1 00:00:00,080 --> 00:00:13,040 Speaker 1: Ye. Welcome to the Bloomberg Surveillance Podcast. I'm Tom Keene 2 00:00:13,480 --> 00:00:17,560 Speaker 1: Jay Ley. We bring you insight from the best in economics, finance, investment, 3 00:00:18,000 --> 00:00:23,520 Speaker 1: and international relations. Find Bloomberg Surveillance on Apple Podcasts, SoundCloud, 4 00:00:23,600 --> 00:00:28,200 Speaker 1: Bloomberg dot com, and of course, on the Bloomberg. A 5 00:00:28,440 --> 00:00:32,120 Speaker 1: speech about the zero lower bound a day before the blackout, 6 00:00:32,840 --> 00:00:36,080 Speaker 1: weeks before the Federal Reserve decision, and woman to believe 7 00:00:36,120 --> 00:00:40,120 Speaker 1: it was purely academic. It's like the smoke signals of 8 00:00:40,280 --> 00:00:44,440 Speaker 1: Arthur Burns pipe. It's it's become a parlor game. And 9 00:00:44,600 --> 00:00:46,360 Speaker 1: the good news is we have the right guest to 10 00:00:46,400 --> 00:00:50,400 Speaker 1: try and parse the silliness of this. Joy James Sweet. 11 00:00:50,560 --> 00:00:54,040 Speaker 1: Just credit Sweet, chief Economist dropping by the studio. Great 12 00:00:54,080 --> 00:00:56,040 Speaker 1: to see it, James, try and make sense of this 13 00:00:56,120 --> 00:00:58,280 Speaker 1: for us. What on earth has happened with FED speak 14 00:00:58,680 --> 00:01:01,040 Speaker 1: in the last twenty four hours. Well, everything I say 15 00:01:01,120 --> 00:01:03,200 Speaker 1: is part of an academic speech and has nothing to 16 00:01:03,200 --> 00:01:07,560 Speaker 1: do course of course. Um yeah, I think the Fed 17 00:01:07,600 --> 00:01:09,760 Speaker 1: has signaled that they're going to cut. I mean we 18 00:01:09,800 --> 00:01:14,399 Speaker 1: expect him to cut by um. The market speculation is 19 00:01:14,560 --> 00:01:18,680 Speaker 1: maybe they'll do fifty. Ironically, the data says maybe they 20 00:01:18,680 --> 00:01:22,319 Speaker 1: should do zero. But but the marketers really debating whether 21 00:01:22,319 --> 00:01:25,160 Speaker 1: they're going to do twenty five or fifty, and the 22 00:01:25,240 --> 00:01:30,000 Speaker 1: comments from from President Williams yesterday really moved the market 23 00:01:30,080 --> 00:01:33,440 Speaker 1: sharply towards expecting fifty. And then he brought that back 24 00:01:33,520 --> 00:01:36,000 Speaker 1: later in the day. Did he bring it back because 25 00:01:36,800 --> 00:01:38,479 Speaker 1: they're not going to do fifty and he doesn't want 26 00:01:38,520 --> 00:01:40,640 Speaker 1: to market miss priced, or did he bring it back 27 00:01:40,680 --> 00:01:43,480 Speaker 1: because they're gonna do fifty. Anyone surprised the market and 28 00:01:43,560 --> 00:01:46,839 Speaker 1: have everyone excited about a dubble shock from the Fed. 29 00:01:46,920 --> 00:01:49,000 Speaker 1: I'm not sure. But you know, again, I don't think 30 00:01:49,000 --> 00:01:51,200 Speaker 1: the data really supports any cut at all, So we're 31 00:01:51,200 --> 00:01:54,559 Speaker 1: going to continue to forecast. The heart of this discussion 32 00:01:55,000 --> 00:01:59,360 Speaker 1: is we're asking too much from one institution forrest about 33 00:01:59,400 --> 00:02:03,600 Speaker 1: the names of the people, governor pharaoh, you know, you know, whatever, 34 00:02:03,680 --> 00:02:08,280 Speaker 1: nobody needs. That just we're asking too much of economists 35 00:02:08,480 --> 00:02:13,040 Speaker 1: who have a toolkit that everyone agrees, including Vice Chairman Clarada, 36 00:02:13,120 --> 00:02:18,200 Speaker 1: with world class research, which is prone to probabilities and likelihoods. Right. 37 00:02:18,320 --> 00:02:20,880 Speaker 1: I think that's fair and I think even you know 38 00:02:20,919 --> 00:02:24,440 Speaker 1: a lot of the presidents, including Mr Williams, were past 39 00:02:25,160 --> 00:02:30,200 Speaker 1: um research heads at their local feds UM, and you know, 40 00:02:30,520 --> 00:02:35,440 Speaker 1: very competent monetary policy experts UM, but you know certain 41 00:02:35,480 --> 00:02:37,680 Speaker 1: kind of people may be prone to a certain kind 42 00:02:37,680 --> 00:02:41,000 Speaker 1: of thinking. And lots of papers about this scary zero bound. 43 00:02:41,120 --> 00:02:43,600 Speaker 1: Let's go to the inertial force. Here is it linear? 44 00:02:43,720 --> 00:02:47,000 Speaker 1: They cut let's forget about fifty basis point they got, 45 00:02:48,200 --> 00:02:50,840 Speaker 1: then they cut another. Always see the date I get 46 00:02:50,840 --> 00:02:54,679 Speaker 1: all that is it. That's a measured green spanning and 47 00:02:54,800 --> 00:02:57,480 Speaker 1: linear move. I don't buy it for a minute. When 48 00:02:57,520 --> 00:03:00,800 Speaker 1: does the umph click in? Well, I think when you 49 00:03:00,960 --> 00:03:05,079 Speaker 1: when you need a sharp move, um, you know you can, 50 00:03:05,160 --> 00:03:08,560 Speaker 1: you can deliver that. But really the question is do 51 00:03:08,600 --> 00:03:12,600 Speaker 1: you need it? Governor Ferrell was saying yesterday. Know, I 52 00:03:12,600 --> 00:03:14,600 Speaker 1: think most people don't think you need it right now. 53 00:03:14,600 --> 00:03:17,280 Speaker 1: I think the communication we've had quite clearly from the FETE, though, 54 00:03:17,360 --> 00:03:19,080 Speaker 1: is that they think they need to get ahead of it. 55 00:03:19,120 --> 00:03:21,560 Speaker 1: They need to do more with less, they have limited ammunition. 56 00:03:21,680 --> 00:03:24,560 Speaker 1: Don't wait for it to materialize, James. I think that's 57 00:03:24,560 --> 00:03:27,240 Speaker 1: why so many people find it so confusing at the moment, 58 00:03:27,240 --> 00:03:29,720 Speaker 1: because they look at the heart day in America and 59 00:03:29,760 --> 00:03:32,440 Speaker 1: they say things are okay. Then they listen to the 60 00:03:32,440 --> 00:03:35,520 Speaker 1: feed and the communication and they're wondering what's wrong, Well, 61 00:03:35,560 --> 00:03:38,360 Speaker 1: the real tailor rule probably should have p M eyes 62 00:03:38,520 --> 00:03:42,040 Speaker 1: and forward break evens in it. And it feels like 63 00:03:42,280 --> 00:03:44,320 Speaker 1: it's a good point. Yeah, it feels like that's what 64 00:03:44,360 --> 00:03:48,480 Speaker 1: they're responding to, because I think the tailor rule that 65 00:03:48,560 --> 00:03:54,360 Speaker 1: has forward actual inflation and you know, forward actual unemployment 66 00:03:55,000 --> 00:03:58,400 Speaker 1: based on really what you can see now, it doesn't 67 00:03:58,480 --> 00:04:02,320 Speaker 1: really suggest a breakdown of inflation or or arise in unemployment. 