1 00:00:13,119 --> 00:00:15,880 Speaker 1: This is Wall Street Week. I'm David Weston bringing you 2 00:00:16,040 --> 00:00:19,720 Speaker 1: stories of capitalism. The world of futures contracts tied to 3 00:00:19,720 --> 00:00:23,360 Speaker 1: specific events is exploding, whether it's predicting the outcome of 4 00:00:23,400 --> 00:00:26,079 Speaker 1: the New York mayoral election or the ending of a 5 00:00:26,079 --> 00:00:29,280 Speaker 1: government shutdown. What did they tell us? And do they 6 00:00:29,320 --> 00:00:34,000 Speaker 1: need regulation like any other futures markets? Plus, President Trump 7 00:00:34,040 --> 00:00:37,959 Speaker 1: has moved against the big Chinese clothing exporters avoiding tariffs 8 00:00:38,040 --> 00:00:41,800 Speaker 1: under the so called deminimous rule. But what other businesses 9 00:00:41,960 --> 00:00:45,519 Speaker 1: is he catching in his net? And last week it 10 00:00:45,600 --> 00:00:48,519 Speaker 1: was healthcare. This week we take another look at an 11 00:00:48,520 --> 00:00:52,160 Speaker 1: area where artificial intelligence is already being used to transform 12 00:00:52,200 --> 00:00:56,960 Speaker 1: a critical sector, this time education. But we start with 13 00:00:57,000 --> 00:01:00,560 Speaker 1: a story about putting a price on the priceless freedom. 14 00:01:01,040 --> 00:01:04,520 Speaker 1: What happens when society concludes that some of us must lose. 15 00:01:04,319 --> 00:01:05,200 Speaker 2: That precious freedom? 16 00:01:05,520 --> 00:01:08,640 Speaker 1: And how do we handle the difficult business of detaining 17 00:01:08,680 --> 00:01:11,240 Speaker 1: people and what we hope is a safe and a 18 00:01:11,360 --> 00:01:15,200 Speaker 1: humane way. It's something we've dealt with for thousands of years, 19 00:01:15,319 --> 00:01:18,480 Speaker 1: but is now front and center amid new efforts to 20 00:01:18,520 --> 00:01:21,119 Speaker 1: detain and deport those who are not in the United 21 00:01:21,120 --> 00:01:25,080 Speaker 1: States legally. It's a story that's in the news every week. 22 00:01:27,600 --> 00:01:31,119 Speaker 3: I have said Congress a detailed funding request laying out 23 00:01:31,160 --> 00:01:35,240 Speaker 3: exactly how we will eliminate these threats to protect our 24 00:01:35,280 --> 00:01:41,280 Speaker 3: homeland and complete the largest deportation operation in American history. 25 00:01:42,240 --> 00:01:45,279 Speaker 1: President Trump was elected in twenty twenty four in part 26 00:01:45,400 --> 00:01:49,160 Speaker 1: because of his strong stance on immigration, keeping out those 27 00:01:49,160 --> 00:01:52,400 Speaker 1: who try to enter the country illegally and deporting those 28 00:01:52,440 --> 00:01:57,120 Speaker 1: who have already come in outside the rules. Whether one 29 00:01:57,160 --> 00:02:01,040 Speaker 1: agrees with his deportation policy or not, it is happening 30 00:02:01,280 --> 00:02:04,600 Speaker 1: on an historic scale, which raises the question of how 31 00:02:04,720 --> 00:02:07,640 Speaker 1: best to do it. There's a lot of money involved. 32 00:02:08,080 --> 00:02:11,040 Speaker 4: Oh, this is big money at a high. 33 00:02:10,960 --> 00:02:11,600 Speaker 5: Rate of return. 34 00:02:12,400 --> 00:02:14,920 Speaker 1: Most of the money is going to private companies who 35 00:02:15,040 --> 00:02:16,600 Speaker 1: run the detention facilities. 36 00:02:16,880 --> 00:02:22,240 Speaker 4: What DHS does is it contracts with other entities, sometimes counties, 37 00:02:22,560 --> 00:02:26,680 Speaker 4: and most often private companies, and those companies run the 38 00:02:26,720 --> 00:02:27,799 Speaker 4: facilities instead. 39 00:02:28,440 --> 00:02:32,520 Speaker 1: There is nothing new about private detention. The first modern 40 00:02:32,560 --> 00:02:36,000 Speaker 1: federal contract dates to the mid nineteen eighties under President 41 00:02:36,120 --> 00:02:41,560 Speaker 1: Ronald Reagan. Corrections Corporation of America today core Civic, helped 42 00:02:41,639 --> 00:02:46,840 Speaker 1: pioneer the model. Damon Heininger, a former correctional officer, now 43 00:02:46,960 --> 00:02:50,680 Speaker 1: leads Corcivic one of the largest private prison and detention 44 00:02:50,840 --> 00:02:52,119 Speaker 1: operators in America. 45 00:02:52,520 --> 00:02:56,120 Speaker 6: Our very first contract was with then I and ASK 46 00:02:56,200 --> 00:02:59,640 Speaker 6: now Immigration custom Enforcement. But the value proposition is to 47 00:02:59,639 --> 00:03:04,280 Speaker 6: bring private sector innovation, being very nimble, being very flexible, 48 00:03:04,680 --> 00:03:07,480 Speaker 6: provide high quality solutions and outcomes that are a very 49 00:03:07,520 --> 00:03:08,560 Speaker 6: low cost to government. 50 00:03:10,880 --> 00:03:14,000 Speaker 1: Along with the ramp up in immigration enforcement has come 51 00:03:14,080 --> 00:03:17,760 Speaker 1: more demand for core civic services. Not even a year 52 00:03:17,800 --> 00:03:20,720 Speaker 1: into its second term, the Trump administration says it has 53 00:03:20,760 --> 00:03:24,280 Speaker 1: deported more than four hundred thousand people, which comes at 54 00:03:24,280 --> 00:03:28,000 Speaker 1: a big cost for the government. Earlier this year, Congress 55 00:03:28,000 --> 00:03:32,080 Speaker 1: passed the One Big, Beautiful Bill, authorizing roughly one hundred 56 00:03:32,120 --> 00:03:36,280 Speaker 1: and seventy billion dollars for immigration and border operations, with 57 00:03:36,440 --> 00:03:41,240 Speaker 1: forty five billion dollars designated just for detention capacity expansion. 58 00:03:41,560 --> 00:03:44,160 Speaker 7: We need detention facilities, We need beds to put them 59 00:03:44,200 --> 00:03:46,360 Speaker 7: in place so that they can have their due process 60 00:03:46,400 --> 00:03:47,600 Speaker 7: before they return home. 61 00:03:48,240 --> 00:03:52,400 Speaker 1: Some sixty thousand people are currently detained, with nearly ninety 62 00:03:52,440 --> 00:03:55,440 Speaker 1: percent of them in private facilities owned or operated by 63 00:03:55,440 --> 00:03:59,520 Speaker 1: companies like cour Civic or Geogroup, its main competitor, and 64 00:03:59,560 --> 00:04:04,560 Speaker 1: that's helped drive profits. According to Corcific's second quarter earnings figures. 65 00:04:04,800 --> 00:04:08,160 Speaker 1: ICE contracts account for a majority of its revenue. 66 00:04:08,360 --> 00:04:10,560 Speaker 6: We don't get involved in policy, but obviously there has 67 00:04:10,600 --> 00:04:12,520 Speaker 6: been a lot of discussion, you know, lead up to 68 00:04:12,560 --> 00:04:15,839 Speaker 6: the campaign and the election last year and this year 69 00:04:15,880 --> 00:04:19,040 Speaker 6: about you know, getting a stronger enforcement on the southwest 70 00:04:19,080 --> 00:04:21,560 Speaker 6: border and also doing a fair amount of interior enforcement 71 00:04:22,040 --> 00:04:26,560 Speaker 6: with DHS and turn our customer ICE. We obviously didn't 72 00:04:26,560 --> 00:04:28,160 Speaker 6: know what the outcome was going to be in the election, 73 00:04:29,080 --> 00:04:33,360 Speaker 6: and I've always stressed this as CEO, to plan well 74 00:04:33,360 --> 00:04:35,560 Speaker 6: in advance before there was, you know, an outcome on 75 00:04:35,600 --> 00:04:38,040 Speaker 6: our national election, especially like the one we had last year. 76 00:04:39,680 --> 00:04:43,800 Speaker 1: Investors moved quickly after the twenty twenty four election. Corcific 77 00:04:43,880 --> 00:04:47,160 Speaker 1: stock jumped before President Trump was even sworn in, as 78 00:04:47,200 --> 00:04:51,719 Speaker 1: Wall Street priced in tighter immigration enforcement and more detention spending. 79 00:04:52,680 --> 00:04:55,520 Speaker 1: The explosion in ICE detentions may have been good for 80 00:04:55,560 --> 00:04:58,279 Speaker 1: the business of companies like Corcivic, but it's come with 81 00:04:58,320 --> 00:05:03,240 Speaker 1: a fair amount of scrutiny and criticism. Margot Schleiner, a 82 00:05:03,320 --> 00:05:06,920 Speaker 1: University of Michigan law professor, previously served in the Department 83 00:05:06,960 --> 00:05:11,200 Speaker 1: of Homeland Security in the Obama administration, overseeing civil rights 84 00:05:11,240 --> 00:05:13,000 Speaker 1: and liberties of detainees. 85 00:05:13,360 --> 00:05:16,359 Speaker 4: There is a range of conditions. Some of the detention 86 00:05:16,560 --> 00:05:20,560 Speaker 4: facilities are better than others, and some of them are 87 00:05:20,600 --> 00:05:23,919 Speaker 4: more stressed than others and are less good at dealing 88 00:05:23,960 --> 00:05:29,200 Speaker 4: with the stress of high populations or particularly needy populations. 89 00:05:29,680 --> 00:05:32,760 Speaker 1: Why is it that the federal government essentially delegates this 90 00:05:33,080 --> 00:05:37,000 Speaker 1: or contracts it out to private companies. 91 00:05:37,440 --> 00:05:40,440 Speaker 4: Congress made a decision a while back that it didn't 92 00:05:40,520 --> 00:05:43,080 Speaker 4: want to set up the Department of Homeland Security to 93 00:05:43,360 --> 00:05:46,919 Speaker 4: run a large system of detention and run it itself. 94 00:05:47,400 --> 00:05:50,920 Speaker 4: So the federal gearau of Prisons has correctional officers who 95 00:05:51,040 --> 00:05:54,560 Speaker 4: run facilities. ICE does not. ICE does not have the 96 00:05:54,640 --> 00:05:57,960 Speaker 4: kinds of people who just day in, day out run 97 00:05:58,040 --> 00:06:01,800 Speaker 4: the facilities. So that's been its general approach since the 98 00:06:01,839 --> 00:06:04,480 Speaker 4: founding of the department in two thousand and three. 99 00:06:06,920 --> 00:06:10,159 Speaker 1: Course Civics says that there are checks and balances in 100 00:06:10,200 --> 00:06:13,920 Speaker 1: place to try to ensure the detainees are being treated humanly. 