1 00:00:09,840 --> 00:00:13,840 Speaker 1: Welcome to the Bloomberg Surveillance Podcast. I'm term Keene Jay Leye. 2 00:00:13,960 --> 00:00:17,560 Speaker 1: 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 --> 00:00:30,159 Speaker 1: Bloomberg dot Com, and of course on the Bloomberg. In 5 00:00:30,160 --> 00:00:33,159 Speaker 1: the bond market, we shape up as follows treasuries tens 6 00:00:33,520 --> 00:00:36,599 Speaker 1: yields unchanged two point six three two on a two 7 00:00:36,680 --> 00:00:40,080 Speaker 1: year note two point four six three percent. That's where 8 00:00:40,120 --> 00:00:42,400 Speaker 1: I want to start this morning, at the front end 9 00:00:42,479 --> 00:00:44,080 Speaker 1: of the yield curve, and we have a great gust 10 00:00:44,120 --> 00:00:46,760 Speaker 1: to do it with Jerum Schneider, PIMCO, Head of short 11 00:00:46,920 --> 00:00:50,320 Speaker 1: term bond Portfolios. Good morning to Jerome, Good morning, Good morning. 12 00:00:50,400 --> 00:00:52,519 Speaker 1: Let's just start with two lines that I picked up 13 00:00:52,560 --> 00:00:54,880 Speaker 1: from the notes I saw in your most recent research. 14 00:00:54,960 --> 00:00:58,200 Speaker 1: Short term bond strategy to manage two things, potential defense 15 00:00:58,240 --> 00:01:01,760 Speaker 1: in volatile markets and the optionality to move to higher 16 00:01:01,800 --> 00:01:05,600 Speaker 1: risk allocations opportunistically. Let's just walk through those two issues 17 00:01:05,680 --> 00:01:07,800 Speaker 1: right now. Why do I need the potential defense in 18 00:01:07,880 --> 00:01:10,839 Speaker 1: volatile markets with VOLTSI love so. The question that remains 19 00:01:10,840 --> 00:01:13,280 Speaker 1: for investors is how long is this economic cycle going 20 00:01:13,280 --> 00:01:16,440 Speaker 1: to persist for and admittedly the financial conditions have eased 21 00:01:16,480 --> 00:01:18,320 Speaker 1: quite a bit since the concerns of the fourth quarter, 22 00:01:18,400 --> 00:01:22,080 Speaker 1: but monetary policy is only one ingredient to to the 23 00:01:22,120 --> 00:01:26,520 Speaker 1: market conditions that helped perpetuate positive returns going forward. Ultimately, 24 00:01:26,560 --> 00:01:29,800 Speaker 1: what we're gonna be dealing with is continued bouts of illiquidity, 25 00:01:29,800 --> 00:01:34,319 Speaker 1: continued increases of volatility within the marketplace. And what's really 26 00:01:34,360 --> 00:01:37,800 Speaker 1: happened is investors, whether your retail or investors or institutional 27 00:01:38,120 --> 00:01:41,679 Speaker 1: have to be thinking about returns or expected returns from 28 00:01:41,680 --> 00:01:44,400 Speaker 1: the construct of not only thinking about, hey, I could 29 00:01:44,400 --> 00:01:47,840 Speaker 1: earn five seven percent or even more obviously historically in 30 00:01:47,840 --> 00:01:51,600 Speaker 1: equities or emerging markets those higher higher beta allocations, but 31 00:01:51,760 --> 00:01:55,240 Speaker 1: doing so with higher volatility expectations. In other words, we 32 00:01:55,320 --> 00:01:58,200 Speaker 1: need to calibrate expectations for returns on a risk adjusted 33 00:01:58,200 --> 00:02:02,040 Speaker 1: basis taking into account more volatility due to the uncertainty 34 00:02:02,080 --> 00:02:04,520 Speaker 1: within the marketplace. So at the moment, volatility is really 35 00:02:04,560 --> 00:02:08,160 Speaker 1: loud cross asset exactly what binds effects, et cetera. But 36 00:02:08,200 --> 00:02:10,440 Speaker 1: think about how quick that healing process happened, and think 37 00:02:10,480 --> 00:02:13,120 Speaker 1: how quickly how many bouts of illiquity we've had simply 38 00:02:13,120 --> 00:02:16,400 Speaker 1: over the past three years and illiquidity. I'm using as 39 00:02:16,440 --> 00:02:19,240 Speaker 1: a proxy here for volatility. But the volatility could emanate 40 00:02:19,280 --> 00:02:22,000 Speaker 1: from politics. It could it could emanate from left tail 41 00:02:22,080 --> 00:02:25,120 Speaker 1: risks such as China trade. And while these risks have 42 00:02:25,240 --> 00:02:28,400 Speaker 1: actually been elongated, the runways of these risks, whether from 43 00:02:28,440 --> 00:02:33,000 Speaker 1: monetary policy, trade policy, or even Brexit, they've elongated. The 44 00:02:33,040 --> 00:02:36,640 Speaker 1: uncertainty could actually persist for quite some time over the 45 00:02:36,760 --> 00:02:38,920 Speaker 1: over the next few years. So while we have a 46 00:02:39,000 --> 00:02:42,240 Speaker 1: relative period of warmth as as spring approaches, don't be 47 00:02:42,480 --> 00:02:44,040 Speaker 1: don't be fooled, and we should be prepared for that 48 00:02:44,120 --> 00:02:46,720 Speaker 1: higher volatility. I just figured this out. Are you here 49 00:02:46,720 --> 00:02:48,200 Speaker 1: to do a double? Is he gonna be on the 50 00:02:48,240 --> 00:02:52,360 Speaker 1: real yield today? One television at least? But you're just 51 00:02:53,000 --> 00:02:55,480 Speaker 1: this is your power, This is the power of feraoh 52 00:02:55,800 --> 00:02:59,360 Speaker 1: booking the brightest guy in the world. Short term paper. Yeah, 53 00:03:00,280 --> 00:03:02,800 Speaker 1: what the commercial paper used to be the thing we 54 00:03:02,800 --> 00:03:06,520 Speaker 1: looked at the pulse. What's the new commercial paper? Guys 55 00:03:06,520 --> 00:03:08,760 Speaker 1: like you look at every day. I'll tell your story, Tom, 56 00:03:08,800 --> 00:03:10,760 Speaker 1: it's really interesting. So two years ago, when I was 57 00:03:10,760 --> 00:03:13,240 Speaker 1: sitting in these seats, we're talking about the evolution of 58 00:03:13,280 --> 00:03:15,840 Speaker 1: money market funds. Money market fund reform and putting people 59 00:03:15,880 --> 00:03:17,600 Speaker 1: to sleep, telling him that the end of the money 60 00:03:17,600 --> 00:03:20,120 Speaker 1: market fund world was coming soon, and we saw a 61 00:03:20,160 --> 00:03:22,760 Speaker 1: trillion dollars worth of prime money market fund assets. Those 62 00:03:22,800 --> 00:03:25,799 Speaker 1: that buy commercial paper go and buy government only money 63 00:03:25,840 --> 00:03:28,400 Speaker 1: organt funds, those buy T bills de risking. So the 64 00:03:28,440 --> 00:03:30,920 Speaker 1: story now has actually changed over the past few months. 65 00:03:31,120 --> 00:03:33,200 Speaker 1: People have been lulled to sleep that they want to 66 00:03:33,280 --> 00:03:35,880 Speaker 1: de risk. They're de risking out of these higher volatility 67 00:03:36,160 --> 00:03:39,560 Speaker 1: classes equities, and they're putting their money, ironically back into 68 00:03:39,560 --> 00:03:42,320 Speaker 1: prime money market funds. There's been a hundred billion dollars 69 00:03:42,320 --> 00:03:44,760 Speaker 1: of new assets put into prime money market funds for 70 00:03:44,800 --> 00:03:47,760 Speaker 1: an incremental return of just a few basis points over 71 00:03:47,800 --> 00:03:50,320 Speaker 1: the safety of T bills. So why would you do that? 72 00:03:50,440 --> 00:03:53,160 Speaker 1: And I think ultimately the world of commercial paper has 73 00:03:53,240 --> 00:03:56,120 Speaker 1: once again become rather complacent to the to the notion 74 00:03:56,240 --> 00:03:58,200 Speaker 1: of any type of credit risk, and we have to 75 00:03:58,240 --> 00:04:01,680 Speaker 1: be more discerning in terms of credit risk. Clearly, you 76 00:04:01,760 --> 00:04:06,160 Speaker 1: can steal that for the real appreciating seeds. So let's 77 00:04:06,200 --> 00:04:08,440 Speaker 1: go to the next line to to have the optionality 78 00:04:08,640 --> 00:04:13,200 Speaker 1: to move to higher risk allocations opportunistically. Are you looking 79 00:04:13,240 --> 00:04:16,040 Speaker 1: for another December kind of moment, not an end of 80 00:04:16,120 --> 00:04:20,200 Speaker 1: cycle moment, but a moment where that risk aversion builds, 81 00:04:20,680 --> 00:04:23,719 Speaker 1: the faith in the cycle fades rapidly, and you get 82 00:04:23,760 --> 00:04:26,360 Speaker 1: to deploy some capital into risk assets. So the starting 83 00:04:26,360 --> 00:04:30,320 Speaker 1: point of analysis and portfolio management is diversification, but more importantly, 84 00:04:30,360 --> 00:04:33,880 