1 00:00:02,520 --> 00:00:08,280 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:20,040 --> 00:00:23,000 Speaker 2: This is Wall Street Week. I'm David Weston, bringing you 3 00:00:23,079 --> 00:00:26,720 Speaker 2: stories of capitalism. As the United States takes away the 4 00:00:26,800 --> 00:00:29,960 Speaker 2: welcome matt for immigrants coming here to work, Japan puts 5 00:00:30,000 --> 00:00:33,320 Speaker 2: it out, figuring out ways to accommodate those from nearby 6 00:00:33,440 --> 00:00:38,280 Speaker 2: Vietnam and far away Brazil. Plus, the biggest change coming 7 00:00:38,320 --> 00:00:40,960 Speaker 2: to your life may be one you don't even notice, 8 00:00:41,240 --> 00:00:43,680 Speaker 2: as those trucks you see carrying cargo all over the 9 00:00:43,720 --> 00:00:48,080 Speaker 2: country lose their human drivers. And we have seen what 10 00:00:48,120 --> 00:00:51,199 Speaker 2: AI is doing right now for doctors and for teachers. 11 00:00:51,680 --> 00:00:53,600 Speaker 2: This week we look at what it's doing for the 12 00:00:53,760 --> 00:00:57,960 Speaker 2: US military, where advanced technology is forcing us to rethink 13 00:00:58,000 --> 00:01:02,480 Speaker 2: how we go about keeping the nations. But we start 14 00:01:02,640 --> 00:01:05,640 Speaker 2: with the state of US banks as earnings began rolling 15 00:01:05,680 --> 00:01:08,760 Speaker 2: in this week. Those earnings give us a snapshot of 16 00:01:08,800 --> 00:01:12,199 Speaker 2: how the financial sector did last quarter, but our special 17 00:01:12,240 --> 00:01:15,319 Speaker 2: contributor Larry Summers takes us through the more sweeping changes 18 00:01:15,560 --> 00:01:16,399 Speaker 2: we are witnessing. 19 00:01:17,680 --> 00:01:21,440 Speaker 3: It suggests that the big banks are building capital and 20 00:01:21,520 --> 00:01:25,399 Speaker 3: are in good shape and have relatively strong balance sheets, 21 00:01:25,480 --> 00:01:28,720 Speaker 3: and it makes the kind of financial crisis that we 22 00:01:28,800 --> 00:01:33,000 Speaker 3: had in two thousand and eight less likely, But I 23 00:01:33,040 --> 00:01:37,560 Speaker 3: don't think it's a fundamentally strong indicator of what's happening 24 00:01:37,720 --> 00:01:43,120 Speaker 3: to the overall economy. I feel pretty good about the 25 00:01:43,200 --> 00:01:46,399 Speaker 3: overall economy and think, if anything, the risks are more 26 00:01:46,480 --> 00:01:48,720 Speaker 3: on the inflation side. 27 00:01:48,960 --> 00:01:50,840 Speaker 2: What do you make of the fact that spreads have 28 00:01:51,040 --> 00:01:54,080 Speaker 2: remained so tight despite everything. I mean, it just seems 29 00:01:54,120 --> 00:01:57,720 Speaker 2: to be a lot of optimism in the credit I worry. 30 00:01:58,040 --> 00:02:04,280 Speaker 3: Less about bank lending than about credit funds lending and 31 00:02:04,320 --> 00:02:10,000 Speaker 3: the growth of non bank private credit, where there's extremely 32 00:02:10,120 --> 00:02:16,440 Speaker 3: rapid growth, lots of new players entering into credit intermediation, 33 00:02:16,800 --> 00:02:24,480 Speaker 3: lots of demand for spread products, much lighter regulation. The 34 00:02:24,520 --> 00:02:27,800 Speaker 3: good news, of course, is that the money that goes 35 00:02:27,880 --> 00:02:31,639 Speaker 3: into those things isn't the kind of fast trigger money 36 00:02:32,000 --> 00:02:37,480 Speaker 3: that tends to sit in banks as uninsured deposits, and 37 00:02:37,600 --> 00:02:39,920 Speaker 3: that means that systemic risk. 38 00:02:40,080 --> 00:02:45,079 Speaker 2: Is lower, So Larry. One of the hallmarks of the 39 00:02:45,120 --> 00:02:49,200 Speaker 2: Trump administration. The second trumpet vistation is deregulation, and specifically 40 00:02:49,200 --> 00:02:52,240 Speaker 2: in the banking area, which is sent a signal I 41 00:02:52,240 --> 00:02:54,920 Speaker 2: think to banks, particularly regional banks. We've seen them some 42 00:02:55,040 --> 00:02:58,960 Speaker 2: consolidation already. Is that a healthy thing? As the practical 43 00:02:59,000 --> 00:03:01,120 Speaker 2: matter to have the regional banks get together. 44 00:03:01,560 --> 00:03:04,160 Speaker 3: Look, I think people always make a mistake when they 45 00:03:04,200 --> 00:03:09,480 Speaker 3: go deregulation good or deregulation bad. I think you have 46 00:03:09,560 --> 00:03:14,360 Speaker 3: to look at the particular area of regulatory policy, and 47 00:03:14,760 --> 00:03:18,760 Speaker 3: when you see a failure to protect market integrity is 48 00:03:18,840 --> 00:03:22,560 Speaker 3: I think we have in some aspects of crypto regulation 49 00:03:23,200 --> 00:03:26,440 Speaker 3: that is, I think a cause for concern. When we 50 00:03:26,520 --> 00:03:33,080 Speaker 3: see huge facilitation of anonymous money, of money that can't 51 00:03:33,160 --> 00:03:36,960 Speaker 3: be traced and that facilitates money laundering, that is I 52 00:03:37,000 --> 00:03:41,240 Speaker 3: think a cause for concern. So I certainly think there 53 00:03:41,280 --> 00:03:45,600 Speaker 3: are areas where we're probably deregulating and moving in the 54 00:03:45,640 --> 00:03:48,760 Speaker 3: wrong direction, or where there would be a case for 55 00:03:48,880 --> 00:03:54,160 Speaker 3: stronger regulation. In the area of consolidation, it's a step 56 00:03:54,200 --> 00:03:59,320 Speaker 3: towards efficiency, because in many cases there are substantial economies 57 00:03:59,360 --> 00:04:04,080 Speaker 3: of scale that can be realized as entities get larger. 58 00:04:04,200 --> 00:04:07,680 Speaker 3: I think it's probably a step towards stability as well, 59 00:04:08,160 --> 00:04:12,320 Speaker 3: when institutions are more profitable, but also when they are 60 00:04:12,800 --> 00:04:19,040 Speaker 3: larger and more diversified and therefore more insulated against risks. 61 00:04:19,200 --> 00:04:24,599 Speaker 3: But welcoming that doesn't mean that these things shouldn't receive 62 00:04:24,839 --> 00:04:27,120 Speaker 3: full antitrust scrutiny. 63 00:04:27,880 --> 00:04:30,680 Speaker 2: While American banks seem to be in good shape, the 64 00:04:30,760 --> 00:04:34,000 Speaker 2: same cannot be said a present Melee of Argentina and 65 00:04:34,040 --> 00:04:38,120 Speaker 2: his PAESO. US Treasury Secretary Scott Bessen helped orchestrate a 66 00:04:38,160 --> 00:04:41,240 Speaker 2: twenty billion dollar swap arrangement to help prop up the 67 00:04:41,240 --> 00:04:44,520 Speaker 2: South American currency, with reports that more may be on 68 00:04:44,600 --> 00:04:48,719 Speaker 2: its way, and intervene directly in currency markets to support 69 00:04:48,720 --> 00:04:51,800 Speaker 2: the Paso. Larry Summers says, it's not the first time 70 00:04:51,880 --> 00:04:55,360 Speaker 2: the US has gotten involved in Latin American finances. You 71 00:04:55,560 --> 00:04:59,560 Speaker 2: actually were there during the Mexican Paso crisis. Beck in 72 00:05:00,320 --> 00:05:03,240 Speaker 2: four What can you tell us about when that makes 73 00:05:03,279 --> 00:05:04,279 Speaker 2: sense and when it does not. 74 00:05:05,160 --> 00:05:08,880 Speaker 3: I'm somebody who's a strong believer that the United States 75 00:05:08,920 --> 00:05:14,719 Speaker 3: has to support global financial stability, that when countries face 76 00:05:15,760 --> 00:05:21,280 Speaker 3: crises involving a sudden loss of liquidity, that their needs 77 00:05:21,680 --> 00:05:24,800 Speaker 3: often to be an active response, and that the United 78 00:05:24,839 --> 00:05:30,400 Speaker 3: States should take a leadership role in such responses. I 79 00:05:30,480 --> 00:05:36,120 Speaker 3: do think that the approach taken so far is new 80 00:05:36,640 --> 00:05:43,240 Speaker 3: and unconventional. First, the United States is going at alone. 