1 00:00:10,960 --> 00:00:13,440 Speaker 1: Well, acky, here we are the final days of two 2 00:00:13,480 --> 00:00:17,400 Speaker 1: thousand sixteen, the year of Brexit, the election of Donald Trump, 3 00:00:17,680 --> 00:00:21,160 Speaker 1: the year we lost Prince and David Bowie. I mean, 4 00:00:21,200 --> 00:00:24,080 Speaker 1: I think as a journalists we did such a terrible 5 00:00:24,880 --> 00:00:29,280 Speaker 1: predicting what would happen this year? And gosh, is it 6 00:00:29,400 --> 00:00:32,959 Speaker 1: even worth trying to think about what might happen next year? 7 00:00:33,040 --> 00:00:36,640 Speaker 1: Maybe the problem is in these predictions were too optimistic. 8 00:00:36,720 --> 00:00:39,479 Speaker 1: We look for the things that we want to see happening. Well, 9 00:00:39,479 --> 00:00:42,199 Speaker 1: how about this, how about we think about the worst 10 00:00:42,400 --> 00:00:45,800 Speaker 1: possible things that could happen next year and beyond, and 11 00:00:45,840 --> 00:00:49,120 Speaker 1: we prepare ourselves for the that worst case scenario that's 12 00:00:49,159 --> 00:00:56,959 Speaker 1: really grim. I like it that. Let's do it. Hi, 13 00:00:57,080 --> 00:01:00,520 Speaker 1: I'm brad Stone, and I for our final episode of 14 00:01:00,560 --> 00:01:04,759 Speaker 1: two thousand sixteen, we bring you the Bloomberg Pessimus Guide 15 00:01:05,040 --> 00:01:08,959 Speaker 1: Global Technology Edition for two thousand seventeen. These are the 16 00:01:09,000 --> 00:01:12,960 Speaker 1: most dire doomsday scenarios are reporters could come up with 17 00:01:13,360 --> 00:01:16,240 Speaker 1: the things that might happen in the world of tech. 18 00:01:16,480 --> 00:01:20,760 Speaker 1: Hopefully not if it all goes terribly wrong. Okay, So 19 00:01:20,840 --> 00:01:24,119 Speaker 1: buckle in and grab some tums to settle that ominously 20 00:01:24,160 --> 00:01:28,120 Speaker 1: grumbling stomach. Or maybe that shouted tequila. Actually, we should 21 00:01:28,120 --> 00:01:41,039 Speaker 1: have a bottle of tekula with us. Now, Well, the 22 00:01:41,120 --> 00:01:43,679 Speaker 1: US is still digesting the shock eleection of Donald Trump. 23 00:01:43,800 --> 00:01:46,160 Speaker 1: So let's start with the potential for doom and gloom 24 00:01:46,200 --> 00:01:49,400 Speaker 1: in Washington, d C. And bringing us that scenario is 25 00:01:49,520 --> 00:01:52,240 Speaker 1: Josh Brustein. He's our reporter in New York and he's 26 00:01:52,240 --> 00:01:54,600 Speaker 1: on the line with us. Hey guys, Okay, Josh, so 27 00:01:54,680 --> 00:01:56,160 Speaker 1: scare the heck out of us. What do you have 28 00:01:56,240 --> 00:01:59,560 Speaker 1: for two seventeen? Well, the great thing um for a 29 00:01:59,600 --> 00:02:02,640 Speaker 1: pessim us in seventeen and the surveillance realm, is that 30 00:02:03,000 --> 00:02:06,440 Speaker 1: there's plenty to be terrified about. Whether your main fear 31 00:02:06,640 --> 00:02:09,840 Speaker 1: is mass surveillance or whether your main fear is the 32 00:02:09,840 --> 00:02:14,560 Speaker 1: intelligence agencies of the United States being being hampered in 33 00:02:14,600 --> 00:02:18,480 Speaker 1: some way or another. That is a perfect pessimas. Okay, 34 00:02:18,560 --> 00:02:20,880 Speaker 1: So there has been this ongoing debate over over the 35 00:02:21,000 --> 00:02:23,600 Speaker 1: Vice Acts, the Foreign Intelligence Surveillance Act. What do you 36 00:02:23,639 --> 00:02:26,880 Speaker 1: think Trump is likely to do with this law? Yeah, 37 00:02:26,880 --> 00:02:29,120 Speaker 1: I mean, I think the thing is that nobody knows 38 00:02:29,280 --> 00:02:32,360 Speaker 1: and that makes people very nervous. This law has been 39 00:02:32,800 --> 00:02:35,560 Speaker 1: very controversial. A major part of it is going to 40 00:02:35,639 --> 00:02:38,320 Speaker 1: come up for renewal, and if it's not renewed by 41 00:02:38,320 --> 00:02:40,600 Speaker 1: the end of the year, it just goes away. And Jess, 42 00:02:40,760 --> 00:02:43,400 Speaker 1: is that a bad thing? Though it is seen as 43 00:02:43,440 --> 00:02:47,600 Speaker 1: a very bad thing. If you are in the intelligence community, 44 00:02:47,960 --> 00:02:50,160 Speaker 1: if you're in the a c. L U or the 45 00:02:50,200 --> 00:02:54,400 Speaker 1: Electronic Frontier Foundation UM or your privacy advocate, then you 46 00:02:54,480 --> 00:02:57,200 Speaker 1: actually kind of might welcome this. They have a website 47 00:02:57,200 --> 00:02:59,920 Speaker 1: where they're actually counting down to the expiration of US 48 00:03:00,000 --> 00:03:02,520 Speaker 1: Section seven two. Of course, Trump as a candidate, you know, 49 00:03:02,639 --> 00:03:05,520 Speaker 1: was for forms of torture. I mean, it does suspect 50 00:03:05,520 --> 00:03:07,280 