Speaker 1: managing downside risks. And we've really viewsed that managing downside 85 00:04:33,920 --> 00:04:36,800 Speaker 1: risk is beginning in two thousand eighteen at Pimcoe, where 86 00:04:36,839 --> 00:04:39,960 Speaker 1: we were really thinking about focusing on corporate credit and 87 00:04:40,000 --> 00:04:44,240 Speaker 1: differentiating corporate credit from the data the index, picking specific 88 00:04:44,279 --> 00:04:46,679 Speaker 1: stories that we liked, under accentuating those that we didn't, 89 00:04:46,800 --> 00:04:49,240 Speaker 1: and diversizing. Now, granted, this was a process that began 90 00:04:49,279 --> 00:04:52,159 Speaker 1: in late two thousand seventeen early two eighteen, and we 91 00:04:52,160 --> 00:04:54,880 Speaker 1: didn't know when when actually the opportunity would would persist, 92 00:04:54,880 --> 00:04:56,520 Speaker 1: but we thought, you know, there would be some some 93 00:04:56,560 --> 00:04:59,400 Speaker 1: tail risks along the way, maybe some rude awakenings, but 94 00:04:59,560 --> 00:05:01,839 Speaker 1: ultimately the growth, the slowing growth that we were seeing 95 00:05:01,880 --> 00:05:04,120 Speaker 1: in the globe economy would translate into opportunity. When it 96 00:05:04,200 --> 00:05:06,800 Speaker 1: happened in the fourth quarter, we had access liquidity. We 97 00:05:06,880 --> 00:05:10,039 Speaker 1: saw some inclinations that liquidity was being dampened back in 98 00:05:10,080 --> 00:05:12,840 Speaker 1: October off of our short term team at PIMCO, and 99 00:05:12,839 --> 00:05:15,160 Speaker 1: that there was. As a result, we became more cautious 100 00:05:15,160 --> 00:05:19,440 Speaker 1: from a liquidity standpoint when those opportunities pent became opportunistic. 101 00:05:19,640 --> 00:05:23,560 Speaker 1: We found that the recalibration in in in UH spreads 102 00:05:23,600 --> 00:05:26,840 Speaker 1: wider actually made things pretty good on a risk adjusta basis. 103 00:05:26,920 --> 00:05:28,960 Speaker 1: To take some of our underweights back to market weight. 104 00:05:29,120 --> 00:05:31,320 Speaker 1: In other words, some of the richness in the market 105 00:05:31,760 --> 00:05:34,280 Speaker 1: was not really attractive. We sold it, and then as 106 00:05:34,279 --> 00:05:37,400 Speaker 1: an active manager, we found an opportunity to actually redeploy 107 00:05:37,520 --> 00:05:40,520 Speaker 1: capital when it became cheap, and more importantly, earned a 108 00:05:40,560 --> 00:05:44,200 Speaker 1: liquidity premium or excess return by providing the market liquidity 109 00:05:44,279 --> 00:05:47,159 Speaker 1: when traditional equity providers like banks, etcetera. Weren't there. So 110 00:05:47,240 --> 00:05:49,560 Speaker 1: you've played it really well the last few months. You 111 00:05:49,640 --> 00:05:52,520 Speaker 1: bought the weakness of December. I just wanted to what 112 00:05:52,600 --> 00:05:56,400 Speaker 1: degree you're fighting the strength of the first quarter. So 113 00:05:56,800 --> 00:05:59,719 Speaker 1: we've seen credits move in dramatic fashion. Some credits that 114 00:05:59,720 --> 00:06:02,520 Speaker 1: were used at Libor plus forty six to nine months 115 00:06:02,560 --> 00:06:04,760 Speaker 1: ago and blew out to Lybra plus three hundred or 116 00:06:04,760 --> 00:06:07,360 Speaker 1: more are back to Libra plus fifty. So there's been 117 00:06:07,360 --> 00:06:09,520 Speaker 1: almost a round trip. So you're right. This is a 118 00:06:09,680 --> 00:06:11,440 Speaker 1: this is a market where you want to be trading 119 00:06:11,960 --> 00:06:14,760 Speaker 1: simply focusing on credit work from the bottom up, and 120 00:06:14,760 --> 00:06:16,520 Speaker 1: and that's what we're doing on our corporate credit side. 121 00:06:16,560 --> 00:06:19,159 Speaker 1: So there is a there's basically a momentum now to 122 00:06:19,200 --> 00:06:22,080 Speaker 1: take a breath. Let's find some opportunities to potentially be 123 00:06:22,160 --> 00:06:24,400 Speaker 1: more defensive. At this point in time, in two thousand 124 00:06:24,360 --> 00:06:27,560 Speaker 1: and six, people were screaming for seven basis points, ten 125 00:06:27,600 --> 00:06:30,400 Speaker 1: basis points. Are we back to that silliness? No, where 126 00:06:30,680 --> 00:06:33,800 Speaker 1: people are just trying to find the next ten basis points. 127 00:06:33,800 --> 00:06:36,360 Speaker 1: It's hundreds of a percentage point. It's more fundamental now. 128 00:06:36,520 --> 00:06:39,200 Speaker 1: So that was more structured, the leverage in the system. 129 00:06:39,200 --> 00:06:42,000 Speaker 1: With structured now it's more fun fundamental, whereby we have 130 00:06:42,040 --> 00:06:44,320 Speaker 1: to differgiate, you know, the you know, the cream from 131 00:06:44,200 --> 00:06:46,000 Speaker 1: the from the rest of from the rest of the product. 132 00:06:46,040 --> 00:06:48,760 Speaker 1: And as a result, that just takes homework, it takes resources. 133 00:06:48,800 --> 00:06:52,480 Speaker 1: And as a result, having diversification, finding other opportunities to 134 00:06:52,520 --> 00:06:55,080 Speaker 1: earn spread an income as we're doing, and mortgages some 135 00:06:55,160 --> 00:06:58,560 Speaker 1: high quality ASSETBAC securities that actually offers your plenty of 136 00:06:58,680 --> 00:07:01,400 Speaker 1: plenty of avenues. More importantly, I think this is The 137 00:07:01,400 --> 00:07:03,960 Speaker 1: thing time is there's no real reason to reach for 138 00:07:04,040 --> 00:07:06,640 Speaker 1: yield when you have portfolios they can yield between three 139 00:07:06,680 --> 00:07:08,919 Speaker 1: and three and a half percent. Those are actually pretty 140 00:07:08,960 --> 00:07:11,720 Speaker 1: attractive in a world where dividends are less than two percent, 141 00:07:11,960 --> 00:07:14,320 Speaker 1: So why take the why take equity risk of this 142 00:07:15,240 --> 00:07:35,040 Speaker 1: than you Pimco as well? What a joy for John 143 00:07:35,080 --> 00:07:39,200 Speaker 1: Farrell and me always have Brian Weezer in our studios 144 00:07:39,200 --> 00:07:41,840 Speaker 1: with pivotal research and of course brilliant work on the 145 00:07:41,880 --> 00:07:45,559 Speaker 1: linkage of advertising into social We learned a few weeks 146 00:07:45,560 --> 00:07:48,840 Speaker 1: ago that he had gone to the dark side and 147 00:07:49,000 --> 00:07:52,040 Speaker 1: returned to the death start the sound side of group 148 00:07:52,080 --> 00:07:55,360 Speaker 1: EM where he is helping them not so much with 149 00:07:55,520 --> 00:07:59,560 Speaker 1: messaging but just thinking about what's out there. And the 150 00:07:59,600 --> 00:08:03,680 Speaker 1: first we've got to talk about is absolute brilliance. Is 151 00:08:03,800 --> 00:08:07,640 Speaker 1: there's not one office dog a group M is there? 152 00:08:07,640 --> 00:08:10,640 Speaker 1: How many are there? Well? It? I work out of 153 00:08:10,640 --> 00:08:14,040 Speaker 1: the Portland, Oregon office of one of our agency's mind 154 00:08:14,080 --> 00:08:18,240 Speaker 1: Share and we have three dogs. You have a three 155 00:08:18,280 --> 00:08:23,200 Speaker 1: dog night. We need a dog in the office. And 156 00:08:23,200 --> 00:08:25,480 Speaker 1: and to be clear, the one of the dogs wears 157 00:08:25,520 --> 00:08:28,360 Speaker 1: a bow tie every day. Tell me a code him 158 00:08:28,360 --> 00:08:32,360 Speaker 1: Tom No, his name is Cooper. I think Cooper, that's cute, 159 00:08:32,920 --> 00:08:35,800 Speaker 1: but it's a wonderful environment. It really is. What told 160 00:08:35,800 --> 00:08:37,360 Speaker 1: you about how different it is. You used to cover 161 00:08:37,440 --> 00:08:40,040 Speaker 1: these companies on the South Side, thinking about a sad 162 00:08:40,120 --> 00:08:42,800 Speaker 1: on w p P. Once upon a time I had cells, 163 00:08:42,840 --> 00:08:46,920 Speaker 1: I had buys. My position on the durability the agency 164 00:08:46,920 --> 00:08:49,760 Speaker 1: model never changed, I'd argue, even when I had cell 165 00:08:49,840 --> 00:08:53,080 Speaker 1: ratings from every agency holding company, I argued I was 166 00:08:53,120 --> 00:08:56,240 Speaker 1: probably the greatest long term champion in the sense that 167 00:08:56,280 --> 00:08:59,000 Speaker 1: I believed in the durability in a way that I 168 00:08:59,040 --> 00:09:02,599 Speaker 1: think very few people, uh in equity markets. Yeah, I 169 00:09:03,240 --> 00:09:06,640 Speaker 1: heard that. Yeah, absolutely. What's the state of advertising right now? 170 00:09:06,679 --> 00:09:09,400 Speaker 1: I mean it is Facebook, Google, Facebook, Google, Facebook Google. 