1 00:00:00,160 --> 00:00:03,760 Speaker 1: This is Bloomberg Business Week with Carol Masser and Jason 2 00:00:03,840 --> 00:00:14,120 Speaker 1: Kelly on Bloomberg Radio. Don't Want So what of our 3 00:00:14,160 --> 00:00:16,960 Speaker 1: next guests picked this song and we'll get into why 4 00:00:17,000 --> 00:00:20,640 Speaker 1: about being evil? Um, And it speaks to a bigger, 5 00:00:20,680 --> 00:00:23,920 Speaker 1: broader theme this year, and it's been a theme this year, 6 00:00:23,960 --> 00:00:25,200 Speaker 1: and it's no dabt going to be a theme in 7 00:00:25,960 --> 00:00:29,320 Speaker 1: big tech, the regulation of it, what big tech stands for, 8 00:00:29,360 --> 00:00:31,960 Speaker 1: what they're all about, how they're impacting our world. Let's 9 00:00:32,000 --> 00:00:34,120 Speaker 1: get into it because Vinnie Carolino is back with us. 10 00:00:34,120 --> 00:00:38,000 Speaker 1: He's chief market strategist at Starvesant Capital Management, Global investment 11 00:00:38,000 --> 00:00:41,720 Speaker 1: strategist at Dafoux Redmount, and also with us is CNN 12 00:00:41,760 --> 00:00:45,800 Speaker 1: Global economic analyst Rona faru Har. She is also author 13 00:00:45,840 --> 00:00:49,120 Speaker 1: of the book Don't Be Evil, Hence the Evil, How 14 00:00:49,159 --> 00:00:52,239 Speaker 1: Big Tech Be Traded, its founding principles and all of us. 15 00:00:52,240 --> 00:00:56,280 Speaker 1: They're both in our Bloomberg Interactive Broker Studio. Nice to 16 00:00:56,360 --> 00:00:59,400 Speaker 1: have you both with us, UM, Jason and I talked 17 00:00:59,400 --> 00:01:01,240 Speaker 1: about this all all the time. First of all, talk 18 00:01:01,320 --> 00:01:05,360 Speaker 1: to us a little bit about the premise for your book. Yeah, well, Um, 19 00:01:05,400 --> 00:01:07,600 Speaker 1: you know I cover the markets and you just look 20 00:01:07,640 --> 00:01:09,759 Speaker 1: at the numbers and you see that basically about eight 21 00:01:10,200 --> 00:01:12,800 Speaker 1: of corporate value is living in ten percent of firms, 22 00:01:12,800 --> 00:01:14,440 Speaker 1: and they're the firms that have the most data, the 23 00:01:14,440 --> 00:01:17,360 Speaker 1: most intellectual property. So this is part of a massive 24 00:01:17,600 --> 00:01:20,240 Speaker 1: shift that's really the industrial revolution of our time. So 25 00:01:20,640 --> 00:01:23,160 Speaker 1: the economic story is there. The political story, I mean, 26 00:01:23,160 --> 00:01:25,720 Speaker 1: we've been living this now for two years. The fact 27 00:01:25,760 --> 00:01:29,080 Speaker 1: that the model of a Google or Facebook in particular 28 00:01:29,200 --> 00:01:33,120 Speaker 1: is highly targeted advertising. It's about targeting us down to 29 00:01:33,280 --> 00:01:37,240 Speaker 1: the individual. But that split society. I mean, it comes 30 00:01:37,240 --> 00:01:39,600 Speaker 1: with side effects. It comes with a lot of great value, 31 00:01:39,640 --> 00:01:42,560 Speaker 1: but it comes with side effects. Then there's the brain science, 32 00:01:42,600 --> 00:01:45,840 Speaker 1: the social issues, and you know, to be honest, I 33 00:01:45,880 --> 00:01:48,240 Speaker 1: got into this topic in some ways for a personal reason. 