Speaker 1: that he will push these things to the limits. So 51 00:03:07,280 --> 00:03:09,920 Speaker 1: what does what does it mean not just for privacy 52 00:03:10,000 --> 00:03:12,320 Speaker 1: of of Americans, but for you know, the u S 53 00:03:12,360 --> 00:03:14,560 Speaker 1: relationship with its allies in Europe and the and the 54 00:03:14,560 --> 00:03:19,320 Speaker 1: status of American Internet companies abroad. Yeah. Absolutely, so if 55 00:03:19,440 --> 00:03:23,080 Speaker 1: Trump is more aggressive with surveillance, something you could do 56 00:03:23,160 --> 00:03:26,000 Speaker 1: even aside from this law, because the president has a 57 00:03:26,040 --> 00:03:29,880 Speaker 1: lot of power just to start claiming just start claiming 58 00:03:29,880 --> 00:03:34,519 Speaker 1: authority under executive orders, and the if he starts being 59 00:03:34,520 --> 00:03:37,320 Speaker 1: more aggressive or is suspected of being more aggressive, because 60 00:03:37,320 --> 00:03:40,000 Speaker 1: it's a possibility that we wouldn't really know what's going on. 61 00:03:40,800 --> 00:03:44,400 Speaker 1: You could see europe uh start to put pressure on 62 00:03:44,480 --> 00:03:48,840 Speaker 1: American companies. We've already seen a lot of skepticism about 63 00:03:49,480 --> 00:03:53,000 Speaker 1: whether American companies can protect the information they have against 64 00:03:53,040 --> 00:03:56,080 Speaker 1: the U. S. Government, and that could become a bit 65 00:03:56,120 --> 00:04:00,760 Speaker 1: of a hot button issue for European governments in Silicon Valley. Okay, Josh, well, 66 00:04:00,840 --> 00:04:03,240 Speaker 1: thanks for bringing that to us today. Absolutely, thank you. 67 00:04:08,960 --> 00:04:11,000 Speaker 1: All right, So, how are you feeling so far? Acky? Uh? 68 00:04:12,320 --> 00:04:25,359 Speaker 1: I really wish we brought that bottle of tequila here. Okay, 69 00:04:25,400 --> 00:04:27,640 Speaker 1: So let's move away from the federal government now and 70 00:04:27,680 --> 00:04:30,960 Speaker 1: take a look at blue chip tech companies. Alex, you 71 00:04:31,000 --> 00:04:33,480 Speaker 1: are our Apple reporter and you're here with us here 72 00:04:33,480 --> 00:04:37,000 Speaker 1: in San Francisco. Hi. So, Alex, welcome to the Bloomberg 73 00:04:37,000 --> 00:04:40,080 Speaker 1: Technology Pessimist Guide, where we're doing our best to ruin 74 00:04:40,120 --> 00:04:43,279 Speaker 1: everyone's day. So tell us what's the worst thing that 75 00:04:43,320 --> 00:04:46,480 Speaker 1: could happen to Apple in two thousandev So Apple could 76 00:04:46,480 --> 00:04:50,000 Speaker 1: lose its credit rating, and this has huge implications for 77 00:04:50,040 --> 00:04:53,440 Speaker 1: a company which has succeeded in keeping shareholders happy by 78 00:04:53,480 --> 00:04:56,280 Speaker 1: rewarding them with cash in recent years while they've been 79 00:04:56,320 --> 00:04:58,680 Speaker 1: waiting for the next blockbuster product to plump up the 80 00:04:58,720 --> 00:05:03,480 Speaker 1: share price Simco, the CEO has sold debt in order 81 00:05:03,520 --> 00:05:07,559 Speaker 1: to fund buybacks and dividends and keep investors pockets plot flush. 82 00:05:07,800 --> 00:05:10,160 Speaker 1: What about iPhone sales? So they continue to prop up 83 00:05:10,160 --> 00:05:13,839 Speaker 1: the company. So iPhone sales are still pretty healthy, but 84 00:05:13,880 --> 00:05:16,599 Speaker 1: they are falling and flattening. Um. So you know, there's 85 00:05:16,640 --> 00:05:19,480 Speaker 1: been at the first revenue decline in over a decade 86 00:05:19,640 --> 00:05:23,080 Speaker 1: in the most recent fiscal year. So the expectation is 87 00:05:23,160 --> 00:05:25,839 Speaker 1: one hopes that the iPhone coming next year will be 88 00:05:25,839 --> 00:05:28,719 Speaker 1: a real blockbuster product. But if for some reason it 89 00:05:28,800 --> 00:05:32,440 Speaker 1: isn't and it's vastly disappointing and it doesn't encourage and 90 00:05:32,640 --> 00:05:36,360 Speaker 1: huge new fresh burst of sales, then that could be 91 00:05:36,400 --> 00:05:39,080 Speaker 1: a problem for Apple. All right, So we should remember 92 00:05:39,120 --> 00:05:43,039 Speaker 1: that Apple is the world's biggest technology company, actually the 93 00:05:43,080 --> 00:05:48,440 Speaker 1: world's biggest company period. So if credit agencies suddenly decide 94 00:05:48,520 --> 00:05:50,960 Speaker 1: that Apple is no longer credit worthy, that's going to 95 00:05:50,960 --> 00:05:53,760 Speaker 1: have implications for every other tech company out there, every 96 00:05:53,800 --> 00:05:57,240 Speaker 1: other publicly traded company in the world. It is something 97 00:05:57,240 --> 00:05:59,800 Speaker 1: that could also have implications for