 --> 00:10:59,880 Speaker 9: geopolitical risks, We've had investors take an additional concern with 178 00:11:00,000 --> 00:11:03,360 Speaker 9: what remains a week economy in China and a week 179 00:11:03,480 --> 00:11:06,560 Speaker 9: manufacturing economy in Europe and still weak in the United 180 00:11:06,559 --> 00:11:09,680 Speaker 9: States outside of data centers and chips, and then an 181 00:11:09,720 --> 00:11:12,880 Speaker 9: earning season. Well, the earning season is basically done. Ninety 182 00:11:12,920 --> 00:11:16,599 Speaker 9: percent of companies have reported, and while the results of 183 00:11:16,920 --> 00:11:20,040 Speaker 9: coming okay, we're still hearing from the non tech companies 184 00:11:20,040 --> 00:11:24,240 Speaker 9: that businesses sluggish, manufacturing, consumer businesses. They're cautious about the 185 00:11:24,320 --> 00:11:26,560 Speaker 9: outlook as well. And I would just say, even though 186 00:11:26,600 --> 00:11:28,760 Speaker 9: we've got ten percent year on year growth at of 187 00:11:28,800 --> 00:11:31,040 Speaker 9: SMP earnings about sixty dollars a share in the second quarter, 188 00:11:32,200 --> 00:11:33,840 Speaker 9: it's all still coming from big cap tech. 189 00:11:34,040 --> 00:11:36,360 Speaker 1: It doesn't help it's in August and so the markets 190 00:11:36,360 --> 00:11:39,200 Speaker 1: are not as liquid as we normally expect, right, But 191 00:11:39,320 --> 00:11:40,760 Speaker 1: what are you expecting for the rest of the year, 192 00:11:40,800 --> 00:11:43,000 Speaker 1: given where we are in earnings right now, all the factors, 193 00:11:43,120 --> 00:11:44,880 Speaker 1: where do you think the SMP five hundreds headed for 194 00:11:44,920 --> 00:11:45,480 Speaker 1: the rest of the year. 195 00:11:45,960 --> 00:11:48,200 Speaker 9: Well, I think market's going to stay volatile. Markets can 196 00:11:48,200 --> 00:11:50,600 Speaker 9: be valuatle particularly any day any week. By the way, 197 00:11:50,640 --> 00:11:53,160 Speaker 9: options and futures markets can be even more volatile. I 198 00:11:53,200 --> 00:11:55,760 Speaker 9: think the volatility is with us. We've been shaken by 199 00:11:55,800 --> 00:11:57,400 Speaker 9: some of the events that that broke out, and I 200 00:11:57,400 --> 00:12:00,560 Speaker 9: think that those risks and those concerns, particularly the election 201 00:12:00,960 --> 00:12:04,000 Speaker 9: and the uncertainty for what that means for taxes and tariffs, 202 00:12:04,000 --> 00:12:07,680 Speaker 9: and I think investors want to see some election outcome 203 00:12:07,760 --> 00:12:11,720 Speaker 9: that keeps taxes low and prevents tariffs from going higher. 204 00:12:12,800 --> 00:12:15,599 Speaker 9: But you know, there's a lot of uncertainty around the elections, 205 00:12:15,960 --> 00:12:19,320 Speaker 9: and I think markets will stay volatile at least until then. 206 00:12:19,559 --> 00:12:21,280 Speaker 1: David, thank you so much for being here. That's David 207 00:12:21,320 --> 00:12:27,000 Speaker 1: Bianco of DWS coming up. Despite the market volatility, the 208 00:12:27,120 --> 00:12:30,760 Speaker 1: US economy continues its relatively strong performance. We ask carbon 209 00:12:30,800 --> 00:12:33,840 Speaker 1: economist Greg Mank for his assessment of the economy and 210 00:12:33,920 --> 00:12:36,240 Speaker 1: what the next president needs to do about it. 211 00:12:36,520 --> 00:12:38,880 Speaker 3: I think we need to at some point move to 212 00:12:38,920 --> 00:12:40,680 Speaker 3: a sustainable fiscal policy. 213 00:12:41,200 --> 00:12:43,439 Speaker 1: That's next on Wall Street Week on Bloomberg. 214 00:12:48,440 --> 00:12:52,680 Speaker 2: This is Bloomberg Wall Street Week with David Weston from 215 00:12:52,800 --> 00:12:53,720 Speaker 2: Bloomberg Radio. 216 00:13:00,320 --> 00:13:03,480 Speaker 1: This is Waltere Week. I'm David Weston. Jobs numbers may 217 00:13:03,480 --> 00:13:06,560 Speaker 1: have come in weaker than expected, and some consumers spending 218 00:13:06,640 --> 00:13:10,400 Speaker 1: may be softening, but overall the economy remains strong and 219 00:13:10,440 --> 00:13:14,480 Speaker 1: inflation is moderating. We talked with Harvard economics professor Greg 220 00:13:14,520 --> 00:13:18,040 Speaker 1: Mankew at the Aspen Economic Strategy Group meetings about how 221 00:13:18,120 --> 00:13:20,720 Speaker 1: the FED is doing and what comes next. 222 00:13:22,640 --> 00:13:23,320 Speaker 7: I think we're there. 223 00:13:23,440 --> 00:13:26,559 Speaker 3: I give FED low marks early on because I thought 224 00:13:26,559 --> 00:13:28,680 Speaker 3: they were slow off the mark, but then they once 225 00:13:28,720 --> 00:13:30,959 Speaker 3: they realized the problem, they reacted vigorously. And I think 226 00:13:30,960 --> 00:13:33,800 Speaker 3: we're basically very close to target now. And if you 227 00:13:33,880 --> 00:13:37,280 Speaker 3: look at the measures of inflation, a big part of 228 00:13:37,280 --> 00:13:40,160 Speaker 3: that is shelter, and shelter we know is measures a lag. 229 00:13:40,200 --> 00:13:44,760 Speaker 3: If you look at the private sector measures of shelter inflation, rents. 230 00:13:44,520 --> 00:13:46,240 Speaker 7: Are basically flat. Now there's no inflation in that. 231 00:13:46,320 --> 00:13:49,240 Speaker 3: So I think the overall measured inflation rates we're get 232 00:13:49,280 --> 00:13:51,079 Speaker 3: to CEPI say that's going to be coming down to 233 00:13:51,120 --> 00:13:52,960 Speaker 3: target in the next six months. 234 00:13:53,160 --> 00:13:56,120 Speaker 1: So the economy is doing well by most measures. A 235 00:13:56,200 --> 00:13:58,480 Speaker 1: lot of people want to move to our country, right, 236 00:13:58,480 --> 00:14:00,400 Speaker 1: they want to invest in our country, and then a 237 00:14:00,440 --> 00:14:03,679 Speaker 1: lot of people are very unhappy with the economy. How 238 00:14:03,720 --> 00:14:05,040 Speaker 1: do you square those two things. 