81 00:05:43,400 --> 00:05:47,200 Speaker 3: Usually and historically, the United States has wanted to share 82 00:05:47,240 --> 00:05:51,360 Speaker 3: the burdens, share the taking of risks, share the responsibility 83 00:05:51,760 --> 00:05:55,680 Speaker 3: with other countries. In general, the Trump administration take the 84 00:05:55,760 --> 00:06:01,240 Speaker 3: defense area, has been the strongest advocate for other countries 85 00:06:01,320 --> 00:06:05,800 Speaker 3: bearing a share of burdens. Yet in this case, the 86 00:06:05,920 --> 00:06:11,440 Speaker 3: United States is going completely alone, providing all the funds itself, 87 00:06:11,960 --> 00:06:15,919 Speaker 3: not seeking to involve the IMF, not seeking to ask 88 00:06:16,800 --> 00:06:20,440 Speaker 3: other countries. Maybe that will turn out to have been 89 00:06:20,480 --> 00:06:24,360 Speaker 3: a good decision because it will have been a profitable investment, 90 00:06:24,880 --> 00:06:29,320 Speaker 3: or because we will reap some substantial political benefit, but 91 00:06:29,520 --> 00:06:35,200 Speaker 3: it's a very unusual step. Second respect in which this 92 00:06:35,480 --> 00:06:42,280 Speaker 3: is unconventional is the degree of risk that is being taken. 93 00:06:42,960 --> 00:06:49,800 Speaker 3: The United States has never before bought a pegged currency 94 00:06:50,720 --> 00:06:57,160 Speaker 3: under attack of an emerging market country. We would never 95 00:06:57,680 --> 00:07:02,479 Speaker 3: during the period when we were reporting Mexico, have taken 96 00:07:02,520 --> 00:07:06,040 Speaker 3: the degree of risk that was involved in buying the 97 00:07:06,080 --> 00:07:11,240 Speaker 3: Mexican paeso, and certainly not when the Mexican peso was 98 00:07:11,360 --> 00:07:19,000 Speaker 3: being defended and Mexico's reserves were being drained. So this 99 00:07:19,440 --> 00:07:25,560 Speaker 3: is a very speculative approach. There may be agreements that 100 00:07:25,600 --> 00:07:29,080 Speaker 3: are in place that we don't know about publicly that 101 00:07:29,480 --> 00:07:34,560 Speaker 3: make this sounder than it appears. It may be that 102 00:07:34,600 --> 00:07:39,000 Speaker 3: this proves to be a shrewd kind of speculation that 103 00:07:39,240 --> 00:07:46,280 Speaker 3: ultimately taxpayers make money on the investment in the Paso. 104 00:07:46,880 --> 00:07:52,160 Speaker 3: So I'm withholding judgment at this point, but I am 105 00:07:52,280 --> 00:07:56,400 Speaker 3: nervous about the approach that's being pursued. 106 00:07:57,680 --> 00:08:01,360 Speaker 2: Although Summers has concerns about the True administration's lifeline to 107 00:08:01,400 --> 00:08:04,600 Speaker 2: Buenos Aires, he says he's less worried about the US 108 00:08:04,720 --> 00:08:09,920 Speaker 2: throwing its weight behind Argentina's incumbent. President Trump wasn't shy 109 00:08:09,960 --> 00:08:13,440 Speaker 2: about supporting President Javier Milay during a meeting between the 110 00:08:13,480 --> 00:08:15,200 Speaker 2: two at the White House this week. 111 00:08:15,720 --> 00:08:19,040 Speaker 4: We are going to work very much with the president. 112 00:08:19,200 --> 00:08:22,040 Speaker 4: We think he's going to win. He should win, and 113 00:08:22,080 --> 00:08:24,000 Speaker 4: if he does win, we're going to be very helpful. 114 00:08:24,040 --> 00:08:25,600 Speaker 4: And if he doesn't win, we're not going to waste 115 00:08:25,640 --> 00:08:29,480 Speaker 4: our time because you have somebody whose philosophy has no 116 00:08:29,600 --> 00:08:31,920 Speaker 4: chance of making Argentina great again. 117 00:08:32,440 --> 00:08:35,920 Speaker 2: The administration, the Trump instruction, likes the reforms the President 118 00:08:35,920 --> 00:08:38,520 Speaker 2: Melia has put into effect. It's just worried he's going 119 00:08:38,520 --> 00:08:40,120 Speaker 2: to get voted out, that he's going to lose the 120 00:08:40,160 --> 00:08:42,760 Speaker 2: midterms in October once they keep him in office, because 121 00:08:42,760 --> 00:08:45,600 Speaker 2: they're afraid of apparentist regime coming back in. Have we 122 00:08:45,720 --> 00:08:48,080 Speaker 2: seen that before, where in a sense it's almost like 123 00:08:48,120 --> 00:08:50,719 Speaker 2: a political intervention to try to help a candidate we 124 00:08:50,920 --> 00:08:52,120 Speaker 2: like keep office. 125 00:08:52,320 --> 00:08:57,480 Speaker 3: I don't remember something that will show clearly linked in 126 00:08:57,520 --> 00:09:01,200 Speaker 3: that way, but in fairness, I think it would be 127 00:09:01,800 --> 00:09:06,760 Speaker 3: right to say that the Western Alliance was strongly committed 128 00:09:06,840 --> 00:09:11,560 Speaker 3: to Russia in the early mid nineteen nineties, and that 129 00:09:11,720 --> 00:09:15,800 Speaker 3: certainly had something to do with believing that Boris Elson's 130 00:09:15,840 --> 00:09:22,000 Speaker 3: government was better than a communist alternative. I think it's 131 00:09:22,040 --> 00:09:26,240 Speaker 3: fair to say, going back to the Marshall Plan, that 132 00:09:26,559 --> 00:09:31,000 Speaker 3: some part of the motivation for the Marshall Plan had 133 00:09:31,040 --> 00:09:34,760 Speaker 3: to do with supporting the good guys at a time 134 00:09:34,840 --> 00:09:40,080 Speaker 3: when there were Stalinist elements who were there in Western Europe. 135 00:09:40,559 --> 00:09:43,280 Speaker 3: So I don't think it would be right to say 136 00:09:43,320 --> 00:09:52,000 Speaker 3: that politics have never entered into US decisions about support programs. 137 00:09:52,679 --> 00:09:56,240 Speaker 3: There is a kind of proximity to an election in 138 00:09:56,320 --> 00:10:02,280 Speaker 3: this case that is unusual, and the degree of commitment 139 00:10:02,920 --> 00:10:10,880 Speaker 3: to a currency of peg is something that is very unusual. 140 00:10:12,440 --> 00:10:15,120 Speaker 2: Coming up, immigration comes to one of the places you 141 00:10:15,200 --> 00:10:18,040 Speaker 2: might not expect it. We look at Japan's approach to 142 00:10:18,080 --> 00:10:21,120 Speaker 2: welcoming workers from around the world to make up for 143 00:10:21,240 --> 00:10:34,520 Speaker 2: its dwindling population. This is a story about the two 144 00:10:34,559 --> 00:10:38,160 Speaker 2: way street of immigration. The United States is known as 145 00:10:38,160 --> 00:10:41,760 Speaker 2: a melting pot created by waves of immigrants. But while 146 00:10:41,840 --> 00:10:44,920 Speaker 2: the US turns away from foreign workers, it turns out 147 00:10:44,960 --> 00:10:49,440 Speaker 2: that Japan, historically much more homogeneous, has its own version 148 00:10:49,520 --> 00:10:52,920 Speaker 2: of growth through immigration. Our colleague chrry On reports that 149 00:10:53,000 --> 00:10:56,199 Speaker 2: making it work requires both the immigrants and the natives 150 00:10:56,520 --> 00:10:57,640 Speaker 2: to make some adjustments. 151 00:11:00,679 --> 00:11:05,360 Speaker 1: At Foxtown Supermarket, shoppers can find everything from Brazilian coffee 152 00:11:05,520 --> 00:11:08,839 Speaker 1: to delicacies like kurana. But this is not a store 153 00:11:08,880 --> 00:11:13,199 Speaker 1: in stall Pawlo. This is Homi Danji in Aichi Prefecture 154 00:11:13,440 --> 00:11:17,360 Speaker 1: in Japan. Bloomberg's Yoshi Nohara has been reporting on this 155 00:11:17,640 --> 00:11:19,040 Speaker 1: unlikely melting pot. 156 00:11:19,840 --> 00:11:23,760 Speaker 5: It's only still a couple of percentage of the total population. 157 00:11:23,880 --> 00:11:26,200 Speaker 6: It's not big. But here's the point. 