1 00:00:05,800 --> 00:00:08,360 Speaker 1: Welcome to the Bloomberg P and L Podcast. I'm Pim 2 00:00:08,400 --> 00:00:11,200 Speaker 1: Fox along with my co host Lisa A. Brahmowitz. Each 3 00:00:11,240 --> 00:00:14,440 Speaker 1: day we bring you the most important, noteworthy, and useful 4 00:00:14,480 --> 00:00:17,079 Speaker 1: interviews for you and your money, whether you're at the 5 00:00:17,120 --> 00:00:20,360 Speaker 1: grocery store or the trading floor. Find the Bloomberg P 6 00:00:20,520 --> 00:00:32,280 Speaker 1: M L Podcast on Apple Podcasts, SoundCloud, and Bloomberg dot com. 7 00:00:32,320 --> 00:00:35,080 Speaker 1: I want to shift your focus to the pharmaceutical industry. 8 00:00:35,080 --> 00:00:37,720 Speaker 1: We've been talking a lot about when President Trump is 9 00:00:37,760 --> 00:00:41,720 Speaker 1: going to shift his focus to lowering drug prices. It 10 00:00:41,720 --> 00:00:43,720 Speaker 1: appears to be that the time is now and joining 11 00:00:43,800 --> 00:00:46,919 Speaker 1: us as Max Neeson Biotech, pharma and healthcare columnists for 12 00:00:47,120 --> 00:00:50,400 Speaker 1: Bloomberg Opinion, our oracle of the pharmaceutical industry and all 13 00:00:50,400 --> 00:00:53,120 Speaker 1: things healthcare. Max, so just lay out what happened with 14 00:00:53,200 --> 00:00:57,160 Speaker 1: Fiser and President Trump's involvement here. Well, as it always 15 00:00:57,200 --> 00:01:00,680 Speaker 1: is with drug pricing, there there's more uh, and there 16 00:01:00,680 --> 00:01:03,800 Speaker 1: appears to be on the surface so UM as it 17 00:01:03,960 --> 00:01:07,120 Speaker 1: usually does kind of on a six month basis. Um 18 00:01:07,240 --> 00:01:09,639 Speaker 1: Fightser raised drug prices on on a number of Medica 19 00:01:09,920 --> 00:01:13,039 Speaker 1: a number of its medications. The President took to Twitter 20 00:01:13,080 --> 00:01:16,840 Speaker 1: to criticize them for that on July nine, and then 21 00:01:17,240 --> 00:01:21,080 Speaker 1: yesterday evening he announced that after speaking to the to 22 00:01:21,240 --> 00:01:24,360 Speaker 1: Fightser CEO on the phone with the Health and Human 23 00:01:24,400 --> 00:01:27,200 Speaker 1: Services sectary alex as R, they had agreed to roll 24 00:01:27,319 --> 00:01:30,600 Speaker 1: back the price hikes they had planned to take, probably 25 00:01:30,640 --> 00:01:34,880 Speaker 1: sometime in the next few weeks, but not permanently. It's 26 00:01:34,959 --> 00:01:39,880 Speaker 1: either until January one or until the President's Drug Price 27 00:01:40,200 --> 00:01:45,160 Speaker 1: Blueprint is enacted, though no difficient definition of enacted there so, 28 00:01:45,280 --> 00:01:48,800 Speaker 1: UM it is a temporary rollback of prices opposed to 29 00:01:48,840 --> 00:01:53,000 Speaker 1: something permanent or drastic max. Is this really what the 30 00:01:53,000 --> 00:01:57,200 Speaker 1: pharmaceutical industry is looking for, which is an ad hoc 31 00:01:57,520 --> 00:02:00,920 Speaker 1: kind of strategy on the part of the administration to 32 00:02:01,080 --> 00:02:04,800 Speaker 1: deal with drug prices? Um? You know, I don't think 33 00:02:04,800 --> 00:02:08,040 Speaker 1: this is how they really prefer to to operate. UM. 34 00:02:08,080 --> 00:02:13,120 Speaker 1: Just kind of unilateral public negotiations UM in kind of 35 00:02:13,160 --> 00:02:15,960 Speaker 1: the public sphere and then just kind of the President 36 00:02:16,040 --> 00:02:18,960 Speaker 1: using the bully probed on an individual basis. I think 37 00:02:19,000 --> 00:02:20,560 Speaker 1: that's what they've been scared up for a long time, 38 00:02:20,760 --> 00:02:23,799 Speaker 1: which is probably why Fiser kind of chose to engage 39 00:02:23,800 --> 00:02:25,760 Speaker 1: in this way. But I think they actually got a 40 00:02:25,800 --> 00:02:28,400 Speaker 1: decent deal out of it. They kind of remove themselves 41 00:02:28,440 --> 00:02:32,680 Speaker 1: from criticism without really giving up all that much. There. 42 00:02:32,800 --> 00:02:35,840 Speaker 1: The reason I asked you is because maybe this is 43 00:02:35,880 --> 00:02:37,960 Speaker 1: the way to do it. I mean, maybe this is 44 00:02:38,000 --> 00:02:40,919 Speaker 1: the way to avoid having the back and forth of 45 00:02:41,040 --> 00:02:46,639 Speaker 1: lobby interests in the Congress trying to manipulate or to 46 00:02:47,000 --> 00:02:51,239 Speaker 1: massage the let potential legislation that would create some kind 47 00:02:51,240 --> 00:02:55,679 Speaker 1: of consistent plan. And maybe this is the way that 48 00:02:55,760 --> 00:02:58,640 Speaker 1: the President has decided to go at it because he 49 00:02:58,720 --> 00:03:01,280 Speaker 1: doesn't want to have to do with the special interest 50 00:03:01,280 --> 00:03:05,360 Speaker 1: groups or the pressure that's been previously applied to drug 51 00:03:05,400 --> 00:03:07,600 Speaker 1: price that that actually makes a lot of sense in 52 00:03:07,600 --> 00:03:09,519 Speaker 1: the sense you give the president kind of a big 53 00:03:09,560 --> 00:03:14,000 Speaker 1: public victory. He gets to play um master negotiator, which 54 00:03:14,080 --> 00:03:16,920 Speaker 1: is a favorite role of his, and and you know, 55 00:03:17,000 --> 00:03:18,919 Speaker 1: have this sort of talking point like, look at me, 56 00:03:19,280 --> 00:03:22,400 Speaker 1: I brought drug prices down, um, even though the actual 57 00:03:22,440 --> 00:03:26,480 Speaker 1: impact on you know, fiser, on patients, on the drug 58 00:03:26,520 --> 00:03:30,880 Speaker 1: pricing problem at all is actually quite minimal. Well, okay, 59 00:03:31,040 --> 00:03:34,720 Speaker 1: so President Trump is one part of this, and fightser 60 00:03:35,040 --> 00:03:38,560 Speaker 1: is certainly a specific story, but more broadly, there were 61 00:03:38,560 --> 00:03:40,720 Speaker 1: a number of drug makers that lowered their prices in 62 00:03:40,800 --> 00:03:43,640 Speaker 1: response to a new California law that aims to provide 63 00:03:43,640 --> 00:03:48,320 Speaker 1: more transparency to the pharmaceutical industry. So even if President 64 00:03:48,320 --> 00:03:51,360 Speaker 1: Trump is taking that out ad hoc approach, states are 65 00:03:51,360 --> 00:03:55,960 Speaker 1: being more systematic in this case California absolutely, and um, 66 00:03:55,960 --> 00:03:58,560 Speaker 1: you know, it's difficult to tell exactly how much of 67 00:03:58,960 --> 00:04:02,000 Speaker 1: those price rollbacks are