239 00:14:05,240 --> 00:14:08,239 Speaker 3: Well, the pandemic had a strange effect on people's finances 240 00:14:08,280 --> 00:14:10,400 Speaker 3: because if you think back to the early pandemic, we 241 00:14:10,440 --> 00:14:12,680 Speaker 3: were sending people lots of checks, whether it's just general 242 00:14:12,720 --> 00:14:16,520 Speaker 3: stimulus checks or expanded unemployment insurance. So people were getting 243 00:14:16,559 --> 00:14:19,040 Speaker 3: lots of income, but they couldn't spend it, so they're 244 00:14:19,080 --> 00:14:21,200 Speaker 3: saving a lot. They are paying back down their credit 245 00:14:21,240 --> 00:14:24,520 Speaker 3: card bills. So even though people are kind of unhappy 246 00:14:24,520 --> 00:14:27,040 Speaker 3: being stuck up in their houses, their finances are pretty good. 247 00:14:27,160 --> 00:14:30,160 Speaker 3: So let's look where are now. All these stimulus checks 248 00:14:30,160 --> 00:14:33,640 Speaker 3: have disappeared. People are spending more and so their credit 249 00:14:33,640 --> 00:14:37,000 Speaker 3: cards bollses are building up, credit card delinquencies are arising 250 00:14:37,040 --> 00:14:38,840 Speaker 3: a little bit. So I think, if you're comparing where 251 00:14:38,880 --> 00:14:41,760 Speaker 3: we were to twenty twenty, well better off because we're 252 00:14:41,760 --> 00:14:42,520 Speaker 3: not in a pandemic. 253 00:14:43,040 --> 00:14:45,200 Speaker 7: But people's finances are actually slightly worse. 254 00:14:45,720 --> 00:14:47,760 Speaker 1: There's a lot of talk about when the federal cut 255 00:14:47,800 --> 00:14:49,960 Speaker 1: how much we will cut? Put that to one side, 256 00:14:50,200 --> 00:14:52,080 Speaker 1: Where do you think we will end up? Where we 257 00:14:52,200 --> 00:14:54,440 Speaker 1: end up through this cycle, Because there's a debate really 258 00:14:54,440 --> 00:14:55,840 Speaker 1: on whether we're going to go back down to really 259 00:14:55,880 --> 00:14:58,240 Speaker 1: low interest rates or whether there are structural factors that 260 00:14:58,360 --> 00:14:59,400 Speaker 1: will keep them elevated. 261 00:14:59,560 --> 00:15:01,240 Speaker 3: I don't think we know, and I think the Fed 262 00:15:01,320 --> 00:15:03,640 Speaker 3: is going to have to play it by year. I 263 00:15:03,800 --> 00:15:06,200 Speaker 3: think there were good reasons why we had a thirty 264 00:15:06,280 --> 00:15:09,800 Speaker 3: year decline in real interest rates prior to the pandemic 265 00:15:10,000 --> 00:15:13,200 Speaker 3: and the recent recent events to what I sent, all 266 00:15:13,200 --> 00:15:14,800 Speaker 3: those forces still in play, and to what I sent, 267 00:15:14,920 --> 00:15:17,680 Speaker 3: they're new forces, like very big budget deficits that are 268 00:15:17,680 --> 00:15:19,120 Speaker 3: going to keep them straights high. I don't think we 269 00:15:19,160 --> 00:15:22,040 Speaker 3: really know yet. Partly it's going to depend on future policy, 270 00:15:22,120 --> 00:15:23,280 Speaker 3: and the next present is going to have to make 271 00:15:23,320 --> 00:15:26,440 Speaker 3: decisions over taxes and spending, and so I don't think 272 00:15:26,440 --> 00:15:27,720 Speaker 3: we really know where the FED is going to end up. 273 00:15:27,720 --> 00:15:29,800 Speaker 3: It's probably lower than it is today, but probably higher 274 00:15:29,800 --> 00:15:32,400 Speaker 3: than it was before the hiking cycle began. 275 00:15:32,880 --> 00:15:35,320 Speaker 1: You mentioned the debt and the deficit. How big are 276 00:15:35,320 --> 00:15:36,440 Speaker 1: problem is after the economy? 277 00:15:36,880 --> 00:15:39,080 Speaker 7: Well, I think there's two problems with the debt. 278 00:15:39,120 --> 00:15:41,760 Speaker 3: I think this is one of the ordinary problems, and 279 00:15:41,760 --> 00:15:45,680 Speaker 3: the extraordinary problems. The ordinary problems are basically passing a 280 00:15:46,120 --> 00:15:48,600 Speaker 3: debt onto our children. You can't leave your children a 281 00:15:48,600 --> 00:15:51,360 Speaker 3: negative bequest unless you're doing it through the government, and 282 00:15:51,360 --> 00:15:53,120 Speaker 3: that's what we're basically doing because we're leaving our children 283 00:15:53,160 --> 00:15:55,160 Speaker 3: in negative bequest by running up the debt. So they're 284 00:15:55,160 --> 00:15:58,200 Speaker 3: going to face higher taxes that will encourage some crowding 285 00:15:58,200 --> 00:16:01,400 Speaker 3: out of private capital, reduced activity growth. And then this 286 00:16:01,480 --> 00:16:03,520 Speaker 3: is the extraordinary problems of debt. You know, could the 287 00:16:03,600 --> 00:16:07,680 Speaker 3: United States turn into Greece or Argentina where there's a 288 00:16:07,760 --> 00:16:10,640 Speaker 3: fiscal crisis. The markets don't think there's going to be one, 289 00:16:10,640 --> 00:16:13,360 Speaker 3: that they're expecting us to be responsible. I hope they're right, 290 00:16:13,640 --> 00:16:16,280 Speaker 3: But the laws of economics that played out in Greece 291 00:16:16,320 --> 00:16:19,200 Speaker 3: and Argentina could happen in the United States. And so 292 00:16:19,240 --> 00:16:21,560 Speaker 3: I think we need to at some point move to 293 00:16:21,560 --> 00:16:23,760 Speaker 3: a sustainable fiscal policy. 294 00:16:23,560 --> 00:16:26,920 Speaker 1: Even short of Greece or Argentina. At some point, does 295 00:16:27,000 --> 00:16:32,360 Speaker 1: the deficit problem curtail the fedibility actually to control the economy, 296 00:16:32,400 --> 00:16:34,880 Speaker 1: because at some point the markets take over in setting 297 00:16:34,960 --> 00:16:35,840 Speaker 1: rates from the FED. 298 00:16:36,560 --> 00:16:38,640 Speaker 7: Well, the FED would have to set higher rates. 299 00:16:38,680 --> 00:16:40,200 Speaker 3: I think that I'm not really worried about the FED 300 00:16:40,280 --> 00:16:43,000 Speaker 3: losing control sort of a fiscal crisis. I'm not worried 301 00:16:43,000 --> 00:16:46,120 Speaker 3: about the FED losing control of the economy, but it 302 00:16:46,200 --> 00:16:48,800 Speaker 3: doesn't mean the FED is going to have to place 303 00:16:48,880 --> 00:16:51,200 Speaker 3: higher rates, and that could be a problem for fiscal policy. 304 00:16:51,240 --> 00:16:54,600 Speaker 3: These higher rates put pressure on the budget deficit, and 305 00:16:54,640 --> 00:16:57,240 Speaker 3: as a result, the tension between fiscal policy makers and 306 00:16:57,240 --> 00:16:59,280 Speaker 3: monetary policy makers could be exacerbated. 307 00:17:00,000 --> 00:17:02,320 Speaker 1: How we defined debt crisis? What are the prospects for 308 00:17:02,360 --> 00:17:04,680 Speaker 1: the next president, whoever it is, we'll have to deal 309 00:17:04,760 --> 00:17:06,159 Speaker 1: with the debt crisis. And I guess that really is 310 00:17:06,160 --> 00:17:07,480 Speaker 1: bond vigilantes, right. 