101 00:06:14,400 --> 00:06:17,159 Speaker 6: Really, since day one, we wanted to meet all national 102 00:06:17,160 --> 00:06:20,600 Speaker 6: standards within our facilities. So American Correction Association, which has 103 00:06:20,640 --> 00:06:24,359 Speaker 6: been around since eighteen seventy, they set national standards on 104 00:06:24,880 --> 00:06:28,080 Speaker 6: our facilities are designed, how big our day rooms how 105 00:06:28,120 --> 00:06:30,920 Speaker 6: big our cells, how big are the food service area, 106 00:06:31,000 --> 00:06:32,279 Speaker 6: medical maintenance, et cetera. 107 00:06:32,560 --> 00:06:34,960 Speaker 1: Do those apply to ongoing treatment while people are in 108 00:06:35,040 --> 00:06:35,640 Speaker 1: your custody. 109 00:06:35,880 --> 00:06:38,599 Speaker 6: They do, yeah, So they'll have general standards come in. 110 00:06:38,720 --> 00:06:42,520 Speaker 6: They'll calibrate the review based on our population. So again 111 00:06:42,960 --> 00:06:45,039 Speaker 6: if they're coming, you know, if we've got auditors coming 112 00:06:45,080 --> 00:06:47,160 Speaker 6: in from the ACA to do a review at a 113 00:06:47,160 --> 00:06:50,000 Speaker 6: facility for an ICE and those standards have again kind 114 00:06:50,000 --> 00:06:53,520 Speaker 6: of evolved over time, not just the standards from ACA, 115 00:06:53,640 --> 00:06:55,880 Speaker 6: but also from our partners. 116 00:06:56,400 --> 00:06:58,479 Speaker 1: But this is important. I'm not sure everyone knows about this. 117 00:06:58,720 --> 00:07:01,520 Speaker 1: There are independent orders from ACA that come in and 118 00:07:01,560 --> 00:07:04,839 Speaker 1: monitor what you're doing, apart from DHS or ICE or 119 00:07:04,839 --> 00:07:06,040 Speaker 1: anyone else in the government. 120 00:07:06,279 --> 00:07:09,440 Speaker 6: Virtually all of our facilities have full time on site 121 00:07:09,600 --> 00:07:13,320 Speaker 6: auditors and monitors. And so these are government employees where 122 00:07:13,320 --> 00:07:15,320 Speaker 6: we are acquired by contract to give them space in 123 00:07:15,360 --> 00:07:17,920 Speaker 6: the facility where they get set up offices and have 124 00:07:18,000 --> 00:07:20,200 Speaker 6: space to where they can do their kind of day 125 00:07:20,200 --> 00:07:23,080 Speaker 6: to day review of the operations. But they got unfettered 126 00:07:23,120 --> 00:07:26,080 Speaker 6: access to facility twenty four hours a day, seven days 127 00:07:26,080 --> 00:07:26,440 Speaker 6: a week. 128 00:07:26,800 --> 00:07:29,680 Speaker 1: We've all seen reports in the press about the mistreatment 129 00:07:29,760 --> 00:07:33,480 Speaker 1: of detainees knowing what you know, how do you respond 130 00:07:33,480 --> 00:07:35,320 Speaker 1: to that? Is it number one, the reports are wrong, 131 00:07:35,760 --> 00:07:37,320 Speaker 1: or number two, it's just not courcific. 132 00:07:37,640 --> 00:07:41,200 Speaker 6: You know, the vast majority, I'd say ninety nine percent 133 00:07:41,200 --> 00:07:44,320 Speaker 6: of the stuff that we see in newspapers or speculated 134 00:07:44,400 --> 00:07:48,240 Speaker 6: in online or on socials is incorrect or false or 135 00:07:48,280 --> 00:07:52,720 Speaker 6: maybe not incomplete relative to information we got in our facilities, 136 00:07:52,760 --> 00:07:55,800 Speaker 6: like one thousand government employees that have unfedered access to 137 00:07:56,200 --> 00:07:59,040 Speaker 6: our operations. Now, having said that, and I often say 138 00:07:59,080 --> 00:08:03,160 Speaker 6: this to investors, Yeah, fourteen thousand employees. Not every single 139 00:08:03,200 --> 00:08:06,000 Speaker 6: employee is going to make the right decision every single day, 140 00:08:06,520 --> 00:08:10,080 Speaker 6: and so and that's the case offer with any large organization. 141 00:08:13,000 --> 00:08:16,120 Speaker 1: There's also the question of where all these detention facilities 142 00:08:16,160 --> 00:08:19,480 Speaker 1: will be located and whether they help or hurt their 143 00:08:19,520 --> 00:08:23,160 Speaker 1: home communities. One of the largest for profit sites in 144 00:08:23,200 --> 00:08:28,120 Speaker 1: the nation is Geogroup's Folkston Ice Processing Center in Georgia. 145 00:08:28,400 --> 00:08:31,600 Speaker 1: In June, the County Commission approved an expansion from just 146 00:08:31,720 --> 00:08:36,239 Speaker 1: over eleven hundred beds to nearly three thousand. County Administrator 147 00:08:36,280 --> 00:08:38,679 Speaker 1: Glenn Hull helped shepherd the deal. 148 00:08:39,240 --> 00:08:42,720 Speaker 5: The process to expand the GEO facility and the contract 149 00:08:42,720 --> 00:08:46,560 Speaker 5: with Ice took about four months. Overall, the relationship is 150 00:08:46,559 --> 00:08:49,520 Speaker 5: good not only from an operating standpoint, but from a 151 00:08:49,559 --> 00:08:53,400 Speaker 5: revenue standpoint. The geogroup in the facility contribute about six 152 00:08:53,480 --> 00:08:56,359 Speaker 5: hundred and fifty five thousand dollars to our tax collections, 153 00:08:56,480 --> 00:09:00,280 Speaker 5: which makes about twelve percent of our overall income. Is 154 00:09:00,280 --> 00:09:05,600 Speaker 5: significant because as we have an outmigration of jobs and industries, 155 00:09:06,240 --> 00:09:08,240 Speaker 5: we need to fill the gap in rural America. This 156 00:09:08,320 --> 00:09:12,160 Speaker 5: is where the rubber meets the road. 157 00:09:14,200 --> 00:09:17,040 Speaker 1: For Folkston, a city with a population of less than 158 00:09:17,080 --> 00:09:21,320 Speaker 1: five thousand people and few job openings, the expansion provides 159 00:09:21,360 --> 00:09:24,319 Speaker 1: both tax revenues and hiring opportunities. 160 00:09:24,520 --> 00:09:27,000 Speaker 5: The job count from the GEO facility is six hundred 161 00:09:27,000 --> 00:09:30,440 Speaker 5: and thirty two, about five hundred and fifty one of 162 00:09:30,480 --> 00:09:34,640 Speaker 5: those are in Georgia, in Folkston specifically one hundred and 163 00:09:34,720 --> 00:09:36,040 Speaker 5: ninety eight jobs. 164 00:09:36,480 --> 00:09:41,280 Speaker 4: There's been a longstanding debate over whether jails and prisons 165 00:09:41,320 --> 00:09:43,679 Speaker 4: and the expansion of jails and prisons, and that's what 166 00:09:43,679 --> 00:09:46,400 Speaker 4: these are. These are basically facilities that are like jails 167 00:09:46,400 --> 00:09:51,600 Speaker 4: and prisons. Whether those expansions actually help communities. I think 168 00:09:51,679 --> 00:09:54,559 Speaker 4: the best evidence is that long term it does not, 169 00:09:55,080 --> 00:09:57,680 Speaker 4: but there's definitely some short term influx of money. 170 00:09:58,360 --> 00:10:02,120 Speaker 5: Charlton County has been through many iterations. Back when US 171 00:10:02,160 --> 00:10:04,920 Speaker 5: One was a vibrant corridor through here before the Interstates 172 00:10:04,920 --> 00:10:07,560 Speaker 5: in the early seventies. We had a mill here in Folkston. 173 00:10:08,200 --> 00:10:11,240 Speaker 5: Some time ago, that mill closed and all of those 174 00:10:11,320 --> 00:10:14,400 Speaker 5: jobs went away. When you have an economy that's based 175 00:10:14,800 --> 00:10:18,000 Speaker 5: on kind of blue collar work and those jobs go away, 176 00:10:19,440 --> 00:10:22,360 Speaker 5: so do the people, so does the tax revenue. I 177 00:10:22,360 --> 00:10:25,440 Speaker 5: think it's important to understand that these servatives lifelines on 178 00:10:25,559 --> 00:10:29,840 Speaker 5: ground zero. Without a detention facility, we wouldn't be surviving 179 00:10:29,880 --> 00:10:30,520 Speaker 5: as a county. 180 00:10:31,679 --> 00:10:33,720 Speaker 8: It's not a lot to offer here when it comes 181 00:10:33,760 --> 00:10:36,040 Speaker 8: to jobs per se, and if there are there on 182 00:10:36,080 --> 00:10:36,880 Speaker 8: a low scale. 183 00:10:39,800 --> 00:10:43,199 Speaker 1: Despite the possible economic benefits of hosting a detention center, 184 00:10:43,600 --> 00:10:46,480 Speaker 1: not everyone there is happy about having one in town. 185 00:10:47,400 --> 00:10:50,960 Speaker 1: Local pastor Antoine Nixon was born and raised in Folkston 186 00:10:51,280 --> 00:10:52,880 Speaker 1: and opposed the expansion. 187 00:10:53,280 --> 00:10:56,760 Speaker 8: My mindset was we failed again. I think it has been 188 00:10:56,920 --> 00:11:00,400 Speaker 8: a growing concern here for the citizens, especially as blade 189 00:11:00,480 --> 00:11:02,920 Speaker 8: with some of the things that our commissioners have been 190 00:11:02,960 --> 00:11:05,920 Speaker 8: okay with. I'm not proud to say we have a 191 00:11:06,000 --> 00:11:09,359 Speaker 8: prison here. There's no kids who are going to graduate, 192 00:11:09,520 --> 00:11:11,440 Speaker 8: who are going to say I want to graduate and 193 00:11:11,480 --> 00:11:13,520 Speaker 8: go work at the prison. I think we ought to 194 00:11:13,559 --> 00:11:17,600 Speaker 8: start providing better outcomes and start looking at what's better 195 00:11:17,640 --> 00:11:21,160 Speaker 8: for our children's future. When you look at our community, 196 00:11:21,320 --> 00:11:23,880 Speaker 8: why is it here? Why did they choose folks? And 197 00:11:24,360 --> 00:11:26,880 Speaker 8: you're not going to go in those maybe educated areas, 198 00:11:27,240 --> 00:11:30,440 Speaker 8: people who are really standing up against things like that, 199 00:11:30,480 --> 00:11:32,600 Speaker 8: You're not going to bring it into those areas. I 200 00:11:32,600 --> 00:11:35,520 Speaker 8: think Folks insists as a prime example and a prime place, 201 00:11:35,559 --> 00:11:38,959 Speaker 8: as many other places do, where you can go in 202 00:11:39,120 --> 00:11:41,920 Speaker 8: and you can dangle the golden apple in front of people, 203 00:11:42,400 --> 00:11:45,760 Speaker 8: and you can just make them these promises that ultimately 204 00:11:46,880 --> 00:11:49,959 Speaker 8: end up being nothing, and then once they suck you dry, 205 00:11:50,200 --> 00:11:52,760 Speaker 8: they move on and find other places. It's like leeches, 206 00:11:53,200 --> 00:11:55,280 Speaker 8: you know, once they have all they have, we don't 207 00:11:55,280 --> 00:11:58,880 Speaker 8: want to get from you, then you're left to offend 208 00:11:58,920 --> 00:12:01,480 Speaker 8: for yourselves. So I'm not happy about that. 209 00:12:04,800 --> 00:12:07,680 Speaker 1: Detention and deportation of those who have entered the United 210 00:12:07,720 --> 00:12:11,840 Speaker 1: States unlawfully remains as controversial as ever, if not more so. 211 00:12:12,840 --> 00:12:15,840 Speaker 1: And although there are those who, like Reverend Nixon, think 212 00:12:15,880 --> 00:12:18,920 Speaker 1: it simply can't be justified no matter what the benefit, 213 00:12:19,280 --> 00:12:21,640 Speaker 1: there are also those who believe this new business of 214 00:12:21,679 --> 00:12:24,320 Speaker 1: detention can be a lifeline for their community. 