1 00:00:02,520 --> 00:00:10,480 Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. This is Bloomberg Business 2 00:00:10,480 --> 00:00:14,400 Speaker 1: Week Daily reporting from the magazine that helps global leaders 3 00:00:14,400 --> 00:00:18,320 Speaker 1: stay ahead with insight on the people, companies, and trends 4 00:00:18,360 --> 00:00:23,360 Speaker 1: shaping today's complex economy. Plus global business finance and tech 5 00:00:23,440 --> 00:00:27,320 Speaker 1: news as it happens. The Bloomberg Business Week Daily Podcast 6 00:00:27,640 --> 00:00:32,040 Speaker 1: with Carol Masser and Tim Stenebeck on Bloomberg Radio. 7 00:00:32,000 --> 00:00:34,360 Speaker 2: Let's get to the micro and Houston, we kind of 8 00:00:34,360 --> 00:00:38,159 Speaker 2: have a problem. OpenAI said it's advanced artificial intelligence models 9 00:00:38,200 --> 00:00:42,800 Speaker 2: inadvertently hacked hugging face in an unprecedented incident that prompted 10 00:00:42,840 --> 00:00:45,360 Speaker 2: fresh calls for curbs the technology. 11 00:00:45,400 --> 00:00:46,040 Speaker 3: Are you worried? 12 00:00:47,120 --> 00:00:50,360 Speaker 4: I am among those who might be a little worried, 13 00:00:50,600 --> 00:00:52,479 Speaker 4: a little perturbed. Yeah, let's see if I Love Love 14 00:00:52,880 --> 00:00:55,280 Speaker 4: says we need to be worried. He's the house of 15 00:00:55,320 --> 00:00:58,240 Speaker 4: Bloomberg Tech. He joins us from our San Francisco bureau. 16 00:00:58,400 --> 00:01:00,360 Speaker 4: And I got a text message just minutes ago from 17 00:01:00,360 --> 00:01:03,080 Speaker 4: some friends. They sent this article to the group chat 18 00:01:03,080 --> 00:01:05,600 Speaker 4: and said, be honest, how many years do we have left? 19 00:01:07,959 --> 00:01:10,880 Speaker 5: This is a big story, but it's not a scandal, 20 00:01:11,080 --> 00:01:15,920 Speaker 5: and it's really important to understand what happened. Chronologically. 21 00:01:16,120 --> 00:01:16,480 Speaker 6: Okay. 22 00:01:16,720 --> 00:01:21,440 Speaker 5: So open ai was testing GPT five point six Soul, 23 00:01:21,520 --> 00:01:25,160 Speaker 5: which is its most advanced publicly released model, and it 24 00:01:25,240 --> 00:01:29,640 Speaker 5: was testing another more powerful but unreleased model for their 25 00:01:29,760 --> 00:01:33,560 Speaker 5: advanced cyber capabilities. Okay, they were running in evaluation of them, 26 00:01:34,160 --> 00:01:37,200 Speaker 5: and what they did was reduce the guardrails on those 27 00:01:37,280 --> 00:01:39,920 Speaker 5: because it was in what's called a sandboxed environment, a 28 00:01:40,000 --> 00:01:43,600 Speaker 5: closed environment, which open ai had control over. And so 29 00:01:43,680 --> 00:01:46,280 Speaker 5: that was the instruction to the models, we want to 30 00:01:46,319 --> 00:01:52,280 Speaker 5: test your cyber capabilities, evaluate them. So the models were like, okay. 31 00:01:52,520 --> 00:01:55,960 Speaker 5: In order to do this to pass the evaluation, the 32 00:01:56,000 --> 00:01:59,560 Speaker 5: models wanted more information, so they exposed what's called a 33 00:01:59,680 --> 00:02:02,520 Speaker 5: zero day floor, basically a floor in the code. It's 34 00:02:02,520 --> 00:02:05,320 Speaker 5: called zero day because the engineers, the human engineers, had 35 00:02:05,360 --> 00:02:09,200 Speaker 5: literally zero days to find and patch it, and in 36 00:02:09,280 --> 00:02:11,280 Speaker 5: doing that, the model has got access to the Internet. 37 00:02:11,480 --> 00:02:12,280 Speaker 7: So their next. 38 00:02:12,080 --> 00:02:16,040 Speaker 5: Logical step was, Okay, our instruction is to pass this evaluation. 39 00:02:17,080 --> 00:02:19,520 Speaker 5: We could do that if we had more information, and 40 00:02:19,680 --> 00:02:22,440 Speaker 5: hugging Face seems like a really logical place to get 41 00:02:22,440 --> 00:02:25,799 Speaker 5: that information because it's a platform that host models specialize 42 00:02:25,800 --> 00:02:30,360 Speaker 5: in this area. So it breached hugging Face systems to 43 00:02:30,440 --> 00:02:32,800 Speaker 5: get the information to try and cheat the evaluation, So 44 00:02:32,840 --> 00:02:36,520 Speaker 5: that part is a concern. But Hugging Face detected that 45 00:02:36,600 --> 00:02:41,680 Speaker 5: this was happening. So this is a lab experiment essentially 46 00:02:41,880 --> 00:02:45,240 Speaker 5: under closed parameters, and the way that the industry has 47 00:02:45,280 --> 00:02:48,760 Speaker 5: reacted is there is some concern because the big takeaways 48 00:02:48,840 --> 00:02:51,079 Speaker 5: it shows what these frontier moors are capable. 49 00:02:51,120 --> 00:02:54,480 Speaker 4: Okay, I'm glad that's where I wanted to go with this, 50 00:02:54,639 --> 00:02:57,600 Speaker 4: because maybe it's fine in a lab if it's Open 51 00:02:57,600 --> 00:03:01,160 Speaker 4: AI and Hugging Face, but if this technology, let's say, 52 00:03:01,520 --> 00:03:04,079 Speaker 4: gets out, and who's to say that the same guardrails 53 00:03:04,080 --> 00:03:06,880 Speaker 4: will be in place with a model maybe one year, 54 00:03:07,000 --> 00:03:09,800 Speaker 4: two years from now in China, for example, that that 55 00:03:09,880 --> 00:03:13,400 Speaker 4: may not have the same guardrails. I think that's concern. 56 00:03:14,000 --> 00:03:16,440 Speaker 5: Now we're getting to the story, you know, the story 57 00:03:16,520 --> 00:03:19,760 Speaker 5: behind all of this, which is these were two American 58 00:03:19,800 --> 00:03:24,560 Speaker 5: companies and open Ai is neconnect with Anthropic, but it's 59 00:03:24,600 --> 00:03:28,960 Speaker 5: a leading frontier lab that was testing American made models 60 00:03:30,480 --> 00:03:35,160 Speaker 5: closed models. Hugging fences defense was to rely on an 61 00:03:35,200 --> 00:03:39,360 Speaker 5: open source model, but where the guardrails limited the ability 62 00:03:39,360 --> 00:03:43,400 Speaker 5: of that model to defend it against a cyber attack, 63 00:03:43,680 --> 00:03:47,000 Speaker 5: which in this case, so happens to have been instigated 64 00:03:47,040 --> 00:03:49,839 Speaker 5: by a US made frontier model. I know that that's 65 00:03:49,840 --> 00:03:52,280 Speaker 5: hard to track, but you're quite rightly pointing out the 66 00:03:52,360 --> 00:03:55,480 Speaker 5: question that loads of people in industry have, which is, 67 00:03:55,720 --> 00:03:59,080 Speaker 5: what if this wasn't open AI. What if this was 68 00:03:59,520 --> 00:04:05,080 Speaker 5: a China model where hugging faces the defender, the defender 69 00:04:05,120 --> 00:04:08,200 Speaker 5: of what's going on isn't able to properly protect itself. 70 00:04:08,560 --> 00:04:10,320 Speaker 5: That is the debate that's been opened. 71 00:04:11,600 --> 00:04:14,680 Speaker 2: I guess my question is, ed, if there are guard 72 00:04:15,160 --> 00:04:19,039 Speaker 2: rails which we all assume will be in place, is 73 00:04:19,080 --> 00:04:21,640 Speaker 2: that one hundred percent or ninety nine point nine percent 74 00:04:21,680 --> 00:04:24,359 Speaker 2: guarantee that things will not go wrong or astray or 75 00:04:24,360 --> 00:04:24,680 Speaker 2: we don't know? 76 00:04:24,800 --> 00:04:28,799 Speaker 5: Okay, So again it's important to go back to open 77 00:04:28,839 --> 00:04:34,440 Speaker 5: AI was testing two specific models with the guardrails intentionally reduced. 