68 00:04:02,680 --> 00:04:04,520 Speaker 1: So you have to have a pro you have to 69 00:04:04,560 --> 00:04:08,360 Speaker 1: be forecasting something meaningful in the absence of cuts to 70 00:04:08,440 --> 00:04:11,920 Speaker 1: justify cuts, and we're not. Is there value to the 71 00:04:12,000 --> 00:04:14,400 Speaker 1: five year five year forward's looking at five years and 72 00:04:14,400 --> 00:04:16,200 Speaker 1: then five years for it from that? Is there too 73 00:04:16,200 --> 00:04:19,080 Speaker 1: far up? It's not. There's not really much value because 74 00:04:19,120 --> 00:04:21,880 Speaker 1: it's it's basically very correlated with oil prices, and it's 75 00:04:21,920 --> 00:04:24,120 Speaker 1: really telling you whether the ten year and the thirty 76 00:04:24,160 --> 00:04:26,159 Speaker 1: year going up and down or not. So what's the 77 00:04:26,480 --> 00:04:30,000 Speaker 1: X axis of tools that would be useful for them? 78 00:04:30,000 --> 00:04:31,640 Speaker 1: And the answer is, I'll a p M E S. 79 00:04:32,200 --> 00:04:35,120 Speaker 1: It's shockingly short, isn't it. Well, the p M I 80 00:04:35,279 --> 00:04:38,240 Speaker 1: S I don't. I think they do definitely respond to 81 00:04:38,320 --> 00:04:41,040 Speaker 1: and we've done some some thorough empirical work showing that 82 00:04:41,120 --> 00:04:44,240 Speaker 1: both the ECB and the FED tend to react to 83 00:04:44,440 --> 00:04:46,760 Speaker 1: sharp moves down in in P M I S. But 84 00:04:46,839 --> 00:04:49,160 Speaker 1: the problem is the labor market tends not to react 85 00:04:49,240 --> 00:04:51,880 Speaker 1: to sharp move downs in PM. You and I did 86 00:04:51,880 --> 00:04:57,240 Speaker 1: thorough empyropical work. Was that over those beers. Paul Sweeney 87 00:04:57,279 --> 00:05:00,719 Speaker 1: out is over for Budweiser's and we did her oracle work. 88 00:05:00,839 --> 00:05:04,480 Speaker 1: And right, James, just thinking about things at the moment. 89 00:05:04,560 --> 00:05:07,280 Speaker 1: The research that you guys have done on industrial production 90 00:05:07,320 --> 00:05:11,360 Speaker 1: worldwide and manufacturing. You came out quite early and said 91 00:05:11,400 --> 00:05:14,160 Speaker 1: that there are some one off factors behind the manufacturing 92 00:05:14,160 --> 00:05:17,120 Speaker 1: recession we are seeing as it was gaining momentum, and 93 00:05:17,160 --> 00:05:20,000 Speaker 1: you think they're fading. And what I find really interesting 94 00:05:20,080 --> 00:05:21,839 Speaker 1: is not where we are right now, where will be 95 00:05:21,960 --> 00:05:24,600 Speaker 1: month end? Were set to get an interest right cut. 96 00:05:24,920 --> 00:05:27,400 Speaker 1: It's what the economy looks like going into the back 97 00:05:27,560 --> 00:05:30,120 Speaker 1: end of this year. And your base case with a 98 00:05:30,240 --> 00:05:32,640 Speaker 1: right cup without a rate cut is things are going 99 00:05:32,680 --> 00:05:34,920 Speaker 1: to improve. So just walk me through that because I 100 00:05:34,920 --> 00:05:38,080 Speaker 1: think it's important, right well, I I really separate the 101 00:05:38,120 --> 00:05:42,400 Speaker 1: real economy into global manufacturing trade industrial production, which of course, 102 00:05:42,440 --> 00:05:47,480 Speaker 1: as a US component and labor market, households in developed economies, 103 00:05:47,520 --> 00:05:52,400 Speaker 1: so on. On the first, on industrial production, basically, trade 104 00:05:52,480 --> 00:05:57,800 Speaker 1: uncertainty tariff fears caused a sharp, sharp slowdown towards the 105 00:05:57,880 --> 00:06:00,440 Speaker 1: end of last year, centered on a drop in imports 106 00:06:00,440 --> 00:06:03,960 Speaker 1: in China. UH. And so from October to March you 107 00:06:04,080 --> 00:06:08,800 Speaker 1: had really bad global industrial production growth, and the Chinese 108 00:06:08,839 --> 00:06:13,280 Speaker 1: industrial production data, for mysterious reasons, didn't really reflect it. 109 00:06:13,360 --> 00:06:16,159 Speaker 1: But um, but I think, you know, I I think 110 00:06:16,520 --> 00:06:18,520 Speaker 1: we had a really big slump. It wasn't a U 111 00:06:18,600 --> 00:06:21,320 Speaker 1: S slump, but it was a big slump. March to May, 112 00:06:21,600 --> 00:06:25,520 Speaker 1: we actually had some recovery. Um. Since since the tariffs 113 00:06:25,560 --> 00:06:27,880 Speaker 1: went up, it's likely that we're back in that slump 114 00:06:27,960 --> 00:06:31,560 Speaker 1: in manufacturing activity. UH. Investment is likely to be pretty 115 00:06:31,560 --> 00:06:34,800 Speaker 1: soft in manufacturing globally. But meanwhile, the labor market in 116 00:06:34,839 --> 00:06:38,640 Speaker 1: the US, nothing has happened. Inflation in the US nothing 117 00:06:38,680 --> 00:06:42,320 Speaker 1: has happened either. Financial services inflation, which they can't measure, 118 00:06:42,560 --> 00:06:45,120 Speaker 1: came down a little bit. Basically, the rest of the 119 00:06:45,160 --> 00:06:48,240 Speaker 1: inflation data are stable, and if anything, core inflation is 120 00:06:48,320 --> 00:06:51,280 Speaker 1: likely to rise. So so you know, basically you've got 121 00:06:51,360 --> 00:06:53,640 Speaker 1: two things. You've got a manufacturing slump, which should really 122 00:06:53,680 --> 00:06:56,520 Speaker 1: get no worse, but not really, you know, we don't 123 00:06:56,560 --> 00:07:00,240 Speaker 1: see robust growth and inflation and and and unemployment where 124 00:07:00,320 --> 00:07:03,800 Speaker 1: basically still nothing is happening. I've never seen Sweeney is 125 00:07:04,000 --> 00:07:06,920 Speaker 1: fired up. What solid mean? Do you? When when the 126 00:07:07,040 --> 00:07:09,880 Speaker 1: when these fancy guys at these institutions say it's a 127 00:07:10,000 --> 00:07:14,360 Speaker 1: solid economy? What solid you? Because right now, I mean 128 00:07:14,680 --> 00:07:18,400 Speaker 1: unemployment is at three six three seven GDP. Growth for 129 00:07:18,440 --> 00:07:19,840 Speaker 1: the first half of the year is going to be 130 00:07:20,080 --> 00:07:24,480 Speaker 1: mid two's um you know, corporates the flow of credit 131 00:07:24,640 --> 00:07:27,119 Speaker 1: to just about anyone who wants it in the US, 132 00:07:27,640 --> 00:07:31,920 Speaker 1: it's pretty good right now. So what's not solid? Really? 