1 00:00:02,720 --> 00:00:07,240 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:20,320 --> 00:00:23,400 Speaker 2: This is Wall Street Week. I'm David Western, bringing you 3 00:00:23,520 --> 00:00:27,480 Speaker 2: stories of capitalism, the wine industry, brace us for another 4 00:00:27,640 --> 00:00:30,640 Speaker 2: round of tariffs on imports to the United States, Why 5 00:00:30,800 --> 00:00:36,000 Speaker 2: being a domestic producer won't necessarily help, and artificial intelligence 6 00:00:36,040 --> 00:00:39,320 Speaker 2: could change the fundamentals of our economy and give central 7 00:00:39,320 --> 00:00:43,159 Speaker 2: banks new tools to monitor and manage it. Plus the 8 00:00:43,240 --> 00:00:47,279 Speaker 2: growing business of wellness wearables and whether they really can 9 00:00:47,440 --> 00:00:51,400 Speaker 2: help us live longer, healthier lives. But we start with 10 00:00:51,440 --> 00:00:54,880 Speaker 2: the profound changes President Trump is bringing or promising to 11 00:00:54,920 --> 00:00:57,840 Speaker 2: bring to us all from the one big, beautiful bill 12 00:00:58,080 --> 00:01:01,720 Speaker 2: to more tariffs. He says, our only day, we welcome 13 00:01:01,760 --> 00:01:04,320 Speaker 2: back our special contributor Larry Summers to tell us what 14 00:01:04,400 --> 00:01:07,080 Speaker 2: is likely to have long lasting effects. 15 00:01:09,120 --> 00:01:12,199 Speaker 3: David, it has to be recognized that it's a big 16 00:01:12,280 --> 00:01:17,360 Speaker 3: legislative accomplishment. It's a larger bill passed sooner than other 17 00:01:17,440 --> 00:01:21,040 Speaker 3: presidents have achieved. But I don't think it's going to 18 00:01:21,120 --> 00:01:24,000 Speaker 3: take the country in the right direction. I think it's 19 00:01:24,080 --> 00:01:29,160 Speaker 3: going to grow our budget deficits in the future very substantially. 20 00:01:29,240 --> 00:01:34,200 Speaker 3: And you're already seeing some market reaction to that. And 21 00:01:34,240 --> 00:01:37,760 Speaker 3: I think that what it does to our social safety 22 00:01:37,800 --> 00:01:42,680 Speaker 3: net is really going to be devastating relative to the 23 00:01:42,720 --> 00:01:45,639 Speaker 3: path the country would have been on if you look 24 00:01:46,160 --> 00:01:48,320 Speaker 3: at cutbacks. 25 00:01:47,680 --> 00:01:49,240 Speaker 4: In the social safety net. 26 00:01:49,320 --> 00:01:53,680 Speaker 3: There were cutbacks in the welfare reform bill that was 27 00:01:53,760 --> 00:01:58,000 Speaker 3: passed during Bill Clinton's presidency. There were cutbacks made by 28 00:01:58,400 --> 00:02:02,880 Speaker 3: President Reagan and his original tax and budget legislation. This 29 00:02:02,960 --> 00:02:07,200 Speaker 3: is the largest cutback in the social safety net that 30 00:02:07,480 --> 00:02:14,440 Speaker 3: anybody has been able to find, and it's coming at 31 00:02:14,480 --> 00:02:18,480 Speaker 3: a time when we appear to be on our trajectory 32 00:02:19,080 --> 00:02:26,320 Speaker 3: to massive cutbacks in spending on scientific research, massive cutbacks 33 00:02:26,320 --> 00:02:35,040 Speaker 3: in support for the arts and humanities, maximum cutbacks in 34 00:02:35,720 --> 00:02:41,480 Speaker 3: support for foreign assistance programs, including ones on which large 35 00:02:41,560 --> 00:02:45,959 Speaker 3: numbers of Aige patients in Africa are dependent for their 36 00:02:46,440 --> 00:02:53,880 Speaker 3: life saving medicines. So this is legislation that, to my mind, 37 00:02:54,440 --> 00:02:59,080 Speaker 3: both compromises our capacity to defend ourselves as a country 38 00:02:59,200 --> 00:03:04,520 Speaker 3: because of all of the debt, and frankly compromises what 39 00:03:04,600 --> 00:03:08,520 Speaker 3: makes our country worth defending in terms of being a 40 00:03:08,600 --> 00:03:12,399 Speaker 3: humane force in terms of the world, in terms of 41 00:03:12,480 --> 00:03:18,000 Speaker 3: our sense of national community and protecting everybody, in terms 42 00:03:18,120 --> 00:03:22,320 Speaker 3: of some of our greatest contributions to humanity in both 43 00:03:22,360 --> 00:03:27,919 Speaker 3: the sciences and the arts, and so I think this 44 00:03:28,000 --> 00:03:34,320 Speaker 3: is a very troubling piece of legislation in ways that 45 00:03:34,480 --> 00:03:38,200 Speaker 3: go beyond the problematic immediate economics. 46 00:03:38,560 --> 00:03:41,000 Speaker 2: This one big, beautiful bill is one part of a 47 00:03:41,040 --> 00:03:44,240 Speaker 2: more sweeping plan that President Trump has for I think 48 00:03:44,280 --> 00:03:47,720 Speaker 2: it's fair to say really redoing the American economy. And 49 00:03:47,840 --> 00:03:50,960 Speaker 2: yet we don't see much reaction yet from the economy 50 00:03:51,000 --> 00:03:52,920 Speaker 2: in the numbers. And I'll give you an example in 51 00:03:53,000 --> 00:03:54,840 Speaker 2: the tariffs. A lot of talk about tariffs, a lot 52 00:03:54,840 --> 00:03:57,120 Speaker 2: of fear about tariffs, and yet if you look at 53 00:03:57,120 --> 00:04:00,200 Speaker 2: the CPI numbers, the PPI numbers are coming out, they 54 00:04:00,240 --> 00:04:03,080 Speaker 2: don't indicate the inflation that most economists predicted. 55 00:04:03,520 --> 00:04:06,320 Speaker 4: I think it's early days. 56 00:04:07,280 --> 00:04:10,520 Speaker 3: It may be that there are a set of other 57 00:04:10,680 --> 00:04:18,080 Speaker 3: developments going on through artificial intelligence, through technology that are 58 00:04:18,600 --> 00:04:24,719 Speaker 3: exerting a deflationary force. It may be that people, given 59 00:04:24,880 --> 00:04:29,440 Speaker 3: all the huge uncertainties about tariffs, are waiting to see 60 00:04:29,480 --> 00:04:35,160 Speaker 3: how it shakes out before they establish their new price structures. 61 00:04:35,800 --> 00:04:39,919 Speaker 3: It may be that for a time it's possible for 62 00:04:40,000 --> 00:04:44,720 Speaker 3: the people in the middle to eat the tariff increases 63 00:04:44,880 --> 00:04:50,320 Speaker 3: in order to try to get market share. I agree, 64 00:04:50,400 --> 00:04:54,760 Speaker 3: I agree with you, David, I would have expected more inflation, 65 00:04:55,040 --> 00:04:58,320 Speaker 3: and what you have always have to do as an 66 00:04:58,360 --> 00:05:03,400 Speaker 3: economist is watch the data and be prepared to change 67 00:05:03,400 --> 00:05:07,600 Speaker 3: your mind. But for now, I think that I wouldn't 68 00:05:07,600 --> 00:05:11,160 Speaker 3: want to rush to a judgment that these tariffs are 69 00:05:11,160 --> 00:05:17,120 Speaker 3: innocuous for inflation. I think the more likely thing is 70 00:05:17,320 --> 00:05:20,320 Speaker 3: that they're going to be somewhat more delayed. 71 00:05:21,360 --> 00:05:23,520 Speaker 4: In their impact. 72 00:05:24,040 --> 00:05:29,800 Speaker 3: Certainly, there have been many careful studies that compared the 73 00:05:29,839 --> 00:05:32,920 Speaker 3: sectors that were tarifted and the sectors that were not 74 00:05:33,120 --> 00:05:37,640 Speaker 3: tariffed during the president's first term and found the tariffs 75 00:05:37,640 --> 00:05:44,320 Speaker 3: did translate into higher prices. So I would rather wait 76 00:05:44,360 --> 00:05:48,600 Speaker 3: and see on this. I think that's the approach that 77 00:05:48,640 --> 00:05:53,760 Speaker 3: the Felleral Reserve is taking, and I think, frankly that 78 00:05:54,000 --> 00:05:56,200 Speaker 3: is the right approach. 79 00:05:56,400 --> 00:05:59,040 Speaker 2: I mean, we've really seen a move up in the 80 00:05:59,080 --> 00:06:03,120 Speaker 2: thirty year yield. We're above five percent relatively constantly. Now 81 00:06:03,279 --> 00:06:05,479 Speaker 2: there are some who are concerned about exactly what that's 82 00:06:05,560 --> 00:06:08,800 Speaker 2: telling us about inflation expectations and term premium. 83 00:06:08,880 --> 00:06:11,440 Speaker 4: David. You know, I look at the. 84 00:06:13,040 --> 00:06:16,760 Speaker 3: Ten year market relative to the twenty year or thirty 85 00:06:16,839 --> 00:06:22,360 Speaker 3: year market as a sign of where the markets see 86 00:06:23,040 --> 00:06:26,400 Speaker 3: very long term rates going over the very long term 87 00:06:26,480 --> 00:06:31,920 Speaker 3: what market participants call the forward rate, and we now 88 00:06:32,040 --> 00:06:37,479 Speaker 3: have forward rates on regular bonds that are well above 89 00:06:38,600 --> 00:06:44,760 Speaker 3: five percent, and forward rates on tips on bonds linked 90 00:06:44,839 --> 00:06:49,200 Speaker 3: to inflation that are well above three percent. And those 91 00:06:49,240 --> 00:06:55,839 Speaker 3: are ominous indicators about our nation's credibility over the medium term. 92 00:06:56,240 --> 00:07:00,280 Speaker 3: They're ominous indicators with respect to the government's ability. 93 00:07:00,440 --> 00:07:04,480 Speaker 4: To issue long term debt. 94 00:07:04,680 --> 00:07:10,680 Speaker 3: They're ominous indicators with respect to the deficit because if 95 00:07:10,720 --> 00:07:15,000 Speaker 3: you look at the projections people quote from the Congressional 96 00:07:15,000 --> 00:07:19,880 Speaker 3: Budget Office from other places, they're building in much lower 97 00:07:20,320 --> 00:07:24,160 Speaker 3: interest rate assumptions. So I think if you look at 98 00:07:24,240 --> 00:07:28,640 Speaker 3: what's happened to bond markets, if you look at what's 99 00:07:28,680 --> 00:07:32,920 Speaker 3: happened to the dollar, you have to view our nation's 100 00:07:32,960 --> 00:07:40,240 Speaker 3: fiscal situation with considerable trepidation and concern. 101 00:07:40,520 --> 00:07:43,560 Speaker 2: While the government grapples with all that debt it's taking on, 102 00:07:43,800 --> 00:07:47,080 Speaker 2: consumers and small businesses have their own responses to Trump 103 00:07:47,120 --> 00:07:50,920 Speaker 2: administration policies, as Bank of America Chairman and CEO Brian 104 00:07:50,960 --> 00:07:52,920 Speaker 2: moynihan explained. 