1 00:00:13,880 --> 00:00:15,160 Speaker 1: This is Wall Street Week. 2 00:00:15,360 --> 00:00:19,239 Speaker 2: I'm David Weston bringing you stories of capitalism this week 3 00:00:19,280 --> 00:00:22,799 Speaker 2: from Detroit, the home of Henry Ford, where we travel 4 00:00:22,880 --> 00:00:26,479 Speaker 2: to see firsthand what President Trump's trade policies are doing 5 00:00:26,560 --> 00:00:29,639 Speaker 2: four and two the auto industry, and to talk with 6 00:00:29,720 --> 00:00:33,320 Speaker 2: Ford's CEO Jim Farley about what he calls the essential 7 00:00:33,360 --> 00:00:38,400 Speaker 2: economy that needs our attention. Plus, from Australia, we bring 8 00:00:38,440 --> 00:00:40,279 Speaker 2: you the story of a nation trying to make a 9 00:00:40,320 --> 00:00:45,440 Speaker 2: big transition from natural resources to innovation, and we begin 10 00:00:45,479 --> 00:00:48,960 Speaker 2: a three part series on AI applied to the real world. 11 00:00:49,400 --> 00:00:52,360 Speaker 2: We have covered the excitement about artificial intelligence and the 12 00:00:52,479 --> 00:00:55,920 Speaker 2: massive demands for capital and for power, but where is 13 00:00:55,960 --> 00:00:59,000 Speaker 2: it being used right now in ways that may justify 14 00:00:59,040 --> 00:01:02,600 Speaker 2: the excitement. We turn first to healthcare, where AI is 15 00:01:02,680 --> 00:01:06,520 Speaker 2: already helping our doctors make diagnoses and develop treatments that 16 00:01:06,640 --> 00:01:11,720 Speaker 2: otherwise might be beyond their reach. But we start with 17 00:01:11,760 --> 00:01:14,880 Speaker 2: the jobs market, where soft data are holding the Federal 18 00:01:14,920 --> 00:01:18,160 Speaker 2: Reserve back from cutting rates more and cutting them faster. 19 00:01:18,720 --> 00:01:22,119 Speaker 2: Steve Rattner is chair and CEO will It Advisors, which 20 00:01:22,160 --> 00:01:25,880 Speaker 2: manages the personal and philanthropic money of Michael Bloomberg, our 21 00:01:25,959 --> 00:01:29,600 Speaker 2: founder and majority shareholder. Steve has been watching the US 22 00:01:29,600 --> 00:01:35,440 Speaker 2: economy closely as an investor and gave us his outlook. Steve, 23 00:01:35,520 --> 00:01:38,759 Speaker 2: the labor market is really a top and center. We've 24 00:01:38,760 --> 00:01:40,720 Speaker 2: got a prior problem before we get to the state 25 00:01:40,760 --> 00:01:43,000 Speaker 2: of the labry, which is one are the numbers because 26 00:01:43,040 --> 00:01:46,440 Speaker 2: of the government shutdown. Why are we shutting down the government. 27 00:01:46,480 --> 00:01:48,400 Speaker 3: Whether it's the right strategy or not, I don't know, 28 00:01:48,440 --> 00:01:50,960 Speaker 3: but there's a legitimate purpose in what the Democrats are 29 00:01:51,000 --> 00:01:54,080 Speaker 3: trying to do, which is healthcare. What most people don't 30 00:01:54,160 --> 00:01:57,840 Speaker 3: understand is that in Trump one point zero, Trump tried 31 00:01:57,840 --> 00:02:01,040 Speaker 3: to kill Obamacare overtly through the front door, so to speak, 32 00:02:01,040 --> 00:02:04,120 Speaker 3: and then John McCain famously voted it down. This time around, 33 00:02:04,120 --> 00:02:06,560 Speaker 3: you've not heard them talk about Obamacare or the ACA 34 00:02:06,640 --> 00:02:08,760 Speaker 3: or any of this stuff, But the fact is they've 35 00:02:08,760 --> 00:02:10,760 Speaker 3: been trying to kill healthcare. There's a lot of stuff 36 00:02:10,760 --> 00:02:14,160 Speaker 3: in the One Big, Beautiful Bill, but there's also this 37 00:02:14,240 --> 00:02:16,160 Speaker 3: provision that would expire at the end of the year 38 00:02:16,160 --> 00:02:19,720 Speaker 3: that provides what are called enhanced premium tax credits that 39 00:02:19,800 --> 00:02:22,359 Speaker 3: people making less than one hundred and fifty thousand dollars 40 00:02:22,360 --> 00:02:24,960 Speaker 3: a year, and particularly those making less than sixty five 41 00:02:25,040 --> 00:02:27,200 Speaker 3: thousand dollars a year that are going to expire at 42 00:02:27,200 --> 00:02:29,359 Speaker 3: the end of this year. This would cost millions of 43 00:02:29,400 --> 00:02:32,600 Speaker 3: Americans their healthcare and it would raise premiums by eighteen 44 00:02:32,680 --> 00:02:35,040 Speaker 3: percent for as many as twenty million Americans who buy 45 00:02:35,080 --> 00:02:36,520 Speaker 3: their insurance on the exchanges. 46 00:02:36,600 --> 00:02:39,040 Speaker 2: As soon as you say it was enhanced premium tax credit, 47 00:02:39,320 --> 00:02:42,240 Speaker 2: you sort of lose me because nobody is sort of saying, 48 00:02:42,480 --> 00:02:44,120 Speaker 2: should we do away with Obamacare or not? 49 00:02:44,960 --> 00:02:46,400 Speaker 1: Well, that's exactly their strategy. 50 00:02:46,440 --> 00:02:49,040 Speaker 3: And the Democrats keep saying this is about healthcare, but 51 00:02:49,120 --> 00:02:52,000 Speaker 3: I don't think the average American really understands about healthcare. 52 00:02:52,160 --> 00:02:54,480 Speaker 3: There are twenty some odd million, twenty twenty two million 53 00:02:54,520 --> 00:02:57,680 Speaker 3: Americans who buy their insurance on the exchanges. The insurance 54 00:02:57,680 --> 00:03:00,680 Speaker 3: companies have already filed premium requests for next year. They 55 00:03:00,680 --> 00:03:02,960 Speaker 3: have to do it by October first, and they're asking 56 00:03:03,000 --> 00:03:05,680 Speaker 3: for an average of an eighteen percent increase in premiums 57 00:03:06,000 --> 00:03:09,040 Speaker 3: for people who buy that insurance because they expect so 58 00:03:09,080 --> 00:03:11,160 Speaker 3: many people to drop out because of the loss of 59 00:03:11,200 --> 00:03:13,799 Speaker 3: these credits that they raised. So people are going to 60 00:03:13,840 --> 00:03:16,280 Speaker 3: find this out now. Now you remember that the Medicaid 61 00:03:16,840 --> 00:03:20,919 Speaker 3: cuts don't take effect after the midterms. This takes effect now, 62 00:03:21,360 --> 00:03:25,080 Speaker 3: and so people may not appreciate it today and support 63 00:03:25,080 --> 00:03:27,720 Speaker 3: the Democrats for that reason, but by the end of 64 00:03:27,760 --> 00:03:29,799 Speaker 3: this year, they're going to find out what this all means. 65 00:03:30,200 --> 00:03:32,720 Speaker 2: So let's turn to the question of the labor market overall. 66 00:03:33,040 --> 00:03:36,040 Speaker 2: You follow this closely, where is it? Is it as 67 00:03:36,040 --> 00:03:37,640 Speaker 2: soft as some people think Right now? 68 00:03:38,480 --> 00:03:40,960 Speaker 3: It's a what people call a no hire, no fire 69 00:03:41,120 --> 00:03:43,600 Speaker 3: labor market. It's kind of frozen. We saw in the 70 00:03:43,600 --> 00:03:46,560 Speaker 3: ADP numbers this week, which are one indicator. They only 71 00:03:46,560 --> 00:03:49,920 Speaker 3: show you the private side of the market, but they're 72 00:03:50,120 --> 00:03:53,760 Speaker 3: sophisticated numbers and no reason to doubt them that hiring 73 00:03:54,520 --> 00:03:55,640 Speaker 3: has really really now. 74 00:03:55,880 --> 00:03:57,040 Speaker 1: I think for two reasons. 75 00:03:57,400 --> 00:04:00,360 Speaker 3: One is uncertainty about the economy and tariffs and all 76 00:04:00,400 --> 00:04:03,000 Speaker 3: the things we've been talking about for so long, and 77 00:04:03,040 --> 00:04:06,120 Speaker 3: the other, potentially is AI. We know that AI is 78 00:04:06,200 --> 00:04:09,240 Speaker 3: going to have a major effect. You saw Doug McMillan 79 00:04:09,280 --> 00:04:12,280 Speaker 3: of Walmart this week, for example. He has said privately 80 00:04:12,320 --> 00:04:14,280 Speaker 3: to people that he thinks of his two point one 81 00:04:14,360 --> 00:04:16,440 Speaker 3: or two point two million people, he might have a 82 00:04:16,480 --> 00:04:19,679 Speaker 3: million people working five years down the road or something 83 00:04:19,800 --> 00:04:22,360 Speaker 3: like that. It's going to have a major effect on 84 00:04:22,400 --> 00:04:24,880 Speaker 3: our country. Ultimately, I think for the better, but with 85 00:04:24,960 --> 00:04:26,760 Speaker 3: a lot of disruption along the way. And I think 86 00:04:26,760 --> 00:04:28,800 Speaker 3: we're beginning to see those signs. 87 00:04:29,120 --> 00:04:32,800 Speaker 2: If the labor markets are slowing at whatever rate, what 88 00:04:32,839 --> 00:04:36,599 Speaker 2: does that tell the BED Because given the reasons they're slowing, 89 00:04:37,120 --> 00:04:39,240 Speaker 2: do short term interest rates have any effect on that? 90 00:04:40,680 --> 00:04:43,400 Speaker 1: Well, in theory they do. It takes a while. 91 00:04:44,000 --> 00:04:47,000 Speaker 3: It's not the most direct transmission mechanism, but obviously if 92 00:04:47,040 --> 00:04:50,000 Speaker 3: you lower the cost of capital, business goes out and 93 00:04:50,040 --> 00:04:52,400 Speaker 3: borrows more, spends more. It's good for the stock market. 94 00:04:52,440 --> 00:04:55,080 Speaker 3: As we have seen. That creates a wealth effect. People 95 00:04:55,080 --> 00:04:58,119 Speaker 3: who have stocks spend more, and that's the whole essence 96 00:04:58,120 --> 00:05:00,720 Speaker 3: of monetary policy. I thought you're going to ask me 97 00:05:00,720 --> 00:05:02,760 Speaker 3: if you don't mind my saying, is that, as Powell 98 00:05:02,800 --> 00:05:05,240 Speaker 3: said in his last press conference, the problem he has 99 00:05:05,240 --> 00:05:06,760 Speaker 3: at the moment is he has to worry about both 100 00:05:06,760 --> 00:05:08,719 Speaker 3: sides of his mandate. Usually he has to worry about 101 00:05:08,720 --> 00:05:10,920 Speaker 3: an employment or inflation. Right now he has to worry 102 00:05:10,960 --> 00:05:14,120 Speaker 3: about both. We're not really in stagflation yet, but we 103 00:05:14,200 --> 00:05:16,839 Speaker 3: have bits of that on both sides of the mandate. 104 00:05:16,920 --> 00:05:19,000 Speaker 3: I think the one thing that maybe hiding in all 105 00:05:19,040 --> 00:05:23,120 Speaker 3: this is again AI AI has the potential and Trump, 106 00:05:23,200 --> 00:05:26,280 Speaker 3: of course, did nothing to enhance AI, particularly other than 107 00:05:26,400 --> 00:05:29,360 Speaker 3: doing a lot of press conferences with Sammulten and whatnot. 108 00:05:29,560 --> 00:05:31,920 Speaker 3: But AI has the potential to be a game changer. 