133 00:07:31,960 --> 00:07:35,080 Speaker 1: The straight James Sweeney with se does David Blanche Flowers 134 00:07:35,280 --> 00:07:37,520 Speaker 1: no doubt listening up in the handover thew Hampshire going 135 00:07:37,680 --> 00:07:40,040 Speaker 1: he doesn't know he's talking about he wants. Do you 136 00:07:40,040 --> 00:07:43,200 Speaker 1: think Danny wants fifty beats? I'm not sure I actually 137 00:07:43,200 --> 00:07:44,840 Speaker 1: I haven't spoke to him for a couple of weeks, 138 00:07:44,880 --> 00:07:47,240 Speaker 1: but imagine it's up there at the fifty level. I 139 00:07:47,240 --> 00:08:03,920 Speaker 1: think he wants a big adjustment. You too short of 140 00:08:04,000 --> 00:08:06,800 Speaker 1: is it now? A chapterin Man of City Groupe The 141 00:08:06,920 --> 00:08:11,000 Speaker 1: Chief Economists and always kept a man on international economics, 142 00:08:11,000 --> 00:08:13,200 Speaker 1: except we're not going to do that on a New 143 00:08:13,280 --> 00:08:16,480 Speaker 1: York Friday. We're going to talk domestic economics with Dr Man. 144 00:08:16,840 --> 00:08:19,360 Speaker 1: What is the research that you see with your vast 145 00:08:19,400 --> 00:08:24,000 Speaker 1: team at City Group? Is it an economy that justifies 146 00:08:24,960 --> 00:08:29,800 Speaker 1: no decision? I guess an out front basis point cut 147 00:08:30,320 --> 00:08:32,840 Speaker 1: or Catherine, can you get all dramatic today and tell 148 00:08:32,880 --> 00:08:37,120 Speaker 1: me we need a fifty basis point cut? Well, you know, 149 00:08:37,240 --> 00:08:41,679 Speaker 1: I think that there's been um, some real challenging communications 150 00:08:42,160 --> 00:08:45,240 Speaker 1: that the FED has been uh presenting to us. I 151 00:08:45,240 --> 00:08:49,240 Speaker 1: think your person who is on before talks about fresh 152 00:08:49,360 --> 00:08:52,520 Speaker 1: clues as if you know, we're trying to uncover the 153 00:08:52,600 --> 00:08:55,400 Speaker 1: true FED, and I think we're The real challenge here 154 00:08:55,559 --> 00:08:59,360 Speaker 1: is that there was a very dramatic pivot from language 155 00:08:59,360 --> 00:09:04,959 Speaker 1: that used the vocabulary of patient monitor data dependence to 156 00:09:05,200 --> 00:09:12,160 Speaker 1: change to language and vocabulary of preemptive, preventive, large and 157 00:09:12,280 --> 00:09:15,600 Speaker 1: bold moves. And so you know, it's like, Wow, how 158 00:09:15,600 --> 00:09:17,640 Speaker 1: do we all of a sudden move from patient and 159 00:09:17,720 --> 00:09:21,640 Speaker 1: monitoring and data dependence to something completely different? Because the 160 00:09:21,720 --> 00:09:26,760 Speaker 1: incoming data for the United States do not warrant change 161 00:09:26,960 --> 00:09:31,439 Speaker 1: in tone, and so it's what happened? What what did 162 00:09:31,440 --> 00:09:34,520 Speaker 1: we see? In? Frankly, the incoming data from the foreign 163 00:09:34,520 --> 00:09:40,000 Speaker 1: economies as well, does not warrant the type of market 164 00:09:40,120 --> 00:09:44,720 Speaker 1: pricing in of dramatic action of you know, fifty basis 165 00:09:44,720 --> 00:09:48,199 Speaker 1: point to mark. The market UM is pricing in much 166 00:09:48,280 --> 00:09:53,600 Speaker 1: more than the data warrant, and they are effectively pricing 167 00:09:53,640 --> 00:09:57,920 Speaker 1: in this recent language change, which is also a puzzle 168 00:09:58,559 --> 00:10:03,200 Speaker 1: because the language change is dramatic without the data being 169 00:10:03,559 --> 00:10:06,280 Speaker 1: with it being dramatic, Doctor man, how did we get 170 00:10:06,320 --> 00:10:09,720 Speaker 1: to the point where we're preemptive? And if you read 171 00:10:09,840 --> 00:10:13,440 Speaker 1: melts or three volumes or Timberlake of the Georgia School 172 00:10:14,120 --> 00:10:17,640 Speaker 1: or Bernanke and the rest of it from academics, where's 173 00:10:17,679 --> 00:10:22,679 Speaker 1: the evidence of preemptive works? Well, we can go back 174 00:10:22,760 --> 00:10:25,920 Speaker 1: to the you know, the long and variable lags UH 175 00:10:25,920 --> 00:10:29,040 Speaker 1: story about monetary policy, that it does take a long 176 00:10:29,120 --> 00:10:33,000 Speaker 1: time for monetary policy to work through um the real 177 00:10:33,200 --> 00:10:36,679 Speaker 1: side of the economy, because in the olden days, that 178 00:10:36,800 --> 00:10:41,319 Speaker 1: was the channel you changed monetary policy. Banks reacted, then 179 00:10:41,360 --> 00:10:44,000 Speaker 1: you had to change in the cost of capital uh 180 00:10:44,120 --> 00:10:46,640 Speaker 1: to cost of credit, and then and then that followed 181 00:10:46,640 --> 00:10:49,720 Speaker 1: through the real economy. And so so you did need 182 00:10:49,800 --> 00:10:54,280 Speaker 1: to look very far forward, uh to prospects for the 183 00:10:54,320 --> 00:10:59,400 Speaker 1: economy and adjust monetary policy appropriately in light of the 184 00:10:59,520 --> 00:11:02,600 Speaker 1: change in economic activity. And then of course also we 185 00:11:02,679 --> 00:11:05,880 Speaker 1: cared a lot about inflation um and that also had 186 00:11:06,200 --> 00:11:09,280 Speaker 1: long and variable lives to affect inflation um. But we're 187 00:11:09,280 --> 00:11:13,360 Speaker 1: at a time right now where what matters more or 188 00:11:13,400 --> 00:11:16,920 Speaker 1: seems to be much more of the dynamic is the 189 00:11:17,000 --> 00:11:20,559 Speaker 1: market expects something to happen, for the FED to move 190 00:11:20,600 --> 00:11:23,199 Speaker 1: in a particular way, and then if the FED does 191 00:11:23,240 --> 00:11:27,920 Speaker 1: not deliver on what the market expects, then that yields 192 00:11:27,960 --> 00:11:33,520 Speaker 1: an implied tightening. The market believes the implied tightening, even 193 00:11:33,559 --> 00:11:36,720 Speaker 1: if there's no change at all, the implied tightening generates 194 00:11:36,720 --> 00:11:40,600 Speaker 1: a real tightening, or they believe so, and that real 195 00:11:40,760 --> 00:11:46,280 Speaker 1: tightening has consequences for the real side economy. And and 196 00:11:46,360 --> 00:11:48,520 Speaker 1: you know, we can argue about whether or not that's 197 00:11:48,600 --> 00:11:51,960 Speaker 1: that's appropriate or to consider, but it means the transmission 198 00:11:51,960 --> 00:11:54,559 Speaker 1: mechanism is very different. So, Katherine, I'm just looking at 199 00:11:54,840 --> 00:11:56,880 Speaker 1: one of your recent notes and the charts that you 200 00:11:56,920 --> 00:11:59,600 Speaker 1: find interesting. I'm looking at the one on job growth. 201 00:11:59,600 --> 00:12:01,360 Speaker 1: I mean, you know, you look at some of the data. 202 00:12:01,520 --> 00:12:04,560 Speaker 1: This is a data dependent FED. The jobs growth would 203 00:12:04,600 --> 00:12:07,080 Speaker 1: suggest that the FED can maybe sit this one out, 204 00:12:07,080 --> 00:12:10,160 Speaker 1: But that's not what the language seems to be. Well, 205 00:12:10,200 --> 00:12:12,280 Speaker 1: that's right. I mean, as I say, we when the 206 00:12:12,559 --> 00:12:15,800 Speaker 1: you know, the language uh was patient, monitor and data 207 00:12:15,840 --> 00:12:18,160 Speaker 1: to pantheons uh and then it and then then it 208 00:12:18,240 --> 00:12:22,679 Speaker 1: really really pivoted very rapidly to this notion of needing 209 00:12:22,720 --> 00:12:27,480 Speaker 1: to have insurance guns or preventive cuts, preemptive uh cuts UH. 210 00:12:27,559 --> 00:12:30,760 Speaker 1: And I think that they're you're getting very different language 211 00:12:30,840 --> 00:12:35,400 Speaker 1: from from different members of the broad body, not just 212 00:12:35,480 --> 00:12:37,480 Speaker 1: the f o MC that there is, the voting body, 213 00:12:37,480 --> 00:12:40,720 Speaker 1: but the broad body