158 00:11:26,720 --> 00:11:30,920 Speaker 5: It's such an aging society here and population shrinking, and 159 00:11:31,040 --> 00:11:35,000 Speaker 5: people coming in tend to be young, much younger than 160 00:11:35,040 --> 00:11:38,079 Speaker 5: most Japanese people. So in other words, these people are 161 00:11:38,120 --> 00:11:41,800 Speaker 5: more visible. They're bumping to these people, you know, on 162 00:11:41,920 --> 00:11:46,160 Speaker 5: trains or you know, going somewhere, and they noticed that 163 00:11:46,200 --> 00:11:50,520 Speaker 5: these people are not just working factories but also serving 164 00:11:50,640 --> 00:11:54,319 Speaker 5: you face to face at convenience stores or restaurants that 165 00:11:54,520 --> 00:11:59,280 Speaker 5: become more visible. So as they experienced some kind of 166 00:12:00,080 --> 00:12:04,760 Speaker 5: comfortable moments, they started thinking, well, is this Japan that 167 00:12:04,880 --> 00:12:05,160 Speaker 5: I know? 168 00:12:05,920 --> 00:12:09,720 Speaker 1: The diversity that surprises the rest of us doesn't surprise 169 00:12:09,800 --> 00:12:13,120 Speaker 1: the residents of a city called Toyota, named after the 170 00:12:13,160 --> 00:12:16,240 Speaker 1: automaker because of its presence in the city since the 171 00:12:16,360 --> 00:12:17,400 Speaker 1: nineteen fifties. 172 00:12:18,120 --> 00:12:21,360 Speaker 5: Most of them don't work for Toyota itself. They work 173 00:12:21,440 --> 00:12:26,679 Speaker 5: for smaller companies that supply for Toyota, and the experience 174 00:12:26,760 --> 00:12:30,360 Speaker 5: has been I would say that it's painful. There's a 175 00:12:30,400 --> 00:12:35,760 Speaker 5: housing complex still hosting many Brazilians. It's an isolation that 176 00:12:35,920 --> 00:12:40,920 Speaker 5: the systematic shortfalls cost for these people. But at the 177 00:12:40,920 --> 00:12:44,959 Speaker 5: same time I see some hopes among newcomers. 178 00:12:45,400 --> 00:12:50,000 Speaker 1: Toyota City is a symptom of Japan's demographics issue. In 179 00:12:50,080 --> 00:12:54,080 Speaker 1: twenty twenty four, Japan's population declined by more than nine 180 00:12:54,200 --> 00:12:59,119 Speaker 1: hundred thousand people, marking the sixteenth consecutive year of contraction. 181 00:13:00,000 --> 00:13:02,880 Speaker 1: At the same time, the number of people aged sixty 182 00:13:02,880 --> 00:13:07,240 Speaker 1: five and over is increasing, leaving employers with fewer working 183 00:13:07,280 --> 00:13:12,040 Speaker 1: age individuals. Over the years, the foreign worker program has 184 00:13:12,080 --> 00:13:14,520 Speaker 1: become essential for the Japanese economy. 185 00:13:15,120 --> 00:13:19,280 Speaker 5: In reality, it's been used as a backdoor for Japan 186 00:13:19,600 --> 00:13:21,560 Speaker 5: to secure cheap workers. 187 00:13:21,720 --> 00:13:24,360 Speaker 6: It's all about money, to be honest. 188 00:13:24,920 --> 00:13:28,560 Speaker 5: And there's been a lot of criticism because you know, 189 00:13:29,200 --> 00:13:33,360 Speaker 5: the program does not allow people to come with their 190 00:13:33,360 --> 00:13:37,440 Speaker 5: family members. And there also there has been cases being 191 00:13:37,480 --> 00:13:41,880 Speaker 5: reported that these trainees, you know, their passports have been 192 00:13:41,920 --> 00:13:44,520 Speaker 5: taken away from them so that they don't run away, 193 00:13:45,240 --> 00:13:48,880 Speaker 5: and the US Department State Department to once criticize that 194 00:13:49,280 --> 00:13:53,640 Speaker 5: this program, you know, is allowing some workers to experience 195 00:13:53,880 --> 00:13:56,079 Speaker 5: forced to labor conditions. 196 00:13:55,600 --> 00:13:57,520 Speaker 6: Which is such a strong term. 197 00:13:57,960 --> 00:14:03,240 Speaker 5: But you know, it's been because you know, once, as 198 00:14:03,280 --> 00:14:06,640 Speaker 5: I said, Japan needs these people, and it's been a 199 00:14:06,760 --> 00:14:12,480 Speaker 5: kind of addiction among small companies using this program because 200 00:14:12,520 --> 00:14:16,320 Speaker 5: they cannot find cheapkas anywhere else in Japan. 201 00:14:16,920 --> 00:14:19,480 Speaker 1: These days, the share of foreign workers in Japan is 202 00:14:19,520 --> 00:14:22,560 Speaker 1: at a record high, making up three percent of the 203 00:14:22,600 --> 00:14:26,960 Speaker 1: population with three point eight million immigrants. It's still a 204 00:14:27,000 --> 00:14:29,960 Speaker 1: far cry from the fifteen percent of the population the 205 00:14:30,040 --> 00:14:34,640 Speaker 1: foreigners comprise in the United States. For workers, the low 206 00:14:34,720 --> 00:14:38,960 Speaker 1: rate of immigration leads to friction on both sides. With 207 00:14:39,080 --> 00:14:43,400 Speaker 1: the rise of the Nationalist Party sanseto resentment towards foreigners 208 00:14:43,440 --> 00:14:47,640 Speaker 1: in Japan has grown, leaving the ruling Liberal Democratic Party 209 00:14:48,040 --> 00:14:51,160 Speaker 1: with the difficult task of managing the tensions. 210 00:14:51,560 --> 00:14:54,520 Speaker 7: People will feel like, oh, this is not Japan, is 211 00:14:54,560 --> 00:14:57,760 Speaker 7: this some other country. So it is there is a 212 00:14:57,800 --> 00:15:00,360 Speaker 7: concern a but the Japanese people with the two much, 213 00:15:02,680 --> 00:15:05,920 Speaker 7: you know, concentration of the foreign people in certain areas. 214 00:15:06,080 --> 00:15:10,680 Speaker 5: Ahead of the July national election, there was a party 215 00:15:10,720 --> 00:15:15,360 Speaker 5: called the Sanseto upstat right wing party is running this 216 00:15:15,520 --> 00:15:19,920 Speaker 5: campaign with the slogans like Japanese fast and. 217 00:15:19,960 --> 00:15:21,720 Speaker 6: Don't break Japan any further. 218 00:15:22,480 --> 00:15:27,560 Speaker 5: Those messages resonated a lot, especially online. 219 00:15:28,280 --> 00:15:30,880 Speaker 1: But Masada says that as important as it is for 220 00:15:30,880 --> 00:15:34,280 Speaker 1: foreigners to adjust to the Japanese way of life, it 221 00:15:34,360 --> 00:15:37,720 Speaker 1: has to be a two way street. It's just as 222 00:15:37,760 --> 00:15:41,040 Speaker 1: important for Japanese people to learn to live and work 223 00:15:41,120 --> 00:15:42,640 Speaker 1: with new cultures. 224 00:15:43,840 --> 00:15:46,840 Speaker 2: Up next, a driver may not be coming to a 225 00:15:46,880 --> 00:15:50,200 Speaker 2: cargo truck near you. We look into the developing story 226 00:15:50,280 --> 00:16:05,640 Speaker 2: of autonomous vehicles for delivering your goods. This is a 227 00:16:05,680 --> 00:16:08,920 Speaker 2: story about the dog that didn't bark, or at least 228 00:16:09,040 --> 00:16:12,160 Speaker 2: that hasn't done much barking yet. When it comes to 229 00:16:12,200 --> 00:16:16,520 Speaker 2: autonomous vehicles, right now, robotaxis are all the rage. Tech 230 00:16:16,560 --> 00:16:20,000 Speaker 2: giants and startups alike are putting hundreds of millions of 231 00:16:20,040 --> 00:16:23,640 Speaker 2: dollars towards creating the perfect driverless cab. But while the 232 00:16:23,720 --> 00:16:27,440 Speaker 2: world focuses on self driving sedan's creeping through city streets, 233 00:16:27,840 --> 00:16:30,320 Speaker 2: our colleague Ed Ludlow brings us the story of a 234 00:16:30,440 --> 00:16:33,760 Speaker 2: quieter race playing out on the open road, the race 235 00:16:33,800 --> 00:16:35,800 Speaker 2: to automate the trucking industry. 236 00:16:40,520 --> 00:16:44,960 Speaker 8: No justice, no fee. We're the precipice of a massive 237 00:16:45,000 --> 00:16:49,360 Speaker 8: industrial revolution, and autonomous trucking will be the crux of that. 238 00:16:49,800 --> 00:16:52,200 Speaker 9: It's a job that isn't for everyone, but the one 239 00:16:52,240 --> 00:16:53,440 Speaker 9: that keeps the world. 240 00:16:53,200 --> 00:16:58,080 Speaker 4: Alive at a time of widespread shutdowns. Truck drivers form 241 00:16:58,160 --> 00:17:00,200 Speaker 4: the lifeblood of our economy. 