215 00:12:24,520 --> 00:12:28,120 Speaker 5: What I would say is, let's have real conversations with 216 00:12:28,240 --> 00:12:30,800 Speaker 5: people that live in rural America as opposed to having 217 00:12:30,880 --> 00:12:35,880 Speaker 5: academic arguments about what tension facility is and what it 218 00:12:35,920 --> 00:12:39,800 Speaker 5: is not. It's one thing to have a human rights conversation, 219 00:12:39,960 --> 00:12:43,239 Speaker 5: it's a completely another one to have an economic development conversation. 220 00:12:43,840 --> 00:12:45,800 Speaker 1: And then there are those who are running a legal 221 00:12:46,000 --> 00:12:49,280 Speaker 1: and profitable business and trying to do it the best 222 00:12:49,280 --> 00:12:50,120 Speaker 1: way they can. 223 00:12:50,600 --> 00:12:54,040 Speaker 6: I think the general review is from a policy perspective, 224 00:12:54,280 --> 00:12:57,000 Speaker 6: We've done a good job. We're very important part of 225 00:12:57,040 --> 00:13:00,199 Speaker 6: the solution for these jurisdictions. And I think from a 226 00:13:00,280 --> 00:13:02,520 Speaker 6: vested perspective, I think the risk that may be assigned 227 00:13:02,520 --> 00:13:04,600 Speaker 6: for our business in the past maybe is not as great. 228 00:13:05,280 --> 00:13:07,679 Speaker 1: In the end, there is no simple answer to the 229 00:13:07,760 --> 00:13:11,080 Speaker 1: question of how the markets can price the freedom of individuals, 230 00:13:11,520 --> 00:13:15,400 Speaker 1: or whether they can. But until a better solution comes along, 231 00:13:15,800 --> 00:13:23,160 Speaker 1: all we can do is our best coming up prognosticators 232 00:13:23,160 --> 00:13:26,320 Speaker 1: putting their money where their mouth is, the brave new 233 00:13:26,400 --> 00:13:29,840 Speaker 1: world of futures in events, contracts, and whether they can 234 00:13:29,840 --> 00:13:32,200 Speaker 1: tell us who the next Mayor of New York will 235 00:13:32,240 --> 00:13:49,400 Speaker 1: be This is a story about telling the future, or 236 00:13:49,480 --> 00:13:52,240 Speaker 1: at least discounting it by paying attention to a whole 237 00:13:52,240 --> 00:13:55,200 Speaker 1: lot of other people telling the future. The business of 238 00:13:55,280 --> 00:13:59,280 Speaker 1: making a market in futures contracts tied to events is exploding. 239 00:14:00,120 --> 00:14:02,840 Speaker 1: Be fun. They may make some people some money, but 240 00:14:02,960 --> 00:14:06,280 Speaker 1: do they tell us anything about what is around the corner. 241 00:14:07,720 --> 00:14:10,760 Speaker 9: I think our goal with forrediction markets is really to 242 00:14:10,840 --> 00:14:13,240 Speaker 9: allow every person that has an opinion on something to 243 00:14:13,320 --> 00:14:16,160 Speaker 9: find the market and that big event that they care 244 00:14:16,200 --> 00:14:17,440 Speaker 9: about to be able to trade on. 245 00:14:18,120 --> 00:14:21,760 Speaker 1: Luana Lopez Lara is the co founder and COO of Calshi, 246 00:14:22,120 --> 00:14:26,040 Speaker 1: the first events contract exchange regulated by the Commodity Futures 247 00:14:26,040 --> 00:14:29,400 Speaker 1: and Trading Commission. The company is currently raising funds at 248 00:14:29,400 --> 00:14:34,720 Speaker 1: evaluation five billion dollars, even as the Intercontinental Exchange seeks 249 00:14:34,760 --> 00:14:39,080 Speaker 1: to buy Calshi's rival Polymarket for closer to ten billion dollars. 250 00:14:39,480 --> 00:14:41,760 Speaker 9: While we went to MIT, but one thing we realize 251 00:14:41,800 --> 00:14:44,120 Speaker 9: is that most trading happens when you have an opinion 252 00:14:44,120 --> 00:14:45,640 Speaker 9: about what's going to happen in the future and you 253 00:14:45,680 --> 00:14:47,840 Speaker 9: find a way to put that in the market. So 254 00:14:47,920 --> 00:14:49,680 Speaker 9: you think this country is going to go into a recession, 255 00:14:49,960 --> 00:14:51,560 Speaker 9: or you think this person is going to win an 256 00:14:51,560 --> 00:14:54,240 Speaker 9: election or Brexit's going to happen, and you find a 257 00:14:54,280 --> 00:14:58,040 Speaker 9: way to express that view in the market to hedge 258 00:14:58,080 --> 00:15:00,000 Speaker 9: some risks that you have, or also to get exposure 259 00:15:00,160 --> 00:15:02,960 Speaker 9: to something. And what we realized was there should be 260 00:15:03,000 --> 00:15:05,120 Speaker 9: a better way to do it for us. Why is 261 00:15:05,160 --> 00:15:07,720 Speaker 9: there no direct exchange that you can just trade directly 262 00:15:07,760 --> 00:15:09,000 Speaker 9: on what you think is going to happen. 263 00:15:09,720 --> 00:15:13,520 Speaker 1: Former Treasury Secretary Larry Summers sees real value in prediction 264 00:15:13,640 --> 00:15:17,000 Speaker 1: markets like the ones Calci and polymarket have created not 265 00:15:17,040 --> 00:15:20,040 Speaker 1: only for the people betting on events, but also for 266 00:15:20,120 --> 00:15:22,320 Speaker 1: those hoping to learn about the outcomes. 267 00:15:22,800 --> 00:15:29,280 Speaker 10: Poly Markets is probably the largest of the various prediction 268 00:15:29,480 --> 00:15:31,280 Speaker 10: markets that are out there. 269 00:15:31,560 --> 00:15:32,800 Speaker 2: There are a number of them. 270 00:15:32,960 --> 00:15:37,240 Speaker 10: I think these are a very useful tool because what 271 00:15:37,320 --> 00:15:43,200 Speaker 10: markets do markets where people can place a bet, take 272 00:15:43,240 --> 00:15:47,840 Speaker 10: a position, is they enable you to get access to 273 00:15:48,760 --> 00:15:52,800 Speaker 10: a kind of consensus opinion where people aren't just talking, 274 00:15:53,480 --> 00:15:55,560 Speaker 10: but they have to put their money where their mouths 275 00:15:55,560 --> 00:15:59,160 Speaker 10: are and they're taking a stake in something. So let 276 00:15:59,200 --> 00:16:03,640 Speaker 10: me give you an exac I don't live in New York. 277 00:16:04,200 --> 00:16:08,920 Speaker 10: It's not my primary interest, but I do follow the 278 00:16:08,960 --> 00:16:12,440 Speaker 10: New York City mayoralty election, there was a development Mayor 279 00:16:12,480 --> 00:16:17,520 Speaker 10: Adams Withdrew. How much did that change the likely prospects 280 00:16:17,560 --> 00:16:20,840 Speaker 10: of the election? One could have formed a judgment by 281 00:16:20,880 --> 00:16:23,400 Speaker 10: trying to think about it. One could have formed a 282 00:16:23,520 --> 00:16:28,080 Speaker 10: judgment by reading a lot of political analysts. In fact, 283 00:16:28,160 --> 00:16:30,680 Speaker 10: you could just look at the prediction markets. That I 284 00:16:30,720 --> 00:16:35,680 Speaker 10: think is the value. It helps us all be more informed, 285 00:16:35,840 --> 00:16:40,360 Speaker 10: more informed about even when there isn't much information available 286 00:16:40,840 --> 00:16:45,320 Speaker 10: about what's likely to happen in politics, what's likely to 287 00:16:45,400 --> 00:16:50,120 Speaker 10: happen in foreign policy? What are the prospects of an 288 00:16:50,200 --> 00:16:54,440 Speaker 10: agreement between Russia and Ukraine. I don't think anybody sensible 289 00:16:54,960 --> 00:17:01,320 Speaker 10: would pretend that these estimates are exactly right. These estimates 290 00:17:01,400 --> 00:17:07,760 Speaker 10: are estimates that can be completely relied on. But I 291 00:17:07,760 --> 00:17:16,520 Speaker 10: think there's a great deal of evidence from elections, from sports, judgments, 292 00:17:16,600 --> 00:17:22,360 Speaker 10: from other efforts at forecasting, that looking at these prediction 293 00:17:22,640 --> 00:17:28,479 Speaker 10: markets provides, what's a better and more reliable sense of 294 00:17:28,520 --> 00:17:31,919 Speaker 10: which way things are going to go than talking to 295 00:17:32,600 --> 00:17:34,480 Speaker 10: a single favorite expert. 296 00:17:34,680 --> 00:17:38,920 Speaker 1: Do prediction markets like polymarkets potentially replace polls? I mean, 297 00:17:38,960 --> 00:17:41,439 Speaker 1: polls will come under some siege. It's gotten harder and 298 00:17:41,440 --> 00:17:43,440 Speaker 1: harder to do them, and by the way, are they 299 00:17:43,480 --> 00:17:45,080 Speaker 1: more susceptible of being gamed? 