1 00:00:05,800 --> 00:00:08,720 Speaker 1: Welcome to the Bloomberg p m L Podcast. I'm pim Fox. 2 00:00:08,760 --> 00:00:11,520 Speaker 1: Along with my co host Lisa Abramowitz. Each day we 3 00:00:11,640 --> 00:00:15,120 Speaker 1: bring you the most important, noteworthy, and useful interviews for 4 00:00:15,200 --> 00:00:17,840 Speaker 1: you and your money, whether you're at the grocery store 5 00:00:17,960 --> 00:00:20,720 Speaker 1: or the trading floor. Find the Bloomberg p m L 6 00:00:20,840 --> 00:00:30,440 Speaker 1: Podcast on Apple Podcasts, SoundCloud and Bloomberg dot Com. A 7 00:00:30,560 --> 00:00:34,280 Speaker 1: rally in the shares of Tesla after the company reports 8 00:00:34,360 --> 00:00:39,440 Speaker 1: his first quarterly profit and a positive free cash flow. Uh. 9 00:00:39,600 --> 00:00:41,960 Speaker 1: Here to tell us more about it is David Coudla. 10 00:00:42,080 --> 00:00:44,839 Speaker 1: He is, of course, the chief investment strategist, founder and 11 00:00:44,920 --> 00:00:49,080 Speaker 1: chief executive of Mainstay Capital and Bloomberg Markets. Brought to 12 00:00:49,080 --> 00:00:52,040 Speaker 1: you by Commonwealth Financial Network, the number one r I, 13 00:00:52,240 --> 00:00:55,080 Speaker 1: a broker dealer that JD Power has named highest and 14 00:00:55,200 --> 00:01:01,120 Speaker 1: independent advisors satisfaction among financial investment firms five times in 15 00:01:01,120 --> 00:01:05,160 Speaker 1: a row. Learn more at Commonwealth and dot com and 16 00:01:05,200 --> 00:01:09,479 Speaker 1: we turned out to Mr Coula to learn more about Tesla. So, David, 17 00:01:09,640 --> 00:01:14,040 Speaker 1: are you a believer or are you still skeptical about Tesla? 18 00:01:14,480 --> 00:01:19,800 Speaker 1: I am still skeptical skeptical about Tesla, and I am 19 00:01:20,600 --> 00:01:24,319 Speaker 1: was very excited about the news yesterday very excited about 20 00:01:24,360 --> 00:01:29,039 Speaker 1: the rally and after hours trading. Unfortunately, I was only 21 00:01:29,560 --> 00:01:31,759 Speaker 1: able to short the stock at a high of three 22 00:01:31,800 --> 00:01:34,600 Speaker 1: twenty one. Today. Maybe I'll have another chance to short 23 00:01:34,640 --> 00:01:38,080 Speaker 1: it higher. It's trading about three twelve now. Um. But 24 00:01:38,280 --> 00:01:42,320 Speaker 1: you know, we we expected with yearning announcement pull ahead, 25 00:01:42,360 --> 00:01:46,280 Speaker 1: we'd have some good results, and we did even better 26 00:01:46,319 --> 00:01:49,480 Speaker 1: than expected. But it's about the sustainability of those results, 27 00:01:49,520 --> 00:01:53,480 Speaker 1: which we don't think they could maintain, and all that 28 00:01:53,560 --> 00:01:55,800 Speaker 1: they did in the third quarter to generate these great 29 00:01:55,840 --> 00:02:00,160 Speaker 1: results and as good as they are, best quarter by 30 00:02:00,160 --> 00:02:04,760 Speaker 1: far in two years. Uh, you know, everything negative cash flow, negative, 31 00:02:04,760 --> 00:02:07,720 Speaker 1: earnings negative in this whopping quarter and the stock is 32 00:02:07,720 --> 00:02:10,320 Speaker 1: trading at three eleven. Yeah. Well, actually I was going 33 00:02:10,360 --> 00:02:12,800 Speaker 1: to just say, what is your target? Because I'm looking 34 00:02:12,880 --> 00:02:16,239 Speaker 1: right now. Bank of America, Mary Lynch. Their target is 35 00:02:16,240 --> 00:02:21,680 Speaker 1: two hundred dollars underperform. Goldman Sachs target to sell. Cowen 36 00:02:21,919 --> 00:02:26,360 Speaker 1: price target to fifty underperform. What's your price target? Our 37 00:02:26,400 --> 00:02:28,639 Speaker 1: twelve month price target on the stock is one eight, 38 00:02:29,280 --> 00:02:31,840 Speaker 1: and we think that longer term, the stock has to 39 00:02:31,880 --> 00:02:35,280 Speaker 1: settle somewhere, uh, down between a hundred and a hundred 40 00:02:35,280 --> 00:02:38,800 Speaker 1: and fifty and we and we say that just compare 41 00:02:38,840 --> 00:02:44,320 Speaker 1: to stocks, compare to automakers general motors trading at a 42 00:02:44,440 --> 00:02:48,200 Speaker 1: PE of about five or six. And if we take 43 00:02:48,280 --> 00:02:51,399 Speaker 1: this number and extrapolate it a number which we don't 44 00:02:51,440 --> 00:02:55,520 Speaker 1: think they can maintain in terms of earnings. UH. Going forward, 45 00:02:56,520 --> 00:02:59,520 Speaker 1: Tesla is trading at would be trading at a price 46 00:02:59,520 --> 00:03:03,720 Speaker 1: to earnings multiple at forty to fifty times, which is 47 00:03:03,800 --> 00:03:07,720 Speaker 1: higher than any automaker anywhere in the world. And you know, 48 00:03:07,760 --> 00:03:10,840 Speaker 1: we know that as we go forward, they have a 49 00:03:10,880 --> 00:03:13,280 Speaker 1: debt repayment that Elon must said in the call last 50 00:03:13,360 --> 00:03:16,720 Speaker 1: night would probably mean a flat you know, no earnings 51 00:03:16,720 --> 00:03:19,000 Speaker 1: in the first quarter. They've got a Model Wide to fund, 52 00:03:19,000 --> 00:03:21,399 Speaker 1: they've got a China Giga factory to fund, They've got 53 00:03:21,400 --> 00:03:25,919 Speaker 1: other operations to fund. And we know, you know, when 54 00:03:25,919 --> 00:03:29,160 Speaker 1: you pass through the numbers, UH, there was a little 55 00:03:29,160 --> 00:03:32,880 Speaker 1: bit of accounting tomfoolery to to uh, to make a 56 00:03:32,880 --> 00:03:37,120 Speaker 1: good number. Elon Musk has been managing this company, headlined 57 00:03:37,120 --> 00:03:39,440 Speaker 1: to headline. In the second quarter, it was about being 58 00:03:39,480 --> 00:03:42,600 Speaker 1: able to say they built five thousand vehicles in one week. 59 00:03:42,840 --> 00:03:46,120 Speaker 1: They only average a little over four thousand Model threes. 