someone like Microsoft. Now 98 00:05:59,839 --> 00:06:02,720 Speaker 1: the is an Apple raises this debt is because while 99 00:06:02,760 --> 00:06:05,480 Speaker 1: it has vast amounts of cash in the bank, most 100 00:06:05,520 --> 00:06:08,120 Speaker 1: of it is offshore. Of the two billion in cash 101 00:06:08,160 --> 00:06:10,800 Speaker 1: reserves that it has, two six billion dollars of that 102 00:06:10,920 --> 00:06:13,919 Speaker 1: is outside the US. In order to repatriate that money, 103 00:06:14,120 --> 00:06:16,280 Speaker 1: Apple has to pay a thirty five pc tax rate 104 00:06:16,320 --> 00:06:18,839 Speaker 1: in the US, which is clearly great news for the Treasury, 105 00:06:19,040 --> 00:06:21,479 Speaker 1: but not such great news for Apple itself. There is 106 00:06:21,520 --> 00:06:23,920 Speaker 1: hope that in Congress they will pass some legislation which 107 00:06:23,960 --> 00:06:26,800 Speaker 1: will reduce that tax bill, which increases the likelihood and 108 00:06:26,800 --> 00:06:29,880 Speaker 1: that Apple's willingness to repatriate money from off shore that 109 00:06:30,000 --> 00:06:33,640 Speaker 1: would essentially reduce their need to sell debt in order 110 00:06:33,680 --> 00:06:35,600 Speaker 1: to reward the shareholders and come up with that sort 111 00:06:35,600 --> 00:06:38,400 Speaker 1: of Donald Trump has signaled the willingness to do that, yes, 112 00:06:38,680 --> 00:06:41,640 Speaker 1: and it's been one of his big campaign pledges that 113 00:06:41,680 --> 00:06:44,719 Speaker 1: would help spare investment in the US. So there is 114 00:06:44,760 --> 00:06:46,840 Speaker 1: a real appetite to make that happen in the new 115 00:06:46,839 --> 00:06:49,360 Speaker 1: White House. Is there a chance where we might see 116 00:06:49,400 --> 00:06:53,120 Speaker 1: another miracle product category spring from Cooper Tino that averts 117 00:06:53,160 --> 00:06:55,560 Speaker 1: this worst case scenario. I mean, that's the question every 118 00:06:55,680 --> 00:06:57,719 Speaker 1: investor in the world is asking. We know they've looked 119 00:06:57,720 --> 00:07:00,920 Speaker 1: at cars, they've broadly scrapped the Apple s hardware part 120 00:07:00,960 --> 00:07:03,040 Speaker 1: of the car project that they're still carrying on with software. 121 00:07:03,279 --> 00:07:06,040 Speaker 1: We think they're exploring some things in glasses. We've reported 122 00:07:06,080 --> 00:07:08,800 Speaker 1: that maybe something in healthcare. We don't quite know what 123 00:07:08,880 --> 00:07:11,240 Speaker 1: that is, maybe just some sort of healthcare platform. They're 124 00:07:11,280 --> 00:07:13,520 Speaker 1: investigating a lot of avenues, but we're yet to see 125 00:07:13,520 --> 00:07:17,040 Speaker 1: anything concrete surface just yet. All right, so we just 126 00:07:17,200 --> 00:07:19,960 Speaker 1: asked you to tell us the worst case scenario. But 127 00:07:20,040 --> 00:07:22,240 Speaker 1: how likely is it? I was speaking to the Moody's 128 00:07:22,240 --> 00:07:24,040 Speaker 1: analyst who looks at this staff as his job, and 129 00:07:24,120 --> 00:07:28,000 Speaker 1: he said that if Apple continues to raise debt at 130 00:07:28,040 --> 00:07:32,960 Speaker 1: a faster pace than it's cash balance increases, then this 131 00:07:33,000 --> 00:07:36,640 Speaker 1: could happen within eighteen months. One hopes that the blockbuster 132 00:07:36,880 --> 00:07:40,760 Speaker 1: new iPhone next year renders such a likelihood impossible, but 133 00:07:40,880 --> 00:07:43,280 Speaker 1: we'll have to look and see. All right, Alex Webb, 134 00:07:43,360 --> 00:07:45,960 Speaker 1: thank you for contributing to the dark mood in the studio. 135 00:07:46,200 --> 00:07:50,680 Speaker 1: We appreciate it. Happy Christmas, sorry I should say Holidays 136 00:07:50,680 --> 00:08:01,240 Speaker 1: in New Yestian so Akey, who's up next? We are 137 00:08:01,280 --> 00:08:04,400 Speaker 1: now going from the blue chips to the unicorns, and 138 00:08:04,440 --> 00:08:07,200 Speaker 1: specifically we're going to talk about the worst thing that 139 00:08:07,240 --> 00:08:10,680 Speaker 1: could happen to Uber, which is now the world's biggest 140 00:08:10,720 --> 00:08:14,280 Speaker 1: private technology company. And we have Eric newcomer here in 141 00:08:14,320 --> 00:08:17,480 Speaker 1: San Francisco. Eric, Eric, Hey, thanks for having me. All right, 142 00:08:17,520 --> 00:08:20,320 Speaker 1: so scare us. What could possibly happen to our beloved Uber? 143 00:08:20,640 --> 00:08:22,920 Speaker 1: You know, what what if? What if the unicorn with 144 00:08:23,000 --> 00:08:26,040 Speaker 1: the biggest private valuation in the world sixty nine billion 145 00:08:26,040 --> 00:08:29,040 Speaker 1: dollars saw its valuation fall? I mean, this