311 00:17:07,320 --> 00:17:10,240 Speaker 3: Well, it is exactly. It's bond bond vigilantes. And I 312 00:17:10,240 --> 00:17:12,280 Speaker 3: don't think we know it's partly psychological. At what point 313 00:17:12,280 --> 00:17:14,920 Speaker 3: do people become scared. I don't think it's gonna happen soon, 314 00:17:15,520 --> 00:17:19,199 Speaker 3: but I think at some point in our lifetimes the 315 00:17:19,240 --> 00:17:20,959 Speaker 3: current policy has to be changed, because if you look 316 00:17:21,000 --> 00:17:24,119 Speaker 3: at the CBO projections on their current policy, that GDP 317 00:17:24,280 --> 00:17:26,080 Speaker 3: ratio is going up to infinity, and we know that's 318 00:17:26,080 --> 00:17:26,760 Speaker 3: not gonna happen. 319 00:17:27,000 --> 00:17:29,360 Speaker 7: So at some point that's gonna change. 320 00:17:29,359 --> 00:17:31,240 Speaker 3: It's probably not gonna change the next few years, but 321 00:17:31,280 --> 00:17:33,040 Speaker 3: it's something we're gonna have to deal with, and how 322 00:17:33,080 --> 00:17:35,400 Speaker 3: to deal with that does not be an easy political problem. 323 00:17:35,760 --> 00:17:38,600 Speaker 1: What about the election coming up? In so far as 324 00:17:38,640 --> 00:17:40,880 Speaker 1: we know anything, we have I think a sense maybe 325 00:17:40,920 --> 00:17:43,359 Speaker 1: if we're a second president, Trump would take us. I 326 00:17:43,359 --> 00:17:45,280 Speaker 1: don't know how much we know abou Kamala Harris, but 327 00:17:45,359 --> 00:17:46,960 Speaker 1: what are the alternatives? You see them? 328 00:17:47,119 --> 00:17:47,359 Speaker 6: Well? 329 00:17:47,440 --> 00:17:50,120 Speaker 3: One of the big issues for me is the role 330 00:17:50,200 --> 00:17:52,800 Speaker 3: of the United States and the global economy. 331 00:17:52,960 --> 00:17:54,800 Speaker 7: I know it's a sort of a bad word these 332 00:17:54,840 --> 00:17:55,760 Speaker 7: days that I'm a globalist. 333 00:17:55,840 --> 00:17:58,439 Speaker 3: I actually believe that integrating the United States with the 334 00:17:58,440 --> 00:18:00,400 Speaker 3: global economy is a good thing. So I'm in favor 335 00:18:00,400 --> 00:18:02,840 Speaker 3: of free trade agreements. I'm in favor of more relaxed 336 00:18:02,880 --> 00:18:06,200 Speaker 3: immigration rules. This is a sort of mainstream view back 337 00:18:06,240 --> 00:18:09,760 Speaker 3: in the Bush and Clinton years. Now is a sort 338 00:18:09,760 --> 00:18:12,160 Speaker 3: of backlash against it. I think that backlash has been 339 00:18:12,320 --> 00:18:14,600 Speaker 3: is ill informed, and I think at some point we 340 00:18:14,640 --> 00:18:16,359 Speaker 3: need to go back to being a leader in the 341 00:18:16,359 --> 00:18:18,359 Speaker 3: global economy, which I think is good not only for 342 00:18:18,400 --> 00:18:20,760 Speaker 3: the rest of the world, but also for the United States. 343 00:18:21,280 --> 00:18:23,720 Speaker 1: What about US relationships with China, when it comes to 344 00:18:23,760 --> 00:18:24,920 Speaker 1: the economy. 345 00:18:25,000 --> 00:18:28,480 Speaker 3: Well, China is the big challenge, not not so much economically, 346 00:18:28,560 --> 00:18:31,560 Speaker 3: but I think politically and especially the threat in Taiwan. 347 00:18:31,600 --> 00:18:34,200 Speaker 3: So I do worry a lot about sort of what's 348 00:18:34,240 --> 00:18:37,080 Speaker 3: going on in that part of the world. I understand 349 00:18:37,359 --> 00:18:39,280 Speaker 3: I don't really like industrial policy in general, but I 350 00:18:39,400 --> 00:18:42,560 Speaker 3: understand the motivation had the Chips Act because almost all 351 00:18:42,560 --> 00:18:45,239 Speaker 3: the high end chips are coming from Taiwan's semiconductor and 352 00:18:45,280 --> 00:18:46,920 Speaker 3: that's a very vulnerable. 353 00:18:46,440 --> 00:18:47,080 Speaker 7: Part of the world. 354 00:18:47,320 --> 00:18:51,960 Speaker 3: So relationship with China is very important, but it's not 355 00:18:51,960 --> 00:18:55,000 Speaker 3: so much an economic relationship as a geopolitical relationship, and 356 00:18:55,040 --> 00:18:56,320 Speaker 3: that's the part we need to negotiate. 357 00:18:56,920 --> 00:19:01,080 Speaker 1: That was Harvard economics professor Greg mank. Whoever wins the 358 00:19:01,160 --> 00:19:05,679 Speaker 1: US presidency in November, they will confront geopolitical hotspots around 359 00:19:05,680 --> 00:19:08,800 Speaker 1: the world, from the Middle East to Ukraine to the 360 00:19:08,880 --> 00:19:12,359 Speaker 1: South China Sea. Michelle Flornoy was the Deputy Secretary of 361 00:19:12,359 --> 00:19:15,199 Speaker 1: Defense under President Obama, and when we sat with her 362 00:19:15,280 --> 00:19:18,879 Speaker 1: at the Aspen Economic Strategy Meetings last week, we started 363 00:19:18,920 --> 00:19:20,919 Speaker 1: with what the Defense Department needs to do to be 364 00:19:20,960 --> 00:19:23,880 Speaker 1: prepared for the range of challenges it faces. 365 00:19:24,400 --> 00:19:26,720 Speaker 4: I think the United States you know, we're a global power. 366 00:19:26,800 --> 00:19:31,399 Speaker 4: We have interests, very real interests in many parts of 367 00:19:31,400 --> 00:19:35,240 Speaker 4: the world. So if we get engaged in say deterring 368 00:19:35,359 --> 00:19:40,280 Speaker 4: a Chinese attack on Taiwan, or deterring Russian aggression beyond 369 00:19:40,359 --> 00:19:43,399 Speaker 4: Ukraine and to NATO, we have to also be able 370 00:19:43,440 --> 00:19:47,119 Speaker 4: to still have presence and deterrence ability elsewhere so that 371 00:19:47,320 --> 00:19:50,040 Speaker 4: the Middle East doesn't also erupt. So you never want 372 00:19:50,080 --> 00:19:52,200 Speaker 4: to be in a situation where your engagement in one 373 00:19:52,240 --> 00:19:58,480 Speaker 4: region basically provokes or encourages another adversary elsewhere to start 374 00:19:58,520 --> 00:20:01,280 Speaker 4: to take advantage of that and start something else. And 375 00:20:01,359 --> 00:20:03,879 Speaker 4: so you really do need to have the ability to 376 00:20:03,960 --> 00:20:07,760 Speaker 4: look across multiple theaters at a time and to be 377 00:20:07,840 --> 00:20:10,919 Speaker 4: fully engaged in one while deterring in the others. 378 00:20:11,240 --> 00:20:13,560 Speaker 1: You mentioned some of the relationships with allies. Obviously we 379 00:20:13,560 --> 00:20:15,920 Speaker 1: have NATO when it comes to Europe, and now there's 380 00:20:15,960 --> 00:20:18,800 Speaker 1: more and more development in Asia, including these talks among 381 00:20:18,880 --> 00:20:21,680 Speaker 1: South Korea, Japan, and the United States we just have happened. 