105 00:07:53,240 --> 00:07:55,760 Speaker 5: So if you look on the consumer side, and our 106 00:07:55,920 --> 00:07:59,320 Speaker 5: seventy million consumers who engage with the economy every day 107 00:07:59,600 --> 00:08:03,000 Speaker 5: and through their accounts and in their it's spend the 108 00:08:03,080 --> 00:08:06,560 Speaker 5: cash and everything about four trillion, five trillion dollars a year. 109 00:08:07,160 --> 00:08:10,440 Speaker 5: That grew up four percent plus the second quarter twenty 110 00:08:10,440 --> 00:08:13,000 Speaker 5: five or the second course of twenty four. So they 111 00:08:13,200 --> 00:08:15,440 Speaker 5: because they're employed and because wage growth, and that's not 112 00:08:15,640 --> 00:08:19,000 Speaker 5: every single consumer, it's but in the large, in the main, 113 00:08:19,400 --> 00:08:22,600 Speaker 5: they are continuing to grow and spend more and that 114 00:08:22,800 --> 00:08:25,600 Speaker 5: helps the economy. And so you're seeing in some of 115 00:08:25,600 --> 00:08:29,320 Speaker 5: the moderate income households there's a little bit of cheff 116 00:08:29,520 --> 00:08:31,800 Speaker 5: moving around to different things. You're seeing people trade from 117 00:08:31,800 --> 00:08:35,480 Speaker 5: wanting another less planes, more cruises earlier, that's leveling out now, 118 00:08:35,559 --> 00:08:37,680 Speaker 5: a lot more going to movies because the movies are good. 119 00:08:37,920 --> 00:08:40,680 Speaker 5: But the end of the day, they're spending discretion necessary 120 00:08:40,679 --> 00:08:44,040 Speaker 5: about the same percentage they traditionally spent. They've got money 121 00:08:44,080 --> 00:08:46,839 Speaker 5: in the accounts, they're employed, and the wage growth has 122 00:08:46,880 --> 00:08:50,120 Speaker 5: been relatively strong, and you know, so they're pretty good shape. 123 00:08:50,120 --> 00:08:52,680 Speaker 5: The credit quality is good. They have equity in their homes, 124 00:08:53,080 --> 00:08:56,280 Speaker 5: they're rate financing the mortgage, so consert is pretty good. 125 00:08:56,280 --> 00:08:57,959 Speaker 5: When you go to small businesses, that's more of the 126 00:08:58,040 --> 00:09:01,240 Speaker 5: question small medium sized businesses, because is the interest rate 127 00:09:01,320 --> 00:09:04,640 Speaker 5: environment hits them harder because they borrow on lines of 128 00:09:04,640 --> 00:09:07,160 Speaker 5: credit short term for a lot of their activities, and 129 00:09:07,240 --> 00:09:10,559 Speaker 5: that rate went up substantially. And anythink about the if 130 00:09:10,559 --> 00:09:12,600 Speaker 5: I'm one hundred million dollar company, a fifty million dollar 131 00:09:12,640 --> 00:09:14,920 Speaker 5: company here in North Carolina, and I'm engaging in the 132 00:09:14,920 --> 00:09:20,120 Speaker 5: world finance, I'm importing goods and manufacturing them, further manufacturing, 133 00:09:20,120 --> 00:09:22,679 Speaker 5: I'm selling them. You know, it got pretty interesting here 134 00:09:22,720 --> 00:09:24,559 Speaker 5: trying to figure out all the trade and terriffs. So 135 00:09:24,600 --> 00:09:26,920 Speaker 5: I think the certainly on the tax rate helps them, 136 00:09:27,520 --> 00:09:29,920 Speaker 5: meaning the big beautiful bill passing and the tax rate, 137 00:09:30,040 --> 00:09:31,800 Speaker 5: that's a very good thing. The alternative would have not 138 00:09:31,840 --> 00:09:35,400 Speaker 5: been good if their tax rates would change. A satisfactory 139 00:09:35,400 --> 00:09:38,640 Speaker 5: resolution to the trade so that they could learn the 140 00:09:38,679 --> 00:09:40,480 Speaker 5: rules of the road over the next thirty sixty ninety 141 00:09:40,559 --> 00:09:42,960 Speaker 5: days and get their plans for next year put together. 142 00:09:43,320 --> 00:09:46,120 Speaker 5: And I think ultimately they're going We've got the satisfactory 143 00:09:46,120 --> 00:09:49,040 Speaker 5: resolution on immigration and population growth. Because the end the day, 144 00:09:49,240 --> 00:09:52,920 Speaker 5: when I'm hearing more from the construction companies, farming companies, 145 00:09:53,280 --> 00:09:56,600 Speaker 5: and travel entertainment type companies, is I'm starting to worry 146 00:09:56,600 --> 00:10:01,400 Speaker 5: about I'm starting to struggle with labor availability and that's 147 00:10:02,160 --> 00:10:04,079 Speaker 5: that we got to make sure they have the workers 148 00:10:04,120 --> 00:10:09,199 Speaker 5: because they will supply great service economy continue to grow up. 149 00:10:09,240 --> 00:10:12,280 Speaker 2: Next, we've been hearing about those tariffs coming our way 150 00:10:12,360 --> 00:10:15,360 Speaker 2: since President Trump returned to office, But where will it 151 00:10:15,440 --> 00:10:18,680 Speaker 2: really affect us in our daily lives? It turns out 152 00:10:18,880 --> 00:10:21,440 Speaker 2: that those of us who drink wine could be on 153 00:10:21,520 --> 00:10:22,400 Speaker 2: the front lines. 154 00:10:23,720 --> 00:10:25,920 Speaker 6: Where you going to find champagne? Where are you going 155 00:10:25,920 --> 00:10:28,600 Speaker 6: to find shout enough to pop? Where you're going to 156 00:10:28,600 --> 00:10:30,720 Speaker 6: find kyanti? You're not going to find it in Oregon 157 00:10:30,800 --> 00:10:37,360 Speaker 6: or California. 158 00:10:41,080 --> 00:10:44,880 Speaker 2: This is a story about bottled poetry. That's what Robert 159 00:10:44,960 --> 00:10:47,920 Speaker 2: Lewis Stevenson called wine, and it's something that many of 160 00:10:48,000 --> 00:10:51,320 Speaker 2: us enjoy regularly, but also something that may be a 161 00:10:51,360 --> 00:10:54,160 Speaker 2: good deal harder to get a hold of if President 162 00:10:54,160 --> 00:10:57,199 Speaker 2: Trump follows through with the tariff threats he's made against 163 00:10:57,240 --> 00:10:58,199 Speaker 2: the European Union. 164 00:11:00,120 --> 00:11:03,040 Speaker 7: We've been taking advantage for many, many years by god. 165 00:11:02,880 --> 00:11:06,600 Speaker 8: Be's vote quote friend in voe, and frankly, the friends 166 00:11:06,640 --> 00:11:08,520 Speaker 8: have been worse than the bos in renegages. 167 00:11:09,600 --> 00:11:12,560 Speaker 2: A new deadline and a new threat. The US could 168 00:11:12,559 --> 00:11:15,440 Speaker 2: impose a thirty percent tariff on imported wine from the 169 00:11:15,480 --> 00:11:19,320 Speaker 2: European Union if no deal is struck by August first, 170 00:11:19,840 --> 00:11:21,080 Speaker 2: we have worked. 171 00:11:20,720 --> 00:11:23,640 Speaker 9: And now are ready to respond with countermeasures. 172 00:11:23,720 --> 00:11:25,480 Speaker 4: It's been a mess. 173 00:11:26,240 --> 00:11:30,040 Speaker 2: In New York, importer Victor Schwartz has spent nearly forty 174 00:11:30,160 --> 00:11:33,880 Speaker 2: years supplying restaurants and wine shops with hand picked bottles 175 00:11:33,920 --> 00:11:38,800 Speaker 2: from small European vineyards. Now tariffs threaten to upend the business. 176 00:11:39,440 --> 00:11:41,880 Speaker 6: That was ten percent, and they threatened fifty percent. 177 00:11:42,160 --> 00:11:44,040 Speaker 2: How different is the effect on your business of a 178 00:11:44,080 --> 00:11:45,040 Speaker 2: twenty versus a ten. 179 00:11:45,559 --> 00:11:48,600 Speaker 6: I mean, in our industry, end of the day, we 180 00:11:48,720 --> 00:11:52,840 Speaker 6: might make five percent as a net profit, five to 181 00:11:52,880 --> 00:11:56,720 Speaker 6: ten percent, So obviously we can't afford ten percent. We 182 00:11:56,760 --> 00:12:00,920 Speaker 6: can't afford twenty percent. But twenty percent is really egregious. 183 00:12:01,720 --> 00:12:05,679 Speaker 6: Twenty percent, I mean, think about what that does. God, 184 00:12:05,720 --> 00:12:08,400 Speaker 6: you know it makes a twenty dollars wine, you know, 185 00:12:08,480 --> 00:12:11,160 Speaker 6: twenty five dollars basically, because you know, there's kind of 186 00:12:11,160 --> 00:12:13,800 Speaker 6: a multiplier effect as it goes through the system. You know, 187 00:12:13,960 --> 00:12:16,840 Speaker 6: it's it's a much bigger impact. And don't forget when 188 00:12:16,880 --> 00:12:20,440 Speaker 6: when we raise a price, it's not as if the 189 00:12:21,160 --> 00:12:23,280 Speaker 6: consumer just accepts it. 190 00:12:23,640 --> 00:12:27,160 Speaker 2: Where do American customers for wine go if they decide 191 00:12:27,160 --> 00:12:29,320 Speaker 2: the price is too high. I'm not going to buy that. 192 00:12:29,880 --> 00:12:32,120 Speaker 6: Where you're going to find champagne, where you're going to 193 00:12:32,160 --> 00:12:35,360 Speaker 6: find shot enough to pop, where you're gonna find Kyanti, 194 00:12:35,480 --> 00:12:37,599 Speaker 6: You're not going to find it in Oregon or California. 195 00:12:37,760 --> 00:12:40,960 Speaker 6: A Finger Lakes wine, let's just be clear, is nothing 196 00:12:41,080 --> 00:12:45,559 Speaker 6: like a Napa Valley wine, nothing like a wine from 197 00:12:45,640 --> 00:12:49,440 Speaker 6: southern Italy or northern Italy or the center of Spain, 198 00:12:49,520 --> 00:12:53,480 Speaker 6: et cetera. My gist is is that these products are 199 00:12:53,520 --> 00:12:57,800 Speaker 6: so connected to their place, and that's what's wonderful and 200 00:12:57,840 --> 00:13:01,360 Speaker 6: interesting about wine. Otherwise, to be the wine company of 201 00:13:01,400 --> 00:13:03,840 Speaker 6: the world and it will come out of a spigot, 202 00:13:04,600 --> 00:13:08,880 Speaker 6: red white rose and sparkling done right. But that's not 203 00:13:08,920 --> 00:13:10,880 Speaker 6: why we love wine. 204 00:13:11,280 --> 00:13:15,040 Speaker 2: And Americans do love their wine. In twenty twenty three, 205 00:13:15,200 --> 00:13:18,560 Speaker 2: we consumed just under nine hundred million gallons of it, 206 00:13:18,920 --> 00:13:21,640 Speaker 2: more than any other country in the world, with a 207 00:13:21,720 --> 00:13:25,640 Speaker 2: value of over one hundred and seven billion dollars. More 208 00:13:25,679 --> 00:13:28,480 Speaker 2: than a third of that is shipped in from abroad, 209 00:13:28,480 --> 00:13:32,360 Speaker 2: making tariffs a real issue for importers, but those in 210 00:13:32,400 --> 00:13:35,079 Speaker 2: the business say it's not just the imports that will 211 00:13:35,120 --> 00:13:40,520 Speaker 2: be hit, it's the entire wine ecosystem. Quartan Ben Anif 212 00:13:40,720 --> 00:13:44,120 Speaker 2: is president of the US Wine Trade Alliance. He has 213 00:13:44,120 --> 00:13:47,760 Speaker 2: a shop in Tribeca that sells fine wine, which typically 214 00:13:47,800 --> 00:13:50,000 Speaker 2: goes for over twenty dollars per bottle. 