78 00:04:34,440 --> 00:04:35,840 Speaker 5: And the way to think about it is, when you 79 00:04:35,880 --> 00:04:39,120 Speaker 5: have the guardrails on, you are testing how a model 80 00:04:39,200 --> 00:04:41,880 Speaker 5: behaves on public roads. I know you guys doing a 81 00:04:41,920 --> 00:04:44,480 Speaker 5: car segment, So say on public roads you want the 82 00:04:44,480 --> 00:04:48,400 Speaker 5: guard rails there in a sandbox environment, a closed environment 83 00:04:48,440 --> 00:04:51,599 Speaker 5: you control, you pair back the guardrails because you want 84 00:04:51,640 --> 00:04:54,880 Speaker 5: to see how it can perform at its greatest capabilities 85 00:04:55,360 --> 00:04:58,440 Speaker 5: closed models, you can do that, you have control that. 86 00:04:58,520 --> 00:05:01,600 Speaker 5: The worrying part here is the Ugging Face part that 87 00:05:01,640 --> 00:05:05,159 Speaker 5: they relied on an open source model, where the guardrails 88 00:05:05,160 --> 00:05:08,240 Speaker 5: that are there for very good reasons also diminish that 89 00:05:08,279 --> 00:05:12,599 Speaker 5: model's ability to defend it against a malicious action, whether 90 00:05:12,640 --> 00:05:14,120 Speaker 5: it was a mistaken action or not. 91 00:05:14,560 --> 00:05:18,680 Speaker 2: Okay, so then all right, this story has definitely caught 92 00:05:18,720 --> 00:05:21,400 Speaker 2: our attention. Just got about forty to fifty seconds left, 93 00:05:21,440 --> 00:05:24,040 Speaker 2: So anybody who's listening, and somebody comes up and like 94 00:05:24,080 --> 00:05:27,479 Speaker 2: did you see this story? You would say not a 95 00:05:27,520 --> 00:05:29,799 Speaker 2: big deal or yeah. 96 00:05:30,160 --> 00:05:34,960 Speaker 5: So the cybersecurity industry, the AI industry, and their investors 97 00:05:35,040 --> 00:05:37,839 Speaker 5: basically think this is a good thing because they want 98 00:05:37,839 --> 00:05:40,919 Speaker 5: the leading labs open AI in this case, to be 99 00:05:41,040 --> 00:05:44,599 Speaker 5: testing what these models are capable of and then reporting back. 100 00:05:44,960 --> 00:05:47,480 Speaker 5: So Open Ai and Hugging Face have got together, they 101 00:05:47,480 --> 00:05:51,360 Speaker 5: have partnered, They've taken steps to make safe the situation 102 00:05:51,920 --> 00:05:55,000 Speaker 5: and do a full investigation, and that report will come out. 103 00:05:55,160 --> 00:05:57,600 Speaker 5: But that's being cheered is a good thing. The information 104 00:05:57,760 --> 00:06:01,320 Speaker 5: from this whole experience will be valuable, all right. 105 00:06:01,200 --> 00:06:05,279 Speaker 2: That makes sense and giving us the context and understand. 106 00:06:04,880 --> 00:06:07,960 Speaker 4: So we're safe for now ED, thank you for now for. 107 00:06:07,960 --> 00:06:13,560 Speaker 2: Now Sky It to be continued DVD ED Ludlow, thank 108 00:06:13,560 --> 00:06:14,840 Speaker 2: you so much. Host to Bloomberg Tech. 109 00:06:16,000 --> 00:06:18,800 Speaker 4: Stay with us. More from Bloomberg Business Week Daily coming 110 00:06:18,880 --> 00:06:19,840 Speaker 4: up after this. 111 00:06:23,760 --> 00:06:27,640 Speaker 1: You're listening to the Bloomberg Business Week Daily Podcast. Catch 112 00:06:27,720 --> 00:06:30,400 Speaker 1: us live weekday afternoons from two to five e's During 113 00:06:30,400 --> 00:06:33,840 Speaker 1: this listen on Applecarplay and Android Auto with the Bloomberg 114 00:06:33,880 --> 00:06:37,320 Speaker 1: Business app, or watch us live on YouTube. 115 00:06:37,640 --> 00:06:41,000 Speaker 2: BMW pulling out of this year's Paris Car Show as 116 00:06:41,040 --> 00:06:43,680 Speaker 2: its new CEO cut costs following a major profit warning. 117 00:06:44,000 --> 00:06:47,160 Speaker 2: Also on the Bloomberg Tesla's cyber truck maybe on its 118 00:06:47,200 --> 00:06:50,359 Speaker 2: way to becoming viewed as the greatest automotive flop of 119 00:06:50,400 --> 00:06:53,640 Speaker 2: all time, similar to the Ford Etzel, which was introduced 120 00:06:53,680 --> 00:06:55,599 Speaker 2: back in nineteen fifty seven. 121 00:06:55,680 --> 00:06:56,920 Speaker 8: There is always, you. 122 00:06:56,839 --> 00:07:00,279 Speaker 4: Know, it's see a lot of them on the road today, No, 123 00:07:00,960 --> 00:07:01,359 Speaker 4: always a. 124 00:07:01,360 --> 00:07:03,480 Speaker 2: Lot going on in the global auto industry, covering all 125 00:07:03,480 --> 00:07:05,640 Speaker 2: the twists and turns the tight curves of the global 126 00:07:05,680 --> 00:07:09,279 Speaker 2: auto industry, from bailouts and buyouts Elon and Nevis to 127 00:07:09,360 --> 00:07:11,880 Speaker 2: China's coming of age. Bloomberg's Keith nultaon who began his 128 00:07:11,960 --> 00:07:14,720 Speaker 2: career back in nineteen eighty five, coming to Bloomberg in 129 00:07:14,720 --> 00:07:17,080 Speaker 2: two thousand and nine amid the Great Financial Crisis and 130 00:07:17,080 --> 00:07:19,600 Speaker 2: the Big three auto bailout. He has written about all 131 00:07:19,640 --> 00:07:21,280 Speaker 2: of it and more. We are delighted to have him 132 00:07:21,280 --> 00:07:23,560 Speaker 2: in New York. His home perch has been in the 133 00:07:23,600 --> 00:07:27,440 Speaker 2: Detroit area, but he is getting ready to wrap up 134 00:07:27,480 --> 00:07:30,400 Speaker 2: his valued and very respected career here at Bloomberg. We 135 00:07:30,440 --> 00:07:33,640 Speaker 2: can't even believe we're not going to be like cal Keith. 136 00:07:33,800 --> 00:07:34,600 Speaker 7: How to talk to him about it? 137 00:07:34,640 --> 00:07:36,000 Speaker 4: I can't believe they're letting you retire. 138 00:07:36,200 --> 00:07:38,800 Speaker 3: Yeah, yeah, yeah, Well you know that's time. It's time. 139 00:07:39,320 --> 00:07:42,960 Speaker 8: Every good career, career has its moment and you know, 140 00:07:43,240 --> 00:07:44,880 Speaker 8: leave out on a high note. 141 00:07:44,920 --> 00:07:47,080 Speaker 4: Well, we are really happy for you. It is sad 142 00:07:47,080 --> 00:07:48,960 Speaker 4: for us, as Carol mentioned, and we do want to 143 00:07:48,960 --> 00:07:51,240 Speaker 4: start at the beginning of your career, all the way 144 00:07:51,280 --> 00:07:55,040 Speaker 4: back in nineteen eighty five, forty one years ago. Take 145 00:07:55,120 --> 00:07:57,200 Speaker 4: us back to that time. You were at the Indianapolis 146 00:07:57,240 --> 00:07:59,360 Speaker 4: News and it's where you began your career. And I 147 00:07:59,360 --> 00:08:02,680 Speaker 4: think it's so because it's like that was when so 148 00:08:02,760 --> 00:08:05,480 Speaker 4: many US automakers were concerned about Japanese. 149 00:08:05,960 --> 00:08:07,560 Speaker 8: Yeah, so that was the big story in that time. 150 00:08:07,760 --> 00:08:09,640 Speaker 8: The way I got the auto beat is my business 151 00:08:09,720 --> 00:08:11,920 Speaker 8: editor at the Indianapolis News, this guy named Ed Lawler. 152 00:08:12,360 --> 00:08:15,119 Speaker 8: He said, So I grew up in Detroit. He said, 153 00:08:15,160 --> 00:08:17,320 Speaker 8: so you're from Detroit, so then you'll cover the auto industry. 154 00:08:17,360 --> 00:08:17,760 Speaker 3: And it's like. 155 00:08:18,000 --> 00:08:21,960 Speaker 8: Okay, but yeah, so you know, it was all about 156 00:08:22,000 --> 00:08:24,560 Speaker 8: the Japanese coming to America in those days. Sounds like 157 00:08:24,600 --> 00:08:27,080 Speaker 8: an echo, doesn't it. And so one of the big 158 00:08:27,120 --> 00:08:29,760 Speaker 8: stories I was covering at that time was Toyota was 159 00:08:29,760 --> 00:08:33,280 Speaker 8: looking for the location of their first American factory India. 160 00:08:33,280 --> 00:08:36,240 Speaker 8: All the states were, you know, vying for it, Indiana included. 161 00:08:36,480 --> 00:08:39,480 Speaker 8: It ended up going to Georgetown, Kentucky. So I was 162 00:08:39,520 --> 00:08:43,520 Speaker 8: there for the groundbreaking of that one and interviewing the 163 00:08:43,679 --> 00:08:47,440 Speaker 8: governor at the time, Martha Laine Collins, and yeah, you know, 164 00:08:47,600 --> 00:08:49,439 Speaker 8: and now here we are with the when will the 165 00:08:49,520 --> 00:08:51,559 Speaker 8: Chinese come to America and where will they locate? 166 00:08:51,600 --> 00:08:53,600 Speaker 3: And the states want them? And you know it'll be 167 00:08:53,760 --> 00:08:54,240 Speaker 3: very similar. 168 00:08:54,240 --> 00:08:55,520 Speaker 2: Do you remember what car are you we driving? 