382 00:20:21,880 --> 00:20:23,840 Speaker 1: How much can that relieve some of the pressure on 383 00:20:23,840 --> 00:20:25,000 Speaker 1: the United States itself? 384 00:20:25,320 --> 00:20:29,800 Speaker 4: First, it's extremely important politically and in terms of demonstrating resolves. 385 00:20:29,800 --> 00:20:34,000 Speaker 4: So if you're trying to affect Hijipang's calculus about whether 386 00:20:34,040 --> 00:20:37,080 Speaker 4: to use force in the region, knowing that he won't 387 00:20:37,119 --> 00:20:40,000 Speaker 4: just have to deal with a US response, but a 388 00:20:40,080 --> 00:20:43,520 Speaker 4: Japanese response, a Korean response, an Australian response, and others 389 00:20:43,560 --> 00:20:46,639 Speaker 4: in the region who you know coming together in coalition 390 00:20:46,800 --> 00:20:49,560 Speaker 4: to try to protect the rules based order in Asia. 391 00:20:49,840 --> 00:20:54,479 Speaker 4: And then these allies increasingly, particularly Japan, Korea, Australia, they 392 00:20:54,520 --> 00:20:59,119 Speaker 4: have real capability to contribute to any sort of crisis situations. 393 00:20:59,359 --> 00:21:01,160 Speaker 1: There are a lot of fiscal pressures in the United 394 00:21:01,200 --> 00:21:03,760 Speaker 1: States right now. At the same time, many people think 395 00:21:03,920 --> 00:21:06,959 Speaker 1: we're going to have to commit more resources actually to defense, 396 00:21:07,040 --> 00:21:08,640 Speaker 1: given some of the things you've just talked about. Larry 397 00:21:08,640 --> 00:21:11,240 Speaker 1: Summers said that repeatedly on this program. We're looking forward, 398 00:21:11,280 --> 00:21:13,720 Speaker 1: we're going to spend more on defense. Are we spending enough? 399 00:21:13,880 --> 00:21:15,359 Speaker 1: Do we need to spend it more and if so, 400 00:21:15,400 --> 00:21:16,040 Speaker 1: how much more? 401 00:21:16,760 --> 00:21:19,720 Speaker 4: Well? I do think that given the way the world 402 00:21:19,880 --> 00:21:23,919 Speaker 4: is evolving in our interests, we probably do need to 403 00:21:24,040 --> 00:21:28,000 Speaker 4: increase our investment. But just throwing money at the problem 404 00:21:28,119 --> 00:21:31,760 Speaker 4: is not going to be enough. At the same time, 405 00:21:31,840 --> 00:21:34,200 Speaker 4: we really have to take a fresh look at what 406 00:21:34,240 --> 00:21:37,399 Speaker 4: we're spending on and where we're investing because in a 407 00:21:37,440 --> 00:21:40,879 Speaker 4: lot of these situations, you know, buying more of the 408 00:21:40,920 --> 00:21:43,840 Speaker 4: same thing that has served us well, you know, in 409 00:21:43,920 --> 00:21:46,879 Speaker 4: a force that was optimized for the Middle East and 410 00:21:46,960 --> 00:21:50,480 Speaker 4: counter terrorism is not necessarily what we need to deal 411 00:21:50,600 --> 00:21:54,920 Speaker 4: in a maritime and air theater predominantly in the Asia 412 00:21:54,960 --> 00:21:57,960 Speaker 4: Pacific and elsewhere. Plus you have this profound period of 413 00:21:58,000 --> 00:22:02,000 Speaker 4: technological disruption, and so so you really have to look at, 414 00:22:02,240 --> 00:22:06,040 Speaker 4: you know, how do we adopt innovation, integrate it into 415 00:22:06,119 --> 00:22:10,000 Speaker 4: the force to enable the legacy forces, we have to 416 00:22:10,640 --> 00:22:15,120 Speaker 4: be able to operate differently and get different outcomes. So 417 00:22:15,200 --> 00:22:19,119 Speaker 4: it's innovation adoption and it's new operational concepts. We have 418 00:22:19,160 --> 00:22:22,600 Speaker 4: to think much more asymmetrically than we have in the past. 419 00:22:22,640 --> 00:22:24,120 Speaker 4: If we're going to keep our edge in the. 420 00:22:24,080 --> 00:22:26,760 Speaker 1: Future, how should we think about the role of technological 421 00:22:26,800 --> 00:22:30,199 Speaker 1: innovation because some of themselves a lot less expensive than some 422 00:22:30,280 --> 00:22:31,640 Speaker 1: of the big weapons systems. 423 00:22:31,800 --> 00:22:34,399 Speaker 4: Right. I think the US is hands down the leader 424 00:22:34,520 --> 00:22:38,439 Speaker 4: globally in technical innovation, and that includes in the defense domain. 425 00:22:38,800 --> 00:22:41,640 Speaker 4: But we are not all that good at innovation adoption 426 00:22:41,880 --> 00:22:45,720 Speaker 4: actually taking particularly if something's coming out of the commercial sector, 427 00:22:45,840 --> 00:22:51,080 Speaker 4: like take for example, small cheap attritable drones. We are 428 00:22:51,160 --> 00:22:54,719 Speaker 4: seeing how that is impacting the war between Russia and Ukraine. 429 00:22:54,720 --> 00:22:58,880 Speaker 4: We are starting to see Iran use them against Israel, 430 00:22:59,119 --> 00:23:02,040 Speaker 4: you know, in an Asia of Asian theater where China 431 00:23:02,080 --> 00:23:05,040 Speaker 4: will always have a quantitative advantage because they have their 432 00:23:05,080 --> 00:23:08,040 Speaker 4: whole force right there. It's their backyard where we have 433 00:23:08,080 --> 00:23:11,000 Speaker 4: to project power from the United States and other regions. 434 00:23:11,840 --> 00:23:14,920 Speaker 4: How do we buy back mass? How do we sort 435 00:23:14,960 --> 00:23:19,040 Speaker 4: of leverage things like drones under sea, on the sea, 436 00:23:19,240 --> 00:23:24,119 Speaker 4: in the air controlled by human operators. How do we 437 00:23:24,240 --> 00:23:29,720 Speaker 4: leverage that to really create new problems for an adversary, 438 00:23:30,160 --> 00:23:32,639 Speaker 4: and how do we use them to contribute to deterrence. 439 00:23:32,960 --> 00:23:37,080 Speaker 4: So the technological piece is absolutely key to keeping our 440 00:23:37,200 --> 00:23:38,000 Speaker 4: edge in the future. 441 00:23:38,400 --> 00:23:42,439 Speaker 1: Financial markets right now are really almost obsessed with artificial intelligence. 442 00:23:43,000 --> 00:23:44,480 Speaker 1: How does that apply a defense area? 443 00:23:44,840 --> 00:23:48,240 Speaker 4: It is being adopted already. I think the Department of 444 00:23:48,280 --> 00:23:51,760 Speaker 4: Defense gets credit and this administration gets credit for setting 445 00:23:51,760 --> 00:23:54,760 Speaker 4: out a framework for responsible AI. How do we keep 446 00:23:54,840 --> 00:23:57,720 Speaker 4: it safe? How do we make sure it behaves appropriately? 