242 00:17:00,520 --> 00:17:03,960 Speaker 9: During the pandemic, truck drivers were hailed as essentral heroes. 243 00:17:04,440 --> 00:17:07,440 Speaker 9: Now five years later, the industry that powered the economy 244 00:17:07,560 --> 00:17:11,280 Speaker 9: is shifting gears towards self driving trucks. The question is 245 00:17:11,320 --> 00:17:15,920 Speaker 9: where the future becomes reality. Before workers and regulators. 246 00:17:15,320 --> 00:17:17,080 Speaker 10: Pushed back, this is the game time. 247 00:17:18,640 --> 00:17:21,240 Speaker 9: Everything around you was most likely brought to you by 248 00:17:21,280 --> 00:17:24,720 Speaker 9: a truck. Just within the United States, trucks account for 249 00:17:24,760 --> 00:17:28,359 Speaker 9: nearly eighty percent of intrastate shipments and are involved in 250 00:17:28,400 --> 00:17:31,880 Speaker 9: the supply chain of all top ten commodities. But now 251 00:17:32,040 --> 00:17:34,639 Speaker 9: the nearly one trillion dollar a year freight industry in 252 00:17:34,640 --> 00:17:37,720 Speaker 9: the US is starting to look very different. If highways 253 00:17:37,760 --> 00:17:40,719 Speaker 9: go driver less, the way goods move will change at 254 00:17:40,760 --> 00:17:43,480 Speaker 9: a national and even international scale. 255 00:17:43,760 --> 00:17:47,479 Speaker 11: ATAMAS trucking has a ton of economic advantages. We can 256 00:17:47,600 --> 00:17:49,600 Speaker 11: drive twenty two hours without stopping. 257 00:17:49,960 --> 00:17:53,760 Speaker 9: OSSA. Fisher is president of Aurora, a company that started 258 00:17:53,800 --> 00:17:57,000 Speaker 9: as a general autonomous driving platform back in twenty seventeen. 259 00:17:57,280 --> 00:18:00,960 Speaker 9: Today it runs fully driverless eighteen wheelers on public freeways 260 00:18:01,119 --> 00:18:02,760 Speaker 9: between Dallas and Houston. 261 00:18:03,200 --> 00:18:06,840 Speaker 11: We've actually found fuel efficiency as well, given the consistency 262 00:18:06,880 --> 00:18:09,480 Speaker 11: of the AARA driver. So we've drove in the same 263 00:18:09,520 --> 00:18:12,880 Speaker 11: freight both manually and in autonomy and seen fourteen percent 264 00:18:13,080 --> 00:18:16,239 Speaker 11: fuel savings, which is really fantastic when that is one 265 00:18:16,320 --> 00:18:19,040 Speaker 11: of the main cost drivers. And then, of course insurance. 266 00:18:19,800 --> 00:18:22,920 Speaker 11: We believe that over time, with our safety record three 267 00:18:22,920 --> 00:18:27,280 Speaker 11: million miles in autonomy already fifty thousand completely driverless miles 268 00:18:27,440 --> 00:18:30,600 Speaker 11: with a perfect safety record, that insurance costs over time 269 00:18:30,720 --> 00:18:32,040 Speaker 11: will also be very favorable. 270 00:18:32,760 --> 00:18:35,520 Speaker 8: Let's take a step back and talk about the US 271 00:18:35,560 --> 00:18:38,959 Speaker 8: trucking industry at present, that is about a nine hundred 272 00:18:38,960 --> 00:18:43,359 Speaker 8: billion dollar market. Maybe the most important pieces are the 273 00:18:43,359 --> 00:18:46,640 Speaker 8: long haul trucking market that's about three hundred billion dollars, 274 00:18:46,960 --> 00:18:50,560 Speaker 8: and then the regional or middle miles segment is three 275 00:18:50,680 --> 00:18:52,160 Speaker 8: hundred billion dollars. 276 00:18:53,160 --> 00:18:58,200 Speaker 9: Santosh Sankos specializes in investing in logistics, transportation technology, and 277 00:18:58,280 --> 00:19:02,439 Speaker 9: autonomous trucking. Goldman expects the self driving truck market to 278 00:19:02,480 --> 00:19:06,399 Speaker 9: increase by more than thirteen thousand percent over the next 279 00:19:06,440 --> 00:19:09,160 Speaker 9: four years, from a mere one hundred and thirty million 280 00:19:09,200 --> 00:19:12,639 Speaker 9: dollars in twenty twenty six to eighteen billion dollars in 281 00:19:12,680 --> 00:19:13,439 Speaker 9: twenty thirty. 282 00:19:14,000 --> 00:19:18,880 Speaker 8: About twenty four states have already established and opened their 283 00:19:18,920 --> 00:19:24,320 Speaker 8: doors for autonomous freight operations. Texas is out in front 284 00:19:24,400 --> 00:19:28,480 Speaker 8: in terms of engendering a regulatory environment where technologists can come, 285 00:19:29,080 --> 00:19:31,120 Speaker 8: harden and scale these deployments. 286 00:19:31,119 --> 00:19:35,159 Speaker 9: Out Aurora launched its first fully autonomous long haul commercial 287 00:19:35,240 --> 00:19:38,359 Speaker 9: rides from Dallas, Texas to Houston back in April twenty 288 00:19:38,400 --> 00:19:41,400 Speaker 9: twenty five. Our producer got a view from the backseat 289 00:19:41,520 --> 00:19:42,919 Speaker 9: of one of their new trucks. 290 00:19:43,240 --> 00:19:45,760 Speaker 11: What is the best use case for an autonomous truck? 291 00:19:45,800 --> 00:19:48,359 Speaker 11: There are lots but the one that we are really 292 00:19:48,760 --> 00:19:52,600 Speaker 11: putting our strategy behind are the long haul. So the 293 00:19:52,600 --> 00:19:56,879 Speaker 11: ones that exceeded hours of service limitation that you know, 294 00:19:56,920 --> 00:20:00,600 Speaker 11: where we could drive twenty hours straight without stopping, has 295 00:20:01,040 --> 00:20:04,280 Speaker 11: tremendous benefits, not just for the efficiency and utilization of 296 00:20:04,320 --> 00:20:07,520 Speaker 11: the truck, which is very important, but important goods that 297 00:20:07,560 --> 00:20:12,600 Speaker 11: need to be expedited, everything from medical supplies to perishable 298 00:20:12,640 --> 00:20:15,280 Speaker 11: fruits and vegetables that need to get where they're going 299 00:20:15,400 --> 00:20:15,959 Speaker 11: very quickly. 300 00:20:16,440 --> 00:20:20,280 Speaker 9: For now, a human observer still rides along in Aurora's trucks. 301 00:20:20,320 --> 00:20:24,080 Speaker 9: The wheel turns itself, but the trust isn't quite there yet. 302 00:20:24,280 --> 00:20:26,800 Speaker 11: We should explain what we're actually doing right now. So 303 00:20:26,840 --> 00:20:29,520 Speaker 11: we are in autonomy, and you can see this is 304 00:20:29,560 --> 00:20:32,840 Speaker 11: a ride observer, but it really does not touch the 305 00:20:32,920 --> 00:20:37,000 Speaker 11: vehicle at all. You see there's no hands on the wheel, 306 00:20:37,080 --> 00:20:40,440 Speaker 11: no feet on the pedals, and the observer is here 307 00:20:40,520 --> 00:20:44,359 Speaker 11: as a request from Pekar, which is the vehicle manufacture, 308 00:20:44,920 --> 00:20:47,760 Speaker 11: but is not required at all for the AURA driver. 309 00:20:47,920 --> 00:20:49,719 Speaker 11: So we are in full autonomy right now. We are 310 00:20:49,760 --> 00:20:50,560 Speaker 11: in grabulus mode. 311 00:20:51,000 --> 00:20:53,879 Speaker 12: Some of the sensors are are mid range light ours. 312 00:20:54,040 --> 00:20:57,320 Speaker 12: That's these ones that you see that are around those 313 00:20:57,359 --> 00:20:59,560 Speaker 12: ones can see about two hundred meters down the road 314 00:20:59,600 --> 00:21:02,679 Speaker 12: and what is going on in the environment, things like 315 00:21:02,920 --> 00:21:06,800 Speaker 12: vehicles down the road. Then we have what's proprietary to AURA, 316 00:21:06,880 --> 00:21:09,199 Speaker 12: which is the first light lighter. If you look at 317 00:21:09,200 --> 00:21:12,800 Speaker 12: the top there, it's that rectangular one in the middle, 318 00:21:13,080 --> 00:21:15,120 Speaker 12: and it is the one that allows us to see 319 00:21:15,359 --> 00:21:17,520 Speaker 12: so far down the road and operate at highway speed. 