are ascribable to the law as 68 00:04:02,000 --> 00:04:05,160 Speaker 1: opposed to whether we just happen to find out about them, 69 00:04:05,720 --> 00:04:07,680 Speaker 1: because of the way that the laws of structure. Basically 70 00:04:08,000 --> 00:04:10,680 Speaker 1: it requires drug makers to to kind of report and 71 00:04:10,760 --> 00:04:14,720 Speaker 1: justify price increases that are of a certain magnitude. But 72 00:04:14,920 --> 00:04:18,320 Speaker 1: it's pretty clear that the transparency has some effect. Having 73 00:04:18,360 --> 00:04:21,280 Speaker 1: to let health plans know in advance, um, which gives 74 00:04:21,279 --> 00:04:23,719 Speaker 1: them time to kind of push back, to react, and 75 00:04:23,760 --> 00:04:25,800 Speaker 1: then having all that sort of in the public sphere 76 00:04:26,040 --> 00:04:28,800 Speaker 1: clearly has an effect. And that's sort of um kind 77 00:04:28,839 --> 00:04:31,919 Speaker 1: of a blueprinted other states or potential the government can follow, 78 00:04:32,320 --> 00:04:34,440 Speaker 1: um if they actually want to have kind of a 79 00:04:34,440 --> 00:04:38,400 Speaker 1: a sustained impact as opposed to this kind of one time, 80 00:04:38,960 --> 00:04:42,440 Speaker 1: short duration sort of thing. Max. Has there been any 81 00:04:42,480 --> 00:04:46,160 Speaker 1: discussion about the way in which healthcare is delivered? I 82 00:04:46,200 --> 00:04:49,680 Speaker 1: don't mean drug prices, but the actual healthcare And the 83 00:04:49,720 --> 00:04:52,640 Speaker 1: reason I ask is because yesterday we had a segment 84 00:04:52,640 --> 00:04:57,240 Speaker 1: in which we focused on innovation in healthcare delivery having 85 00:04:57,320 --> 00:05:01,920 Speaker 1: to do with hospitals. For example, where some hospitals would 86 00:05:01,960 --> 00:05:06,520 Speaker 1: specialize in heart surgery, other hospitals would specialize in other 87 00:05:06,680 --> 00:05:10,640 Speaker 1: kinds of surgical procedures or services. Right now, you have 88 00:05:10,839 --> 00:05:14,160 Speaker 1: no idea how much they cost from hospital to hospital, 89 00:05:14,400 --> 00:05:18,080 Speaker 1: so every hospital ends up almost duplicating the services and 90 00:05:18,120 --> 00:05:21,320 Speaker 1: they compete with each other rather than delivering uh, what 91 00:05:21,320 --> 00:05:25,800 Speaker 1: would be called efficient healthcare. So I think the lack 92 00:05:25,880 --> 00:05:29,799 Speaker 1: of price transparency and the lack of UM really prices 93 00:05:29,839 --> 00:05:33,520 Speaker 1: and healthcare that are just available or interpretable to the 94 00:05:33,560 --> 00:05:37,000 Speaker 1: average consumer is really one of the most difficult things. 95 00:05:37,040 --> 00:05:41,279 Speaker 1: It's it's basically impossible to comparison shop UM with any 96 00:05:41,360 --> 00:05:44,240 Speaker 1: kind of like real pricing data or real data on 97 00:05:44,400 --> 00:05:49,640 Speaker 1: outcomes on quality UM. The the data is bad. Hospitals 98 00:05:50,680 --> 00:05:53,520 Speaker 1: usually for the most part, are interested in obfuscating that 99 00:05:53,600 --> 00:05:56,839 Speaker 1: data about what something costs and why, which is why 100 00:05:56,920 --> 00:05:59,560 Speaker 1: you hear about those bills where you have you know, 101 00:05:59,680 --> 00:06:03,160 Speaker 1: thous and dollar charges for for aspirin, for for minor 102 00:06:03,279 --> 00:06:06,440 Speaker 1: care UM just because you know they that's not the 103 00:06:06,480 --> 00:06:08,280 Speaker 1: real price the one they negotiate with your insurance, the 104 00:06:08,320 --> 00:06:10,640 Speaker 1: real price, but it's impossible to find the real price 105 00:06:10,640 --> 00:06:13,880 Speaker 1: and compare it. So yeah, it's a miserable situation. Well, 106 00:06:13,960 --> 00:06:16,880 Speaker 1: and and trying to to fix the entire healthcare system 107 00:06:17,200 --> 00:06:18,920 Speaker 1: is a heavy one and one that we could spend 108 00:06:18,920 --> 00:06:21,719 Speaker 1: hours on. Uh. There are some things that are going 109 00:06:21,880 --> 00:06:24,840 Speaker 1: on right now though with respect to Obamacare, which is 110 00:06:24,880 --> 00:06:27,680 Speaker 1: sort of heralded as a solution but has been uh 111 00:06:28,240 --> 00:06:30,800 Speaker 1: really kind of tried to at least the attempt has 112 00:06:30,800 --> 00:06:33,400 Speaker 1: been to try to pare it back in the recent 113 00:06:33,600 --> 00:06:37,480 Speaker 1: few years, and the latest events have to do with 114 00:06:37,920 --> 00:06:42,880 Speaker 1: reducing the payments to high risk pools as well as 115 00:06:43,520 --> 00:06:46,640 Speaker 1: President Trump cutting some of the grants that help consumers 116 00:06:46,640 --> 00:06:51,640 Speaker 1: get Obamacare. How significant are these developments in your view? Um, 117 00:06:51,680 --> 00:06:54,360 Speaker 1: You know, for the most part, it depends on particularly 118 00:06:54,360 --> 00:06:58,240 Speaker 1: the major one, which is the risk corridors or the sorry, 119 00:06:58,240 --> 00:07:01,760 Speaker 1: the risk adjustments. Basically that UM ensures that have the 120 00:07:01,800 --> 00:07:05,640 Speaker 1: sickest population UM they get paid back to a certain 121 00:07:05,640 --> 00:07:09,080 Speaker 1: extent by insurers that have a healthier population UM. It 122 00:07:09,120 --> 00:07:11,520 Speaker 1: helps them kind of stay in business and just adjust 123 00:07:11,560 --> 00:07:16,840 Speaker 1: and and make basically ensuring those sacre patients more viable. Um, 124 00:07:16,880 --> 00:07:20,120 Speaker 1: it's I bet like that it's likely that those payments 125 00:07:20,160 --> 00:07:22,240 Speaker 1: will resume at some point. It was due to kind 126 00:07:22,240 --> 00:07:26,480 Speaker 1: of just this weird, quirky New Mexico court case. But um, 127 00:07:26,600 --> 00:07:30,080 Speaker 1: and even though that's going on just today or last night, 128 00:07:30,160 --> 00:07:33,160 Speaker 1: rather we just got news that another insurer was expanding 129 00:07:33,200 --> 00:07:36,440 Speaker 1: in a couple of A markets. So even though things 130 00:07:36,480 --> 00:07:39,800 Speaker 1: are kind of still uncertain and and kind of prone 131 00:07:39,800 --> 00:07:42,000 Speaker 1: to shake up and and prone to what many would 132 00:07:42,040 --> 00:07:46,080 Speaker 1: call sabotage, um, there are still people interested in this market, 133 00:07:46,200 --> 00:07:48,960 Speaker 1: as