that addresses the state of the 214 00:12:40,800 --> 00:12:43,200 Speaker 1: U S economy, state of the global economy and how 215 00:12:43,240 --> 00:12:46,160 Speaker 1: it impacts the US economy as part of the decision 216 00:12:46,160 --> 00:12:48,600 Speaker 1: making process. So you are starting to you know, you 217 00:12:48,640 --> 00:12:52,120 Speaker 1: are seeing quite a range of views uh in in 218 00:12:52,240 --> 00:12:56,520 Speaker 1: the in the commentary. But but the market had definitely 219 00:12:56,840 --> 00:13:03,199 Speaker 1: tended to um focus quite a bit more on this preemptive, preventive, 220 00:13:03,840 --> 00:13:07,280 Speaker 1: large and bold moves that have come out in in 221 00:13:07,320 --> 00:13:12,360 Speaker 1: the most recent testimony and other commentary of the past 222 00:13:12,400 --> 00:13:14,760 Speaker 1: several days too short of visit, Dr Man, thank you 223 00:13:14,800 --> 00:13:17,240 Speaker 1: so much for joining us. Really wonderful to get a 224 00:13:17,280 --> 00:13:20,480 Speaker 1: briefing from City Group chief economist Catherine Man this morning. 225 00:13:35,640 --> 00:13:38,600 Speaker 1: It's rare, Paul, that we have someone dark in the 226 00:13:38,640 --> 00:13:42,079 Speaker 1: door who has a degree in natural philosophy from William 227 00:13:42,080 --> 00:13:46,079 Speaker 1: and Mary, which is so no, but I mean, I 228 00:13:46,160 --> 00:13:48,319 Speaker 1: mean in physics right now, William and Mary. It's the 229 00:13:48,360 --> 00:13:54,280 Speaker 1: William Smallhow small was Thomas Jefferson's iconic mentor, Like Jefferson 230 00:13:54,360 --> 00:13:57,280 Speaker 1: always said, this guy is a guy that jumped started in, 231 00:13:57,400 --> 00:14:00,440 Speaker 1: Lisa Ellis with us. What was it like doing physics 232 00:14:00,480 --> 00:14:03,520 Speaker 1: where you're supposed to do history. You go to William 233 00:14:03,559 --> 00:14:06,120 Speaker 1: Mary to do American history and you got out of 234 00:14:06,200 --> 00:14:10,560 Speaker 1: slide rule? Yeah, yeah, that's right. Well when the Mary 235 00:14:10,679 --> 00:14:14,520 Speaker 1: is beautiful as you highlighted, steeped in history. I was 236 00:14:14,559 --> 00:14:18,640 Speaker 1: actually there during the three hundredth anniversary and the institution 237 00:14:18,800 --> 00:14:22,720 Speaker 1: and um. But it's the natural sciences are strong too. 238 00:14:22,760 --> 00:14:25,480 Speaker 1: There's a lot of um. The NASA has a big 239 00:14:25,480 --> 00:14:28,440 Speaker 1: and wrong that barely describes the physics program as one 240 00:14:28,480 --> 00:14:30,440 Speaker 1: of the best in the country. Paul, why don't you 241 00:14:30,440 --> 00:14:33,080 Speaker 1: bring it from offa Nathan's and Lisa Ellis here on 242 00:14:33,160 --> 00:14:35,680 Speaker 1: a on a three year old computer company that ain't 243 00:14:35,680 --> 00:14:38,080 Speaker 1: get done? You know, I was saying earlier. I pulled 244 00:14:38,120 --> 00:14:40,000 Speaker 1: up the five year chart on IBM, and it's returned 245 00:14:40,000 --> 00:14:42,360 Speaker 1: to whopping negative one point two percent over the last 246 00:14:42,600 --> 00:14:44,280 Speaker 1: five years. Just looking at the stocks at LASA, I 247 00:14:44,320 --> 00:14:48,120 Speaker 1: know they reported earnings. What's what's the key takeaway here? 248 00:14:48,120 --> 00:14:50,200 Speaker 1: I know you're not really constructive on the stock here, 249 00:14:50,200 --> 00:14:53,000 Speaker 1: but what was the takeaway you took out of the earnings? Yeah, 250 00:14:53,040 --> 00:14:57,560 Speaker 1: there's they were mixed results. That's the overall takeaway. The 251 00:14:57,640 --> 00:15:00,400 Speaker 1: two that this strong the big positive and the strong 252 00:15:00,480 --> 00:15:05,120 Speaker 1: negative pretty bipolar. Actually. UM on the on the positive side, 253 00:15:05,240 --> 00:15:07,920 Speaker 1: the software number, which is a lot of what tends 254 00:15:07,960 --> 00:15:10,760 Speaker 1: to move the stock on earnings, was quite good five 255 00:15:10,800 --> 00:15:13,520 Speaker 1: point four percent for their software business. That's a very 256 00:15:13,520 --> 00:15:17,080 Speaker 1: profitable business, very important to the sort of perpetuation of 257 00:15:17,120 --> 00:15:20,280 Speaker 1: the franchise. UM. On the flip side, however, their cloud 258 00:15:20,440 --> 00:15:24,600 Speaker 1: number was terrible. It was five percent in the quarter. 259 00:15:25,000 --> 00:15:28,360 Speaker 1: That's down from twelve percent growth a last quarter. And 260 00:15:28,400 --> 00:15:30,240 Speaker 1: this was a business that just in as of two 261 00:15:30,280 --> 00:15:34,280 Speaker 1: thousand seventeen was doing. And obviously you compare that to 262 00:15:34,600 --> 00:15:37,600 Speaker 1: you know, Microsoft to just reported and for Microsoft Azure, 263 00:15:37,600 --> 00:15:41,280 Speaker 1: they're putting up numbers like an excess of sixty percent growth. 264 00:15:41,320 --> 00:15:44,280 Speaker 1: So alright, so that brings us to red Hat. Red Hat, 265 00:15:44,440 --> 00:15:48,320 Speaker 1: as I recall, was a big acquisition presumably going to 266 00:15:48,360 --> 00:15:50,440 Speaker 1: help them in the cloud. What can you tell us 267 00:15:50,440 --> 00:15:52,600 Speaker 1: about red Hat You think that is going to do 268 00:15:52,640 --> 00:15:55,000 Speaker 1: what IBM needs to be done to their cloud business? Well, 269 00:15:55,000 --> 00:15:58,840 Speaker 1: it it definitely helps UM, you know, within the scale 270 00:15:59,040 --> 00:16:03,400 Speaker 1: of IBM. Unfortunately, just from a financial perspective, it doesn't 271 00:16:03,400 --> 00:16:06,360 Speaker 1: move the needle that much just because of the difference 272 00:16:06,360 --> 00:16:11,640 Speaker 1: in scale UM. But from a competitiveness perspective, it helps them. 273 00:16:11,840 --> 00:16:15,440 Speaker 1: The real area where IBM is struggling right now in 274 00:16:15,560 --> 00:16:18,960 Speaker 1: cloud is in the platform as a service business. That's 275 00:16:18,960 --> 00:16:22,200 Speaker 1: where they would compete with like a Microsoft Azure. Red 276 00:16:22,240 --> 00:16:24,800 Speaker 1: Hat has one of the leading products in that space, 277 00:16:24,840 --> 00:16:28,080 Speaker 1: their open Shift product. Again, financially not a big deal, 278 00:16:28,120 --> 00:16:30,400 Speaker 1: it's only a few hundred million in revenue, but more 279 00:16:30,520 --> 00:16:35,760 Speaker 1: strategically it should help IBM positioning in that space where 280 00:16:35,800 --> 00:16:39,800 Speaker 1: IBMS product, uh you know, has been lagging those piers. 