169 00:08:56,160 --> 00:08:56,400 Speaker 3: Yeah? 170 00:08:56,440 --> 00:08:59,559 Speaker 8: In those days I had a Renault Alliance Oh wow, 171 00:09:00,160 --> 00:09:04,079 Speaker 8: uaw hands and Kenosha Wisconsin. When Chrysler and Renot had 172 00:09:04,080 --> 00:09:05,000 Speaker 8: a joint venture. 173 00:09:05,080 --> 00:09:07,160 Speaker 2: Well, I'm so glad you mentioned Chrysler because one of 174 00:09:07,200 --> 00:09:11,360 Speaker 2: the icons or iconic figures of the auto industry is 175 00:09:11,440 --> 00:09:14,720 Speaker 2: Leiah Coca and we actually have a picture nineteen eighty 176 00:09:14,800 --> 00:09:17,120 Speaker 2: nine of you with him, and we'll bring it up 177 00:09:17,160 --> 00:09:21,800 Speaker 2: for everybody. Yea, yeah, tell us about this moment for 178 00:09:21,920 --> 00:09:26,320 Speaker 2: this on radio. It is Keith Norton a few years ago, 179 00:09:26,800 --> 00:09:27,319 Speaker 2: a couple. 180 00:09:27,160 --> 00:09:27,880 Speaker 7: Of decades ago. 181 00:09:28,040 --> 00:09:32,280 Speaker 8: Poor hair, no no mustache, though Leah Coca was like 182 00:09:32,720 --> 00:09:36,760 Speaker 8: he was iconic. Yeah no, So that's obviously a scrum 183 00:09:36,800 --> 00:09:40,079 Speaker 8: and I'm clearly laughing too hard to try and impress 184 00:09:40,160 --> 00:09:40,600 Speaker 8: the chairman. 185 00:09:41,720 --> 00:09:42,600 Speaker 3: But no, he was something. 186 00:09:42,640 --> 00:09:45,760 Speaker 8: I mean, I remember when he came back from Japan 187 00:09:46,400 --> 00:09:49,160 Speaker 8: and this is when the original George Bush had gotten 188 00:09:49,200 --> 00:09:51,439 Speaker 8: sick on the lap of the Prime Minister of Japan, 189 00:09:51,600 --> 00:09:54,439 Speaker 8: or if you remember that moment in history. So Leito 190 00:09:54,559 --> 00:09:56,240 Speaker 8: comes back and he gives a speech. 191 00:09:56,360 --> 00:09:59,640 Speaker 2: She'ms so charming amid the geopolitical backdrop today, but go ahead. 192 00:10:00,280 --> 00:10:01,000 Speaker 3: He goes back and. 193 00:10:01,280 --> 00:10:03,800 Speaker 8: Gives a speech the Detroit Economic Club all about how 194 00:10:03,840 --> 00:10:06,280 Speaker 8: we should essentially and he used this kind of verbiage 195 00:10:06,640 --> 00:10:09,280 Speaker 8: go to war with the Japanese, which you know, given 196 00:10:09,320 --> 00:10:11,360 Speaker 8: the whole World War II thing was probably not the 197 00:10:11,400 --> 00:10:14,760 Speaker 8: right way to put it, but but that was Leiah Coca. 198 00:10:14,840 --> 00:10:17,120 Speaker 2: He pulled no punches and had to deal with his 199 00:10:17,160 --> 00:10:17,800 Speaker 2: own crisis. 200 00:10:17,800 --> 00:10:20,280 Speaker 8: Though it is absolutely I mean he kind of he 201 00:10:20,400 --> 00:10:23,360 Speaker 8: saved not more than kind of. He definitely saved Chrysler. 202 00:10:23,440 --> 00:10:26,320 Speaker 8: They were in dire straits. I remember he got fired 203 00:10:26,679 --> 00:10:29,800 Speaker 8: by Henry Ford the second right, Hank the Deuce fired 204 00:10:29,880 --> 00:10:34,080 Speaker 8: him Ayakoka's father and Mustang, and then he you know, 205 00:10:34,320 --> 00:10:38,200 Speaker 8: rehabilitated himself by saving Chrysler. It was a great time 206 00:10:38,240 --> 00:10:41,800 Speaker 8: to cover the industry because there were such swashbuckling characters 207 00:10:41,800 --> 00:10:44,200 Speaker 8: in those days, Bob Lotts, all these guys that just 208 00:10:44,880 --> 00:10:46,120 Speaker 8: said what was on their mind. 209 00:10:47,120 --> 00:10:49,560 Speaker 4: It wasn't all the auto industry for you, though, some 210 00:10:49,640 --> 00:10:52,359 Speaker 4: other highlights in your career. Even before coming to Bloomberg. 211 00:10:52,400 --> 00:10:55,400 Speaker 4: Senior correspondent BusinessWeek, you covered the auto industry your auto reporter, 212 00:10:55,480 --> 00:10:58,640 Speaker 4: the Detroit News before Bloomberg, though you were Detroit bureau 213 00:10:58,679 --> 00:11:01,439 Speaker 4: chief for ten years over at Newsweek. We covered the 214 00:11:01,440 --> 00:11:05,560 Speaker 4: auto industry there, but also some other stories, including. 215 00:11:05,360 --> 00:11:10,120 Speaker 2: Stewart and this little known man put on the cover. 216 00:11:10,520 --> 00:11:12,480 Speaker 2: Oh yeah, he's president today, Donald Trump. 217 00:11:12,559 --> 00:11:14,559 Speaker 8: Yeah, I spent a couple of days with the Donald 218 00:11:15,040 --> 00:11:18,000 Speaker 8: and Milania. Yeah there's the cover. Yeah, that's the one 219 00:11:18,000 --> 00:11:19,360 Speaker 8: I wrote for those on radio. 220 00:11:19,480 --> 00:11:22,680 Speaker 2: It says it's Donald Trump back in his apprentice days. 221 00:11:22,920 --> 00:11:24,640 Speaker 3: You're fired, right, So he was. 222 00:11:25,640 --> 00:11:27,160 Speaker 8: I mean, the story on how that ended up on 223 00:11:27,200 --> 00:11:30,280 Speaker 8: the cover was This all sounds braggy, But I was 224 00:11:30,320 --> 00:11:33,679 Speaker 8: talking to Jack Welch. I did this all in one day. 225 00:11:33,679 --> 00:11:36,400 Speaker 8: I was talking to Jack Welch, former chairman of GE, 226 00:11:36,920 --> 00:11:39,360 Speaker 8: and he told me it was his favorite show to watch. 227 00:11:40,000 --> 00:11:42,160 Speaker 8: He and Susie would watch it every night. 228 00:11:42,000 --> 00:11:43,559 Speaker 7: Run his network, Right, it's. 229 00:11:43,360 --> 00:11:44,120 Speaker 3: On his network. 230 00:11:44,640 --> 00:11:47,640 Speaker 8: And so then and so then we reached out to Trump, 231 00:11:47,679 --> 00:11:49,600 Speaker 8: who in those days there were no PR people. You 232 00:11:49,679 --> 00:11:52,280 Speaker 8: just called his secretary and he gets on the phone 233 00:11:52,400 --> 00:11:56,120 Speaker 8: and he told me that, in no uncertain terms, we 234 00:11:56,160 --> 00:11:58,720 Speaker 8: should put him on the cover. And I'm like, well, 235 00:11:58,800 --> 00:12:00,360 Speaker 8: right now, it's just a business story. We're going to 236 00:12:00,400 --> 00:12:02,120 Speaker 8: do a business dury in the business section. No, you 237 00:12:02,120 --> 00:12:04,560 Speaker 8: should tell Rick Smith, who was you know, the publisher 238 00:12:04,559 --> 00:12:06,880 Speaker 8: at the time, put me on the cover. And then 239 00:12:06,920 --> 00:12:10,360 Speaker 8: when it went to the the meeting that day, the 240 00:12:10,480 --> 00:12:12,360 Speaker 8: editor said, okay, let's put him on the cover. I 241 00:12:12,400 --> 00:12:14,600 Speaker 8: called him back and he said, I'm glad you listened. 242 00:12:14,679 --> 00:12:16,160 Speaker 7: So so then. 243 00:12:16,040 --> 00:12:18,040 Speaker 3: We both flew to mar A Lago and hung out 244 00:12:18,040 --> 00:12:18,600 Speaker 3: for a couple of days. 245 00:12:18,600 --> 00:12:20,520 Speaker 4: Well, what did you find about him on that on 246 00:12:20,520 --> 00:12:23,679 Speaker 4: that trip to mar Alago? Obviously no prediction that he 247 00:12:23,720 --> 00:12:25,520 Speaker 4: would one day become president, and. 248 00:12:25,360 --> 00:12:27,640 Speaker 8: So well, I mean he was he was a Democrat then, 249 00:12:28,200 --> 00:12:31,400 Speaker 8: and and he had run as a Democrat sort of 250 00:12:31,440 --> 00:12:34,520 Speaker 8: run for more of a marketing exercise in the in 251 00:12:34,640 --> 00:12:37,720 Speaker 8: the Gore Bush election, the election of two thousands. So 252 00:12:37,760 --> 00:12:39,680 Speaker 8: we talked a little bit about that, and he and 253 00:12:39,720 --> 00:12:43,000 Speaker 8: Malania at that time were engaged. They got married a 254 00:12:43,080 --> 00:12:44,760 Speaker 8: year later, and of course Bill and Hillary were at 255 00:12:44,760 --> 00:12:46,480 Speaker 8: the wedding. There's the famous picture of the four. 256 00:12:46,360 --> 00:12:48,280 Speaker 3: Of them together right time. 257 00:12:49,080 --> 00:12:51,480 Speaker 8: So yeah, but yeah, he was, you. 258 00:12:51,440 --> 00:12:52,959 Speaker 3: Know, he was the Donald. 