opposed to years past where everyone's just kind of fleeing. Well, 134 00:07:49,080 --> 00:07:51,320 Speaker 1: but this is actually fascinating to me. And one thing 135 00:07:51,320 --> 00:07:53,840 Speaker 1: that I'm wondering. As insures expand in a c A 136 00:07:53,920 --> 00:07:58,800 Speaker 1: expand their Obamacare coverage, does that mean that people still 137 00:07:58,840 --> 00:08:01,960 Speaker 1: like this program? Does that mean that the program is 138 00:08:02,000 --> 00:08:06,400 Speaker 1: being made more lucrative for the health insurers? Um. I 139 00:08:06,440 --> 00:08:09,280 Speaker 1: think it's a little bit of both. The in the 140 00:08:09,360 --> 00:08:12,040 Speaker 1: sense that insurers are are a little bit more experienced 141 00:08:12,040 --> 00:08:14,320 Speaker 1: with what it takes to be profitable in this market, 142 00:08:14,520 --> 00:08:17,720 Speaker 1: even though it keeps changing and getting weird every year. 143 00:08:18,120 --> 00:08:21,880 Speaker 1: But um, as for the consumer experience, it's very much 144 00:08:21,920 --> 00:08:26,960 Speaker 1: bifurcated between people that get subsidies and people that don't. UM. 145 00:08:27,080 --> 00:08:29,960 Speaker 1: For someone who gets a subsidy, you can get relatively 146 00:08:30,000 --> 00:08:33,800 Speaker 1: affordable insurance without paying that much. But because of the 147 00:08:33,840 --> 00:08:36,680 Speaker 1: fact that the law has been sort of systematically undermined 148 00:08:37,240 --> 00:08:40,000 Speaker 1: um or at the very least not helped as in um. 149 00:08:40,080 --> 00:08:42,040 Speaker 1: You know, things that could be done to improve it 150 00:08:42,120 --> 00:08:45,640 Speaker 1: have have just not happened because of the administration Congress. 151 00:08:46,160 --> 00:08:49,760 Speaker 1: Um premiums have gone up for people that have just 152 00:08:50,360 --> 00:08:52,880 Speaker 1: aren't in that kind of income cut off the lots 153 00:08:52,880 --> 00:08:55,600 Speaker 1: some good subsidies, so it's it's really terrible for them, 154 00:08:55,640 --> 00:08:58,480 Speaker 1: but but still good for a good chunk of the market, 155 00:08:58,480 --> 00:09:02,040 Speaker 1: which is more than that to its subsidies. Max. You know, 156 00:09:02,080 --> 00:09:04,319 Speaker 1: one of the things that happens when you have the 157 00:09:04,440 --> 00:09:08,720 Speaker 1: healthcare reform debate is always here about the cost of 158 00:09:09,080 --> 00:09:12,280 Speaker 1: end of life care and how much that is outsized. 159 00:09:12,920 --> 00:09:15,760 Speaker 1: That turns out to not necessarily be so. I mean, 160 00:09:15,800 --> 00:09:21,760 Speaker 1: there's an estimate that between eleven to of total health 161 00:09:21,840 --> 00:09:27,400 Speaker 1: care spending is on that end of life time for patients. 162 00:09:28,080 --> 00:09:31,480 Speaker 1: Is is the focus should the focus then be on 163 00:09:31,800 --> 00:09:37,360 Speaker 1: chronic long term conditions? Is that really where the bulk 164 00:09:37,440 --> 00:09:39,920 Speaker 1: of the money goes. I mean, if if there's anywhere 165 00:09:39,920 --> 00:09:41,640 Speaker 1: that you can move the dial just sort of in 166 00:09:41,640 --> 00:09:45,440 Speaker 1: the largest possible population that requires, um the greatest amount 167 00:09:45,440 --> 00:09:47,640 Speaker 1: of healthcare spending over the longer term and has the 168 00:09:47,679 --> 00:09:51,800 Speaker 1: most benefit if you can kind of manage those conditions. Well, UM, 169 00:09:51,840 --> 00:09:54,760 Speaker 1: it's definitely that population. And for the most part, it's 170 00:09:54,760 --> 00:09:58,000 Speaker 1: something that's that's not done especially well unless you're lucky 171 00:09:58,080 --> 00:10:01,240 Speaker 1: enough to have UM, you know, series of healthcare providers 172 00:10:01,240 --> 00:10:04,559 Speaker 1: that are are really engaged or UM un employer that's 173 00:10:04,600 --> 00:10:07,880 Speaker 1: kind of actively engaging and managing those costs. Otherwise you 174 00:10:08,120 --> 00:10:12,120 Speaker 1: kind of just get left alone. And you know, it's, uh, 175 00:10:12,240 --> 00:10:15,880 Speaker 1: it's not easy without kind of expert advice and input 176 00:10:16,200 --> 00:10:18,320 Speaker 1: and monitoring and on all of that. And we're only 177 00:10:18,840 --> 00:10:21,120 Speaker 1: just starting to get better than that. And uh that 178 00:10:21,200 --> 00:10:23,280 Speaker 1: that's something where there's a lot of very for improvement. 179 00:10:23,600 --> 00:10:25,640 Speaker 1: Just real quick. I do want to note that the 180 00:10:25,720 --> 00:10:29,920 Speaker 1: SMP five Healthcare index has fallen nearly a percentage point 181 00:10:29,960 --> 00:10:32,880 Speaker 1: today and that this a lot of people are attributing 182 00:10:32,920 --> 00:10:37,160 Speaker 1: this to what happened with Visor. Do you expect President 183 00:10:37,200 --> 00:10:40,000 Speaker 1: Trump to take a similar tap with other pharmacy companies 184 00:10:40,000 --> 00:10:42,520 Speaker 1: in the near future. Um, you know, now now that 185 00:10:42,559 --> 00:10:45,679 Speaker 1: he's gotten the sense that if he gets uh gets 186 00:10:45,679 --> 00:10:49,040 Speaker 1: a CEO on on the phone and and uh jets 187 00:10:49,040 --> 00:10:51,360 Speaker 1: them a while, that they might actually do something, he 188 00:10:51,679 --> 00:10:53,920 Speaker 1: may very well give it, give it a shot again, 189 00:10:54,559 --> 00:10:57,160 Speaker 1: especially because you know we're we're in this is drug 190 00:10:57,200 --> 00:10:59,880 Speaker 1: price hike season middle of the year July one, so 191 00:11:00,000 --> 00:11:01,960 Speaker 1: there are a number of other kind of prominent firms 192 00:11:02,000 --> 00:11:05,600 Speaker 1: that have increased prices. He may give it a shot. Um, 193 00:11:05,640 --> 00:11:07,720 Speaker 1: you know I would have even if it happens, even 194 00:11:07,760 --> 00:11:10,800 Speaker 1: if the entire drug industry rolls back this round of 195 00:11:10,840 --> 00:11:13,240 Speaker 1: drug price increases. You have to keep in mind that 196 00:11:13,520 --> 00:11:15,640 Speaker 1: every single price hike that they've done for the past 197 00:11:15,720 --> 00:11:19,200 Speaker 1: decade is still there. So this is really uh, at 198 00:11:19,200 --> 00:11:21,760 Speaker 1: the end of the day. On the margins, Um, I'm 199 00:11:21,800 --> 00:11:25,960 Speaker 1: hopeful that some something more