171 00:09:09,640 --> 00:09:11,560 Speaker 1: Do you go in and try to change that with 172 00:09:11,640 --> 00:09:14,880 Speaker 1: a new w PP after sir Martin or do you 173 00:09:15,000 --> 00:09:17,400 Speaker 1: live with Facebook? No? Well, I mean here's the thing, 174 00:09:17,679 --> 00:09:21,400 Speaker 1: And I think when I joined I worked for another 175 00:09:21,440 --> 00:09:24,200 Speaker 1: holding company in two thousand three ten, and when I 176 00:09:24,200 --> 00:09:26,920 Speaker 1: showed up, they're also coming from all street. The weird 177 00:09:27,000 --> 00:09:29,679 Speaker 1: thing was how everyone's complaining about how dependent they were 178 00:09:29,720 --> 00:09:34,480 Speaker 1: on television, how bad it was alternatives. Well, they have 179 00:09:34,520 --> 00:09:36,960 Speaker 1: alternaate exactly. And but the point is that even then, 180 00:09:37,240 --> 00:09:40,240 Speaker 1: every advertiser needed to think what was our alternative. In cinema, 181 00:09:40,280 --> 00:09:43,200 Speaker 1: for example, was something that was held out as a hope, right, 182 00:09:43,240 --> 00:09:45,680 Speaker 1: the idea that you could put it put money into 183 00:09:45,720 --> 00:09:48,000 Speaker 1: on screen advertising in different minds, and then of course 184 00:09:48,000 --> 00:09:51,599 Speaker 1: the Internet was considered an alternative. There will be alternatives, 185 00:09:51,640 --> 00:09:54,480 Speaker 1: and every advertiser needs to think of the best alternative 186 00:09:54,480 --> 00:09:57,040 Speaker 1: negotiating agreement. You have been more than anyone I know, 187 00:09:57,480 --> 00:10:00,720 Speaker 1: strongest on you know what TV will survive, etcetera. Now 188 00:10:00,720 --> 00:10:04,160 Speaker 1: there's certain networks I'll lead with NBC Universal who are 189 00:10:04,160 --> 00:10:08,000 Speaker 1: saying we need fewer ads in prime time or on YouTube. 190 00:10:08,000 --> 00:10:10,280 Speaker 1: They have those silly ads where you hit skip ad 191 00:10:10,360 --> 00:10:13,320 Speaker 1: to get through them. That's a guy like you're it's 192 00:10:13,360 --> 00:10:16,680 Speaker 1: your fault we're seeing those ads. What is the new Brian? 193 00:10:16,760 --> 00:10:20,640 Speaker 1: We'ser attention span? Well for seconds? To be clear, I 194 00:10:20,679 --> 00:10:24,079 Speaker 1: think that clutter is a significant problem, and I think 195 00:10:24,120 --> 00:10:26,959 Speaker 1: that individuals. It doesn't take thought leadership to know that 196 00:10:27,080 --> 00:10:28,960 Speaker 1: John and I are living in it it. I know, but 197 00:10:29,000 --> 00:10:32,480 Speaker 1: I think every advertiser has to think differently about whether 198 00:10:32,559 --> 00:10:34,360 Speaker 1: or not they should be allocating as much money to 199 00:10:35,040 --> 00:10:37,920 Speaker 1: UH spots and dots for lack of a better do 200 00:10:38,040 --> 00:10:41,440 Speaker 1: you guys know how much we hate that garbage cluttering 201 00:10:41,559 --> 00:10:44,160 Speaker 1: up everything out there. We're not going to a commercial 202 00:10:44,200 --> 00:10:48,960 Speaker 1: break now, are we, No, I know, But it's people. 203 00:10:49,200 --> 00:10:51,880 Speaker 1: I mean, can you put any more ads on the uniforms? 204 00:10:51,920 --> 00:10:55,560 Speaker 1: And English question? To what degree? Halfway established a bit 205 00:10:55,600 --> 00:10:58,760 Speaker 1: of a false economy web by the advertiser believes that 206 00:10:58,800 --> 00:11:02,280 Speaker 1: they're getting clicks and each month, but actually there's nothing 207 00:11:02,360 --> 00:11:05,760 Speaker 1: behind it. So this is arguably the most important thing 208 00:11:06,000 --> 00:11:08,920 Speaker 1: when it comes to UH. Well, a lot of the 209 00:11:08,920 --> 00:11:10,959 Speaker 1: people I work with things that are focusing on it's 210 00:11:10,960 --> 00:11:14,760 Speaker 1: things like viewability of ads. It's things like, uh, knowing 211 00:11:14,840 --> 00:11:19,280 Speaker 1: that ads were actually engaging with consumers. Unfortunately, the industry 212 00:11:19,360 --> 00:11:21,480 Speaker 1: is really good at optimizing trees and not so good 213 00:11:21,480 --> 00:11:24,800 Speaker 1: at optimizing forests. And what that means practically is, uh, 214 00:11:24,840 --> 00:11:27,520 Speaker 1: there's a focus on keeping costs down, which means quality 215 00:11:27,679 --> 00:11:30,760 Speaker 1: may also go down. It's really hard to justify that. 216 00:11:30,840 --> 00:11:33,720 Speaker 1: Let's just say you're you could own the entirety of 217 00:11:33,720 --> 00:11:35,600 Speaker 1: an AD block, and maybe that AD block maybe a 218 00:11:35,600 --> 00:11:38,720 Speaker 1: thirty second block instead of two minutes. That might be 219 00:11:38,840 --> 00:11:42,440 Speaker 1: worth five times or ten times as much. The problem 220 00:11:42,480 --> 00:11:44,840 Speaker 1: is so much of the industry so focused on the 221 00:11:45,040 --> 00:11:48,640 Speaker 1: cost per unit, and that focus on the individual unit 222 00:11:48,720 --> 00:11:50,439 Speaker 1: is a big problem and we actually need to in 223 00:11:50,760 --> 00:11:53,760 Speaker 1: my position, we need to help marketers think more holistically 224 00:11:54,080 --> 00:11:56,360 Speaker 1: about how to optimize the entirety of what they're doing. 225 00:11:56,960 --> 00:11:59,160 Speaker 1: So it is a problem, and I think people might 226 00:11:59,559 --> 00:12:04,400 Speaker 1: try to helps is that the Pluck go the distance 227 00:12:06,760 --> 00:12:10,679 Speaker 1: radio is a really strong media. Actually all seriousness, I mean, 228 00:12:10,720 --> 00:12:14,600 Speaker 1: but that's a good example that radio and audio more 229 00:12:14,679 --> 00:12:17,280 Speaker 1: generally speaking, is something that more markers should be thinking of. 230 00:12:18,200 --> 00:12:20,839 Speaker 1: Stop they're doing. I mean, we have been humbled by 231 00:12:20,880 --> 00:12:24,400 Speaker 1: the success of our podcast. John and I collectively fell 232 00:12:24,440 --> 00:12:27,400 Speaker 1: off the chair when Bank of America first stepped up 233 00:12:27,440 --> 00:12:30,920 Speaker 1: and said we believe in your project, Apple Music, Spotify 234 00:12:31,000 --> 00:12:34,720 Speaker 1: and the whole thing. There's Brian, They're doing audio because 235 00:12:34,720 --> 00:12:38,320 Speaker 1: they don't have tough time to score around with video. Right. Well, 236 00:12:38,400 --> 00:12:41,600 Speaker 1: I think that frankly that because television and video based 237 00:12:41,600 --> 00:12:44,960 Speaker 1: advertising it is expensive at a per unit basis, is 238 00:12:45,000 --> 00:12:47,920 Speaker 1: perceived as being cluttered. Uh. There is a real problem, 239 00:12:48,000 --> 00:12:50,160 Speaker 1: especially with younger audiences, in terms of reaching them on 240 00:12:50,240 --> 00:12:53,240 Speaker 1: traditional TV. You're reaching frequency curves look all out of whack. 