109 00:05:32,000 --> 00:05:34,839 Speaker 3: If we can raise our productivity rate by half a 110 00:05:34,839 --> 00:05:37,120 Speaker 3: percentage point or a percentage point, it would have a 111 00:05:37,160 --> 00:05:40,240 Speaker 3: dramatic effect on growth. It would allow for more growth 112 00:05:40,279 --> 00:05:42,840 Speaker 3: without more inflation. I think that if the Fed were 113 00:05:42,880 --> 00:05:45,799 Speaker 3: to follow what Trump and Myron want and cut interest 114 00:05:45,839 --> 00:05:48,360 Speaker 3: rates by two hundred basis points, you'd see a fair 115 00:05:48,360 --> 00:05:50,880 Speaker 3: amount of inflation. And right now we're not even a 116 00:05:50,960 --> 00:05:52,360 Speaker 3: two percent, as we both know. 117 00:05:52,800 --> 00:05:55,599 Speaker 2: Prison Trump came to office promising to help an industry 118 00:05:55,640 --> 00:05:58,839 Speaker 2: you know, well, you helped restructure it under President Obama, 119 00:05:58,920 --> 00:06:02,080 Speaker 2: the auto industry. What has been the net effect of 120 00:06:02,120 --> 00:06:03,280 Speaker 2: the various policies. 121 00:06:03,400 --> 00:06:05,480 Speaker 3: I think if you talk to auto executives, and some 122 00:06:06,160 --> 00:06:07,880 Speaker 3: of them have said this more or less publicly, some 123 00:06:07,920 --> 00:06:10,440 Speaker 3: have said it more privately, I don't think really much 124 00:06:10,480 --> 00:06:13,000 Speaker 3: of anything good has come out of this, and the 125 00:06:13,000 --> 00:06:15,080 Speaker 3: stock market, if you look at the share prices, would 126 00:06:15,120 --> 00:06:17,440 Speaker 3: agree with that much of anything good has come out 127 00:06:17,440 --> 00:06:17,640 Speaker 3: of this. 128 00:06:17,680 --> 00:06:18,719 Speaker 1: For the auto industry. 129 00:06:19,760 --> 00:06:22,719 Speaker 3: I think Mary Barrow of GM has been reasonably candid 130 00:06:22,800 --> 00:06:26,360 Speaker 3: about the challenges opposes for them. As Jim farley Ford 131 00:06:26,400 --> 00:06:29,320 Speaker 3: has pointed out, GM imports more of its cars from 132 00:06:29,440 --> 00:06:33,159 Speaker 3: Mexico and Canada than FOUR does, so that is a 133 00:06:33,200 --> 00:06:36,320 Speaker 3: problem for them. The taxes on the import taxes on 134 00:06:36,600 --> 00:06:39,320 Speaker 3: auto parts is a huge problem for them. But also 135 00:06:39,440 --> 00:06:43,200 Speaker 3: the stop start policies on electrification and cafe standards and 136 00:06:43,240 --> 00:06:45,479 Speaker 3: so on, where you go from one administration to the 137 00:06:45,520 --> 00:06:47,760 Speaker 3: other and you're trying to make five year capital plans 138 00:06:47,800 --> 00:06:50,240 Speaker 3: and product plans and then suddenly there's a u turn 139 00:06:50,320 --> 00:06:52,320 Speaker 3: in policies that's not good for them. 140 00:06:52,600 --> 00:06:55,400 Speaker 2: You pointed outing is really striking. On the one hand, 141 00:06:55,520 --> 00:06:59,360 Speaker 2: the expiration this week, the ev tax credits in another hand, 142 00:06:59,400 --> 00:07:02,440 Speaker 2: the cafe say so sort of giving them the one 143 00:07:02,480 --> 00:07:04,800 Speaker 2: hand and taking away on the other hand. So net 144 00:07:04,920 --> 00:07:07,320 Speaker 2: net is that going to help the auto industry. 145 00:07:07,680 --> 00:07:10,280 Speaker 3: Well, but what those two things do is it drives 146 00:07:10,320 --> 00:07:14,080 Speaker 3: no pun intended the auto industry from producing small electric 147 00:07:14,200 --> 00:07:17,240 Speaker 3: electric or very fuel efficient cars for producing larger cars 148 00:07:17,240 --> 00:07:18,920 Speaker 3: that are less fuel efficient. So then they have to 149 00:07:18,960 --> 00:07:22,920 Speaker 3: retool and reorganize all their production plans. And sure, maybe 150 00:07:22,920 --> 00:07:25,960 Speaker 3: if those policies stay in place indefinitely, it would be 151 00:07:26,000 --> 00:07:28,440 Speaker 3: good for them. Bigger cars tend to have higher profit margins, 152 00:07:28,480 --> 00:07:30,880 Speaker 3: they're easier for us to compete against the foreign cars 153 00:07:30,920 --> 00:07:33,200 Speaker 3: that are where they can make smaller cars more efficiently, 154 00:07:33,200 --> 00:07:35,920 Speaker 3: and so on and so forth. But we will have 155 00:07:35,960 --> 00:07:39,679 Speaker 3: another president in three years and this may all change again, 156 00:07:39,800 --> 00:07:43,680 Speaker 3: and so it's it's not really great for American prosperity 157 00:07:43,680 --> 00:07:46,880 Speaker 3: and capitalism to have these kind of stop start policies. 158 00:07:47,000 --> 00:07:49,160 Speaker 3: I think the government does have an important role in 159 00:07:49,400 --> 00:07:53,360 Speaker 3: policy where they're what they call externalities, where they're effects 160 00:07:53,120 --> 00:07:55,840 Speaker 3: of what a private actor does that aren't captured in 161 00:07:55,880 --> 00:07:59,600 Speaker 3: the price mechanism in the market. And emissions and climate 162 00:07:59,640 --> 00:08:02,440 Speaker 3: are the best example of that that. If you just 163 00:08:02,560 --> 00:08:04,760 Speaker 3: let auto companies make whatever they want, you didn't have 164 00:08:04,800 --> 00:08:07,559 Speaker 3: cafe standards, you didn't have ev tax credits, and so forth, 165 00:08:08,080 --> 00:08:11,240 Speaker 3: we have bigger polluting cars. But if I were a god, 166 00:08:11,320 --> 00:08:13,040 Speaker 3: I would have done this very differently. I would have 167 00:08:13,040 --> 00:08:15,960 Speaker 3: done it using the tax system, big tax on gasoline, 168 00:08:16,000 --> 00:08:19,720 Speaker 3: things like that to incentivize people and companies to move 169 00:08:19,760 --> 00:08:22,920 Speaker 3: in the right direction, not these sort of very Jerry 170 00:08:23,000 --> 00:08:27,480 Speaker 3: rig complicated, on again, off again regulatory policies. Why do 171 00:08:27,560 --> 00:08:30,480 Speaker 3: we do it this way? Because consumers don't see it. 172 00:08:30,520 --> 00:08:32,800 Speaker 3: They don't see the costs of cafe standards. It's not 173 00:08:32,840 --> 00:08:34,080 Speaker 3: part of the American culture. 174 00:08:34,280 --> 00:08:37,520 Speaker 2: To come full circle, what do these policies and policy 175 00:08:37,600 --> 00:08:41,160 Speaker 2: changes mean for employment in the auto industry, including the 176 00:08:41,160 --> 00:08:41,760 Speaker 2: parts business. 177 00:08:41,880 --> 00:08:42,840 Speaker 1: Well, let's just step back. 178 00:08:42,880 --> 00:08:46,040 Speaker 3: I think not just the policies, but the auto industry, 179 00:08:46,080 --> 00:08:48,000 Speaker 3: and I did spend some time in the auto industry. 180 00:08:48,440 --> 00:08:52,160 Speaker 3: The auto industry has some really significant challenges. When you 181 00:08:52,160 --> 00:08:54,000 Speaker 3: look at what the Chinese are doing, and yes, we 182 00:08:54,040 --> 00:08:56,120 Speaker 3: have huge tariffs on their cars, so they don't come 183 00:08:56,160 --> 00:08:58,280 Speaker 3: here at the moment, But when you look at what 184 00:08:58,280 --> 00:09:00,560 Speaker 3: they're able to do and the prices at which they're 185 00:09:00,559 --> 00:09:03,559 Speaker 3: able to produce and sell cars efficiently, and how good 186 00:09:03,600 --> 00:09:05,480 Speaker 3: the cars have become. They didn't have to make cars 187 00:09:05,480 --> 00:09:07,440 Speaker 3: for a long time. We kind of taught them how 188 00:09:07,480 --> 00:09:09,680 Speaker 3: to make cars. At the wage rates we pay. With 189 00:09:09,720 --> 00:09:13,240 Speaker 3: the other cost structure that we have in this country, 190 00:09:12,600 --> 00:09:14,760 Speaker 3: it is going to be it would be very tough 191 00:09:14,760 --> 00:09:17,080 Speaker 3: for us to compete on a completely level playing field. 192 00:09:17,120 --> 00:09:20,959 Speaker 3: So the question then becomes how much protectionism, if you will, 193 00:09:21,280 --> 00:09:23,280 Speaker 3: do you want to afford our auto industry, because we 194 00:09:23,320 --> 00:09:25,120 Speaker 3: think it's important to have an auto industry. 195 00:09:25,280 --> 00:09:27,520 Speaker 2: If you talk to auto executives, as I know you do, 196 00:09:27,800 --> 00:09:29,920 Speaker 2: they say, it's not a completely level playing field. Can 197 00:09:30,000 --> 00:09:33,959 Speaker 2: we out innovate China to be able to overcome whatever 198 00:09:33,960 --> 00:09:34,920 Speaker 2: substances being given. 199 00:09:35,080 --> 00:09:36,960 Speaker 3: I think even if we gave the subsidies, I think 200 00:09:37,000 --> 00:09:40,560 Speaker 3: you'd find that the Chinese can produce better, cheaper cars 201 00:09:40,600 --> 00:09:44,440 Speaker 3: than we can. I think their innovation is exceptional. I 202 00:09:44,440 --> 00:09:47,280 Speaker 3: think their costs of production is exceptional. 203 00:09:48,120 --> 00:09:50,560 Speaker 2: One of the advantages China has is a very large 204 00:09:50,600 --> 00:09:54,760 Speaker 2: domestic market. Are we seeing sort of an epical change actually, 205 00:09:54,800 --> 00:09:57,160 Speaker 2: from a world in which the United States was really 206 00:09:57,200 --> 00:09:59,760 Speaker 2: a leader in auto production to one where we're not 207 00:09:59,800 --> 00:10:02,120 Speaker 2: going to be We're going to be the equivalent of 208 00:10:02,160 --> 00:10:02,800 Speaker 2: a Europe. 209 00:10:02,920 --> 00:10:04,320 Speaker 1: Well, I think we'll be somewhere in between. 210 00:10:04,480 --> 00:10:08,760 Speaker 3: I think Europe's car industry has had many, many struggles, 211 00:10:08,840 --> 00:10:11,720 Speaker 3: and the extent they've been successful, it's heavily been the 212 00:10:11,720 --> 00:10:17,280 Speaker 3: German luxury cars exporting to China. Ironically, but yeah, look, 213 00:10:17,400 --> 00:10:19,679 Speaker 3: China does have, as you say, one point three billion people, 214 00:10:19,720 --> 00:10:22,840 Speaker 3: and they're increasingly prosperous and able to buy cars. You know, 215 00:10:22,880 --> 00:10:25,160 Speaker 3: over fifty percent of the cars sold in China now 216 00:10:25,200 --> 00:10:27,840 Speaker 3: are evs. I mean they are in a whole other 217 00:10:28,000 --> 00:10:31,520 Speaker 3: world compared to us in the transition away from internal 218 00:10:31,520 --> 00:10:33,679 Speaker 3: combustion engines into electrification. 219 00:10:35,600 --> 00:10:38,200 Speaker 2: Coming up, we continue the discussion of how the auto 220 00:10:38,240 --> 00:10:40,520 Speaker 2: industry is varing and what needs to be done to 221 00:10:40,600 --> 00:10:43,800 Speaker 2: ensure the strength of industries like it that make things 222 00:10:44,240 --> 00:10:56,400 Speaker 2: with Jim Farley, President and CEO of the Ford Motor Company. 