materialized, but you know, this 200 00:11:26,040 --> 00:11:29,319 Speaker 1: might stick shake stocks in the near term. I don't 201 00:11:29,360 --> 00:11:32,040 Speaker 1: think it's going to be a wait for the long term, 202 00:11:32,040 --> 00:11:34,240 Speaker 1: like a fig leaf, I guess you could describe it. 203 00:11:34,280 --> 00:11:38,800 Speaker 1: Thanks very much, Max Neeson, Bloomberg Opinion columnists for all 204 00:11:38,880 --> 00:11:42,920 Speaker 1: things related to healthcare. Of course, we appreciate your comments 205 00:11:43,000 --> 00:11:45,840 Speaker 1: and your thoughts. You can follow Max on Twitter at 206 00:11:46,240 --> 00:12:06,160 Speaker 1: Max Neeson. Our next guest is here in Reality. He 207 00:12:06,440 --> 00:12:09,560 Speaker 1: is budget art. He is the co founder and president 208 00:12:09,600 --> 00:12:13,400 Speaker 1: of hi A Technologies. They're based in Los Angeles. And 209 00:12:13,440 --> 00:12:15,560 Speaker 1: the reason why I say object that you are here 210 00:12:15,600 --> 00:12:18,840 Speaker 1: in reality is because you are focused on the world 211 00:12:19,320 --> 00:12:24,680 Speaker 1: of artificial intelligence and avatars and creating the software and 212 00:12:24,679 --> 00:12:31,640 Speaker 1: the platforms to actually have meaningful conversations with machines exactly. 213 00:12:31,760 --> 00:12:37,600 Speaker 1: This is a technology that has been used through the 214 00:12:37,600 --> 00:12:41,600 Speaker 1: the Institute of Creative Technologies at USC the un University 215 00:12:41,640 --> 00:12:44,679 Speaker 1: of Southern California and the West Coast, and it was 216 00:12:44,760 --> 00:12:48,280 Speaker 1: a military funded research. Forty million dollars went into it, 217 00:12:48,720 --> 00:12:52,200 Speaker 1: and um, we're the first ones to commercialize this outside 218 00:12:52,240 --> 00:12:56,640 Speaker 1: of the institute. They use it for military training and 219 00:12:56,720 --> 00:13:02,240 Speaker 1: avatar interacting with humans to to test out certain situations 220 00:13:02,280 --> 00:13:09,319 Speaker 1: with them and more specifically PTSD therapy Teumatic stress disorders therapy, right, 221 00:13:09,800 --> 00:13:13,720 Speaker 1: so UM veterans with PTSD. It turns out respond to 222 00:13:13,800 --> 00:13:18,680 Speaker 1: avatars much better in the in terms of opening up 223 00:13:18,880 --> 00:13:22,520 Speaker 1: and revealing more about themselves, feeling more comfortable, taking more 224 00:13:22,600 --> 00:13:28,199 Speaker 1: time to be interviewed than psychiatrists. So they had some 225 00:13:29,320 --> 00:13:32,400 Speaker 1: famous papers published on this and it's been a couple 226 00:13:32,400 --> 00:13:34,600 Speaker 1: of years, but now it's the technology has come to 227 00:13:34,600 --> 00:13:38,040 Speaker 1: a point that it's cloud based, you know, client server, 228 00:13:38,400 --> 00:13:42,320 Speaker 1: it's very easily transported to mobile devices, et cetera. So 229 00:13:42,440 --> 00:13:45,280 Speaker 1: we decided to commercialize it. We have an exclusive license 230 00:13:45,320 --> 00:13:49,679 Speaker 1: from USC and we're going to use it for HR 231 00:13:49,800 --> 00:13:55,440 Speaker 1: and specifically in recruiting. We will conduct pre screening interviews 232 00:13:55,520 --> 00:13:59,679 Speaker 1: for companies that hire large numbers of people and find 233 00:13:59,679 --> 00:14:03,920 Speaker 1: itficult to do a good job in screening candidates and 234 00:14:03,960 --> 00:14:07,160 Speaker 1: finding the right kind of people. Can we just talk 235 00:14:07,200 --> 00:14:11,559 Speaker 1: a little bit about the concept of replicating a conversation 236 00:14:12,080 --> 00:14:15,880 Speaker 1: in real time with a computer, Because when you have Alexa, 237 00:14:15,920 --> 00:14:18,959 Speaker 1: for example, and you ask her a question, so give 238 00:14:19,000 --> 00:14:22,320 Speaker 1: you a response based on an Internet search, but it's 239 00:14:22,320 --> 00:14:25,960 Speaker 1: not carrying on a conversation. So how how does that work? 240 00:14:26,040 --> 00:14:29,320 Speaker 1: Is that? Is that incredibly complicated to make happen? It 241 00:14:29,400 --> 00:14:32,960 Speaker 1: is complicated, and it starts out with a lot of 242 00:14:33,600 --> 00:14:38,720 Speaker 1: manual work. You have to script these conversations. But the 243 00:14:38,760 --> 00:14:42,640 Speaker 1: magical thing behind it is the machine learning. It learns 244 00:14:42,960 --> 00:14:45,800 Speaker 1: from its experiences and then it gets better and better 245 00:14:45,840 --> 00:14:48,920 Speaker 1: over time. It gets better much quicker than humans get 246 00:14:48,920 --> 00:14:53,360 Speaker 1: better in anything. So um, yes, it is complex, but UM, 247 00:14:53,480 --> 00:14:56,280 Speaker 1: you know you you you have to start with a 248 00:14:56,360 --> 00:15:01,360 Speaker 1: base product. For example, UM, in interviewing, we have these 249 00:15:01,720 --> 00:15:05,720 Speaker 1: job modules. It's a question and answer module. So for 250 00:15:05,840 --> 00:15:09,400 Speaker 1: a junior accountant position, there is a set of twenty 251 00:15:09,840 --> 00:15:14,119 Speaker 1: questions that the company has decided to ask their potential employees. 252 00:15:14,920 --> 00:15:20,440 Speaker 1: And uh, these are crafted by combination of our experts 253 00:15:20,440 --> 00:15:24,840 Speaker 1: that we found and they're specific questions. So the questions 254 00:15:24,840 --> 00:15:28,120 Speaker 1: are listed and then there's buckets of answers for each question. 