34 00:01:48,240 --> 00:01:50,080 Speaker 1: I came home one day, I was looking at a 35 00:01:50,080 --> 00:01:52,560 Speaker 1: credit card bill and there were all these tiny charges 36 00:01:52,560 --> 00:01:55,600 Speaker 1: dollar ninety three dollars. I thought, my god, I've been hacked, 37 00:01:56,080 --> 00:01:58,360 Speaker 1: and then I realized, my ten year old son has 38 00:01:58,400 --> 00:02:01,960 Speaker 1: my passwords. Turns out he had racked up nine dollars 39 00:02:01,960 --> 00:02:06,680 Speaker 1: in a supposedly free online soccer game that was tracking 40 00:02:06,760 --> 00:02:09,359 Speaker 1: him and selling him in app purchases. And I thought, 41 00:02:09,360 --> 00:02:10,880 Speaker 1: you know, as a mother, I was horrified, But as 42 00:02:10,919 --> 00:02:12,640 Speaker 1: a as a business journalist, I thought, I want to 43 00:02:12,639 --> 00:02:15,919 Speaker 1: know everything about this. Yeah, so Thenny come on in here, 44 00:02:15,960 --> 00:02:19,560 Speaker 1: because as ron is so wisely noted at the top, 45 00:02:19,680 --> 00:02:22,600 Speaker 1: I mean, this is a business story front and center. 46 00:02:23,120 --> 00:02:26,640 Speaker 1: And for investors, what's not to love about tech? I mean, 47 00:02:26,760 --> 00:02:29,280 Speaker 1: Carol just went through the numbers at the close of 48 00:02:29,520 --> 00:02:33,000 Speaker 1: how well these big tech stocks have done. Investors have 49 00:02:33,040 --> 00:02:35,280 Speaker 1: made a ton of money here. Fair missing out too 50 00:02:35,320 --> 00:02:38,720 Speaker 1: if you don't chip on the on the investment training here. Sure, absolutely, 51 00:02:38,800 --> 00:02:42,560 Speaker 1: I think I think the listeners investors should know that 52 00:02:42,600 --> 00:02:46,760 Speaker 1: there is an investment paradigm here, uh, that they need 53 00:02:46,840 --> 00:02:50,240 Speaker 1: to dig a little bit deeper into and understand. Ronna 54 00:02:50,320 --> 00:02:53,720 Speaker 1: talks about it with the the targeted ads and the 55 00:02:53,720 --> 00:02:58,320 Speaker 1: revenue streams that are coming from from this surveillance capitalism 56 00:02:58,360 --> 00:03:01,760 Speaker 1: system that is going on. Uh. And it's interesting when 57 00:03:01,840 --> 00:03:05,560 Speaker 1: Rona mentions about that she was hacked, she thought she 58 00:03:05,639 --> 00:03:08,000 Speaker 1: might have been hacked. And as I begin to explore 59 00:03:08,280 --> 00:03:12,440 Speaker 1: this area thanks to Rona, that I realized that, yeah, 60 00:03:12,840 --> 00:03:15,360 Speaker 1: we're being hacked, but we're not being hacked in the 61 00:03:15,400 --> 00:03:18,080 Speaker 1: manner of you know, you would ordinarily think about it 62 00:03:18,120 --> 00:03:22,280 Speaker 1: that the drill down into our behavior. So you really 63 00:03:22,280 --> 00:03:24,880 Speaker 1: need to understand that it on on a whole range 64 00:03:24,919 --> 00:03:26,720 Speaker 1: of level. Well, we talked about the business we cover 65 00:03:26,800 --> 00:03:29,000 Speaker 1: this week about Google and the generals at Google. Right. 66 00:03:29,360 --> 00:03:32,000 Speaker 1: You know, for a long time, Silicon Valley wars a 67 00:03:32,040 --> 00:03:35,920 Speaker 1: badge of honor, kind of Washington hating them, and now 68 00:03:35,960 --> 00:03:38,240 Speaker 1: all of a sudden that their middle age. A lot 69 00:03:38,240 --> 00:03:41,000 Speaker 1: of these big tech companies, especially something like a Google, 70 00:03:41,360 --> 00:03:44,040 Speaker 1: you know, they want to be involved in those big 71 00:03:44,080 --> 00:03:47,800 Speaker 1: government contracts and they're doing so, but they've got a 72 00:03:47,840 --> 00:03:49,960 Speaker 1: lot of their employees not happy about it. This whole 73 00:03:49,960 --> 00:03:52,560 Speaker 1: idea of you know, not doing evil like this was 74 