60 00:03:46,720 --> 00:03:49,480 Speaker 1: They only averaged a little over four thousand model threes 61 00:03:49,560 --> 00:03:51,880 Speaker 1: per week in the third quarter, after hitting that five 62 00:03:51,920 --> 00:03:55,720 Speaker 1: thousand a week benchmark. Now he's got the headline of 63 00:03:55,760 --> 00:03:59,760 Speaker 1: this great quarter. Um, you know, are our bare thesis 64 00:03:59,800 --> 00:04:03,880 Speaker 1: is meant like some bears on on Tesla going bankrupt. 65 00:04:04,160 --> 00:04:06,880 Speaker 1: It's just that it's someday that the stock comes down 66 00:04:06,880 --> 00:04:11,640 Speaker 1: to reality and trades closer to where other automakers trade, 67 00:04:12,320 --> 00:04:15,000 Speaker 1: even if it is a sustainable business and it's not 68 00:04:15,040 --> 00:04:17,440 Speaker 1: going to be at three hundred or three fifty or 69 00:04:17,440 --> 00:04:20,240 Speaker 1: four hundred dollars a share, how do you respond, David, 70 00:04:20,320 --> 00:04:23,760 Speaker 1: to those investors or even those analysts who say that 71 00:04:23,800 --> 00:04:27,960 Speaker 1: Tesla is not just an automobile company. Tesla is a 72 00:04:28,040 --> 00:04:33,360 Speaker 1: technology company, and it will expand its offering to not 73 00:04:33,520 --> 00:04:39,360 Speaker 1: just include automobiles sold through their own dealerships, but an 74 00:04:39,360 --> 00:04:44,600 Speaker 1: attempt to lease automobiles for rides sharing or to compete 75 00:04:44,680 --> 00:04:50,600 Speaker 1: against Uber and Lift, and also to provide charging that 76 00:04:50,680 --> 00:04:57,839 Speaker 1: would really change the way people interact with their vehicles. Right, So, uh, 77 00:04:58,040 --> 00:05:01,200 Speaker 1: I understand that that thesis that both talked about, But 78 00:05:01,440 --> 00:05:05,600 Speaker 1: we've got now a ten year old company that drives 79 00:05:05,680 --> 00:05:11,640 Speaker 1: more than of its revenue from selling automobiles less than 80 00:05:11,640 --> 00:05:14,240 Speaker 1: ten percent of the revenue comes from those other businesses, 81 00:05:14,279 --> 00:05:19,160 Speaker 1: whether it's solar panels or energy storage or UH solar 82 00:05:19,640 --> 00:05:23,240 Speaker 1: roof shingles, all of those those other business activities in 83 00:05:23,279 --> 00:05:27,840 Speaker 1: the energy division and solar divisions of the company. H 84 00:05:28,000 --> 00:05:31,000 Speaker 1: As we look forward into the future of mobility, there 85 00:05:31,080 --> 00:05:33,960 Speaker 1: is this assumption and Elon Musk talked about it last 86 00:05:34,040 --> 00:05:37,160 Speaker 1: night in the conference call. There's this assumption by the 87 00:05:37,160 --> 00:05:41,000 Speaker 1: Tesla bulls that that Tesla will just own the future 88 00:05:41,000 --> 00:05:43,440 Speaker 1: of e V it will own the future of autonomous, 89 00:05:43,480 --> 00:05:45,640 Speaker 1: it will own the future of ride sharing, it will 90 00:05:45,680 --> 00:05:51,320 Speaker 1: own the future of robotaxis and autonomous Weymo is recognized 91 00:05:51,320 --> 00:05:55,080 Speaker 1: as the industry leader UH. You know General Motors when 92 00:05:55,279 --> 00:06:01,000 Speaker 1: when UH, When UM soft Bank UH passed on an 93 00:06:01,040 --> 00:06:04,520 Speaker 1: investment in Tesla, they made an investment in General Motors 94 00:06:04,560 --> 00:06:10,080 Speaker 1: Cruise division. When Honda, Japanese automaker wanted to look for 95 00:06:10,480 --> 00:06:15,279 Speaker 1: their next generation of electric vehicle battery architecture, they bought 96 00:06:15,279 --> 00:06:17,520 Speaker 1: it from GM. When they wanted to team up with 97 00:06:17,560 --> 00:06:20,720 Speaker 1: somebody to work on autonomous, they did with GM. So 98 00:06:20,800 --> 00:06:24,040 Speaker 1: there are a lot of competing technologies out there. I 99 00:06:24,120 --> 00:06:30,320 Speaker 1: know there are those faithful UH people, followers of Tesla, 100 00:06:30,800 --> 00:06:32,920 Speaker 1: but the future is going to look a lot different 101 00:06:32,920 --> 00:06:35,200 Speaker 1: than what they might think and maybe what Elon Musk 102 00:06:35,320 --> 00:06:38,440 Speaker 1: might might be preaching. David Coodla, thank you so much 103 00:06:38,480 --> 00:06:41,119 Speaker 1: for being with us and sharing your views. David Coodla's founder, 104 00:06:41,200 --> 00:06:45,440 Speaker 1: chief executive officer, and chief investment strategist of Mainstay Capital 105 00:06:45,600 --> 00:06:49,719 Speaker 1: Bold call shares below two hundred and the next twelve 106 00:06:49,880 --> 00:06:53,320 Speaker 1: months interesting to see. Right now shares up eight point 107 00:06:53,360 --> 00:06:57,680 Speaker 1: four percent, trading just below three huh actually right now 108 00:06:57,839 --> 00:07:11,560 Speaker 1: nearly three hundred and twelve dollars to share. There has 109 00:07:11,640 --> 00:07:14,920 Speaker 1: been a report in the New York Times about President 110 00:07:14,960 --> 00:07:18,480 Speaker 1: Trump's phone, his cell phone, his personal cell phone, uh, 111 00:07:18,520 --> 00:07:21,000 Speaker 1: and the fact that he continues to use it and 112 00:07:21,080 --> 00:07:24,840 Speaker 1: it makes him susceptible to hacking. He has denied this, 113 00:07:25,160 --> 00:07:27,760 Speaker 1: so has the Chinese government. What do we make of this? 114 00:07:28,600 --> 00:07:31,119 Speaker 1: I don't know, but Clint Watts probably does. He's senior 115 00:07:31,120 --> 00:07:34,280 Speaker 1: fellow at the Foreign Policy Research Institute, also senior Fellow 116 00:07:34,320 --> 00:07:36,520 Speaker 1: at the Center for Cyber and Homeland Security at the 117 00:07:36,520 --> 00:07:40,360 Speaker 1: George Washington University. Thank you so much, Clint for being 118 00:07:40,560 --> 00:07:44,960 Speaker 1: with us. So what was your impression of this particular story? 