223 00:11:00,320 --> 00:11:03,840 Speaker 2: Is a story about the essential economy. That's what Ford 224 00:11:03,960 --> 00:11:06,600 Speaker 2: CEO Jim Farley calls the part of the US market 225 00:11:06,640 --> 00:11:10,480 Speaker 2: where things get built, moved, or fixed. This week he 226 00:11:10,480 --> 00:11:13,200 Speaker 2: held a series of meetings at the restored Michigan Central 227 00:11:13,240 --> 00:11:16,079 Speaker 2: Rail station in Detroit, and we traveled there to hear 228 00:11:16,160 --> 00:11:20,280 Speaker 2: directly from him what needs to be done. 229 00:11:20,440 --> 00:11:23,800 Speaker 4: Ninety five million people huge part of our GDP that 230 00:11:23,920 --> 00:11:25,040 Speaker 4: basically build things. 231 00:11:25,280 --> 00:11:26,679 Speaker 5: Think about factory. 232 00:11:26,240 --> 00:11:32,000 Speaker 4: Workers, construction workers, the people who move things, rail workers, 233 00:11:32,160 --> 00:11:37,120 Speaker 4: truck drivers, and the people that fixed things. Think about plumbers, electricians, 234 00:11:37,679 --> 00:11:41,760 Speaker 4: people who are mechanics on your vehicle. That's the economy 235 00:11:41,920 --> 00:11:46,000 Speaker 4: is huge, and we have about a million people shortage today. 236 00:11:46,520 --> 00:11:49,200 Speaker 2: What's going on there? Why do why we short that 237 00:11:49,240 --> 00:11:50,360 Speaker 2: many essential workers. 238 00:11:50,920 --> 00:11:54,400 Speaker 4: It's a combination of things, David, But I think the 239 00:11:54,400 --> 00:11:58,040 Speaker 4: biggest thing is the societal prestige of these jobs has 240 00:11:58,160 --> 00:12:00,960 Speaker 4: changed from our parents and our grandpay parents. You know, 241 00:12:01,120 --> 00:12:03,200 Speaker 4: people all want to go to a four year degree 242 00:12:03,240 --> 00:12:05,400 Speaker 4: and then they want to go into the white collar workforce. 243 00:12:05,640 --> 00:12:08,920 Speaker 5: That's what my grandfather told me. He was a factory worker. Hey, Jim, I. 244 00:12:08,840 --> 00:12:11,080 Speaker 4: Don't want you to have to work this hard, you know, 245 00:12:11,160 --> 00:12:14,559 Speaker 4: go to college. And the irony of the irony is 246 00:12:14,679 --> 00:12:17,280 Speaker 4: we have all these data centers, all this new technology 247 00:12:17,480 --> 00:12:21,959 Speaker 4: or all out still requires electricians, construction workers, you know, 248 00:12:22,120 --> 00:12:25,000 Speaker 4: and we have this huge shortage. The second thing is 249 00:12:25,120 --> 00:12:29,320 Speaker 4: our country has really pulled back on investing in education programs, 250 00:12:29,360 --> 00:12:31,840 Speaker 4: the local college support. 251 00:12:31,760 --> 00:12:33,120 Speaker 5: For these kinds of jobs. 252 00:12:33,200 --> 00:12:36,120 Speaker 4: There's no high schools around vocational programs anymore. 253 00:12:36,160 --> 00:12:38,480 Speaker 5: That's the exception, not the rule anymore. 254 00:12:38,800 --> 00:12:43,360 Speaker 4: I think the last thing is permitting all the regulatory 255 00:12:43,440 --> 00:12:46,959 Speaker 4: requirements on these jobs is really tough, especially for small business, 256 00:12:47,360 --> 00:12:50,120 Speaker 4: and it just makes these jobs hard and complicated. 257 00:12:50,360 --> 00:12:53,560 Speaker 2: How does AI fit with the essential economy? 258 00:12:53,840 --> 00:12:56,360 Speaker 4: Well, I hope that it will be a help, but 259 00:12:56,480 --> 00:12:59,240 Speaker 4: it's hard to say that today we're going to have 260 00:12:59,280 --> 00:13:02,400 Speaker 4: a lot of wins for the essential economy of the I. 261 00:13:02,520 --> 00:13:05,000 Speaker 4: For example, we have to build all these data centers 262 00:13:05,040 --> 00:13:09,839 Speaker 4: all the transmission lines thus can require plumbers, you know, electricians, 263 00:13:10,320 --> 00:13:13,840 Speaker 4: a lot of technical people to do all that construction workers. 264 00:13:14,200 --> 00:13:17,360 Speaker 4: But on the other hand, over the last twenty years, 265 00:13:17,800 --> 00:13:21,679 Speaker 4: the essential economy productivity has actually gone down. Wise, white 266 00:13:21,679 --> 00:13:24,920 Speaker 4: collar productivity has gone up twenty to thirty percent. That's 267 00:13:24,960 --> 00:13:28,640 Speaker 4: like for the average essential worker, that's like thirty thousand 268 00:13:28,679 --> 00:13:32,000 Speaker 4: dollars a year. So we don't have a good track 269 00:13:32,040 --> 00:13:35,800 Speaker 4: wreck here here of applying new technology like AI to 270 00:13:35,880 --> 00:13:37,760 Speaker 4: make these jobs more productive. 271 00:13:38,280 --> 00:13:40,520 Speaker 5: It, you know, automation, things like that. 272 00:13:40,840 --> 00:13:44,360 Speaker 4: Those innovations really took jobs out of the job market 273 00:13:44,440 --> 00:13:46,640 Speaker 4: and out of the essential economy. 274 00:13:47,280 --> 00:13:48,199 Speaker 5: I think we'd have to. 275 00:13:48,480 --> 00:13:52,679 Speaker 4: Twist the technology through the lens of these critical jobs 276 00:13:52,840 --> 00:13:54,319 Speaker 4: for it to be the opposite. 277 00:13:54,559 --> 00:13:57,480 Speaker 2: The government plays a central role in turning around the 278 00:13:57,600 --> 00:14:02,120 Speaker 2: essential economy, with President Trump emphasize manufacturing and the need 279 00:14:02,200 --> 00:14:05,320 Speaker 2: for trade schools. What Farley has yet to see the 280 00:14:05,360 --> 00:14:08,640 Speaker 2: real results, including with those auto tariffs. 281 00:14:09,440 --> 00:14:10,760 Speaker 5: It's too early to tell. 282 00:14:11,000 --> 00:14:13,760 Speaker 4: I haven't seen a lot of new plant announcements of 283 00:14:13,800 --> 00:14:17,000 Speaker 4: my competitors who import. You know, our market is fifty 284 00:14:17,000 --> 00:14:20,200 Speaker 4: percent import So what I was looking for is how 285 00:14:20,240 --> 00:14:22,400 Speaker 4: many of those imported vehicles are now being built in 286 00:14:22,400 --> 00:14:25,400 Speaker 4: the US. We haven't seen too many announcements. What hasn't 287 00:14:25,480 --> 00:14:27,760 Speaker 4: changed is Ford's commitment to the US. We build eighty 288 00:14:27,800 --> 00:14:32,640 Speaker 4: percent of our vehicles here with the largest employer UAW workers, 289 00:14:32,960 --> 00:14:35,680 Speaker 4: and where the largest export are by far from the US. 290 00:14:36,520 --> 00:14:38,680 Speaker 5: I would say it is too early to. 291 00:14:38,600 --> 00:14:42,760 Speaker 4: Tell, but I'm seeing the tone at the top in 292 00:14:42,880 --> 00:14:48,920 Speaker 4: DC be very thoughtful about the parts tariffs, which more 293 00:14:48,960 --> 00:14:52,400 Speaker 4: than twenty percent of our tariff of our profits has 294 00:14:52,480 --> 00:14:52,960 Speaker 4: lost on. 295 00:14:52,920 --> 00:14:53,960 Speaker 5: These parts tariffs. 296 00:14:54,080 --> 00:14:56,560 Speaker 4: So yes, we make in the US, but we import 297 00:14:56,600 --> 00:14:59,120 Speaker 4: parts from all around the world because some of them, 298 00:14:59,160 --> 00:15:01,720 Speaker 4: like wiring looms, we can't even buy in the US. 299 00:15:02,480 --> 00:15:05,960 Speaker 4: The tax the tariffs on those parts, maybe twenty percent 300 00:15:06,040 --> 00:15:08,680 Speaker 4: of your F one fifty is twenty percent of our 301 00:15:08,720 --> 00:15:12,360 Speaker 4: profit gone. And I'm seeing a tone at the top 302 00:15:12,720 --> 00:15:16,120 Speaker 4: in DC where they are listening to us very carefully 303 00:15:16,400 --> 00:15:21,480 Speaker 4: because they understand long term that twenty two billion dollars 304 00:15:21,480 --> 00:15:24,680 Speaker 4: of headwind for US will not be good for the US, 305 00:15:24,880 --> 00:15:27,600 Speaker 4: especially for a company like Ford. I don't know what 306 00:15:27,600 --> 00:15:29,840 Speaker 4: they're going to do, but boy if we had a 307 00:15:29,840 --> 00:15:32,680 Speaker 4: lot of conversations with Commerce and the President about this, 308 00:15:33,760 --> 00:15:37,040 Speaker 4: and I remain very optimistic that they'll make the right adjustments. 309 00:15:37,240 --> 00:15:39,880 Speaker 4: If they make the right adjustments on tariffs, with the 310 00:15:39,920 --> 00:15:41,840 Speaker 4: EPA rule, some of the. 311 00:15:41,760 --> 00:15:44,840 Speaker 5: Tax changes like PTC, I think. 312 00:15:44,840 --> 00:15:47,840 Speaker 4: We will see a much stronger US industry in the 313 00:15:47,880 --> 00:15:49,880 Speaker 4: coming years, and I'm sure hopeful of that. 314 00:15:50,760 --> 00:15:53,640 Speaker 2: When we spoke to Farley a year ago, before President 315 00:15:53,680 --> 00:15:57,640 Speaker 2: Trump's election, the auto industry was working and investing hard 316 00:15:57,760 --> 00:16:01,800 Speaker 2: to meet government targets for electric vehicles. Since then, EB 317 00:16:01,960 --> 00:16:05,720 Speaker 2: policy has been turned on its head, making automakers' lives 318 00:16:05,880 --> 00:16:10,520 Speaker 2: dramatically more complicated. Roger Penske, founder of the Penske Corporation 319 00:16:10,640 --> 00:16:14,000 Speaker 2: and owner of IndyCar, says it's the result of ignoring 320 00:16:14,080 --> 00:16:15,520 Speaker 2: what the customer wants. 321 00:16:15,880 --> 00:16:18,960 Speaker 6: No one asked the customer what they wanted number one 322 00:16:18,960 --> 00:16:22,560 Speaker 6: and number two, we didn't have the infrastructure, and the 323 00:16:22,640 --> 00:16:26,880 Speaker 6: expectation on range wasn't there. So everything came together the 324 00:16:26,920 --> 00:16:31,560 Speaker 6: way they sold electric vehicles with government support. The seventy 325 00:16:31,600 --> 00:16:34,800 Speaker 6: five hundred dollars is going away. When you think about that, 326 00:16:34,800 --> 00:16:37,600 Speaker 6: that's fifty billion that the government is going to be 327 00:16:37,640 --> 00:16:40,880 Speaker 6: able to put, hopefully towards something that's better off for 328 00:16:40,960 --> 00:16:43,480 Speaker 6: our economy and better off for the country. But I 329 00:16:43,480 --> 00:16:46,520 Speaker 6: would say they're going through a transition, all of them, 330 00:16:46,680 --> 00:16:50,480 Speaker 6: said Barry bearra Has Farley has you know, they're moving 331 00:16:50,560 --> 00:16:53,000 Speaker 6: to lower cost EV vehicles. 332 00:16:53,440 --> 00:16:56,640 Speaker 2: You at Ford, like the other automakers, have really made 333 00:16:56,680 --> 00:17:00,800 Speaker 2: a lot of capital investment commitments for an EV. What 334 00:17:01,000 --> 00:17:02,240 Speaker 2: happens to those investments? 