00:03:52,600 --> 00:03:55,480 Speaker 1: something that they held as part of their corporate culture 75 00:03:55,920 --> 00:03:58,360 Speaker 1: and now things are changing. Yeah, and it was always 76 00:03:58,440 --> 00:04:00,720 Speaker 1: baked into the business model, Frank, I mean, evil was 77 00:04:01,280 --> 00:04:03,760 Speaker 1: part of the business model. If you think that from 78 00:04:03,840 --> 00:04:06,240 Speaker 1: day one, I mean you go back to the very 79 00:04:06,240 --> 00:04:10,680 Speaker 1: first paper that Larry Page and Serge Brand, founders of Google, wrote. Um. 80 00:04:10,720 --> 00:04:12,720 Speaker 1: It had a section at the bottom that said, if 81 00:04:12,760 --> 00:04:16,400 Speaker 1: you monetize a big search engine with targeted advertising, the 82 00:04:16,440 --> 00:04:18,920 Speaker 1: interests of the users and those of the advertisers are 83 00:04:18,960 --> 00:04:20,880 Speaker 1: going to come into conflict at some point. And so 84 00:04:20,920 --> 00:04:24,320 Speaker 1: they actually advocated for an open search engine, maybe something 85 00:04:24,360 --> 00:04:27,560 Speaker 1: in the academic sphere. But um, you know, it's interesting 86 00:04:27,560 --> 00:04:30,719 Speaker 1: that he's making a fascinating point because this disruption is 87 00:04:30,720 --> 00:04:33,520 Speaker 1: not just about the four or five Silicon Valley giants. 88 00:04:33,839 --> 00:04:36,600 Speaker 1: It's coming to every business model. So one of the 89 00:04:36,640 --> 00:04:38,480 Speaker 1: things that I find so fascinating you just look at 90 00:04:38,520 --> 00:04:41,920 Speaker 1: the insurance industry. For example, you can now have sensors 91 00:04:41,920 --> 00:04:44,280 Speaker 1: in your house, in your car that will give you 92 00:04:44,680 --> 00:04:46,640 Speaker 1: a discount if you know, if you're taking care of 93 00:04:46,680 --> 00:04:50,280 Speaker 1: your plumbing um or you're stopping quickly, but you might 94 00:04:50,320 --> 00:04:53,040 Speaker 1: get a black mark if your kid is smoking weed 95 00:04:53,240 --> 00:04:57,000 Speaker 1: in the bedroom. What does that do? That disrupts the 96 00:04:57,240 --> 00:05:01,440 Speaker 1: entire insurance business model of pulled rit think about that. 97 00:05:01,440 --> 00:05:04,960 Speaker 1: That's coming to every industry. The the implications are really profound. 98 00:05:05,720 --> 00:05:08,640 Speaker 1: And so we've only got about a minute left in 99 00:05:08,640 --> 00:05:10,800 Speaker 1: this first segment. We're gonna keep you around for another 100 00:05:11,000 --> 00:05:13,479 Speaker 1: what's the biggest surprise to you writing this book? What 101 00:05:13,560 --> 00:05:16,640 Speaker 1: jumped out? You know, in some ways the fact that 102 00:05:16,680 --> 00:05:19,040 Speaker 1: it was always there, It was hiding in plain site. 103 00:05:19,279 --> 00:05:21,599 Speaker 1: You know that that paper And frankly, this goes to 104 00:05:21,640 --> 00:05:23,000 Speaker 1: my point. I mean, one of the side and one 105 00:05:23,040 --> 00:05:26,080 Speaker 1: of the social side effects of this high speed media 106 00:05:26,120 --> 00:05:28,080 Speaker 1: landscape that we're in is that people don't read as much. 107 00:05:28,120 --> 00:05:30,720 Speaker 1: And I gotta think that nobody read the initial paper 108 00:05:30,839 --> 00:05:32,800 Speaker 1: because we kind of would have known where we were 109 00:05:32,800 --> 00:05:35,000 Speaker 1: going to be. And we want to continue our conversation 110 00:05:35,480 --> 00:05:37,800 Speaker 1: still with us as Vinne Catalino, Chief market strategist of 111 00:05:37,880 --> 00:05:41,000 Speaker 1: It stuff Isn't Capital Management Global investment strategist to Faux 112 00:05:41,040 --> 00:05:44,520 Speaker 1: Red Mount and also still with us as ron book 113 00:05:44,760 --> 00:05:47,600 Speaker 1: is Don't be Evil, how big Tech betrayed its founding 114 00:05:47,600 --> 00:05:49,680 Speaker 1: principles and all of us. This is great for your 115 00:05:49,680 --> 00:05:55,600 Speaker 1: stocking stuffers this holiday season because big stock that's okay, 116 00:05:55,640 --> 00:05:57,640 Speaker 1: there's like this is like a must read because it 117 00:05:57,680 --> 00:06:01,560 Speaker 1: does um it's the story or it is and it 118 00:06:01,600 --> 00:06:04,440 Speaker 1: impact no matter what industry you said before the break. 119 00:06:04,560 --> 00:06:05,760 Speaker 1: You know, one of the things we need to get 120 00:06:05,800 --> 00:06:08,320 Speaker 1: into is China's role in all of this. Tell us 121 00:06:08,320 --> 00:06:10,280 Speaker 1: a little bit about that, where you see how that 122 00:06:10,320 --> 00:06:14,479 Speaker 1: plays in right well, this is the story, the the 123 00:06:14,520 --> 00:06:17,279 Speaker 1: economy story right now, tech trade wars. This is really 124 00:06:17,320 --> 00:06:20,040 Speaker 1: about China and the US going different ways in terms 125 00:06:20,040 --> 00:06:21,640 Speaker 1: of how the Internet is going to be governed, in 126 00:06:21,720 --> 00:06:24,800 Speaker 1: terms of strategic technologies, I keep hearing from a lot 127 00:06:24,800 --> 00:06:27,440 Speaker 1: of Chinese investors that they believe they're going to have 128 00:06:27,480 --> 00:06:29,760 Speaker 1: their own ecosystem. They've already got their own big tech 129 00:06:29,800 --> 00:06:32,200 Speaker 1: players Ali Baba, Tencent, But I do all those, but 130 00:06:32,279 --> 00:06:34,800 Speaker 1: that they are going to actually have their their own 131 00:06:35,360 --> 00:06:37,920 Speaker 1: supply chains, their own consumer brands. I mean, a company 132 00:06:37,960 --> 00:06:40,320 Speaker 1: like show Me is already doing better in some in 133 00:06:40,360 --> 00:06:43,000 Speaker 1: some areas than Apple in China. So this is a 134 00:06:43,000 --> 00:06:47,440 Speaker 1: big split coming, and it's really interesting. It's provoking some fascinating, 135 00:06:47,600 --> 00:06:50,680 Speaker 1: um uh, strange bedfellows in the US. You know, you 136 00:06:50,720 --> 00:06:53,920 Speaker 1: see a company like Google saying, well, you know, maybe 137 00:06:53,920 --> 00:06:56,520 Speaker 1: we should be a national champion here, um, you know, 138 00:06:56,640 --> 00:06:59,880 Speaker 1: teaming up with the Defense Department, uh, thinking about how 139 00:06:59,880 --> 00:07:02,560 Speaker 1: to kind of ring fence the the ecosystem in the US. 140 00:07:02,600 --> 00:07:05,520 Speaker 1: Same again with Amazon, they've tried to ring fence government purchasing. 141 00:07:06,080 --> 00:07:09,120 Speaker 1: My worry is the overall ecosystem, though. I worry that 142 00:07:09,440 --> 00:07:11,320 Speaker 1: you're going to end up with a scenario where four 143 00:07:11,440 --> 00:07:15,160 Speaker 1: or five big players have the entire pie, and that 144 00:07:15,240 --> 00:07:18,960 Speaker 1: doesn't work. We've got to make room for other other companies. Really, 145 00:07:19,200 --> 00:07:21,560 Speaker 1: so four or five players sort of connes that there 146 00:07:21,600 --> 00:07:25,880 Speaker 1: would be