is a 146 00:08:29,040 --> 00:08:32,800 Speaker 1: company that's been built around sort of the ever growing 147 00:08:33,000 --> 00:08:35,800 Speaker 1: rise of that that number. We've just watched it. When 148 00:08:35,800 --> 00:08:37,680 Speaker 1: it was three billion, we thought it was crazy and 149 00:08:37,760 --> 00:08:39,240 Speaker 1: up and up and up and up. You know, there 150 00:08:39,240 --> 00:08:41,520 Speaker 1: are no short sellers against it. It's it's a number 151 00:08:41,520 --> 00:08:44,360 Speaker 1: that's climbed. So what happens if it falls? So who cares? What? 152 00:08:44,360 --> 00:08:46,360 Speaker 1: What would be the impact of that? The problem is 153 00:08:46,400 --> 00:08:50,960 Speaker 1: one for Uber morale would be heard. Some employees would 154 00:08:50,960 --> 00:08:54,360 Speaker 1: see their stock compensation fall dramatically. A lot of them 155 00:08:54,400 --> 00:08:56,760 Speaker 1: have sort of counted on that is why they're working 156 00:08:56,800 --> 00:09:00,400 Speaker 1: there and not sort of the lush offices of Google. Uh. 157 00:09:00,559 --> 00:09:04,080 Speaker 1: Uber's reputation would fall. You know, it's been this gigantic titan. 158 00:09:04,360 --> 00:09:06,760 Speaker 1: But I think you know the problems for Uber aside 159 00:09:06,840 --> 00:09:09,160 Speaker 1: it would be a bad sign for Silicon Valley. This 160 00:09:09,200 --> 00:09:11,800 Speaker 1: is a valuation that towers above all other ones. It 161 00:09:11,840 --> 00:09:14,600 Speaker 1: sort of sets the brominter for what companies should be worth. 162 00:09:14,840 --> 00:09:17,960 Speaker 1: If Uber's value is misguided, then I think a lot 163 00:09:18,040 --> 00:09:19,839 Speaker 1: of other companies are gonna have to question how much 164 00:09:19,840 --> 00:09:22,880 Speaker 1: their worth and that could have broad implications for you know, 165 00:09:22,960 --> 00:09:25,719 Speaker 1: not just these technology workers, but also the broader San 166 00:09:25,760 --> 00:09:29,600 Speaker 1: Francisco economy too. Right, maybe housing prices would come down 167 00:09:29,640 --> 00:09:32,400 Speaker 1: to Well, that's a good point. I mean, would a 168 00:09:32,480 --> 00:09:36,360 Speaker 1: decline in unicorn valuations be good for some people presumably 169 00:09:36,400 --> 00:09:39,400 Speaker 1: incumbents and everyone that these unicorns have been able to 170 00:09:39,880 --> 00:09:43,040 Speaker 1: use a flood of cheap money to sort of suppress 171 00:09:43,400 --> 00:09:46,600 Speaker 1: I mean, Uber has helped drown out the taxi cab market. Here. 172 00:09:46,840 --> 00:09:48,839 Speaker 1: If it turned out Uber was operating on margins that 173 00:09:48,880 --> 00:09:52,280 Speaker 1: were unsustainable, had to raise its margins, heard its valuation, 174 00:09:53,000 --> 00:09:55,240 Speaker 1: lower its market share, that might see we might see 175 00:09:55,240 --> 00:09:58,360 Speaker 1: a resurgence in taxi cabs, or maybe Uber sort of 176 00:09:58,360 --> 00:10:00,920 Speaker 1: steamrolled too many of them to really make a strong comeback. 177 00:10:01,120 --> 00:10:03,760 Speaker 1: I mean, it really is hard to imagine Uber making 178 00:10:03,800 --> 00:10:06,240 Speaker 1: money off of these uberpool routes. I mean they are 179 00:10:06,400 --> 00:10:10,040 Speaker 1: so cheap. Yeah, for Uber. It's all about volume, drive 180 00:10:10,120 --> 00:10:13,040 Speaker 1: price to the bottom, and then expand into pool, expand 181 00:10:13,080 --> 00:10:16,040 Speaker 1: into food delivery. So they know the margins are going 182 00:10:16,080 --> 00:10:17,600 Speaker 1: to be pretty thin, but they just need to do 183 00:10:17,640 --> 00:10:19,520 Speaker 1: a lot of them. Let me see this though, as 184 00:10:19,520 --> 00:10:22,840 Speaker 1: a consumer, I think the worst case scenario for me 185 00:10:23,040 --> 00:10:24,800 Speaker 1: would be for Lift to go out of business and 186 00:10:24,800 --> 00:10:27,040 Speaker 1: for Uber to have a complete monopoly over the San 187 00:10:27,040 --> 00:10:30,640 Speaker 1: Francisco market and check out prices like crazy and make 188 00:10:31,040 --> 00:10:34,720 Speaker 1: my transportation costs spike. I think that's a fair point. 