300 00:17:46,040 --> 00:17:50,159 Speaker 10: David, I don't think they replace poles anymore than the 301 00:17:50,240 --> 00:17:57,960 Speaker 10: stock market replaces earnings forecasts. The stock market reflects earnings 302 00:17:58,000 --> 00:18:02,480 Speaker 10: forecasts and helps people process what a company is worth 303 00:18:03,000 --> 00:18:07,640 Speaker 10: given divergent discussions. But it wouldn't be able to function 304 00:18:08,280 --> 00:18:13,800 Speaker 10: if there weren't data, and there weren't analyzes and projections 305 00:18:13,840 --> 00:18:18,400 Speaker 10: of future earnings. So no, I don't think that prediction 306 00:18:18,560 --> 00:18:22,320 Speaker 10: markets are a substitute for polls. They are a way 307 00:18:22,400 --> 00:18:27,399 Speaker 10: of processing the information in polls if you're not yourself 308 00:18:27,560 --> 00:18:33,520 Speaker 10: a polster in a much better in a much better way. 309 00:18:33,680 --> 00:18:37,720 Speaker 1: Scott Rasmussen is a traditional polster and editor at large 310 00:18:37,720 --> 00:18:40,960 Speaker 1: of ballot Pedia, and as challenging as the business of 311 00:18:41,000 --> 00:18:44,280 Speaker 1: polling has been in recent years, it's far from clear 312 00:18:44,400 --> 00:18:46,240 Speaker 1: that prediction markets can replace it. 313 00:18:46,640 --> 00:18:49,320 Speaker 11: The definition of a good poll for most people is 314 00:18:49,359 --> 00:18:53,199 Speaker 11: one that confirms what they want to see, and a 315 00:18:53,240 --> 00:18:56,800 Speaker 11: lot of the criticism about polling is because of perceived 316 00:18:56,840 --> 00:19:01,080 Speaker 11: inaccuracies or perceived biases, and a lot of that has 317 00:19:01,160 --> 00:19:04,960 Speaker 11: to do with the difference between conducting a poll and 318 00:19:05,119 --> 00:19:08,720 Speaker 11: interpreting the poll, between the polling and the analysis twenty 319 00:19:08,840 --> 00:19:12,159 Speaker 11: sixteen is a year that I constantly hear people say 320 00:19:12,240 --> 00:19:16,159 Speaker 11: the polling was wrong. Well, actually, the national polls in 321 00:19:16,200 --> 00:19:20,640 Speaker 11: twenty sixteen showed that Hillary Clinton would win the popular 322 00:19:20,760 --> 00:19:23,800 Speaker 11: vote by three points. She won by two. On the 323 00:19:23,840 --> 00:19:27,200 Speaker 11: state by state polls, there were forty seven states where 324 00:19:27,240 --> 00:19:30,879 Speaker 11: there were no surprises whatsoever. There were three states that 325 00:19:30,920 --> 00:19:33,040 Speaker 11: were a shock, the big Blue Wall States. 326 00:19:33,160 --> 00:19:34,080 Speaker 2: My point to all. 327 00:19:33,960 --> 00:19:36,560 Speaker 11: Of this is that the analysis, the way you look 328 00:19:36,600 --> 00:19:39,360 Speaker 11: at the polls and the way you read them, has 329 00:19:39,400 --> 00:19:42,600 Speaker 11: as much to do with the polling is or with 330 00:19:42,720 --> 00:19:44,960 Speaker 11: the interpretation of the polls as anything else. 331 00:19:45,200 --> 00:19:48,880 Speaker 1: Scott, how has technology changed your business? Maybe made it harder, 332 00:19:49,040 --> 00:19:53,960 Speaker 1: particularly in getting representative samples in the things like cell phones. 333 00:19:53,960 --> 00:19:55,040 Speaker 2: As was a landlines. 334 00:19:55,320 --> 00:19:58,240 Speaker 11: We do almost all of our work now online through 335 00:19:58,280 --> 00:20:01,159 Speaker 11: different panels and text to pro coaches and other things. 336 00:20:01,240 --> 00:20:04,720 Speaker 11: Every one of those, every polling methodology you use, and 337 00:20:04,760 --> 00:20:08,320 Speaker 11: this has been true forever, has certain biases that you 338 00:20:08,440 --> 00:20:09,440 Speaker 11: have to correct for. 339 00:20:10,119 --> 00:20:10,919 Speaker 2: When we were. 340 00:20:10,800 --> 00:20:13,800 Speaker 11: Doing phone polling exclusively, if you had to call into 341 00:20:13,840 --> 00:20:17,640 Speaker 11: an urban area, you didn't get as many people answering 342 00:20:17,680 --> 00:20:20,919 Speaker 11: the phones because they were out doing other things. So 343 00:20:21,200 --> 00:20:24,760 Speaker 11: we would have to consciously work to place more calls 344 00:20:24,800 --> 00:20:29,400 Speaker 11: into those areas. Today, I believe that pollsters can get 345 00:20:29,440 --> 00:20:33,240 Speaker 11: a pretty good sample if you're careful with your methodology, 346 00:20:33,800 --> 00:20:38,720 Speaker 11: if you're talking about a survey of registered voters or 347 00:20:38,760 --> 00:20:44,320 Speaker 11: a survey of all adults. Because we have census data, 348 00:20:44,400 --> 00:20:46,919 Speaker 11: we have good models for what that should look like. 349 00:20:47,480 --> 00:20:50,960 Speaker 11: Where pollsters have struggled in recent years and will continue 350 00:20:50,960 --> 00:20:54,639 Speaker 11: to struggle is in the question of likely voters, who's 351 00:20:54,720 --> 00:20:57,880 Speaker 11: actually going to show up on election day, and as 352 00:20:57,880 --> 00:21:00,879 Speaker 11: we all know, in close races, that's decisive. 353 00:21:01,320 --> 00:21:04,840 Speaker 1: And now there's a new technology in town called artificial intelligence. 354 00:21:05,040 --> 00:21:07,520 Speaker 1: How is that changing polling? Is it making it better? 355 00:21:07,800 --> 00:21:10,080 Speaker 11: The thing that first made polling better, by the way, 356 00:21:10,240 --> 00:21:12,400 Speaker 11: was having a lot more competition and a lot more 357 00:21:12,440 --> 00:21:17,400 Speaker 11: polsters because we actually had to improve our game. Right now, 358 00:21:17,560 --> 00:21:22,399 Speaker 11: artificial intelligence is reshaping all kinds of industries in ways 359 00:21:22,440 --> 00:21:26,000 Speaker 11: that we can't imagine. I'm working on a project with Jigsaw, 360 00:21:26,359 --> 00:21:29,480 Speaker 11: which is a techt incubator inside of Google. We're going 361 00:21:29,560 --> 00:21:33,359 Speaker 11: to have a national conversation with five people from every 362 00:21:33,440 --> 00:21:36,800 Speaker 11: congressional district. The difference in this poll this is not 363 00:21:36,840 --> 00:21:39,840 Speaker 11: to determine who's going to win the mid terms or 364 00:21:39,920 --> 00:21:44,480 Speaker 11: the twenty twenty eight presidential election, We're focused on what 365 00:21:44,520 --> 00:21:46,919 Speaker 11: does it mean to be an American in the twenty 366 00:21:46,960 --> 00:21:50,560 Speaker 11: first century. This gets to one of the core problems 367 00:21:50,560 --> 00:21:53,480 Speaker 11: in polling, and this is where AI can help. 368 00:21:54,600 --> 00:21:55,640 Speaker 2: We don't all speak the. 369 00:21:55,600 --> 00:21:59,200 Speaker 11: Same language anymore, so what we're doing with this project 370 00:21:59,280 --> 00:22:04,040 Speaker 11: we're asking questions, but we're letting respondents answer in their 371 00:22:04,160 --> 00:22:05,040 Speaker 11: own words. 372 00:22:05,320 --> 00:22:08,480 Speaker 1: We also are seeing a rapid uptake in so called 373 00:22:08,480 --> 00:22:12,399 Speaker 1: prediction markets, or these futures markets and tied to specific events. 374 00:22:12,440 --> 00:22:15,800 Speaker 1: Contract how do those fit into the world of polling? 375 00:22:15,880 --> 00:22:18,600 Speaker 1: If at all, they don't fit into the world of polling. 376 00:22:18,680 --> 00:22:21,560 Speaker 11: But I will tell you that on election night when 377 00:22:21,600 --> 00:22:24,439 Speaker 11: I'm doing whether I'm doing analysis or if I'm sitting 378 00:22:24,440 --> 00:22:28,280 Speaker 11: at home, I watch the prediction markets because you've got 379 00:22:28,280 --> 00:22:32,080 Speaker 11: a whole crowd sourced army of people out there who 380 00:22:32,160 --> 00:22:34,800 Speaker 11: are looking for the latest news. And if I see 381 00:22:34,840 --> 00:22:38,800 Speaker 11: a candidate going from a seventy percent bet to a 382 00:22:38,920 --> 00:22:42,520 Speaker 11: forty percent bet, quickly I say, oh, something has gone 383 00:22:42,560 --> 00:22:44,920 Speaker 11: on here. I need to investigate and find out what 384 00:22:44,920 --> 00:22:49,720 Speaker 11: it was leading up to election night. Though prediction markets 385 00:22:49,800 --> 00:22:55,080 Speaker 11: are heavily dependent on polling, if you don't have polling, 386 00:22:55,920 --> 00:22:58,680 Speaker 11: the prediction markets wouldn't be nearly as good as they 387 00:22:58,680 --> 00:23:03,920 Speaker 11: are right now. What prediction markets have done is there, 388 00:23:03,920 --> 00:23:07,840 Speaker 11: in effect another analyst. They're looking at all of the data. 389 00:23:07,880 --> 00:23:11,480 Speaker 11: They're looking at the poles, They're also looking maybe at 390 00:23:11,920 --> 00:23:14,560 Speaker 11: who's voting early. They may be looking at some other 391 00:23:14,640 --> 00:23:18,520 Speaker 11: factors going on in the campaign, and they assemble that 392 00:23:18,760 --> 00:23:22,720 Speaker 11: in a way that an analyst can't. No individual analysts 393 00:23:22,760 --> 00:23:25,760 Speaker 11: could combine all of that information. So I like the 394 00:23:25,760 --> 00:23:29,280 Speaker 11: crowdsourcing aspect of it, but again it's not really a 395 00:23:29,359 --> 00:23:32,800 Speaker 11: replacement for polling. There's all kinds of data that's out 396 00:23:32,840 --> 00:23:35,399 Speaker 11: there today that we can look at and analyze and 397 00:23:35,440 --> 00:23:39,160 Speaker 11: think about, and prediction markets are another important tool. 398 00:23:41,840 --> 00:23:45,879 Speaker 1: Up next, President Trump triggers the law of unintended consequences, 399 00:23:46,160 --> 00:23:49,639 Speaker 1: aiming for big Chinese apparel companies and hitting a small 400 00:23:49,840 --> 00:23:53,919 Speaker 1: watercolor artist in Devonshire with sweeping tariff changes. 401 00:24:05,320 --> 00:24:06,320 Speaker 2: This is a story. 402 00:24:06,040 --> 00:24:09,440 Speaker 1: About painting with a very broad brush. In late July 403 00:24:09,640 --> 00:24:12,040 Speaker 1: of this year, President Trump declared an end to a 404 00:24:12,119 --> 00:24:15,920 Speaker 1: tariff exemption for small shipments. It may have been meant 405 00:24:15,920 --> 00:24:19,119 Speaker 1: to hit some large Chinese companies, but it's also hitting 406 00:24:19,200 --> 00:24:22,440 Speaker 1: small businesses on the other side of the world. 407 00:24:23,720 --> 00:24:26,320 Speaker 2: To Menormis. It's very it's a big deal. 408 00:24:26,680 --> 00:24:29,600 Speaker 3: It's a big scam going on against our country gets 409 00:24:29,960 --> 00:24:33,199 Speaker 3: really small businesses, and we've ended we put an end 410 00:24:33,200 --> 00:24:33,520 Speaker 3: to it. 411 00:24:35,920 --> 00:24:39,920 Speaker 1: Three thousand miles away from Washington across the Atlantic, Harriet 412 00:24:39,920 --> 00:24:43,640 Speaker 1: Dewinton has built a business painting with the smallest of brushes. 