a monopoly Yeah, and yet we seem to 147 00:07:26,520 --> 00:07:30,880 Speaker 1: need a new definition for monopoly, right, because monopoly pertains 148 00:07:30,960 --> 00:07:35,200 Speaker 1: to price increases generally as a rule in an industrial economy, 149 00:07:35,240 --> 00:07:41,520 Speaker 1: but in this kind of surveillance, capitalistic information economy, that's 150 00:07:41,560 --> 00:07:43,640 Speaker 1: really not the case. Yeah, and you know that the 151 00:07:43,680 --> 00:07:46,160 Speaker 1: point about price is so profound because you're absolutely right 152 00:07:46,240 --> 00:07:48,320 Speaker 1: that the whole Chicago school, you know, as long as 153 00:07:48,320 --> 00:07:51,520 Speaker 1: prices are going down, everything's fine, doesn't fit when you're 154 00:07:51,560 --> 00:07:53,760 Speaker 1: you're not doing a transaction in dollars, You're doing it 155 00:07:53,800 --> 00:07:56,600 Speaker 1: in data. It's a barter transaction. So that's not a 156 00:07:56,800 --> 00:07:58,800 Speaker 1: that that's a very opaque market. And that's why you 157 00:07:58,840 --> 00:08:01,200 Speaker 1: have this asymmetry and this kind of superstar effect with 158 00:08:01,200 --> 00:08:04,200 Speaker 1: an Amazon or Google. They've got all the information. You 159 00:08:04,240 --> 00:08:06,560 Speaker 1: don't have any information. So I think we are going 160 00:08:06,600 --> 00:08:08,440 Speaker 1: to need some regulatory shifts to deal with that. So 161 00:08:08,520 --> 00:08:11,560 Speaker 1: as you sort of finished reporting and writing this, and 162 00:08:11,600 --> 00:08:13,280 Speaker 1: as you go out and talk to people, I mean, 163 00:08:13,320 --> 00:08:15,240 Speaker 1: as we've been saying, we're not just saying because you're 164 00:08:15,280 --> 00:08:16,800 Speaker 1: I mean, it is the story of our time. It's 165 00:08:16,800 --> 00:08:21,480 Speaker 1: incredibly timely. Are you more optimistic less optimistic? Have you 166 00:08:21,600 --> 00:08:24,679 Speaker 1: changed your own sort of behavior or your thoughts about 167 00:08:24,680 --> 00:08:27,760 Speaker 1: this through the process. Well, I absolutely have. I mean 168 00:08:27,760 --> 00:08:30,160 Speaker 1: I've done a digital detox. It was kind of actually 169 00:08:30,160 --> 00:08:33,080 Speaker 1: by force that I Um, last Christmas, I dropped my 170 00:08:33,160 --> 00:08:36,000 Speaker 1: cell phone on Christmas Eve was a company issued phone, 171 00:08:36,000 --> 00:08:37,640 Speaker 1: and I couldn't get a replacement, and it was like, 172 00:08:37,880 --> 00:08:40,600 Speaker 1: you know, going off cigarettes or something. For forty eight hours, 173 00:08:40,600 --> 00:08:43,680 Speaker 1: I was twitchy. I kept reaching in my pocket. So, um, 174 00:08:43,760 --> 00:08:45,760 Speaker 1: I do that regularly now. And I've actually cut down 175 00:08:45,840 --> 00:08:47,800 Speaker 1: the amount of times that I check email, which, by 176 00:08:47,800 --> 00:08:49,960 Speaker 1: the way, it takes you five minutes to reset every 177 00:08:50,000 --> 00:08:53,160 Speaker 1: time you interrupt. So your productivity, I mean, that's a 178 00:08:53,160 --> 00:08:56,959 Speaker 1: whole another topic. Lost product The productivity conundrum probably has 179 00:08:57,000 --> 00:08:59,520 Speaker 1: something to do with technology and the effects of these firsts. No, 180 00:08:59,559 --> 00:09:01,520 Speaker 1: I think it's a really good point, and you see 181 00:09:01,559 --> 00:09:04,079 Speaker 1: more and more advice saying, you know, check your emails 182 00:09:04,120 --> 00:09:06,680 Speaker 1: certainly you know a certain amount of time times to day, 183 00:09:06,880 --> 00:09:09,760 Speaker 1: not constantly letting it kind of interrupt your workflow. I 184 00:09:09,800 --> 00:09:13,800 Speaker 1: do wonder though, in terms of regulatory oversight, where it's 185 00:09:13,840 --> 00:09:17,000 Speaker 1: all headache. Well, there's there's two big schools of thought. 