189 00:10:34,920 --> 00:10:37,640 Speaker 1: Lift has about a billion dollars in the bank probably 190 00:10:37,640 --> 00:10:40,760 Speaker 1: and is slowly spending down that money, and maybe you know, 191 00:10:40,760 --> 00:10:43,640 Speaker 1: about six million dollars a year or less, so it 192 00:10:43,679 --> 00:10:45,320 Speaker 1: has some time to figure it out. But at some 193 00:10:45,400 --> 00:10:48,240 Speaker 1: point there's a ticking time bomb for Lift to figure 194 00:10:48,240 --> 00:10:51,040 Speaker 1: out how it gets more money, goes public, gets money 195 00:10:51,040 --> 00:10:53,719 Speaker 1: from those investors, or gets bought and has somebody subsidized 196 00:10:53,760 --> 00:10:57,760 Speaker 1: their business. Okay, taking time bombs, Thanks for unsettling us further, Eric, 197 00:10:58,600 --> 00:11:07,839 Speaker 1: Good job, Brad. Are you ready for the next one? 198 00:11:08,000 --> 00:11:09,839 Speaker 1: There's more? I feel like I need to go get 199 00:11:09,880 --> 00:11:16,080 Speaker 1: some anxiety medication. Well, this one is particularly scary. Lizette Chapman, 200 00:11:16,240 --> 00:11:19,240 Speaker 1: you are our venture capital reporter here in San Francisco, 201 00:11:19,480 --> 00:11:21,960 Speaker 1: and you're going to tell us about how personal health 202 00:11:22,040 --> 00:11:25,320 Speaker 1: data could be collected against our will and used against us. 203 00:11:25,600 --> 00:11:28,160 Speaker 1: What do you mean could be collected? It is being 204 00:11:28,160 --> 00:11:34,040 Speaker 1: collected right now, and we're volunteering it. Everybody has not everybody, 205 00:11:34,080 --> 00:11:37,120 Speaker 1: but like sixty million plus people have absolutely fallen in 206 00:11:37,200 --> 00:11:41,800 Speaker 1: love with all of these different wearable devices with different 207 00:11:41,800 --> 00:11:44,880 Speaker 1: sensors on it that track our steps that you know, 208 00:11:45,000 --> 00:11:48,160 Speaker 1: track our sleeping, and sensors are getting better and better. 209 00:11:48,600 --> 00:11:50,600 Speaker 1: So what's the problem. I thought all this was supposed 210 00:11:50,640 --> 00:11:53,000 Speaker 1: to make us feel healthier. It's true, a lot of 211 00:11:53,000 --> 00:11:55,800 Speaker 1: people have improved their health because they're able to quantify 212 00:11:56,360 --> 00:11:59,920 Speaker 1: how their habits range day to day and even sometimes 213 00:12:00,000 --> 00:12:04,360 Speaker 1: compete with their friends. The problem is that a lot 214 00:12:04,440 --> 00:12:08,160 Speaker 1: of people are just volunteering this information. You're not even reading, 215 00:12:08,360 --> 00:12:10,120 Speaker 1: you know, kind of like the terms and conditions when 216 00:12:10,120 --> 00:12:13,160 Speaker 1: you download an app. You just click it and move on. Well, 217 00:12:13,800 --> 00:12:16,040 Speaker 1: that could be a problem going forward. But how much 218 00:12:16,120 --> 00:12:18,920 Speaker 1: data can these insurers or employers really get out of 219 00:12:18,960 --> 00:12:21,080 Speaker 1: the number of steps we take every day? We'll see, 220 00:12:21,120 --> 00:12:23,600 Speaker 1: But that's just it. It's not just about steps anymore. 221 00:12:23,640 --> 00:12:27,440 Speaker 1: I mean now sensors are becoming so cheap and so 222 00:12:27,559 --> 00:12:30,440 Speaker 1: much better than they have been in past years that 223 00:12:30,480 --> 00:12:33,959 Speaker 1: they're able to monitor everything from blood oxygen levels to 224 00:12:34,440 --> 00:12:36,560 Speaker 1: heart rate to to you name it, it, just like 225 00:12:36,600 --> 00:12:39,000 Speaker 1: you would in a doctor's appointment. So the worst key 226 00:12:39,000 --> 00:12:42,400 Speaker 1: scenario is these insurers might use this against us that 227 00:12:42,800 --> 00:12:45,000 Speaker 1: you know, they'll see the number of steps we're taking, 228 00:12:45,120 --> 00:12:48,440 Speaker 1: our heart rate, our blood oxygen levels and say, maybe 229 00:12:48,440 --> 00:12:51,080 Speaker 1: this person has a good chance of getting heart disease 230 00:12:51,120 --> 00:12:54,440 Speaker 1: down the road. It could happen. We're volunteering this information. 231 00:12:54,840 --> 00:12:58,640 Speaker 1: I haven't seen any legislation saying hey, you know, uh, 232 00:12:59,360 --> 00:13:02,560 Speaker 1: you know, don't reach out to these different customers because 233 00:13:02,600 --> 00:13:04,840 Speaker 1: it hasn't happened yet. They're not they haven't made the 234 00:13:04,840 --> 00:13:08,720 Speaker 1: connection yet between this massive trove of data that's being 235 00:13:08,760 --> 00:13:16,920 Speaker 1: generated every day by Americans to um to health insurance policies. Yet, 236 00:13:17,120 --> 00:13:21,920 Speaker 1: just earlier this year, ETNA UM gave out fifty thousand 237 00:13:22,000 --> 00:13:24,960 Speaker 1: Apple watches to their employees so they can start working 238 00:13:25,000 --> 00:13:27,040 Speaker 1: on a joint venture with Apple to figure out how 239 00:13:27,080 --> 00:13:29,480 Speaker 1: to use the data. So I think it's might be coming. 