119 00:07:45,000 --> 00:07:49,320 Speaker 1: Do you believe it? I want to say no, but 120 00:07:49,400 --> 00:07:50,960 Speaker 1: every time I say no in a story like this, 121 00:07:51,080 --> 00:07:54,280 Speaker 1: it turns out to be a yes. So I think 122 00:07:54,320 --> 00:07:58,240 Speaker 1: there are some important things in the article um to 123 00:07:58,320 --> 00:08:01,280 Speaker 1: think about. And one was the notion that the Chinese 124 00:08:01,320 --> 00:08:05,320 Speaker 1: government essentially identifies friends of President Trump and then goes 125 00:08:05,360 --> 00:08:08,440 Speaker 1: to influence the friends to tell Donald Trump and his 126 00:08:08,600 --> 00:08:12,200 Speaker 1: friends what to think about different issues. And whether that 127 00:08:12,280 --> 00:08:15,360 Speaker 1: happens on a cell phone intercept or in reality through 128 00:08:15,400 --> 00:08:20,320 Speaker 1: business associates, this is a very adept influenced strategy I've 129 00:08:20,360 --> 00:08:22,640 Speaker 1: also seen. I think this is actually the second or 130 00:08:22,760 --> 00:08:26,040 Speaker 1: third story that I've seen about the president's cell phone 131 00:08:26,040 --> 00:08:28,640 Speaker 1: maybe not being secured or used in a secure way. 132 00:08:28,680 --> 00:08:30,960 Speaker 1: If you remember back to the start of the Obama 133 00:08:31,000 --> 00:08:34,920 Speaker 1: administration when President Obama was addicted to his BlackBerry, you 134 00:08:34,960 --> 00:08:36,600 Speaker 1: know this has come back to the two cousin eight, 135 00:08:37,120 --> 00:08:39,360 Speaker 1: and there was a lot of worry about how they 136 00:08:39,360 --> 00:08:41,600 Speaker 1: would secure it. I think they eventually convinced him to 137 00:08:41,679 --> 00:08:44,960 Speaker 1: not use it because of the security issue. I'm not 138 00:08:45,040 --> 00:08:48,680 Speaker 1: sure that that sort of convincing has been done in 139 00:08:48,679 --> 00:08:51,160 Speaker 1: in the White House. And and one thing just tangentially 140 00:08:51,200 --> 00:08:53,720 Speaker 1: is we've also seen a White House where a lot 141 00:08:53,800 --> 00:08:55,880 Speaker 1: of aids, a lot of people that work in the 142 00:08:55,880 --> 00:08:58,800 Speaker 1: White not S are recording each other. So every time 143 00:08:58,840 --> 00:09:00,600 Speaker 1: I have doubt about these stories that we would have 144 00:09:01,160 --> 00:09:04,080 Speaker 1: on secure lines that could be intercepted, I do find 145 00:09:04,679 --> 00:09:07,000 Speaker 1: uh that there are users in the White House of 146 00:09:07,080 --> 00:09:11,640 Speaker 1: cellphones uh, making recordings, uh, using things sort of in 147 00:09:11,720 --> 00:09:14,880 Speaker 1: a loose fashion, which could could lead to something being insecure. 148 00:09:15,360 --> 00:09:17,560 Speaker 1: So I'm not convinced one way or another yet, but 149 00:09:17,600 --> 00:09:21,200 Speaker 1: I think the approach, whether it's intercepting a cellphone conversation 150 00:09:21,320 --> 00:09:24,840 Speaker 1: or trying to influence the president through his business associates, uh, 151 00:09:25,080 --> 00:09:28,120 Speaker 1: that I think it's very true. Clint Watch, Do you 152 00:09:28,160 --> 00:09:33,280 Speaker 1: believe that authorities will intercept those individuals or individual who 153 00:09:33,480 --> 00:09:37,640 Speaker 1: is responsible for those packages containing crew pipe bombs and 154 00:09:37,880 --> 00:09:41,160 Speaker 1: envelopes with white powder that were addressed to the Clinton's 155 00:09:41,200 --> 00:09:44,439 Speaker 1: at their Chappaquad, New York home, or to Barack Obama 156 00:09:44,520 --> 00:09:48,160 Speaker 1: in Washington, d C. Or indeed John Brennan att CNN 157 00:09:48,640 --> 00:09:53,640 Speaker 1: New York Studios. Yes, I would actually expect him to 158 00:09:53,640 --> 00:09:57,400 Speaker 1: do it fairly quickly. Um, if you look at the 159 00:09:57,520 --> 00:10:00,440 Speaker 1: packages they intercepted overnight, it seems like that they know 160 00:10:00,520 --> 00:10:02,560 Speaker 1: what to look for. They've gone through the US postal 161 00:10:02,640 --> 00:10:05,000 Speaker 1: system and been able to identify other ones that have 162 00:10:05,080 --> 00:10:08,640 Speaker 1: been distributed. This gives them very strong leads to go 163 00:10:08,720 --> 00:10:10,959 Speaker 1: back to where was the point of origin for these 164 00:10:11,000 --> 00:10:14,800 Speaker 1: packages when they were disseminated. The other thing that's interesting 165 00:10:14,800 --> 00:10:17,720 Speaker 1: about to ask this case as opposed to the last 166 00:10:17,760 --> 00:10:19,800 Speaker 1: reason one, which was Austin. If you remember we had 167 00:10:19,840 --> 00:10:24,120 Speaker 1: bombings in Austin, this show is a very weak delivery system. 168 00:10:24,280 --> 00:10:27,040 Speaker 1: You know, there wasn't much reconnaissance done and the packages 169 00:10:27,160 --> 00:10:32,480 Speaker 1: are are rendered to law enforcement and they're not exploded. 