335 00:17:02,280 --> 00:17:02,560 Speaker 6: Now? 336 00:17:02,920 --> 00:17:05,760 Speaker 4: What's an important question for our country. First of all, 337 00:17:05,840 --> 00:17:10,080 Speaker 4: Ford never talked about an all electric Ford we always 338 00:17:10,080 --> 00:17:13,479 Speaker 4: set a customer choice. We have hybrid the f one 339 00:17:13,560 --> 00:17:16,320 Speaker 4: fifty best selling vehicle in the United States. Almost a 340 00:17:16,400 --> 00:17:20,119 Speaker 4: third is now hybrid. So we never bet the farm 341 00:17:20,160 --> 00:17:23,160 Speaker 4: on electrification, and we were a first mover. We've been 342 00:17:23,240 --> 00:17:25,320 Speaker 4: number two to TESTAF I think for three years now. 343 00:17:26,000 --> 00:17:30,040 Speaker 4: But that is the biggest next up question. How does 344 00:17:30,080 --> 00:17:37,200 Speaker 4: Ford adjustice assets from moving to making large scale battery operations. 345 00:17:37,240 --> 00:17:38,639 Speaker 5: We have three battery. 346 00:17:38,280 --> 00:17:41,400 Speaker 4: Plants in Kentucky and Tennessee, another one here in Michigan 347 00:17:41,480 --> 00:17:47,480 Speaker 4: and Marshall, so four plus two assembly plants dedicated to electrics. 348 00:17:47,840 --> 00:17:49,680 Speaker 5: You know, what do we do with those assets. 349 00:17:50,080 --> 00:17:51,719 Speaker 4: I'm not going to go into the details, but all 350 00:17:51,760 --> 00:17:55,000 Speaker 4: I say is they're some of the best factories we've 351 00:17:55,040 --> 00:17:59,399 Speaker 4: ever built. We've designed them flexibly and we'll make the 352 00:17:59,480 --> 00:18:00,800 Speaker 4: right decision for the company. 353 00:18:01,000 --> 00:18:02,680 Speaker 5: We're not going to allow. 354 00:18:02,400 --> 00:18:06,040 Speaker 4: These to be mothballed, and we have more decisions to 355 00:18:06,040 --> 00:18:08,280 Speaker 4: make on those battery plants and assembly plants in the 356 00:18:08,320 --> 00:18:11,640 Speaker 4: coming months and years with this change, because a year 357 00:18:11,680 --> 00:18:14,240 Speaker 4: ago we didn't see the customer like we do today, 358 00:18:14,760 --> 00:18:18,119 Speaker 4: and boy are we seeing customers by hybrids and partial 359 00:18:18,119 --> 00:18:22,280 Speaker 4: electric solutions. So America is moving more to low co 360 00:18:22,520 --> 00:18:27,960 Speaker 4: two footprint powertrains, but they are not accepting full electrics 361 00:18:28,000 --> 00:18:30,320 Speaker 4: anywhere near what we thought when you. 362 00:18:30,280 --> 00:18:32,680 Speaker 2: Talk about some of the policies coming out of Washington. 363 00:18:32,760 --> 00:18:34,399 Speaker 2: On the one hand, as you mentioned, there's the seventy 364 00:18:34,400 --> 00:18:36,320 Speaker 2: five hundred dollars credit, yeah, the way this way, Yes. 365 00:18:36,480 --> 00:18:38,560 Speaker 2: On the other hand, you've got the cafe Yes. So 366 00:18:38,720 --> 00:18:41,199 Speaker 2: there's some gives and takes on this. How does that 367 00:18:41,200 --> 00:18:42,879 Speaker 2: all come up for the Ford Motor Company. 368 00:18:43,600 --> 00:18:46,439 Speaker 5: Well, it's accelerating this awkward moment. 369 00:18:46,640 --> 00:18:49,600 Speaker 4: Basically, the seventy five hundred dollars, which could be up 370 00:18:49,680 --> 00:18:53,280 Speaker 4: to you know, twenty thirty percent of the purchase price 371 00:18:53,480 --> 00:18:56,240 Speaker 4: is gone and makes evs a lot more expensive. 372 00:18:56,440 --> 00:18:57,760 Speaker 1: But think about Ford. 373 00:18:57,520 --> 00:19:00,440 Speaker 4: As a global company. We're not just the most American company. 374 00:19:00,560 --> 00:19:03,880 Speaker 4: We compete around the globe. And in China. 375 00:19:03,640 --> 00:19:06,080 Speaker 5: Fifty percent of the vehicles are electric. In Europe is 376 00:19:06,160 --> 00:19:06,880 Speaker 5: thirty percent. 377 00:19:07,359 --> 00:19:10,000 Speaker 4: So we have to set up our industrial system to 378 00:19:10,000 --> 00:19:13,120 Speaker 4: compete not just in the four or five percent here 379 00:19:13,160 --> 00:19:16,919 Speaker 4: in America with maybe a higher mix of hybrids or erevs, 380 00:19:17,440 --> 00:19:20,679 Speaker 4: but in China or the rest of the world. We 381 00:19:20,760 --> 00:19:24,440 Speaker 4: still have to make this ev future profitable. To do that, 382 00:19:24,600 --> 00:19:26,720 Speaker 4: we made a big bet four years ago. We did 383 00:19:26,720 --> 00:19:29,040 Speaker 4: in secret. We came up with a small group of 384 00:19:29,080 --> 00:19:32,520 Speaker 4: people on the West Coast and we redesigned the way 385 00:19:32,560 --> 00:19:34,600 Speaker 4: we make and design a vehicle. We call it the 386 00:19:34,680 --> 00:19:37,119 Speaker 4: Universe Electric Vehicles, kind of a model t kind of 387 00:19:37,119 --> 00:19:39,520 Speaker 4: moment for the company. That vehicle will be coming out 388 00:19:39,520 --> 00:19:41,520 Speaker 4: in about a year and a half. That is our 389 00:19:41,640 --> 00:19:44,800 Speaker 4: way of competing with BYD's costs they've been added for 390 00:19:44,840 --> 00:19:48,199 Speaker 4: twenty years. These markets are huge outside the US. We 391 00:19:48,280 --> 00:19:52,399 Speaker 4: still have to be successful globally. We can't just draw 392 00:19:52,440 --> 00:19:54,639 Speaker 4: a big wall around the US and say that's what 393 00:19:54,760 --> 00:19:55,879 Speaker 4: Ford Motor Company is. 394 00:19:56,440 --> 00:19:58,840 Speaker 2: Another subject we discussed a year ago was China, Yes, 395 00:19:58,880 --> 00:20:02,199 Speaker 2: and what's going on? Bring us forward one year? For 396 00:20:02,240 --> 00:20:03,159 Speaker 2: example with BYD. 397 00:20:03,640 --> 00:20:06,520 Speaker 4: Yeah, I mean a year ago, Tessa was the number 398 00:20:06,520 --> 00:20:10,240 Speaker 4: one seller globally electric vehicles and VW was a top 399 00:20:10,320 --> 00:20:13,320 Speaker 4: rand in China. The world's biggest market is almost twenty 400 00:20:13,359 --> 00:20:16,480 Speaker 4: seven million. The US is sixteen. Maybe on a good 401 00:20:16,520 --> 00:20:17,960 Speaker 4: day you imagine. 402 00:20:17,520 --> 00:20:18,280 Speaker 1: How big that is. 403 00:20:18,400 --> 00:20:20,880 Speaker 5: It's not twice as big, but it's a big market. 404 00:20:21,680 --> 00:20:25,160 Speaker 4: Now BOID is not only out selling Volkswagen, they're now 405 00:20:25,200 --> 00:20:28,240 Speaker 4: the number one electric player in the world. A year later, 406 00:20:28,800 --> 00:20:32,240 Speaker 4: very powerful company, vertically integrated like Henry Ford did in 407 00:20:32,280 --> 00:20:32,880 Speaker 4: the thirties. 408 00:20:33,440 --> 00:20:35,240 Speaker 5: We're very humble, but it's the same token. 409 00:20:35,400 --> 00:20:38,840 Speaker 4: Ford has announced our Universe Electric platform to be built 410 00:20:38,840 --> 00:20:42,320 Speaker 4: in Louisville, Kentucky, and we think it's basically a wash 411 00:20:42,480 --> 00:20:46,600 Speaker 4: with BYD made in Mexico because we've thrown innovation at it. 412 00:20:47,800 --> 00:20:50,080 Speaker 5: So a lot has changed, and we've. 413 00:20:49,920 --> 00:20:56,720 Speaker 4: Seen Europe it's been really the battleground for globally for 414 00:20:56,800 --> 00:21:00,320 Speaker 4: the Chinese. Now it looks like this month, I bet 415 00:21:00,359 --> 00:21:02,280 Speaker 4: you the Chinese will be close to ten percent of 416 00:21:02,280 --> 00:21:05,840 Speaker 4: the European EV market. Many of the brands weren't even 417 00:21:05,840 --> 00:21:10,640 Speaker 4: on sale a year ago, mg G Le others. Now 418 00:21:10,640 --> 00:21:14,879 Speaker 4: they're very successful like BID in Europe. It's really a moment, 419 00:21:15,640 --> 00:21:19,119 Speaker 4: a transitional moment for our industry where everyone used to 420 00:21:19,160 --> 00:21:22,520 Speaker 4: talk about South Korea or Japan. In my eyes as 421 00:21:22,560 --> 00:21:26,160 Speaker 4: the CEO, China is the place where the most fit 422 00:21:26,240 --> 00:21:29,600 Speaker 4: industrial systems are getting created, not just for electric vehicles, 423 00:21:29,800 --> 00:21:33,520 Speaker 4: but for internal combustion vehicles. And without projects like UEV, 424 00:21:34,440 --> 00:21:37,679 Speaker 4: companies will will be under thread of existence. 425 00:21:38,280 --> 00:21:42,159 Speaker 2: You were on the front lines when Japanese manufacturers are 426 00:21:42,160 --> 00:21:44,119 Speaker 2: going to stole market share they I'd say, yes. But 427 00:21:44,200 --> 00:21:48,240 Speaker 2: bringing forward to today, there is a different economic structure in China. 428 00:21:49,040 --> 00:21:50,920 Speaker 1: There is subsidies, yes there is. 429 00:21:51,200 --> 00:21:56,320 Speaker 2: Can the American auto industry outcompete those subsidies, out innovate 430 00:21:56,359 --> 00:21:57,160 Speaker 2: those subsidies. 431 00:21:57,720 --> 00:22:01,639 Speaker 4: That's a very difficult answer to to give you right now. 432 00:22:01,720 --> 00:22:04,640 Speaker 4: I think it's too really to answer your question because 433 00:22:04,680 --> 00:22:07,600 Speaker 4: we're only in the second inning of these pure electric vehicles, 434 00:22:07,920 --> 00:22:11,479 Speaker 4: and the innovation cycles so fast we could be talking 435 00:22:11,480 --> 00:22:13,200 Speaker 4: in a year or two of a whole new battery 436 00:22:13,240 --> 00:22:17,000 Speaker 4: technology that could either change that equation. But I will 437 00:22:17,040 --> 00:22:21,280 Speaker 4: say at this point they have a big lead. When 438 00:22:21,320 --> 00:22:24,480 Speaker 4: you look at a BYD, we think on average four 439 00:22:24,520 --> 00:22:27,960 Speaker 4: to five thousand dollars a vehicle of direct support from 440 00:22:27,960 --> 00:22:31,840 Speaker 4: the Chinese government that's exported to Europe and New Mexico 441 00:22:31,920 --> 00:22:32,679 Speaker 4: and places like that. 442 00:22:33,760 --> 00:22:35,200 Speaker 5: Can we make up for that. 443 00:22:35,680 --> 00:22:39,320 Speaker 4: Plus their scale, their industrial scale of being ten times 444 00:22:39,320 --> 00:22:41,919 Speaker 4: bigger than the US, I'd say it's pretty difficult at 445 00:22:41,920 --> 00:22:44,520 Speaker 4: this point. I will tell you the level of risk 446 00:22:44,560 --> 00:22:47,960 Speaker 4: that we've had to take on UEV on the execution side, 447 00:22:48,119 --> 00:22:50,960 Speaker 4: large unit castings, building a vehicle in a way we've 448 00:22:51,000 --> 00:22:51,879 Speaker 4: never built before. 