259 00:12:52,960 --> 00:12:56,160 Speaker 8: We watched The Apprentice together and he would, you know, 260 00:12:56,280 --> 00:12:59,400 Speaker 8: turn down the sound when it was the contestants segments, 261 00:12:59,480 --> 00:13:01,680 Speaker 8: and then he was on, he would turn it up 262 00:13:01,720 --> 00:13:03,880 Speaker 8: to a deafening volume so no one in the room 263 00:13:03,920 --> 00:13:04,440 Speaker 8: could talk. 264 00:13:06,000 --> 00:13:09,880 Speaker 2: So fascinating, like the places that being a journalist takes you, 265 00:13:10,000 --> 00:13:13,960 Speaker 2: right and to certain people. All right, let's talk to 266 00:13:14,000 --> 00:13:18,480 Speaker 2: your time at Bloomberg seventeen years you came at what 267 00:13:18,559 --> 00:13:21,480 Speaker 2: was a really difficult time for the world, for the US, 268 00:13:21,559 --> 00:13:25,920 Speaker 2: for the financial industry, and for automakers. Who can forget right, 269 00:13:26,040 --> 00:13:29,200 Speaker 2: the automakers be hauled before Congress. 270 00:13:28,760 --> 00:13:31,040 Speaker 6: The CEOs right, the bailout. 271 00:13:31,160 --> 00:13:33,559 Speaker 8: Yeah, no, it was amazing because it was right as 272 00:13:33,559 --> 00:13:36,080 Speaker 8: I was leading Newsweek and starting at Bloomberg. When I 273 00:13:36,120 --> 00:13:39,480 Speaker 8: was here from my training in New York, Obama was 274 00:13:39,520 --> 00:13:44,240 Speaker 8: being inaugurated for his first term, and you know, George W. 275 00:13:44,240 --> 00:13:46,120 Speaker 8: Bush had sort of handed them behind him to save 276 00:13:46,200 --> 00:13:49,640 Speaker 8: those companies. And famously they were all asked, you know, 277 00:13:49,640 --> 00:13:51,640 Speaker 8: they'd got in trouble for oh yeah, there we go. 278 00:13:51,800 --> 00:13:54,280 Speaker 8: They got in trouble for flying in for the hearings 279 00:13:54,280 --> 00:13:56,160 Speaker 8: and they're told they should. 280 00:13:55,960 --> 00:13:57,600 Speaker 3: Drive in for them. So that's what they did. 281 00:13:57,679 --> 00:14:00,679 Speaker 8: They were all asked to, you know, give up their pay, 282 00:14:00,840 --> 00:14:03,680 Speaker 8: and everybody except Alan Malalley at Ford said they would. 283 00:14:03,880 --> 00:14:08,800 Speaker 8: Alan Malalley said, yeah, I'm fine. Another ones who didn't 284 00:14:08,840 --> 00:14:10,080 Speaker 8: take the bail I was just going to say they 285 00:14:10,080 --> 00:14:10,559 Speaker 8: didn't did that. 286 00:14:10,679 --> 00:14:10,839 Speaker 3: Well. 287 00:14:11,360 --> 00:14:13,920 Speaker 4: A lot has happened in the auto industry since then, 288 00:14:13,960 --> 00:14:17,480 Speaker 4: and it kind of brings us to almost two today, 289 00:14:17,880 --> 00:14:20,360 Speaker 4: the rise of evs and the way that you know, 290 00:14:20,640 --> 00:14:23,400 Speaker 4: even conversations that we had with you on our program 291 00:14:23,480 --> 00:14:25,720 Speaker 4: in the last six years at the beginning of that, 292 00:14:25,760 --> 00:14:27,200 Speaker 4: and that's that's how long I've been doing this. That's 293 00:14:27,200 --> 00:14:29,400 Speaker 4: why I went with the six year number. At the 294 00:14:29,440 --> 00:14:31,080 Speaker 4: beginning of that, we talked to you about the huge 295 00:14:31,080 --> 00:14:34,000 Speaker 4: investments that these US companies were making in evs, how 296 00:14:34,040 --> 00:14:37,360 Speaker 4: difficult it was to get a Ford Mocky, and the 297 00:14:37,440 --> 00:14:39,800 Speaker 4: wait list that a customer would have to wait on, 298 00:14:39,840 --> 00:14:42,920 Speaker 4: and getting a lightning pickup truck. And just a few 299 00:14:43,200 --> 00:14:45,840 Speaker 4: years after that, we're learning that these investments are being 300 00:14:45,840 --> 00:14:48,080 Speaker 4: paired back and the customers just aren't there for. 301 00:14:48,040 --> 00:14:50,040 Speaker 8: These It was one of the biggest misses ever. And 302 00:14:50,080 --> 00:14:54,440 Speaker 8: I think we sometimes our view of it isn't long enough. 303 00:14:54,480 --> 00:14:56,560 Speaker 8: We think, Okay, so Trump got an office and you 304 00:14:56,600 --> 00:14:59,240 Speaker 8: took away the EV incentives and that killed the market. No, 305 00:14:59,360 --> 00:15:03,280 Speaker 8: the market was withering before he got elected. Americans just 306 00:15:03,880 --> 00:15:06,960 Speaker 8: we live in a very big country, so range anxiety 307 00:15:07,080 --> 00:15:11,120 Speaker 8: is real. It's easier in other places that are more compact, 308 00:15:11,920 --> 00:15:15,680 Speaker 8: that have a better infrastructure for recharging vehicles. So particularly 309 00:15:15,720 --> 00:15:17,680 Speaker 8: in the vast middle of the country where I live, 310 00:15:18,400 --> 00:15:21,240 Speaker 8: you know, people just aren't sold on evs, particularly. 311 00:15:20,800 --> 00:15:22,160 Speaker 3: People who drive pickup trucks. 312 00:15:22,160 --> 00:15:25,000 Speaker 8: The big bust of the whole EV American EV cycle 313 00:15:25,440 --> 00:15:28,400 Speaker 8: was putting in pickup trucks first, because if you use 314 00:15:28,440 --> 00:15:32,760 Speaker 8: an EV pickup truck to tow or haul, the battery 315 00:15:32,800 --> 00:15:35,480 Speaker 8: is depleted in no time. So it's just not practical. 316 00:15:35,600 --> 00:15:38,920 Speaker 8: So everybody has taken a step back. I actually think 317 00:15:38,920 --> 00:15:40,800 Speaker 8: in some ways Trump does them a favor because it 318 00:15:40,800 --> 00:15:43,040 Speaker 8: gives them time to regroup as long as they keep 319 00:15:43,080 --> 00:15:46,320 Speaker 8: those barriers to the Chinese and hopefully they'll come up 320 00:15:46,360 --> 00:15:47,200 Speaker 8: with a better solution. 321 00:15:48,160 --> 00:15:50,360 Speaker 2: You know, we have another picture of you, I believe 322 00:15:50,400 --> 00:15:53,000 Speaker 2: with Allan Malay, and this was in two thousand and nine, 323 00:15:53,120 --> 00:15:56,160 Speaker 2: former Ford CEO. You said you reminded us he didn't 324 00:15:56,160 --> 00:15:59,000 Speaker 2: take the bail out. Then where are you, guys? 325 00:15:59,240 --> 00:15:59,560 Speaker 3: Were? 326 00:16:00,240 --> 00:16:02,560 Speaker 8: I think in that picture being inducted into the Automotive 327 00:16:02,560 --> 00:16:07,640 Speaker 8: Hall of Fame and we're in Detroit. I think, is 328 00:16:07,680 --> 00:16:10,040 Speaker 8: it two thousand and nine, Yeah, it might be a little. 329 00:16:09,920 --> 00:16:10,400 Speaker 3: Later than that. 330 00:16:10,920 --> 00:16:13,440 Speaker 8: I think it's more at the end of his time. 331 00:16:13,640 --> 00:16:15,520 Speaker 8: But it was in two thousand and nine that he 332 00:16:15,560 --> 00:16:19,840 Speaker 8: avoided the bailout, and you know, he saved Ford along 333 00:16:19,880 --> 00:16:23,000 Speaker 8: with his boss Bill Ford never forget the Ford family 334 00:16:23,080 --> 00:16:25,760 Speaker 8: runs forward. So but yeah, but no, he was a 335 00:16:25,840 --> 00:16:29,080 Speaker 8: rock star and he was great to work with, and 336 00:16:29,160 --> 00:16:30,960 Speaker 8: he also was one of those guys who pulled no 337 00:16:31,040 --> 00:16:33,360 Speaker 8: punches and had things on his mind. 338 00:16:33,600 --> 00:16:36,000 Speaker 2: You know, we were thinking about you. You really you're 339 00:16:36,000 --> 00:16:37,760 Speaker 2: a lot of fun to talk to and we just 340 00:16:37,960 --> 00:16:41,960 Speaker 2: love covering this world with you. You've inspired a lot 341 00:16:41,960 --> 00:16:44,640 Speaker 2: of us, and it seems like you've also inspired someone 342 00:16:44,680 --> 00:16:46,800 Speaker 2: who's very near and dear to your daughter or of 343 00:16:46,880 --> 00:16:49,520 Speaker 2: another picture we want to pull up and maybe you 344 00:16:49,520 --> 00:16:52,000 Speaker 2: could tell us a little bit about this. That's you 345 00:16:52,040 --> 00:16:55,360 Speaker 2: For those on radio, it's Keith obviously, and then there's 346 00:16:55,400 --> 00:16:55,880 Speaker 2: your daughter. 