215 00:13:51,679 --> 00:13:54,840 Speaker 10: Distributors and importers, even those by the way, that represent 216 00:13:55,120 --> 00:13:59,120 Speaker 10: US domestic wines about seventy five percent of their revenue 217 00:13:59,120 --> 00:14:03,320 Speaker 10: comes from wine. So that's one of the really interesting 218 00:14:03,360 --> 00:14:06,840 Speaker 10: things about this on the tariffront, all of the major 219 00:14:06,880 --> 00:14:11,160 Speaker 10: domestic wine producing organizations, from Wine Institute to NAPA Valley 220 00:14:11,200 --> 00:14:14,400 Speaker 10: mint Ners to Wine America, they're all against tariffs on 221 00:14:14,440 --> 00:14:20,680 Speaker 10: imported wine because they understand their domestic growers. Their producers 222 00:14:21,280 --> 00:14:24,640 Speaker 10: rely on healthy wine distributors for access to market. 223 00:14:25,520 --> 00:14:29,480 Speaker 2: Put another way, because state laws prevent domestic vineyards from 224 00:14:29,520 --> 00:14:34,280 Speaker 2: supplying restaurants and wine shops, they need distributors, and the 225 00:14:34,320 --> 00:14:39,160 Speaker 2: distributors rely critically on selling imports alongside their domestic wines. 226 00:14:40,000 --> 00:14:43,560 Speaker 2: That's why those who import fine wines like Victor Schwartz, 227 00:14:43,720 --> 00:14:46,200 Speaker 2: and those who sell it to US like Ben Anniff, 228 00:14:46,640 --> 00:14:49,800 Speaker 2: have no doubt that tariffs will cripple their business selling 229 00:14:49,840 --> 00:14:53,360 Speaker 2: both foreign and domestic wines. But there's another part of 230 00:14:53,400 --> 00:14:57,280 Speaker 2: the business, the value wine business, where a bottle or 231 00:14:57,320 --> 00:15:02,840 Speaker 2: its equivalent typically costs less than a LIE, and producers 232 00:15:02,880 --> 00:15:06,600 Speaker 2: for this segment, like Stuart Spencer in California's Central Valley, 233 00:15:07,040 --> 00:15:10,560 Speaker 2: say they need protection from multinational companies bringing in cheap, 234 00:15:10,920 --> 00:15:15,240 Speaker 2: subsidized imports that force American growers out of business. 235 00:15:16,440 --> 00:15:18,760 Speaker 7: There is a lot of what we call bulk wine 236 00:15:18,800 --> 00:15:21,840 Speaker 7: coming in in these big twenty foot bladder containers, and 237 00:15:21,880 --> 00:15:24,000 Speaker 7: it is this bulk wine that is really undercut in 238 00:15:24,040 --> 00:15:25,280 Speaker 7: California grapegrowers. 239 00:15:26,120 --> 00:15:28,640 Speaker 2: There's a lot of talk about the difference between free 240 00:15:28,640 --> 00:15:33,080 Speaker 2: trade and fair trade. From your experience as a grower, 241 00:15:33,120 --> 00:15:37,120 Speaker 2: but also from your dealing with Lodi, there are unfairnesses 242 00:15:37,280 --> 00:15:39,040 Speaker 2: in some of the exports to the United States. 243 00:15:40,080 --> 00:15:42,600 Speaker 7: I mean it's a completely unfair market. I mean we 244 00:15:42,640 --> 00:15:46,400 Speaker 7: are competing in a global marketplace. The European Union spends 245 00:15:46,440 --> 00:15:49,240 Speaker 7: you know, over two billion year between EU money and 246 00:15:49,880 --> 00:15:53,960 Speaker 7: member state money propping up their wine sector. They are 247 00:15:54,000 --> 00:15:58,240 Speaker 7: not only paying growers to inventnors to distill access wine 248 00:15:58,280 --> 00:15:59,920 Speaker 7: and buy it up, but they're also paying them to 249 00:16:00,040 --> 00:16:02,760 Speaker 7: plant new vineyards. And they spend hundreds of millions of 250 00:16:02,760 --> 00:16:05,200 Speaker 7: dollars in market promotion all around the world. And the 251 00:16:05,280 --> 00:16:08,320 Speaker 7: US is the number one target market. They also have 252 00:16:08,440 --> 00:16:11,520 Speaker 7: trade barriers, so it gets really complex when you get 253 00:16:11,560 --> 00:16:13,360 Speaker 7: in the weeds. But we are not playing on a 254 00:16:13,440 --> 00:16:14,320 Speaker 7: level field. 255 00:16:14,560 --> 00:16:18,360 Speaker 2: Last year, California wineries, which make nearly ninety percent of 256 00:16:18,440 --> 00:16:21,800 Speaker 2: US wine, we're stuck with more than five hundred thousand 257 00:16:21,920 --> 00:16:26,200 Speaker 2: excess tons of grapes. Now seventy seven million gallons of 258 00:16:26,240 --> 00:16:28,800 Speaker 2: wine are sitting in storage tanks. 259 00:16:28,520 --> 00:16:30,400 Speaker 7: And you can still see some of the grapes on 260 00:16:30,480 --> 00:16:33,720 Speaker 7: the vines. We have thousands of acres of grapes that 261 00:16:33,760 --> 00:16:36,840 Speaker 7: are being torn out right now, and we have small 262 00:16:36,880 --> 00:16:39,600 Speaker 7: farms and family businesses that are up for sale because 263 00:16:39,640 --> 00:16:43,400 Speaker 7: there's just not a prospect for them moving forward. My 264 00:16:43,480 --> 00:16:45,320 Speaker 7: family's been in this for fifty years, and I talked 265 00:16:45,320 --> 00:16:47,640 Speaker 7: to old timers that been in it for multi generations, 266 00:16:47,640 --> 00:16:49,800 Speaker 7: and they've never seen it as challenging as we are. 267 00:16:49,920 --> 00:16:50,160 Speaker 8: Now. 268 00:16:50,760 --> 00:16:53,120 Speaker 7: Seventy percent of all wine sales in this country is 269 00:16:53,160 --> 00:16:56,280 Speaker 7: controlled by about a handful of five to six large 270 00:16:56,360 --> 00:17:00,960 Speaker 7: multinational companies. They're bringing wine in bulk, blending it in 271 00:17:01,000 --> 00:17:04,320 Speaker 7: with California wine up to twenty five percent and calling 272 00:17:04,359 --> 00:17:08,440 Speaker 7: American appellation it's a federal loophole. We have, you know, 273 00:17:08,880 --> 00:17:11,679 Speaker 7: millions of gallons filled up in tanks right now in 274 00:17:11,680 --> 00:17:15,000 Speaker 7: California that don't have a home. But simultaneously, twenty four 275 00:17:15,000 --> 00:17:18,119 Speaker 7: million gallons of bulk wine is poured into California, coming 276 00:17:18,160 --> 00:17:21,399 Speaker 7: in at super low prices and undercutting the California grapegrower. 277 00:17:21,880 --> 00:17:24,800 Speaker 2: Are you in favor of the tariffs the President Trump 278 00:17:24,840 --> 00:17:25,919 Speaker 2: is talking about. 279 00:17:25,720 --> 00:17:27,959 Speaker 7: Well, I think if I was to speak to our 280 00:17:28,080 --> 00:17:30,880 Speaker 7: seven hundred grape growers that I represent, I think many 281 00:17:30,880 --> 00:17:33,240 Speaker 7: of them would support the tariffs to help level the 282 00:17:33,280 --> 00:17:35,960 Speaker 7: playing field. And I think what we would really hope 283 00:17:36,080 --> 00:17:39,040 Speaker 7: is that would bring these other trading partners to the 284 00:17:39,080 --> 00:17:42,480 Speaker 7: table to negotiate fair trade. The challenge we see with 285 00:17:42,520 --> 00:17:44,760 Speaker 7: what's going on with a lot of the trade negotiations 286 00:17:44,800 --> 00:17:47,360 Speaker 7: now is wine is just upon in a larger story 287 00:17:47,680 --> 00:17:50,160 Speaker 7: and the issues are about bigger issues. But I think 288 00:17:50,240 --> 00:17:52,639 Speaker 7: none of us, you know, want to see tariffs in 289 00:17:52,800 --> 00:17:55,360 Speaker 7: place permanently. I think what we really want to see 290 00:17:55,440 --> 00:17:56,800 Speaker 7: is really free and fair trade. 291 00:17:57,960 --> 00:18:02,920 Speaker 2: Some domestic producers kicking californ or complain about unfairness from 292 00:18:03,040 --> 00:18:08,480 Speaker 2: Europe because there are subsidies given to vineyards over in Europe. 293 00:18:09,119 --> 00:18:11,320 Speaker 2: Are tariff's an effective way to deal with that problem? 294 00:18:11,680 --> 00:18:14,240 Speaker 10: I feel really, really really bad for those guys. But 295 00:18:14,400 --> 00:18:16,560 Speaker 10: a tariff is not going to solve their problem. Farmers 296 00:18:16,600 --> 00:18:19,920 Speaker 10: that grow grapes to sell until, for instance, grocery store 297 00:18:19,960 --> 00:18:24,000 Speaker 10: boxed wine, and that's terrific for them. It's a great 298 00:18:24,000 --> 00:18:28,040 Speaker 10: product for certain customers. The demand for those products is collapsing. 299 00:18:28,560 --> 00:18:31,239 Speaker 10: You know, people aren't buying bulk wine the way they 300 00:18:31,280 --> 00:18:31,520 Speaker 10: used to. 301 00:18:33,840 --> 00:18:37,000 Speaker 2: Whether tariffs could give some relief to bulk wine producers 302 00:18:37,119 --> 00:18:40,600 Speaker 2: or not, they certainly would have unintended effects on the 303 00:18:40,640 --> 00:18:44,440 Speaker 2: American economy overall. You have spent some of your time 304 00:18:44,480 --> 00:18:48,960 Speaker 2: down in Washington trying to explain to lawmakers policymakers exactly 305 00:18:48,960 --> 00:18:52,280 Speaker 2: what this would mean for the wine business. What would 306 00:18:52,359 --> 00:18:54,720 Speaker 2: you want them to understand that maybe they don't understand 307 00:18:54,800 --> 00:18:57,200 Speaker 2: right now about the business and the effects of tariffs. 