255 00:15:28,920 --> 00:15:31,080 Speaker 1: What we do is we listen to the answer. We 256 00:15:31,160 --> 00:15:36,640 Speaker 1: assign what we heard to a particular bucket. Okay, can 257 00:15:36,760 --> 00:15:42,520 Speaker 1: can avatars be funny? Yes? So there's an algorithm for 258 00:15:42,520 --> 00:15:47,240 Speaker 1: for humor. Yes, And you have to understand we have 259 00:15:47,320 --> 00:15:50,600 Speaker 1: to be very careful in applying that to situations like this. 260 00:15:50,720 --> 00:15:54,520 Speaker 1: So we have actually tested that you know, uh, people 261 00:15:54,960 --> 00:15:57,280 Speaker 1: UM do not take it lightly. And this is this 262 00:15:57,360 --> 00:16:02,280 Speaker 1: happened in Syrie and Alexa as well. UM, the majority 263 00:16:02,320 --> 00:16:06,840 Speaker 1: of the people do not think it's funny enough, or 264 00:16:07,120 --> 00:16:11,640 Speaker 1: it's it's it's it's appropriate. So what we decided to do, 265 00:16:11,960 --> 00:16:15,520 Speaker 1: literally in this particular case, since it's a an enterprise 266 00:16:15,520 --> 00:16:18,320 Speaker 1: sale for us, we decided to stay on the safe 267 00:16:18,320 --> 00:16:21,160 Speaker 1: side and not quite go there, and we just go 268 00:16:21,560 --> 00:16:24,840 Speaker 1: very polite and very curious, and we just we just 269 00:16:25,040 --> 00:16:28,960 Speaker 1: don't go there. But it's definitely possible. Tell us a 270 00:16:29,000 --> 00:16:31,120 Speaker 1: little bit about how you got involved in this, because 271 00:16:31,160 --> 00:16:36,480 Speaker 1: you have a wide ranging career in technology in Silicon Valley. Uh, 272 00:16:36,520 --> 00:16:41,640 Speaker 1: Fabricate Labs for example of micro Fabrica is another one 273 00:16:41,680 --> 00:16:45,800 Speaker 1: of the companies. I believe that you were uh involved 274 00:16:45,800 --> 00:16:50,440 Speaker 1: with your You seem to have this sort of perspective 275 00:16:50,440 --> 00:16:55,960 Speaker 1: of turning what is sort of almost mundane thing, you know, 276 00:16:56,040 --> 00:17:00,360 Speaker 1: concepts into something uh precious and and and you know 277 00:17:00,440 --> 00:17:05,520 Speaker 1: driven by technology. Yeah. Um, you know, pioneering brand new 278 00:17:05,600 --> 00:17:11,960 Speaker 1: ideas um is my passion and uh that is a 279 00:17:12,040 --> 00:17:15,560 Speaker 1: pattern in my background to some of those ideas. Some 280 00:17:15,600 --> 00:17:18,679 Speaker 1: of the pioneering efforts have not been that successful, but 281 00:17:19,200 --> 00:17:23,439 Speaker 1: it is what I like and changing paradigms, changing the 282 00:17:23,480 --> 00:17:27,560 Speaker 1: way things are done today, um, disrupting the way things 283 00:17:27,600 --> 00:17:31,439 Speaker 1: are done today, especially when it comes to making people 284 00:17:31,520 --> 00:17:36,760 Speaker 1: more productive, um, and making people more accurate in the 285 00:17:36,760 --> 00:17:39,840 Speaker 1: way they do things. Well what we do for example, 286 00:17:39,960 --> 00:17:43,520 Speaker 1: let me say analogy is an Excel sheet. You know, Uh, 287 00:17:43,640 --> 00:17:48,919 Speaker 1: we didn't Excel sheet did not dislodge accountants from their jobs. 288 00:17:49,440 --> 00:17:51,919 Speaker 1: It's a tool and it doesn't you know, much like 289 00:17:52,000 --> 00:17:56,200 Speaker 1: Ai is Budget. I could spend the next hour speaking 290 00:17:56,200 --> 00:17:58,480 Speaker 1: with you, probably more. Thank you so much for being here. 291 00:17:58,600 --> 00:18:00,280 Speaker 1: It was really great having you A have to have 292 00:18:00,320 --> 00:18:03,200 Speaker 1: you back. Budget A rat is co founder and president 293 00:18:03,240 --> 00:18:07,240 Speaker 1: of hi A Technologies in Los Angeles, creating technology that 294 00:18:07,280 --> 00:18:26,960 Speaker 1: will soon replace me. When we talk about trade skirmishes 295 00:18:27,000 --> 00:18:30,000 Speaker 1: between China and the US, many are quick to point 296 00:18:30,000 --> 00:18:33,520 Speaker 1: out that China's imports from the US aren't large enough 297 00:18:33,560 --> 00:18:37,280 Speaker 1: to match Trump's tariffs taller for dollar, meaning that they're 298 00:18:37,400 --> 00:18:41,359 Speaker 1: unable to hit back really with the same force that 299 00:18:41,400 --> 00:18:44,800 Speaker 1: the U s is. But is that true? Joining us now, 300 00:18:44,880 --> 00:18:49,200 Speaker 1: Carl Weinberg, He's chief economist high Frequency Economics in New York. Carl, 301 00:18:49,480 --> 00:18:51,760 Speaker 1: thank you so much for being here. So let's just 302 00:18:51,840 --> 00:18:54,320 Speaker 1: start there. I mean, is it true that China really 303 00:18:54,359 --> 00:18:57,000 Speaker 1: can't hit back dollar for dollar just due to the 304 00:18:57,040 --> 00:19:00,600 Speaker 1: fact that isn't import enough from the US. High Lisa, 305 00:19:00,640 --> 00:19:03,280 Speaker 1: good morning, Thanks for having me on your program. Um, 306 00:19:03,359 --> 00:19:05,199 Speaker 1: China has a lot of weapons that can use that 307 00:19:05,280 --> 00:19:08,680 Speaker 1: aren't strictly tariffs that could be engaged in the trade war. 308 00:19:09,320 --> 00:19:12,439 Speaker 1: The most likely that we are that we are prone 309 00:19:12,480 --> 00:19:15,439 Speaker 1: to see and perhaps have already started to see, are 310 00:19:15,480 --> 00:19:19,280 Speaker 1: just boycotts against American branded goods. Um. You know, US 311 00:19:19,320 --> 00:19:23,040 Speaker 1: automobile makers reported drop in sales in May of U 312 00:19:23,240 --> 00:19:26,840 Speaker 1: S branded vehicles. That we've seen the Chinese do this before, 313 00:19:26,960 --> 00:19:30,960 Speaker 1: boycotting brands from Japan and South Korea when tensions have 314 00:19:31,320 --> 00:19:35,439 Speaker 1: risen on the political front against those nations. UM. The 315 00:19:35,520 --> 00:19:39,000 Speaker 1: US companies have about a hundred billion dollars of direct 316 00:19:39,080 --> 00:19:44,240 Speaker 1: investment in in China in projects to make goods for 317 00:19:44,440 --> 00:19:47,800 Speaker 1: Chinese consumers in China, and every one of those companies 318 00:19:47,840 --> 00:19:50,240 Speaker 1: is vulnerable. And there's some of our biggest corporate names, 319 00:19:50,920 --> 00:19:54,800 Speaker 1: Carl Weinberg as an example. BMW says it will build 320 00:19:54,880 --> 00:19:59,200 Speaker 1: more of its SUVs overseas and not in South Carolina 321 00:19:59,720 --> 00:20:04,399 Speaker 1: be because of China's retaliation in the auto industry. Do 322 00:20:04,440 --> 00:20:07,560 Speaker 1: you believe that? Sorry, go ahead exactly, Tim, And we 323 00:20:07,880 --> 00:20:11,680 Speaker 1: just reported on Bloomberg the story yesterday of Tesla promising 324 00:20:11,720 --> 00:20:13,720 Speaker 1: to build a plant there to build half a million 325 00:20:13,800 --> 00:20:16,520 Speaker 1: vehicles per year in China. We don't know how real 326 00:20:16,600 --> 00:20:19,120 Speaker 1: that is, but the intention is certainly real. And if 327 00:20:19,160 --> 00:20:22,880 Speaker 1: Tesla can do it, others can do it GM right now. 328 00:20:22,880 --> 00:20:25,080 Speaker 1: According to an article I read on Bloomberg a few 329 00:20:25,080 --> 00:20:28,920 Speaker 1: weeks ago, produces more cars in China under ten joint 330 00:20:29,000 --> 00:20:31,600 Speaker 1: ventures and two direct investments than it does in the 331 00:20:31,640 --> 00:20:35,280 Speaker 1: United States. So big companies are vulnerable, and some companies 332 00:20:35,560 --> 00:20:38,280 Speaker 1: get a great proportion of their profit, does not most 333 00:20:38,280 --> 00:20:42,120 Speaker 1: of them from transactions in China. So, Carl, how much 334 00:20:42,119 --> 00:20:44,679 Speaker 1: of this is being priced in that China will retaliate 335 00:20:44,920 --> 00:20:47,560 Speaker 1: in the manner that you say, with sort of boycotting 336 00:20:47,680 --> 00:20:52,320 Speaker 1: or discriminating against US companies they're doing business in China. Lisa, 337 00:20:52,359 --> 00:20:54,120 Speaker 1: I wish I could give you an answer to that. 338 00:20:54,160 --> 00:20:56,880 Speaker 1: There's no way to know. We've never really seen anything 339 00:20:56,920 --> 00:21:00,000 Speaker 1: like this before. The only prior that we can point 340 00:21:00,080 --> 00:21:03,280 Speaker 1: two is that when China has engaged in a spat 341 00:21:03,359 --> 00:21:07,679 Speaker 1: with Japan, Japanese brands were boycotted. When they engaged in 342 00:21:07,720 --> 00:21:11,119 Speaker 1: a spat with Korea, Korean brands were boycotted. And this 343 00:21:11,160 --> 00:21:15,960 Speaker 1: can be a very very effective counter tool in any case. 344 00:21:16,400 --> 00:21:18,919 Speaker 1: You know the name of the game here. The objective 345 00:21:19,080 --> 00:21:21,800 Speaker 1: of the Trump tariffs is to try to short circuit 346 00:21:21,880 --> 00:21:26,080 Speaker 1: China's Made in China program, and that's worth about a 347 00:21:26,119 --> 00:21:29,520 Speaker 1: trillion dollars a year to China's economy forever once they 348 00:21:29,600 --> 00:21:32,680 Speaker 1: succeeded it, and I think they will. And whatever pain 349 00:21:32,800 --> 00:21:35,680 Speaker 1: that China feels on tariffs and on this trade war 350 00:21:36,040 --> 00:21:40,199 Speaker 1: is peanuts compared to the potential gains from China, all right, 351 00:21:40,320 --> 00:21:42,880 Speaker 1: So that is one way that China could hit back 352 00:21:43,160 --> 00:21:45,480 Speaker 1: is through some of the measures that could take against 353 00:21:45,520 --> 00:21:48,000 Speaker 1: US companies. What about its treasury holdings, I mean, do 354 00:21:48,040 --> 00:21:50,320 Speaker 1: you expect that that to be on the table with 355 00:21:50,400 --> 00:21:55,280 Speaker 1: China potentially liquidating it's holdings. Well, there's a risk of 356 00:21:55,359 --> 00:21:58,639 Speaker 1: that at any time that China could decide to let go. 357 00:21:58,720 --> 00:22:01,800 Speaker 1: Of course, they do have their own capital invested in 358 00:22:01,840 --> 00:22:05,320 Speaker 1: those bonds, so that if they undermine the market, they 359 00:22:05,359 --> 00:22:08,399 Speaker 1: do undermine the present value of their portfolio. But if 360 00:22:08,400 --> 00:22:14,040 Speaker 1: they're planning on holding to maturity, then they can finesse 361 00:22:14,119 --> 00:22:17,879 Speaker 1: the capital losses and they can probably make a profit 362 00:22:17,920 --> 00:22:20,040 Speaker 1: on the bonds that they sell. So I would say 363 00:22:20,080 --> 00:22:23,200 Speaker 1: that there is a risk, but I would say it's 364 00:22:23,200 --> 00:22:27,000 Speaker 1: a tail risk right now, mainly because China sees itself 365 00:22:27,000 --> 00:22:30,879 Speaker 1: as a world player and its future is involved in globalism, 366 00:22:31,200 --> 00:22:33,720 Speaker 1: and to undermine the U S Treasury market would undermine 367 00:22:33,760 --> 00:22:37,240 Speaker 1: the world economy, and that's not in China's interest. It 368 00:22:37,280 --> 00:22:41,240 Speaker 1: has more focused tools at its disposal that it can use. 369 00:22:42,119 --> 00:22:47,240 Speaker 1: Carl or about using currency as a weapon in this war, Well, 370 00:22:47,480 --> 00:22:49,919 Speaker 1: it would seem, you know almost that they are. You know, 371 00:22:50,000 --> 00:22:53,560 Speaker 1: we see the yuan getting cheaper, and um, you know 372 00:22:53,680 --> 00:22:56,679 Speaker 1: that certainly is one way to offset the impact of tariffs. 373 00:22:57,160 --> 00:23:00,800 Speaker 1: The thing is they don't really have to go that route. 374 00:23:01,280 --> 00:23:04,520 Speaker 1: I'm writing tonight from our customers at High Frequency Economics, 375 00:23:04,560 --> 00:23:08,359 Speaker 1: where our readers hear a story about trade diversion and 376 00:23:08,400 --> 00:23:14,159 Speaker 1: alternative sources for goods, and um, China can can really 377 00:23:14,560 --> 00:23:17,720 Speaker 1: escape through this tariff war with very little effect on 378 00:23:17,800 --> 00:23:21,480 Speaker 1: its domestic economy. Um, the losses that they would face 379 00:23:21,520 --> 00:23:24,439 Speaker 1: on sales to the US would be minimal, and the 380 00:23:24,480 --> 00:23:27,080 Speaker 1: impact on their consumers of the tariffs the US is 381 00:23:27,119 --> 00:23:30,880 Speaker 1: proposing would be I think negligible. Thank you very much 382 00:23:30,920 --> 00:23:33,320 Speaker 1: for spending time with us. Carl Weinberg is the chief 383 00:23:33,320 --> 00:23:38,280 Speaker 1: economist for High Frequency Economics. You can follow Carl on 384 00:23:38,280 --> 00:23:43,800 Speaker 1: Twitter at c B Weinberg. The topic tariffs and President 385 00:23:43,840 --> 00:24:04,760 Speaker 1: Donald Trump's trade war and retaliation from China. Fake accounts, Yes, 386 00:24:04,840 --> 00:24:08,400 Speaker 1: fake accounts at Twitter, potentially fake accounts at Facebook, fake 387 00:24:08,440 --> 00:24:12,760 Speaker 1: accounts almost everywhere. Here to tell us more about social 388 00:24:12,800 --> 00:24:16,359 Speaker 1: media and advertising is David Garrity. He is the chief 389 00:24:16,359 --> 00:24:19,679 Speaker 1: executive of g v A Research and you can of 390 00:24:19,720 --> 00:24:22,879 Speaker 1: course follow David as we do on Twitter at a 391 00:24:22,960 --> 00:24:26,280 Speaker 1: g v A Research. David always a pleasure, Thanks for 392 00:24:26,359 --> 00:24:30,920 Speaker 1: coming into the studio. Uh, you're not a fake account. 