347 00:16:56,040 --> 00:17:00,080 Speaker 8: Yeah, that's Nora behind me and we're both scrumming Jim Farrar, 348 00:17:00,360 --> 00:17:03,000 Speaker 8: who is now Ford CEO. I think that's at a 349 00:17:03,040 --> 00:17:05,640 Speaker 8: New York auto show maybe in twenty nineteen. So yeah, 350 00:17:05,720 --> 00:17:08,040 Speaker 8: Nara has worked in different places. She was also at 351 00:17:08,040 --> 00:17:10,440 Speaker 8: the Detroit News like I was. She was the Wall 352 00:17:10,480 --> 00:17:15,439 Speaker 8: Street Journal business insider. So she's she's she's great, and 353 00:17:15,440 --> 00:17:18,440 Speaker 8: it's great to have your your child in the days 354 00:17:18,480 --> 00:17:18,720 Speaker 8: with you. 355 00:17:19,800 --> 00:17:22,159 Speaker 3: So she's she's, she's wonderful. 356 00:17:22,160 --> 00:17:23,680 Speaker 8: And that was a great moment, and that was taken 357 00:17:24,040 --> 00:17:29,080 Speaker 8: That picture was taken by Ford's chief communications officer, Mark Truby. 358 00:17:29,119 --> 00:17:32,200 Speaker 8: He was he was monitoring the scrum and he saw 359 00:17:32,240 --> 00:17:36,439 Speaker 8: that shot and got our voices, our faces of skepticism. 360 00:17:37,440 --> 00:17:39,879 Speaker 2: Which sometimes have to do when you're covering an industry. 361 00:17:40,840 --> 00:17:43,360 Speaker 2: We've got to wrap up. But as you look forward 362 00:17:45,359 --> 00:17:47,680 Speaker 2: to your world, and are you going to be keeping 363 00:17:47,680 --> 00:17:48,960 Speaker 2: an eye on the auto industry? 364 00:17:49,080 --> 00:17:50,879 Speaker 3: Oh, I mean I live in Detroit. It's hard not to. 365 00:17:51,119 --> 00:17:53,360 Speaker 2: Yeah, you know, well, what's your thoughts? Like you've only 366 00:17:53,359 --> 00:17:54,399 Speaker 2: got thirty or forty seconds? 367 00:17:54,440 --> 00:18:00,480 Speaker 8: Like, yeah, I think it's a really perilous for the 368 00:18:00,480 --> 00:18:04,000 Speaker 8: American auto industry. They really do need to reinvent themselves 369 00:18:04,000 --> 00:18:06,520 Speaker 8: before the Chinese arrive. And as Bill Ford said last week, 370 00:18:06,600 --> 00:18:08,240 Speaker 8: they are going to arrive. 371 00:18:08,680 --> 00:18:09,720 Speaker 3: And if the. 372 00:18:09,680 --> 00:18:14,040 Speaker 8: American auto industry isn't ready lower priced cars, electric vehicles, 373 00:18:14,040 --> 00:18:15,480 Speaker 8: if they aren't ready. 374 00:18:15,440 --> 00:18:16,199 Speaker 3: They won't survive. 375 00:18:16,920 --> 00:18:18,919 Speaker 2: Are you driving a ev I'm not. 376 00:18:19,840 --> 00:18:23,320 Speaker 4: I'm a moss back, not even a hybrid. 377 00:18:23,840 --> 00:18:24,119 Speaker 6: No. 378 00:18:24,280 --> 00:18:26,520 Speaker 3: My brother, my older brother has a hybrid. But yet 379 00:18:26,600 --> 00:18:26,960 Speaker 3: I do not. 380 00:18:27,119 --> 00:18:31,159 Speaker 2: I keep waiting for what's the car? Here's another one. 381 00:18:31,400 --> 00:18:34,200 Speaker 2: You're a jem. We wish you well. Thank you, Keith 382 00:18:34,240 --> 00:18:37,439 Speaker 2: not and Bloomberg News Auto Reporter. You will be missed. 383 00:18:37,840 --> 00:18:38,639 Speaker 2: This is Bloomberg. 384 00:18:40,080 --> 00:18:42,840 Speaker 4: Stay with us. More from Bloomberg Business Week Daily coming 385 00:18:42,920 --> 00:18:43,880 Speaker 4: up after this. 386 00:18:47,840 --> 00:18:51,720 Speaker 1: You're listening to the Bloomberg Business Week Daily podcast. Catch 387 00:18:51,760 --> 00:18:54,440 Speaker 1: us live weekday afternoons from two to five e's during 388 00:18:54,680 --> 00:18:58,600 Speaker 1: Listen on Applecarplay and Android Auto with the Bloomberg Business app, 389 00:18:58,760 --> 00:19:00,440 Speaker 1: or watch us live on you Tube. 390 00:19:02,200 --> 00:19:03,800 Speaker 2: And you know, fast forward to kind of where we 391 00:19:03,840 --> 00:19:05,720 Speaker 2: are today, right Tim, I mean in a big way. 392 00:19:06,240 --> 00:19:09,080 Speaker 2: So much is coming at us, and you layer on 393 00:19:09,280 --> 00:19:13,240 Speaker 2: AI and how everything is moving so rapidly. You do 394 00:19:13,359 --> 00:19:16,200 Speaker 2: think how it's transforming so much. And our next guest 395 00:19:16,320 --> 00:19:19,560 Speaker 2: talks about how AI is quietly quietly transforming some of 396 00:19:19,600 --> 00:19:24,800 Speaker 2: the world's biggest industrial sectors. So we're talking about automotive, defense, aviation, 397 00:19:25,680 --> 00:19:28,840 Speaker 2: and that many companies still risk being left behind. 398 00:19:28,880 --> 00:19:32,160 Speaker 4: Here we got with us Roy high Loca, Oliver Wyman's 399 00:19:32,200 --> 00:19:36,320 Speaker 4: partner in the firm's Transportation and Services and Digital Practices group. Rory, 400 00:19:36,359 --> 00:19:38,480 Speaker 4: good to have you with us this afternoon. You and 401 00:19:38,480 --> 00:19:40,400 Speaker 4: the folks at Oliver Wyman have this big report out 402 00:19:40,560 --> 00:19:44,359 Speaker 4: on the industrial AI divide. How AI is quietly transforming, 403 00:19:44,359 --> 00:19:49,040 Speaker 4: As Carol mentioned, some of these big industrial sectors. What 404 00:19:49,720 --> 00:19:52,320 Speaker 4: is happening and who could get left behind? 405 00:19:54,680 --> 00:19:56,960 Speaker 9: That's a great question, and I think we're seeing a 406 00:19:56,960 --> 00:19:59,840 Speaker 9: couple of things happen right now, and it varies a 407 00:19:59,840 --> 00:20:04,480 Speaker 9: bit sector bi sector where AI is rapidly becoming a 408 00:20:04,520 --> 00:20:10,280 Speaker 9: focus area for you know, automotive manufacturers and automotive companies, airlines, 409 00:20:11,320 --> 00:20:17,280 Speaker 9: rail companies, industrial mining, you name it, and it's become 410 00:20:17,800 --> 00:20:21,959 Speaker 9: something that is already starting to generate real revenue returns 411 00:20:22,200 --> 00:20:25,440 Speaker 9: for companies that have embraced it early. If you think 412 00:20:25,440 --> 00:20:30,159 Speaker 9: about leveraging AI for better price decision, better servicing of 413 00:20:30,200 --> 00:20:34,080 Speaker 9: offers for consumer facing transportation companies, you're starting to see 414 00:20:34,119 --> 00:20:37,679 Speaker 9: those benefits. But at the same time you're seeing, you know, 415 00:20:37,960 --> 00:20:42,439 Speaker 9: real differences in how companies are evolving when it comes 416 00:20:42,440 --> 00:20:47,080 Speaker 9: to embracing AI for delivering you know, complex, very real 417 00:20:47,119 --> 00:20:50,880 Speaker 9: world transportation offering. So think about you know, rail network, 418 00:20:51,320 --> 00:20:54,640 Speaker 9: airlines and airspace, you know, cars on the road. 419 00:20:55,200 --> 00:20:56,880 Speaker 6: So there's there's a lot that's happening. 