308 00:18:57,480 --> 00:19:01,000 Speaker 10: The United States has been talking about their concerns with 309 00:19:01,040 --> 00:19:04,960 Speaker 10: respect to a trade deficit. We import more European wines 310 00:19:05,080 --> 00:19:07,879 Speaker 10: than we sell American wines Europe. But the reality is 311 00:19:08,080 --> 00:19:11,879 Speaker 10: we have a huge economic surplus on the sale of 312 00:19:11,920 --> 00:19:15,119 Speaker 10: European wines in the United States. You know, we import 313 00:19:15,160 --> 00:19:18,400 Speaker 10: about five point three billion dollars worth of wine from 314 00:19:18,400 --> 00:19:23,159 Speaker 10: the European Union into the United States. But American businesses 315 00:19:23,240 --> 00:19:27,560 Speaker 10: make almost twenty three billion dollars from the sale of 316 00:19:27,560 --> 00:19:31,920 Speaker 10: those products, making a big margin on wine. For a restaurant, 317 00:19:31,960 --> 00:19:35,760 Speaker 10: it is not a luxury, it is an absolute necessity 318 00:19:35,920 --> 00:19:37,520 Speaker 10: for their very survival. 319 00:19:37,760 --> 00:19:41,439 Speaker 2: If in fact, tariff's do get imposed, what are the 320 00:19:41,920 --> 00:19:44,439 Speaker 2: likely long term effects in the wine business? 321 00:19:44,560 --> 00:19:48,879 Speaker 10: Contraction? And then you know what that means. Contraction means 322 00:19:49,920 --> 00:19:53,280 Speaker 10: American businesses closing and firing all their employees. 323 00:19:53,480 --> 00:19:56,800 Speaker 2: What about the uncertainty itself quite apart from the tariffs. 324 00:19:57,040 --> 00:19:59,800 Speaker 10: I mean, I'll tell you I had phone calls from 325 00:20:00,320 --> 00:20:03,800 Speaker 10: you know, wine distributors who said, you know, my grandfather 326 00:20:03,960 --> 00:20:07,920 Speaker 10: started this business. We were in terrific shape and growing 327 00:20:07,960 --> 00:20:11,800 Speaker 10: and hiring in January, and now I might have to 328 00:20:11,840 --> 00:20:14,719 Speaker 10: decide if I'm going to close the doors in two weeks. 329 00:20:15,080 --> 00:20:18,360 Speaker 10: You know, when they're they're put into this position when 330 00:20:18,359 --> 00:20:21,159 Speaker 10: their choice is either to pay a tariff that they 331 00:20:21,160 --> 00:20:25,040 Speaker 10: cannot afford because these these are small businesses, or don't 332 00:20:25,040 --> 00:20:28,080 Speaker 10: bring in wine. Don't bring in the wine that represents 333 00:20:28,600 --> 00:20:31,040 Speaker 10: seventy five percent of the revenue for your next three 334 00:20:31,119 --> 00:20:33,440 Speaker 10: or six months. You know, we had restaurants from South 335 00:20:33,440 --> 00:20:37,520 Speaker 10: Florida say, in the summertime, we need sancer and rose. 336 00:20:37,920 --> 00:20:41,280 Speaker 10: That's what keeps our businesses alive. And there really is 337 00:20:41,920 --> 00:20:44,080 Speaker 10: no substitute for these products. 338 00:20:43,840 --> 00:20:45,160 Speaker 6: And one of our favorite. 339 00:20:45,760 --> 00:20:49,199 Speaker 2: So in your wine store you have Bordeaux. If you 340 00:20:49,240 --> 00:20:52,120 Speaker 2: can't get the Bordeaux, will the customer say that's okay, 341 00:20:52,160 --> 00:20:52,880 Speaker 2: I'll take the cab. 342 00:20:53,280 --> 00:20:54,400 Speaker 10: The answer, flat lay is. 343 00:20:54,359 --> 00:20:56,520 Speaker 6: Now we talk about terror. 344 00:20:56,560 --> 00:20:56,760 Speaker 4: War. 345 00:20:57,480 --> 00:20:59,879 Speaker 6: It's a word that gets bandied about. It sounds fan 346 00:21:00,000 --> 00:21:02,359 Speaker 6: and see it's forign, but it really well, it means 347 00:21:02,680 --> 00:21:05,920 Speaker 6: terrowar land terror and it just means the place, right, 348 00:21:05,960 --> 00:21:10,680 Speaker 6: it's just geography. Part of terrawar is the human element, 349 00:21:10,960 --> 00:21:14,160 Speaker 6: the culture, the civilization, the people, the people who've been 350 00:21:14,320 --> 00:21:19,040 Speaker 6: on this piece of land in southern Italy for multiple generations. 351 00:21:19,240 --> 00:21:22,359 Speaker 6: They cook certain kinds of food, they make certain kinds 352 00:21:22,400 --> 00:21:25,960 Speaker 6: of wine that go with those foods. And it's very specific, right, 353 00:21:26,240 --> 00:21:28,960 Speaker 6: I mean, don't you love to drink an Italian wine 354 00:21:28,960 --> 00:21:31,199 Speaker 6: when you're having your spaghetti and meat sauce. 355 00:21:32,160 --> 00:21:35,040 Speaker 2: Schwartz is trying to hold off the administration as the 356 00:21:35,119 --> 00:21:38,760 Speaker 2: lead plaintiff in a lawsuit challenging the tariffs. The US 357 00:21:38,840 --> 00:21:42,119 Speaker 2: Court of International Trade ruled in his favor, but the 358 00:21:42,160 --> 00:21:45,280 Speaker 2: appeals Court stayed the injunction to give itself time to 359 00:21:45,320 --> 00:21:49,280 Speaker 2: hear the case. If the tariff's going to effect four 360 00:21:49,440 --> 00:21:54,040 Speaker 2: European wines August one, how long will it take before 361 00:21:54,119 --> 00:21:55,320 Speaker 2: we see it in our lives. 362 00:21:56,440 --> 00:21:58,879 Speaker 10: I think you'll start to see it pretty quickly. You know, 363 00:22:00,320 --> 00:22:03,919 Speaker 10: the first tariffed wines only have only now started to 364 00:22:03,960 --> 00:22:07,600 Speaker 10: come in, so distributors have still been selling through some 365 00:22:07,880 --> 00:22:09,880 Speaker 10: wines that didn't have tariffs on them. You're gonna start 366 00:22:09,880 --> 00:22:12,119 Speaker 10: seeing those prices come now. You're gonna start seeing a 367 00:22:12,160 --> 00:22:14,800 Speaker 10: lot less choice. You know, there are importers and distributors 368 00:22:14,880 --> 00:22:17,320 Speaker 10: that have halted all of their shipments because they're not 369 00:22:17,320 --> 00:22:19,280 Speaker 10: sure they can afford to bring them in now. At 370 00:22:19,320 --> 00:22:22,760 Speaker 10: the same time, they have no substitutes for them. They're 371 00:22:22,760 --> 00:22:25,920 Speaker 10: not buying more domestic wine, for instance, they can't afford 372 00:22:25,960 --> 00:22:28,280 Speaker 10: to they need to sell these European wines in order 373 00:22:28,280 --> 00:22:31,600 Speaker 10: to buy more American wine. In a nutshell, American businesses 374 00:22:31,760 --> 00:22:34,879 Speaker 10: are incredibly good at selling European wine and they support 375 00:22:35,000 --> 00:22:36,840 Speaker 10: huge numbers of jobs in the United States. So some 376 00:22:36,920 --> 00:22:39,520 Speaker 10: of the most famous importers actually got into the business 377 00:22:39,840 --> 00:22:42,920 Speaker 10: because they were in France or Italy during World War two, 378 00:22:42,960 --> 00:22:45,080 Speaker 10: said oh my god, I love this. This is what 379 00:22:45,160 --> 00:22:47,920 Speaker 10: I do want to do with my life, and their 380 00:22:47,960 --> 00:22:51,920 Speaker 10: classic American entrepreneurial success stories. As a matter of fact, 381 00:22:52,040 --> 00:22:54,719 Speaker 10: many of the most famous European wines in the world. 382 00:22:55,240 --> 00:23:00,800 Speaker 10: They're famous today because they were discovered by American wine 383 00:22:59,840 --> 00:23:03,639 Speaker 10: and they were brought back. They tasted ten thousand wines, 384 00:23:03,680 --> 00:23:05,720 Speaker 10: said these three are the best, and they were right. 385 00:23:06,040 --> 00:23:07,879 Speaker 10: Funny story. One of the first guys to do that, 386 00:23:07,880 --> 00:23:09,879 Speaker 10: by the way, was Thomas Jefferson. You know, he went 387 00:23:09,920 --> 00:23:13,320 Speaker 10: to Burgundy. He bought Mohersche and Mahrsokudor for he and 388 00:23:13,359 --> 00:23:15,760 Speaker 10: George Washington. He bought Chateau de Kem for he and 389 00:23:15,760 --> 00:23:17,680 Speaker 10: George Washington, and today those are still some of the 390 00:23:17,720 --> 00:23:20,040 Speaker 10: greatest wines on the planet. He had a pretty good paltte, 391 00:23:20,040 --> 00:23:20,280 Speaker 10: I think. 392 00:23:21,000 --> 00:23:25,840 Speaker 2: And now, ironically American's affinity for European wines, nurtured by 393 00:23:25,840 --> 00:23:29,440 Speaker 2: the likes of George Washington and Thomas Jefferson, may be 394 00:23:29,640 --> 00:23:33,359 Speaker 2: challenged by the most recent occupant of their high office 395 00:23:34,200 --> 00:23:36,679 Speaker 2: and perhaps make it more difficult for us to enjoy 396 00:23:36,720 --> 00:23:40,640 Speaker 2: that bottled poetry they discovered two hundred and fifty years 397 00:23:40,640 --> 00:23:48,639 Speaker 2: ago in Fine French wines up next. From our wrists 398 00:23:48,720 --> 00:23:51,840 Speaker 2: to our fingers, everyone seems to be wearing some device 399 00:23:51,920 --> 00:23:55,240 Speaker 2: to monitor how healthy our habits are. We visit the 400 00:23:55,320 --> 00:24:10,160 Speaker 2: wonderful world of Whoop to see what's really possible. This 401 00:24:10,280 --> 00:24:13,879 Speaker 2: is a story about the Fountain of Youth. Since long 402 00:24:13,960 --> 00:24:17,520 Speaker 2: before Ponce de Leon supposedly got lost looking for it 403 00:24:17,560 --> 00:24:21,199 Speaker 2: in the swamps of Florida in fifteen thirteen, humans have 404 00:24:21,280 --> 00:24:23,920 Speaker 2: been on a quest for a longer and healthier life, 405 00:24:24,359 --> 00:24:27,919 Speaker 2: leading to everything from exercise regimens to diet crazes to 406 00:24:27,960 --> 00:24:32,200 Speaker 2: weight loss drugs. Now, as in everything else, big tech 407 00:24:32,480 --> 00:24:35,679 Speaker 2: is in the game. But our elaborate wellness device is 408 00:24:35,760 --> 00:24:39,080 Speaker 2: really worth it? Or does the path longevity boil down 409 00:24:39,119 --> 00:24:41,919 Speaker 2: to just a few fundamental principles. 