186 00:09:17,040 --> 00:09:19,319 Speaker 1: I mean, one is that, look, we can work within 187 00:09:19,400 --> 00:09:22,920 Speaker 1: the existing system and just make some small tweaks. The 188 00:09:23,000 --> 00:09:25,400 Speaker 1: other thought, and this is more of how the Europeans 189 00:09:25,400 --> 00:09:28,559 Speaker 1: are going, is we're going to need a really profound reshaping. 190 00:09:28,559 --> 00:09:31,720 Speaker 1: I mean, the Europeans are talking about public data banks, 191 00:09:31,800 --> 00:09:33,960 Speaker 1: you know, where you would be the public sector would 192 00:09:33,960 --> 00:09:36,240 Speaker 1: own this this data. But on the other hand, you've 193 00:09:36,280 --> 00:09:38,640 Speaker 1: got California coming in saying, hey, maybe we need a 194 00:09:38,800 --> 00:09:41,920 Speaker 1: digital Sovereign Wealth Fund, because at the end of the day, 195 00:09:42,000 --> 00:09:45,079 Speaker 1: if data is the new oil, you have to make 196 00:09:45,080 --> 00:09:49,360 Speaker 1: sure that that value can be shared. Well you can. 197 00:09:49,400 --> 00:09:53,120 Speaker 1: I just need something run a set earlier in regards 198 00:09:53,200 --> 00:09:59,559 Speaker 1: to uh, the capture of information and the and the 199 00:09:59,600 --> 00:10:02,000 Speaker 1: and to a patient of what is about to occur 200 00:10:02,080 --> 00:10:04,360 Speaker 1: and you know, dropping the phone, things of that sort. 201 00:10:04,679 --> 00:10:07,760 Speaker 1: One of the concerns that's been expressed has been whether 202 00:10:07,880 --> 00:10:11,920 Speaker 1: or not the machine learning will have accomplished so much 203 00:10:12,600 --> 00:10:15,640 Speaker 1: that it knows what you're going to do and anticipation 204 00:10:15,760 --> 00:10:20,040 Speaker 1: of and therefore it isn't you have free will you don't? Yeah, 205 00:10:20,480 --> 00:10:22,600 Speaker 1: it really does come down to that, It really has. 206 00:10:22,679 --> 00:10:27,360 Speaker 1: It has a definitely Yeah that's correct as a matrix 207 00:10:27,400 --> 00:10:31,320 Speaker 1: feel to it. For sure. It's a whole other level like, yeah, 208 00:10:32,040 --> 00:10:34,080 Speaker 1: I think I think we just go back and think 209 00:10:34,120 --> 00:10:37,600 Speaker 1: we just went into the matrix. All right. Thank you 210 00:10:37,760 --> 00:10:41,720 Speaker 1: both so much. We really really appreciate it. Rana Fuhar. 211 00:10:41,960 --> 00:10:45,040 Speaker 1: The book is Don't Be Evil, how Big Tech portrayed 212 00:10:45,080 --> 00:10:47,559 Speaker 1: its founding principles and all of us. Uh. It is 213 00:10:47,600 --> 00:10:52,160 Speaker 1: a must read. It's incredibly thought provoking. It will spur conversations, 214 00:10:52,240 --> 00:10:55,280 Speaker 1: probably spur some different behavior as well. Has given us 215 00:10:55,280 --> 00:10:57,319 Speaker 1: a lot to think about our thanks as well to 216 00:10:57,400 --> 00:11:00,920 Speaker 1: any Catalina cheap market strategies, first Ice Capital Management and 217 00:11:01,080 --> 00:11:05,000 Speaker 1: of course global investment strategies over at defaut Red Mount