420 00:20:57,040 --> 00:21:00,280 Speaker 9: We're seeing some real differences in kind of company that 421 00:21:00,320 --> 00:21:04,199 Speaker 9: have always been more focused on deploying AI as a 422 00:21:04,240 --> 00:21:07,720 Speaker 9: real transformation of the business and their people versus companies 423 00:21:07,800 --> 00:21:09,800 Speaker 9: leaving it kind of more as a This is something 424 00:21:09,800 --> 00:21:12,160 Speaker 9: that the technology team has to go and figure out 425 00:21:12,520 --> 00:21:14,760 Speaker 9: for how we embedded in our business. And so we're 426 00:21:14,760 --> 00:21:18,240 Speaker 9: seeing real, real differences across the industries. It still feels 427 00:21:18,240 --> 00:21:20,919 Speaker 9: like really early days in terms of the level of 428 00:21:21,040 --> 00:21:23,600 Speaker 9: change and evolution that we're going to see, though, you know. 429 00:21:23,560 --> 00:21:27,080 Speaker 2: I'm looking at some various you know, technology statists, excuse me. 430 00:21:27,119 --> 00:21:31,400 Speaker 2: In this report, like here, fifty eight percent of aviation maintenance, repair, 431 00:21:31,440 --> 00:21:34,400 Speaker 2: and overhaul professionals said that their value expectations from AI 432 00:21:34,440 --> 00:21:36,600 Speaker 2: investments are being met or exceeded. That's up from twenty 433 00:21:36,640 --> 00:21:39,760 Speaker 2: percent and twenty twenty four. How much when we look 434 00:21:39,800 --> 00:21:43,480 Speaker 2: at stuff moving around, whether it's you know, planes, trains, 435 00:21:43,560 --> 00:21:47,560 Speaker 2: or automobiles and go where you want, Yeah, is actually 436 00:21:47,840 --> 00:21:51,440 Speaker 2: utilizing AI and it's making a difference. Are they all 437 00:21:51,520 --> 00:21:53,760 Speaker 2: just at the beginning, Because it feels like, you know, 438 00:21:53,840 --> 00:21:56,040 Speaker 2: AI's been around for a long time, and I understand 439 00:21:56,080 --> 00:21:58,919 Speaker 2: what's happened in the last three three years is taking 440 00:21:58,960 --> 00:22:01,919 Speaker 2: it to a whole other level. But give us an 441 00:22:01,960 --> 00:22:03,800 Speaker 2: idea of how much is already happening. 442 00:22:05,320 --> 00:22:06,400 Speaker 6: It's a great question. 443 00:22:06,720 --> 00:22:10,080 Speaker 9: I think if you think about certain things like manufacturing, 444 00:22:10,920 --> 00:22:16,880 Speaker 9: parts or cars or engines. AI and particularly machine learning 445 00:22:17,080 --> 00:22:20,919 Speaker 9: have already helped companies realize substantial value. It allows you 446 00:22:20,960 --> 00:22:25,160 Speaker 9: to much better model and predict behaviors of a closed system, 447 00:22:25,800 --> 00:22:28,800 Speaker 9: allows you to design things, keep a factory up and 448 00:22:28,880 --> 00:22:33,080 Speaker 9: running at a higher rate, which creates better economics. You're 449 00:22:33,119 --> 00:22:36,280 Speaker 9: seeing it really in use and at scale in a 450 00:22:36,359 --> 00:22:40,120 Speaker 9: number of those kinds of use cases. You mentioned airline maintenance, 451 00:22:40,160 --> 00:22:44,119 Speaker 9: great example where just having better visibility into the likelihood 452 00:22:44,560 --> 00:22:46,640 Speaker 9: of a maintenance event and then being able to put 453 00:22:46,680 --> 00:22:48,440 Speaker 9: the part in the right place, have the team ready 454 00:22:48,440 --> 00:22:50,920 Speaker 9: to go. That drives a real efficiency and we've really 455 00:22:51,000 --> 00:22:54,240 Speaker 9: seen that take off when you think about using AI 456 00:22:54,480 --> 00:22:59,040 Speaker 9: to do things like manage complex airspace and improve what 457 00:22:59,160 --> 00:23:02,280 Speaker 9: it's like on a third stay in LaGuardia or going 458 00:23:02,320 --> 00:23:05,080 Speaker 9: into Heathrow. You know, we're starting to see that technology 459 00:23:05,119 --> 00:23:07,840 Speaker 9: be put to the test. But you're dealing with a 460 00:23:07,920 --> 00:23:11,560 Speaker 9: need to change a massive open system, you know, use 461 00:23:11,640 --> 00:23:15,879 Speaker 9: the interplay of the weather, aircraft, networks, things on the ground, 462 00:23:16,000 --> 00:23:18,320 Speaker 9: and that's a much harder problem to solve. So I 463 00:23:18,320 --> 00:23:21,600 Speaker 9: think we're still in the early days of really figuring 464 00:23:21,640 --> 00:23:23,560 Speaker 9: out how that's going to come together, and so I 465 00:23:23,560 --> 00:23:25,879 Speaker 9: think there's a lot of change to come and that 466 00:23:25,960 --> 00:23:30,360 Speaker 9: will impact companies that touch all aspects of those operations 467 00:23:30,400 --> 00:23:31,880 Speaker 9: and of that kind of value chain. 468 00:23:31,960 --> 00:23:33,400 Speaker 4: I think a lot of people are thinking about AI 469 00:23:33,480 --> 00:23:35,360 Speaker 4: as a sort of layer of technology the same way 470 00:23:35,359 --> 00:23:38,080 Speaker 4: that we thought of the Internet and just being online, 471 00:23:38,160 --> 00:23:40,360 Speaker 4: you know, maybe twenty five years ago, and how that's 472 00:23:40,400 --> 00:23:44,040 Speaker 4: sort of everything we do now is online. What areas 473 00:23:44,040 --> 00:23:47,520 Speaker 4: though right now are missing the not missing the opportunity, 474 00:23:47,520 --> 00:23:50,919 Speaker 4: but have the most have the most opportunity for disruption, 475 00:23:51,160 --> 00:23:53,440 Speaker 4: Like we don't see it yet because the technology is 476 00:23:53,480 --> 00:23:55,119 Speaker 4: not quite there, but maybe we will in two or 477 00:23:55,160 --> 00:23:55,639 Speaker 4: three years. 478 00:23:56,960 --> 00:23:58,119 Speaker 6: Yeah, it's a great question. 479 00:23:58,520 --> 00:24:01,560 Speaker 9: I mean, I think the the one sector we've probably seen, 480 00:24:01,600 --> 00:24:05,320 Speaker 9: how you know, seen play out really quickly over the 481 00:24:05,400 --> 00:24:08,120 Speaker 9: last couple of years, given just the nature of what's 482 00:24:08,160 --> 00:24:09,959 Speaker 9: happening in the world is the defense sector. 483 00:24:10,760 --> 00:24:11,760 Speaker 6: Certainly the role that. 484 00:24:11,760 --> 00:24:16,120 Speaker 9: AI plays in using drones in the battlefield, we've seen 485 00:24:16,160 --> 00:24:20,080 Speaker 9: that and we've seen the effects of disruption happening. New 486 00:24:20,280 --> 00:24:25,320 Speaker 9: entrants into that sector bring that technology to life. If 487 00:24:25,359 --> 00:24:28,240 Speaker 9: I think about, you know, in other areas like rail 488 00:24:28,440 --> 00:24:32,280 Speaker 9: or aviation, I think the companies that are able to 489 00:24:32,720 --> 00:24:37,600 Speaker 9: really rethink and transform how they manage their business, run 490 00:24:37,640 --> 00:24:41,480 Speaker 9: their business, automate their business, change the role of people 491 00:24:41,520 --> 00:24:44,040 Speaker 9: in the workforce, and do that quickly. They are going 492 00:24:44,080 --> 00:24:48,400 Speaker 9: to find both economic efficiency and frankly improve the consumer 493 00:24:48,440 --> 00:24:51,240 Speaker 9: and customer experience. And I think that's where you're going 494 00:24:51,280 --> 00:24:53,560 Speaker 9: to see some real divides. And the challenge is for 495 00:24:53,560 --> 00:24:56,679 Speaker 9: a lot of these companies, you still need all of 496 00:24:56,720 --> 00:25:00,480 Speaker 9: your technology infrastructure to come to get other and be 497 00:25:00,520 --> 00:25:02,840 Speaker 9: able to support the use of AI. And that's one 498 00:25:02,840 --> 00:25:06,600 Speaker 9: of the really big lists, you know, modernizing systems, making 499 00:25:06,640 --> 00:25:11,080 Speaker 9: the data available to inform, train and support these kinds 500 00:25:11,080 --> 00:25:14,639 Speaker 9: of models and tools, Putting the physical infrastructure in place 501 00:25:14,640 --> 00:25:15,720 Speaker 9: so you have computer vision. 502 00:25:15,800 --> 00:25:17,240 Speaker 6: All those things are really key. 503 00:25:17,480 --> 00:25:21,120 Speaker 2: Rory, just thirty seconds left here, US versus China, US 504 00:25:21,200 --> 00:25:25,760 Speaker 2: versus Europe in terms of industries, embracing AI for the 505 00:25:25,760 --> 00:25:27,640 Speaker 2: good if you will, and just quickly, if you could. 506 00:25:28,760 --> 00:25:31,879 Speaker 9: Yeah, really quickly. I think all three are going to 507 00:25:31,880 --> 00:25:34,040 Speaker 9: make great strides. I know that's a bit of a 508 00:25:34,480 --> 00:25:35,920 Speaker 9: you know, muted answer, but. 509 00:25:36,000 --> 00:25:38,000 Speaker 2: If you have to pick one, who's ahead of the 510 00:25:38,080 --> 00:25:39,000 Speaker 2: race here. 511 00:25:39,480 --> 00:25:42,159 Speaker 9: If I have to pick one, I think the innovation 512 00:25:42,560 --> 00:25:45,879 Speaker 9: coming out of the US is really remarkable. I think 513 00:25:46,160 --> 00:25:47,640 Speaker 9: you know, and we're going to We're going to see 514 00:25:47,640 --> 00:25:49,160 Speaker 9: that play out in the next couple of years. 