170 00:10:32,760 --> 00:10:35,960 Speaker 1: This gives you tremendous amount of human forensics to look for. 171 00:10:36,720 --> 00:10:39,199 Speaker 1: You can actually look at how they were constructed, if 172 00:10:39,200 --> 00:10:41,960 Speaker 1: they had ever maybe tried to test these before. And 173 00:10:42,000 --> 00:10:44,600 Speaker 1: so I imagine right now there's actually quite a bit 174 00:10:44,640 --> 00:10:48,240 Speaker 1: of evidence. I think it's ten packages that I have 175 00:10:48,400 --> 00:10:52,560 Speaker 1: seen have been intercepted or picked up. There's they've been 176 00:10:52,640 --> 00:10:56,560 Speaker 1: routed to UH targets to make sense based on UH 177 00:10:56,720 --> 00:11:00,680 Speaker 1: certain ideological mindset. So they're going to you know, descend 178 00:11:00,679 --> 00:11:02,840 Speaker 1: on that fairly quickly. I think they actually have a 179 00:11:02,880 --> 00:11:04,640 Speaker 1: lot of evidence to go on, as opposed to some 180 00:11:04,960 --> 00:11:07,880 Speaker 1: like in the Austin bombings back in March, when the 181 00:11:07,920 --> 00:11:12,120 Speaker 1: devices are exploding when they are are delivered you know, 182 00:11:12,200 --> 00:11:14,880 Speaker 1: directly by the assailant or set up in an I e. D. Fashion. 183 00:11:15,240 --> 00:11:17,720 Speaker 1: That's much tougher to go on. So Clint, do you 184 00:11:17,720 --> 00:11:21,600 Speaker 1: have any sense of why the packages didn't explode and 185 00:11:21,840 --> 00:11:24,280 Speaker 1: whether they were intended to explode or more intended as 186 00:11:24,280 --> 00:11:28,960 Speaker 1: a warning. Yeah, just looking at it, you know, just 187 00:11:29,000 --> 00:11:32,320 Speaker 1: from the pictures I've seen. Usually look for a few things. One, 188 00:11:33,280 --> 00:11:36,360 Speaker 1: what is the actually the explosive material, and if this 189 00:11:36,440 --> 00:11:38,880 Speaker 1: is mostly black powder from what I've heard, or or 190 00:11:38,960 --> 00:11:42,000 Speaker 1: other things just packed into a pipe, that is usually 191 00:11:42,040 --> 00:11:45,160 Speaker 1: your least sophisticated. If you remember back to the terrorism 192 00:11:45,760 --> 00:11:49,480 Speaker 1: era with al Qaeda and the Zazi case for example, 193 00:11:49,480 --> 00:11:53,560 Speaker 1: we were worried about peroxide based explosives. Um plastic or 194 00:11:53,559 --> 00:11:56,440 Speaker 1: military grade would be even more sophisticated. But the other 195 00:11:56,480 --> 00:11:58,920 Speaker 1: part of the device with the triggering device is that 196 00:11:59,000 --> 00:12:02,720 Speaker 1: a remote that needed pressure debtonated. None of these went off, 197 00:12:02,800 --> 00:12:05,320 Speaker 1: which means these could just have a device trapped to 198 00:12:05,360 --> 00:12:08,400 Speaker 1: the outside that never could have you know, detonated the 199 00:12:08,440 --> 00:12:11,320 Speaker 1: device at all. What we do know those these were 200 00:12:11,360 --> 00:12:13,600 Speaker 1: explosive devices, That's what you know. We've seen that from 201 00:12:13,600 --> 00:12:16,880 Speaker 1: several reports now and that they could have exploded. So 202 00:12:17,240 --> 00:12:19,959 Speaker 1: I think the danger is there. I think the signal 203 00:12:20,040 --> 00:12:22,640 Speaker 1: has definitely been sent, but it's not the most sophisticate, 204 00:12:23,240 --> 00:12:26,400 Speaker 1: sophisticated bomb maker that you might encounter. Clint, I'm wondering, 205 00:12:26,679 --> 00:12:29,520 Speaker 1: from your experience in the U. S. Army and as 206 00:12:29,559 --> 00:12:33,720 Speaker 1: an FBI Special Agent on Joint Terrorism, I'm just wondering, 207 00:12:34,240 --> 00:12:38,000 Speaker 1: do you think that the risk from domestic domestic terrorism 208 00:12:38,040 --> 00:12:41,760 Speaker 1: has increased more than it has been in recent years. 209 00:12:44,000 --> 00:12:47,080 Speaker 1: I don't even think it's increased more now. That is 210 00:12:47,120 --> 00:12:49,840 Speaker 1: definitely the case. Domestics should be our main focus as 211 00:12:49,840 --> 00:12:53,520 Speaker 1: opposed to international. But even going back about six years, 212 00:12:54,160 --> 00:12:57,480 Speaker 1: you could see this train coming. If it wasn't for ISIS, 213 00:12:57,480 --> 00:13:01,719 Speaker 1: and they're sort of spectacular attacks, success attacks overseas um 214 00:13:01,840 --> 00:13:05,600 Speaker 1: rallying a lot of Americans to join online, we would 215 00:13:05,640 --> 00:13:09,440 Speaker 1: probably be more focused already on domestic extremism. To me, 216 00:13:09,559 --> 00:13:14,720 Speaker 1: it's is far outpacing international extremism. UM. That doesn't mean, 217 00:13:15,000 --> 00:13:17,719 Speaker 1: you know, in this case, we couldn't. This could be 218 00:13:17,760 --> 00:13:20,800 Speaker 1: an international extremist for some reason, trying to cloak themselves. 219 00:13:20,800 --> 00:13:23,760 Speaker 1: I think that's unlikely. Uh. And so just based on 220 00:13:23,800 --> 00:13:26,079 Speaker 1: the targets and impact. It looks like it's it's towards 221 00:13:26,120 --> 00:13:28,920 Speaker 1: domestic extremism, but we should also look at a sort 222 00:13:28,960 --> 00:13:31,920 Speaker 1: of a wild card hypothesis too, which is, if you 223 00:13:31,960 --> 00:13:35,000 Speaker 1: want to create panic in the United States, particularly in 224 00:13:35,000 --> 00:13:37,920 Speaker 1: these very divisive times of an election period, it's also 225 00:13:37,960 --> 00:13:42,680 Speaker 1: an opportunity for a foreign government UH an intel service 226 00:13:43,000 --> 00:13:46,080 Speaker 1: to create a provocation which stirs peer and panic in 227 00:13:46,120 --> 00:13:49,400 Speaker 1: the audience space too. I think that's highly unlikely, but 228 00:13:49,720 --> 00:13:51,880 Speaker 1: we shouldn't entirely rule that out, and I'm sure that's 229 00:13:51,880 --> 00:13:54,720 Speaker 1: what the invest investigation the FBI is pursuing right now 230 00:13:54,800 --> 00:13:58,840 Speaker 1: is trying to determine. Clint Clint Watts, Thank you, Senior Fellow, 231 00:13:59,000 --> 00:14:02,920 Speaker 1: Foreign Policy Search Institute, also Senior Fellow at the Center 232 00:14:03,000 --> 00:14:06,880 Speaker 1: for Cyber and Homeland Security at the George Washington University. 