281 00:16:39,840 --> 00:16:42,720 Speaker 1: I look at the vectors of their income statement and frankly, 282 00:16:42,760 --> 00:16:45,520 Speaker 1: if you didn't know what was IBM their constructive EBITA 283 00:16:46,040 --> 00:16:51,680 Speaker 1: manage rising, free cash flow manage rising. The revenue line 284 00:16:51,840 --> 00:16:55,600 Speaker 1: is an unmitigated disaster at the board level. Are they 285 00:16:55,640 --> 00:17:02,200 Speaker 1: just simply managing IBM to be a smaller company? Uh, realistically, yes, 286 00:17:02,360 --> 00:17:05,439 Speaker 1: I mean they are in you know that sort of 287 00:17:05,480 --> 00:17:09,000 Speaker 1: like shrink to grow type of mode. Does that work 288 00:17:09,040 --> 00:17:12,720 Speaker 1: in your experience at Mackenzie years ago? Does Mackenzie do 289 00:17:12,840 --> 00:17:16,840 Speaker 1: shrink to grow? What is that? Well? I mean meaning 290 00:17:16,920 --> 00:17:21,760 Speaker 1: there are pieces of IBM that are in areas of 291 00:17:21,760 --> 00:17:24,439 Speaker 1: of of I T that are in structural decline and 292 00:17:24,480 --> 00:17:28,200 Speaker 1: they've got to work their way well the challenges that 293 00:17:28,640 --> 00:17:32,840 Speaker 1: you know, IBM is really about major large customers where 294 00:17:32,880 --> 00:17:35,160 Speaker 1: they are selling almost like an all you can eat. 295 00:17:35,200 --> 00:17:43,520 Speaker 1: They sell hundreds of millions in software servers services. Microsoft 296 00:17:43,560 --> 00:17:46,439 Speaker 1: does that as as well. That's true, but they the 297 00:17:46,480 --> 00:17:49,360 Speaker 1: point is you can't really break apart those pieces. It's 298 00:17:49,400 --> 00:17:52,920 Speaker 1: like the you know, it's like these large customer situations. 299 00:17:52,920 --> 00:17:55,680 Speaker 1: So they sort of have to just manage manage through that, 300 00:17:56,400 --> 00:17:59,399 Speaker 1: you know, runoff of some of the declining area. Do 301 00:17:59,480 --> 00:18:04,480 Speaker 1: not know this? They have three employees? Yeah, wow, that's right. 302 00:18:04,640 --> 00:18:09,600 Speaker 1: It's because they still are the within IBM um they 303 00:18:09,640 --> 00:18:14,400 Speaker 1: have over forty you know, over half of IBM's revenue 304 00:18:14,480 --> 00:18:19,080 Speaker 1: is in services. They actually are still although Accenture is 305 00:18:19,440 --> 00:18:22,320 Speaker 1: inching up on them, the single largest I T services 306 00:18:22,359 --> 00:18:24,560 Speaker 1: player out there. That's why they that's the hundreds of 307 00:18:24,560 --> 00:18:27,520 Speaker 1: thousands of employees are there services. So at least I 308 00:18:27,520 --> 00:18:30,560 Speaker 1: see on August two they're having an investor webcast. They're 309 00:18:30,560 --> 00:18:32,040 Speaker 1: not even gonna bring you in for the rubber chicken. 310 00:18:32,240 --> 00:18:34,240 Speaker 1: It's just a webcast. So what do they need to 311 00:18:34,280 --> 00:18:37,880 Speaker 1: get across to the street on this August two kind 312 00:18:37,880 --> 00:18:40,879 Speaker 1: of webcast with this kind of their investor meeting. The 313 00:18:41,920 --> 00:18:46,280 Speaker 1: UM Well that the key thing in the immediate term 314 00:18:46,280 --> 00:18:48,639 Speaker 1: for the street is going to be numbers. There is 315 00:18:48,680 --> 00:18:53,560 Speaker 1: some uncertainty around how red Hat because it's a software company, 316 00:18:53,600 --> 00:18:56,080 Speaker 1: there's this purchase Accounty Dynamics is going to fold in. 317 00:18:56,160 --> 00:18:59,000 Speaker 1: So they're going to give new twenty nineteen guidance. They're 318 00:18:59,040 --> 00:19:01,560 Speaker 1: also going to give some new medium term guidance around 319 00:19:01,880 --> 00:19:04,720 Speaker 1: the revenue and earnings and free cash flow impacts on 320 00:19:04,760 --> 00:19:07,680 Speaker 1: the business for the street. Honestly, a lot of it 321 00:19:07,680 --> 00:19:10,520 Speaker 1: will be about that, but I think to get the 322 00:19:10,560 --> 00:19:14,360 Speaker 1: stock to move positively, they'll need to give more tangible 323 00:19:15,119 --> 00:19:19,400 Speaker 1: UM nearer term meaning twenty type of time frame synergies 324 00:19:19,440 --> 00:19:21,399 Speaker 1: from Red Hat and they talk a lot about the 325 00:19:21,440 --> 00:19:23,840 Speaker 1: concept of the synergies, but not a lot about the 326 00:19:23,920 --> 00:19:27,720 Speaker 1: numbers for ten years. I mean they're all concept. I mean, 327 00:19:28,040 --> 00:19:30,560 Speaker 1: you know the stock is up this year. Let's give 328 00:19:30,640 --> 00:19:33,720 Speaker 1: a little bit of a break. Okay, there are one 329 00:19:33,720 --> 00:19:37,199 Speaker 1: your targets one right, that's right. How soon do we 330 00:19:37,240 --> 00:19:39,360 Speaker 1: get to your target? I mean is this by next week? 331 00:19:39,480 --> 00:19:42,960 Speaker 1: Or uh no, we would we I mean our price 332 00:19:42,960 --> 00:19:45,159 Speaker 1: targets are when your price targets, so that would be 333 00:19:45,200 --> 00:19:48,800 Speaker 1: a one year time frame. Um, the big, the big 334 00:19:48,880 --> 00:19:51,400 Speaker 1: question marks are going to be you know, the this 335 00:19:52,119 --> 00:19:55,959 Speaker 1: you know will Will red Hat. Really it can't just 336 00:19:56,200 --> 00:19:59,480 Speaker 1: be red Hat folded into IBM. Red Hat has to 337 00:19:59,600 --> 00:20:04,240 Speaker 1: make the broader IBM businesses be better to really change 338 00:20:04,280 --> 00:20:07,160 Speaker 1: that long term, like you said, that long term negative 339 00:20:07,200 --> 00:20:10,560 Speaker 1: trajectory on the revenue, and that's what we'll all be 340 00:20:10,600 --> 00:20:14,040 Speaker 1: watching for. Just does that cloud number get better? Right now? 341 00:20:14,080 --> 00:20:18,159 Speaker 1: Cloud number five percent versus azure at sixty three I 342 00:20:18,200 --> 00:20:20,600 Speaker 1: think percent or sixty seven was the number they put up. 343 00:20:21,080 --> 00:20:24,400 Speaker 1: You know that those growth rates need to start normalizing. 344 00:20:24,600 --> 00:20:26,879 Speaker 1: You're sit in the same room with Moffatt Nathanson. Do 345 00:20:26,920 --> 00:20:30,600 Speaker 1: you have to hear them? They do? They sit right 346 00:20:30,640 --> 00:20:32,520 Speaker 1: to write down the hall from me and you just 347 00:20:32,760 --> 00:20:37,760 Speaker 1: you just all day. It's media, media, cable. What's the content? 348 00:20:37,960 --> 00:20:41,959 Speaker 1: Very quickly, what's the content of IBM? It's a Nathanson question. 349 00:20:42,400 --> 00:20:46,800 Speaker 1: What's the content of IBM, meaning like what what's their future? 350 00:20:46,880 --> 00:20:52,800 Speaker 1: What's the code? Yeah, that means cloud software at services. 351 00:20:52,840 --> 00:20:55,760 Speaker 1: That's I mean they there with the d n A 352 00:20:55,960 --> 00:21:02,560 Speaker 1: and the specialty is infrastructures. Is is big enterprise I 353 00:21:02,800 --> 00:21:08,960 Speaker 1: T infrastructure services like the platforms that run business software. 