447 00:23:57,760 --> 00:24:00,480 Speaker 4: How do we make sure, you know, it's trans parent, 448 00:24:00,600 --> 00:24:03,159 Speaker 4: that we're following data rules and so forth. We're not 449 00:24:03,200 --> 00:24:05,960 Speaker 4: so sure as whether our adversaries will be so responsible 450 00:24:06,000 --> 00:24:08,159 Speaker 4: in how they develop it. But we see it coming 451 00:24:08,200 --> 00:24:13,760 Speaker 4: into in the intelligence field, helping analysts sort through the 452 00:24:13,800 --> 00:24:18,880 Speaker 4: massive amounts of information to focus on what's important for insight. 453 00:24:20,440 --> 00:24:24,600 Speaker 4: We see it in the area of maintenance, preventative maintenance, 454 00:24:24,640 --> 00:24:27,879 Speaker 4: so getting data that suggests you need to repair something 455 00:24:27,920 --> 00:24:31,280 Speaker 4: before it breaks, which takes less time and is cheaper. 456 00:24:31,760 --> 00:24:35,080 Speaker 4: And then we'll eventually seeing applying it to this human 457 00:24:35,200 --> 00:24:37,399 Speaker 4: machine teaming. I talked about where you have a single 458 00:24:37,680 --> 00:24:42,560 Speaker 4: human being operating very large, large numbers of unmanned systems, 459 00:24:42,920 --> 00:24:44,800 Speaker 4: so you still have the human in the loop, but 460 00:24:44,880 --> 00:24:49,840 Speaker 4: you've bought back a lot of mass for your forces. Ultimately, 461 00:24:49,880 --> 00:24:52,400 Speaker 4: I also think it's going to be important to give 462 00:24:52,480 --> 00:24:54,840 Speaker 4: us an edge and decision making. If we can process 463 00:24:55,440 --> 00:25:00,240 Speaker 4: information faster, get insight faster, tee up options fast, master 464 00:25:00,400 --> 00:25:03,960 Speaker 4: for the human decision maker, we will have an edge 465 00:25:04,000 --> 00:25:06,040 Speaker 4: in any future crisis. 466 00:25:06,680 --> 00:25:10,639 Speaker 1: That was former Deputy Secretary Defense Michelle Flornoy, managing partner 467 00:25:10,680 --> 00:25:15,800 Speaker 1: and co founder of West Exec Advisors, coming up to 468 00:25:15,800 --> 00:25:19,119 Speaker 1: the rush. Degenerative AI has shifted enormous amounts of capital 469 00:25:19,160 --> 00:25:21,800 Speaker 1: and driven tech shares higher. We talked about whether it's 470 00:25:21,840 --> 00:25:25,240 Speaker 1: all gone too far too fast with AI pioneer Jack 471 00:25:25,320 --> 00:25:27,000 Speaker 1: Heitory of Sandbox AQ. 472 00:25:27,560 --> 00:25:30,960 Speaker 5: The bigger part of the economy really needs a different 473 00:25:31,080 --> 00:25:35,280 Speaker 5: kind of AI, and that AI is large quantitative bondus. 474 00:25:36,680 --> 00:25:39,040 Speaker 1: That's next on Wall Street Week on Bloomberg. 475 00:25:40,400 --> 00:25:44,600 Speaker 2: This is Bloomberg Wall Street Week with David Weston from 476 00:25:44,720 --> 00:25:45,679 Speaker 2: Bloomberg Radio. 477 00:25:52,400 --> 00:25:55,040 Speaker 1: This is Wall Street Week. I'm David Weston. Invidios stock 478 00:25:55,040 --> 00:25:58,000 Speaker 1: has gone through the roof the so called Hyperscalers are 479 00:25:58,040 --> 00:26:00,760 Speaker 1: investing tens of billions of dollars as the dream of 480 00:26:00,840 --> 00:26:04,480 Speaker 1: generave AI has seized the imagination of investors. But now 481 00:26:04,600 --> 00:26:07,120 Speaker 1: some people are second guessing the land rush to take 482 00:26:07,200 --> 00:26:09,720 Speaker 1: us through what's real and what's overdone. Welcome now a 483 00:26:09,800 --> 00:26:13,440 Speaker 1: pioneer in the field. He's Jack Hitory, CEO of Sandbox AQ. 484 00:26:13,640 --> 00:26:15,320 Speaker 1: So Jack, so good to have you with us. Thank 485 00:26:15,320 --> 00:26:17,080 Speaker 1: you for being here. So for those of us who 486 00:26:17,119 --> 00:26:19,199 Speaker 1: really are not that well versus this, give us your 487 00:26:19,280 --> 00:26:22,320 Speaker 1: overall take. We have seen some backing off, some suggestions, 488 00:26:22,359 --> 00:26:24,639 Speaker 1: for example in Vidio may have some delays in some 489 00:26:24,680 --> 00:26:27,360 Speaker 1: of their chips. Maybe some questions about it. How much 490 00:26:27,400 --> 00:26:29,000 Speaker 1: is real and how much of it is hype? 491 00:26:30,359 --> 00:26:32,760 Speaker 5: David Firstell, great to see you this is these are 492 00:26:32,920 --> 00:26:36,879 Speaker 5: very exciting times right now for AI. Not only the 493 00:26:36,960 --> 00:26:39,400 Speaker 5: kind of AI that people have seen before, like chat, 494 00:26:39,440 --> 00:26:42,960 Speaker 5: GPT and AI focused on words, but also AI now 495 00:26:43,000 --> 00:26:46,240 Speaker 5: applied to the biggest industries in the world to make 496 00:26:46,320 --> 00:26:51,679 Speaker 5: new drugs, to make new alloys, to lightweight vehicles, to. 497 00:26:50,920 --> 00:26:53,720 Speaker 8: Create new energy opportunities for the world. 498 00:26:53,960 --> 00:26:56,160 Speaker 5: This is a great moment for AI that takes us 499 00:26:56,160 --> 00:26:59,800 Speaker 5: beyond the chat GPT. When we look at Nvidia to 500 00:26:59,840 --> 00:27:02,840 Speaker 5: your question about Nvidia, we see a company with a 501 00:27:02,880 --> 00:27:06,320 Speaker 5: great set of high performance chips, initially the g and 502 00:27:06,359 --> 00:27:08,840 Speaker 5: GPU of course for graphics, and then of course to 503 00:27:08,960 --> 00:27:13,000 Speaker 5: large language models. But now there's new demand curves coming 504 00:27:13,000 --> 00:27:15,719 Speaker 5: to Invidia, and so I think, well, there may be 505 00:27:15,760 --> 00:27:17,560 Speaker 5: a couple of initial bumps in the road here in 506 00:27:17,600 --> 00:27:20,280 Speaker 5: the near term. What I recommend people do is look 507 00:27:20,320 --> 00:27:24,280 Speaker 5: at the mid and long term nature of opportunity, which 508 00:27:24,359 --> 00:27:27,840 Speaker 5: now has a big demand curve for quantitative AI, which 509 00:27:27,880 --> 00:27:29,760 Speaker 5: is the next wave AI that's now hitting. 