241 00:12:53,559 --> 00:12:55,720 Speaker 1: There's a lot of interest in audio. Now that said, 242 00:12:56,320 --> 00:12:59,200 Speaker 1: I could have said that five years ago, and radio 243 00:12:59,440 --> 00:13:03,240 Speaker 1: broadly to find early grew right. There's a shift to 244 00:13:03,280 --> 00:13:07,080 Speaker 1: spend to Spotify, Pandora to some degree, but the industry 245 00:13:07,120 --> 00:13:11,960 Speaker 1: collectively didn't grow. That said, we can still advocate for Hey, Marketer, 246 00:13:12,120 --> 00:13:14,240 Speaker 1: if you haven't thought of this, you really need to. 247 00:13:15,080 --> 00:13:17,559 Speaker 1: Let's talk about the privacy scandal that has engulfed the 248 00:13:17,600 --> 00:13:20,240 Speaker 1: likes of Facebook over the last twelve months. How does 249 00:13:20,280 --> 00:13:25,560 Speaker 1: that change things for your clients? So every brand has 250 00:13:25,600 --> 00:13:28,240 Speaker 1: to think whether they think or not they should be 251 00:13:28,280 --> 00:13:31,360 Speaker 1: thinking about whether or not risks and rewards of working 252 00:13:31,360 --> 00:13:33,920 Speaker 1: with any given media partner are worthwhile, right, and so 253 00:13:34,120 --> 00:13:38,440 Speaker 1: when it comes to the privacy issues with Facebook in particular, 254 00:13:38,520 --> 00:13:42,560 Speaker 1: but more generally, we call it brand safety, right, Uh, 255 00:13:42,720 --> 00:13:45,240 Speaker 1: brands have to consider if their association with a media 256 00:13:45,280 --> 00:13:48,840 Speaker 1: owner has a negative effect or if because the consumer 257 00:13:48,960 --> 00:13:51,880 Speaker 1: might see that they might be self loathing essentially, and 258 00:13:51,880 --> 00:13:54,480 Speaker 1: while they're consuming the medium, think I hate being on 259 00:13:54,520 --> 00:13:57,120 Speaker 1: this medium and I hate the advertisers who are making 260 00:13:57,120 --> 00:14:03,079 Speaker 1: this possible. I mean, that can happen, but more generally, UH, 261 00:14:03,120 --> 00:14:07,600 Speaker 1: there's a risk of association with media owners too, in 262 00:14:07,600 --> 00:14:09,600 Speaker 1: the mind of people who are not using the platform right, 263 00:14:09,720 --> 00:14:15,200 Speaker 1: enabling bad behaviors, enabling societal problems, whatever. Every brand has 264 00:14:15,240 --> 00:14:18,440 Speaker 1: to consider whether or not their brand is tarnished by 265 00:14:18,480 --> 00:14:22,680 Speaker 1: any association. The vast majority of advertisers have not considered 266 00:14:22,800 --> 00:14:25,480 Speaker 1: that they are negatively impacted at this point in time. 267 00:14:26,000 --> 00:14:29,080 Speaker 1: But that is the way to think about it. Again, 268 00:14:29,120 --> 00:14:31,200 Speaker 1: even in my prior role, I still would have said 269 00:14:31,840 --> 00:14:35,280 Speaker 1: Facebook and the lights are going to keep growing, because 270 00:14:35,280 --> 00:14:37,920 Speaker 1: that's not a connection. That's let's think about private data 271 00:14:38,360 --> 00:14:42,160 Speaker 1: as a currency. At some point, private datoris a currency 272 00:14:42,200 --> 00:14:44,400 Speaker 1: is going to become so expensive that the advertiser and 273 00:14:44,440 --> 00:14:47,400 Speaker 1: advertising incrementally is going to have to get more expensive. 274 00:14:47,440 --> 00:14:49,200 Speaker 1: Let's say, yes, Okay, now I see what you're saying. 275 00:14:49,320 --> 00:14:51,760 Speaker 1: So I would argue that every brand needs to make 276 00:14:51,760 --> 00:14:54,960 Speaker 1: sure that if they can't persuade a consumer to supply 277 00:14:55,200 --> 00:14:58,600 Speaker 1: willingly their data to the brand for use in targeting, 278 00:14:58,640 --> 00:15:01,640 Speaker 1: they probably don't deserve it. And that goes back to 279 00:15:01,680 --> 00:15:03,080 Speaker 1: the whole idea. What is a brand? Of brand is 280 00:15:03,080 --> 00:15:04,920 Speaker 1: supposed to be a mark of trust. Of brand is 281 00:15:04,960 --> 00:15:07,080 Speaker 1: supposed to be a mark of why a consumer should 282 00:15:07,080 --> 00:15:10,280 Speaker 1: want to engage. You think, Feral thinks this is an interview. 283 00:15:10,800 --> 00:15:13,720 Speaker 1: You're here to consult. What does radio need to do 284 00:15:13,760 --> 00:15:18,320 Speaker 1: to stay red. It's fiber right now. I mean, come on, 285 00:15:18,800 --> 00:15:21,640 Speaker 1: give me some thought leadership on how to drive Bloomberg 286 00:15:21,680 --> 00:15:24,440 Speaker 1: surveillance forward over the next five years. Oh yeah, I know, 287 00:15:24,520 --> 00:15:27,160 Speaker 1: i'd focus. Well, I mean, I'm going to bridge these 288 00:15:27,160 --> 00:15:30,800 Speaker 1: two comments here because to the extent that if you 289 00:15:30,840 --> 00:15:36,360 Speaker 1: can imagine a world where concerns around privacy are enhanced, 290 00:15:37,080 --> 00:15:40,800 Speaker 1: where laws and regulations are more pronounced with respect how 291 00:15:40,880 --> 00:15:45,200 Speaker 1: data is shared, and the relative advantage of using different 292 00:15:45,200 --> 00:15:50,160 Speaker 1: media will evolve. Radio doesn't quite have that issue, at 293 00:15:50,240 --> 00:15:52,840 Speaker 1: least traditional audio. Now that said, I think that every 294 00:15:52,840 --> 00:15:55,280 Speaker 1: advertiser wants to find ways to use data that they have. 295 00:15:55,400 --> 00:15:59,080 Speaker 1: I would argue an advertiser's best position to do gather 296 00:15:59,120 --> 00:16:02,000 Speaker 1: a ton of data on the insights of consumers using 297 00:16:02,040 --> 00:16:04,760 Speaker 1: a given media, use a ton of data on the 298 00:16:04,800 --> 00:16:07,200 Speaker 1: output of an impact, and then let the art take 299 00:16:07,240 --> 00:16:09,360 Speaker 1: the work in the middle. Yeah, we did all that 300 00:16:09,480 --> 00:16:12,160 Speaker 1: and we come up with John's TV platform. The real yield. 301 00:16:12,360 --> 00:16:15,040 Speaker 1: I mean, they saw a pure fixed income. That's all 302 00:16:15,360 --> 00:16:17,360 Speaker 1: that one of the Brian Weezer. This has been painful. 303 00:16:17,440 --> 00:16:27,400 Speaker 1: Thank you so much as well. Congratulations. Do you know 304 00:16:27,480 --> 00:16:34,800 Speaker 1: what I'm you have no idea. Yeah, Field of Dreams, okay, 305 00:16:36,480 --> 00:16:42,200 Speaker 1: Radiota out of the corn field. No, no idea, Okay, 306 00:16:42,520 --> 00:16:44,520 Speaker 1: thank you, I mean it works. Brian Weezer, thank you 307 00:16:44,560 --> 00:16:48,080 Speaker 1: so much. I'm sorry, but I think this. Yeah, that's 308 00:16:48,080 --> 00:16:51,560 Speaker 1: a baseball movie. You have seen that movie. Okay, But anyways, 309 00:16:51,840 --> 00:16:58,600 Speaker 1: just as really the association is is you just you 310 00:16:58,640 --> 00:17:01,360 Speaker 1: work into it. It's like you don't it's heaven. No, 311 00:17:01,520 --> 00:17:04,800 Speaker 1: it's Bloomberg surveillance. And we almost went with that phrase. 312 00:17:18,720 --> 00:17:21,720 Speaker 1: I have been so so so waiting for this conversation. 313 00:17:21,840 --> 00:17:24,520 Speaker 1: We're gonna go for you two hours NonStop. I'm kidding, 314 00:17:25,040 --> 00:17:27,440 Speaker 1: of course, but we're gonna carve out on a Friday, 315 00:17:28,440 --> 00:17:31,680 Speaker 1: clear language on what's all the rage. And it may 316 00:17:31,680 --> 00:17:35,120 Speaker 1: be one you're in nanotechnology and this, that and the other, 317 00:17:35,960 --> 00:17:39,639 Speaker 1: but once again it is the use and abuse of 318 00:17:39,680 --> 00:17:43,760 Speaker 1: the phrase artificial intelligence. All of this goes back to 319 00:17:43,840 --> 00:17:48,760 Speaker 1: a meeting in nineteen at Dartmouth College where the the 320 00:17:48,840 --> 00:17:52,840 Speaker 1: brains of Pittsburgh showed up from the University of Pittsburgh 321 00:17:52,880 --> 00:17:56,560 Speaker 1: in a small school called Carnegie Mellon University and Allen 322 00:17:56,640 --> 00:18:00,119 Speaker 1: Newell and her Herbert Simon and other giants. Actually, he 323 00:18:00,200 --> 00:18:04,040 Speaker 1: tried to figure out how we would handle computers and machines. 324 00:18:04,119 --> 00:18:06,840 Speaker 1: Flash forward and out of Pittsburgh at n y U 325 00:18:06,880 --> 00:18:09,760 Speaker 1: as vacant dar and he joins us this morning. We 326 00:18:09,800 --> 00:18:12,360 Speaker 1: are honored to have you here, professor. There's so much 327 00:18:12,480 --> 00:18:17,120 Speaker 1: to straighten out here. What is most wrong in the 328 00:18:17,160 --> 00:18:21,480 Speaker 1: new vogue of artificial intelligence is it's covered by the media. 