354 00:21:09,520 --> 00:21:12,879 Speaker 1: So this includes everything from the servers, the storage, the 355 00:21:12,920 --> 00:21:15,719 Speaker 1: middleware layers, the service you know, the labor that wraps 356 00:21:15,760 --> 00:21:19,119 Speaker 1: around that. And that's what's moving into these cloud models 357 00:21:19,119 --> 00:21:21,159 Speaker 1: and they need to be a leader there. Lisa, thank us. 358 00:21:21,200 --> 00:21:24,960 Speaker 1: Lisa Ellis Moffatt Nathanson with a cell on international business 359 00:21:38,440 --> 00:21:42,679 Speaker 1: right now out of Stanford, and not a body joins us. 360 00:21:43,040 --> 00:21:48,040 Speaker 1: She has been just superb on questioning banks in the 361 00:21:48,080 --> 00:21:50,960 Speaker 1: shadows of the shadows within our banking system were under 362 00:21:51,000 --> 00:21:54,119 Speaker 1: the professor body join us this morning. Wonderful to have 363 00:21:54,160 --> 00:21:58,240 Speaker 1: you with us. Professor you're right about the leverage ratchet effect. 364 00:21:58,680 --> 00:22:01,479 Speaker 1: Are we leveraging up a global system as we did 365 00:22:01,560 --> 00:22:05,280 Speaker 1: in two thousand five? In two thousand six. Yeah, I 366 00:22:05,320 --> 00:22:09,760 Speaker 1: think that basically the system continues to be built on 367 00:22:10,000 --> 00:22:12,879 Speaker 1: piles and piles of that, and we you know, it 368 00:22:12,960 --> 00:22:17,160 Speaker 1: all worked wonderfully with leverage on the upside, but there's 369 00:22:17,240 --> 00:22:19,800 Speaker 1: leverage on the upside. Is a character of the leverage 370 00:22:19,800 --> 00:22:22,879 Speaker 1: build up this time? Is it different than what we 371 00:22:22,920 --> 00:22:29,600 Speaker 1: saw twelve years ago? You know, they're they're always variations 372 00:22:29,680 --> 00:22:33,960 Speaker 1: on exactly what it is that that's the underlying assets. 373 00:22:34,040 --> 00:22:38,040 Speaker 1: But fundamentally, you know, that is that you invest in 374 00:22:38,160 --> 00:22:41,360 Speaker 1: various things. You call them, you know, loans or other 375 00:22:41,640 --> 00:22:47,080 Speaker 1: real world, real world economy firms, our small businesses, you know, 376 00:22:47,280 --> 00:22:52,760 Speaker 1: leverage loans. They now call them covenant light loans, you know, 377 00:22:53,000 --> 00:22:59,960 Speaker 1: not subprime mortgages, um where households are as indebted. But yeah, 378 00:23:00,280 --> 00:23:04,840 Speaker 1: that is a huge part of the economy. So, professor, 379 00:23:05,040 --> 00:23:07,399 Speaker 1: just ten years after the financial crisis, wonder if you 380 00:23:07,440 --> 00:23:09,480 Speaker 1: could just give us a sense of or your sense 381 00:23:09,480 --> 00:23:12,719 Speaker 1: of kind of the state of the US financial system. 382 00:23:12,920 --> 00:23:16,280 Speaker 1: Is it safer than it was pre crisis? I don't 383 00:23:16,320 --> 00:23:21,280 Speaker 1: think it's fundamentally safer. I think, uh, it's you know, 384 00:23:21,320 --> 00:23:24,560 Speaker 1: you could imagine where the next big shock will come from. 385 00:23:24,600 --> 00:23:28,600 Speaker 1: Will it come from from you? Know, business or corporations 386 00:23:28,760 --> 00:23:32,199 Speaker 1: default or will it come from I'm kind of scared 387 00:23:32,240 --> 00:23:37,080 Speaker 1: of any kind of cyber problem. Uh, any many hacking 388 00:23:37,320 --> 00:23:41,280 Speaker 1: or some systems crashing and all of a sudden, uh, 389 00:23:41,560 --> 00:23:45,080 Speaker 1: you know, if a fragile system collapses. So I think 390 00:23:45,119 --> 00:23:49,359 Speaker 1: that it's still remains very difficult to see through a 391 00:23:49,520 --> 00:23:54,320 Speaker 1: system that's so connected and so global and so uh. 392 00:23:54,359 --> 00:23:56,080 Speaker 1: There is a lot of debt, and a lot of 393 00:23:56,880 --> 00:23:59,600 Speaker 1: debt that we don't see it off balance sheet commitment, 394 00:24:00,040 --> 00:24:04,160 Speaker 1: things that can sort of trigger all kinds of contagion 395 00:24:04,440 --> 00:24:08,439 Speaker 1: mechanisms that we saw. So I'm not I'm not feeling 396 00:24:08,480 --> 00:24:12,000 Speaker 1: that it's much different. Professor. In your paper, you have 397 00:24:12,040 --> 00:24:17,400 Speaker 1: a wonderful literature review of thinking about our behavior when 398 00:24:17,440 --> 00:24:20,000 Speaker 1: we leverage up and you go back to you know, 399 00:24:20,040 --> 00:24:22,640 Speaker 1: I love the paper from the late eighties Jacob Frankel 400 00:24:23,000 --> 00:24:27,280 Speaker 1: Folks now the chairman of JP Morgan International, Michael Dooley 401 00:24:27,440 --> 00:24:30,800 Speaker 1: legendary and Peter Wickham as well, and I remember that 402 00:24:30,880 --> 00:24:34,960 Speaker 1: paper is being foundational to the fact that we leverage 403 00:24:36,080 --> 00:24:39,480 Speaker 1: and then we're successful, so we feel good, So we 404 00:24:39,600 --> 00:24:43,480 Speaker 1: leverage more and we feel successful, so we feel good, 405 00:24:43,480 --> 00:24:46,120 Speaker 1: which everybody would say, well, that's a normal human condition. 406 00:24:46,760 --> 00:24:50,119 Speaker 1: What's changed now from the time of Frankel, Dooley and 407 00:24:50,160 --> 00:24:54,320 Speaker 1: Wickham not much. Our paper that you're referring to, for 408 00:24:54,359 --> 00:24:57,200 Speaker 1: me was a revelation because for a long time we 409 00:24:57,600 --> 00:25:00,960 Speaker 1: you know, we teach basic corporate finance and we teach 410 00:25:01,000 --> 00:25:03,800 Speaker 1: about that and equity funding, and we have a very 411 00:25:03,960 --> 00:25:07,120 Speaker 1: static way of thinking about it. You know, you sort 412 00:25:07,160 --> 00:25:09,199 Speaker 1: of put in place that and equity and then the 413 00:25:09,240 --> 00:25:11,639 Speaker 1: world ends, at least in the story we tell, and 414 00:25:11,640 --> 00:25:14,639 Speaker 1: we talk about how the risk gets split between that 415 00:25:14,720 --> 00:25:16,840 Speaker 1: and equity and all of that. But if you look 416 00:25:16,880 --> 00:25:20,199 Speaker 1: at it for a living, breathing, you know, firm, what 417 00:25:20,400 --> 00:25:23,600 Speaker 1: ends up happening is that there's a very uh over 418 00:25:23,760 --> 00:25:27,800 Speaker 1: time because you keep making decisions both investments exactly balancy, 419 00:25:28,440 --> 00:25:31,040 Speaker 1: you become sort of addicted to it. And that's what 420 00:25:31,080 --> 00:25:34,199 Speaker 1: we explored. And it's interesting because a while ago, when 421 00:25:34,200 --> 00:25:37,800 Speaker 1: I got into banking, someone us talking about why about 422 00:25:37,880 --> 00:25:41,480 Speaker 1: leveraging exactly what you're saying that if you gamble with 423 00:25:41,600 --> 00:25:44,840 Speaker 1: board money and you succeed, then you think leverage is 424 00:25:44,840 --> 00:25:47,680 Speaker 1: wonderful and that you're smart. Yeah, you're jenous. So now 425 00:25:47,680 --> 00:25:50,600 Speaker 1: thank