413 00:24:44,440 --> 00:24:48,640 Speaker 12: It began with wedding stationery, beautiful hand painted illustrated designs, 414 00:24:48,920 --> 00:24:53,760 Speaker 12: and that quickly transformed into a much broader range of things. 415 00:24:54,200 --> 00:24:57,240 Speaker 1: From a cottage in the quiet countryside of Devon, England, 416 00:24:57,560 --> 00:25:01,000 Speaker 1: de Winton paints, post videos and well's the supply she 417 00:25:01,200 --> 00:25:03,120 Speaker 1: uses to customers around the world. 418 00:25:03,720 --> 00:25:07,480 Speaker 12: I taught I write watercolor books, which we sell around 419 00:25:07,480 --> 00:25:10,840 Speaker 12: the world, and also art supplies because a lot of 420 00:25:10,840 --> 00:25:13,879 Speaker 12: people see me painting on YouTube and you can have 421 00:25:13,920 --> 00:25:18,679 Speaker 12: fun really turning this piece into something rather sophisticated. So 422 00:25:19,160 --> 00:25:23,200 Speaker 12: the business still involves painting commissions and doing some really 423 00:25:23,200 --> 00:25:26,480 Speaker 12: fun illustration work for companies and brands. We're getting now 424 00:25:26,600 --> 00:25:30,080 Speaker 12: to about a four hundred thousand pound turnover a year, 425 00:25:31,119 --> 00:25:33,720 Speaker 12: so we're you know, it's keeping us really busy and 426 00:25:33,760 --> 00:25:34,359 Speaker 12: really happy. 427 00:25:35,480 --> 00:25:38,320 Speaker 1: Dewinton was used to the challenges of running a small business, 428 00:25:38,720 --> 00:25:41,000 Speaker 1: but none compared to the day she learned that the 429 00:25:41,119 --> 00:25:44,719 Speaker 1: rule she relied on to ship goods abroad was going away. 430 00:25:45,400 --> 00:25:49,719 Speaker 12: I was on Instagram and I was I noticed a 431 00:25:49,800 --> 00:25:53,080 Speaker 12: post from a fellow small business owner who runs a 432 00:25:53,119 --> 00:25:56,639 Speaker 12: creative business talking about this, and I it was the 433 00:25:56,680 --> 00:26:00,000 Speaker 12: first I'd heard, and I thought, that's that sounds very strng. 434 00:26:01,040 --> 00:26:04,080 Speaker 1: The rule exempting tariffs on small shipments dates back to 435 00:26:04,080 --> 00:26:08,399 Speaker 1: the nineteen thirties and was designed for administrative efficiency. It 436 00:26:08,480 --> 00:26:11,680 Speaker 1: started with packages worth less than a dollar, and eventually 437 00:26:11,720 --> 00:26:14,719 Speaker 1: it grew to five dollars, to ten dollars, to two 438 00:26:14,840 --> 00:26:19,240 Speaker 1: hundred dollars, eventually reaching eight hundred dollars in twenty sixteen. 439 00:26:19,680 --> 00:26:23,119 Speaker 13: The Dimennamus tariff rule at the moment was that if 440 00:26:23,160 --> 00:26:25,840 Speaker 13: you brought in less than eight hundred dollars worth of imports, 441 00:26:26,200 --> 00:26:27,240 Speaker 13: you were exempted. 442 00:26:26,840 --> 00:26:27,560 Speaker 2: From all tariffs. 443 00:26:27,680 --> 00:26:31,119 Speaker 13: And it was dates to the nineteen thirties, back to 444 00:26:31,160 --> 00:26:34,679 Speaker 13: the Smooth Holly era, and it was meant to exempt 445 00:26:34,720 --> 00:26:38,720 Speaker 13: people from all the paperwork and compliance costs and sorting 446 00:26:38,760 --> 00:26:41,560 Speaker 13: through the tariff schedules for small transactions. 447 00:26:41,760 --> 00:26:45,240 Speaker 1: Douglas holtz Eken is president of the American Action Forum 448 00:26:45,400 --> 00:26:48,440 Speaker 1: and a former director of the Congressional Budget. 449 00:26:48,080 --> 00:26:52,040 Speaker 13: Office, compliance costs are reel. Administrative costs serreel, and you 450 00:26:52,080 --> 00:26:55,879 Speaker 13: don't want to spend those dollars on tiny transactions that 451 00:26:55,920 --> 00:26:58,879 Speaker 13: don't amount to much in the economy. So that was 452 00:26:58,920 --> 00:27:00,000 Speaker 13: a sensible thing to happen. 453 00:27:00,720 --> 00:27:01,040 Speaker 9: The whole. 454 00:27:01,080 --> 00:27:03,639 Speaker 1: Seekin says that over the years, the use of the 455 00:27:03,680 --> 00:27:07,560 Speaker 1: Dominimus rule has exploded, with US Customs and Border Protection 456 00:27:07,680 --> 00:27:11,680 Speaker 1: reporting nearly one point four billion packages in twenty twenty four, 457 00:27:12,200 --> 00:27:15,080 Speaker 1: averaging about three point seven million a day. 458 00:27:15,680 --> 00:27:18,800 Speaker 13: There are two rationales given for taking it away. Rationale 459 00:27:18,880 --> 00:27:22,720 Speaker 13: number one is fentanyl and the shipment of packages from 460 00:27:22,800 --> 00:27:25,320 Speaker 13: China and to the United States containing fentanyl and not 461 00:27:25,480 --> 00:27:28,600 Speaker 13: being inspected because they are declared to be under the 462 00:27:28,680 --> 00:27:31,480 Speaker 13: eight hundred dollars dominionist rule. So as part of fighting 463 00:27:31,560 --> 00:27:34,520 Speaker 13: the fentanyl trafficking, the administration thought, we'll get rid of 464 00:27:34,520 --> 00:27:36,919 Speaker 13: the Dominius rule. We'll look at every package. And the 465 00:27:36,960 --> 00:27:40,840 Speaker 13: second is the competition from the online retailers in China, 466 00:27:41,000 --> 00:27:44,040 Speaker 13: and if you're a US retailer and you're paying local 467 00:27:44,040 --> 00:27:47,640 Speaker 13: sales taxes, the Chinese retailers sending it to the US 468 00:27:47,640 --> 00:27:52,000 Speaker 13: without any terriff for tax What developed was large online 469 00:27:52,080 --> 00:27:55,720 Speaker 13: Chinese retailers who automated the process of breaking up large 470 00:27:55,720 --> 00:27:59,199 Speaker 13: shipments into small under eight hundred dollars bundles, dodging all 471 00:27:59,240 --> 00:28:01,680 Speaker 13: the tariffs entire and that's what caught the attention of 472 00:28:01,720 --> 00:28:05,520 Speaker 13: the administration. Seventy percent of the value of things brought 473 00:28:05,520 --> 00:28:08,440 Speaker 13: in under the Dominianist rule or from China. These are 474 00:28:08,440 --> 00:28:11,240 Speaker 13: low cost retailers, and Americans liked the prices. 475 00:28:11,400 --> 00:28:14,240 Speaker 7: Today, I am doing another huge team hall. 476 00:28:14,359 --> 00:28:18,040 Speaker 2: This one is massive. Two leggings for like nine dollars. 477 00:28:18,240 --> 00:28:21,120 Speaker 11: Let's see, we can make it right three two. 478 00:28:21,400 --> 00:28:25,200 Speaker 1: What President Trump may have taken away a strategic advantage 479 00:28:25,240 --> 00:28:28,440 Speaker 1: for Shian and Timu, but small business owners like Harriet 480 00:28:28,480 --> 00:28:32,040 Speaker 1: Dewinton say they're also paying the price, a price that 481 00:28:32,119 --> 00:28:35,359 Speaker 1: began simply with confusion about how it would all work 482 00:28:35,680 --> 00:28:38,040 Speaker 1: and what it would mean. What is that done to 483 00:28:38,080 --> 00:28:38,760 Speaker 1: your business? 484 00:28:39,000 --> 00:28:43,400 Speaker 12: It's had a really sizable impact on the business, starting 485 00:28:43,520 --> 00:28:49,040 Speaker 12: with the beginning of hearing about the news and in 486 00:28:49,080 --> 00:28:52,000 Speaker 12: all honesty being in disbelief that it was about to happen. 487 00:28:52,200 --> 00:28:56,160 Speaker 1: About sixty percent of Dewinton's online sales came from the US. 488 00:28:56,800 --> 00:28:59,600 Speaker 1: After a transition period when the US imposes of flat 489 00:28:59,680 --> 00:29:03,120 Speaker 1: duty on all shipments. Ultimately, everything she ships to the 490 00:29:03,200 --> 00:29:07,080 Speaker 1: United States apart from books, will be subject to tariffs 491 00:29:07,280 --> 00:29:11,000 Speaker 1: calculated as a percentage of the item's declared value, depending 492 00:29:11,000 --> 00:29:14,880 Speaker 1: on what it is and where it's from. Confused, so 493 00:29:15,120 --> 00:29:15,720 Speaker 1: is Harriet. 494 00:29:16,320 --> 00:29:20,240 Speaker 12: It felt quite uncertain and unclear as to what the 495 00:29:20,360 --> 00:29:25,320 Speaker 12: actual facts of the exemption ending were going to be, 496 00:29:26,000 --> 00:29:30,440 Speaker 12: so we very quickly started just trying to research as 497 00:29:30,520 --> 00:29:32,240 Speaker 12: much as possible. That's the best thing you can do, 498 00:29:32,320 --> 00:29:35,680 Speaker 12: isn't it. When change, change comes. We used to change 499 00:29:36,480 --> 00:29:39,120 Speaker 12: and we act nimbly on our feet, but this one 500 00:29:39,160 --> 00:29:41,680 Speaker 12: was just a little bit difficult to get a true 501 00:29:41,720 --> 00:29:43,360 Speaker 12: sense of what it was going to mean for us. 502 00:29:43,480 --> 00:29:47,520 Speaker 12: We realized that we needed to shut our shop entirely 503 00:29:47,720 --> 00:29:50,960 Speaker 12: towards the end of August just so we could see 504 00:29:51,560 --> 00:29:52,840 Speaker 12: what it actually meant. 505 00:29:53,400 --> 00:29:57,360 Speaker 1: Rathnascharade built her company flavor Cloud to help businesses like 506 00:29:57,440 --> 00:30:00,320 Speaker 1: Dewinton's navigate this shifting trade andronment. 507 00:30:00,960 --> 00:30:05,960 Speaker 14: So really, most consumer purchases are under the eight hundred dollars, 508 00:30:06,200 --> 00:30:10,719 Speaker 14: so it didn't need to have formal customs declarations, didn't 509 00:30:10,880 --> 00:30:15,040 Speaker 14: need to have additional data around you know what exactly 510 00:30:15,120 --> 00:30:20,200 Speaker 14: is coming into the country, so typically trade requires harmonized 511 00:30:20,200 --> 00:30:24,800 Speaker 14: commodity codes. It basically allows for the customs official at 512 00:30:24,840 --> 00:30:26,760 Speaker 14: the border to say, you know, what are you bringing 513 00:30:26,880 --> 00:30:30,280 Speaker 14: into the country, what is the value of those goods 514 00:30:30,320 --> 00:30:33,560 Speaker 14: where was it made. What that allows the customs official 515 00:30:33,640 --> 00:30:38,600 Speaker 14: to do is then assess duties, taxes and fees. Typically 516 00:30:38,600 --> 00:30:43,240 Speaker 14: that also includes tariffs in this case, and that's being 517 00:30:43,320 --> 00:30:48,680 Speaker 14: assessed now on goods of any value. So regardless of 518 00:30:48,680 --> 00:30:51,240 Speaker 14: where you source and bring the goods from, you are 519 00:30:51,280 --> 00:30:54,200 Speaker 14: seeing a massive implication with deminimus. 