393 00:24:31,400 --> 00:24:34,160 Speaker 1: But how do we know that there isn't someone out 394 00:24:34,160 --> 00:24:39,840 Speaker 1: there on social media impersonating David Garrity? Um, apart from 395 00:24:39,880 --> 00:24:42,200 Speaker 1: the fact of you know, my own activity and those 396 00:24:42,200 --> 00:24:45,600 Speaker 1: of people who basically host my social media activities, you know, 397 00:24:45,600 --> 00:24:48,440 Speaker 1: trying to make sure that nobody's out there squatting. Uh. 398 00:24:48,480 --> 00:24:51,920 Speaker 1: So there's a certain amount of self policing that takes place. 399 00:24:51,960 --> 00:24:55,840 Speaker 1: But clearly we have a circumstance here with social media platforms, 400 00:24:56,160 --> 00:24:59,679 Speaker 1: you know, not necessarily subscribing to the good business practices 401 00:25:00,440 --> 00:25:05,199 Speaker 1: you know, traditional um news organizations and or other technology 402 00:25:05,240 --> 00:25:09,880 Speaker 1: companies had subscribed to in terms of verifying users identities. Clearly, 403 00:25:10,119 --> 00:25:12,920 Speaker 1: you know, we're seeing a belated response on the part 404 00:25:12,920 --> 00:25:15,440 Speaker 1: of Twitter to suddenly go out and purge one out 405 00:25:15,440 --> 00:25:18,159 Speaker 1: of every five of their user accounts. As at the 406 00:25:18,240 --> 00:25:20,639 Speaker 1: end of March, I mean, the number of seventy million 407 00:25:20,720 --> 00:25:23,399 Speaker 1: was somewhat staggering, especially when you consider that they have 408 00:25:23,760 --> 00:25:27,480 Speaker 1: seventy million users in the US alone. Now, clearly we 409 00:25:27,520 --> 00:25:30,320 Speaker 1: don't know what the geographic you know, dispersion was or 410 00:25:30,359 --> 00:25:33,360 Speaker 1: distribution was of these seventy million accounts that were being 411 00:25:33,400 --> 00:25:36,000 Speaker 1: acted by Twitter, but it would be very interesting to 412 00:25:36,040 --> 00:25:39,480 Speaker 1: go through and see, you know, these accounts, what kind 413 00:25:39,520 --> 00:25:42,879 Speaker 1: of social positions into or political positions were they espousing 414 00:25:43,080 --> 00:25:46,000 Speaker 1: to what extent was that basically a very clear measure 415 00:25:46,040 --> 00:25:50,560 Speaker 1: of manipulation of public opinion through Twitter, you know, And 416 00:25:50,680 --> 00:25:53,280 Speaker 1: the question really is begged here if this is what 417 00:25:53,320 --> 00:25:56,000 Speaker 1: they've done, now, what are they going to be going 418 00:25:56,040 --> 00:25:59,560 Speaker 1: to do on an ongoing basis? What's Facebook going to 419 00:25:59,600 --> 00:26:02,720 Speaker 1: be doing? You know, do we have a consensus potentially 420 00:26:03,119 --> 00:26:06,280 Speaker 1: in Congress, or do we have a consensus overseas, say 421 00:26:06,320 --> 00:26:10,040 Speaker 1: within the EU to make sure that these practices are 422 00:26:10,080 --> 00:26:12,520 Speaker 1: being that the bars being raised. So what I'm trying 423 00:26:12,560 --> 00:26:16,679 Speaker 1: to understand is also what are the consequences consequences from 424 00:26:16,680 --> 00:26:20,400 Speaker 1: a corporate standpoint of some of these social media companies 425 00:26:20,440 --> 00:26:22,639 Speaker 1: actually taking action? I mean, Twitter, yes, we know that 426 00:26:22,680 --> 00:26:25,520 Speaker 1: they're going to be culling about one fifth of all 427 00:26:25,520 --> 00:26:29,760 Speaker 1: of their active monthly users calling them fake bots are 428 00:26:29,800 --> 00:26:34,840 Speaker 1: impersonating uh impersonating identities, but their shares are still up 429 00:26:34,840 --> 00:26:37,840 Speaker 1: more than this here. No, I mean, certainly one could 430 00:26:37,840 --> 00:26:39,600 Speaker 1: say on the flip side that they're gonna be able 431 00:26:39,640 --> 00:26:42,240 Speaker 1: to come back to advertisers and say, look, okay, we've 432 00:26:42,240 --> 00:26:44,959 Speaker 1: gone through, we've vetted our accounts. You're really dealing with 433 00:26:45,040 --> 00:26:48,080 Speaker 1: real people here, and from that standpoint, we can justify 434 00:26:48,160 --> 00:26:50,760 Speaker 1: our ad rates. And you know, we've we've you know, 435 00:26:50,880 --> 00:26:53,879 Speaker 1: come to the altar, we've confessed our sins, you know, 436 00:26:54,000 --> 00:26:57,240 Speaker 1: absolve us and let us go forward. And the question 437 00:26:57,240 --> 00:26:59,480 Speaker 1: really boils down to, from a social and from a 438 00:26:59,480 --> 00:27:03,200 Speaker 1: policy endpoint, is that good enough? My view would be no, 439 00:27:03,480 --> 00:27:06,800 Speaker 1: because you know, what happened once clearly could happen again. 440 00:27:07,280 --> 00:27:09,440 Speaker 1: I think that this might be an interesting opportunity to 441 00:27:09,480 --> 00:27:14,359 Speaker 1: apply it a new technology, say blockchain, to verify user 442 00:27:14,440 --> 00:27:17,840 Speaker 1: identity when it counts are opened, and also to update 443 00:27:17,920 --> 00:27:20,960 Speaker 1: that record as new posts are being added. Hold on 444 00:27:21,040 --> 00:27:23,280 Speaker 1: a second, I'm sorry, because people use blockchain and a 445 00:27:23,280 --> 00:27:25,359 Speaker 1: lot of liberal ways and and they're sort of almost 446 00:27:25,359 --> 00:27:27,240 Speaker 1: a joke in the newsroom that you know, if you 447 00:27:27,280 --> 00:27:29,399 Speaker 1: have a problem, Just say blockchain and it will go away. 448 00:27:29,800 --> 00:27:31,880 Speaker 1: Are you basically saying that everybody should have a digital 449 00:27:31,880 --> 00:27:35,879 Speaker 1: fingerprint and that basically is used to sort of peg 450 00:27:35,920 --> 00:27:38,760 Speaker 1: their social media presences? In reality? Is that that type 451 00:27:38,760 --> 00:27:41,240 Speaker 1: of thing and that the blockchain technology will be used 452 00:27:41,240 --> 00:27:43,879 Speaker 1: to give everyone that digital fingerprint? Or is it? My 453 00:27:43,960 --> 00:27:46,280 Speaker 1: concept here would be say that for Twitter themselves, they 454 00:27:46,320 --> 00:27:49,680 Speaker 1: could develop a utility token, which, you know, in which 455 00:27:49,840 --> 00:27:53,560 Speaker 1: users would have to employ in order to access their platform. 