320 00:21:18,440 --> 00:21:20,560 Speaker 9: So what will it take for trust to build to 321 00:21:20,640 --> 00:21:23,080 Speaker 9: the point where trucks don't even have steering wheels or 322 00:21:23,119 --> 00:21:26,560 Speaker 9: brake pedals. Paul Hannapel is working the problem in Europe 323 00:21:26,680 --> 00:21:29,919 Speaker 9: as head of Automotive and Logistics at Bitcom, a German 324 00:21:29,960 --> 00:21:34,480 Speaker 9: digital trade association. He breaks the technology down into five levels, 325 00:21:34,800 --> 00:21:38,200 Speaker 9: the latter three of which are stepping stones to full autonomy. 326 00:21:38,880 --> 00:21:42,400 Speaker 13: Level three means at some point the car takes over 327 00:21:42,440 --> 00:21:45,360 Speaker 13: the control, for example at a motorway when it's following 328 00:21:45,400 --> 00:21:49,480 Speaker 13: another car, and there actually the car has the responsibility 329 00:21:49,600 --> 00:21:52,760 Speaker 13: before example, it can be on your phone, but at 330 00:21:52,800 --> 00:21:56,119 Speaker 13: any moment you have to be able to take over 331 00:21:56,440 --> 00:22:00,880 Speaker 13: or to take back the control. And there's bigger jump afterwards. 332 00:22:00,880 --> 00:22:04,439 Speaker 13: Its level four. There you jump into the car, you 333 00:22:04,520 --> 00:22:07,240 Speaker 13: won't drive it, so you can just go from A 334 00:22:07,480 --> 00:22:11,679 Speaker 13: to B without driving, but it's important you are always 335 00:22:11,680 --> 00:22:15,880 Speaker 13: staying in this operational domain. Level five at the end 336 00:22:16,119 --> 00:22:19,800 Speaker 13: is not even planned that a person could drive this car, 337 00:22:20,119 --> 00:22:23,120 Speaker 13: so there is, for example, no steering wheel in this car. 338 00:22:23,240 --> 00:22:27,880 Speaker 13: The infrastructure of the whole car is made that only software, 339 00:22:28,000 --> 00:22:31,240 Speaker 13: only their technology, is driving the car. There is no 340 00:22:31,640 --> 00:22:35,000 Speaker 13: assistant driver who could even possibly drive the car. 341 00:22:35,560 --> 00:22:38,600 Speaker 9: While the US and China race ahead with real world testing, 342 00:22:38,680 --> 00:22:41,959 Speaker 9: Europe's taking the slow route, writing the rule book first. 343 00:22:42,440 --> 00:22:45,520 Speaker 9: In Germany, autonomous vehicles are not yet fully deployed due 344 00:22:45,560 --> 00:22:49,000 Speaker 9: to strict regulations. Victoria Brossa is a member of the 345 00:22:49,000 --> 00:22:52,680 Speaker 9: German Parliament working to make autonomous vehicles a reality. 346 00:22:53,200 --> 00:22:58,320 Speaker 14: In twenty seventeen, the German government passed the first law 347 00:22:58,720 --> 00:23:03,280 Speaker 14: to allow autonomous driving, and in twenty twenty one there 348 00:23:03,400 --> 00:23:06,879 Speaker 14: was a second law and now we are allowed to 349 00:23:06,960 --> 00:23:10,399 Speaker 14: test autonomous driving in Germany up to level four. In 350 00:23:10,440 --> 00:23:13,760 Speaker 14: the US, the testing is done before the legislation, so 351 00:23:14,000 --> 00:23:18,280 Speaker 14: we see that American companies can test their autonomous vehicles 352 00:23:18,320 --> 00:23:23,120 Speaker 14: on the streets. So we're lacking behind the development in US, 353 00:23:23,440 --> 00:23:26,159 Speaker 14: but we already have the standards in place. 354 00:23:29,320 --> 00:23:31,520 Speaker 15: When it comes to safety. We take it as our 355 00:23:31,600 --> 00:23:35,480 Speaker 15: highest priority. Unlike in the US, where you can launch 356 00:23:35,520 --> 00:23:40,000 Speaker 15: something and afterwards the regulators would come in and see 357 00:23:40,000 --> 00:23:42,439 Speaker 15: if everything was safe, in Europe you first need to 358 00:23:42,480 --> 00:23:43,280 Speaker 15: pass US check. 359 00:23:43,840 --> 00:23:47,360 Speaker 9: Hendrik Kramer is the CEO of fern Ride, a private 360 00:23:47,440 --> 00:23:50,879 Speaker 9: driverless trucking company that's making waves in Germany. It's the 361 00:23:50,920 --> 00:23:54,920 Speaker 9: first company to receive certification for an autonomous terminal tractor. 362 00:23:55,040 --> 00:23:57,680 Speaker 9: Although it's able to operate at ports, it cannot yet 363 00:23:57,720 --> 00:23:59,399 Speaker 9: operate on public roads. 364 00:24:00,080 --> 00:24:04,320 Speaker 15: Coming from a use case that is automating trucks and 365 00:24:04,400 --> 00:24:08,640 Speaker 15: container terminals today, so moving cargo in the horizontal transport 366 00:24:08,680 --> 00:24:11,880 Speaker 15: in confined areas. We have a huge scaling journey ahead 367 00:24:11,920 --> 00:24:15,560 Speaker 15: of us, also into many defense applications, but also trucking 368 00:24:15,560 --> 00:24:18,040 Speaker 15: on public roads. There are situations where it comes to 369 00:24:18,080 --> 00:24:21,480 Speaker 15: its limits. There are situations when it's negotiating with itself 370 00:24:21,680 --> 00:24:24,359 Speaker 15: is it safe enough or not. It always takes the 371 00:24:24,480 --> 00:24:27,480 Speaker 15: conservative approach. We had a very recent example with the 372 00:24:27,520 --> 00:24:30,359 Speaker 15: seagull landing in front of an autonomous truck and a 373 00:24:30,480 --> 00:24:33,320 Speaker 15: human would recognize, hey, this seagull would just fly away 374 00:24:33,359 --> 00:24:36,800 Speaker 15: once it comes closer, but our truck recognized it as 375 00:24:36,840 --> 00:24:40,560 Speaker 15: a large enough object, it stopped for It waited there 376 00:24:40,560 --> 00:24:42,959 Speaker 15: for another ten seconds, and then connected with the remote 377 00:24:42,960 --> 00:24:45,960 Speaker 15: operator that then connected to that vehicles saw on the 378 00:24:45,960 --> 00:24:49,040 Speaker 15: camera stream. Okay, it's just a seagull. Continue your mission, 379 00:24:49,080 --> 00:24:51,800 Speaker 15: autonomous truck, and then re resolved that situation. 380 00:24:52,280 --> 00:24:54,520 Speaker 9: If we can work out things like how to recognize 381 00:24:54,520 --> 00:24:58,320 Speaker 9: a seagull, the benefits from autonomous trucking could be profound, 382 00:24:58,440 --> 00:25:01,040 Speaker 9: both in safety and cost savings. 383 00:25:01,520 --> 00:25:04,760 Speaker 11: Driving is one of the most complex things humans do 384 00:25:05,320 --> 00:25:07,520 Speaker 11: every single day, and we don't even know it, and 385 00:25:07,600 --> 00:25:11,800 Speaker 11: so in that complexity lies an opportunity. If we glance 386 00:25:11,840 --> 00:25:14,199 Speaker 11: down at our phone, if we glance back at our child, 387 00:25:14,840 --> 00:25:17,360 Speaker 11: if heaven forbid, we've had one glass too many to drink. 388 00:25:17,400 --> 00:25:21,960 Speaker 11: When we're out about town, accidents happen and they're dire. 389 00:25:22,080 --> 00:25:26,120 Speaker 11: Forty thousand people dry on American roadways every single year. 390 00:25:26,640 --> 00:25:29,080 Speaker 11: That is like a seven thirty seven falling from the 391 00:25:29,119 --> 00:25:33,640 Speaker 11: sky every week. If a plane file from the sky 392 00:25:33,640 --> 00:25:36,919 Speaker 11: every week, we wouldn't stand for. Autonomous vehicles don't get tired, 393 00:25:38,000 --> 00:25:41,359 Speaker 11: they certainly don't drink, They are always on, and they 394 00:25:41,359 --> 00:25:44,240 Speaker 11: have superhuman capabilities like being able to see all the 395 00:25:44,240 --> 00:25:45,399 Speaker 11: way around their heads. 