449 00:22:52,440 --> 00:22:54,400 Speaker 5: It's not a guaranteed project. 450 00:22:54,400 --> 00:22:56,440 Speaker 4: This is all going to come together just like we thought, 451 00:22:56,800 --> 00:22:59,359 Speaker 4: and so we think we're even with them right now. 452 00:22:59,480 --> 00:23:03,320 Speaker 5: But this bead of innovation we're seeing is very humbling 453 00:23:03,359 --> 00:23:03,639 Speaker 5: for me. 454 00:23:04,119 --> 00:23:06,760 Speaker 4: Even if we have a stable platform on cost today, 455 00:23:07,200 --> 00:23:09,840 Speaker 4: will that platform be competitive in five years when the 456 00:23:09,880 --> 00:23:12,880 Speaker 4: battery tech does another full cycle of innovation. 457 00:23:13,359 --> 00:23:14,040 Speaker 5: Hard to say. 458 00:23:14,600 --> 00:23:18,760 Speaker 4: I'm thinking about twenty thirty two right now. That's to 459 00:23:18,800 --> 00:23:21,280 Speaker 4: answer your question. I'm there. I have to make those 460 00:23:21,280 --> 00:23:24,080 Speaker 4: capital choices now for twenty thirty two. To answer your 461 00:23:24,119 --> 00:23:27,680 Speaker 4: question right now, I think there's a chance. But without 462 00:23:28,119 --> 00:23:32,160 Speaker 4: huge support where it's more of a level of playing field, 463 00:23:32,720 --> 00:23:36,560 Speaker 4: where it isn't in Europe, I think it's gonna be 464 00:23:36,640 --> 00:23:39,760 Speaker 4: really tough, maybe the toughest battle of my career. 465 00:23:42,080 --> 00:23:42,600 Speaker 1: Up next. 466 00:23:42,760 --> 00:23:46,240 Speaker 2: The Australian economy has been powered for three decades by 467 00:23:46,280 --> 00:23:49,879 Speaker 2: its rich natural resources. We travel down Under to see 468 00:23:49,920 --> 00:23:53,639 Speaker 2: firsthand its efforts to diversify into new and more cutting 469 00:23:53,720 --> 00:24:05,520 Speaker 2: edge areas of growth. This is a story about the 470 00:24:05,640 --> 00:24:09,400 Speaker 2: need to be good as well as lucky. Australia has 471 00:24:09,440 --> 00:24:12,280 Speaker 2: been lucky in the natural resources that have been driving 472 00:24:12,320 --> 00:24:16,240 Speaker 2: its uninterrupted growth for three decades, but now it needs 473 00:24:16,280 --> 00:24:19,080 Speaker 2: to be good in innovation and investing to keep that 474 00:24:19,200 --> 00:24:24,200 Speaker 2: growth going. As our colleague Heidie stroud Watt reports from Sydney. 475 00:24:26,640 --> 00:24:29,720 Speaker 7: Australia has long been known as the lucky country, a 476 00:24:29,800 --> 00:24:34,000 Speaker 7: land of sun, surf and abundant resources. For those looking 477 00:24:34,040 --> 00:24:37,560 Speaker 7: to drive its future, fortune has been harder to find. 478 00:24:38,160 --> 00:24:40,680 Speaker 8: We now find ourselves at a place where there is 479 00:24:40,800 --> 00:24:45,480 Speaker 8: basically no interested growth capital in Australia. Our last round, 480 00:24:45,800 --> 00:24:49,119 Speaker 8: which was a world record setting Series B, eighty percent 481 00:24:49,160 --> 00:24:53,359 Speaker 8: of the funds inbound were foreign direct investment. Only three 482 00:24:53,520 --> 00:24:57,240 Speaker 8: out of twenty investors who joined us were from Australia, 483 00:24:57,720 --> 00:25:01,440 Speaker 8: so we are obligated to look outside. It's the lack 484 00:25:01,480 --> 00:25:02,359 Speaker 8: of familiarity. 485 00:25:02,960 --> 00:25:06,160 Speaker 7: Michael Biersuk is a founder and CEO of q Control, 486 00:25:06,320 --> 00:25:08,480 Speaker 7: a quantum tech startup based in Sydney. 487 00:25:09,040 --> 00:25:12,000 Speaker 8: It's not about loyalty to Australia or disloyalty. It's about 488 00:25:12,000 --> 00:25:15,199 Speaker 8: economic opportunity. The US and the UK right now are 489 00:25:15,240 --> 00:25:18,240 Speaker 8: making massive investments from the public sector and of course 490 00:25:18,280 --> 00:25:22,440 Speaker 8: the private sector in advancing this technology for sovereign capability. 491 00:25:22,600 --> 00:25:26,440 Speaker 8: Australia has not yet done that, and we're looking forward to. 492 00:25:26,400 --> 00:25:27,800 Speaker 1: That kind of investment in the future. 493 00:25:27,920 --> 00:25:31,920 Speaker 8: This is a real map of data coming from commercial airplanes. 494 00:25:32,080 --> 00:25:34,879 Speaker 8: At q Control, we focus on this new field of 495 00:25:34,920 --> 00:25:38,840 Speaker 8: quantum technology and our specialty is building new kinds of 496 00:25:38,880 --> 00:25:42,879 Speaker 8: AI that make this emerging technology area actually useful for 497 00:25:43,119 --> 00:25:46,640 Speaker 8: end applications. One of the core areas that we've invested 498 00:25:46,640 --> 00:25:50,120 Speaker 8: in is helping navigate when there is no GPS. Now, 499 00:25:50,200 --> 00:25:54,640 Speaker 8: on a daily basis, GPS completely governs our lives. Unfortunately, 500 00:25:55,160 --> 00:25:59,560 Speaker 8: the reliability of GPS has diminished tremendously just in the 501 00:25:59,600 --> 00:26:03,160 Speaker 8: last year. Since March of twenty twenty four, we've seen 502 00:26:03,200 --> 00:26:07,560 Speaker 8: almost one thousand flights a day commercial aviation flights disrupted 503 00:26:07,600 --> 00:26:11,680 Speaker 8: by deliberate GPS jamming. This is emerging as a major 504 00:26:11,720 --> 00:26:14,359 Speaker 8: threat and it's not just in commercial aviation. Defense sees 505 00:26:14,440 --> 00:26:17,959 Speaker 8: exactly the same challenges. We set out to try and 506 00:26:18,000 --> 00:26:20,639 Speaker 8: fix this through our work in quantum sensing, and we 507 00:26:20,800 --> 00:26:25,440 Speaker 8: built a new technology that lets us navigate with our GPS. 508 00:26:25,880 --> 00:26:29,160 Speaker 7: It's a business of tomorrow, the kind many believes should 509 00:26:29,160 --> 00:26:33,040 Speaker 7: play a far greater role in shaping Australia's economy of tomorrow. 510 00:26:33,480 --> 00:26:35,600 Speaker 9: Well, we've won the risk of falling behind. I mean, 511 00:26:35,640 --> 00:26:38,800 Speaker 9: there's no question about that. You've got you know, you've 512 00:26:38,880 --> 00:26:40,639 Speaker 9: got to stay at the head, at the front of 513 00:26:40,680 --> 00:26:45,119 Speaker 9: the pack. And to do that you have to you know, 514 00:26:45,160 --> 00:26:47,640 Speaker 9: you've got to keep innovating. You've got to keep asking 515 00:26:47,800 --> 00:26:51,280 Speaker 9: every day. Our our policy is working. Complacency is a 516 00:26:51,359 --> 00:26:55,080 Speaker 9: killer in politics and government, as in business. 517 00:26:55,880 --> 00:26:59,320 Speaker 7: But before examining the Australian economy of tomorrow, it's worth 518 00:26:59,320 --> 00:27:03,320 Speaker 7: explaining the economy of today. It was nineteen sixty four 519 00:27:03,320 --> 00:27:06,560 Speaker 7: when the author Donald Horn famously took aim at Australia, 520 00:27:06,960 --> 00:27:10,200 Speaker 7: calling it a lucky country run mainly by second rate 521 00:27:10,240 --> 00:27:13,440 Speaker 7: people who share its luck. It was a job at 522 00:27:13,480 --> 00:27:17,359 Speaker 7: its complacency, a country whose wealth, he argued, came not 523 00:27:17,400 --> 00:27:23,879 Speaker 7: from innovation or ingenuity, but from its abundant natural resources iron, ore, coal, gold. 524 00:27:24,280 --> 00:27:25,159 Speaker 7: The list goes on. 525 00:27:25,680 --> 00:27:28,920 Speaker 10: There's no doubt that the economy has a pretty narrow base. 526 00:27:29,480 --> 00:27:32,960 Speaker 7: Jennifer Westercott is a senior advisor at KPMG and a 527 00:27:33,040 --> 00:27:36,840 Speaker 7: former CEO of the Australian Business Council. She says that 528 00:27:36,880 --> 00:27:40,480 Speaker 7: although the country's economy might not be highly diversified, it's 529 00:27:40,600 --> 00:27:42,720 Speaker 7: proven to be very lucrative so far. 530 00:27:43,200 --> 00:27:46,520 Speaker 10: I think the economy still has incredible kind of foundations 531 00:27:46,560 --> 00:27:48,920 Speaker 10: in mining and resources, and there's still a long long 532 00:27:48,960 --> 00:27:50,240 Speaker 10: way to go in resources. 533 00:27:50,880 --> 00:27:54,280 Speaker 7: Australia ranked second in the world for median wealth per capita, 534 00:27:54,560 --> 00:27:58,560 Speaker 7: but its narrow base is showing fragility. Commodity earnings are 535 00:27:58,600 --> 00:28:01,080 Speaker 7: expected to fall to two hundred than seventy one billion 536 00:28:01,160 --> 00:28:04,000 Speaker 7: US dollars this year about three hundred and eighty five 537 00:28:04,000 --> 00:28:07,240 Speaker 7: billion Australian dollars and continue to drop over the next 538 00:28:07,280 --> 00:28:12,280 Speaker 7: two years on folding prices and an uncertain global economy. Well, 539 00:28:12,320 --> 00:28:15,600 Speaker 7: the Reserve Bank of Australia recently slashed it's forecasts for 540 00:28:15,680 --> 00:28:17,440 Speaker 7: economic growth and productivity. 541 00:28:17,800 --> 00:28:20,240 Speaker 10: So I think you know that the first thing we 542 00:28:20,280 --> 00:28:22,879 Speaker 10: need to do is make sure that we protect and 543 00:28:22,960 --> 00:28:27,120 Speaker 10: strengthen that incredible base that's propped up living standards for many, 544 00:28:27,160 --> 00:28:33,000 Speaker 10: many years and has made the country extremely prosperous and 545 00:28:33,040 --> 00:28:35,840 Speaker 10: so there's lots of ways of diversifying the economy and 546 00:28:35,960 --> 00:28:39,080 Speaker 10: driving greater innovation in the economy. The first is to 547 00:28:39,160 --> 00:28:42,880 Speaker 10: drive higher levels of investment, and investment as a share 548 00:28:42,920 --> 00:28:46,160 Speaker 10: of GDP is around the same level was in the 549 00:28:46,240 --> 00:28:48,960 Speaker 10: nineteen ninety so we need to get investment happening. That's 550 00:28:49,000 --> 00:28:52,000 Speaker 10: about lower taxes or on more competitive tax system. It's 551 00:28:52,040 --> 00:28:55,840 Speaker 10: also about reducing regulation. We've also got to drive more innovation. 552 00:28:56,000 --> 00:29:00,000 Speaker 10: That's about skilling our population, making sure that we draw 553 00:29:00,360 --> 00:29:03,040 Speaker 10: the new skills the future in racing things like AI 554 00:29:03,800 --> 00:29:06,920 Speaker 10: and going with sectors of the economy that are in 555 00:29:06,960 --> 00:29:10,160 Speaker 10: our comparative advantage and things that we can scale on. 556 00:29:11,440 --> 00:29:15,120 Speaker 7: And the economy's fragility places even more importance on setting 557 00:29:15,160 --> 00:29:18,800 Speaker 7: up its future. Australia, for all its wealth, spends just 558 00:29:18,880 --> 00:29:21,960 Speaker 7: one point seven percent of GDP on research and development, 559 00:29:22,440 --> 00:29:25,760 Speaker 7: well below the OECD average of two point seven percent. 