520 00:30:53,960 --> 00:30:57,480 Speaker 1: For small businesses like Dewinton's. The end of Deminimus came 521 00:30:57,560 --> 00:31:00,920 Speaker 1: as a surprise, but insiders say it was a long 522 00:31:01,000 --> 00:31:04,600 Speaker 1: time coming, with the US standing out for years with 523 00:31:04,720 --> 00:31:08,120 Speaker 1: one of the world's most generous thresholds at eight hundred dollars, 524 00:31:08,520 --> 00:31:11,400 Speaker 1: far higher than in Europe, Canada or Japan. 525 00:31:12,280 --> 00:31:16,360 Speaker 14: Ultimately, all governments want a piece of the tax revenue. 526 00:31:16,560 --> 00:31:21,360 Speaker 14: So as global e commerce has grown, cross border e 527 00:31:21,400 --> 00:31:25,520 Speaker 14: commerce has grown, and it is a continuously growing pie. 528 00:31:25,800 --> 00:31:28,400 Speaker 14: You know, everyone wants a piece of that pie. So 529 00:31:28,880 --> 00:31:31,320 Speaker 14: we've seen that happen around the world. 530 00:31:31,680 --> 00:31:34,400 Speaker 1: But economists warn the real shift will be felt in 531 00:31:34,440 --> 00:31:36,640 Speaker 1: the pocketbooks of American consumers. 532 00:31:37,240 --> 00:31:39,840 Speaker 13: This is the hard truth that the administration really doesn't 533 00:31:39,880 --> 00:31:42,520 Speaker 13: like to face. Tariffs are taxes on imports, and they're 534 00:31:42,560 --> 00:31:45,720 Speaker 13: paid by Americans. I've been to either businesses or consumers. 535 00:31:46,640 --> 00:31:50,200 Speaker 13: It is their hope that foreign producers will cut their 536 00:31:50,240 --> 00:31:53,520 Speaker 13: prices so that the net cost to the consumer doesn't 537 00:31:53,520 --> 00:31:56,320 Speaker 13: go up. There's no evidence that that's going to be 538 00:31:56,440 --> 00:31:59,320 Speaker 13: the case on a large scale. And so if you've 539 00:31:59,320 --> 00:32:01,320 Speaker 13: put the tariffs place, and we have a lot of 540 00:32:01,320 --> 00:32:03,320 Speaker 13: tariffs in place now, about three hundred and eighty billion 541 00:32:03,360 --> 00:32:05,720 Speaker 13: dollars a year, that's a three hundred and eighty billion 542 00:32:05,760 --> 00:32:08,240 Speaker 13: dollar year tax increase on Americans. This is a highly 543 00:32:08,280 --> 00:32:11,160 Speaker 13: distortionary tax. It's going to force all these small businesses 544 00:32:11,200 --> 00:32:13,840 Speaker 13: to change the way they run their operations and impose 545 00:32:13,880 --> 00:32:15,880 Speaker 13: a lot of costs in them. You look at whether 546 00:32:15,880 --> 00:32:19,480 Speaker 13: it's a fair tax. These are regressive taxes paid more 547 00:32:19,520 --> 00:32:22,760 Speaker 13: by small businesses and low income individuals, and it's an 548 00:32:22,800 --> 00:32:26,920 Speaker 13: incredibly complicated, expensive thing to comply with and to administer. 549 00:32:27,320 --> 00:32:28,280 Speaker 13: It isn't the way you want to. 550 00:32:28,280 --> 00:32:30,440 Speaker 12: Raise a lot of revenue head to etsy to get 551 00:32:30,480 --> 00:32:34,640 Speaker 12: both of these we are selling to the USA again. Hallelujah. 552 00:32:35,160 --> 00:32:38,680 Speaker 12: We've been looking at all the options and my main 553 00:32:40,000 --> 00:32:43,040 Speaker 12: focus is to remove the hassle for the US customer 554 00:32:43,080 --> 00:32:46,160 Speaker 12: because they're so important to us. So we've decided to 555 00:32:46,840 --> 00:32:49,720 Speaker 12: pay at the source with Royal Mail for the time being, 556 00:32:50,680 --> 00:32:53,760 Speaker 12: and they do a service where we cover the cost 557 00:32:53,800 --> 00:32:56,080 Speaker 12: of the tariff and then it's down to me as 558 00:32:56,080 --> 00:32:59,760 Speaker 12: a shop owner to decide where I make that difference up. 559 00:33:00,080 --> 00:33:02,840 Speaker 12: So we've looked at just increasing our shipping costs a 560 00:33:02,880 --> 00:33:05,800 Speaker 12: little bit, and in one or two examples we've just 561 00:33:06,200 --> 00:33:08,880 Speaker 12: slightly raised the price of the product itself to be 562 00:33:08,960 --> 00:33:14,160 Speaker 12: able to stay in business. If you haven't already had 563 00:33:14,200 --> 00:33:17,480 Speaker 12: this land on your doormat today, they're no worries because 564 00:33:17,520 --> 00:33:19,880 Speaker 12: I'm going to show you how to do this tutorial. 565 00:33:20,080 --> 00:33:22,800 Speaker 1: Viewed through the broad brush of the economy, it's all 566 00:33:22,800 --> 00:33:26,240 Speaker 1: pretty simple. Costs have gone up and so to must 567 00:33:26,280 --> 00:33:30,040 Speaker 1: the price. But for Harriet Dewinton, who has now reopened 568 00:33:30,040 --> 00:33:33,440 Speaker 1: her online shop back in Devon, it's not that easy. 569 00:33:33,920 --> 00:33:35,880 Speaker 1: Do you have a sense of the long term effect 570 00:33:35,920 --> 00:33:36,720 Speaker 1: on your business? 571 00:33:37,040 --> 00:33:37,200 Speaker 2: Well? 572 00:33:37,240 --> 00:33:41,560 Speaker 12: I do think just returning to people's consciousness as the 573 00:33:41,680 --> 00:33:44,200 Speaker 12: seller they want to buy from is going to take 574 00:33:44,240 --> 00:33:48,000 Speaker 12: a bit of time and my hope is that we 575 00:33:48,400 --> 00:33:52,120 Speaker 12: sell a number of products that are unique to my business, 576 00:33:52,800 --> 00:33:56,080 Speaker 12: and I'm a trusted voice on the Internet when it 577 00:33:56,120 --> 00:33:59,760 Speaker 12: comes to art supplies and watercolor in particular. The American 578 00:33:59,840 --> 00:34:04,560 Speaker 12: or adients have been a really, really important part of 579 00:34:04,640 --> 00:34:07,760 Speaker 12: de Winton Paper Coat, taking it from just working down 580 00:34:07,800 --> 00:34:11,520 Speaker 12: in Devon and I love your little cottage and reaching 581 00:34:11,560 --> 00:34:14,400 Speaker 12: out to the world. I've just found that the culture 582 00:34:14,440 --> 00:34:18,600 Speaker 12: of watercolor painting in particular has just been embraced so 583 00:34:18,800 --> 00:34:21,879 Speaker 12: much by the American audience as well as the rest 584 00:34:21,880 --> 00:34:22,360 Speaker 12: of the world. 585 00:34:22,880 --> 00:34:25,160 Speaker 1: For her, it all goes back to the passion and 586 00:34:25,320 --> 00:34:28,680 Speaker 1: people who have turned her art into a global community 587 00:34:29,080 --> 00:34:32,760 Speaker 1: and a global business. She hopes that both will endure 588 00:34:33,400 --> 00:34:39,239 Speaker 1: no matter the costs. Coming up AI is coming to 589 00:34:39,280 --> 00:34:57,680 Speaker 1: a schoolroom near you, if it isn't already there. This 590 00:34:57,840 --> 00:35:01,000 Speaker 1: is the second story in our series on artificial intelligence 591 00:35:01,040 --> 00:35:05,080 Speaker 1: being applied here and now where it matters most. Last 592 00:35:05,080 --> 00:35:07,640 Speaker 1: week we brought you the story of the AI used 593 00:35:07,640 --> 00:35:11,120 Speaker 1: by your doctor. This week it's about your teacher, the 594 00:35:11,160 --> 00:35:13,879 Speaker 1: one you depend on to educate your children, the one 595 00:35:13,920 --> 00:35:16,880 Speaker 1: we all depend on to prepare the next generation of workers, 596 00:35:17,239 --> 00:35:20,239 Speaker 1: the one that we all remember from our own childhood. 597 00:35:22,280 --> 00:35:25,120 Speaker 15: Whenever I talk to people about their experiences in education, 598 00:35:25,400 --> 00:35:28,480 Speaker 15: they always named this one teacher who was so influential 599 00:35:28,520 --> 00:35:31,640 Speaker 15: and they got into this discipline because this teacher was 600 00:35:31,680 --> 00:35:34,960 Speaker 15: amazing and they inspired them where students said. 601 00:35:34,800 --> 00:35:39,600 Speaker 1: What Professor Shamya Kurumbaya now teaches those teachers. As a 602 00:35:39,600 --> 00:35:42,879 Speaker 1: professor at the University of Wisconsin. She lives at the 603 00:35:42,920 --> 00:35:47,040 Speaker 1: intersection of education and tech, focusing on the application of 604 00:35:47,160 --> 00:35:49,960 Speaker 1: artificial intelligence in America's classrooms. 605 00:35:50,160 --> 00:35:52,279 Speaker 15: Why we know that teacher in the front of the 606 00:35:52,320 --> 00:35:54,920 Speaker 15: classroom and students in front of their laptops is not 607 00:35:54,960 --> 00:35:57,359 Speaker 15: a model that works. It's broken. Last couple of years 608 00:35:57,440 --> 00:36:00,080 Speaker 15: especially has been very exciting, and I would say the 609 00:36:00,160 --> 00:36:04,799 Speaker 15: big change is the ability for teachers to customize what's 610 00:36:04,800 --> 00:36:05,920 Speaker 15: happening with AIS. 611 00:36:06,120 --> 00:36:08,400 Speaker 2: So what else to look for on your Kira dashboard. 612 00:36:08,719 --> 00:36:12,239 Speaker 1: Lance Key is one of those classroom teachers adapting and 613 00:36:12,320 --> 00:36:16,040 Speaker 1: adapting AI as he teaches computer science in the Putnam 614 00:36:16,040 --> 00:36:19,560 Speaker 1: County School System just east of Nashville, Tennessee. 615 00:36:19,640 --> 00:36:20,760 Speaker 2: Computer science curriculum. 