329 00:18:21,960 --> 00:18:27,800 Speaker 1: What drives you nuts the most? Um? Really happy to 330 00:18:27,840 --> 00:18:30,679 Speaker 1: be here, Tom, And incidentally, Hope Simon was one of 331 00:18:30,680 --> 00:18:33,720 Speaker 1: my mentors and graduate school and on my thesis committee, 332 00:18:33,760 --> 00:18:36,920 Speaker 1: so I was honored to have him um and get 333 00:18:36,960 --> 00:18:40,960 Speaker 1: to know him. I think AI has seen several hype 334 00:18:40,960 --> 00:18:44,679 Speaker 1: cycles lost four decades full disclosure, my brother was in 335 00:18:44,720 --> 00:18:47,119 Speaker 1: one of them. Continued, So you know we've so so 336 00:18:47,400 --> 00:18:50,000 Speaker 1: I've seen several of these hype cycles during my career, 337 00:18:50,200 --> 00:18:53,119 Speaker 1: and h we're seeing another one at the moment. What 338 00:18:53,160 --> 00:18:56,160 Speaker 1: do we get wrong? What's the hype that drives you nuts? Well, 339 00:18:56,200 --> 00:18:58,680 Speaker 1: you know the by the way, I think this one 340 00:18:58,760 --> 00:19:00,800 Speaker 1: is different, right, So as just want to qualify that. 341 00:19:00,880 --> 00:19:04,359 Speaker 1: But I think the there's a lot of misunderstanding that 342 00:19:04,440 --> 00:19:09,560 Speaker 1: AI will solve everything that machines have, you know, will 343 00:19:09,760 --> 00:19:13,679 Speaker 1: have become incredibly intelligent and they'll replace us. And you 344 00:19:13,720 --> 00:19:16,359 Speaker 1: know that may well happen in the very long term, 345 00:19:16,480 --> 00:19:20,080 Speaker 1: but I think that we ought to have some somewhat 346 00:19:20,200 --> 00:19:23,160 Speaker 1: muted expectations. I mean, we've certainly come a long way, 347 00:19:23,800 --> 00:19:26,760 Speaker 1: but I think we have a really long way to 348 00:19:26,800 --> 00:19:29,720 Speaker 1: go go. Jack Clark, writing for Bloomberg four years ago, says, 349 00:19:29,760 --> 00:19:32,560 Speaker 1: this time is different. You just alluded to that what's 350 00:19:32,600 --> 00:19:38,119 Speaker 1: different now within artificial intelligence or these machines running our lives. 351 00:19:38,520 --> 00:19:43,920 Speaker 1: So I think what's what's different now is that we've 352 00:19:44,000 --> 00:19:49,000 Speaker 1: made a dent in solving perception kinds of problems. So 353 00:19:49,160 --> 00:19:53,840 Speaker 1: machines have gotten a lot better at seeing, hearing, getting 354 00:19:53,840 --> 00:19:57,200 Speaker 1: better at reading um And I think what that does 355 00:19:57,560 --> 00:20:01,720 Speaker 1: is it changes the nature of interaction with the machine. 356 00:20:01,760 --> 00:20:06,000 Speaker 1: The machine can now ingest inputs directly from the environment 357 00:20:06,119 --> 00:20:09,120 Speaker 1: instead of them being formatted in a way and structured 358 00:20:09,119 --> 00:20:10,720 Speaker 1: in the way that it can understand. Right, So it's 359 00:20:10,720 --> 00:20:13,560 Speaker 1: able to deal with more instructured kind of input like 360 00:20:13,640 --> 00:20:17,480 Speaker 1: humans are. And and that's interesting in many domains, including healthcare. 361 00:20:17,560 --> 00:20:19,520 Speaker 1: But you can look at images and stuff like that. 362 00:20:19,560 --> 00:20:21,520 Speaker 1: But in your high school math in India, I'm sure 363 00:20:21,520 --> 00:20:23,840 Speaker 1: you got to this quickly. Linear regressions and you've got 364 00:20:23,920 --> 00:20:25,880 Speaker 1: a signal on the back end, which is that risk 365 00:20:26,040 --> 00:20:29,280 Speaker 1: that's out there, that unknowable that's out there. We're all 366 00:20:29,320 --> 00:20:31,600 Speaker 1: living this right now with the emotion of two plane 367 00:20:31,600 --> 00:20:34,920 Speaker 1: crashes over software in a cockpit that just flat out 368 00:20:35,160 --> 00:20:38,479 Speaker 1: didn't work. That's certainly what the FAA is getting too quickly. 369 00:20:38,960 --> 00:20:42,280 Speaker 1: Is there a new reliance on machines and on computer 370 00:20:42,400 --> 00:20:47,880 Speaker 1: technology where that risk, that sigma it becomes too unknowable. Well, 371 00:20:47,920 --> 00:20:51,919 Speaker 1: you know, you're you're raising an incredibly important question. And 372 00:20:52,160 --> 00:20:54,040 Speaker 1: you know, one of my questions really is when do 373 00:20:54,040 --> 00:20:56,280 Speaker 1: we trust machines? And the way I look at trust 374 00:20:56,359 --> 00:20:59,280 Speaker 1: is in terms of risk. In my mind, it boils 375 00:20:59,280 --> 00:21:01,399 Speaker 1: down to how often will the machine be wrong? And 376 00:21:01,440 --> 00:21:04,800 Speaker 1: what are the consequences when the machine is wrong? Things 377 00:21:04,840 --> 00:21:08,800 Speaker 1: like drive a less cars, autopilot. The consequences of era 378 00:21:08,920 --> 00:21:13,240 Speaker 1: are severe. Uh. And so those kinds of decisions tend 379 00:21:13,280 --> 00:21:16,200 Speaker 1: to be particularly sensitive. When they talk about handing control. 380 00:21:16,960 --> 00:21:19,240 Speaker 1: You're in class throwing chalk at somebody at n WANT. 381 00:21:19,280 --> 00:21:22,040 Speaker 1: You're the dumbest kid in engineering n y U. And 382 00:21:22,080 --> 00:21:25,159 Speaker 1: if you look at the risk distribution, we all understand 383 00:21:25,200 --> 00:21:28,000 Speaker 1: it's not gaussy and it's not a normal bell curve 384 00:21:28,400 --> 00:21:30,639 Speaker 1: like the height of our high school class. It can 385 00:21:30,680 --> 00:21:34,080 Speaker 1: be a goofy distribution or whatever, which is like if 386 00:21:34,119 --> 00:21:37,719 Speaker 1: I paint ten thou BMW's one of them will be wrong. 387 00:21:38,240 --> 00:21:42,600 Speaker 1: That's a lot different than airplanes crash exactly. What's what's 388 00:21:42,680 --> 00:21:45,200 Speaker 1: different about airplanes and driver a less cars is you're 389 00:21:45,240 --> 00:21:48,600 Speaker 1: not really interested in their distribution. You're not exactly gonna 390 00:21:48,640 --> 00:21:50,919 Speaker 1: really interested in the average case. You're interested in the 391 00:21:50,960 --> 00:21:56,239 Speaker 1: worst case. Right, it's that worst case Rumsfeldian unknown that 392 00:21:56,280 --> 00:21:58,919 Speaker 1: you're really concerned about. That you may not have counter 393 00:22:00,040 --> 00:22:02,440 Speaker 1: guy like you with the privileges studying with the CMU 394 00:22:02,520 --> 00:22:05,560 Speaker 1: guys at Pittsburgh a million years ago. If if you 395 00:22:05,680 --> 00:22:08,240 Speaker 1: look at these tech boys out and still these children 396 00:22:08,280 --> 00:22:11,520 Speaker 1: out in Silicon Valley saying we're gonna do driverless cars, 397 00:22:12,000 --> 00:22:14,320 Speaker 1: you've got in your head all those risks out there, 398 00:22:14,640 --> 00:22:18,760 Speaker 1: When do we actually get driverless cars? Given how each 399 00:22:18,880 --> 00:22:24,200 Speaker 1: of us perceives the next traffic accigen accident very slowly, 400 00:22:25,280 --> 00:22:27,680 Speaker 1: is my prediction. I don't think this is going to 401 00:22:27,800 --> 00:22:30,760 Speaker 1: happen overnight. I think we're going to have to get 402 00:22:30,920 --> 00:22:34,040 Speaker 1: used to living with these new entities on the on 403 00:22:34,119 --> 00:22:37,439 Speaker 1: the road. I think they'll be highly restricted to begin with, 404 00:22:37,560 --> 00:22:40,000 Speaker 1: and we'll just have to wait and see what kinds 405 00:22:40,000 --> 00:22:43,080 Speaker 1: of mistakes they make in the wild, which we haven't 406 00:22:43,119 --> 00:22:45,879 Speaker 1: seen yet. Right, those are the unknowns that we have 407 00:22:46,000 --> 00:22:48,119 Speaker 1: yet to see, and unless we see them, we're not 408 00:22:48,200 --> 00:22:50,480 Speaker 1: going to trust them. The wild is the potholes on 409 00:22:50,600 --> 00:22:53,199 Speaker 1: fifty nights. Exactly here, Anderic, if you're just joining us, 410 00:22:53,280 --> 00:22:56,200 Speaker 1: v Sandar with us with an important conversation on all 411 00:22:56,200 --> 00:22:59,640 Speaker 1: this uproar over AI. Okay, so we're gonna have driverless cars, 412 00:22:59,640 --> 00:23:02,600 Speaker 1: and we've established the risks that are out there. There's 413 00:23:02,640 --> 00:23:07,919 Speaker 1: a whole financial thing where now we're extrapolating out billions 414 00:23:07,960 --> 00:23:12,080 Speaker 1: of dollars of equity value on hopes and dreams of 415 00:23:12,119 --> 00:23:16,080 Speaker 1: the technology you're actually expert in, how do you respond 416 00:23:16,119 --> 00:23:19,800 Speaker 1: to finance? This has a timeline of two years, where 417 00:23:19,800 --> 00:23:23,280 Speaker 