233 00:14:07,320 --> 00:14:11,840 Speaker 1: You can follow him on Twitter at Selected Wisdom, and 234 00:14:11,920 --> 00:14:15,400 Speaker 1: his most recent book is Messing with the Enemy, Surviving 235 00:14:15,400 --> 00:14:19,320 Speaker 1: in a Social media world of hackers, terrorists, Russians, and 236 00:14:19,600 --> 00:14:33,880 Speaker 1: fake news. Yesterday Wall Street hated big Tech. Today it 237 00:14:34,040 --> 00:14:36,440 Speaker 1: loves it, and we're looking at green on the screen, 238 00:14:36,720 --> 00:14:41,080 Speaker 1: led by Twitter shares up nearly eighteen percent after reporting 239 00:14:41,120 --> 00:14:45,080 Speaker 1: earnings that beat expectations. Amazon and Alphabet parent company of Google. 240 00:14:45,400 --> 00:14:47,640 Speaker 1: Such a report earnings after the bell, but those shares 241 00:14:47,680 --> 00:14:51,120 Speaker 1: also climbing higher up give or take about four percent. 242 00:14:51,320 --> 00:14:53,560 Speaker 1: Scott Kessler joining US now head of equity research for 243 00:14:53,600 --> 00:14:57,080 Speaker 1: cf are, a Research in New York. Scott, let's just 244 00:14:57,120 --> 00:14:59,840 Speaker 1: start with Twitter because we actually have data to look through. 245 00:15:00,160 --> 00:15:03,720 Speaker 1: Can you talk about how they outperformed and what this 246 00:15:03,800 --> 00:15:06,480 Speaker 1: means about sort of some of these big tech companies 247 00:15:06,480 --> 00:15:09,320 Speaker 1: and social media companies being responsible when it comes to 248 00:15:09,440 --> 00:15:15,840 Speaker 1: having members that count or users that count. Thanks. Yeah, Um, look, 249 00:15:15,840 --> 00:15:17,640 Speaker 1: I mean when you see a stock like this moving 250 00:15:17,640 --> 00:15:20,240 Speaker 1: in by the way, I mean, Twitter is no stranger 251 00:15:20,400 --> 00:15:24,320 Speaker 1: to post earnings um volatility. I mean it's a stock 252 00:15:24,360 --> 00:15:27,840 Speaker 1: that moves quite a bit after their report results. Um. 253 00:15:27,960 --> 00:15:31,000 Speaker 1: This time, it seems they are performed pretty nicely. I 254 00:15:31,040 --> 00:15:36,000 Speaker 1: think most notable was the revenue increase, which was far 255 00:15:36,080 --> 00:15:40,960 Speaker 1: above the consensus. They reported about seven fifty eight million 256 00:15:41,000 --> 00:15:44,120 Speaker 1: dollars in revenue and folks were looking for about seven 257 00:15:44,320 --> 00:15:46,880 Speaker 1: million dollars in revenue. And I think there are a 258 00:15:46,920 --> 00:15:50,080 Speaker 1: number of factors that contributed to that, but most obviously 259 00:15:50,160 --> 00:15:54,920 Speaker 1: as they're continuing to focus on their strategy of health 260 00:15:55,000 --> 00:15:58,960 Speaker 1: on the platform as well as providing a good experience 261 00:15:59,000 --> 00:16:03,120 Speaker 1: for both users and marketers. And then in addition to that, 262 00:16:03,320 --> 00:16:06,080 Speaker 1: I think we would be remiss if we didn't acknowledge 263 00:16:06,120 --> 00:16:09,720 Speaker 1: that they might have been benefiting to some extent from 264 00:16:09,760 --> 00:16:12,200 Speaker 1: some of the challenges that Facebook has been going through recently. 265 00:16:13,240 --> 00:16:16,400 Speaker 1: Scott Kessler, can you speak to the video aspect of 266 00:16:16,880 --> 00:16:20,720 Speaker 1: Twitter's strategy? And also as a disclaimer, Bloomberg of course 267 00:16:20,760 --> 00:16:25,600 Speaker 1: provides at TikTok, the twenty four hour streaming network for 268 00:16:25,680 --> 00:16:29,640 Speaker 1: business and financial news for Twitter, tell us about video 269 00:16:29,680 --> 00:16:33,080 Speaker 1: and how that plays into their future. Yeah, so him, 270 00:16:33,160 --> 00:16:36,200 Speaker 1: I think it's pretty clear that UM over the last 271 00:16:36,440 --> 00:16:40,760 Speaker 1: year or so, Twitter is kind of refocused its strategy 272 00:16:40,840 --> 00:16:43,080 Speaker 1: to some extent, and one of the big kind of 273 00:16:43,520 --> 00:16:47,920 Speaker 1: UM thrust for them from a growth perspective is video. 274 00:16:48,000 --> 00:16:53,400 Speaker 1: So they highlighted UM a number of UM new agreements 275 00:16:53,840 --> 00:16:58,200 Speaker 1: with respect to their Live initiative, and their live initiative 276 00:16:58,320 --> 00:17:00,480 Speaker 1: is you know, really at the core of a company 277 00:17:00,560 --> 00:17:04,680 Speaker 1: is about which is really about UM getting people information 278 00:17:04,720 --> 00:17:08,399 Speaker 1: and content exactly when they want. If you use a 279 00:17:08,440 --> 00:17:12,800 Speaker 1: search engine, if you use UM some type of news portal, 280 00:17:13,280 --> 00:17:15,679 Speaker 1: you might have to wait until it's surfaced with an 281 00:17:15,760 --> 00:17:19,560 Speaker 1: algorithm or someone can actually report on it. Um. Twitter, 282 00:17:19,640 --> 00:17:21,399 Speaker 1: I think for a lot of people is really the 283 00:17:21,520 --> 00:17:24,560 Speaker 1: go to