you so much, and automounting with us from Stanford, 426 00:25:50,600 --> 00:26:06,520 Speaker 1: the leverage, ratchet effect. Me A Feynman joins us. Now 427 00:26:06,920 --> 00:26:11,160 Speaker 1: the curator in photography at the museum, with an ample 428 00:26:11,280 --> 00:26:15,600 Speaker 1: history of massaging black and white film, and she joins us, Uh, 429 00:26:15,640 --> 00:26:19,040 Speaker 1: now me a congratulations on your show. Have you been 430 00:26:19,080 --> 00:26:21,680 Speaker 1: stunned by the turnout? I visited a couple of days 431 00:26:21,680 --> 00:26:26,440 Speaker 1: ago and it was packed. Is it had a genuine interest? Oh? 432 00:26:26,560 --> 00:26:30,560 Speaker 1: Hi Tom, Uh Yeah, it's been a very popular show. Uh, 433 00:26:30,840 --> 00:26:33,760 Speaker 1: but I did expect that. I mean, everybody likes the moon, 434 00:26:34,320 --> 00:26:37,280 Speaker 1: you know, it's and and with the anniversary, there's just 435 00:26:37,320 --> 00:26:41,680 Speaker 1: so much going on around Apollo Leap and thinking about IP. 436 00:26:42,200 --> 00:26:44,879 Speaker 1: I love the imagery and then you slam it at 437 00:26:44,880 --> 00:26:48,479 Speaker 1: the end with the TV set, the old antique nine 438 00:26:48,920 --> 00:26:51,679 Speaker 1: TV set of Walter Cronkite. Why did you do that? 439 00:26:51,720 --> 00:26:54,960 Speaker 1: Why did you go from the still photography all the 440 00:26:54,960 --> 00:26:58,199 Speaker 1: way through the imagery, the paintings and then right at 441 00:26:58,240 --> 00:27:02,760 Speaker 1: the end, boom, there we were in ninety nine. Um, well, 442 00:27:03,760 --> 00:27:05,720 Speaker 1: we thought it would be. We had to show the 443 00:27:05,840 --> 00:27:08,840 Speaker 1: video in some way because it really was the first 444 00:27:09,000 --> 00:27:13,640 Speaker 1: worldwide media events that everybody tuned into and that's how 445 00:27:13,680 --> 00:27:17,119 Speaker 1: most people experienced this moment. And so we felt that 446 00:27:17,200 --> 00:27:20,439 Speaker 1: putting it on a vintage television set and letting people 447 00:27:20,760 --> 00:27:24,879 Speaker 1: watch the CBS news footage, uh and see Neil Armstrong's 448 00:27:24,880 --> 00:27:27,479 Speaker 1: first steps on the moon was a way of bringing 449 00:27:27,520 --> 00:27:31,000 Speaker 1: that experience to life in a visceral way. There are 450 00:27:31,040 --> 00:27:35,439 Speaker 1: imageries within all of our past about science, and of 451 00:27:35,520 --> 00:27:38,120 Speaker 1: course the huge up or now of some would say 452 00:27:38,160 --> 00:27:40,439 Speaker 1: the death of science. We think New Jersey Institute of 453 00:27:40,480 --> 00:27:44,720 Speaker 1: Technology for their commitment on Bloomberg surveillance to science. But 454 00:27:44,800 --> 00:27:47,160 Speaker 1: you know, I think of Peter Coyote and the keys 455 00:27:48,000 --> 00:27:51,280 Speaker 1: clinking and et and they're just these these images that 456 00:27:51,320 --> 00:27:54,320 Speaker 1: we have, and so much of that comes from Tom Hanks, 457 00:27:54,320 --> 00:27:57,840 Speaker 1: the actor. He wrote the introduction to your book. Explain 458 00:27:57,920 --> 00:28:01,440 Speaker 1: how Tom Hanks looks at your Moon show in this 459 00:28:01,600 --> 00:28:07,399 Speaker 1: moment of Apollo history. Well, Tom Hanks's introduction is really 460 00:28:07,440 --> 00:28:11,360 Speaker 1: beautiful and poetic, and he sort of steps back and, uh, 461 00:28:11,359 --> 00:28:14,000 Speaker 1: you know, looked at the big picture of you know 462 00:28:14,040 --> 00:28:18,320 Speaker 1: what the mystery of the moon and how this mysterious 463 00:28:18,520 --> 00:28:22,399 Speaker 1: shining orb in the sky has always fascinated human being 464 00:28:22,800 --> 00:28:27,320 Speaker 1: from the very beginning of time. Um and looks at 465 00:28:27,359 --> 00:28:30,120 Speaker 1: the different ways you know, sort of talks thinks about 466 00:28:30,119 --> 00:28:33,600 Speaker 1: the different ways that people have interpreted the Moon, you know, 467 00:28:33,640 --> 00:28:36,600 Speaker 1: as a goddess um as you know, and and you know, 468 00:28:36,720 --> 00:28:39,760 Speaker 1: up into the space age where we actually were able 469 00:28:39,800 --> 00:28:43,240 Speaker 1: to um get human beings there to another planet in 470 00:28:43,560 --> 00:28:46,640 Speaker 1: time and putting the chronology together, what was the biggest 471 00:28:46,680 --> 00:28:49,800 Speaker 1: surprise for you, I mean your expert in you know, 472 00:28:49,880 --> 00:28:53,520 Speaker 1: the derogatypes and the photography and the film and all 473 00:28:53,560 --> 00:28:55,760 Speaker 1: that in the art and the other exhibit as well, 474 00:28:55,760 --> 00:29:01,160 Speaker 1: But what was the biggest surprise for you in that chronology? Um? Well, 475 00:29:01,200 --> 00:29:03,600 Speaker 1: I had never really thought about the far side of 476 00:29:03,640 --> 00:29:06,360 Speaker 1: the moon, the decide that we never see and how 477 00:29:06,400 --> 00:29:10,360 Speaker 1: that is always been the ultimate mystery, something that human 478 00:29:10,400 --> 00:29:14,360 Speaker 1: eyes had never seen until nineteen nine when the Soviets 479 00:29:14,440 --> 00:29:18,600 Speaker 1: sent an orbiter around the Moon with the camera inside 480 00:29:18,600 --> 00:29:21,280 Speaker 1: of it, and then they sent some pictures back. And 481 00:29:21,680 --> 00:29:26,440 Speaker 1: this photograph that's in the exhibition is the first time anyone, 482 00:29:26,680 --> 00:29:29,160 Speaker 1: any human being has ever seen this. And that that 483 00:29:29,200 --> 00:29:32,440 Speaker 1: I found kind of moving and and and you know 484 00:29:32,440 --> 00:29:34,480 Speaker 1: a little thrilling, you know. So that was that was 485 00:29:34,520 --> 00:29:36,600 Speaker 1: a surprise for me. If you're joining us on this 486 00:29:36,720 --> 00:29:38,920 Speaker 1: day of a pottle eleven Mia Fineman with us with 487 00:29:39,000 --> 00:29:43,040 Speaker 1: a spectacular show at the Metropolitan Museum of Art of 488 00:29:43,080 --> 00:29:46,320 Speaker 1: the imagery of the Moon and goes back to Friederich 489 00:29:46,360 --> 00:29:48,720 Speaker 1: painting that we all grew up with. Yeah, everything, But 490 00:29:48,840 --> 00:29:50,480 Speaker 1: did you do you have a copy of good Night 491 00:29:50,520 --> 00:29:53,320 Speaker 1: Moon in there, the children's book that everybody grew up. 492 00:29:54,120 --> 00:29:56,800 Speaker 1: We've got that in the gift shop. The gift shop. 493 00:29:57,720 --> 00:30:00,200 Speaker 1: You couldn't get a first edition a good Night to 494 00:30:00,720 --> 00:30:04,520 Speaker 1: show the literature of it. There's so much popular culture 495 00:30:04,600 --> 00:30:06,920 Speaker 1: around the Moon, um. And you know, if we if 496 00:30:06,920 --> 00:30:09,800 Speaker 1: we had, like you know, another few galleries could have 497 00:30:09,880 --> 