410 00:24:44,200 --> 00:24:47,400 Speaker 8: Wearable technology and the sensors that we have today will 411 00:24:47,440 --> 00:24:49,879 Speaker 8: continue to unlock new capabilities. 412 00:24:50,400 --> 00:24:53,359 Speaker 11: Eventually we will write a point where we can really 413 00:24:53,400 --> 00:24:58,480 Speaker 11: monitor our processes and intervene early on before a disease 414 00:24:58,520 --> 00:24:59,160 Speaker 11: could develop. 415 00:25:00,200 --> 00:25:02,639 Speaker 9: Something on your body. It starts to say a lot 416 00:25:02,680 --> 00:25:03,480 Speaker 9: about who you are. 417 00:25:05,320 --> 00:25:09,040 Speaker 1: We think that wearables are a key to the MAHA 418 00:25:09,080 --> 00:25:12,600 Speaker 1: agenda making America healthy again, and we are going to 419 00:25:13,080 --> 00:25:16,399 Speaker 1: My vision is at every American and is wearing a 420 00:25:16,400 --> 00:25:17,919 Speaker 1: wearable within four years. 421 00:25:18,800 --> 00:25:22,440 Speaker 2: Health and Human Services Secretary Robert F. Kennedy Junior's goal 422 00:25:22,640 --> 00:25:26,959 Speaker 2: might seem ambitious, but the market for global wellness wearables 423 00:25:27,200 --> 00:25:31,359 Speaker 2: is on the rise. Data firm IDC expects global revenues 424 00:25:31,359 --> 00:25:34,199 Speaker 2: to grow from sixty three billion dollars last year to 425 00:25:34,320 --> 00:25:37,560 Speaker 2: nearly seventy eight billion dollars by twenty twenty nine. 426 00:25:38,200 --> 00:25:42,000 Speaker 12: With wearable technology, you need to build something that's either 427 00:25:42,119 --> 00:25:43,920 Speaker 12: cool or invisible. 428 00:25:43,359 --> 00:25:44,200 Speaker 9: We like to say. 429 00:25:44,960 --> 00:25:48,280 Speaker 2: Will Ahmed is the founder and CEO of Whoop, one 430 00:25:48,280 --> 00:25:51,680 Speaker 2: of the major players in the wellness wearable field. As 431 00:25:51,720 --> 00:25:54,600 Speaker 2: of its last funding round in twenty twenty one, it 432 00:25:54,720 --> 00:25:58,399 Speaker 2: was valued at three point six billion dollars. Will first 433 00:25:58,440 --> 00:26:01,040 Speaker 2: came up with the idea during his time as captain 434 00:26:01,160 --> 00:26:02,639 Speaker 2: of Harvard's squash team. 435 00:26:03,440 --> 00:26:07,920 Speaker 12: Who builds wearable technology that's really designed to improve performance 436 00:26:08,000 --> 00:26:10,720 Speaker 12: and health. The company was founded out of the Harvard 437 00:26:10,720 --> 00:26:14,600 Speaker 12: Innovation Lab twelve years ago. We started with the world's 438 00:26:14,680 --> 00:26:19,360 Speaker 12: best athletes, where we were really designing high performance technology 439 00:26:19,440 --> 00:26:21,600 Speaker 12: to replace what at the time was a lot of 440 00:26:21,640 --> 00:26:26,360 Speaker 12: medical technology. An electrocardiogram, a PSG machine, the gold standard 441 00:26:26,359 --> 00:26:30,320 Speaker 12: for sleep, a chest strap, which measures heart rate during exercise. 442 00:26:30,720 --> 00:26:34,800 Speaker 12: This is the original prototype that was built in twenty twelve. 443 00:26:35,080 --> 00:26:38,280 Speaker 12: Now it looks ridiculous in a million ways, but it 444 00:26:38,320 --> 00:26:42,080 Speaker 12: could measure this thing called heart rate variability from the wrist, 445 00:26:42,320 --> 00:26:45,840 Speaker 12: which was a breakthrough. We wanted to take these sophisticated 446 00:26:45,880 --> 00:26:49,399 Speaker 12: but antiquated pieces of technology and put them in a 447 00:26:49,480 --> 00:26:52,800 Speaker 12: much smaller form factor, and the business has really evolved 448 00:26:53,440 --> 00:26:57,640 Speaker 12: quite beautifully from being a very high performance athletic product 449 00:26:58,000 --> 00:27:01,320 Speaker 12: to now being a tool that many people are using 450 00:27:01,600 --> 00:27:02,399 Speaker 12: to live longer. 451 00:27:02,960 --> 00:27:06,800 Speaker 2: While wellness wearables today are heavily focused on new technology 452 00:27:06,840 --> 00:27:09,480 Speaker 2: that can measure heart health, it was more than one 453 00:27:09,560 --> 00:27:12,720 Speaker 2: hundred years ago when the idea for the pedometer was 454 00:27:12,760 --> 00:27:16,240 Speaker 2: first patented. Poehler raised the stakes in the eighties with 455 00:27:16,280 --> 00:27:19,800 Speaker 2: the first wireless heart rate monitor, but it was fitbit 456 00:27:20,040 --> 00:27:24,160 Speaker 2: in the early two thousands it took the industry mainstream. Today, 457 00:27:24,240 --> 00:27:28,400 Speaker 2: the wellness wearables arena is crowded, attracting billions in investment 458 00:27:28,520 --> 00:27:30,639 Speaker 2: and spawning new startups regularly. 459 00:27:31,119 --> 00:27:34,280 Speaker 8: Wearables today are nearly ubiquitous. I think, certainly when I 460 00:27:34,359 --> 00:27:36,199 Speaker 8: go out and about, I do see people with an 461 00:27:36,240 --> 00:27:39,720 Speaker 8: Apple watch, a whoop, an aura. We do see people 462 00:27:39,720 --> 00:27:43,119 Speaker 8: wearing with various devices out in the world today. 463 00:27:43,800 --> 00:27:46,919 Speaker 2: Alex Morgan is a partner in Cosla Ventures, where he 464 00:27:46,960 --> 00:27:51,320 Speaker 2: focuses on investment in emerging biotech, healthcare, and data science. 465 00:27:51,680 --> 00:27:55,359 Speaker 8: For many wearables, the competitive landscape is a challenge. We 466 00:27:55,480 --> 00:27:59,320 Speaker 8: certainly saw in some of the first generation of activity 467 00:27:59,359 --> 00:28:02,720 Speaker 8: based wearables that were many companies that were all measuring 468 00:28:02,800 --> 00:28:05,840 Speaker 8: activity and heart rate and it was very hard to 469 00:28:05,920 --> 00:28:09,879 Speaker 8: compete with a product that really didn't provide unique information 470 00:28:09,960 --> 00:28:12,760 Speaker 8: and it was much more about brand and packaging and 471 00:28:12,800 --> 00:28:15,400 Speaker 8: perhaps influencer And there are ways that you can win 472 00:28:15,520 --> 00:28:19,680 Speaker 8: in a competitive landscape with better branding, better marketing, better 473 00:28:19,720 --> 00:28:24,119 Speaker 8: access to influencers. We tend to look for technology that 474 00:28:24,240 --> 00:28:29,240 Speaker 8: is unique, often protectable with IP that offers unique benefit, 475 00:28:29,400 --> 00:28:31,320 Speaker 8: and that is something that we particularly look for. 476 00:28:32,359 --> 00:28:36,760 Speaker 2: Samsung recently released the Galaxy Watch eight, offering new health 477 00:28:36,840 --> 00:28:41,560 Speaker 2: tracking capabilities. In late twenty twenty four, Apple announced its 478 00:28:41,640 --> 00:28:45,560 Speaker 2: latest smartwatch with advanced features like a sleep apnea detector. 479 00:28:46,200 --> 00:28:50,560 Speaker 2: But not every company is successful. Amazon discontinued its Halo 480 00:28:50,720 --> 00:28:54,320 Speaker 2: fitness band in twenty twenty three, underscoring the challenge of 481 00:28:54,440 --> 00:28:58,360 Speaker 2: entering this market without a clear edge. With multiple devices 482 00:28:58,360 --> 00:29:01,640 Speaker 2: to choose from. How does one brand set itself apart 483 00:29:01,680 --> 00:29:05,160 Speaker 2: from the pack? Wearables have become popular in various ways. 484 00:29:05,400 --> 00:29:07,840 Speaker 2: How do you compare what's your market niche? 485 00:29:07,960 --> 00:29:11,480 Speaker 12: So we've designed the product to be worn very easily 486 00:29:12,800 --> 00:29:17,560 Speaker 12: in whatever location you want. And what does that ultimately achieve, Well, 487 00:29:17,560 --> 00:29:20,120 Speaker 12: it achieves a solution where you can be collecting this 488 00:29:20,200 --> 00:29:24,680 Speaker 12: health data twenty four to seven and continuous data is 489 00:29:24,800 --> 00:29:26,320 Speaker 12: ultimately what makes. 490 00:29:27,080 --> 00:29:28,720 Speaker 9: Our tool so successful. 491 00:29:29,160 --> 00:29:32,200 Speaker 12: You know, A real challenge I think with other products 492 00:29:32,240 --> 00:29:34,760 Speaker 12: that came before Whoop is they would give you these 493 00:29:34,800 --> 00:29:38,320 Speaker 12: sort of snapshots along the way. We collect an enormous 494 00:29:38,360 --> 00:29:41,920 Speaker 12: amount of data on the human body, physiological data. It's 495 00:29:41,960 --> 00:29:44,880 Speaker 12: really accurate. We've tuned the sensors to be really accurate. 496 00:29:45,400 --> 00:29:46,360 Speaker 12: But what that also. 497 00:29:46,200 --> 00:29:48,360 Speaker 9: Means is we're not going to do a thousand other things. 498 00:29:48,760 --> 00:29:54,040 Speaker 12: Right, We're not a smart watch, we're not doing phone calls. 499 00:29:54,160 --> 00:29:55,720 Speaker 12: You know, you're not going to call an uber with 500 00:29:55,800 --> 00:29:57,360 Speaker 12: your Whoop. But at the end of the day, we 501 00:29:57,360 --> 00:30:00,240 Speaker 12: don't spend that much time thinking about competition. Were just 502 00:30:00,280 --> 00:30:02,920 Speaker 12: incredibly focused on how do we drive health outcomes? 503 00:30:03,600 --> 00:30:07,040 Speaker 2: So, how does a wearable company help drive health outcomes? 504 00:30:07,800 --> 00:30:12,400 Speaker 2: For Whoop it's about differentiating itself through technology, using sophisticated 505 00:30:12,400 --> 00:30:17,080 Speaker 2: biometric monitoring and data collection for more actionable health insights. 506 00:30:17,720 --> 00:30:20,560 Speaker 2: Its most recent models are the Whoop five point zero 507 00:30:20,760 --> 00:30:21,959 Speaker 2: and Whoop MG. 508 00:30:23,000 --> 00:30:25,360 Speaker 12: It's got a fourteen day battery life, it's got more 509 00:30:25,400 --> 00:30:29,400 Speaker 12: accurate sensing. The Whoop MG has medical clearances, so it's 510 00:30:29,400 --> 00:30:35,360 Speaker 12: able to do ECG monitoring, AFIB detection, blood pressure insights. 