396 00:25:45,480 --> 00:25:48,240 Speaker 9: Well autonomous trucks could be safer and more reliable. The 397 00:25:48,280 --> 00:25:51,280 Speaker 9: big question for business comes down to cost, which will 398 00:25:51,320 --> 00:25:55,280 Speaker 9: be cheaper man or machine. Goldman Sachs analysts expect the 399 00:25:55,320 --> 00:25:58,440 Speaker 9: cost per mile four driverless trucks to fall from six 400 00:25:58,480 --> 00:26:01,480 Speaker 9: dollars fifteen this year to a dollar eighty nine in 401 00:26:01,600 --> 00:26:05,560 Speaker 9: twenty thirty. Human driven trucks could see their costs increase 402 00:26:05,880 --> 00:26:08,720 Speaker 9: from two dollars sixty one to two dollars eighty per 403 00:26:08,760 --> 00:26:12,359 Speaker 9: mile over the same period as driver wages rise. That 404 00:26:12,400 --> 00:26:14,679 Speaker 9: would put autonomous trucks safely in the lead in the 405 00:26:14,720 --> 00:26:19,440 Speaker 9: near future. But does that mean an entire workforce gets replaced? 406 00:26:20,040 --> 00:26:20,920 Speaker 2: He kind of just needs to. 407 00:26:23,080 --> 00:26:23,600 Speaker 10: I'd like to plit. 408 00:26:23,680 --> 00:26:30,040 Speaker 1: It gives WI pick simply laws kicking. 409 00:26:32,040 --> 00:26:34,800 Speaker 9: Some drivers may be anxious, but trucking companies would say, 410 00:26:34,840 --> 00:26:37,879 Speaker 9: the problem today is that we have too few people 411 00:26:38,000 --> 00:26:41,359 Speaker 9: wanting to drive trucks, not too many. In the many 412 00:26:41,480 --> 00:26:45,600 Speaker 9: years I've been covering autonomous trucking. The technology companies will say, 413 00:26:45,960 --> 00:26:50,159 Speaker 9: look at how difficult it is for the polage companies 414 00:26:50,240 --> 00:26:53,240 Speaker 9: to get drivers. The cynic would say they're just not 415 00:26:53,440 --> 00:26:56,240 Speaker 9: hiring as a hedge against the idea that in the 416 00:26:56,240 --> 00:26:59,000 Speaker 9: future they won't need them anyway. What is the reality 417 00:26:59,080 --> 00:27:01,040 Speaker 9: of that that lay market dynamic. 418 00:27:01,400 --> 00:27:04,480 Speaker 8: What we're effectively seeing is the fact that a lot 419 00:27:04,520 --> 00:27:07,560 Speaker 8: of the truck drivers are aging and retiring out of 420 00:27:07,600 --> 00:27:11,840 Speaker 8: the market. Right You're talking about ages between fifty and 421 00:27:11,920 --> 00:27:16,959 Speaker 8: sixty five. And when you're operating a vehicle, a heavy 422 00:27:17,040 --> 00:27:21,240 Speaker 8: machine on the highways over long distances for extended periods 423 00:27:21,280 --> 00:27:26,320 Speaker 8: of time, experience matters. That's how you engender safety and reliability. 424 00:27:27,000 --> 00:27:28,800 Speaker 10: If you look at the average age of a truck 425 00:27:28,880 --> 00:27:30,879 Speaker 10: drive in the United States, it used to be thirty 426 00:27:30,920 --> 00:27:33,320 Speaker 10: five twenty years ago, it's been forty five ten years ago, 427 00:27:33,359 --> 00:27:34,280 Speaker 10: it's now fifty five. 428 00:27:34,640 --> 00:27:37,920 Speaker 9: Leo Ron is the founder and former CEO at uber Freight. 429 00:27:38,080 --> 00:27:41,280 Speaker 9: He's currently the COO of autonomous trucking company Wabi. 430 00:27:41,640 --> 00:27:44,280 Speaker 10: We have a chronic shortage of trug dive in US 431 00:27:44,320 --> 00:27:47,520 Speaker 10: today and I think for the foreseable future. As you 432 00:27:47,560 --> 00:27:52,679 Speaker 10: start calling self driving, gradually you augment those jobs, you 433 00:27:52,840 --> 00:27:57,640 Speaker 10: fulfill the empty spots in the demand for those jobs. 434 00:27:57,680 --> 00:28:00,320 Speaker 10: Many young folks do not want to take on this job, 435 00:28:00,400 --> 00:28:03,960 Speaker 10: being three hundred days on the road, not being able 436 00:28:04,000 --> 00:28:07,520 Speaker 10: to raise a family, not being able to have predictable 437 00:28:07,560 --> 00:28:09,200 Speaker 10: access to sleep and food. 438 00:28:09,640 --> 00:28:11,639 Speaker 15: Thirty five percent of the truck drivers we have in 439 00:28:11,680 --> 00:28:15,199 Speaker 15: Germany have an Eastern European passport, and therefore, once there 440 00:28:15,200 --> 00:28:18,200 Speaker 15: as a conflict, they need to service in their home countries. 441 00:28:18,240 --> 00:28:23,760 Speaker 15: And while demand is doubling, supply of drivers is coming 442 00:28:23,800 --> 00:28:27,280 Speaker 15: even shorter. And the only answer to that is autonomous trucking. 443 00:28:28,119 --> 00:28:31,800 Speaker 9: Whatever the obstacles or concerns, whether drivers like it or not, 444 00:28:31,920 --> 00:28:34,840 Speaker 9: autonomous trucking is seemingly here to stay and it can 445 00:28:34,840 --> 00:28:36,960 Speaker 9: bring with it a boost for the economies of both 446 00:28:36,960 --> 00:28:39,000 Speaker 9: the United States and Europe. 447 00:28:39,120 --> 00:28:42,479 Speaker 11: The autonomous industry can add over four hundred and fifty 448 00:28:42,680 --> 00:28:46,320 Speaker 11: thousand jobs to the American economy over the next fifteen years. 449 00:28:46,440 --> 00:28:48,880 Speaker 11: So not only are we filling a labor gap, but 450 00:28:48,920 --> 00:28:50,840 Speaker 11: we're also growing the American economy. 451 00:28:51,080 --> 00:28:53,080 Speaker 15: We are in those early days of the Internet, like 452 00:28:53,160 --> 00:28:55,920 Speaker 15: nineteen ninety five something like that, and we will see 453 00:28:55,960 --> 00:28:59,000 Speaker 15: this technology is scaling in Europe in many different applications 454 00:28:59,000 --> 00:29:01,160 Speaker 15: and all over the world. Being on the forefront of 455 00:29:01,200 --> 00:29:02,200 Speaker 15: that is super excited. 456 00:29:02,440 --> 00:29:04,960 Speaker 11: We're really just at the beginning of what I think 457 00:29:05,000 --> 00:29:08,960 Speaker 11: will be a really profound, meaningful shift for America and 458 00:29:09,000 --> 00:29:09,400 Speaker 11: the world. 459 00:29:11,880 --> 00:29:12,360 Speaker 6: Coming up. 460 00:29:12,400 --> 00:29:15,720 Speaker 2: The third story in our series about AI applied to 461 00:29:15,760 --> 00:29:19,200 Speaker 2: the here and the now, how new technology is changing 462 00:29:19,200 --> 00:29:34,400 Speaker 2: the way the Pentagon prepares for war. This is the 463 00:29:34,440 --> 00:29:37,520 Speaker 2: third story in our series on where artificial intelligence is 464 00:29:37,560 --> 00:29:41,360 Speaker 2: already making a difference. Last week it was teachers using 465 00:29:41,400 --> 00:29:44,680 Speaker 2: AI in the classroom. This week it's the effect it's 466 00:29:44,680 --> 00:29:48,240 Speaker 2: having on the huge bureaucracy that is the US military, 467 00:29:48,600 --> 00:29:51,400 Speaker 2: where it's not so much what is already deployed as 468 00:29:51,400 --> 00:29:54,720 Speaker 2: it is changing the entire theory of warfare and how 469 00:29:54,720 --> 00:29:57,320 Speaker 2: to prepare for it. 470 00:29:59,080 --> 00:30:01,000 Speaker 16: The future of war is going to come when you 471 00:30:01,120 --> 00:30:05,080 Speaker 16: take that very large quantity of vehicles and robotic systems 472 00:30:05,360 --> 00:30:07,680 Speaker 16: and marry it with an intelligence that can see, think, 473 00:30:07,680 --> 00:30:08,840 Speaker 16: and act on the battlefield. 474 00:30:09,240 --> 00:30:12,040 Speaker 17: It's really about a changing nature of warfare where we're 475 00:30:12,040 --> 00:30:15,440 Speaker 17: looking at how to incorporate autonomy into all kinds of 476 00:30:15,480 --> 00:30:16,800 Speaker 17: different operations. 477 00:30:17,120 --> 00:30:19,719 Speaker 18: Warfare is going to be fought with a mixture of 478 00:30:20,000 --> 00:30:21,920 Speaker 18: kind of a human and a machine. 479 00:30:22,560 --> 00:30:25,480 Speaker 2: The US military has long put a premium on avoiding 480 00:30:25,520 --> 00:30:29,640 Speaker 2: mistakes at all costs, but with artificial intelligence, the government 481 00:30:29,760 --> 00:30:32,600 Speaker 2: might need to take a page out of Mark Zuckerberg's playbook, 482 00:30:32,800 --> 00:30:35,600 Speaker 2: Move fast and Break Things. If it's going to keep 483 00:30:35,640 --> 00:30:37,240 Speaker 2: up with technological change. 