515 00:25:49,280 --> 00:25:52,160 Speaker 2: All right, really appreciate it. Good stuff in depth report, 516 00:25:52,280 --> 00:25:55,119 Speaker 2: that's for sure. Rory Haylaka He is Oliver Wyman's partner 517 00:25:55,160 --> 00:25:58,120 Speaker 2: in the firms Transportation and Services and Digital Practices Group. 518 00:25:58,200 --> 00:26:00,720 Speaker 2: Joining us right here in New New York City. 519 00:26:02,320 --> 00:26:05,080 Speaker 4: Stay with us. More from Bloomberg Business Week Daily coming 520 00:26:05,200 --> 00:26:06,080 Speaker 4: up after this. 521 00:26:10,480 --> 00:26:14,320 Speaker 1: You're listening to the Bloomberg Business Week Daily Podcast. Catch 522 00:26:14,400 --> 00:26:17,080 Speaker 1: us live weekday afternoons from two to five e's during 523 00:26:17,280 --> 00:26:21,200 Speaker 1: Listen on Applecarplay and Android Auto with the Bloomberg Business app, 524 00:26:21,359 --> 00:26:23,560 Speaker 1: or watch us live on YouTube. 525 00:26:24,280 --> 00:26:25,840 Speaker 2: Well, when you think about what's going on in our 526 00:26:25,840 --> 00:26:27,720 Speaker 2: world to spend the AI build and so on and 527 00:26:27,720 --> 00:26:30,360 Speaker 2: so forth, I feel like there's a lot of big 528 00:26:30,400 --> 00:26:33,199 Speaker 2: money in that, and I don't know, there's a lot 529 00:26:33,240 --> 00:26:34,960 Speaker 2: of big money in interesting places. We just came off 530 00:26:34,960 --> 00:26:37,119 Speaker 2: the World Cup. We've talked so much about money in sport. 531 00:26:37,359 --> 00:26:39,320 Speaker 2: There's a lot going on in our world. 532 00:26:39,359 --> 00:26:41,800 Speaker 4: I'm glad you mentioned money in sports. That's a good segue. 533 00:26:41,840 --> 00:26:42,359 Speaker 2: You're welcome. 534 00:26:42,359 --> 00:26:44,600 Speaker 4: We got Chris mor Angi with US President co cio 535 00:26:44,680 --> 00:26:47,639 Speaker 4: of Value also portfolio manager, the goals ETF at the 536 00:26:47,640 --> 00:26:51,160 Speaker 4: gabelly opportunities in live sports. That's what goals stands for. 537 00:26:51,520 --> 00:26:53,359 Speaker 4: We're gonna get to the sports ETF in just a minute. 538 00:26:53,359 --> 00:26:55,760 Speaker 4: We talked about this with you in the past, Chris, 539 00:26:56,240 --> 00:26:58,120 Speaker 4: but we want to talk about the market environment first, 540 00:26:58,160 --> 00:27:00,800 Speaker 4: and you know, earnings is the backdrop part, but it 541 00:27:00,840 --> 00:27:04,040 Speaker 4: also has to do with five dollars a gallon diesel, 542 00:27:04,119 --> 00:27:07,600 Speaker 4: four dollars a gallon gas and challenges when it comes 543 00:27:07,600 --> 00:27:10,320 Speaker 4: to geopolitics and when it comes to politics as well. 544 00:27:10,920 --> 00:27:12,800 Speaker 4: What do you make of the environment that we're in 545 00:27:12,880 --> 00:27:13,280 Speaker 4: right now? 546 00:27:14,240 --> 00:27:15,840 Speaker 10: So, yeah, a lot to keep track of. First of all, 547 00:27:16,000 --> 00:27:20,520 Speaker 10: delighted to be here. We categorize them as the three t's, Trump, technology, 548 00:27:20,680 --> 00:27:24,560 Speaker 10: and treasuries. Not meant to be political, but obviously these 549 00:27:24,560 --> 00:27:26,800 Speaker 10: are the things that people are thinking about. It's as 550 00:27:26,800 --> 00:27:32,520 Speaker 10: you mentioned around the price of gas, tariffs, the midterms, technology, 551 00:27:32,680 --> 00:27:35,879 Speaker 10: AI need I say more, and then treasuries in the 552 00:27:35,880 --> 00:27:38,960 Speaker 10: back of everyone's mind, sticky inflation, a two trillion dollar 553 00:27:39,040 --> 00:27:42,199 Speaker 10: deficit and almost forty trillion debt and how we're going 554 00:27:42,280 --> 00:27:45,320 Speaker 10: to deal with that long term. And you know, a 555 00:27:45,359 --> 00:27:48,159 Speaker 10: lot has been thrown at this market this year and 556 00:27:48,200 --> 00:27:50,200 Speaker 10: we're still up a lot. And one way to read 557 00:27:50,200 --> 00:27:53,440 Speaker 10: that is we've successfully climbed that wall of worries. That's 558 00:27:53,440 --> 00:27:57,840 Speaker 10: what markets do. Another read is that market is being complacent. 559 00:27:57,920 --> 00:28:01,320 Speaker 10: I tend to lean a little towards the ladder, but 560 00:28:01,440 --> 00:28:03,639 Speaker 10: on the former earnings are up a lot, and if 561 00:28:03,680 --> 00:28:05,479 Speaker 10: you look at it, the market is actually cheaper than 562 00:28:05,520 --> 00:28:06,640 Speaker 10: it was at the beginning of the year. 563 00:28:07,359 --> 00:28:09,000 Speaker 2: Doesn't mean it can't get cheaper, right. 564 00:28:10,080 --> 00:28:12,040 Speaker 10: That is for sure, and a lot of that is 565 00:28:12,080 --> 00:28:13,760 Speaker 10: going to have to do with what we hear from 566 00:28:13,880 --> 00:28:17,959 Speaker 10: Google in just forty five minutes or so. This locomotive 567 00:28:18,040 --> 00:28:22,600 Speaker 10: is being pulled by AI spend and you know post 568 00:28:22,680 --> 00:28:24,600 Speaker 10: Close is going to spend. 569 00:28:24,440 --> 00:28:27,040 Speaker 7: Three hundred billion, four hundred billion next year. It's going 570 00:28:27,119 --> 00:28:27,800 Speaker 7: to be a big number. 571 00:28:27,840 --> 00:28:30,600 Speaker 10: There's a reason they did their offering last month, and 572 00:28:30,640 --> 00:28:34,480 Speaker 10: that really is just dragging the utilities, the energy complex, 573 00:28:34,520 --> 00:28:36,840 Speaker 10: all these other picks and shovels plays that are driving 574 00:28:36,840 --> 00:28:37,960 Speaker 10: earnings and driving the market. 575 00:28:38,040 --> 00:28:38,560 Speaker 3: Yeah, that's right. 576 00:28:38,600 --> 00:28:40,920 Speaker 2: The company did what an eighty five billion dollar equity 577 00:28:41,000 --> 00:28:44,600 Speaker 2: raise last month, and let's pull by factset are expecting 578 00:28:44,640 --> 00:28:48,360 Speaker 2: one hundred and eighty seven point one billion in capital. 579 00:28:47,960 --> 00:28:50,360 Speaker 4: Expenditures this year, So that's real money. 580 00:28:50,440 --> 00:28:51,720 Speaker 2: That is real money. 581 00:28:51,840 --> 00:28:55,000 Speaker 4: Hey, Chris, you mentioned that you know you're you're leaning 582 00:28:55,000 --> 00:28:57,000 Speaker 4: toward the ladder, and that's the idea of the market 583 00:28:57,000 --> 00:28:59,840 Speaker 4: being complacent. So one side of the coin is earnings 584 00:28:59,840 --> 00:29:02,240 Speaker 4: and AI spend driving, but the other side of the 585 00:29:02,280 --> 00:29:05,200 Speaker 4: coin is complacency, and I'm wondering if you're seeing any 586 00:29:05,240 --> 00:29:05,400 Speaker 4: of that. 587 00:29:07,000 --> 00:29:08,840 Speaker 7: Yeah, again, I think there is a little complacency some 588 00:29:08,920 --> 00:29:09,080 Speaker 7: of it. 589 00:29:09,160 --> 00:29:10,480 Speaker 10: By the way, you know, we used to use the 590 00:29:10,480 --> 00:29:13,120 Speaker 10: initials or of the name Tina, and there really isn't 591 00:29:13,840 --> 00:29:16,840 Speaker 10: an alternative at the moment. Well, we'll talk about an 592 00:29:16,840 --> 00:29:19,120 Speaker 10: alternative in a minute, probably, but you know, gold is 593 00:29:19,160 --> 00:29:22,240 Speaker 10: underperformed a little bit for probably some idiosyncratic reasons. 594 00:29:22,840 --> 00:29:24,480 Speaker 7: There aren't a lot of great places. 595 00:29:24,120 --> 00:29:28,600 Speaker 10: To put your money, especially one where inflation remains untamed. 596 00:29:29,160 --> 00:29:30,840 Speaker 10: So you go to the equity markets, and so far 597 00:29:31,160 --> 00:29:33,640 Speaker 10: that's worked out for US investors. 598 00:29:33,920 --> 00:29:36,560 Speaker 4: You put your money on the Knicks. Is one way 599 00:29:36,600 --> 00:29:38,720 Speaker 4: to I bet or. 600 00:29:38,680 --> 00:29:40,520 Speaker 2: In Spain or Venezuela or Spain. 