510 00:27:30,760 --> 00:27:33,200 Speaker 1: So Jack, from your experience, you guys are really heavy 511 00:27:33,280 --> 00:27:36,159 Speaker 1: hitter investors. Is it patient capital? I mean when they 512 00:27:36,200 --> 00:27:37,639 Speaker 1: see some of these bumps along the road, do they 513 00:27:37,640 --> 00:27:39,480 Speaker 1: get nervous? Did they call you up? Or are they 514 00:27:39,520 --> 00:27:40,440 Speaker 1: in it for the long haul. 515 00:27:42,040 --> 00:27:44,840 Speaker 5: Most investors that call me up, and many do, are 516 00:27:44,880 --> 00:27:46,880 Speaker 5: in it for the long haul. They see the value 517 00:27:46,960 --> 00:27:49,480 Speaker 5: of these kind of platforms. They see the demand from 518 00:27:49,520 --> 00:27:53,000 Speaker 5: the large data centers, from many companies buying these kinds 519 00:27:53,000 --> 00:27:55,800 Speaker 5: of chips. But what's most important now is the next 520 00:27:56,400 --> 00:27:59,800 Speaker 5: wave of AI that's coming. The initial wave, of course, 521 00:27:59,880 --> 00:28:03,840 Speaker 5: was focus on downloading the Internet training language models like chat, 522 00:28:03,880 --> 00:28:07,280 Speaker 5: GPT and other kinds of large language models known as llms. 523 00:28:07,760 --> 00:28:10,359 Speaker 5: That's certainly a good first wave, and there are certain 524 00:28:10,440 --> 00:28:15,520 Speaker 5: key applications of llms. David, for example, customer service. Right 525 00:28:15,560 --> 00:28:17,680 Speaker 5: now we all agree, I think we can have better 526 00:28:17,720 --> 00:28:21,560 Speaker 5: customer service from telcos and cable operators and airlines and 527 00:28:21,600 --> 00:28:24,399 Speaker 5: folks like that, and I think the chat GPTs of 528 00:28:24,400 --> 00:28:26,920 Speaker 5: the world will help with that. But the bigger part 529 00:28:27,040 --> 00:28:30,200 Speaker 5: of the economy really needs a different kind of AI, 530 00:28:30,520 --> 00:28:35,119 Speaker 5: and that AI is large quantitative models l qms instead 531 00:28:35,160 --> 00:28:39,520 Speaker 5: of just l lms. And that's because we really want 532 00:28:39,520 --> 00:28:42,920 Speaker 5: to see new drug drugs in the marketplace, new treatments 533 00:28:42,960 --> 00:28:47,080 Speaker 5: for brain cancer, pancreatic cancer, for Alzheimer's, for dementia. I 534 00:28:47,120 --> 00:28:50,720 Speaker 5: think everyone watching this show today David knows a loved one, 535 00:28:50,880 --> 00:28:54,800 Speaker 5: a family friend who is impacted by these diseases. Forty 536 00:28:54,880 --> 00:28:58,160 Speaker 5: years of research has not yielded much for the patients 537 00:28:58,400 --> 00:29:01,520 Speaker 5: that are afflicted by these conditions. What we're seeing now 538 00:29:01,600 --> 00:29:04,560 Speaker 5: is the advent of a new, bigger AI, an AI 539 00:29:04,640 --> 00:29:10,080 Speaker 5: that handles quantitative relationships. It understands chemistry, it understands physics, 540 00:29:10,080 --> 00:29:14,840 Speaker 5: It understands financial relationships for asset management and portfolio construction 541 00:29:15,120 --> 00:29:16,280 Speaker 5: for financial services. 542 00:29:16,600 --> 00:29:17,960 Speaker 8: These are the big verticals. 543 00:29:18,000 --> 00:29:22,320 Speaker 5: Biopharma is eight trillion in size, financial services ten trillion, 544 00:29:22,520 --> 00:29:27,360 Speaker 5: Automotive and aerospace fifteen trillion, and finally chemicals energy materials 545 00:29:27,720 --> 00:29:31,720 Speaker 5: another fifteen trillion, part of the one hundred trillion dollar 546 00:29:31,840 --> 00:29:36,160 Speaker 5: GDP economy worldwide. These are the bigger applications that are 547 00:29:36,200 --> 00:29:38,120 Speaker 5: now upon us in the world of AI. 548 00:29:38,360 --> 00:29:41,680 Speaker 1: So Jack, that's very helpful. The distinction between large language 549 00:29:41,720 --> 00:29:45,320 Speaker 1: model and large quantitative model big difference. That's one queue. 550 00:29:45,320 --> 00:29:48,600 Speaker 1: Another queue is quantum. You're involved in quantum. What does 551 00:29:48,680 --> 00:29:50,840 Speaker 1: quantum add to the mix when it comes to AI. 552 00:29:52,120 --> 00:29:55,520 Speaker 5: What's fascinating is that let's look at AI and quantum, David, 553 00:29:55,560 --> 00:29:58,680 Speaker 5: as part of a bigger hole of advanced compute and 554 00:29:58,760 --> 00:30:01,120 Speaker 5: so when we look at AI, I think now people 555 00:30:01,200 --> 00:30:03,920 Speaker 5: get a feel of how powerful that is already, and 556 00:30:04,080 --> 00:30:06,440 Speaker 5: it's going to even get more powerful over the next 557 00:30:06,480 --> 00:30:09,880 Speaker 5: two three four years. And now into the world of quantum. 558 00:30:10,320 --> 00:30:12,400 Speaker 5: Most people when they think about quantum, they think about 559 00:30:12,480 --> 00:30:15,880 Speaker 5: quantum computers, and certainly quantum computers are going to have 560 00:30:15,960 --> 00:30:19,400 Speaker 5: a wonderful impact on our society. Give us even more 561 00:30:19,600 --> 00:30:23,200 Speaker 5: computer on top of and with the AI that we 562 00:30:23,400 --> 00:30:27,320 Speaker 5: just discussed. But what's not often discussed is the other 563 00:30:27,400 --> 00:30:31,760 Speaker 5: applications of quantum. For example, quantum sensors. These are senses 564 00:30:31,800 --> 00:30:34,920 Speaker 5: that are here today, their room temperature, they're very compact 565 00:30:35,000 --> 00:30:38,120 Speaker 5: their solid state, and for example, we can use them 566 00:30:38,200 --> 00:30:40,520 Speaker 5: to improve medical diagnostics. 567 00:30:40,800 --> 00:30:42,760 Speaker 1: Bring us to the here and now, right now in 568 00:30:42,800 --> 00:30:45,040 Speaker 1: concrete terms. I was with somebody recently from one of 569 00:30:45,040 --> 00:30:47,360 Speaker 1: the hyperscalers saying, though you know, a little bit like 570 00:30:47,480 --> 00:30:50,280 Speaker 1: electricity and the latter part last century, you have the 571 00:30:50,320 --> 00:30:52,600 Speaker 1: power planning of the grid, and then you have the appliances, 572 00:30:53,080 --> 00:30:54,960 Speaker 1: and what we need in part with AI are the 573 00:30:55,000 --> 00:30:57,520 Speaker 1: appliances as it were. You had an announcement just this 574 00:30:57,600 --> 00:31:02,160 Speaker 1: week with the Mayo Clinic about some cardia analysis, and wow, 575 00:31:02,200 --> 00:31:04,000 Speaker 1: you can help with that. What is that and how 576 00:31:04,040 --> 00:31:05,520 Speaker 1: close is it to being real? 577 00:31:06,120 --> 00:31:07,200 Speaker 8: That's a great point, David. 