484 00:30:37,320 --> 00:30:40,000 Speaker 18: We as an army have done an incredibly poor job 485 00:30:40,080 --> 00:30:43,920 Speaker 18: over the last three or four decades of just saying, hey, 486 00:30:43,920 --> 00:30:45,480 Speaker 18: if you have an idea that we think could be 487 00:30:45,480 --> 00:30:49,000 Speaker 18: powerful for soldiers, get it to us as quickly as possible. 488 00:30:49,480 --> 00:30:52,320 Speaker 2: The Secretary of the US Army, Dan Driscoll, is the 489 00:30:52,360 --> 00:30:54,880 Speaker 2: point person for getting the Pentagon to take a whole 490 00:30:54,960 --> 00:30:57,760 Speaker 2: new approach, driven in large part by AI. 491 00:30:58,320 --> 00:31:00,239 Speaker 18: The way that we used to require things as an 492 00:31:00,320 --> 00:31:03,200 Speaker 18: army is we'd have sixteen steps that a thing would 493 00:31:03,200 --> 00:31:05,440 Speaker 18: have to go through before we wrote a check, and 494 00:31:05,680 --> 00:31:08,720 Speaker 18: any of the stops along those sixteen could send it 495 00:31:08,800 --> 00:31:11,320 Speaker 18: back to the beginning. And with the incentive structure where 496 00:31:11,760 --> 00:31:15,280 Speaker 18: saying yes was punished and saying no is rewarded, most 497 00:31:15,360 --> 00:31:17,640 Speaker 18: times it would end up in this doom loop of 498 00:31:17,760 --> 00:31:20,600 Speaker 18: kind of forever decision making. And we are collapsing all 499 00:31:20,640 --> 00:31:23,720 Speaker 18: of that down. So everyone will report directly to the 500 00:31:23,800 --> 00:31:25,680 Speaker 18: Chief of Staff of the Army and I, and we 501 00:31:25,720 --> 00:31:29,440 Speaker 18: will hold them accountable for going very quickly and testing 502 00:31:29,480 --> 00:31:30,479 Speaker 18: new things and learning. 503 00:31:32,840 --> 00:31:37,000 Speaker 2: Former US Department of Defense Deputy Secretary Kathleen Hicks agrees 504 00:31:37,160 --> 00:31:40,760 Speaker 2: that these changes are essential, but she also warns that 505 00:31:40,800 --> 00:31:43,920 Speaker 2: they're not easy to what extent is there resistance in 506 00:31:43,920 --> 00:31:47,320 Speaker 2: the Pentagon for really making the changes that AI may require. 507 00:31:47,720 --> 00:31:52,480 Speaker 17: Culture change overall, I think is really our biggest challenge, 508 00:31:52,560 --> 00:31:55,440 Speaker 17: and it isn't just in the Pentagon, it's all across 509 00:31:55,480 --> 00:32:00,520 Speaker 17: the stakeholders on Capitol Hill, throughout industry. There are a 510 00:32:00,560 --> 00:32:04,000 Speaker 17: lot of invested incentives in doing things the way they've 511 00:32:04,000 --> 00:32:08,760 Speaker 17: always been done. But AI is being used, especially away 512 00:32:08,880 --> 00:32:11,960 Speaker 17: from the battlefield, in terms of bringing in lots of 513 00:32:12,080 --> 00:32:14,880 Speaker 17: data and then using AI to quickly sift through that 514 00:32:15,000 --> 00:32:18,440 Speaker 17: data and make sense of it. So if you think back, 515 00:32:18,480 --> 00:32:22,240 Speaker 17: for example, to the wars in Iraq and Afghanistan, where 516 00:32:22,320 --> 00:32:27,520 Speaker 17: Americans face challenges around IEDs, these explosive devices that were 517 00:32:27,520 --> 00:32:31,080 Speaker 17: often buried in the earth, you can imagine how AI 518 00:32:31,480 --> 00:32:35,400 Speaker 17: is already being used to look at pictures visually to 519 00:32:35,520 --> 00:32:39,120 Speaker 17: understand different data that's coming in. We really are just 520 00:32:39,160 --> 00:32:43,400 Speaker 17: at the beginning of that maturation cycle where you could 521 00:32:43,440 --> 00:32:48,880 Speaker 17: imagine different autonomous systems. I think that is the next frontier. 522 00:32:50,600 --> 00:32:53,520 Speaker 2: Ryan Saying is the president and co founder of one 523 00:32:53,560 --> 00:32:55,880 Speaker 2: of the companies hoping to drive the change in the 524 00:32:56,040 --> 00:33:00,280 Speaker 2: US defense Pasture Shield AI is an aerospace and technology 525 00:33:00,360 --> 00:33:04,120 Speaker 2: company moving at breakneck speed to develop the AI power drones. 526 00:33:04,320 --> 00:33:06,320 Speaker 2: Secretary Driscoll says he needs. 527 00:33:06,880 --> 00:33:10,680 Speaker 16: For the last twenty years, adversaries have modernized and enhanced 528 00:33:10,720 --> 00:33:15,959 Speaker 16: their defense capabilities or their war fighting capabilities, and our 529 00:33:16,000 --> 00:33:19,840 Speaker 16: ability to deter conflict in the future depends on the 530 00:33:19,880 --> 00:33:23,719 Speaker 16: adoption of new technologies to make our war fighters more effective, 531 00:33:23,880 --> 00:33:26,400 Speaker 16: and chief among them is AI and autonomy. 532 00:33:26,680 --> 00:33:30,000 Speaker 2: What does AI make available but otherwise you would not 533 00:33:30,120 --> 00:33:31,400 Speaker 2: have from other technology. 534 00:33:31,560 --> 00:33:33,440 Speaker 16: I think the most fundamental thing that it does is 535 00:33:33,520 --> 00:33:38,120 Speaker 16: it enables the deployment of effective mass on the battlefield. 536 00:33:38,640 --> 00:33:43,680 Speaker 16: You can see in Ukraine millions upon millions of drones 537 00:33:43,720 --> 00:33:46,600 Speaker 16: and missiles being produced, but they're limited in their ability 538 00:33:46,640 --> 00:33:49,160 Speaker 16: to see, think and act based on what's going on 539 00:33:49,240 --> 00:33:51,640 Speaker 16: in the battlefield. They might be remote controlled by a 540 00:33:51,720 --> 00:33:54,800 Speaker 16: very focused operator who's connected to them via fiber optic cable. 541 00:33:55,160 --> 00:33:58,640 Speaker 16: But this huge volume of robotic systems, whether they're drones, 542 00:33:58,720 --> 00:34:01,920 Speaker 16: land vehicles or both surrounder see vehicles don't have their 543 00:34:01,920 --> 00:34:04,200 Speaker 16: own ability to see, think and act on the battlefield, 544 00:34:04,240 --> 00:34:08,719 Speaker 16: and then therefore their effectiveness is limited. The future of 545 00:34:08,760 --> 00:34:11,319 Speaker 16: war is going to come when you take that very 546 00:34:11,480 --> 00:34:15,520 Speaker 16: large quantity of vehicles and robotic systems and marry it 547 00:34:15,600 --> 00:34:17,680 Speaker 16: with an intelligence that can see, think, and act on 548 00:34:17,680 --> 00:34:19,360 Speaker 16: the battlefield so that it is effective. 549 00:34:20,080 --> 00:34:22,960 Speaker 18: If you think of having to defend against a swarm 550 00:34:23,040 --> 00:34:26,920 Speaker 18: of one thousand incoming drones, a human brain is not 551 00:34:27,040 --> 00:34:30,400 Speaker 18: capable of pulling off that decision making at that scale 552 00:34:30,440 --> 00:34:34,080 Speaker 18: and the speed required. It's a really complex problem that 553 00:34:34,280 --> 00:34:36,799 Speaker 18: just human beings are not well suited to answer on 554 00:34:36,840 --> 00:34:38,960 Speaker 18: their own. And then if you think that you're in 555 00:34:39,000 --> 00:34:42,719 Speaker 18: a wartime area and your enemy has those types of 556 00:34:42,840 --> 00:34:47,560 Speaker 18: defensive capabilities that are run by artificial intelligence, it's going 557 00:34:47,600 --> 00:34:49,319 Speaker 18: to be really hard for a human being to plan 558 00:34:49,400 --> 00:34:51,919 Speaker 18: an attack in that space. And so in a lot 559 00:34:51,920 --> 00:34:54,799 Speaker 18: of ways, what part of warfare may look like is 560 00:34:55,080 --> 00:34:59,040 Speaker 18: artificial intelligence driven. Drone on drone fighting may be the 561 00:34:59,120 --> 00:35:01,440 Speaker 18: new future of the front line for a while. 