616 00:36:20,760 --> 00:36:24,040 Speaker 16: They've really gamified it and they made it colorful and 617 00:36:24,360 --> 00:36:27,040 Speaker 16: engaging and exciting for the students. And also you know, 618 00:36:27,160 --> 00:36:28,719 Speaker 16: inside of there, if they try to do things that 619 00:36:28,760 --> 00:36:31,920 Speaker 16: they can't it again will redirect them. It'll give them, oh, 620 00:36:31,960 --> 00:36:33,640 Speaker 16: there's an error here, and then then go to the 621 00:36:33,719 --> 00:36:36,520 Speaker 16: chatbot and try to work through what the error is, 622 00:36:36,760 --> 00:36:38,680 Speaker 16: and then it'll redirect them to where they need to go. 623 00:36:39,200 --> 00:36:41,680 Speaker 7: I think the big opportunity that stems from MEI in 624 00:36:41,680 --> 00:36:45,800 Speaker 7: the classroom is the ability to personalize instruction to individual students. 625 00:36:46,360 --> 00:36:50,360 Speaker 1: Andrea Passinetti is co founder and CEO of the company Kira. 626 00:36:51,000 --> 00:36:55,080 Speaker 1: It provides the education software platform to Lance Key's classroom, 627 00:36:55,239 --> 00:36:58,040 Speaker 1: as well as to many of the largest school districts 628 00:36:58,120 --> 00:36:59,360 Speaker 1: across the United States. 629 00:37:00,000 --> 00:37:04,320 Speaker 7: If AI, a teacher has an unlimited number of teaching 630 00:37:04,360 --> 00:37:09,360 Speaker 7: assistants to support that process, and the teacher can provide guidance, guardrails, 631 00:37:09,400 --> 00:37:12,120 Speaker 7: and guidelines on how to provide that support to students. 632 00:37:12,480 --> 00:37:14,719 Speaker 7: That looks like students working on a terminal and having 633 00:37:14,719 --> 00:37:17,720 Speaker 7: an AI tutor that they can query either in written 634 00:37:17,760 --> 00:37:20,560 Speaker 7: text by typing on a keyboard or in spoken word 635 00:37:20,680 --> 00:37:22,840 Speaker 7: by speaking directly to the terminal and the computer. 636 00:37:23,200 --> 00:37:24,040 Speaker 2: Yeah chet bot. 637 00:37:24,719 --> 00:37:28,400 Speaker 1: That personalization is something that Key is experiencing every day 638 00:37:28,600 --> 00:37:30,160 Speaker 1: in his Tennessee classroom. 639 00:37:30,520 --> 00:37:34,319 Speaker 16: It allows me to have a more personalized relationship with 640 00:37:34,320 --> 00:37:37,920 Speaker 16: my students in the classroom. Before there was one of 641 00:37:37,960 --> 00:37:40,600 Speaker 16: me and for every question that came up, I was 642 00:37:40,680 --> 00:37:43,520 Speaker 16: like bouncing around the room just answering questions all the time. 643 00:37:43,719 --> 00:37:46,239 Speaker 16: But now we've got you a tutor that can help 644 00:37:46,280 --> 00:37:49,840 Speaker 16: them along the way, and I can build personalized tutors 645 00:37:49,880 --> 00:37:52,240 Speaker 16: for them too, So if it's on a specific content area, 646 00:37:52,520 --> 00:37:55,239 Speaker 16: I can say, okay, today we're working on solving two 647 00:37:55,280 --> 00:37:56,239 Speaker 16: step equations. 648 00:37:56,560 --> 00:37:58,040 Speaker 2: Here's a two step tutor that. 649 00:37:58,000 --> 00:38:01,000 Speaker 16: My students can work with alone and ask it questions, 650 00:38:01,160 --> 00:38:03,400 Speaker 16: so then I can walk around and then one on one. 651 00:38:03,320 --> 00:38:04,200 Speaker 2: Check with students. 652 00:38:04,360 --> 00:38:06,840 Speaker 16: I've also got a dashboard on the backside that will 653 00:38:06,880 --> 00:38:09,719 Speaker 16: alert me if there's a student that's having problems, so 654 00:38:09,760 --> 00:38:12,000 Speaker 16: then I know real time that I need to go 655 00:38:12,160 --> 00:38:13,719 Speaker 16: check on Johnny or Susie. 656 00:38:13,760 --> 00:38:17,720 Speaker 1: Over and above that personalization, AI can also provide teachers 657 00:38:17,760 --> 00:38:20,640 Speaker 1: like Lance Key with some much needed relief. 658 00:38:21,160 --> 00:38:25,160 Speaker 16: My wife previously was an English teacher, and I recall 659 00:38:25,719 --> 00:38:30,040 Speaker 16: grading essays being very daunting for her because she would 660 00:38:30,080 --> 00:38:33,200 Speaker 16: have to score one hundred and fifty essays every time 661 00:38:33,239 --> 00:38:36,279 Speaker 16: if they wrote an essay in the classroom. So using 662 00:38:36,360 --> 00:38:39,280 Speaker 16: a Rubert, being able to upload our district rubricks into 663 00:38:39,440 --> 00:38:42,920 Speaker 16: Kira and being able to use one, they're. 664 00:38:42,760 --> 00:38:44,360 Speaker 2: Greater off of that rubric. 665 00:38:44,560 --> 00:38:47,640 Speaker 16: But also their AI detection, their plagiarism detection, and their 666 00:38:47,640 --> 00:38:50,719 Speaker 16: feedback writer has been amazing. So we can load all 667 00:38:50,719 --> 00:38:52,920 Speaker 16: the papers up into that. It will score it off 668 00:38:52,920 --> 00:38:54,759 Speaker 16: for us, It will give us all the feedback and 669 00:38:54,800 --> 00:38:57,080 Speaker 16: the teachers can then just review it. And I think 670 00:38:57,120 --> 00:38:59,319 Speaker 16: that's the big thing that we can focus on with 671 00:38:59,440 --> 00:39:01,600 Speaker 16: A is the time that it gives teachers back in 672 00:39:01,600 --> 00:39:03,680 Speaker 16: the day. By twenty thirty, we're going to need to 673 00:39:03,760 --> 00:39:07,120 Speaker 16: hire thirty million teachers because we've got teachers retiring and 674 00:39:07,120 --> 00:39:08,840 Speaker 16: people not going into the field, so we're gonna have 675 00:39:08,840 --> 00:39:09,960 Speaker 16: a teacher shortage that's. 676 00:39:09,840 --> 00:39:12,160 Speaker 2: Coming along with a high burnout. 677 00:39:12,200 --> 00:39:14,560 Speaker 16: Right, So I think AI can help us with some 678 00:39:14,640 --> 00:39:16,880 Speaker 16: of the repetitive processes that we do over and over 679 00:39:16,920 --> 00:39:17,399 Speaker 16: and over again. 680 00:39:18,280 --> 00:39:21,160 Speaker 1: For all its promise today, it turns out that the 681 00:39:21,239 --> 00:39:24,840 Speaker 1: use of AI for education isn't all that new. While 682 00:39:24,960 --> 00:39:28,560 Speaker 1: large language models are only just making their debut in schools, 683 00:39:28,960 --> 00:39:32,560 Speaker 1: other forms of AI have a long history. How long 684 00:39:33,200 --> 00:39:35,480 Speaker 1: has either artificial intelligence or maybe we should call it 685 00:39:35,560 --> 00:39:38,120 Speaker 1: machine learning going back? How long has it been used 686 00:39:38,160 --> 00:39:38,840 Speaker 1: in the classroom. 687 00:39:39,000 --> 00:39:43,440 Speaker 15: You'd be surprised how AI and education. Users of it 688 00:39:43,520 --> 00:39:47,360 Speaker 15: in education have a lot of common sort of origins, 689 00:39:47,440 --> 00:39:51,400 Speaker 15: and so I come from an academic background where my 690 00:39:51,560 --> 00:39:57,239 Speaker 15: advisor's advisors advisor back in nineteen eighties was creating what 691 00:39:57,280 --> 00:39:59,920 Speaker 15: we now call it cognitive tutors that were being u 692 00:40:00,280 --> 00:40:04,440 Speaker 15: in Pittsburgh classrooms, and people have studied. There's work done 693 00:40:04,480 --> 00:40:08,840 Speaker 15: in the nineteen nineties on ethnography basically of how teachers 694 00:40:08,880 --> 00:40:11,520 Speaker 15: are using AI in the classroom. What does introduction of 695 00:40:11,560 --> 00:40:15,040 Speaker 15: AI in the classroom change in the social structures student 696 00:40:15,080 --> 00:40:18,480 Speaker 15: student interaction or student AI student teacher interaction. 697 00:40:19,239 --> 00:40:21,879 Speaker 1: It's only in the last few years that AI has 698 00:40:21,920 --> 00:40:24,600 Speaker 1: developed to the point where companies like Kira can put 699 00:40:24,600 --> 00:40:28,240 Speaker 1: it to use in the classroom. According to surveys by RAND, 700 00:40:28,480 --> 00:40:30,960 Speaker 1: as of the twenty twenty three to twenty four school year, 701 00:40:31,440 --> 00:40:34,600 Speaker 1: a quarter of all US teachers were already using AI 702 00:40:34,760 --> 00:40:39,279 Speaker 1: to teach students and Passinetti says, the key is the conversation. 703 00:40:39,239 --> 00:40:41,279 Speaker 7: We use AI as a catch all term for a 704 00:40:41,280 --> 00:40:45,600 Speaker 7: lot of different technologies. In reality, what's happened with the 705 00:40:45,640 --> 00:40:48,880 Speaker 7: most recent wave of AI, with LLLMS in particular, is 706 00:40:48,920 --> 00:40:53,719 Speaker 7: a more discursive medium. So AI now, unlike even three 707 00:40:53,840 --> 00:40:58,360 Speaker 7: four years ago, is able to have real conversations with students. 708 00:40:58,440 --> 00:41:00,759 Speaker 7: It's able to engage with student on a level that 709 00:41:00,840 --> 00:41:04,560 Speaker 7: feels more human in some respects. We founded Kira four 710 00:41:04,680 --> 00:41:09,960 Speaker 7: years ago before AI was popular. AI wasn't cool, it 711 00:41:10,080 --> 00:41:13,840 Speaker 7: seemed premature, and in many cases it was entirely verbotan. 712 00:41:14,239 --> 00:41:16,000 Speaker 7: There are a lot of districts that said, we can't 713 00:41:16,040 --> 00:41:18,719 Speaker 7: really be talking about AI with parents because it gives 714 00:41:18,719 --> 00:41:21,279 Speaker 7: them a lot of anxiety, or there's a lack of 715 00:41:21,360 --> 00:41:23,960 Speaker 7: understanding about what a I can do, and so the 716 00:41:24,000 --> 00:41:29,239 Speaker 7: whole conversation would die at the very beginning. So I 717 00:41:29,239 --> 00:41:34,319 Speaker 7: would say there's been a radical shift where that anxiety 718 00:41:34,360 --> 00:41:38,480 Speaker 7: and resistance has given way to curiosity. 719 00:41:38,560 --> 00:41:41,920 Speaker 1: And some of that curiosity has turned into plain necessity. 720 00:41:42,239 --> 00:41:45,040 Speaker 1: As school districts across the country struggled to deal with 721 00:41:45,080 --> 00:41:48,239 Speaker 1: teaching students in a multitude of languages. 722 00:41:48,080 --> 00:41:51,400 Speaker 15: It's important to bring students home language into their classrooms. 