source when it comes to breaking news, and 284 00:17:24,640 --> 00:17:28,560 Speaker 1: so they've really capitalized on that by um, I think 285 00:17:28,960 --> 00:17:32,280 Speaker 1: focusing on video as an opportunity, and they've done well. 286 00:17:32,320 --> 00:17:35,440 Speaker 1: They've signed a lot of agreements, I think importantly though, 287 00:17:35,520 --> 00:17:39,520 Speaker 1: they've gained some traction and you can charge more for 288 00:17:39,560 --> 00:17:42,480 Speaker 1: the video advertising than the run of the mill um 289 00:17:42,520 --> 00:17:46,280 Speaker 1: advertising that Twitter might have been uh, you know, involved 290 00:17:46,280 --> 00:17:49,440 Speaker 1: with predominantly over the last number of years. All right, Scott, 291 00:17:49,480 --> 00:17:51,600 Speaker 1: Let's shift for focus a little bit to Amazon and 292 00:17:51,600 --> 00:17:54,520 Speaker 1: Alphabet both expected to report earnings after the bell today. 293 00:17:54,640 --> 00:17:58,840 Speaker 1: Amazon shares up nearly fift year to date, pretty massive run. 294 00:17:59,080 --> 00:18:00,760 Speaker 1: Do they have to actually know that they're able to 295 00:18:00,760 --> 00:18:02,880 Speaker 1: generate a big profit this time or do people still 296 00:18:02,920 --> 00:18:06,439 Speaker 1: not care? You know? What's interesting about these companies? And 297 00:18:06,520 --> 00:18:12,320 Speaker 1: I can put everyone from Amazon to Twitter um in 298 00:18:12,400 --> 00:18:16,240 Speaker 1: this kind of bucket of companies where people absolutely want 299 00:18:16,280 --> 00:18:19,320 Speaker 1: to see the growth. But growth is by far um 300 00:18:19,359 --> 00:18:22,200 Speaker 1: the most important thing. But then when you can throw 301 00:18:22,280 --> 00:18:27,520 Speaker 1: in um profitability or significant profitability, especially when you might 302 00:18:27,640 --> 00:18:30,760 Speaker 1: surprise people, and I think Amazon has done that. I 303 00:18:30,800 --> 00:18:34,840 Speaker 1: think Tesla did that yesterday. I think that the magnitude 304 00:18:34,920 --> 00:18:37,520 Speaker 1: of Twitter's beat was pretty significant. Even a company like 305 00:18:37,600 --> 00:18:43,640 Speaker 1: Microsoft that reported yesterday, they're upside in terms of the 306 00:18:43,760 --> 00:18:48,600 Speaker 1: UM revenue and earnings numbers versus expectations. Those give people 307 00:18:48,680 --> 00:18:50,960 Speaker 1: confidence and that's really what I think a lot of 308 00:18:51,000 --> 00:18:54,959 Speaker 1: investors are looking for right now. Scott Tesler, do you 309 00:18:55,040 --> 00:18:59,880 Speaker 1: believe that Amazon, with its variety of revenue streams, whether 310 00:19:00,080 --> 00:19:06,040 Speaker 1: the Amazon Web Services or it's hardware sales, it's online offerings, 311 00:19:06,080 --> 00:19:10,399 Speaker 1: and of course the actual store itself, do you believe 312 00:19:10,440 --> 00:19:16,160 Speaker 1: that Amazon is set for another round of growth? Well, Pim, 313 00:19:16,160 --> 00:19:19,600 Speaker 1: what I can say is, um, we have another team 314 00:19:19,600 --> 00:19:22,960 Speaker 1: member here to Nomobile who covers Amazon, and so my 315 00:19:23,119 --> 00:19:25,600 Speaker 1: thoughts on the company are more broad. But I think 316 00:19:25,600 --> 00:19:27,359 Speaker 1: it's fair to say, and we've been positive on the 317 00:19:27,359 --> 00:19:31,359 Speaker 1: stock for UM years at this point. Um, Yes, there 318 00:19:31,359 --> 00:19:34,160 Speaker 1: are a lot of levers for growth. UM. They seem 319 00:19:34,200 --> 00:19:36,520 Speaker 1: to be doing a very good job at moving into 320 00:19:36,560 --> 00:19:41,240 Speaker 1: new categories. But I would also say that there are 321 00:19:41,520 --> 00:19:45,760 Speaker 1: competitors everywhere and they're looking to take share from Amazon. 322 00:19:45,880 --> 00:19:48,600 Speaker 1: I think in the court e commerce market it's pretty 323 00:19:48,680 --> 00:19:51,639 Speaker 1: challenging and it's becoming challenging and some of the cloud 324 00:19:51,680 --> 00:19:55,040 Speaker 1: services offerings as well. But there are other areas that 325 00:19:55,040 --> 00:19:58,760 Speaker 1: they're moving into where they're the upstart, they're kind of 326 00:19:58,760 --> 00:20:01,199 Speaker 1: trying to take share, and so it'll be interesting to 327 00:20:01,200 --> 00:20:03,639 Speaker 1: see how that plays out in the context of what 328 00:20:03,680 --> 00:20:07,200 Speaker 1: they report later, especially from a cost and expensive perspective. 329 00:20:07,280 --> 00:20:11,360 Speaker 1: Everyone's looking at UM cost going up because of UM 330 00:20:11,480 --> 00:20:16,399 Speaker 1: the US Postal service contract and UM increasing expenses related 331 00:20:16,440 --> 00:20:19,040 Speaker 1: to that. All right, thanks very much for spending time 332 00:20:19,080 --> 00:20:21,440 Speaker 1: with us. Scott Kessler is the head of equity research 333 00:20:21,840 --> 00:20:25,159 Speaker 1: for c f r A Research, and he can be 334 00:20:25,200 --> 00:20:30,040 Speaker 1: followed on Twitter at Kessler c f r A. I'll 335 00:20:30,040 --> 00:20:32,680 Speaker 1: just say that where the tech companies go is really 336 00:20:32,680 --> 00:20:34,919 Speaker 1: where the market is probably going to go in the 337 00:20:34,920 --> 00:20:36,960 Speaker 1: near term. Anyway, they've been the real driver, right so 338 00:20:37,240 --> 00:20:40,840 Speaker 1: if these are disappointing earnings, it'll be an interesting day. 339 00:20:41,400 --> 00:20:44,400 Speaker 1: Right now, though shares a Twitter higher by more than 340 00:20:44,800 --> 00:20:49,160 Speaker 1: seventeen per cent. You're listening to Bloomberg Markets. I'm pim 341 00:20:49,240 --> 00:21:02,720 Speaker 1: Fox along with my co host Lisa abron Woods. We 342 00:21:02,760 --> 00:21:07,080 Speaker 1: are broadcasting live from the Bloomberg Interactive Broker's studios. Brian 343 00:21:07,160 --> 00:21:09,919 Speaker 1: Eggar joining us now Senior Gaming and lodging analyst with 344 00:21:09,960 --> 00:21:14,520 Speaker 1: Bloomberg Intelligence to talk about his experience buying a ticket 345 00:21:14,840 --> 00:21:18,359 Speaker 1: to win one point six billion dollars in the Mega 346 00:21:18,400 --> 00:21:22,880 Speaker 1: Millions competition that was drawn Tuesday night. Did you win? 347 00:21:23,560 --> 00:21:25,119 Speaker 1: I did not win. That's why I'm here today, as 348 00:21:25,160 --> 00:21:28,320 Speaker 1: I explained to him. But with the one out of chance, 349 00:21:28,440 --> 00:21:30,040 Speaker 1: would you have? I don't know if I would have quit, 350 00:21:30,160 --> 00:21:33,560 Speaker 1: but my sense of urgency might have might have been modified. 351 00:21:33,880 --> 00:21:35,879 Speaker 1: I'm so glad you didn't win, because we're so pleased 352 00:21:35,920 --> 00:21:39,120 Speaker 1: to see you. So let's just talk about how much 353 00:21:39,160 --> 00:21:43,320 Speaker 1: attention both this as well as the record powerball drawing 354 00:21:43,320 --> 00:21:46,800 Speaker 1: that will occur Saturday night, how that's brought to the 355 00:21:46,960 --> 00:21:50,560 Speaker 1: entire lottery industry, and who's benefiting from that business wise. Sure, so, 356 00:21:50,720 --> 00:21:53,840 Speaker 1: there is a bit of a trend the watery operators 357 00:21:53,840 --> 00:21:56,600 Speaker 1: that basically tighten y odds somewhat, which brings in more play, 358 00:21:56,680 --> 00:22:00,199 Speaker 1: results in less frequent but much harder jackpots, and that 359 00:22:00,280 --> 00:22:03,960 Speaker 1: in turn attracts more lottery ticket sales. And watery ticket 360 00:22:04,000 --> 00:22:06,840 Speaker 1: sales for multi state games have grown in about a 361 00:22:06,880 --> 00:22:09,200 Speaker 1: four and half percent, Ray Brannam over the last number 362 00:22:09,200 --> 00:22:15,040 Speaker 1: of years. Brian, the companies behind this technology i GT 363 00:22:15,480 --> 00:22:20,240 Speaker 1: International Game Technology and Scientific Games. Right, this is basically 364 00:22:20,280 --> 00:22:23,480 Speaker 1: a duopoly because there's been a lot of consolidation in 365 00:22:23,520 --> 00:22:26,000 Speaker 1: this industry. Yeah, that's true. I g T is nearly 366 00:22:26,080 --> 00:22:29,840 Speaker 1: eight of US watery sales. Side games is about twelve percent. 367 00:22:29,880 --> 00:22:32,440 Speaker 1: There's a company called Intra lot that's tem percent. But 368 00:22:32,440 --> 00:22:37,520 Speaker 1: you're absolutely right, it is effectively duopoly. Okay, so it's 369 00:22:37,560 --> 00:22:41,359 Speaker 1: just the states. Are the companies that run the lotteries 370 00:22:41,480 --> 00:22:44,480 Speaker 1: for the states or the duopoly that make the money. 371 00:22:44,520 --> 00:22:47,200 Speaker 1: But are there any other companies that sort of benefit 372 00:22:47,240 --> 00:22:50,119 Speaker 1: as people get into the mood to plunk down money 373 00:22:50,119 --> 00:22:52,600 Speaker 1: that they may most likely will never see again. So 374 00:22:52,640 --> 00:22:54,960 Speaker 1: the way, well, there's the states themselves, if you will 375 00:22:55,000 --> 00:22:58,560 Speaker 1: get South Carolina with a big winner or New York State. Basically, 376 00:22:59,359 --> 00:23:03,120 Speaker 1: thirty cents on a dollar spent goes to various state 377 00:23:03,280 --> 00:23:07,120 Speaker 1: education funds. The lottery companies themselves basically get a small 378 00:23:07,160 --> 00:23:10,520 Speaker 1: percent one or two percent of the lottery ticket sales 379 00:23:10,880 --> 00:23:14,600 Speaker 1: in return for basically either running these facility management contracts 380 00:23:14,680 --> 00:23:17,840 Speaker 1: or a lottery management agreement. So the lottery players do 381 00:23:17,960 --> 00:23:19,960 Speaker 1: get a piece of the action, if you will, when 382 00:23:19,960 --> 00:23:23,359 Speaker 1: people buy tickets. Brian, is this also a case of 383 00:23:23,480 --> 00:23:27,639 Speaker 1: being able to adjust the odds. That is something that 384 00:23:27,680 --> 00:23:31,160 Speaker 1: happened back in two thousand, fifteen and seventeen the Powerball 385 00:23:31,280 --> 00:23:34,800 Speaker 1: and Mega Millions lotteries slightly tighten the odds. I mean, 386 00:23:34,920 --> 00:23:39,040 Speaker 1: it is true that the resulting higher payouts and less 387 00:23:39,400 --> 00:23:41,600 Speaker 1: less chance of winning results in a little more excitement, 388 00:23:41,880 --> 00:23:44,760 Speaker 1: more media attention, and that does result in an elevator 389 00:23:44,840 --> 00:23:47,119 Speaker 1: level of ticket sales, which, if it's a secular trend, 390 00:23:47,359 --> 00:23:49,639 Speaker 1: would benefit UM, the I D T s and side 391 00:23:49,640 --> 00:23:52,480 Speaker 1: games of the world. You know, I gotta say, do 392 00:23:52,520 --> 00:23:56,640 Speaker 1: you buy lottery tickets? Him? Do you buy lottery tickets? Brian? 