578 00:31:07,320 --> 00:31:10,479 Speaker 5: It's the applications of AI and quantum sensing that are 579 00:31:10,520 --> 00:31:13,120 Speaker 5: the most exciting things, not just the theory. And so 580 00:31:13,240 --> 00:31:16,240 Speaker 5: this week, as you pointed out, we in the Mao 581 00:31:16,320 --> 00:31:20,520 Speaker 5: Clinic announced that our AI and device are inside the 582 00:31:20,520 --> 00:31:23,880 Speaker 5: Mayo Clinic right now in yet another clinical trial. We 583 00:31:23,960 --> 00:31:26,960 Speaker 5: already completed trials now at UCSF Hospital and the Mount 584 00:31:27,000 --> 00:31:29,960 Speaker 5: Signi Hospital, New York, and now this new trial is 585 00:31:30,000 --> 00:31:33,040 Speaker 5: starting at the Mayo Clinic, one of the premier hospitals 586 00:31:33,080 --> 00:31:35,600 Speaker 5: of the entire world. Very very proud to be working 587 00:31:35,600 --> 00:31:38,080 Speaker 5: with the Mayo Clinic on this and very excited to 588 00:31:38,080 --> 00:31:41,440 Speaker 5: see some of the outcomes. What's important about that is 589 00:31:41,440 --> 00:31:46,320 Speaker 5: that the ability to bring quantitative AI LQMS combined with 590 00:31:46,480 --> 00:31:50,880 Speaker 5: advanced sensors in a single format of this box allow 591 00:31:51,600 --> 00:31:54,480 Speaker 5: us to put that in the cardiac hospital and look 592 00:31:54,520 --> 00:31:57,560 Speaker 5: at real patients in real time and how their heart 593 00:31:57,600 --> 00:32:00,520 Speaker 5: is doing in a way that no other modality can do. 594 00:32:01,000 --> 00:32:04,400 Speaker 5: What's happened is that quantum sensors have become room temperature, 595 00:32:04,520 --> 00:32:07,680 Speaker 5: they become very portable and small format, low power draw 596 00:32:08,080 --> 00:32:11,920 Speaker 5: but the challenge was how to read this huge plethora, 597 00:32:12,280 --> 00:32:15,640 Speaker 5: this fountain of information coming out of these sensors. That's 598 00:32:15,680 --> 00:32:19,440 Speaker 5: where AI comes in. Specifically, that's where lqms come in. 599 00:32:19,760 --> 00:32:22,560 Speaker 5: You cannot add a chat GPT to this box and 600 00:32:22,640 --> 00:32:24,080 Speaker 5: have it interpret those signals. 601 00:32:24,400 --> 00:32:26,640 Speaker 8: Being trained on Wikipedia. 602 00:32:25,960 --> 00:32:28,360 Speaker 5: And Reddit and Twitter is not going to help you 603 00:32:28,560 --> 00:32:31,200 Speaker 5: with this kind of signal. Rather, this kind of training 604 00:32:31,240 --> 00:32:35,080 Speaker 5: requires quantitative data. So this is a major step forward 605 00:32:35,360 --> 00:32:39,600 Speaker 5: for cardiovasculine disease. Doctor Toby Cosgrove, who used to head 606 00:32:39,680 --> 00:32:42,960 Speaker 5: up the Cleveland Clinic for the past thirteen years, called 607 00:32:43,040 --> 00:32:47,720 Speaker 5: it the key to transforming cardiovascular medicine. So it's great 608 00:32:47,720 --> 00:32:52,280 Speaker 5: to see this kind of advancement combining AI and quantum jack. 609 00:32:52,360 --> 00:32:55,120 Speaker 1: That was very helpful and very informative. One last question 610 00:32:55,160 --> 00:32:57,280 Speaker 1: in a very different area, and that's the area of defense. 611 00:32:57,680 --> 00:33:00,160 Speaker 1: We've talked to some people in the defense establishment. What's 612 00:33:00,200 --> 00:33:02,760 Speaker 1: going on, but even maybe an arms race when it 613 00:33:02,800 --> 00:33:06,880 Speaker 1: comes to AI and use of AI in defense contracting. 614 00:33:07,000 --> 00:33:09,040 Speaker 1: Where are we that in so far as you can 615 00:33:09,040 --> 00:33:09,760 Speaker 1: tell us about it. 616 00:33:10,360 --> 00:33:12,760 Speaker 8: Sure, defense is a very very critical application. 617 00:33:12,920 --> 00:33:16,560 Speaker 5: National security is of paramount importance for both United States 618 00:33:16,600 --> 00:33:19,240 Speaker 5: and its allies. I think we all agree with the 619 00:33:19,280 --> 00:33:22,960 Speaker 5: confabgrations going on around the world. It's even more important 620 00:33:23,000 --> 00:33:25,480 Speaker 5: today than it was just a few years ago. This 621 00:33:25,560 --> 00:33:28,320 Speaker 5: is both on the cyber aspect as well as the 622 00:33:28,400 --> 00:33:32,000 Speaker 5: kinetic aspect of war and of conflict. And so when 623 00:33:32,040 --> 00:33:34,440 Speaker 5: we look at the applications, let me give some examples. 624 00:33:35,440 --> 00:33:39,400 Speaker 5: If we take navigation, when you're in a wartime scenario, 625 00:33:39,440 --> 00:33:42,520 Speaker 5: even a pre wartime scenario, the first thing your adversary 626 00:33:42,560 --> 00:33:46,719 Speaker 5: will do, David, is they will block and jam GPS. 627 00:33:47,400 --> 00:33:49,280 Speaker 5: You want to do that as an adversary because you 628 00:33:49,320 --> 00:33:52,280 Speaker 5: want to stop planes from coming at you and dropping bombs. 629 00:33:52,520 --> 00:33:54,680 Speaker 5: You want to stop missiles from coming at you, you 630 00:33:54,720 --> 00:33:56,280 Speaker 5: want to stop drones from coming at you. 631 00:33:56,320 --> 00:33:57,240 Speaker 8: And you see it today. 632 00:33:57,800 --> 00:34:01,800 Speaker 5: Russia right now is jamming not only would be Ukraine, 633 00:34:01,840 --> 00:34:03,880 Speaker 5: but over enough of area of Europe that it has 634 00:34:03,960 --> 00:34:08,800 Speaker 5: disrupted thousands of commercial flights. Thousands of commercial flights have 635 00:34:08,880 --> 00:34:12,279 Speaker 5: been disrupted by Russ's jamming of GPS. And this is 636 00:34:12,320 --> 00:34:15,760 Speaker 5: another application of both AI and advancedensing. 637 00:34:16,080 --> 00:34:18,960 Speaker 1: How is that the case, Jack, thank you so much 638 00:34:19,000 --> 00:34:21,920 Speaker 1: as has been really terribly helpful. That is Jack Heitory 639 00:34:22,040 --> 00:34:25,920 Speaker 1: of Sandbox AQ. If you want a friend in Washington, 640 00:34:26,080 --> 00:34:29,200 Speaker 1: get a dog. President Truman is thought to have said 641 00:34:29,200 --> 00:34:31,920 Speaker 1: this when he was running for reelection back in nineteen 642 00:34:31,960 --> 00:34:34,960 Speaker 1: forty eight. There is little doubt that the companionship of 643 00:34:35,000 --> 00:34:37,600 Speaker 1: an animal can be a consolation when it feels like 644 00:34:37,640 --> 00:34:40,080 Speaker 1: no one else is on our side. Maybe that's why 645 00:34:40,120 --> 00:34:43,360 Speaker 1: nearly every one of our presidents has had a cherished pet, 646 00:34:43,840 --> 00:34:47,000 Speaker 1: going all the way back to George Washington's foxhounds, and 647 00:34:47,080 --> 00:34:50,680 