240 00:13:29,920 --> 00:13:33,040 Speaker 1: That sounds scary. Does the potential repeal of the Affordable 241 00:13:33,080 --> 00:13:37,239 Speaker 1: Care Act Obamacare does a factor into this legal ambiguity 242 00:13:37,280 --> 00:13:40,520 Speaker 1: that might allow insurance or healthcare companies to penalize us 243 00:13:40,559 --> 00:13:44,720 Speaker 1: for unhealthiness. Perhaps it's kind of mes you know, you 244 00:13:44,800 --> 00:13:47,280 Speaker 1: look at that, that's kind of a big, hot red 245 00:13:47,400 --> 00:13:50,400 Speaker 1: mess right now. I think the larger point that that 246 00:13:50,520 --> 00:13:52,320 Speaker 1: you're getting at, I think is a good one, which 247 00:13:52,360 --> 00:13:55,480 Speaker 1: is that you know, health insurance is broken right now 248 00:13:55,520 --> 00:13:58,319 Speaker 1: and they're looking for new revenue streams. There's high cost, 249 00:13:58,640 --> 00:14:03,760 Speaker 1: low satisfaction um with the big data, the use of 250 00:14:03,800 --> 00:14:06,040 Speaker 1: big data, this is a new revenue stream and if 251 00:14:06,080 --> 00:14:09,480 Speaker 1: you think about it, you know we're just giving it away. 252 00:14:09,520 --> 00:14:11,720 Speaker 1: It's kind of like when you download an app and 253 00:14:11,760 --> 00:14:14,720 Speaker 1: you just agree to terms and conditions. Well, thanks for 254 00:14:14,720 --> 00:14:22,240 Speaker 1: sharing that with us today, Lizette Brad. I think it's 255 00:14:22,280 --> 00:14:24,280 Speaker 1: time for you to kick it off my Apple Watch 256 00:14:24,360 --> 00:14:28,320 Speaker 1: right now, at least change your privacy settings on your tracker. Well, 257 00:14:28,320 --> 00:14:37,800 Speaker 1: I'm going for a run. So this brings us to 258 00:14:38,000 --> 00:14:42,840 Speaker 1: our very last scenario of ready for one more bread? 259 00:14:43,720 --> 00:14:46,360 Speaker 1: Probably not, but let's do it. This one, you could say, 260 00:14:46,400 --> 00:14:50,960 Speaker 1: has been long overdue. Here's Dina Bass calling in from Seattle. Hey, DNS, 261 00:14:51,120 --> 00:14:53,880 Speaker 1: what have you got for us? So? My scenario is 262 00:14:53,880 --> 00:14:57,440 Speaker 1: that the a group of hackers working for a hostile 263 00:14:57,520 --> 00:15:00,720 Speaker 1: nation state or a stateless entity like a terrorist, scre criminals, 264 00:15:00,800 --> 00:15:03,520 Speaker 1: or hacked of US will hack into the industrial systems 265 00:15:03,600 --> 00:15:07,040 Speaker 1: at UM some critical infrastructure, say a power plant or 266 00:15:07,080 --> 00:15:10,640 Speaker 1: a damn public transport. They will tweak the software too, 267 00:15:11,040 --> 00:15:13,440 Speaker 1: in a manner that shuts down or destroys the hardware 268 00:15:13,480 --> 00:15:15,960 Speaker 1: and damaging the facility. Yeah, this sort of thing has 269 00:15:15,960 --> 00:15:18,520 Speaker 1: been possible for a while. I'm thinking of that attacked 270 00:15:18,560 --> 00:15:22,480 Speaker 1: by US and Israeli hackers on an Iranian nuclear facility 271 00:15:22,520 --> 00:15:25,480 Speaker 1: a few years ago with the stuck snet virus. Yes, absolutely, 272 00:15:25,520 --> 00:15:27,960 Speaker 1: that's exactly what they did there. They used a virus 273 00:15:28,000 --> 00:15:30,720 Speaker 1: to basically speed up the way the center fugees in 274 00:15:31,120 --> 00:15:34,400 Speaker 1: the reactor we're spinning and damaged um um. And you know, 275 00:15:34,480 --> 00:15:36,840 Speaker 1: the US government has been doing some tests going back 276 00:15:36,840 --> 00:15:38,560 Speaker 1: as far as two thousand seven, if you look at 277 00:15:38,720 --> 00:15:41,080 Speaker 1: Fred Kaplan's book, where they were trying to see whether 278 00:15:41,120 --> 00:15:43,440 Speaker 1: a remote programmer in d C could take out a 279 00:15:43,520 --> 00:15:46,280 Speaker 1: twenty seven ton power generator all the way across the 280 00:15:46,280 --> 00:15:48,640 Speaker 1: country in Idaho. And of course it took just twenty 281 00:15:48,640 --> 00:15:50,760 Speaker 1: one lines of code to send the generator to an 282 00:15:50,840 --> 00:15:53,840 Speaker 1: untimely death. That's incredible. I mean, one of our previous 283 00:15:53,880 --> 00:15:58,520 Speaker 1: podcast episodes was about how these hackers in Russia managed 284 00:15:58,560 --> 00:16:02,240 Speaker 1: to penetrate the Democratic now stional committees email servers and 285 00:16:02,720 --> 00:16:05,320 Speaker 1: they were able to read quite a bit of havoc 286 00:16:05,480 --> 00:16:09,440 Speaker 1: on the election campaign cycle. So if they were able 287 00:16:09,480 --> 00:16:12,240 Speaker 1: to hack emails in this way, he really makes you 288 00:16:12,320 --> 00:16:16,800 Speaker 1: think what happens if they actually hack physical infrastructure that 289 00:16:16,840 --> 00:16:19,960 Speaker 1: could put real lives in danger. Sure. Absolutely, we've had 290 00:16:20,000 --> 00:16:22,920 Speaker 1: a couple of, you know, examples