560 00:29:26,440 --> 00:29:29,560 Speaker 7: Malcolm Turnbull was the Prime Minister of Australia between twenty 561 00:29:29,640 --> 00:29:34,040 Speaker 7: fifteen and twenty eighteen, and before that ran Goldman Sachs Australia. 562 00:29:34,720 --> 00:29:37,400 Speaker 9: At the level of the economy, the big priority has 563 00:29:37,440 --> 00:29:41,880 Speaker 9: always got to be productivity and that is driven by innovation. 564 00:29:42,080 --> 00:29:45,760 Speaker 9: So you've got to You've got, at one level get 565 00:29:45,840 --> 00:29:49,080 Speaker 9: rid of as much regulation and red tape as you can. 566 00:29:49,200 --> 00:29:52,400 Speaker 9: At the same time you've really got to supercharge innovation. 567 00:29:52,960 --> 00:29:55,320 Speaker 9: This was a really important part of my time as 568 00:29:55,400 --> 00:30:00,080 Speaker 9: PMS by Big first Big Economic Agenda, the National Innovation 569 00:30:00,200 --> 00:30:05,840 Speaker 9: and Science Agenda in twenty fifteen that gave tech, R 570 00:30:05,880 --> 00:30:10,680 Speaker 9: and D, investment, venture capital a huge lift and which 571 00:30:10,800 --> 00:30:12,280 Speaker 9: they're still benefiting from. 572 00:30:12,440 --> 00:30:14,120 Speaker 1: But you've got to do it again. 573 00:30:14,240 --> 00:30:16,720 Speaker 9: You know, you can't just do it once and put 574 00:30:16,720 --> 00:30:18,200 Speaker 9: it aside and say that's it. 575 00:30:18,320 --> 00:30:20,800 Speaker 7: How do you characterize the environment when it comes to 576 00:30:20,800 --> 00:30:24,000 Speaker 7: tech an innovation. Do you think it's not getting as 577 00:30:24,080 --> 00:30:26,880 Speaker 7: much attention or as credit as it should be or 578 00:30:26,920 --> 00:30:29,400 Speaker 7: do you think that progress has stalled? 579 00:30:30,000 --> 00:30:34,680 Speaker 9: Well, look, Australia does not spend enough on research and development. False, 580 00:30:34,720 --> 00:30:38,120 Speaker 9: Australian business doesn't and we're not spending enough in the 581 00:30:38,480 --> 00:30:43,960 Speaker 9: pure research primary research realm of universities and research institutions 582 00:30:44,000 --> 00:30:46,360 Speaker 9: are so that needs more encouragement. 583 00:30:47,240 --> 00:30:50,800 Speaker 7: As a result, Australia's foreign direct investment is still heavily 584 00:30:50,840 --> 00:30:55,360 Speaker 7: skewed towards mining, forcing companies like que Control to look 585 00:30:55,400 --> 00:30:56,760 Speaker 7: overseas for investment. 586 00:30:57,360 --> 00:31:01,480 Speaker 8: We have not seen the same level of major tech successes, 587 00:31:01,920 --> 00:31:07,400 Speaker 8: minting billionaires building generational technology businesses in Australia as we've 588 00:31:07,440 --> 00:31:09,800 Speaker 8: seen in the United States, and as a result, I 589 00:31:09,800 --> 00:31:13,920 Speaker 8: think that lack of familiarity has made people just less 590 00:31:13,960 --> 00:31:17,479 Speaker 8: willing to take some bets. And our objective, frankly, is 591 00:31:17,560 --> 00:31:19,840 Speaker 8: to show by example, whether you're in government or a 592 00:31:19,880 --> 00:31:23,920 Speaker 8: local investor base, that the upsides are enormous and real 593 00:31:24,160 --> 00:31:27,400 Speaker 8: and we're just trying with everything we can to deliver 594 00:31:27,440 --> 00:31:28,320 Speaker 8: on that opportunity. 595 00:31:28,920 --> 00:31:32,400 Speaker 11: Could there be that kind of transformative sort of movement 596 00:31:32,520 --> 00:31:36,000 Speaker 11: in Australia And if so, what are the policy measures, 597 00:31:36,040 --> 00:31:38,880 Speaker 11: what are the things in terms of supporting that possibility 598 00:31:38,920 --> 00:31:39,560 Speaker 11: that you see. 599 00:31:39,800 --> 00:31:41,959 Speaker 8: I think there's no question that the opportunity is there. 600 00:31:42,160 --> 00:31:44,920 Speaker 8: I came to Australia from the United States because the 601 00:31:45,080 --> 00:31:47,120 Speaker 8: research community was so strong when I was an academic 602 00:31:47,160 --> 00:31:47,320 Speaker 8: at the. 603 00:31:47,320 --> 00:31:48,200 Speaker 2: University of Sydney. 604 00:31:48,720 --> 00:31:54,520 Speaker 8: The bigger challenge has been building an industry base around 605 00:31:54,600 --> 00:31:58,720 Speaker 8: the transition from basic science over it to industrial applications 606 00:31:59,760 --> 00:32:02,600 Speaker 8: in Australia has been lagging. That we were the first 607 00:32:02,680 --> 00:32:06,280 Speaker 8: venture back company in the field in Australia. The challenges 608 00:32:06,280 --> 00:32:09,560 Speaker 8: we haven't seen that many more in the last seven years. 609 00:32:10,120 --> 00:32:14,000 Speaker 7: Westerncott, who also serves as Chancellor of Western Sydney University, 610 00:32:14,320 --> 00:32:17,640 Speaker 7: believes Australia should be capitalizing on funding cuts to US 611 00:32:17,840 --> 00:32:21,000 Speaker 7: universities to attract talent and drive innovation. 612 00:32:21,600 --> 00:32:23,360 Speaker 10: I think the first thing that we need to do is, 613 00:32:23,400 --> 00:32:26,600 Speaker 10: of course, continue to invest in our universities, continue to 614 00:32:26,680 --> 00:32:30,080 Speaker 10: invest in research and development. But we also need to 615 00:32:30,160 --> 00:32:33,480 Speaker 10: send very consistent and clear policy signals that we're open 616 00:32:33,800 --> 00:32:36,640 Speaker 10: for international students. Now we have a cap on international 617 00:32:36,680 --> 00:32:40,360 Speaker 10: students at the moment, which is very controversial in Australia. 618 00:32:40,400 --> 00:32:43,160 Speaker 10: We need to send that message that we want international 619 00:32:43,200 --> 00:32:46,320 Speaker 10: students one of our biggest exports. We also need to 620 00:32:46,320 --> 00:32:51,200 Speaker 10: make sure that we are encouraging skilled migration from young people. 621 00:32:52,000 --> 00:32:54,520 Speaker 10: You know, KPMG did a study several years ago which 622 00:32:54,520 --> 00:32:56,800 Speaker 10: showed that we'd add thirty billion dollars to the economy 623 00:32:56,800 --> 00:33:00,400 Speaker 10: by targeting those highly skilled young people. When I run 624 00:33:00,440 --> 00:33:03,480 Speaker 10: the Business Council of Australia, the key message that big 625 00:33:03,480 --> 00:33:06,200 Speaker 10: employers will give to me is that they want more skill, 626 00:33:06,480 --> 00:33:10,360 Speaker 10: very specialized skills. So we should be really targeting those 627 00:33:10,400 --> 00:33:12,840 Speaker 10: young people from the United States, common and even work 628 00:33:12,880 --> 00:33:16,720 Speaker 10: in Australia greatest country on Earth and really targeting those 629 00:33:16,800 --> 00:33:20,200 Speaker 10: super skills. So it's an opportunity, but we can't just 630 00:33:20,360 --> 00:33:22,520 Speaker 10: kind of sit back and expect it to happen. We're 631 00:33:22,560 --> 00:33:25,480 Speaker 10: going to have to have deliberate and purposeful policies and 632 00:33:25,560 --> 00:33:28,600 Speaker 10: actions to encourage those people to come to Australia. 633 00:33:28,680 --> 00:33:31,520 Speaker 9: We have a huge opportunity in terms of talent. And 634 00:33:32,000 --> 00:33:36,000 Speaker 9: while no one, no Australian government wants to go out 635 00:33:36,040 --> 00:33:41,120 Speaker 9: there and say to talented people in other countries, you're 636 00:33:41,160 --> 00:33:43,440 Speaker 9: living in a terrible country, come and live in our house. 637 00:33:43,520 --> 00:33:44,880 Speaker 1: You've got to be tactful about this. 638 00:33:45,400 --> 00:33:48,840 Speaker 9: But let's face it, there is a war for talent 639 00:33:49,160 --> 00:33:53,479 Speaker 9: human talent, and we have some very attractive things going on, 640 00:33:53,520 --> 00:33:55,120 Speaker 9: which is why, by the way, we need to be 641 00:33:55,160 --> 00:34:00,760 Speaker 9: putting more money into research because those scientists and technologists 642 00:34:00,800 --> 00:34:02,760 Speaker 9: will want to come here and work with it. But 643 00:34:02,880 --> 00:34:05,920 Speaker 9: you think about the livability of our cities for example, 644 00:34:06,440 --> 00:34:08,880 Speaker 9: you know we have some of the most livable cities 645 00:34:08,880 --> 00:34:09,400 Speaker 9: in the world. 646 00:34:09,960 --> 00:34:14,160 Speaker 7: Ultimately, lifestyle can only take you so far. If Australia 647 00:34:14,200 --> 00:34:16,920 Speaker 7: wants to remain competitive, it needs to be more than 648 00:34:17,000 --> 00:34:19,640 Speaker 7: just a great place to surf. It needs to be 649 00:34:19,680 --> 00:34:22,920 Speaker 7: a great place to invest. It's luck may not be 650 00:34:23,000 --> 00:34:26,360 Speaker 7: running out entirely, but to get the economy humming and 651 00:34:26,440 --> 00:34:30,759 Speaker 7: needs investment and innovation, it needs to be good as 652 00:34:30,800 --> 00:34:32,040 Speaker 7: well as lucky. 653 00:34:32,480 --> 00:34:34,879 Speaker 9: I think Australia is lucky, but we've made a lot 654 00:34:34,880 --> 00:34:39,239 Speaker 9: of our own luck too, so I'm very optimistic about Australia's. 655 00:34:38,680 --> 00:34:44,080 Speaker 2: Future coming up. AI may already be playing a big 656 00:34:44,200 --> 00:34:46,960 Speaker 2: role in your next visit to the doctor without your 657 00:34:47,040 --> 00:34:50,000 Speaker 2: even knowing about it. We tell the story of artificial 658 00:34:50,040 --> 00:35:05,480 Speaker 2: intelligence in healthcare next. This is not yet another story 659 00:35:05,520 --> 00:35:08,640 Speaker 2: about the promise of artificial intelligence. It's the first in 660 00:35:08,680 --> 00:35:11,439 Speaker 2: a series of stories that go beyond the hope and 661 00:35:11,680 --> 00:35:14,399 Speaker 2: the hype to see where AI is making a real 662 00:35:14,440 --> 00:35:18,759 Speaker 2: difference today. Starting with something important to us. All our 663 00:35:18,840 --> 00:35:19,560 Speaker 2: medical care. 664 00:35:21,120 --> 00:35:23,000 Speaker 4: Yes, yeah, good morning. 665 00:35:23,239 --> 00:35:27,840 Speaker 2: On any given day Tennessee, based on coologists Samyukta Mulanghi 666 00:35:28,320 --> 00:35:30,919 Speaker 2: could see as many as twenty five patients. 