511 00:30:35,880 --> 00:30:39,600 Speaker 12: So these are all really powerful innovations that frankly just 512 00:30:39,600 --> 00:30:40,440 Speaker 12: didn't exist. 513 00:30:40,760 --> 00:30:44,400 Speaker 8: Whatever wearable technology is doing to measure or interact with 514 00:30:44,440 --> 00:30:48,800 Speaker 8: an individual, the accelerating ability of a machine learning to 515 00:30:49,000 --> 00:30:52,800 Speaker 8: improve the capability of that measurement or that intervention is 516 00:30:52,840 --> 00:30:55,280 Speaker 8: only accelerating. We are translating to face where there are 517 00:30:55,360 --> 00:31:01,360 Speaker 8: wearable technologies that aren't just providing insights but actually provide interventions. 518 00:31:01,400 --> 00:31:04,200 Speaker 8: They're actually therapeutic in some way, And I think that 519 00:31:04,760 --> 00:31:08,000 Speaker 8: is going to accelerate the current adoption even more because 520 00:31:08,040 --> 00:31:12,840 Speaker 8: I do think that most people, most customers, don't necessarily 521 00:31:12,880 --> 00:31:15,720 Speaker 8: want insights. Most people want solutions to problems that they have, 522 00:31:16,560 --> 00:31:22,320 Speaker 8: whether it's problems sleeping, problems with depression, concerns about wait wellness. 523 00:31:22,320 --> 00:31:26,400 Speaker 2: Wearables have certainly become popular, but are they a good business? 524 00:31:27,000 --> 00:31:30,000 Speaker 2: And what takes a wearable from being nice to have 525 00:31:30,520 --> 00:31:32,320 Speaker 2: to being a half to have. 526 00:31:33,000 --> 00:31:36,160 Speaker 8: When we evaluate a company, there's no single way we 527 00:31:36,200 --> 00:31:41,120 Speaker 8: evaluate it. So sometimes we invest in unique technology. And 528 00:31:41,160 --> 00:31:44,240 Speaker 8: that's actually the majority of my time trying to identify 529 00:31:44,960 --> 00:31:49,120 Speaker 8: unique technology that is crossing over into being productizable and 530 00:31:49,200 --> 00:31:53,400 Speaker 8: translatable into products that we believe will provide real value 531 00:31:53,520 --> 00:31:56,040 Speaker 8: to customers in patients. And that's I think one of 532 00:31:56,080 --> 00:31:58,360 Speaker 8: the first things we want to do, is this product 533 00:31:58,480 --> 00:32:01,840 Speaker 8: or technology really able to help people in a potentially 534 00:32:01,880 --> 00:32:06,000 Speaker 8: powerful way. Another technology that I'm really excited about that's 535 00:32:06,040 --> 00:32:08,920 Speaker 8: being used is a company called Flow Neuroscience. 536 00:32:09,040 --> 00:32:09,880 Speaker 4: So this is on. 537 00:32:09,880 --> 00:32:13,479 Speaker 8: The market in the UK and Europe. Over ten thousand 538 00:32:13,480 --> 00:32:16,440 Speaker 8: people a month use it and it also uses a 539 00:32:16,840 --> 00:32:21,360 Speaker 8: gentle electric stimulation to treat mild to modern depression and 540 00:32:21,440 --> 00:32:22,680 Speaker 8: general anxiety disorder. 541 00:32:23,600 --> 00:32:25,080 Speaker 4: Numerous clinical trials. 542 00:32:24,800 --> 00:32:27,960 Speaker 8: Have shown benefit, so in some of the more recent 543 00:32:27,960 --> 00:32:32,000 Speaker 8: clinical trials about sixteen percent improvement and remission and depressive 544 00:32:32,000 --> 00:32:34,760 Speaker 8: symptoms compared to twenty percent in placebo. So the ideal 545 00:32:34,920 --> 00:32:39,760 Speaker 8: startup for US is a technology that provides unique, powerful, 546 00:32:39,840 --> 00:32:43,560 Speaker 8: patient and customer benefit in some way. They're able to say, 547 00:32:43,680 --> 00:32:47,120 Speaker 8: here's an innovation in technology that is crossing over from 548 00:32:47,160 --> 00:32:52,520 Speaker 8: basic science and research and moving into an opportunity to 549 00:32:52,560 --> 00:32:55,200 Speaker 8: go out and help many people that have a particular 550 00:32:55,240 --> 00:32:58,880 Speaker 8: problem and able through that in a unique and special way. Certainly, 551 00:32:59,640 --> 00:33:02,680 Speaker 8: there are are people using things like continuous glucose monitoring 552 00:33:02,920 --> 00:33:04,960 Speaker 8: that you may not know that they're using, but if 553 00:33:04,960 --> 00:33:07,760 Speaker 8: you ask someone with diabetes, that is probably how they 554 00:33:07,760 --> 00:33:10,760 Speaker 8: may be managing it today if you're intolindependent diabetic. So 555 00:33:11,240 --> 00:33:14,080 Speaker 8: I would say that it has crossed the chasm into 556 00:33:14,120 --> 00:33:16,720 Speaker 8: being something that's speculative into being a product that's just 557 00:33:16,800 --> 00:33:19,080 Speaker 8: part of the everyday lives for many Americans. 558 00:33:19,280 --> 00:33:21,280 Speaker 12: We're now in fifty markets. I think one of the 559 00:33:21,320 --> 00:33:23,840 Speaker 12: biggest changes for Whoop in the last two years is 560 00:33:23,880 --> 00:33:26,800 Speaker 12: going from being almost entirely a US business to being 561 00:33:26,800 --> 00:33:30,440 Speaker 12: in global business that obviously introduces new challenges, but also 562 00:33:31,040 --> 00:33:35,120 Speaker 12: enormous growth. And so we've seen the business growing considerably 563 00:33:35,320 --> 00:33:38,520 Speaker 12: in the last twelve months, seventy percent year over year growth, 564 00:33:38,520 --> 00:33:39,720 Speaker 12: which is really exciting. 565 00:33:40,360 --> 00:33:42,880 Speaker 2: We don't have the data yet to show that wearables 566 00:33:42,920 --> 00:33:46,760 Speaker 2: like whoop will actually make us healthier, much less, live longer, 567 00:33:47,400 --> 00:33:49,920 Speaker 2: and some believe the path to the fountain of youth 568 00:33:50,120 --> 00:33:53,200 Speaker 2: ultimately means doing the things our mothers have taught us 569 00:33:53,320 --> 00:33:54,400 Speaker 2: through the generations. 570 00:33:55,120 --> 00:33:57,600 Speaker 11: The secret of healthy life is very simple and it 571 00:33:57,640 --> 00:34:00,760 Speaker 11: doesn't require an industry to maintain it. Ignore all that 572 00:34:00,880 --> 00:34:06,400 Speaker 11: noise and focus on these five fundamental components from diet 573 00:34:06,920 --> 00:34:09,399 Speaker 11: to exercise, stress, sleep, and so on. 574 00:34:09,840 --> 00:34:14,480 Speaker 2: Albert Laslo Barabasi is a network science physicist. He believes 575 00:34:14,480 --> 00:34:18,200 Speaker 2: wearables may help users stay healthy, but there's no secret 576 00:34:18,360 --> 00:34:19,960 Speaker 2: about the keys to success. 577 00:34:20,840 --> 00:34:22,960 Speaker 11: As long as you focus on these basics and you 578 00:34:23,040 --> 00:34:27,600 Speaker 11: make sure that these are guaranteed, you are actually setting 579 00:34:27,640 --> 00:34:31,480 Speaker 11: yourself up for a healthy lifestyle. Everything else is more 580 00:34:31,560 --> 00:34:34,239 Speaker 11: or less an intervention that is trying to correct the 581 00:34:34,360 --> 00:34:39,680 Speaker 11: problem because these have not been probably observed. Certainly, having 582 00:34:39,719 --> 00:34:43,000 Speaker 11: access to all the data points about us does give 583 00:34:43,120 --> 00:34:46,120 Speaker 11: us a sense of control, and I think that sense 584 00:34:46,120 --> 00:34:49,440 Speaker 11: of control fails when we realize that we don't know 585 00:34:49,480 --> 00:34:52,880 Speaker 11: how to correct that. And we're all going to encounter 586 00:34:52,960 --> 00:34:56,280 Speaker 11: that moment in our lives where the numbers are flashing 587 00:34:56,360 --> 00:34:58,960 Speaker 11: and showing that something is out of balance. 588 00:34:59,600 --> 00:35:02,439 Speaker 2: Where do wearables need to go from here? If they're 589 00:35:02,480 --> 00:35:07,839 Speaker 2: to become proactive health companions? Not surprisingly artificial intelligence may 590 00:35:07,880 --> 00:35:09,120 Speaker 2: be part of the answer. 591 00:35:09,920 --> 00:35:13,240 Speaker 12: We've used artificial intelligence for a decade to really improve 592 00:35:13,280 --> 00:35:16,440 Speaker 12: our algorithms for sensing, and the result I think is 593 00:35:16,560 --> 00:35:18,799 Speaker 12: being able to demonstrate in the market we're the most 594 00:35:18,800 --> 00:35:22,360 Speaker 12: performance product. We still have the world's best athletes that 595 00:35:22,440 --> 00:35:26,520 Speaker 12: were whoop, we have medically approved features, and we have 596 00:35:26,560 --> 00:35:29,560 Speaker 12: consumers that swear by our accuracy. You don't really get 597 00:35:29,560 --> 00:35:33,080 Speaker 12: those combination of things if the underlying data isn't really good. 598 00:35:33,600 --> 00:35:36,560 Speaker 11: Eventually we will write to a point where we can really 599 00:35:36,640 --> 00:35:41,680 Speaker 11: monitor our processes and intervene early on before a disease 600 00:35:41,719 --> 00:35:44,239 Speaker 11: could develop. One way to think about it is that 601 00:35:44,320 --> 00:35:47,480 Speaker 11: every single disease that I will have throughout my lifetime 602 00:35:47,600 --> 00:35:51,360 Speaker 11: is already bit in me developing because I already born 603 00:35:51,440 --> 00:35:54,919 Speaker 11: with all the mutations and all the defects that will 604 00:35:54,960 --> 00:36:00,440 Speaker 11: eventually just develop themselves and manifest themselves with age. Is 605 00:36:00,480 --> 00:36:04,200 Speaker 11: how can we capture that early and how can correct 606 00:36:04,200 --> 00:36:08,360 Speaker 11: that before it's too late? And in that respect, wearable 607 00:36:08,440 --> 00:36:11,239 Speaker 11: technologies as well as the many monitoring devices that the 608 00:36:11,280 --> 00:36:17,279 Speaker 11: technology is making possible eventually will be the answer. Are 609 00:36:17,320 --> 00:36:19,759 Speaker 11: they the answer right now? Not necessarily. 