1: you've got a timeline at twenty years well in you know, 418 00:23:23,440 --> 00:23:28,280 Speaker 1: in in finance. Um, it's you know, as strange as 419 00:23:28,280 --> 00:23:31,720 Speaker 1: it may sound, this this is a an easier problem 420 00:23:31,840 --> 00:23:36,760 Speaker 1: than uh than you know, automated and and and and 421 00:23:36,920 --> 00:23:41,159 Speaker 1: uh you know, autopilot on cockpits, where the consequences of 422 00:23:41,200 --> 00:23:46,600 Speaker 1: EA are really large. In finance, you can actually look 423 00:23:46,640 --> 00:23:49,199 Speaker 1: at their you know, coming back to those distributions, they 424 00:23:49,240 --> 00:23:51,880 Speaker 1: become important, right because you have to have expectations about 425 00:23:51,880 --> 00:23:54,000 Speaker 1: how how often the machine will be wrong. But the 426 00:23:54,040 --> 00:23:57,360 Speaker 1: time this is critical. The timelines, Professor are so different 427 00:23:57,840 --> 00:24:01,520 Speaker 1: from experienced, grizzled guys like you versus what I hear 428 00:24:01,560 --> 00:24:04,280 Speaker 1: from the tech crew. You're you're working on two different 429 00:24:04,320 --> 00:24:07,960 Speaker 1: two regimes of time, aren't you. Yes. I think that 430 00:24:08,840 --> 00:24:12,199 Speaker 1: it takes time to get used to looking at the 431 00:24:12,240 --> 00:24:15,440 Speaker 1: behavior of these machines in any kind of domain. Um, 432 00:24:15,600 --> 00:24:19,640 Speaker 1: And I think that one has to have expectations that, 433 00:24:19,840 --> 00:24:23,600 Speaker 1: you know, when you build a machine to predict financial markets, 434 00:24:24,080 --> 00:24:27,240 Speaker 1: you have to you had there's gonna be some time 435 00:24:27,320 --> 00:24:29,440 Speaker 1: over which you have to observe its behavior to get 436 00:24:29,480 --> 00:24:33,639 Speaker 1: comfortable with it as well. So I think, uh, you know, 437 00:24:33,680 --> 00:24:36,119 Speaker 1: I think expectations have to be realistic. You can't just 438 00:24:36,240 --> 00:24:39,280 Speaker 1: think about we're gonna have these intelligent machines making investment decisions. 439 00:24:39,320 --> 00:24:42,760 Speaker 1: What's your timeline on self driving cars? You're starting with 440 00:24:42,800 --> 00:24:45,480 Speaker 1: Mari Barr of GM who's all pumped up about this. 441 00:24:45,560 --> 00:24:49,080 Speaker 1: She's a legit engineer. She gets the math. What's your 442 00:24:49,080 --> 00:24:52,440 Speaker 1: timeline on self driving cars? To Mary Bar like I said, 443 00:24:52,520 --> 00:24:55,560 Speaker 1: I think it depends on the evidence that we get 444 00:24:55,600 --> 00:24:59,720 Speaker 1: from the wild right when when we actually start looking 445 00:24:59,800 --> 00:25:03,440 Speaker 1: at these vehicles, uh, you know, we'll get some data. 446 00:25:03,600 --> 00:25:05,159 Speaker 1: But if I were to sort of throw out a 447 00:25:05,400 --> 00:25:08,720 Speaker 1: you know, a number out there, I'd say that we'll 448 00:25:08,760 --> 00:25:11,240 Speaker 1: be looking at least five years before we can get 449 00:25:11,240 --> 00:25:14,399 Speaker 1: comfortable that we have enough data to trust machines but 450 00:25:14,600 --> 00:25:16,800 Speaker 1: driving us around. Thank you so so much. I would 451 00:25:16,960 --> 00:25:18,760 Speaker 1: kill to have you together with one of that. But 452 00:25:19,000 --> 00:25:21,280 Speaker 1: bring Jolson up at m I t the two of 453 00:25:21,280 --> 00:25:23,920 Speaker 1: you together would just be lights out. It's dark. There's 454 00:25:23,920 --> 00:25:27,399 Speaker 1: some n y you truly expertise in all the rage 455 00:25:27,520 --> 00:25:30,880 Speaker 1: the vogue that is artificial intelligence. I think you can 456 00:25:30,880 --> 00:25:35,200 Speaker 1: tell from my question, you know right right, maybe no 457 00:25:35,400 --> 00:25:38,760 Speaker 1: artificial intelligence to tie a bow tie, Professor dar, thank 458 00:25:38,800 --> 00:25:56,920 Speaker 1: you so much. Uptown into the west and it has 459 00:25:56,960 --> 00:26:02,240 Speaker 1: been a flat lands for well over twelve fifteen years. 460 00:26:02,440 --> 00:26:05,560 Speaker 1: Is called Hudson Yards for those you globally on the 461 00:26:05,600 --> 00:26:09,280 Speaker 1: shores of the Hudson River, our Viviana hurtado is that 462 00:26:09,520 --> 00:26:15,080 Speaker 1: the newly acclaimed Hudson Yards with six beautiful skyscrapers and 463 00:26:15,200 --> 00:26:18,439 Speaker 1: a lot of hopes. Viviana, what is the major hope 464 00:26:18,880 --> 00:26:22,439 Speaker 1: of this development of Hudson Yards. Well, you know it 465 00:26:22,560 --> 00:26:26,840 Speaker 1: has been said Tom that this development Hudson Yards could 466 00:26:27,080 --> 00:26:31,760 Speaker 1: very well shift the culture, the arts. Uh. You know, 467 00:26:32,080 --> 00:26:36,639 Speaker 1: so much of the economic vibrancy of Manhattan to the 468 00:26:36,720 --> 00:26:39,960 Speaker 1: West Side. And so today we are just about a 469 00:26:40,359 --> 00:26:43,720 Speaker 1: less than an hour away from the real big kickoff 470 00:26:43,800 --> 00:26:47,199 Speaker 1: half happening. Um. Certainly a lot of excitement. There has 471 00:26:47,240 --> 00:26:50,280 Speaker 1: been music in my chest. But that's really the hope 472 00:26:50,680 --> 00:26:54,959 Speaker 1: and the vision particularly so many people involved, as you 473 00:26:54,960 --> 00:26:58,119 Speaker 1: were saying, over the course of fifteen plus years, but 474 00:26:58,280 --> 00:27:02,120 Speaker 1: really today the show, the vision is going to belong 475 00:27:02,200 --> 00:27:05,919 Speaker 1: to related in Oxford, Viviana. How much is still the 476 00:27:06,040 --> 00:27:09,879 Speaker 1: vision and how much is actually actuality at this point? 477 00:27:09,920 --> 00:27:14,800 Speaker 1: How much commerce? How much a residential space has been killed? Yeah, definitely. 478 00:27:14,880 --> 00:27:18,000 Speaker 1: So right now what we're seeing is just about half 479 00:27:18,160 --> 00:27:21,080 Speaker 1: of what is going to be the full Hussant Yards 480 00:27:21,119 --> 00:27:24,919 Speaker 1: complex opening, So the public square and gardens is going 481 00:27:24,960 --> 00:27:27,320 Speaker 1: to open. We know that there's residential half of that 482 00:27:27,480 --> 00:27:28,960 Speaker 1: is going to be for sale, half of it is 483 00:27:28,960 --> 00:27:31,399 Speaker 1: going to be rental. Uh, and there's going to be 484 00:27:31,400 --> 00:27:34,480 Speaker 1: the commercial space as well that opens today with global 485 00:27:34,560 --> 00:27:38,320 Speaker 1: brands being located there. Loreal USA, one of those big 486 00:27:38,400 --> 00:27:41,359 Speaker 1: names that's taken up shop here. Speaking of taking up shop, 487 00:27:41,440 --> 00:27:44,440 Speaker 1: we do have the shops and that's being anchored by 488 00:27:44,560 --> 00:27:47,560 Speaker 1: Neiman Marcus, migrating from the big d of Dallas to 489 00:27:47,680 --> 00:27:51,200 Speaker 1: the big Apple of New York City. But again, today 490 00:27:51,400 --> 00:27:55,800 Speaker 1: marks what is only the half opening in many ways 491 00:27:55,800 --> 00:27:58,560 Speaker 1: and opening, but only half the project. It's going to 492 00:27:58,680 --> 00:28:02,600 Speaker 1: still be several more years until the full project is 493 00:28:03,359 --> 00:28:06,080 Speaker 1: built and ringing in. By the way, guys, at twenty 494 00:28:06,119 --> 00:28:10,240 Speaker 1: five billion dollars, Vivianna, I have to wonder, given all 495 00:28:10,280 --> 00:28:13,520 Speaker 1: of the development in Long Island City, given all of 496 00:28:13,520 --> 00:28:18,360 Speaker 1: the development downtown, is there enough demand for this? Well, 497 00:28:18,400 --> 00:28:22,080 Speaker 1: that's the question on everybody's mind, right, Lisa, because the 498 00:28:22,080 --> 00:28:24,000 Speaker 1: truth of the matter is we are living in a 499 00:28:24,040 --> 00:28:29,160 Speaker 1: time where you have retail. We're just talking about retail 500 00:28:29,280 --> 00:28:31,880 Speaker 1: and the shops opening up here anchored by Neiman. Well, 501 00:28:32,080 --> 00:28:36,840 Speaker 1: this has not been a good retails season, has it, uh? 502 