667 00:35:31,360 --> 00:35:35,560 Speaker 12: Medical oncologists like me are pressed for time and we're 668 00:35:35,560 --> 00:35:38,360 Speaker 12: overwhelmed with sort of clinical duties, trying to do that 669 00:35:38,480 --> 00:35:42,600 Speaker 12: work of charting in ahead of time, seeing the patient, 670 00:35:42,880 --> 00:35:47,520 Speaker 12: documenting after the fact, and also now trying to squeeze 671 00:35:47,560 --> 00:35:49,759 Speaker 12: in record retrieval in the middle of everything is just 672 00:35:49,920 --> 00:35:53,840 Speaker 12: very impossible, and so you have the concept of pajama 673 00:35:53,920 --> 00:35:56,680 Speaker 12: time where oncologists and other physicians are just sort of 674 00:35:56,719 --> 00:35:59,640 Speaker 12: finishing up their daily work at home after you know, 675 00:35:59,680 --> 00:36:01,520 Speaker 12: the kids are put to bed and after dinner, and 676 00:36:01,560 --> 00:36:05,400 Speaker 12: it just contributes to a lot of provider burnout. 677 00:36:05,600 --> 00:36:09,840 Speaker 2: Doctor Mulongi is not alone. All across the country, doctors 678 00:36:09,880 --> 00:36:13,920 Speaker 2: in all specialties are often stretched thin. The American Medical 679 00:36:13,960 --> 00:36:17,600 Speaker 2: Association found that almost half of US physicians experienced at 680 00:36:17,719 --> 00:36:21,239 Speaker 2: least one symptom of burnout. It's a little wonder that 681 00:36:21,280 --> 00:36:24,239 Speaker 2: our doctors are feeling the burden given the explosion of 682 00:36:24,320 --> 00:36:28,480 Speaker 2: medical research. So Daniel Nadler decided to do something about it. 683 00:36:29,280 --> 00:36:32,040 Speaker 13: The rate of doubling of medical knowledge in nineteen fifty 684 00:36:32,120 --> 00:36:35,719 Speaker 13: was roughly every fifty years. In twenty twenty five, they're 685 00:36:35,760 --> 00:36:40,239 Speaker 13: different estimates and they're different numbers. In a study in 686 00:36:40,440 --> 00:36:43,080 Speaker 13: the British Medical Journal and another study in Nature, they 687 00:36:43,120 --> 00:36:45,440 Speaker 13: found that the rate of doubling of medical knowledge was 688 00:36:45,520 --> 00:36:46,719 Speaker 13: seventy three days. 689 00:36:47,160 --> 00:36:51,839 Speaker 2: Nadler's PhD thesis from Harvard was on analyzing derivatives, which 690 00:36:51,880 --> 00:36:55,680 Speaker 2: he turned into a startup using machine learning for financial analysis. 691 00:36:56,160 --> 00:36:59,080 Speaker 2: After selling that company for half a billion dollars, he 692 00:36:59,160 --> 00:37:02,040 Speaker 2: turned his attention to helping doctors make sense of the 693 00:37:02,080 --> 00:37:05,319 Speaker 2: tsunami of medical research coming their way. 694 00:37:05,360 --> 00:37:07,960 Speaker 13: We looked at this and we did another analysis, and 695 00:37:07,960 --> 00:37:10,600 Speaker 13: we said, let's ignore the doubling for a second and 696 00:37:10,800 --> 00:37:15,000 Speaker 13: just ask the question if you had to read just 697 00:37:15,120 --> 00:37:18,839 Speaker 13: the top third of peer reviewed medical literature just within 698 00:37:18,880 --> 00:37:21,640 Speaker 13: your specialty, which is not ideal. Right, that means no 699 00:37:21,719 --> 00:37:24,319 Speaker 13: cardiologist is reading anything in neurology and vice versa, which 700 00:37:24,360 --> 00:37:26,200 Speaker 13: is not ideal. But even if you just said that, 701 00:37:27,200 --> 00:37:31,040 Speaker 13: how long would it take every day for a specialist 702 00:37:31,200 --> 00:37:33,760 Speaker 13: to just read the top third of peer reviewed medical 703 00:37:33,760 --> 00:37:37,239 Speaker 13: literature just within their specialty? And the answer turned out 704 00:37:37,239 --> 00:37:41,000 Speaker 13: to be something about something like nine hours. So practically 705 00:37:41,000 --> 00:37:43,560 Speaker 13: that's obviously impossible. They would never see patients, or never 706 00:37:43,600 --> 00:37:45,759 Speaker 13: see their family, or they would never sleep. 707 00:37:46,040 --> 00:37:49,000 Speaker 2: And that led Nadler to found Open Evidence in twenty 708 00:37:49,040 --> 00:37:49,560 Speaker 2: twenty two. 709 00:37:50,040 --> 00:37:53,239 Speaker 13: Open Evidence is designed to do for physicians what the 710 00:37:53,280 --> 00:37:56,560 Speaker 13: advent of computer systems, let's call it that on Wall 711 00:37:56,600 --> 00:37:59,080 Speaker 13: Street achieved for Wall Street knowledge workers. 712 00:38:00,239 --> 00:38:04,320 Speaker 2: Evidence is an AI model trained on medical literature, carefully 713 00:38:04,400 --> 00:38:07,600 Speaker 2: curated to ensure high quality results for. 714 00:38:07,600 --> 00:38:09,680 Speaker 13: The first time in the last let's call it three 715 00:38:10,000 --> 00:38:13,520 Speaker 13: four five years maximum, we've reached a point in the 716 00:38:13,560 --> 00:38:18,279 Speaker 13: sophistication of artificial intelligence of computers broadly that they can 717 00:38:18,280 --> 00:38:20,839 Speaker 13: store not just the right letters and the right words 718 00:38:20,880 --> 00:38:22,759 Speaker 13: and the right order, but they can understand the semantic 719 00:38:22,880 --> 00:38:26,800 Speaker 13: meaning of the findings of these studies. So Open Evidence 720 00:38:27,120 --> 00:38:30,400 Speaker 13: is an artificial intelligence. It's a computer system that's able 721 00:38:30,480 --> 00:38:34,560 Speaker 13: to understand the semantic meaning of the findings of these 722 00:38:34,600 --> 00:38:37,320 Speaker 13: studies so that it can act as a brain extender 723 00:38:37,600 --> 00:38:40,359 Speaker 13: to physicians who have even in the best and most 724 00:38:40,400 --> 00:38:43,680 Speaker 13: generous reading of what they have to do as a 725 00:38:43,680 --> 00:38:45,480 Speaker 13: physician in terms of keeping up with the pace of 726 00:38:45,520 --> 00:38:48,000 Speaker 13: medical knowledge, such that they don't have to spend nine 727 00:38:48,040 --> 00:38:50,480 Speaker 13: hours a day just reading the top third of pure 728 00:38:50,560 --> 00:38:52,400 Speaker 13: viewed medical journals. 729 00:38:53,080 --> 00:38:56,359 Speaker 2: Doctors are piling into the platform, which says it has 730 00:38:56,400 --> 00:38:59,360 Speaker 2: signed up around fifty percent of all doctors in America 731 00:38:59,719 --> 00:39:04,400 Speaker 2: and adding sixty five thousand every month. Investors are piling 732 00:39:04,440 --> 00:39:07,719 Speaker 2: in two Its latest funding round valued the company at 733 00:39:07,840 --> 00:39:12,080 Speaker 2: three point five billion dollars. What is it that Open 734 00:39:12,160 --> 00:39:14,880 Speaker 2: Evidence should be relied upon to do and what do 735 00:39:14,920 --> 00:39:17,759 Speaker 2: we still need the physician to do. For example, can 736 00:39:17,840 --> 00:39:19,960 Speaker 2: open evidence diagnose no? 737 00:39:20,440 --> 00:39:24,480 Speaker 13: So the physician is still relied upon to do everything 738 00:39:24,480 --> 00:39:28,160 Speaker 13: that a physician was always relied upon to do. I 739 00:39:28,200 --> 00:39:33,359 Speaker 13: see open evidence as a continuum or a continuation of 740 00:39:33,400 --> 00:39:39,160 Speaker 13: a very traditional technology called search. Right, So historically physicians 741 00:39:40,120 --> 00:39:42,920 Speaker 13: needed to search for findings and medical journals. That's not 742 00:39:42,960 --> 00:39:46,200 Speaker 13: a new behavior. They've been doing that for years and 743 00:39:46,280 --> 00:39:48,960 Speaker 13: years and years. One way to think about this is 744 00:39:49,640 --> 00:39:52,600 Speaker 13: we spend a lot of time as a society celebrating 745 00:39:52,640 --> 00:39:56,320 Speaker 13: the golden age of biotechnology, and we should. Right, every 746 00:39:56,800 --> 00:39:59,960 Speaker 13: metric and proxy you look at in the data shows 747 00:40:00,080 --> 00:40:03,760 Speaker 13: that we are accelerating the rate of drug discovery, including 748 00:40:03,800 --> 00:40:07,320 Speaker 13: using artificial intelligence. We're accelerating the rate of drug development, 749 00:40:07,560 --> 00:40:09,959 Speaker 13: and so we celebrate that we're in this golden age 750 00:40:10,000 --> 00:40:13,080 Speaker 13: and it's amazing, this golden age of biotechnology. But what's 751 00:40:13,120 --> 00:40:16,680 Speaker 13: not talked about a lot or enough is that this 752 00:40:17,000 --> 00:40:20,319 Speaker 13: golden age of biotechnology is really the dark ages for 753 00:40:20,320 --> 00:40:24,239 Speaker 13: physicians in terms of burnout. We expect that almost all 754 00:40:24,239 --> 00:40:27,240 Speaker 13: physicians in the United States will be on the platform 755 00:40:27,360 --> 00:40:28,640 Speaker 13: within the next year. 756 00:40:29,480 --> 00:40:32,880 Speaker 2: Doctor David Reich is President and chief Clinical Officer for 757 00:40:33,040 --> 00:40:36,840 Speaker 2: Mount Sinai Health System in New York. He uses Open Evidence, 758 00:40:37,080 --> 00:40:39,760 Speaker 2: but as part of a larger range of AI models 759 00:40:39,800 --> 00:40:41,799 Speaker 2: they are integrating into their hospitals. 760 00:40:42,160 --> 00:40:44,759 Speaker 14: I have the app on my phone and it's available 761 00:40:44,920 --> 00:40:48,800 Speaker 14: through our medical school library, and people do use it. However, 762 00:40:49,000 --> 00:40:52,520 Speaker 14: we do also work with chat GPT, and they've created 763 00:40:52,560 --> 00:40:56,080 Speaker 14: an environment where we can have our medical students ask 764 00:40:56,239 --> 00:41:00,000 Speaker 14: questions that contain protected health information, and that protected health 765 00:41:00,040 --> 00:41:04,480 Speaker 14: health information stays within the CyberSecure. 766 00:41:04,040 --> 00:41:07,720 Speaker 3: Environment that we work so hard to maintain. 767 00:41:08,120 --> 00:41:12,520 Speaker 14: And so I think cybersecurity remains a key consideration in 768 00:41:12,600 --> 00:41:16,160 Speaker 14: any tools that are a great assistance to us, and 769 00:41:16,239 --> 00:41:19,040 Speaker 14: I'm sure that Open Evidence will work very hard to 770 00:41:19,640 --> 00:41:21,000 Speaker 14: address that along with others. 771 00:41:21,000 --> 00:41:22,840 Speaker 1: But I'm very enthusiastic about it. 772 00:41:22,920 --> 00:41:23,879 Speaker 14: I think it's a great tool. 773 00:41:24,560 --> 00:41:27,400 Speaker 2: One of the reasons Open Evidence has become so popular 774 00:41:27,440 --> 00:41:30,840 Speaker 2: with doctors so quickly is that it licenses the best 775 00:41:30,920 --> 00:41:35,320 Speaker 2: medical literature from trusted sources, and it's free to doctors, 776 00:41:35,440 --> 00:41:37,839 Speaker 2: relying on advertising for its revenue. 