of that around the world. Also, 291 00:16:22,960 --> 00:16:26,640 Speaker 1: there was hacking of of a damn in Westchester, New 292 00:16:26,720 --> 00:16:30,520 Speaker 1: York that was attributed to the Iranians. In we had 293 00:16:30,560 --> 00:16:32,880 Speaker 1: was sort of the first reported takedown of an electrical 294 00:16:32,880 --> 00:16:35,760 Speaker 1: power grid in Ukraine. Uh And the point you make 295 00:16:35,800 --> 00:16:37,640 Speaker 1: about the d n C hack is a good one 296 00:16:37,640 --> 00:16:39,560 Speaker 1: as well, because that that happened with you know, as 297 00:16:39,560 --> 00:16:41,640 Speaker 1: far as we know, not a lot of repercussions, and 298 00:16:42,160 --> 00:16:45,080 Speaker 1: you know, my sense is that that could embolden people 299 00:16:45,120 --> 00:16:48,040 Speaker 1: in twenty s to try something more seriously. Well, Dina, 300 00:16:48,120 --> 00:16:51,400 Speaker 1: considering all these very public instances, how prepared our computer 301 00:16:51,520 --> 00:16:55,000 Speaker 1: security experts for these kinds of attacks? So it's gotten. 302 00:16:55,040 --> 00:16:58,479 Speaker 1: Things have gotten better than stucks net. You know, companies 303 00:16:58,520 --> 00:17:01,120 Speaker 1: like Semens, which was theo that have made the controls 304 00:17:01,160 --> 00:17:05,120 Speaker 1: that were impacted in Iran, have worked to harden their systems. 305 00:17:05,200 --> 00:17:08,120 Speaker 1: They now go in and signed contracts with these companies 306 00:17:08,160 --> 00:17:10,800 Speaker 1: to go in and update the systems more frequently. But 307 00:17:11,000 --> 00:17:14,119 Speaker 1: you know, there's still a fair amount of issues. Just 308 00:17:14,200 --> 00:17:18,160 Speaker 1: last week, there's a Presidential Commission on Enhancing National Cybersecurity 309 00:17:18,160 --> 00:17:20,080 Speaker 1: and put they put out a report and a number 310 00:17:20,080 --> 00:17:23,600 Speaker 1: of the recommendations were related to these issues of defending 311 00:17:24,040 --> 00:17:27,240 Speaker 1: key infrastructure um, you know, and also they talked about 312 00:17:27,280 --> 00:17:29,359 Speaker 1: coming up with clear ideas of who does what and 313 00:17:29,640 --> 00:17:31,679 Speaker 1: you know, to respond to these kinds of attacks, and 314 00:17:31,680 --> 00:17:35,400 Speaker 1: what are the rules of engagement for state and local governments, 315 00:17:35,440 --> 00:17:38,400 Speaker 1: for the for the federal government, and also for private 316 00:17:38,400 --> 00:17:41,200 Speaker 1: companies who are are going to be helping out to protect. 317 00:17:41,640 --> 00:17:44,600 Speaker 1: And of course, as we bring more and more of 318 00:17:44,640 --> 00:17:47,640 Speaker 1: our systems connected to the Internet, the stakes are only 319 00:17:47,640 --> 00:17:50,920 Speaker 1: getting higher. Yeah. Absolutely, that was something that was flagged 320 00:17:50,920 --> 00:17:52,760 Speaker 1: by the Commission as well, and they were talking about, 321 00:17:52,760 --> 00:17:56,159 Speaker 1: you know, our cyber and physical worlds increasingly converge. You know, 322 00:17:56,200 --> 00:18:00,119 Speaker 1: earlier this year we saw that massive distributed denial of 323 00:18:00,440 --> 00:18:03,200 Speaker 1: service attack that took down a decent chunk of the Internet, 324 00:18:03,240 --> 00:18:05,879 Speaker 1: and that was basically, you know, people, the Internet was 325 00:18:05,920 --> 00:18:09,320 Speaker 1: attacked via webcams and and DVRs. It's great that we 326 00:18:09,359 --> 00:18:11,880 Speaker 1: have all of these Internet of things devices, but they're 327 00:18:11,880 --> 00:18:14,520 Speaker 1: now on networks that are connecting or can connect in 328 00:18:14,560 --> 00:18:17,520 Speaker 1: certain ways to our critical infrastructure, which is another thing 329 00:18:17,560 --> 00:18:20,920 Speaker 1: this Presidential Commission brought up. These two things are converging 330 00:18:21,040 --> 00:18:24,840 Speaker 1: the largely unsecured world of Internet of things devices and 331 00:18:24,960 --> 00:18:28,360 Speaker 1: our critical infrastructure. Well, let's keep our fingers crossed. None 332 00:18:28,359 --> 00:18:30,359 Speaker 1: of this is making me feel any better. Thanks for 333 00:18:30,440 --> 00:18:45,480 Speaker 1: that prediction, Dina, Thank you. How are you feeling, Hockey? 334 00:18:47,440 --> 00:18:49,720 Speaker 1: I don't know. I don't know if I'm ready for 335 00:18:49,760 --> 00:18:55,400 Speaker 1: twenty Neither apard the Optimist Guide the two after this episode. 