723 00:41:51,800 --> 00:41:56,600 Speaker 15: A very promising benefited opportunity for AI here is to 724 00:41:56,640 --> 00:41:59,720 Speaker 15: bridge the gap between the language that the teacher speaks 725 00:41:59,760 --> 00:42:02,760 Speaker 15: and different languages that the students in the classroom speak. 726 00:42:03,200 --> 00:42:06,920 Speaker 15: Thirty two states in the United States reported that their 727 00:42:06,960 --> 00:42:11,200 Speaker 15: students speak over two hundred languages, and these thirty two 728 00:42:11,200 --> 00:42:15,200 Speaker 15: states have a deficit of bilingual resource teachers. So there 729 00:42:15,239 --> 00:42:17,560 Speaker 15: are all these opportunities out there in terms of what's 730 00:42:17,560 --> 00:42:21,880 Speaker 15: already working in the classroom, but there is limited human resources. 731 00:42:21,960 --> 00:42:25,040 Speaker 7: The number of districts that can now teach languages like 732 00:42:25,200 --> 00:42:30,399 Speaker 7: Mandarin or Arabic or you know, French and Spanish, where 733 00:42:30,560 --> 00:42:33,279 Speaker 7: historically would have required teaching a native or hiring a 734 00:42:33,360 --> 00:42:36,960 Speaker 7: native speaker of that language and as a result been 735 00:42:37,040 --> 00:42:40,200 Speaker 7: much more, much more difficult to achieve for a district. 736 00:42:40,200 --> 00:42:44,520 Speaker 7: The ability to do that now is immediate. Districts can 737 00:42:44,920 --> 00:42:49,000 Speaker 7: leverage AI tools to teach a new foreign subject or 738 00:42:49,040 --> 00:42:51,000 Speaker 7: a foreign language as a subject in a way that 739 00:42:51,040 --> 00:42:52,160 Speaker 7: they couldn't have in the past. 740 00:42:53,280 --> 00:42:56,279 Speaker 1: For all its advantages, the widespread use of AI in 741 00:42:56,280 --> 00:42:59,759 Speaker 1: the classroom is not without its risks, risks we've all 742 00:42:59,800 --> 00:43:04,000 Speaker 1: heard about with other applications of AI, things like hallucinations 743 00:43:04,120 --> 00:43:07,680 Speaker 1: and bias, but also risks that are unique to education. 744 00:43:08,480 --> 00:43:11,120 Speaker 1: Pew study from last year showed that a quarter of 745 00:43:11,160 --> 00:43:14,480 Speaker 1: teachers think AI tools do more harm than good in 746 00:43:14,520 --> 00:43:19,120 Speaker 1: the classroom. Only six percent say AI is beneficial. 747 00:43:19,160 --> 00:43:22,319 Speaker 15: Any piece of AI that is generating text anywhere where 748 00:43:22,360 --> 00:43:25,200 Speaker 15: it is student facing. You have to be careful about 749 00:43:25,600 --> 00:43:30,080 Speaker 15: the implications hallucinations have in this specific context of education. 750 00:43:30,440 --> 00:43:32,680 Speaker 15: The goal in the classroom is for us to help 751 00:43:32,760 --> 00:43:39,520 Speaker 15: students learn. Hallucination is absolutely detrimental to students learning any 752 00:43:39,520 --> 00:43:43,560 Speaker 15: sort of misinformation it could. I would also talk about 753 00:43:43,680 --> 00:43:51,160 Speaker 15: potential biases, potential toxic content, the kinds of any kind 754 00:43:51,200 --> 00:43:54,560 Speaker 15: of text that the AI is generating that is harmful 755 00:43:54,680 --> 00:43:56,880 Speaker 15: for students learning and well being. 756 00:43:57,320 --> 00:44:00,520 Speaker 1: But perhaps a greater risk with AI applied to classroom 757 00:44:00,719 --> 00:44:03,440 Speaker 1: is the risk of expecting too much of it. 758 00:44:03,520 --> 00:44:06,839 Speaker 7: AI is a tool like anything else. It has very 759 00:44:06,920 --> 00:44:11,359 Speaker 7: very real limitations. The fact that it's discursive and it 760 00:44:11,840 --> 00:44:16,400 Speaker 7: resembles an interaction with a human in some isolated cases 761 00:44:17,440 --> 00:44:20,520 Speaker 7: makes it feel a lot more advanced than technologies we've 762 00:44:20,520 --> 00:44:24,319 Speaker 7: interacted with in the past, But as of today, AI's 763 00:44:24,320 --> 00:44:28,040 Speaker 7: boundaries are still limited and it's very easy to overstate. 764 00:44:28,120 --> 00:44:31,040 Speaker 7: There's a lot of hype. It's very easy to feed 765 00:44:31,080 --> 00:44:35,040 Speaker 7: into a narrative of fear, but the reality is AI 766 00:44:35,160 --> 00:44:39,000 Speaker 7: is still answering fairly discreete questions being asked by students 767 00:44:39,080 --> 00:44:42,560 Speaker 7: and supporting teachers with the answering of those questions. I 768 00:44:42,600 --> 00:44:47,040 Speaker 7: think students interacting with computers one on one is definitely 769 00:44:47,080 --> 00:44:53,759 Speaker 7: something that schools, district, teachers, parents, students themselves need to 770 00:44:53,800 --> 00:44:59,160 Speaker 7: be careful of. It can take students out of interpersonal relationships, 771 00:44:59,200 --> 00:45:02,200 Speaker 7: It can all students abilities to interact with their peers. 772 00:45:03,320 --> 00:45:06,000 Speaker 7: So AI is by no means a panacea. 773 00:45:06,200 --> 00:45:09,240 Speaker 15: Over ninety percent of innovation in AI for education fails. 774 00:45:09,680 --> 00:45:12,279 Speaker 15: I believe that it is because there is a They 775 00:45:12,320 --> 00:45:15,480 Speaker 15: don't do not consider what's the kind of practices that's 776 00:45:15,480 --> 00:45:17,720 Speaker 15: happening in the classroom, and it's built in this black box. 777 00:45:18,360 --> 00:45:21,880 Speaker 15: Second is also often in computer science. I am I 778 00:45:21,920 --> 00:45:25,160 Speaker 15: have a background in computer science. Often we make oversimplifying 779 00:45:25,360 --> 00:45:28,319 Speaker 15: assumptions about real world context. So we are sort of 780 00:45:28,480 --> 00:45:31,799 Speaker 15: assuming that these factors do not play a role, and 781 00:45:31,840 --> 00:45:35,280 Speaker 15: we build a system for this ideal context. But classroom, 782 00:45:35,320 --> 00:45:38,879 Speaker 15: real world classrooms are far from ideal. Students have very 783 00:45:38,920 --> 00:45:42,200 Speaker 15: different needs. There is a lot of learner variability. They 784 00:45:42,239 --> 00:45:45,400 Speaker 15: come in with a lot of preconceptions that often is 785 00:45:45,520 --> 00:45:47,040 Speaker 15: hard for us to catch right away. 786 00:45:47,520 --> 00:45:50,400 Speaker 1: So with all the hype and a fair amount of failure, 787 00:45:50,920 --> 00:45:54,839 Speaker 1: how can an investor short out which AI education applications 788 00:45:54,880 --> 00:45:56,160 Speaker 1: have the most promise. 789 00:45:56,760 --> 00:45:59,520 Speaker 15: The AI hype certainly has led to a lot of 790 00:45:59,520 --> 00:46:04,359 Speaker 15: funding for what I would call as systems that are generated. 791 00:46:06,040 --> 00:46:08,279 Speaker 15: To put a lipstick on the pig, is that the 792 00:46:08,280 --> 00:46:13,000 Speaker 15: phrase if you haven't thought about fundamentally, how are you 793 00:46:13,040 --> 00:46:15,759 Speaker 15: going to improve education? And you're only coming from the 794 00:46:15,880 --> 00:46:18,080 Speaker 15: point of view, we have this AI tool and we're 795 00:46:18,120 --> 00:46:20,759 Speaker 15: going to find something to apply to It's not going 796 00:46:20,800 --> 00:46:24,279 Speaker 15: to work. It's yet another fancy tool, but the underlying 797 00:46:24,400 --> 00:46:27,200 Speaker 15: things that has been broken in education continues to be. 798 00:46:27,160 --> 00:46:33,280 Speaker 1: Broken, whether it's investing or teaching or learning. Everyone agrees 799 00:46:33,360 --> 00:46:35,640 Speaker 1: that in the end, it all comes back to the 800 00:46:35,719 --> 00:46:37,280 Speaker 1: teachers themselves. 801 00:46:37,560 --> 00:46:39,720 Speaker 7: I think that's the real promise of AI. It allows 802 00:46:39,760 --> 00:46:41,759 Speaker 7: teachers to do what they do best. When you think 803 00:46:41,800 --> 00:46:44,799 Speaker 7: of your favorite teacher, you don't think of, you know, 804 00:46:44,880 --> 00:46:47,120 Speaker 7: the teacher who taught derivatives the best. Do you think 805 00:46:47,120 --> 00:46:50,760 Speaker 7: of the teacher who encouraged you, who made you feel smart, 806 00:46:50,800 --> 00:46:54,120 Speaker 7: who made you feel capable, who made you feel like 807 00:46:54,160 --> 00:46:56,839 Speaker 7: you could learn anything and accomplish anything. And I think 808 00:46:56,880 --> 00:47:01,000 Speaker 7: that's where teachers really shine. That's where is really a 809 00:47:01,080 --> 00:47:03,480 Speaker 7: democratizing power. I would say in education. 810 00:47:03,400 --> 00:47:05,680 Speaker 15: There is something about that human intent. There is something 811 00:47:05,719 --> 00:47:10,000 Speaker 15: about a human being caring about a child, about a student, 812 00:47:10,320 --> 00:47:12,480 Speaker 15: which I don't think AI is able to do that. 813 00:47:12,880 --> 00:47:13,880 Speaker 2: My dad just a few. 814 00:47:13,719 --> 00:47:16,640 Speaker 16: Years ago, he changed a headlight in the car and 815 00:47:17,040 --> 00:47:18,479 Speaker 16: I'm like, why don't you take it this moere. 816 00:47:18,360 --> 00:47:18,759 Speaker 2: To get it done? 817 00:47:18,760 --> 00:47:20,800 Speaker 16: He said, I'll watch the YouTube video and it taught 818 00:47:20,800 --> 00:47:22,680 Speaker 16: me how to do it. So I think that we 819 00:47:22,760 --> 00:47:25,920 Speaker 16: have some dispensations that are happening in education right now 820 00:47:25,920 --> 00:47:29,319 Speaker 16: where some shifts are happening. Teachers are not being minimized, 821 00:47:29,600 --> 00:47:31,640 Speaker 16: but our roles are changing a little bit to where 822 00:47:31,680 --> 00:47:34,640 Speaker 16: we're guiding students to learning and we're able to personalize 823 00:47:34,640 --> 00:47:36,360 Speaker 16: the learning more because we have the time to be 824 00:47:36,400 --> 00:47:39,240 Speaker 16: able to do that. 825 00:47:39,239 --> 00:47:41,120 Speaker 1: That does it for us Here at Wall Street Week, 826 00:47:41,280 --> 00:47:44,360 Speaker 1: I'm David Weston. See you next week for more stories 827 00:47:44,400 --> 00:48:00,200 Speaker 1: of capitalism.