393 00:23:56,680 --> 00:24:00,000 Speaker 1: On a regular basis? On a regular basis, I do not. Um. 394 00:24:00,040 --> 00:24:04,119 Speaker 1: Do you know that two thirds of Americans gamble and 395 00:24:04,200 --> 00:24:08,640 Speaker 1: that last year they spent nearly seventy three billion dollars 396 00:24:08,680 --> 00:24:11,800 Speaker 1: on traditional lottery tickets and that is equal to more 397 00:24:11,840 --> 00:24:16,119 Speaker 1: than two hundred dollars a person. Is there evidence that 398 00:24:16,200 --> 00:24:20,600 Speaker 1: people are more likely to increase that amount if the 399 00:24:20,640 --> 00:24:23,800 Speaker 1: total jackpot is bigger but they have fewer chances? In 400 00:24:23,800 --> 00:24:26,560 Speaker 1: other words, is this like a get rich quick kind 401 00:24:26,560 --> 00:24:29,679 Speaker 1: of scheme? Or is there enjoyment and sort of you know, 402 00:24:29,800 --> 00:24:32,640 Speaker 1: see what you get chances if you get ten bucks, great, 403 00:24:32,720 --> 00:24:36,960 Speaker 1: if you get one out of three hundred and three million, 404 00:24:37,680 --> 00:24:41,320 Speaker 1: that's correct, So it's power. What happens, um is that 405 00:24:41,440 --> 00:24:44,520 Speaker 1: although the jack pots are quite large and the odds 406 00:24:44,520 --> 00:24:49,760 Speaker 1: of winning become infinitestinally small, nevertheless that the headline of 407 00:24:49,800 --> 00:24:52,680 Speaker 1: that jackpot size and the associated excitement does bring in 408 00:24:52,680 --> 00:24:54,880 Speaker 1: a little bit more play. And I think that's exactly 409 00:24:54,880 --> 00:24:57,879 Speaker 1: what the lottery operators, as well as the wattery equipment 410 00:24:57,880 --> 00:25:01,200 Speaker 1: suppliers White to White to observe. And is it worth 411 00:25:01,240 --> 00:25:05,320 Speaker 1: noting that the companies behind the lottery and gaming and 412 00:25:05,480 --> 00:25:10,400 Speaker 1: slot machines I G T and Scientific Games, these are 413 00:25:10,480 --> 00:25:14,359 Speaker 1: international businesses, the internationally you know. I G T is 414 00:25:14,400 --> 00:25:18,840 Speaker 1: actually fifty owned by an Italian conglomerate, the Augustini, and 415 00:25:18,880 --> 00:25:20,840 Speaker 1: they get a large percentage of the watery business in 416 00:25:20,880 --> 00:25:23,439 Speaker 1: Italy itself. And look at I G and Scientific Games. 417 00:25:23,760 --> 00:25:26,800 Speaker 1: They age get about their business from kind of the 418 00:25:26,880 --> 00:25:30,800 Speaker 1: North American lottery business, and of that a certain percentage 419 00:25:31,200 --> 00:25:34,080 Speaker 1: goes to these multi state draw games which have become 420 00:25:34,080 --> 00:25:36,480 Speaker 1: ever more popular. Yeah, you know, I'm gonna be a 421 00:25:36,480 --> 00:25:39,119 Speaker 1: Debbie downer here for a second. But the sort of 422 00:25:39,160 --> 00:25:42,440 Speaker 1: traditional belief is that lotteries are taxes on the poor. 423 00:25:42,520 --> 00:25:44,240 Speaker 1: That's sort of something that a lot of people will 424 00:25:44,280 --> 00:25:47,199 Speaker 1: say because people who are lower income are more likely 425 00:25:47,520 --> 00:25:49,720 Speaker 1: to buy tickets. That is sort of the feeling. Is 426 00:25:49,720 --> 00:25:53,800 Speaker 1: that true? I mean, that regressive scenario maybe maybe accurate. 427 00:25:53,840 --> 00:25:56,280 Speaker 1: I think one one thing I have read is that 428 00:25:56,320 --> 00:25:59,040 Speaker 1: as the jack bots get larger, uh, some of more 429 00:25:59,040 --> 00:26:02,520 Speaker 1: affluent individuals would not be drawn to a smaller jackpot, 430 00:26:02,960 --> 00:26:05,639 Speaker 1: might be more inclined to play. That being said, the odds, 431 00:26:05,680 --> 00:26:08,040 Speaker 1: of course at one out of three hundred point to 432 00:26:08,200 --> 00:26:12,119 Speaker 1: five million to one are not exactly making that a 433 00:26:13,280 --> 00:26:16,040 Speaker 1: rational expectation of winning. But nevertheless, there is a little 434 00:26:16,040 --> 00:26:19,280 Speaker 1: bit more excitement in the jackpots attract attention. So I 435 00:26:19,320 --> 00:26:25,360 Speaker 1: think he's just said yes, but it's everybody. And honestly, 436 00:26:25,440 --> 00:26:27,159 Speaker 1: I mean I know a lot of people who of 437 00:26:27,320 --> 00:26:31,639 Speaker 1: all types who bought for this particular And there are 438 00:26:31,640 --> 00:26:34,159 Speaker 1: forty four states that have watteries and and basically in 439 00:26:34,240 --> 00:26:36,000 Speaker 1: terms of the contracts I g. T s and twenty 440 00:26:36,000 --> 00:26:38,520 Speaker 1: five of them, and scientific games is about ten of them. 441 00:26:38,800 --> 00:26:41,440 Speaker 1: So it is, with the exception of four continental states, 442 00:26:41,480 --> 00:26:44,920 Speaker 1: a very popular activity. Well, we're very glad you didn't win, 443 00:26:44,960 --> 00:26:47,159 Speaker 1: because We're very glad to have had you here. Brian 444 00:26:47,320 --> 00:26:52,280 Speaker 1: Edgar is our senior Gaming and Lodging analyst for Bloomberg Intelligence, 445 00:26:52,680 --> 00:26:55,800 Speaker 1: telling us all about the one and a half billion 446 00:26:55,880 --> 00:27:02,800 Speaker 1: dollar Powerball winner and the technology behind it. Thanks for 447 00:27:02,880 --> 00:27:05,520 Speaker 1: listening to the Bloomberg P and L podcast. You can 448 00:27:05,560 --> 00:27:09,399 Speaker 1: subscribe and listen to interviews at Apple Podcasts, SoundCloud, or 449 00:27:09,440 --> 00:27:12,919 Speaker 1: whatever podcast platform you prefer. I'm pim Fox. I'm on 450 00:27:12,960 --> 00:27:16,800 Speaker 1: Twitter at pim Fox. I'm on Twitter at Lisa Abramo. 451 00:27:16,920 --> 00:27:19,520 Speaker 1: It's one before the podcast. You can always catch us 452 00:27:19,560 --> 00:27:21,119 Speaker 1: worldwide on Bloomberg Radio