00:30:12,560 Speaker 1: gone down that road. But we just we had to 498 00:30:12,640 --> 00:30:15,240 Speaker 1: make a lot of hard decisions about what to include 499 00:30:15,240 --> 00:30:18,200 Speaker 1: and what not in the in the exhibition itself, and 500 00:30:18,280 --> 00:30:20,960 Speaker 1: some very good decisions. Well, Mia, Fireman, thank you so 501 00:30:21,080 --> 00:30:24,440 Speaker 1: much for joining us an incredibly busy week, a successful 502 00:30:24,480 --> 00:30:30,120 Speaker 1: show at the Metropolitan muse uh Museum of our Apollos 503 00:30:30,200 --> 00:30:32,800 Speaker 1: mus The Moon in the age of photography really can't 504 00:30:32,840 --> 00:30:36,280 Speaker 1: say enough about it as well right now with my 505 00:30:36,440 --> 00:30:39,960 Speaker 1: past in his past, Robert Moon shows up. Of course 506 00:30:40,000 --> 00:30:43,280 Speaker 1: he has provided huge leadership for surveillance on the STEM 507 00:30:43,360 --> 00:30:46,000 Speaker 1: Report every morning and the science of it. And you 508 00:30:46,080 --> 00:30:49,640 Speaker 1: and I go way back on this to the absolute 509 00:30:49,720 --> 00:30:52,800 Speaker 1: sweat Bob Moon and it's seen in Mia Fireman Show. 510 00:30:53,600 --> 00:30:57,800 Speaker 1: We didn't know what we were landing on, did we know? 511 00:30:58,280 --> 00:31:01,960 Speaker 1: Not until we finally clue. We finally put some sort 512 00:31:02,000 --> 00:31:06,040 Speaker 1: of spacecraft up there to take high resolution pictures. And 513 00:31:06,200 --> 00:31:08,440 Speaker 1: you know, you and I were kids. We wore kids once. 514 00:31:09,040 --> 00:31:11,760 Speaker 1: It's hard to believe. I bow tie on the boats 515 00:31:13,160 --> 00:31:15,600 Speaker 1: and uh. And when I was a kid, my mother 516 00:31:15,720 --> 00:31:20,720 Speaker 1: worked for Hughes Aircraft and they built the Surveyor lander 517 00:31:21,320 --> 00:31:23,400 Speaker 1: and uh. And I remember all the while she was 518 00:31:23,440 --> 00:31:27,040 Speaker 1: doing the wiring, she was doing the harnesses that held 519 00:31:27,080 --> 00:31:29,240 Speaker 1: all the wires that ran the cameras and and that 520 00:31:29,320 --> 00:31:32,360 Speaker 1: sort of thing. I remember her telling me what she 521 00:31:32,480 --> 00:31:35,680 Speaker 1: was doing and not quite really understanding the significance of 522 00:31:35,720 --> 00:31:38,080 Speaker 1: it and what's great about it. Whether it's surveyor, which 523 00:31:38,120 --> 00:31:41,240 Speaker 1: my father was hugely excited about, he did not it 524 00:31:41,320 --> 00:31:43,840 Speaker 1: was an ranger, which was as a kid, was so 525 00:31:43,920 --> 00:31:46,400 Speaker 1: exciting because they're going to fly it right into the 526 00:31:46,400 --> 00:31:49,960 Speaker 1: moon and they'll be that last photo before and this, folks, 527 00:31:50,000 --> 00:31:52,640 Speaker 1: this is way before nine. It's like five six, seven 528 00:31:52,680 --> 00:31:58,400 Speaker 1: years before is it was all bolts and steel. You know, 529 00:31:58,520 --> 00:32:01,920 Speaker 1: you think about it, directors, how did we, in that 530 00:32:02,120 --> 00:32:04,600 Speaker 1: very short amount of time get to the Moon. And 531 00:32:04,720 --> 00:32:08,640 Speaker 1: I think about all these stories, like my mom's story, 532 00:32:08,840 --> 00:32:13,239 Speaker 1: of all these people all across the country having a 533 00:32:13,400 --> 00:32:16,480 Speaker 1: role in getting us there. I mean, she had a 534 00:32:16,520 --> 00:32:20,200 Speaker 1: personal role in finding out if we could even land 535 00:32:20,320 --> 00:32:22,560 Speaker 1: on the surface of the Moon by helping to build 536 00:32:22,600 --> 00:32:25,840 Speaker 1: that spacecraft. People built the rocket engines, people built the 537 00:32:27,360 --> 00:32:30,440 Speaker 1: lander that Neil Armstrong and Buzz Aldrin used. That was 538 00:32:30,480 --> 00:32:34,200 Speaker 1: a Grumming product, and and all of those different things. 539 00:32:35,080 --> 00:32:37,560 Speaker 1: Indulge me with just a quick bit of family folk clues. 540 00:32:37,640 --> 00:32:40,040 Speaker 1: When I was growing up, I came home from school 541 00:32:40,120 --> 00:32:42,400 Speaker 1: one day a little kid, and there was a Manila 542 00:32:42,480 --> 00:32:45,080 Speaker 1: envelope sitting on the living room table, and I asked 543 00:32:45,080 --> 00:32:47,880 Speaker 1: my mom, what's that? And she showed it to me. 544 00:32:47,960 --> 00:32:51,000 Speaker 1: She pulled out a picture of herself. It was a 545 00:32:51,040 --> 00:32:57,000 Speaker 1: glossy picture of her in a one piece piece bathing suit, right, 546 00:32:57,480 --> 00:33:00,640 Speaker 1: And I what's that? She said, Well, they needed to 547 00:33:00,680 --> 00:33:04,200 Speaker 1: test the cameras for the surveyor, and so they took 548 00:33:04,240 --> 00:33:07,560 Speaker 1: this picture to the resolution studies and that sort of thing. 549 00:33:07,960 --> 00:33:12,040 Speaker 1: And I didn't know whether that meant that her picture 550 00:33:12,160 --> 00:33:13,840 Speaker 1: was going to be on the Moon. To this day, 551 00:33:14,320 --> 00:33:17,040 Speaker 1: I think that I looked at the schematic for the surveyor. 552 00:33:17,560 --> 00:33:19,720 Speaker 1: There's a little little part of the surveyor that says 553 00:33:19,840 --> 00:33:23,400 Speaker 1: focusing target, and I wonder if my mom is up 554 00:33:23,400 --> 00:33:27,960 Speaker 1: there there's a focusing target. I got a million anecdotes 555 00:33:27,960 --> 00:33:30,320 Speaker 1: of this, folks, and I'll just give you one. When 556 00:33:30,360 --> 00:33:34,000 Speaker 1: I was fifteen, I couldn't go to Germany. There was 557 00:33:34,040 --> 00:33:37,760 Speaker 1: a stamp in my passport that I was not allowed 558 00:33:37,800 --> 00:33:40,840 Speaker 1: to go to Germany. That's how tense it was. It 559 00:33:40,880 --> 00:33:44,320 Speaker 1: was really as you know it, Hughes. My father was 560 00:33:44,360 --> 00:33:46,800 Speaker 1: with the Eastman Kodak company up in the land of 561 00:33:46,880 --> 00:33:50,760 Speaker 1: genesc and uh, it was, you know, within all the 562 00:33:50,760 --> 00:33:54,560 Speaker 1: remembrance of it, it was really serious time, wasn't it was? 563 00:33:54,600 --> 00:33:56,920 Speaker 1: It wasn't We were in a race. We were in 564 00:33:56,920 --> 00:33:59,240 Speaker 1: a race to the moon, and it was serious. Thank 565 00:33:59,280 --> 00:34:04,040 Speaker 1: you so much for those remembrances. Thanks for listening to 566 00:34:04,080 --> 00:34:08,600 Speaker 1: the Bloomberg Surveillance podcast. Subscribe and listen to interviews on 567 00:34:08,680 --> 00:34:14,520 Speaker 1: Apple Podcasts, SoundCloud, or whichever podcast platform you prefer. I'm 568 00:34:14,560 --> 00:34:17,839 Speaker 1: on Twitter at Tom Keane before the podcast. You can 569 00:34:17,880 --> 00:34:21,080 Speaker 1: always catch us worldwide. I'm Bloomberg Radio