610 00:36:20,080 --> 00:36:22,759 Speaker 8: We are translating to face where there are wearable technologies 611 00:36:23,000 --> 00:36:27,960 Speaker 8: that aren't just providing insights but actually providing interventions. They're 612 00:36:28,000 --> 00:36:29,680 Speaker 8: actually therapeutic in some way. 613 00:36:29,800 --> 00:36:32,240 Speaker 12: If we look at the next few years, right, health 614 00:36:32,280 --> 00:36:36,239 Speaker 12: monitoring will start by enabling individuals and being kind of 615 00:36:36,239 --> 00:36:39,480 Speaker 12: a continuation of that doctor's office, and I think in 616 00:36:39,520 --> 00:36:43,000 Speaker 12: the long run it'll ultimately replace the doctor's office. 617 00:36:43,440 --> 00:36:45,960 Speaker 2: At the point where wearables can replace our visits to 618 00:36:45,960 --> 00:36:49,440 Speaker 2: the doctor, or even make those visits a bit less frequent, 619 00:36:50,000 --> 00:36:52,920 Speaker 2: they will truly move from the nice to the necessary 620 00:36:52,960 --> 00:36:55,439 Speaker 2: for us all, and that could lead to a sort 621 00:36:55,440 --> 00:36:59,520 Speaker 2: of technological fountain of youth found not in the swamps 622 00:36:59,520 --> 00:37:04,080 Speaker 2: of Florida, but right on your wrist coming up. Some 623 00:37:04,120 --> 00:37:06,680 Speaker 2: of us may be worried about artificial intelligence coming for 624 00:37:06,800 --> 00:37:10,399 Speaker 2: our jobs, but does that include all those economists at 625 00:37:10,400 --> 00:37:23,000 Speaker 2: the Federal Reserve that's next on Wall Street Week. This 626 00:37:23,120 --> 00:37:26,799 Speaker 2: is a story about an invisible hand guiding the economy. No, 627 00:37:27,200 --> 00:37:30,200 Speaker 2: not the invisible hand of self interest that Adam Smith 628 00:37:30,239 --> 00:37:33,200 Speaker 2: wrote about in the Wealth of Nations. This invisible hand 629 00:37:33,320 --> 00:37:36,600 Speaker 2: is much more modern. It's that artificial intelligence we keep 630 00:37:36,640 --> 00:37:40,000 Speaker 2: hearing so much about. Our special contributor. Larry Summers is 631 00:37:40,040 --> 00:37:43,080 Speaker 2: a macroeconomist who also sits on the board of Open AI. 632 00:37:43,719 --> 00:37:46,800 Speaker 2: Who better to ask what AI could mean for Adam 633 00:37:46,840 --> 00:37:47,960 Speaker 2: Smith's economy. 634 00:37:49,640 --> 00:37:52,040 Speaker 3: My guess is that this is going to raise the 635 00:37:52,080 --> 00:37:55,799 Speaker 3: neutral rate of interest over time, both because of the 636 00:37:55,880 --> 00:37:58,480 Speaker 3: massive investment that's going to need to take place in 637 00:37:58,600 --> 00:38:03,440 Speaker 3: data centers and because of the acceleration. 638 00:38:03,520 --> 00:38:05,920 Speaker 4: In the rate of growth. 639 00:38:06,000 --> 00:38:10,400 Speaker 3: So I think the so called our star is likely 640 00:38:10,480 --> 00:38:16,759 Speaker 3: to be higher, perhaps even considerably higher, because of AI. 641 00:38:17,760 --> 00:38:21,000 Speaker 3: I think it's possible that it's going to be a 642 00:38:21,040 --> 00:38:28,000 Speaker 3: disinflationary force because of acceleration of productivity growth, as the 643 00:38:28,040 --> 00:38:33,360 Speaker 3: Internet was during the nineteen nineties. 644 00:38:33,719 --> 00:38:37,680 Speaker 2: The economic potential of AI varies widely, with some saying 645 00:38:37,719 --> 00:38:41,280 Speaker 2: that it can disrupt jobs, whole industries, and even pose 646 00:38:41,400 --> 00:38:42,880 Speaker 2: existential challenges. 647 00:38:43,200 --> 00:38:46,600 Speaker 11: It's not yet like replacing jobs in the way to 648 00:38:46,680 --> 00:38:48,239 Speaker 11: the degree that people thought it was going to. 649 00:38:48,320 --> 00:38:50,239 Speaker 9: Of course jobs will change, and of course some jobs 650 00:38:50,239 --> 00:38:51,160 Speaker 9: will totally go away. 651 00:38:51,280 --> 00:38:53,960 Speaker 13: It really is an existential threat. Some people say this 652 00:38:54,040 --> 00:38:56,880 Speaker 13: is just science fiction, and until fairly recently I believed 653 00:38:56,880 --> 00:38:58,839 Speaker 13: it was a long way off. Now I think it's 654 00:38:58,960 --> 00:39:02,520 Speaker 13: quite likely that sometime in the next twenty years these 655 00:39:02,520 --> 00:39:04,839 Speaker 13: things will get smarter than us and we really need 656 00:39:04,880 --> 00:39:06,080 Speaker 13: to worry about what happens. 657 00:39:06,080 --> 00:39:08,640 Speaker 2: Then, So let me ask you about one particular perhaps 658 00:39:08,719 --> 00:39:11,360 Speaker 2: rule of thumb or rule, and that is the relationship 659 00:39:11,360 --> 00:39:13,680 Speaker 2: between inflation on the one hand and unemployment on the other, 660 00:39:14,040 --> 00:39:17,000 Speaker 2: which has been an important issue for the Federal Reserve, 661 00:39:17,040 --> 00:39:19,799 Speaker 2: for example, with its dual mandate to address both of those. 662 00:39:19,880 --> 00:39:22,240 Speaker 2: Do you think it could change that relationship? 663 00:39:22,760 --> 00:39:28,399 Speaker 3: It certainly could, and you can make arguments in both directions. 664 00:39:28,520 --> 00:39:33,560 Speaker 3: Perhaps the more flexible economy means that rates of inflation 665 00:39:33,800 --> 00:39:38,480 Speaker 3: or prices will be more sensitive to demand and unemployment 666 00:39:39,560 --> 00:39:47,960 Speaker 3: than they were before. Perhaps the more rapid underlying productivity 667 00:39:48,200 --> 00:39:54,719 Speaker 3: growth will mean that there's less sensitivity because when there's 668 00:39:54,760 --> 00:39:57,799 Speaker 3: an increase in demand, the economy will be able to 669 00:39:57,840 --> 00:40:04,560 Speaker 3: accommodate it more easily because there's more capacity fundamentally in 670 00:40:04,600 --> 00:40:09,000 Speaker 3: the economy. I think it's difficult to know, and if 671 00:40:09,000 --> 00:40:12,600 Speaker 3: I had to guess, the effects that I described on 672 00:40:12,680 --> 00:40:16,279 Speaker 3: the neutral interest rate and so forth are probably going 673 00:40:16,360 --> 00:40:20,240 Speaker 3: to be more salient than any change in the slope 674 00:40:20,440 --> 00:40:25,560 Speaker 3: of the Phillips curve of that relationship you referred to 675 00:40:26,280 --> 00:40:34,680 Speaker 3: between inflation and unemployment. But nobody can be confident in 676 00:40:34,719 --> 00:40:37,200 Speaker 3: their judgments about this kind of thing. 677 00:40:37,840 --> 00:40:40,920 Speaker 2: There's the uncertainty about what AI could mean for the economy, 678 00:40:41,360 --> 00:40:43,600 Speaker 2: but there are also questions about what it could mean 679 00:40:43,640 --> 00:40:47,080 Speaker 2: for central banks trying to set monetary policy and how 680 00:40:47,080 --> 00:40:49,960 Speaker 2: it could change the way they gather and analyze data 681 00:40:50,000 --> 00:40:53,239 Speaker 2: about the economy. Sasha Stefan of the Frankfort School of 682 00:40:53,239 --> 00:40:57,320 Speaker 2: Finance has studied the ways AI might change monetary policy 683 00:40:57,360 --> 00:40:59,440 Speaker 2: transmission specifically. 684 00:40:59,480 --> 00:41:02,200 Speaker 14: And this is all the most common or most natural 685 00:41:02,239 --> 00:41:06,080 Speaker 14: thing people are thinking about here is it will improve forecasting. 686 00:41:06,120 --> 00:41:07,920 Speaker 14: And I think this is sort of where everybody's interested. 687 00:41:08,280 --> 00:41:10,840 Speaker 14: How our interest rates going how does the economy develop 688 00:41:10,880 --> 00:41:14,160 Speaker 14: going forward? How is inflation going forward? So here we're 689 00:41:14,200 --> 00:41:16,440 Speaker 14: going to see a lot already happening in terms of 690 00:41:16,680 --> 00:41:18,840 Speaker 14: I and I think here there's also a lot to 691 00:41:18,920 --> 00:41:19,920 Speaker 14: learn going forward. 692 00:41:20,560 --> 00:41:23,320 Speaker 2: Is it likely to make forecasting more accurate? Do you think? 693 00:41:24,080 --> 00:41:25,200 Speaker 14: I think definitely. 694 00:41:25,360 --> 00:41:25,520 Speaker 8: So. 695 00:41:25,880 --> 00:41:29,640 Speaker 14: I think one dimension of AI is basically increasing the 696 00:41:29,719 --> 00:41:34,480 Speaker 14: toolbox and the methodologies, improving on the methodologies thereby also 697 00:41:34,520 --> 00:41:39,040 Speaker 14: making forecasting much more precise. Also basically using models to 698 00:41:39,200 --> 00:41:43,239 Speaker 14: use existing data and use them in completely novel ways. Right, So, 699 00:41:43,280 --> 00:41:45,640 Speaker 14: how can we use bond market data? How can we 700 00:41:45,719 --> 00:41:48,480 Speaker 14: use loan market data. So we can employ these models 701 00:41:48,880 --> 00:41:52,000 Speaker 14: to look at things that we haven't been able to 702 00:41:52,040 --> 00:41:54,600 Speaker 14: do before. We can look at websites, we can use 703 00:41:55,320 --> 00:42:01,200 Speaker 14: images from satellites, we can use social media. Right, So, 704 00:42:01,400 --> 00:42:04,359 Speaker 14: actually I have a study, a recent one in which 705 00:42:04,400 --> 00:42:08,799 Speaker 14: we try to use AI and Twitter or x in 706 00:42:08,880 --> 00:42:13,080 Speaker 14: order to generate an index about what do individuals like 707 00:42:13,200 --> 00:42:16,480 Speaker 14: households have an idea about how inflation is going to 708 00:42:16,520 --> 00:42:18,839 Speaker 14: develop going forward? Right, So, this is what we call 709 00:42:18,880 --> 00:42:22,719 Speaker 14: inflation expectations, and this is something where central banks, the 710 00:42:22,760 --> 00:42:26,000 Speaker 14: Federal Reserve but also the European Central Banks are looking 711 00:42:26,080 --> 00:42:28,719 Speaker 14: much more closely at compared to what we call like 712 00:42:28,800 --> 00:42:31,880 Speaker 14: realize inflation what we have. So, what do actually people 713 00:42:31,960 --> 00:42:36,360 Speaker 14: households expect in terms of inflation going forward? Because this 714 00:42:36,480 --> 00:42:39,319 Speaker 14: is going to affect how they're going to behave, what 715 00:42:39,400 --> 00:42:41,879 Speaker 14: kinds of products are they going to buy, what kinds 716 00:42:41,920 --> 00:42:45,520 Speaker 14: of sort of how are they going to save going forward? 