Speaker 1: those presidential pets haven't all been dogs. Thomas Jefferson broke 648 00:34:50,719 --> 00:34:55,160 Speaker 1: from mister Washington by keeping mockingbirds. Andrew Jackson had a 649 00:34:55,200 --> 00:34:58,240 Speaker 1: parent named Paul, who had to be removed from Jackson's 650 00:34:58,280 --> 00:35:01,680 Speaker 1: funeral because of the parrot's foul language. You can decide 651 00:35:01,719 --> 00:35:04,080 Speaker 1: where the bird may have picked that up. But despite 652 00:35:04,120 --> 00:35:07,560 Speaker 1: the assorted goats, sheep, and pigs kept by presidents down 653 00:35:07,640 --> 00:35:10,800 Speaker 1: through the ages, by far the most popular presidential pets 654 00:35:10,840 --> 00:35:14,720 Speaker 1: have been dogs. The most famous was FDR's beloved Falla, 655 00:35:15,160 --> 00:35:19,240 Speaker 1: a Scottish terrier who was President Roosevelt's constant companion. Fala 656 00:35:19,400 --> 00:35:22,360 Speaker 1: even became an issue in mister Roosevelt's campaign for reelection 657 00:35:22,400 --> 00:35:25,880 Speaker 1: in nineteen forty four, when Republicans claimed that millions of 658 00:35:25,920 --> 00:35:29,320 Speaker 1: taxpayer dollars had been spent sending a destroyer to retrieve 659 00:35:29,360 --> 00:35:32,600 Speaker 1: the dog from the Aleutian Islands after he'd been left behind. 660 00:35:33,040 --> 00:35:35,160 Speaker 1: That turned out to be an urban myth, but President 661 00:35:35,239 --> 00:35:39,160 Speaker 1: Roosevelt took it on directly and humorously in a national 662 00:35:39,239 --> 00:35:41,400 Speaker 1: radio dress in the middle of the campaign. 663 00:35:42,000 --> 00:35:45,680 Speaker 10: The Republican leaders are not in contempt with a pack 664 00:35:46,120 --> 00:35:49,600 Speaker 10: on me, are on my wife, are on my son? 665 00:35:51,520 --> 00:35:54,440 Speaker 10: They now include my little dog foul. 666 00:35:55,880 --> 00:35:58,480 Speaker 1: By the way, Fala is the only presidential pet to 667 00:35:58,480 --> 00:36:01,279 Speaker 1: be immortalized as a bronze statue as part of the 668 00:36:01,360 --> 00:36:04,640 Speaker 1: Roosevelt Memorial on the National Mall in Washington. Since then, 669 00:36:04,680 --> 00:36:07,759 Speaker 1: there's been a steady succession of dogs in the White House, 670 00:36:07,800 --> 00:36:11,360 Speaker 1: from Ronald Reagan's Rex and Lucky to George Herbert Walker 671 00:36:11,400 --> 00:36:12,480 Speaker 1: Bush's Millie. 672 00:36:12,719 --> 00:36:13,440 Speaker 7: To George W. 673 00:36:13,520 --> 00:36:16,680 Speaker 1: Bush's Barney, made famous by the Barney Cam showing up 674 00:36:16,719 --> 00:36:24,279 Speaker 1: every Christmas from the White House. Indeed, it was only 675 00:36:24,280 --> 00:36:27,200 Speaker 1: President Trump who went without a pet in recent years, 676 00:36:27,400 --> 00:36:30,280 Speaker 1: but not all of the stories about presidents or presidential 677 00:36:30,320 --> 00:36:33,400 Speaker 1: wannabes have been happy ones when it comes to the animals. 678 00:36:33,680 --> 00:36:35,960 Speaker 1: Those of us of a certain age remember the controversy 679 00:36:35,960 --> 00:36:38,400 Speaker 1: when President Johnson decided to show off his hounds they 680 00:36:38,400 --> 00:36:41,239 Speaker 1: were called Him and Her, by picking them up by 681 00:36:41,280 --> 00:36:44,520 Speaker 1: the ears and hearing them yelp, something the President insisted 682 00:36:44,719 --> 00:36:48,080 Speaker 1: was good for them. President Biden's German shepherd commander was 683 00:36:48,160 --> 00:36:51,000 Speaker 1: banished to a farm after repeatedly biting members of the 684 00:36:51,000 --> 00:36:51,800 Speaker 1: Secret Service. 685 00:36:52,239 --> 00:36:55,640 Speaker 8: He put hey doing lord. 686 00:36:56,239 --> 00:36:59,240 Speaker 1: But some in national politics have gone well past making 687 00:36:59,239 --> 00:37:02,319 Speaker 1: their dogs yell or sending him to a farm. South 688 00:37:02,360 --> 00:37:05,720 Speaker 1: Dakota Governor Christy Noam, in the running to become Donald 689 00:37:05,760 --> 00:37:08,360 Speaker 1: Trump's running mate, at one point this year, did herself 690 00:37:08,400 --> 00:37:11,680 Speaker 1: no favors when she admitted in her autobiogree that she'd 691 00:37:11,760 --> 00:37:14,919 Speaker 1: shot her dog when he misbehaved. And then this week, 692 00:37:15,040 --> 00:37:19,000 Speaker 1: presidential candidate RFK Junior, in an interview with Roseanne Barr, 693 00:37:19,360 --> 00:37:22,160 Speaker 1: admitted that he'd come across a dead bear cub in 694 00:37:22,239 --> 00:37:24,920 Speaker 1: upstate New York, put it in his car, drove it 695 00:37:24,960 --> 00:37:28,360 Speaker 1: to Manhattan, and then staged its death by bicycle in 696 00:37:28,440 --> 00:37:31,640 Speaker 1: Central Park because, according to mister Kennedy, he didn't want 697 00:37:31,640 --> 00:37:32,600 Speaker 1: the meat to go. 698 00:37:32,560 --> 00:37:35,320 Speaker 8: To waste in Central Park. 699 00:37:35,360 --> 00:37:40,000 Speaker 1: And we'll make it look like however it all really happened. 700 00:37:40,040 --> 00:37:43,160 Speaker 1: It was a long way from FDR's beloved Falla. But 701 00:37:43,560 --> 00:37:45,880 Speaker 1: then again, let's go back to the source of that 702 00:37:45,920 --> 00:37:48,839 Speaker 1: supposed Truman quote about getting a dog if you want 703 00:37:48,840 --> 00:37:51,440 Speaker 1: a friend in Washington. It turns out that there is 704 00:37:51,560 --> 00:37:54,799 Speaker 1: no evidence President Truman ever said anything about making a 705 00:37:54,840 --> 00:37:57,520 Speaker 1: friend of a dog. Indeed, when he was asked about 706 00:37:57,560 --> 00:38:00,239 Speaker 1: a Cocker Spaniel he'd been given by a supporter, said 707 00:38:00,239 --> 00:38:04,319 Speaker 1: simply that it was around someplace. The someplace turned out 708 00:38:04,320 --> 00:38:07,520 Speaker 1: to be Ohio, where Feller had been sent, because, as 709 00:38:07,520 --> 00:38:09,840 Speaker 1: the President put it, I didn't ask for him and 710 00:38:09,920 --> 00:38:12,640 Speaker 1: I don't need him. But if my own dogs, Whiskey 711 00:38:12,680 --> 00:38:16,040 Speaker 1: and Walker, are watching, don't worry. We have no plans 712 00:38:16,080 --> 00:38:18,920 Speaker 1: of shipping you to Ohio. That does it for this 713 00:38:18,960 --> 00:38:22,040 Speaker 1: episode of Wall Street Week. I'm David Weston. This is Bloomberg. 714 00:38:22,239 --> 00:38:29,840 Speaker 1: See you next week.