777 00:41:38,239 --> 00:41:45,360 Speaker 13: We write from the start licensed content, licensed journals, licensed information, 778 00:41:45,920 --> 00:41:50,160 Speaker 13: medical information from the relevant copyright holders. So we have 779 00:41:50,440 --> 00:41:55,360 Speaker 13: a agreement with the Massachusetts Medical Society, which owns the 780 00:41:55,400 --> 00:41:58,280 Speaker 13: new maudral Medicine. It's a nonprofit. We have a licensing 781 00:41:58,320 --> 00:42:01,960 Speaker 13: agreement with the American Medicalists Asociation. Again it's a nonprofit, 782 00:42:01,960 --> 00:42:04,719 Speaker 13: but it owns the journal of the American Medical Association 783 00:42:05,120 --> 00:42:09,319 Speaker 13: as well as all the specialty journals JAMMA Oncology, JAMMA Neurology, 784 00:42:09,600 --> 00:42:11,000 Speaker 13: and so on and so on and so on, not 785 00:42:11,400 --> 00:42:15,520 Speaker 13: just those. So we took a very different approach to copyright, 786 00:42:15,600 --> 00:42:19,800 Speaker 13: to licensing, to all of this, and we're extremely proud 787 00:42:19,840 --> 00:42:22,840 Speaker 13: of the fact that we're probably the only PUREI company 788 00:42:23,440 --> 00:42:26,560 Speaker 13: in America that is not currently being sued for copyright violations. 789 00:42:26,640 --> 00:42:29,520 Speaker 13: Our business model is the exact same as Google, which 790 00:42:29,560 --> 00:42:32,600 Speaker 13: is I think it's public. Google is one of our 791 00:42:32,680 --> 00:42:36,200 Speaker 13: largest investors and has been an enormous patron to the 792 00:42:36,239 --> 00:42:38,839 Speaker 13: company in many ways. So you can build very successful 793 00:42:39,640 --> 00:42:43,279 Speaker 13: software's of service companies, and there are many examples of 794 00:42:43,719 --> 00:42:47,600 Speaker 13: ten twenty thirty forty fifty one hundred billion dollars market 795 00:42:47,600 --> 00:42:50,600 Speaker 13: cap software's to service companies. But once you start getting 796 00:42:50,640 --> 00:42:54,000 Speaker 13: into the rare air of multi trillion market cap companies, 797 00:42:54,320 --> 00:42:56,759 Speaker 13: it's notable that they are that almost all of them, 798 00:42:57,200 --> 00:43:00,600 Speaker 13: again excepting Nvidia and Apple, are either primarily the advertising 799 00:43:00,640 --> 00:43:03,600 Speaker 13: business models or have advertising as a significant component of 800 00:43:03,600 --> 00:43:05,880 Speaker 13: what they do. And I think the explanation there is 801 00:43:06,560 --> 00:43:09,440 Speaker 13: it is the business model that most aligns the incentives 802 00:43:09,480 --> 00:43:12,680 Speaker 13: of the platform and the users to make sure you're 803 00:43:12,680 --> 00:43:16,760 Speaker 13: delivering to your users the highest quality product possible, because 804 00:43:16,760 --> 00:43:20,680 Speaker 13: your incentive is not reducing cost. Your incentive is attracting 805 00:43:20,680 --> 00:43:22,640 Speaker 13: more users and increasing your engagement. 806 00:43:23,080 --> 00:43:26,680 Speaker 2: AI tools like open Evidence are already providing much needed 807 00:43:26,719 --> 00:43:29,839 Speaker 2: help to physicians who must focus on their patients even 808 00:43:29,880 --> 00:43:32,160 Speaker 2: as they need to keep up with the deluge of 809 00:43:32,200 --> 00:43:35,680 Speaker 2: new research. But for AI to realize it's full potential 810 00:43:35,680 --> 00:43:39,160 Speaker 2: in healthcare, doctor Reich says it needs to become fully 811 00:43:39,239 --> 00:43:41,040 Speaker 2: integrated into the workflow. 812 00:43:41,200 --> 00:43:46,600 Speaker 14: But Mount Sinai is doing the genetic information analysis on 813 00:43:46,960 --> 00:43:51,120 Speaker 14: up to a million patients in partnership with Regeneron, and 814 00:43:51,360 --> 00:43:54,680 Speaker 14: we're several hundred thousand patients into this, and we have 815 00:43:54,760 --> 00:44:00,200 Speaker 14: a vast trove of information on patient medical images. We 816 00:44:00,280 --> 00:44:04,200 Speaker 14: have incredible information in our electronic health record. Now, when 817 00:44:04,239 --> 00:44:07,440 Speaker 14: we start to marry all of those data sources together 818 00:44:08,080 --> 00:44:10,759 Speaker 14: and follow the promise of AI in the not too 819 00:44:10,840 --> 00:44:13,400 Speaker 14: distant future, I should be able to say to you 820 00:44:13,480 --> 00:44:16,560 Speaker 14: when you come in not only did I screen you 821 00:44:16,600 --> 00:44:17,960 Speaker 14: and I found particular risks. 822 00:44:18,000 --> 00:44:19,160 Speaker 1: Not only do I have. 823 00:44:19,480 --> 00:44:22,719 Speaker 14: Care pathways which suggest how I should go forward, but 824 00:44:22,840 --> 00:44:26,960 Speaker 14: it's specific to you, to your family history, to your 825 00:44:27,000 --> 00:44:30,759 Speaker 14: genetic markers, and hopefully the giving you the best and 826 00:44:30,800 --> 00:44:35,360 Speaker 14: safest possible experience. So think of the future as being 827 00:44:35,480 --> 00:44:40,799 Speaker 14: much more personalized. And the advance of technology is so 828 00:44:41,080 --> 00:44:44,800 Speaker 14: inspiring right now that I think that what I've witnessed 829 00:44:44,800 --> 00:44:48,000 Speaker 14: over several decades of medicine could vastly change in the 830 00:44:48,040 --> 00:44:52,120 Speaker 14: next several years, as long as we learn the lessons 831 00:44:52,200 --> 00:44:56,440 Speaker 14: of past mistakes of being maybe too exuberant about technology 832 00:44:56,960 --> 00:45:01,400 Speaker 14: and making sure that people who are truly in touch 833 00:45:01,520 --> 00:45:06,560 Speaker 14: with that social contract between patients in this nation and 834 00:45:06,719 --> 00:45:10,560 Speaker 14: the payers and the government and the providers, that we 835 00:45:10,640 --> 00:45:12,200 Speaker 14: actually find really good solutions. 836 00:45:12,440 --> 00:45:15,760 Speaker 2: Where doctor Reich emphasizes the integration of all the data 837 00:45:15,840 --> 00:45:19,600 Speaker 2: into a single workflow, Daniel Nadler envisions a world connecting 838 00:45:19,640 --> 00:45:22,759 Speaker 2: physicians with others around the world who are working on 839 00:45:22,800 --> 00:45:25,040 Speaker 2: the same clinical challenges. 840 00:45:24,960 --> 00:45:27,319 Speaker 13: As open evidence develops. If we go back to my 841 00:45:27,400 --> 00:45:30,080 Speaker 13: metaphor of the sort of nineteen forties World War two 842 00:45:30,120 --> 00:45:35,400 Speaker 13: telephone operator who's routing and connecting a human to another 843 00:45:35,480 --> 00:45:38,920 Speaker 13: human on a battlefield. In this case, it might actually 844 00:45:40,239 --> 00:45:42,279 Speaker 13: end up in a world where the AI is the 845 00:45:42,360 --> 00:45:46,400 Speaker 13: least interesting part of the technology, and what's really happening 846 00:45:46,719 --> 00:45:49,960 Speaker 13: is the AI is serving as connective tissue between a 847 00:45:50,040 --> 00:45:54,080 Speaker 13: human and a human, between a human physician presenting some 848 00:45:55,000 --> 00:45:59,840 Speaker 13: atypical combination of symptoms and another human somewhere in the 849 00:45:59,880 --> 00:46:03,279 Speaker 13: country that is an expert on that and where the 850 00:46:03,400 --> 00:46:06,600 Speaker 13: job of the AI is as far as possible from 851 00:46:06,640 --> 00:46:09,239 Speaker 13: answering the question and as much more about getting out 852 00:46:09,239 --> 00:46:11,160 Speaker 13: of the way as quickly as possible and connecting that 853 00:46:11,200 --> 00:46:14,600 Speaker 13: one human to another human. And that's a very wonderful 854 00:46:14,600 --> 00:46:17,840 Speaker 13: and sort of optimistic vision for what the future of 855 00:46:17,880 --> 00:46:21,000 Speaker 13: AI can be. You know, most scenarios for the future 856 00:46:21,040 --> 00:46:24,080 Speaker 13: of AI, or many are very dystopian. I can't common 857 00:46:24,120 --> 00:46:26,080 Speaker 13: on what happens outside of medicine, but in medicine, I 858 00:46:26,080 --> 00:46:29,400 Speaker 13: think you have a very beautiful possibility where the technology 859 00:46:29,560 --> 00:46:32,200 Speaker 13: ends up serving as connective tissue between a human and 860 00:46:32,200 --> 00:46:32,600 Speaker 13: a human. 861 00:46:33,680 --> 00:46:36,440 Speaker 2: But whether it's connecting the doctor with the data or 862 00:46:36,480 --> 00:46:39,640 Speaker 2: the human with the human. Right now, AI products like 863 00:46:39,680 --> 00:46:43,600 Speaker 2: open Evidence are providing much needed relief for practicing physicians 864 00:46:43,920 --> 00:46:45,120 Speaker 2: like Sam Mulanghi. 865 00:46:45,560 --> 00:46:48,000 Speaker 12: I would say that for me, open evidence solves like 866 00:46:48,280 --> 00:46:54,520 Speaker 12: two rather unrelated but maybe orthogonal problems. One is that 867 00:46:55,080 --> 00:46:58,200 Speaker 12: actually open evidence taps into the entire medical corpus of 868 00:46:58,239 --> 00:47:02,000 Speaker 12: academic literature, which actually, for the most part, tends to 869 00:47:02,000 --> 00:47:04,399 Speaker 12: be paywalled. The second thing that it does, I think 870 00:47:04,520 --> 00:47:07,160 Speaker 12: is just sort of marrying the best of current AI 871 00:47:07,200 --> 00:47:11,719 Speaker 12: capabilities around natural language processing and reasoning to try and 872 00:47:11,760 --> 00:47:15,600 Speaker 12: interpret my requests and retrieve records easily in a very 873 00:47:15,680 --> 00:47:19,400 Speaker 12: time efficient manner. So having sort of smart AI tooling 874 00:47:19,480 --> 00:47:24,279 Speaker 12: that is able to provide fast queries has just been 875 00:47:24,320 --> 00:47:26,719 Speaker 12: really been a game changer for me, and that is. 876 00:47:26,719 --> 00:47:30,200 Speaker 2: One application of artificial intelligence that is making a real 877 00:47:30,239 --> 00:47:35,880 Speaker 2: difference in the here and now. Next week will continue 878 00:47:35,920 --> 00:47:38,759 Speaker 2: our exploration of where AI is already being put to 879 00:47:38,800 --> 00:47:43,000 Speaker 2: good use, this time when it comes to education that 880 00:47:43,040 --> 00:47:44,960 Speaker 2: does it for us Here at Wall Street Week, I'm 881 00:47:45,040 --> 00:48:00,239 Speaker 2: David Weston. See you next week for more stories of capitalism.