456 00:27:53,720 --> 00:27:55,520 Speaker 1: You know, if what Twitter wants to be able to 457 00:27:55,560 --> 00:28:00,600 Speaker 1: put forward, absent the imposition of regulation and oversaw, if 458 00:28:00,600 --> 00:28:03,480 Speaker 1: they wanted to adopt a solution that might allow a 459 00:28:03,560 --> 00:28:08,600 Speaker 1: better tracking and verification, um, you know, around what activities 460 00:28:08,600 --> 00:28:11,200 Speaker 1: are taking place on their site. Blockchain in this case 461 00:28:11,320 --> 00:28:15,439 Speaker 1: actually is something of possible application and value. And I 462 00:28:15,480 --> 00:28:17,879 Speaker 1: think it's something where you would have a distributed network 463 00:28:17,880 --> 00:28:20,639 Speaker 1: because you have many people, many parties, who are actually 464 00:28:20,840 --> 00:28:25,119 Speaker 1: interested in establishing and confirming and upholding the veracity of 465 00:28:25,200 --> 00:28:28,040 Speaker 1: what's going across social media platforms. You know, this is 466 00:28:28,119 --> 00:28:33,520 Speaker 1: possible for Twitter to consider a blockchain application. David Garty, 467 00:28:33,840 --> 00:28:35,639 Speaker 1: I just want to bring in the concept of a 468 00:28:35,720 --> 00:28:40,400 Speaker 1: middleman or middle person, right, and I understand that blockchain 469 00:28:40,880 --> 00:28:44,920 Speaker 1: and bitcoin and a lot of cryptocurrencies, why we sometimes 470 00:28:45,040 --> 00:28:49,040 Speaker 1: shouldn't conflate them. It is the goal to get rid 471 00:28:49,080 --> 00:28:53,600 Speaker 1: of the costs, the friction cost that exists in the 472 00:28:53,640 --> 00:28:58,400 Speaker 1: middle of transactions. Would that be accurate? No, entirely, okay, 473 00:28:58,440 --> 00:29:01,360 Speaker 1: And until such time as we actually have technologies developed 474 00:29:01,360 --> 00:29:05,440 Speaker 1: to support blockchain applications that have faster transaction times and 475 00:29:05,440 --> 00:29:09,720 Speaker 1: lower transaction costs than existing centralized providers, you know, those 476 00:29:09,760 --> 00:29:13,560 Speaker 1: are obstacles to blockchain adoption long term. Okay, let me 477 00:29:13,640 --> 00:29:16,120 Speaker 1: just see if I can strike a blow for the 478 00:29:16,160 --> 00:29:19,920 Speaker 1: middle person. You know, in the world of book selling, 479 00:29:20,520 --> 00:29:23,360 Speaker 1: they used to be this concept that a publisher would 480 00:29:23,360 --> 00:29:26,760 Speaker 1: ship a certain amount of books to a retail establishment. 481 00:29:27,440 --> 00:29:31,440 Speaker 1: What the retail establishment didn't sell, they were allowed and 482 00:29:31,520 --> 00:29:36,680 Speaker 1: then send back to the bookseller to the publisher. In 483 00:29:36,720 --> 00:29:41,520 Speaker 1: another model, the book publisher sent books to the retailer 484 00:29:42,040 --> 00:29:45,280 Speaker 1: and they didn't take anything back. When the New York 485 00:29:45,320 --> 00:29:49,000 Speaker 1: Times went to look at best seller lists, they decided 486 00:29:49,080 --> 00:29:52,040 Speaker 1: that it was more important to look at those organizations 487 00:29:52,080 --> 00:29:56,920 Speaker 1: that could send the books back that were unsold, because 488 00:29:57,240 --> 00:30:02,000 Speaker 1: they had no reason to fake the results. You had 489 00:30:02,120 --> 00:30:04,880 Speaker 1: retailers who were faking results by saying, oh yeah, this 490 00:30:04,960 --> 00:30:08,600 Speaker 1: was a best seller and it became a self fulfilling prophecy. 491 00:30:08,680 --> 00:30:12,000 Speaker 1: So I'm wondering who would that middle person be in 492 00:30:12,040 --> 00:30:15,760 Speaker 1: these kinds of transactions. In the middle person in case 493 00:30:15,800 --> 00:30:19,200 Speaker 1: of this example might very well be a consortium that 494 00:30:19,240 --> 00:30:22,239 Speaker 1: would be formed between the publishers and those retailers who 495 00:30:22,280 --> 00:30:26,840 Speaker 1: would agree to participate. Um, you know, arguably, assuming that 496 00:30:26,880 --> 00:30:30,240 Speaker 1: there is you know, frictionless application, you know, low cost 497 00:30:30,360 --> 00:30:33,320 Speaker 1: or transactions or loss for processing, you know, you could 498 00:30:33,320 --> 00:30:38,320 Speaker 1: potentially apply a blockchain tracker to each book that was 499 00:30:38,360 --> 00:30:40,720 Speaker 1: going out and being sold, and then you could use 500 00:30:40,760 --> 00:30:45,040 Speaker 1: you know, barcode readers that potentially update the related block 501 00:30:45,120 --> 00:30:49,280 Speaker 1: chains and the distributed nodes that would be supporting the application. 502 00:30:49,360 --> 00:30:52,320 Speaker 1: This blockchain would be operated by the consortium of these 503 00:30:52,360 --> 00:30:57,960 Speaker 1: different organizations. Would that work for social media as well? Well? Potentially, yes, 504 00:30:58,240 --> 00:31:00,200 Speaker 1: I think in the case here looking at it, or 505 00:31:00,200 --> 00:31:02,360 Speaker 1: if one of the problems that we have is the 506 00:31:02,560 --> 00:31:06,640 Speaker 1: ease with which these fake accounts can be opened. Impersonating myself, 507 00:31:06,680 --> 00:31:11,640 Speaker 1: for impersonating you are God forbid impersonating Lisa exactly. But 508 00:31:11,760 --> 00:31:15,280 Speaker 1: Lisa Brahma wits one. By the way, precisely, not too 509 00:31:15,360 --> 00:31:19,520 Speaker 1: three or four exactly. But um, from that standpoint, you know, 510 00:31:20,080 --> 00:31:23,360 Speaker 1: if we can find the platform that technology can be 511 00:31:23,480 --> 00:31:26,640 Speaker 1: used to help to enable this kind of verification of 512 00:31:26,720 --> 00:31:30,640 Speaker 1: identity and veracity of content. Arguably, given all that we 513 00:31:30,680 --> 00:31:33,720 Speaker 1: have suffered, if you will, as a society because of 514 00:31:33,760 --> 00:31:37,600 Speaker 1: the manipulation of social media, we would all benefit. David Garrity, 515 00:31:38,200 --> 00:31:41,640 Speaker 1: the one and only, the non impersonatable David Garritty, the 516 00:31:41,720 --> 00:31:44,959 Speaker 1: chief executive of g v A Research here in our 517 00:31:45,000 --> 00:31:52,920 Speaker 1: eleven three oh studios. Thank you so much. Thanks for 518 00:31:53,000 --> 00:31:55,640 Speaker 1: listening to the Bloomberg P and L podcast. You can 519 00:31:55,680 --> 00:31:59,480 Speaker 1: subscribe and listen to interviews at Apple Podcasts, SoundCloud, or 520 00:31:59,520 --> 00:32:03,000 Speaker 1: whatever podcast platform you prefer. I'm pim Fox. I'm on 521 00:32:03,040 --> 00:32:06,920 Speaker 1: Twitter at pim Fox. I'm on Twitter at Lisa Abramo 522 00:32:06,920 --> 00:32:09,640 Speaker 1: wits one. Before the podcast, you can always catch us 523 00:32:09,680 --> 00:32:11,240 Speaker 1: worldwide on Bloomberg Radio.