717 00:42:45,600 --> 00:42:47,560 Speaker 14: And also firms are going to be affected by that 718 00:42:47,640 --> 00:42:50,480 Speaker 14: because they see if customers don't buy, the shelves are 719 00:42:50,480 --> 00:42:54,160 Speaker 14: going to remain full and they basically report a completely 720 00:42:54,160 --> 00:42:56,000 Speaker 14: different bottom or top line going forward. 721 00:42:56,040 --> 00:42:58,359 Speaker 2: What are the risks of expanding out the data that way, 722 00:42:58,440 --> 00:43:01,000 Speaker 2: I mean a large language models and larger based on 723 00:43:01,120 --> 00:43:04,680 Speaker 2: what human beings have generated, and human beings are not perfect, 724 00:43:05,000 --> 00:43:07,520 Speaker 2: and you can get hallucinations that way. What are the risks, 725 00:43:07,520 --> 00:43:10,160 Speaker 2: particularly as you go to things like social media for example. 726 00:43:10,080 --> 00:43:13,799 Speaker 14: X information sensitivity is always run right, which means that 727 00:43:14,640 --> 00:43:18,240 Speaker 14: if new information arises that can cause maybe a complete 728 00:43:18,280 --> 00:43:21,120 Speaker 14: meltdown of the market. Stocks are going to be sold, 729 00:43:21,280 --> 00:43:24,000 Speaker 14: And of course AI makes it even more likely that 730 00:43:24,120 --> 00:43:26,880 Speaker 14: these risks are actually or these new information is actually 731 00:43:26,880 --> 00:43:31,920 Speaker 14: going to emerge, right. But also another risk is, for example, interconnectedness, 732 00:43:31,920 --> 00:43:36,560 Speaker 14: and AI also will connect institutions more going forward, there 733 00:43:36,640 --> 00:43:39,000 Speaker 14: might be shared data, there might be shared platforms that 734 00:43:39,080 --> 00:43:42,560 Speaker 14: might increase interconnectedness. So if one domino drops, the others 735 00:43:42,640 --> 00:43:46,080 Speaker 14: might actually drop as well. So amplification of existing risks 736 00:43:46,120 --> 00:43:50,480 Speaker 14: is definitely one problem. Another problem that's very frequently mentioned 737 00:43:50,520 --> 00:43:52,920 Speaker 14: if you talk to practitioners in this field is also 738 00:43:53,719 --> 00:43:55,719 Speaker 14: a kind of what we call a model bias. Right, 739 00:43:55,800 --> 00:43:59,239 Speaker 14: So what happens if we use data that has been 740 00:43:59,320 --> 00:44:02,080 Speaker 14: generated by a model that has all better been falls 741 00:44:02,120 --> 00:44:04,920 Speaker 14: to begin with, and then we continue to use their 742 00:44:05,040 --> 00:44:08,160 Speaker 14: data going forward, there will be so colled perpetuation of 743 00:44:08,440 --> 00:44:12,160 Speaker 14: these biases going forward, and like garbage in garbage out. 744 00:44:12,080 --> 00:44:15,280 Speaker 2: We hear from some experts in AI that we're getting 745 00:44:15,280 --> 00:44:17,439 Speaker 2: to a point, maybe past a point where we don't 746 00:44:17,480 --> 00:44:20,120 Speaker 2: actually know how it works. It's a black box in 747 00:44:20,160 --> 00:44:23,399 Speaker 2: that sense. If that's true, how can a center back 748 00:44:23,440 --> 00:44:25,600 Speaker 2: rely on it? How can it verify it or certify 749 00:44:25,640 --> 00:44:28,799 Speaker 2: it to make sure that it's making correct inferences. 750 00:44:28,840 --> 00:44:31,319 Speaker 14: That's an absolute important point. This is on the one end, 751 00:44:31,320 --> 00:44:34,359 Speaker 14: this is definitely a risk, right because if things go wrong, 752 00:44:34,480 --> 00:44:38,240 Speaker 14: the question, the trust that might be there quickly goes away, 753 00:44:38,400 --> 00:44:40,800 Speaker 14: and then the things might even be worth going forward. 754 00:44:41,080 --> 00:44:45,920 Speaker 14: One of the potential powerful applications of AI for central 755 00:44:45,960 --> 00:44:52,120 Speaker 14: banks is to not replace necessarily, but to complement the 756 00:44:52,280 --> 00:44:56,239 Speaker 14: professional forecasting that the central banks usually rely on. They 757 00:44:56,280 --> 00:44:58,759 Speaker 14: usually do on a quarterly basis. Ask a lot of 758 00:44:58,760 --> 00:45:02,239 Speaker 14: professionals and investm banks and other institutions as to what 759 00:45:02,280 --> 00:45:06,080 Speaker 14: do they expect the economy to develop going forward, specifically 760 00:45:06,120 --> 00:45:09,279 Speaker 14: when it comes to inflation, and now it might be 761 00:45:09,320 --> 00:45:14,480 Speaker 14: actually possible to set up an AI model that exactly 762 00:45:14,640 --> 00:45:19,480 Speaker 14: does that. So it basically it is trained on the individual, 763 00:45:19,520 --> 00:45:23,759 Speaker 14: it's trained on the cvs of the professional forecasters, how 764 00:45:23,800 --> 00:45:26,319 Speaker 14: they have actually voted, or what they have done in 765 00:45:26,600 --> 00:45:31,000 Speaker 14: previous forecasts, that they did, what is their job, what 766 00:45:31,160 --> 00:45:34,799 Speaker 14: they learned, and then asked the model instead of the forecaster, 767 00:45:35,080 --> 00:45:38,279 Speaker 14: And the existing research already tells us that there is 768 00:45:38,320 --> 00:45:40,719 Speaker 14: a high degree of overlap in terms of what the 769 00:45:40,760 --> 00:45:45,600 Speaker 14: professional forecast actually would tell him or herself and what 770 00:45:45,680 --> 00:45:48,919 Speaker 14: the model actually sells. Right, But then the one risk, 771 00:45:48,960 --> 00:45:51,279 Speaker 14: and now coming back to your question, is that how 772 00:45:51,280 --> 00:45:55,640 Speaker 14: can we actually make sure that the model actually uses 773 00:45:55,719 --> 00:45:58,360 Speaker 14: only the information that is available at the time of 774 00:45:58,400 --> 00:46:02,239 Speaker 14: a forecast itself is a train basically on information that 775 00:46:02,280 --> 00:46:05,120 Speaker 14: it should not know because it actually happened afterwards. 776 00:46:05,200 --> 00:46:07,719 Speaker 2: Well, I wonder about the systemic risk because not being 777 00:46:07,719 --> 00:46:12,800 Speaker 2: a conputer science, it's possible that AI could spot relationships 778 00:46:13,000 --> 00:46:15,239 Speaker 2: that otherwise humans might miss. I mean, so you have 779 00:46:15,280 --> 00:46:18,520 Speaker 2: something like Silicon Valley Bank in the United States. Is 780 00:46:18,560 --> 00:46:23,440 Speaker 2: it possible AI would have spotted imbalances earlier that humans. 781 00:46:23,080 --> 00:46:26,759 Speaker 14: Missed, specifically with a Silicon value bank, it would have 782 00:46:27,400 --> 00:46:30,480 Speaker 14: recognized this because it's also interesting that people did not 783 00:46:30,600 --> 00:46:33,480 Speaker 14: realize this because it was obvious to be to be clear, 784 00:46:34,200 --> 00:46:37,280 Speaker 14: because actually all the publicly available data was pointing exactly 785 00:46:37,320 --> 00:46:39,759 Speaker 14: at that, but no, you're absolutely right. So these kind 786 00:46:39,840 --> 00:46:44,640 Speaker 14: of systemically important banks, also other banks risks emerging based 787 00:46:44,680 --> 00:46:48,520 Speaker 14: on for example, liquidity consideration might be detected much much 788 00:46:48,560 --> 00:46:52,000 Speaker 14: earlier compared to what the regulator or supervisor might actually 789 00:46:52,080 --> 00:46:52,839 Speaker 14: might actually see. 790 00:46:52,960 --> 00:46:56,560 Speaker 2: And what we need human beings economists presumably to check 791 00:46:56,640 --> 00:46:59,160 Speaker 2: what's going on to make corrections, because you can have 792 00:46:59,320 --> 00:47:02,319 Speaker 2: hallucination in any model, and if you don't correct them, 793 00:47:02,320 --> 00:47:04,040 Speaker 2: as I understand, they just compound. 794 00:47:04,320 --> 00:47:07,160 Speaker 14: I think this is exactly one danger that people might 795 00:47:07,239 --> 00:47:10,200 Speaker 14: actually try to rely on AI and these models too 796 00:47:10,280 --> 00:47:14,520 Speaker 14: much and think, Okay, these models actually know what's going 797 00:47:14,600 --> 00:47:15,040 Speaker 14: to happen. 798 00:47:15,440 --> 00:47:20,120 Speaker 4: But the idea how to assess these. 799 00:47:20,040 --> 00:47:22,960 Speaker 14: Outcomes or these outputs, that's a crucial one. And this 800 00:47:23,080 --> 00:47:26,600 Speaker 14: means I think also for us as not only as economists, 801 00:47:26,600 --> 00:47:28,920 Speaker 14: but also as educators, we need to make sure that 802 00:47:29,000 --> 00:47:32,560 Speaker 14: people understand the underlying theory and economics actually in order 803 00:47:32,600 --> 00:47:35,799 Speaker 14: to evaluate is that what the model actually tells me, 804 00:47:36,120 --> 00:47:39,040 Speaker 14: is this something that's actually plausible or something that's not right? 805 00:47:39,120 --> 00:47:41,319 Speaker 14: So I think that puts a lot of pressure on 806 00:47:41,320 --> 00:47:44,360 Speaker 14: on us also as educators, but then also on the 807 00:47:44,400 --> 00:47:47,400 Speaker 14: different institutions, do they actually train the people right going forward. 808 00:47:49,120 --> 00:47:50,839 Speaker 2: That does it for us Here at Wall Street Week, 809 00:47:51,040 --> 00:47:54,000 Speaker 2: I'm David Weston. See you next week for more stories 810 00:47:54,040 --> 00:48:00,640 Speaker 2: of capitalism.