00:28:36,880 --> 00:28:38,880 Speaker 1: And we know that here at least in Manhattan we 503 00:28:38,880 --> 00:28:42,920 Speaker 1: saw the iconic Lord and Taylor Clothes Henry Bendel as well. 504 00:28:43,240 --> 00:28:46,360 Speaker 1: They are making a very bold and a very big 505 00:28:46,440 --> 00:28:50,719 Speaker 1: pitch here opening a bricks and mortar store when the market, 506 00:28:50,800 --> 00:28:55,760 Speaker 1: certainly and the conventional wisdom is saying that that customers 507 00:28:55,800 --> 00:28:59,080 Speaker 1: are migrating online, uh, and so that is going to 508 00:28:59,160 --> 00:29:01,360 Speaker 1: be the big pitch what they are hoping, of course, 509 00:29:01,480 --> 00:29:04,960 Speaker 1: and that with this shift uh that's going to come 510 00:29:05,000 --> 00:29:06,480 Speaker 1: to the West Side, it's going to bring in a 511 00:29:06,480 --> 00:29:09,520 Speaker 1: lot of foot traffic, uh, tourists who are going to 512 00:29:09,640 --> 00:29:11,840 Speaker 1: be nearby at the high Line, as well as the 513 00:29:11,840 --> 00:29:13,959 Speaker 1: people who are going to work and live here. And 514 00:29:14,000 --> 00:29:17,120 Speaker 1: that it's going to really give uh this vibrancy and 515 00:29:17,160 --> 00:29:21,080 Speaker 1: economic vibrancy to what some people are calling a vertical 516 00:29:21,160 --> 00:29:24,120 Speaker 1: retail space. But that Lisa, you and I may remember 517 00:29:24,320 --> 00:29:26,880 Speaker 1: as just being called them all Vivian or Toto, thank 518 00:29:26,920 --> 00:29:30,120 Speaker 1: you so much to Hudson Yards overlooking the Hudson River. 519 00:29:30,480 --> 00:29:32,640 Speaker 1: As well, I should tell you that Michael Bloomberg, the 520 00:29:32,680 --> 00:29:36,120 Speaker 1: founder majority owner of Bloomberg LP, played a role in 521 00:29:36,160 --> 00:29:40,120 Speaker 1: the development of the Hudson Yard project as Mayor of 522 00:29:40,880 --> 00:29:58,040 Speaker 1: New York. Lisa Brown Winson, Tom Keenan, what we're gonna 523 00:29:58,080 --> 00:30:00,160 Speaker 1: do here like I used to do a lot or 524 00:30:00,200 --> 00:30:02,080 Speaker 1: and maybe we'll get back to it in two thousand 525 00:30:02,160 --> 00:30:05,280 Speaker 1: nineteen as well, is find books where you go, you 526 00:30:05,360 --> 00:30:09,440 Speaker 1: hate the author because you go, this looks so interesting, 527 00:30:09,640 --> 00:30:13,120 Speaker 1: so good, I have to push seven other books aside 528 00:30:13,240 --> 00:30:16,680 Speaker 1: and read it. You will do that with ten Caesar's 529 00:30:16,760 --> 00:30:20,040 Speaker 1: because every time you've tried to do Roman history, your 530 00:30:20,080 --> 00:30:25,000 Speaker 1: eyes have glazed over those high above Cayuga's waters in Ithaca. 531 00:30:25,120 --> 00:30:28,959 Speaker 1: Have had the good fortune of ancient history for years 532 00:30:29,240 --> 00:30:33,560 Speaker 1: with Barry Strauss. He's truly legendary within the study of 533 00:30:33,720 --> 00:30:36,680 Speaker 1: Rome and bringing it to life, and he has succeeded 534 00:30:36,720 --> 00:30:41,760 Speaker 1: in doing that with ten Caesar's. Roman Emperors from Augustus 535 00:30:41,800 --> 00:30:48,080 Speaker 1: to Constantine. Barry, congratulations on the accessibility of ten Caesar's. 536 00:30:48,600 --> 00:30:52,040 Speaker 1: But you've written seven other books or whatever on Rome. 537 00:30:52,360 --> 00:30:57,120 Speaker 1: Why ten Caesar's. Well, thanks so much for the kind words. 538 00:30:57,160 --> 00:31:01,600 Speaker 1: It's good to be hated, uh Tense Cazars. You know, 539 00:31:01,720 --> 00:31:05,000 Speaker 1: I was interested in this subject ever since I saw 540 00:31:05,200 --> 00:31:08,760 Speaker 1: I Claudius in the nineteen seventies, uh, and it really 541 00:31:08,800 --> 00:31:11,520 Speaker 1: got me into the idea that there's something special about 542 00:31:12,080 --> 00:31:14,720 Speaker 1: uh these Roman emperors, and they just had a remarkable 543 00:31:14,760 --> 00:31:19,800 Speaker 1: ability to market themselves and make their own stories larger 544 00:31:19,840 --> 00:31:22,280 Speaker 1: than anything else that was going on in the Roman 545 00:31:22,320 --> 00:31:23,720 Speaker 1: world at the time. And I think that's one of 546 00:31:23,720 --> 00:31:26,400 Speaker 1: the reasons we're still interested in them today. This is 547 00:31:26,400 --> 00:31:29,040 Speaker 1: so important, But far more important is the fact Lisa 548 00:31:29,120 --> 00:31:33,320 Speaker 1: Bramowitz has had forty seven cups of coffee in Rome 549 00:31:33,360 --> 00:31:36,240 Speaker 1: and is completely up to speed on all this. She 550 00:31:36,280 --> 00:31:40,240 Speaker 1: will read your book anxiously at waits the movie coming 551 00:31:40,240 --> 00:31:43,600 Speaker 1: out in three years. Lisa jump in here with Professor Strauss. Well, 552 00:31:43,800 --> 00:31:47,400 Speaker 1: it's it's it's not only fascinating from the personalities who 553 00:31:47,400 --> 00:31:49,200 Speaker 1: made their stories larger than life, but this is the 554 00:31:49,200 --> 00:31:51,720 Speaker 1: foundation of democracy as we know it. And I'm wondering 555 00:31:52,280 --> 00:31:57,480 Speaker 1: with these ten Caesars, is there a unifying factor beneath 556 00:31:57,720 --> 00:32:02,920 Speaker 1: their vibrant and diverse personalities. Oh yeah, I think that 557 00:32:04,760 --> 00:32:07,800 Speaker 1: all of them were able to make change their friend. 558 00:32:09,120 --> 00:32:10,840 Speaker 1: And you know, in a way, the model of the 559 00:32:10,920 --> 00:32:13,640 Speaker 1: Roman Empire is one that comes from a more recent 560 00:32:13,720 --> 00:32:16,640 Speaker 1: Italian novel, and that is, if we want things to 561 00:32:16,920 --> 00:32:20,840 Speaker 1: stay the same, everything has to change. And these emperors 562 00:32:20,880 --> 00:32:22,600 Speaker 1: really knew how to do it. They knew how to 563 00:32:22,680 --> 00:32:26,920 Speaker 1: keep Rome Roman by making it less Roman. Ironically, is 564 00:32:26,960 --> 00:32:29,760 Speaker 1: that may seem they moved the They're able to govern 565 00:32:29,960 --> 00:32:33,840 Speaker 1: without Italy, without Rome, to bring new people into the 566 00:32:33,920 --> 00:32:37,480 Speaker 1: elite um, and to accept change and I think that's 567 00:32:37,480 --> 00:32:39,520 Speaker 1: one of the reasons why the empire lasted so long. 568 00:32:40,960 --> 00:32:43,600 Speaker 1: One thing that I love about the book is the 569 00:32:43,640 --> 00:32:48,280 Speaker 1: titles for each of the chapters, Vespassion, the Commoner, Nero 570 00:32:48,400 --> 00:32:53,520 Speaker 1: the Entertainer, Tiberius the Tyrant. Which was your favorite Caesar 571 00:32:53,600 --> 00:32:57,880 Speaker 1: to write about? I think Hadrian, you know, because he's 572 00:32:57,920 --> 00:33:02,160 Speaker 1: so complicated. Uh he said, a combination of of good 573 00:33:02,280 --> 00:33:08,160 Speaker 1: and evil, of you know, extraordinary talent and just cheer wickedness. Um, 574 00:33:08,200 --> 00:33:10,760 Speaker 1: it's kind of hard to beat that. And also such 575 00:33:10,800 --> 00:33:16,280 Speaker 1: a such a vast Kansas, from Hadrian's wall to Germany 576 00:33:16,320 --> 00:33:19,280 Speaker 1: to Athens, to Jerusalem to North Africa. You know, he's 577 00:33:19,320 --> 00:33:21,960 Speaker 1: just all over the place. Very stress for this. The 578 00:33:22,000 --> 00:33:25,640 Speaker 1: book is tend Caesar's Roman Emperors from Augustus to Constantine. 