336 00:18:55,440 --> 00:18:58,919 Speaker 1: Oh my gosh, I know, what's your worst case scenario, 337 00:18:59,040 --> 00:19:02,000 Speaker 1: brad Well. I mean, everyone gave gave me a lot 338 00:19:02,040 --> 00:19:03,879 Speaker 1: of food for thought here, you know. The one that 339 00:19:03,920 --> 00:19:07,240 Speaker 1: I contributed to the larger Bloomberg Pessimist Guide was that 340 00:19:07,280 --> 00:19:10,520 Speaker 1: we would find out that the law enforcement authorities were 341 00:19:10,600 --> 00:19:13,399 Speaker 1: wire tapping some of these passive listening devices like the 342 00:19:13,440 --> 00:19:16,040 Speaker 1: Amazon Echo or even some of our phones that are 343 00:19:16,080 --> 00:19:20,399 Speaker 1: kind of waiting for these watchwords like Hello, Google or Alexa. 344 00:19:20,480 --> 00:19:22,359 Speaker 1: You know, can they just turn those on and listen? 345 00:19:22,800 --> 00:19:24,879 Speaker 1: And if they do do that and or may be 346 00:19:24,960 --> 00:19:28,159 Speaker 1: able to, you know, to to amass some intelligence on 347 00:19:28,240 --> 00:19:29,920 Speaker 1: some bad guys. But what is it due to the 348 00:19:29,960 --> 00:19:32,760 Speaker 1: overall trust in these devices that are now populating our lives? 349 00:19:32,800 --> 00:19:36,200 Speaker 1: That's my question for two thousand and seventeen. How about you, well, 350 00:19:36,560 --> 00:19:39,080 Speaker 1: how about this? How about I reject the premise altogether 351 00:19:39,119 --> 00:19:43,080 Speaker 1: because I'm feeling terrible right now, and I give you 352 00:19:43,480 --> 00:19:47,119 Speaker 1: an optimist prediction next year. All right. So I was 353 00:19:47,200 --> 00:19:50,879 Speaker 1: talking to our biotech reporter Caroline Chen earlier, and she 354 00:19:51,000 --> 00:19:54,560 Speaker 1: was telling me about, um, all these really incredible advances 355 00:19:54,600 --> 00:19:59,280 Speaker 1: that doctors, researchers, biotech companies are making right now in 356 00:19:59,320 --> 00:20:02,560 Speaker 1: this in the few yild of cancer research. UM. Apparently 357 00:20:02,640 --> 00:20:08,240 Speaker 1: there's a new method called immunotherapy where instead of using 358 00:20:08,400 --> 00:20:12,480 Speaker 1: radiation to just kill off entire parts of your body, right, UM, 359 00:20:12,680 --> 00:20:16,040 Speaker 1: you can go in UH and implement this really targeted 360 00:20:16,119 --> 00:20:19,720 Speaker 1: form of therapy that makes your immune system attack the 361 00:20:19,840 --> 00:20:24,840 Speaker 1: cancer cells. And so far in university labs and the 362 00:20:24,960 --> 00:20:28,000 Speaker 1: biotech research labs, UH they're producing a lot of really 363 00:20:28,000 --> 00:20:31,360 Speaker 1: good results. That sounds very promising. It's two thousand seventeen, 364 00:20:31,400 --> 00:20:34,240 Speaker 1: the year when I make a difference in patients lives. Well, 365 00:20:34,359 --> 00:20:37,560 Speaker 1: let let's see. It sounds like I'm some therapies are 366 00:20:37,680 --> 00:20:41,159 Speaker 1: up for FDA approval right now, so they could become 367 00:20:41,200 --> 00:20:44,680 Speaker 1: widely available pretty soon. And you know, maybe they wouldn't 368 00:20:44,680 --> 00:20:47,719 Speaker 1: cure cancer altogether, maybe they wouldn't be able to prevent it, 369 00:20:47,800 --> 00:20:51,639 Speaker 1: but maybe it could make cancer this form of chronic 370 00:20:51,680 --> 00:20:54,480 Speaker 1: disease that you live with for the rest of your life, 371 00:20:54,560 --> 00:20:57,560 Speaker 1: kind of like kind of like AIDS. So a very 372 00:20:57,680 --> 00:21:06,720 Speaker 1: nice optimistic prediction to end to end our podcast today, 373 00:21:07,880 --> 00:21:10,719 Speaker 1: and that's it for this year on the Decrypted Podcast. 374 00:21:10,920 --> 00:21:13,639 Speaker 1: Thanks for listening. We're going to take a short break 375 00:21:13,720 --> 00:21:16,080 Speaker 1: for the holidays next week, but we'll be back with 376 00:21:16,160 --> 00:21:19,560 Speaker 1: a full slate of new episodes starting on January ten. 377 00:21:19,880 --> 00:21:22,359 Speaker 1: If you have an iPhone, please subscribe on your native 378 00:21:22,359 --> 00:21:25,160 Speaker 1: podcast app and leave us a rating and review there. 379 00:21:25,320 --> 00:21:28,120 Speaker 1: It helps more listeners discover the show and tell us 380 00:21:28,160 --> 00:21:31,080 Speaker 1: what you think could be the worst possible thing that 381 00:21:31,119 --> 00:21:35,719 Speaker 1: could happen in I'm on Twitter at Akio seven and 382 00:21:35,800 --> 00:21:39,800 Speaker 1: I'm at Brad Stone. This episode was produced by Pied Good, 383 00:21:39,880 --> 00:21:43,320 Speaker 1: Cary Magnus, Henrickson, and Liz Smith, with help from Aaron Plack. 384 00:21:43,760 --> 00:21:47,359 Speaker 1: Alec McCabe is head of Bloomberg Podcasts. We'll see you 385 00:21:47,359 --> 00:22:01,600 Speaker 1: next year, Happy New Year, Land Land