562 00:35:03,640 --> 00:35:06,720 Speaker 2: As Secretary Driscoll and his colleagues at the Pentagon spur 563 00:35:06,880 --> 00:35:10,080 Speaker 2: the organization to develop high tech weaponry for the future, 564 00:35:10,280 --> 00:35:13,759 Speaker 2: they're watching it get deployed right now in Ukraine. 565 00:35:14,040 --> 00:35:17,520 Speaker 18: Ukraine is considered by many to be the Silicon Valley 566 00:35:17,520 --> 00:35:20,640 Speaker 18: of war. We are hoping to repeat those lessons learned 567 00:35:20,680 --> 00:35:23,319 Speaker 18: through our processes and our systems here. But what we 568 00:35:23,440 --> 00:35:27,680 Speaker 18: do know is drone warfare is completely upending and altering 569 00:35:27,760 --> 00:35:30,040 Speaker 18: how wars have been fought and how people have thought 570 00:35:30,040 --> 00:35:32,560 Speaker 18: about fighting. We have got to get to a place 571 00:35:32,600 --> 00:35:34,560 Speaker 18: where we can update things quickly. I was just a 572 00:35:34,600 --> 00:35:36,960 Speaker 18: couple of weeks ago at a base and looking at 573 00:35:37,000 --> 00:35:40,160 Speaker 18: one of our kind of air and missile defense systems, 574 00:35:40,400 --> 00:35:44,200 Speaker 18: and the laptop that was running this system was thirty 575 00:35:44,400 --> 00:35:47,759 Speaker 18: plus years old. This soldier using it was twenty two. 576 00:35:47,920 --> 00:35:50,560 Speaker 18: So this computer he is trying to use is eight 577 00:35:50,680 --> 00:35:52,759 Speaker 18: years older than the soldier. You have to be able 578 00:35:52,800 --> 00:35:55,640 Speaker 18: to update things within two weeks, and so it is 579 00:35:55,719 --> 00:35:59,080 Speaker 18: not just a failed system. It is a sinfully failed. 580 00:35:58,800 --> 00:36:02,920 Speaker 17: System, but a lot of work from the US military 581 00:36:03,000 --> 00:36:07,920 Speaker 17: side with Ukrainians. Also, our NATO allies work closely with 582 00:36:08,280 --> 00:36:12,000 Speaker 17: the Ukrainians. The Brits, for example, are very engaged in 583 00:36:12,080 --> 00:36:16,600 Speaker 17: learning from what's happening there. The Russians are also learning, 584 00:36:16,640 --> 00:36:19,680 Speaker 17: and we have seen improvements from them. But I do 585 00:36:19,760 --> 00:36:22,680 Speaker 17: think we're very engaged looking at what's happening in the 586 00:36:22,760 --> 00:36:30,120 Speaker 17: Ukraine War and trying to learn our own lessons from it. 587 00:36:30,120 --> 00:36:33,040 Speaker 2: It's not just AI and drones that are coming to warfare. 588 00:36:33,200 --> 00:36:37,280 Speaker 2: It's also new technology like autonomous vehicles, as German avy 589 00:36:37,320 --> 00:36:41,000 Speaker 2: trucking company fern Ride is demonstrating right now in Europe. 590 00:36:41,600 --> 00:36:43,320 Speaker 2: Hendrik Kramer is the CEO. 591 00:36:44,000 --> 00:36:47,719 Speaker 15: So right now we have this pressure cooker moment in 592 00:36:47,920 --> 00:36:51,880 Speaker 15: Europe where the geopolitical situation and the war in Ukraine 593 00:36:51,960 --> 00:36:55,920 Speaker 15: and the potential conflict of nat when with Russia is 594 00:36:56,000 --> 00:37:00,160 Speaker 15: leading to a huge demand for unmanned systems and ground autonomy. 595 00:37:00,560 --> 00:37:03,440 Speaker 15: Unlike the drone systems in the air, it has not 596 00:37:03,640 --> 00:37:07,640 Speaker 15: been deployed and developed. Therefore, I think the impact will 597 00:37:07,680 --> 00:37:11,719 Speaker 15: be broadly in defense and also civil logistics. So one 598 00:37:11,800 --> 00:37:15,960 Speaker 15: of the most important defense application is very similar to 599 00:37:16,040 --> 00:37:19,239 Speaker 15: a hub to hub autonomous trucking product, where you are, 600 00:37:19,480 --> 00:37:23,319 Speaker 15: for example, having a coupling bridge between Poland and Lithania, 601 00:37:23,520 --> 00:37:27,560 Speaker 15: where Belarus and Russia are having this very small gap 602 00:37:27,640 --> 00:37:32,080 Speaker 15: to connect the Baltic States with Poland and mainland NATO countries, 603 00:37:32,120 --> 00:37:34,239 Speaker 15: and I think this is one of the applications where 604 00:37:34,239 --> 00:37:36,840 Speaker 15: it will be very dangerous to put people into trucks 605 00:37:37,040 --> 00:37:40,279 Speaker 15: on public roads, and therefore this is a fantastic applications 606 00:37:40,320 --> 00:37:43,960 Speaker 15: where the same technology that is working for civil or 607 00:37:44,080 --> 00:37:48,800 Speaker 15: defense or vice versa can be developed and scaled right now. 608 00:37:51,239 --> 00:37:54,000 Speaker 2: It's one thing to see the future, it's another to 609 00:37:54,040 --> 00:37:58,080 Speaker 2: move aggressively to reach it and shield AI's Ryan Saying says, 610 00:37:58,200 --> 00:38:01,200 Speaker 2: there's still work to be done. If that is the 611 00:38:01,239 --> 00:38:03,480 Speaker 2: future of war, how much of it is in the present. 612 00:38:03,800 --> 00:38:07,640 Speaker 2: How much is AI already being used in combat situations? 613 00:38:07,960 --> 00:38:11,759 Speaker 16: It's being used selectively today, It's not deployed at very 614 00:38:11,800 --> 00:38:14,719 Speaker 16: large scale. And I think a lot of that is 615 00:38:14,960 --> 00:38:20,480 Speaker 16: just the friction that exists between defense departments globally and 616 00:38:20,600 --> 00:38:23,400 Speaker 16: an industry. If you look around the United States, I 617 00:38:23,440 --> 00:38:26,359 Speaker 16: guess specifically, there's so many examples of industry moving out 618 00:38:26,400 --> 00:38:29,720 Speaker 16: at light speed, and our own defense department has shown 619 00:38:29,719 --> 00:38:33,440 Speaker 16: its capability to mobilize at light speed. But there has 620 00:38:33,520 --> 00:38:35,960 Speaker 16: been a lot of friction in the acquisition system that 621 00:38:36,040 --> 00:38:38,759 Speaker 16: slows down the government industry partnership, and I think that 622 00:38:38,840 --> 00:38:42,920 Speaker 16: has been responsible for slowing down the adoption of AI. 623 00:38:43,040 --> 00:38:47,480 Speaker 16: Despite many of the capabilities existing today and being battlefield 624 00:38:47,600 --> 00:38:48,760 Speaker 16: ready today. 625 00:38:51,040 --> 00:38:54,440 Speaker 2: As promising as AI is in giving the United States 626 00:38:54,600 --> 00:38:57,920 Speaker 2: new war fighting capabilities, it is not a replacement for 627 00:38:57,960 --> 00:39:00,800 Speaker 2: the soldier anymore than it can be for your doctor 628 00:39:01,080 --> 00:39:01,800 Speaker 2: or your teacher. 629 00:39:02,280 --> 00:39:04,920 Speaker 17: We're going to need everyone it's all hands on deck. 630 00:39:04,960 --> 00:39:07,279 Speaker 17: As I used to say at DUD. We need our 631 00:39:07,400 --> 00:39:13,799 Speaker 17: traditional defense manufacturers, particularly for the scale of manufacturing that 632 00:39:13,840 --> 00:39:17,719 Speaker 17: we require, for their knowledge and deep expertise, and we 633 00:39:17,800 --> 00:39:20,960 Speaker 17: need that innovation that's coming all across the sector, but 634 00:39:21,080 --> 00:39:24,680 Speaker 17: particularly from the startup community. At the end of the day, 635 00:39:25,000 --> 00:39:28,200 Speaker 17: warfare has to remain a human act of judgment. But 636 00:39:28,239 --> 00:39:32,959 Speaker 17: AI can really help bring speed and precision to all 637 00:39:33,080 --> 00:39:36,760 Speaker 17: kinds of aspects of military operations. 638 00:39:39,640 --> 00:39:41,719 Speaker 2: That does it for us here at Wall Street Week, 639 00:39:41,880 --> 00:39:45,120 Speaker 2: I'm David Weston. See you next week for more stories 640 00:39:45,160 --> 00:39:59,080 Speaker 2: of capitalism.