579 00:33:25,960 --> 00:33:28,800 Speaker 1: And of course we deliver this to your folks on 580 00:33:29,000 --> 00:33:32,400 Speaker 1: March fifteen, The IDEs of March. Barry, when you teach 581 00:33:32,480 --> 00:33:35,080 Speaker 1: the course, what is the thing that we get most 582 00:33:35,200 --> 00:33:37,960 Speaker 1: wrong about the eyes of March? We use it, we 583 00:33:38,080 --> 00:33:41,800 Speaker 1: say it, we do it constantly. Correct us, how should 584 00:33:41,840 --> 00:33:47,520 Speaker 1: we interpret Roman IDEs of March? Oh? God, where to begin? Well, 585 00:33:47,560 --> 00:33:51,080 Speaker 1: season wasn't killed on the capitol on the Capitol line, 586 00:33:51,120 --> 00:33:53,880 Speaker 1: Hill um. And he wasn't killed in the senate as 587 00:33:53,920 --> 00:33:55,920 Speaker 1: Shakespeare says. He wasn't killed in the senate house that 588 00:33:55,960 --> 00:34:00,720 Speaker 1: we tourists see Rome. It all took place about half 589 00:34:00,800 --> 00:34:03,320 Speaker 1: mile Oi and the law of Argentina if you're if 590 00:34:03,320 --> 00:34:07,200 Speaker 1: you're walking around Rome. But I guess the biggest thing 591 00:34:07,600 --> 00:34:11,000 Speaker 1: is that we think that Brutus and cash Us were 592 00:34:11,040 --> 00:34:14,040 Speaker 1: the guys behind it, and Brutus is the one who 593 00:34:14,080 --> 00:34:18,680 Speaker 1: has said who who betrayed Caesar? Well, you know, um, 594 00:34:18,719 --> 00:34:21,759 Speaker 1: So I think the biggest thing actually is into Brute. 595 00:34:21,960 --> 00:34:25,960 Speaker 1: Caesar never actually said it to Brute. He probably just 596 00:34:26,120 --> 00:34:31,600 Speaker 1: groaned as he was being killeda said in Greek, you too, 597 00:34:31,640 --> 00:34:34,759 Speaker 1: my son, which could have been a real insult if 598 00:34:34,760 --> 00:34:37,120 Speaker 1: he actually said it. Because the room Caesar hadn't a 599 00:34:37,239 --> 00:34:40,520 Speaker 1: head an affair with Brutus's mother, and the rumor was 600 00:34:40,560 --> 00:34:43,319 Speaker 1: that Brutus was his illegitate son, and so Caesar saying 601 00:34:43,320 --> 00:34:45,680 Speaker 1: in a way, you just killed your father and that's 602 00:34:45,680 --> 00:34:48,400 Speaker 1: a terrible crime. According to Romo, just to see how 603 00:34:48,600 --> 00:34:51,800 Speaker 1: stro just crushes our illusions. So much of this Lisa's 604 00:34:51,960 --> 00:34:55,200 Speaker 1: Roman excitement. And I think of Darius Aria of the 605 00:34:55,239 --> 00:34:59,160 Speaker 1: great filma film and photographer PBS who want me around 606 00:34:59,520 --> 00:35:02,359 Speaker 1: Rome on. But for anybody who's not been to Rome 607 00:35:03,040 --> 00:35:07,160 Speaker 1: your advantages read Barry Strauss ted Caesar's And what's so 608 00:35:07,200 --> 00:35:09,879 Speaker 1: cool to me, Lisa is it just comes alive. It does. 609 00:35:10,120 --> 00:35:14,360 Speaker 1: And of course these personalities are really colorful, Professor Strauss, 610 00:35:14,400 --> 00:35:17,640 Speaker 1: I'd love to get your sins. As the Roman Empire 611 00:35:17,760 --> 00:35:21,239 Speaker 1: progressed early Middle and then as it really was on 612 00:35:21,280 --> 00:35:25,600 Speaker 1: the precipice of failure, how did some of the leaders 613 00:35:25,719 --> 00:35:29,640 Speaker 1: fail to adapt to change in a way that gave 614 00:35:29,640 --> 00:35:35,320 Speaker 1: it lasting power? How did they fail to adapt to change? Uh? Well, 615 00:35:35,360 --> 00:35:39,000 Speaker 1: I you know, Diocletian would be a good example a 616 00:35:39,120 --> 00:35:42,719 Speaker 1: failure to adapt. M He, like many Romans, after a 617 00:35:42,800 --> 00:35:50,399 Speaker 1: half century of wars, invasions, defeats, epidemics, inflation, he thought 618 00:35:50,400 --> 00:35:52,239 Speaker 1: there was a problem with the gods. You know, the 619 00:35:52,239 --> 00:35:54,600 Speaker 1: gods just weren't on Rome side. And the way he 620 00:35:54,680 --> 00:35:57,080 Speaker 1: looked at it is the way to save Rome was 621 00:35:57,160 --> 00:35:59,600 Speaker 1: to get rid of the atheists, which is how he 622 00:35:59,680 --> 00:36:02,520 Speaker 1: looked at the Christians, because they didn't believe in the 623 00:36:02,560 --> 00:36:07,440 Speaker 1: Olympian gods. So he started the Great Prosecution of the Christians, 624 00:36:07,800 --> 00:36:12,000 Speaker 1: which is quite terrible but also an utter failure. And 625 00:36:12,040 --> 00:36:15,319 Speaker 1: in the end he had to admit defeat. He is 626 00:36:15,360 --> 00:36:18,560 Speaker 1: succeeded by Constantine, who is the first Christian emperor, and 627 00:36:18,600 --> 00:36:21,000 Speaker 1: turns it around and says, yeah, you're right, we do 628 00:36:21,040 --> 00:36:22,920 Speaker 1: have a problem with the gods. But the solution is 629 00:36:22,960 --> 00:36:25,960 Speaker 1: not doubling down on the old religion. It's accepting a 630 00:36:26,040 --> 00:36:29,200 Speaker 1: new religion, UM and he sets the empire on the 631 00:36:29,280 --> 00:36:32,920 Speaker 1: road to becoming Christian and also to surviving. So I 632 00:36:32,960 --> 00:36:36,440 Speaker 1: think Clesia's an example of somebody who tried to dig 633 00:36:36,480 --> 00:36:38,759 Speaker 1: his heels into the past and it just wasn't going 634 00:36:38,800 --> 00:36:41,200 Speaker 1: to work. One other aspect that I love about the 635 00:36:41,200 --> 00:36:45,239 Speaker 1: book is your highlighting of women and the relationships of 636 00:36:45,280 --> 00:36:49,360 Speaker 1: some of the powerful women and the ten Caesar's throughout 637 00:36:49,520 --> 00:36:52,040 Speaker 1: these three hundred plus years. Can you talk a little 638 00:36:52,080 --> 00:36:55,239 Speaker 1: bit about that. Yeah, you know, we the Romans were 639 00:36:55,280 --> 00:36:58,440 Speaker 1: definitely macho and sometimes misogynistic, and we tend to just 640 00:36:58,520 --> 00:37:01,760 Speaker 1: focus on the men. But all of these emperors, beginning 641 00:37:01,800 --> 00:37:06,239 Speaker 1: with Augustus, knew that in order to be successful, UH, 642 00:37:06,280 --> 00:37:09,680 Speaker 1: they would also have to reach out to women. Augustus 643 00:37:09,800 --> 00:37:13,080 Speaker 1: is married to Olivia for fifty four years. It's one 644 00:37:13,080 --> 00:37:18,360 Speaker 1: of the most interesting partnerships between UM two powerful people 645 00:37:18,400 --> 00:37:23,759 Speaker 1: in history. Um august Olivia was a Roman noble. She 646 00:37:23,880 --> 00:37:26,960 Speaker 1: wasn't just Augustus's wife, but she was his advisor. He 647 00:37:27,040 --> 00:37:29,400 Speaker 1: consulted her, he brought her with her when he traveled 648 00:37:29,400 --> 00:37:34,400 Speaker 1: around the empire. She was very shrewd um and they were. 649 00:37:34,440 --> 00:37:37,440 Speaker 1: They were a true power couple working together. Then we 650 00:37:37,480 --> 00:37:42,040 Speaker 1: find some others like Vespasian, who rises from being a 651 00:37:42,080 --> 00:37:44,839 Speaker 1: commoner to being emperor, and he's very much helped by 652 00:37:44,880 --> 00:37:49,200 Speaker 1: the fact that his mistress is a slave woman who 653 00:37:49,320 --> 00:37:53,120 Speaker 1: is working for UM, one of the top imperial women, 654 00:37:53,360 --> 00:37:58,719 Speaker 1: UM caligulous grandmother, Claudius's mother and Antonia. She helps Vespasian 655 00:37:58,800 --> 00:38:01,960 Speaker 1: and ultimately he becomes emperor. He's now a widower, she 656 00:38:02,040 --> 00:38:05,439 Speaker 1: becomes his common law wife. So it's stories like that 657 00:38:05,600 --> 00:38:10,239 Speaker 1: and like that. Who really gives we have more to 658 00:38:10,280 --> 00:38:12,080 Speaker 1: talk about. We're gonna have to have you back here 659 00:38:12,120 --> 00:38:15,560 Speaker 1: to get through another seven uh emperors as well ten 660 00:38:15,680 --> 00:38:21,080 Speaker 1: Caesar's Roman Emperors from Augustus to Constantine. It is shockingly accessible. 661 00:38:21,480 --> 00:38:24,760 Speaker 1: I think of Robert Hughes Classic Rome and many many 662 00:38:24,840 --> 00:38:28,280 Speaker 1: other books, and you get through them. But Barry Strauss 663 00:38:28,320 --> 00:38:31,040 Speaker 1: just said, does it incredibly well? In course, you remember 664 00:38:31,080 --> 00:38:33,120 Speaker 1: him as author of the Death of Caesar, whos did 665 00:38:33,120 --> 00:38:37,080 Speaker 1: so well as well ten Caesar's from Cornell's Barry Strauss. 666 00:38:37,239 --> 00:38:41,120 Speaker 1: We thank him today. Thanks for listening to the Bloomberg 667 00:38:41,160 --> 00:38:47,120 Speaker 1: Surveillance podcast. Subscribe and listen to interviews on Apple Podcasts, SoundCloud, 668 00:38:47,480 --> 00:38:51,680 Speaker 1: or whichever podcast platform you prefer. I'm on Twitter at 669 00:38:51,719 --> 00:38:56,000 Speaker 1: Tom Keane before the podcast. You can always catch us worldwide. 670 00:38:56,440 --> 00:39:02,520 Speaker 1: I'm Bloomberg Radio.