601 00:29:40,680 --> 00:29:41,680 Speaker 7: Spain was a good bet. 602 00:29:41,680 --> 00:29:44,160 Speaker 4: Well, maybe this is a good segue to talk about 603 00:29:44,440 --> 00:29:46,840 Speaker 4: the goals ETF and the top holding is Madison Square 604 00:29:46,880 --> 00:29:50,040 Speaker 4: garden sports. It's about eight percent of the ETF. So 605 00:29:50,080 --> 00:29:52,520 Speaker 4: I say, you know, put your money on the next. 606 00:29:52,320 --> 00:29:56,360 Speaker 4: But when you were alluding to this idea of where 607 00:29:56,400 --> 00:29:59,000 Speaker 4: to put your money, you you still think that that's 608 00:29:59,040 --> 00:30:02,719 Speaker 4: sports and live op oor tunities are a real opportunity 609 00:30:02,720 --> 00:30:03,680 Speaker 4: for investors right now. 610 00:30:04,760 --> 00:30:06,840 Speaker 10: Absolutely, And you know, part of the reason that we 611 00:30:06,920 --> 00:30:10,720 Speaker 10: started this ETF, and we've been following public sports franchises 612 00:30:10,720 --> 00:30:13,640 Speaker 10: for decades. Actually when I got into this business twenty 613 00:30:13,640 --> 00:30:16,440 Speaker 10: something years ago, started by Volume Cable Vision, which is 614 00:30:16,720 --> 00:30:19,720 Speaker 10: which is the predecessor to Madison Square Garden of course 615 00:30:19,720 --> 00:30:24,320 Speaker 10: in Liberty, which spun out the Lan Rais. And you know, 616 00:30:24,320 --> 00:30:27,560 Speaker 10: there's a reason that really rich people buy sports franchises 617 00:30:27,600 --> 00:30:31,479 Speaker 10: because over history they've been great stores of value. They 618 00:30:31,560 --> 00:30:35,040 Speaker 10: are economically resilient, they're sort of tribal. There's a limited 619 00:30:35,080 --> 00:30:37,480 Speaker 10: number of them, and guess what, they're also ai resilient. 620 00:30:37,800 --> 00:30:39,720 Speaker 10: There aren't a lot of sectors that can say that 621 00:30:40,320 --> 00:30:43,200 Speaker 10: this is one of them. There's about thirty or so 622 00:30:43,360 --> 00:30:46,280 Speaker 10: publicly traded sports franchises in leagues around the world. Some 623 00:30:46,320 --> 00:30:48,560 Speaker 10: of them are hard to access, they're in Europe, they 624 00:30:48,640 --> 00:30:51,800 Speaker 10: lightly trade. We've got all of them here, and obviously 625 00:30:51,840 --> 00:30:53,880 Speaker 10: the big holding is about in the square Garden Sports, 626 00:30:53,920 --> 00:30:56,760 Speaker 10: the Knicks and the Rangers. They're separating that entity towards 627 00:30:56,800 --> 00:30:58,600 Speaker 10: the end of the year. We think that opens up 628 00:30:58,600 --> 00:30:58,920 Speaker 10: a lot of. 629 00:30:58,840 --> 00:30:59,719 Speaker 7: Options for them. 630 00:31:00,200 --> 00:31:02,200 Speaker 2: So why our treasury is the top holding. 631 00:31:04,560 --> 00:31:06,640 Speaker 10: Well, no, that's not right. The we we got about 632 00:31:06,640 --> 00:31:10,320 Speaker 10: five percent and five percent in cash Madison, Square Garden 633 00:31:10,360 --> 00:31:12,959 Speaker 10: Sports are about ten percent, and then the Braves are 634 00:31:13,000 --> 00:31:16,480 Speaker 10: a number two at eight percent. Ok. Sorry, So yeah, 635 00:31:16,880 --> 00:31:18,240 Speaker 10: you know, we're getting a lot of floes and we're 636 00:31:18,240 --> 00:31:19,240 Speaker 10: putting those to work. 637 00:31:20,960 --> 00:31:21,600 Speaker 7: Judiciously. 638 00:31:21,720 --> 00:31:25,200 Speaker 2: Okay, maybe this, maybe this isn't updated. Yeah, because eighty percent, right, 639 00:31:25,240 --> 00:31:27,840 Speaker 2: you're looking to make sure is allocated at any given time. 640 00:31:28,800 --> 00:31:29,240 Speaker 3: That's right. 641 00:31:30,400 --> 00:31:34,200 Speaker 2: Do higher valuations of various teams and so on and 642 00:31:34,200 --> 00:31:36,480 Speaker 2: so forth make for a good thing for you guys? 643 00:31:37,760 --> 00:31:41,080 Speaker 10: Yeah, I mean, listen, you look at at how sports 644 00:31:41,120 --> 00:31:44,840 Speaker 10: franchises have performed over the last twelve or so years. 645 00:31:45,040 --> 00:31:47,120 Speaker 10: There's a lot of third party data sets that show 646 00:31:47,880 --> 00:31:50,520 Speaker 10: that the value of the Big four teams, the teams 647 00:31:50,560 --> 00:31:52,800 Speaker 10: in the big four leagues in the United States have 648 00:31:52,920 --> 00:31:55,680 Speaker 10: gone up about fifteen percent. That's out paced the S 649 00:31:55,720 --> 00:31:58,120 Speaker 10: and P five hundred and you know, you just pick 650 00:31:58,200 --> 00:32:00,200 Speaker 10: up the paper every few days and you'll find other 651 00:32:00,240 --> 00:32:03,080 Speaker 10: sports transaction. If it's we had the Seahawks most recently, 652 00:32:03,720 --> 00:32:06,920 Speaker 10: the big ones for of course, the Lakers and the 653 00:32:06,960 --> 00:32:12,320 Speaker 10: Giants trading recently for record values. I think should any 654 00:32:12,360 --> 00:32:16,200 Speaker 10: of the teams that we own in public form trade, 655 00:32:16,200 --> 00:32:17,800 Speaker 10: they'll also trade it at big numbers. 656 00:32:18,600 --> 00:32:19,880 Speaker 7: So, you know those we're. 657 00:32:19,600 --> 00:32:22,320 Speaker 10: Buying those franchises, anybody can buy those franchises by buying 658 00:32:22,400 --> 00:32:24,640 Speaker 10: them in the market. We're buying a package of them 659 00:32:24,880 --> 00:32:27,320 Speaker 10: for well below what we think they're intrinsic value might be. 660 00:32:27,680 --> 00:32:28,920 Speaker 7: Hey, talk to us. 661 00:32:28,840 --> 00:32:32,760 Speaker 2: About flows, interests by investors to actually come into this fund. 662 00:32:32,800 --> 00:32:34,280 Speaker 2: What can you tell us about what kind of money 663 00:32:34,320 --> 00:32:34,920 Speaker 2: has been coming in. 664 00:32:35,720 --> 00:32:37,840 Speaker 10: Yeah, so you know, ETFs are a bit of a 665 00:32:37,920 --> 00:32:41,000 Speaker 10: chicken and egg. You know, investors want to see flows, 666 00:32:41,040 --> 00:32:42,960 Speaker 10: they want to see liquidity. We just launched this in 667 00:32:43,040 --> 00:32:46,600 Speaker 10: January and we're seeing a lot of interest. And it's funny, 668 00:32:46,600 --> 00:32:48,960 Speaker 10: it's not just from retail investors, your average Joe who 669 00:32:48,960 --> 00:32:51,720 Speaker 10: wants to participate in this. We're having calls with family 670 00:32:51,800 --> 00:32:55,440 Speaker 10: offices who you know, desperately want to own sports franchises. 671 00:32:55,440 --> 00:32:57,800 Speaker 10: Maybe aren't big enough to write a billion dollar check 672 00:32:57,840 --> 00:32:59,920 Speaker 10: to get a minority stake, and they want liquidity, and 673 00:33:00,160 --> 00:33:03,000 Speaker 10: so they're looking at US, and so we've had a 674 00:33:03,040 --> 00:33:05,640 Speaker 10: lot of really great conversations about it, getting a lot 675 00:33:05,640 --> 00:33:08,280 Speaker 10: of interest, and you know, we think that the universe 676 00:33:08,440 --> 00:33:12,920 Speaker 10: of potential targets is going to grow. You'll see uplistings 677 00:33:12,960 --> 00:33:16,320 Speaker 10: of European soccer clubs to the US, perhaps even some 678 00:33:17,280 --> 00:33:19,960 Speaker 10: leagues go public, franchises go public. 679 00:33:21,000 --> 00:33:23,280 Speaker 2: Yeah, it's been interesting. Yeah, you do wonder I mean, 680 00:33:23,320 --> 00:33:25,080 Speaker 2: do you feel like so there is interest like coming 681 00:33:25,120 --> 00:33:26,520 Speaker 2: off the World Cup just real quickly? 682 00:33:27,560 --> 00:33:29,920 Speaker 7: Yeah, absolutely, the World Cup smashing success. 683 00:33:30,120 --> 00:33:33,840 Speaker 10: Yeah, you know, ratings forty million households tune into the final. 684 00:33:33,880 --> 00:33:36,760 Speaker 7: That's a big number. Spurred interests in the US and 685 00:33:36,800 --> 00:33:40,240 Speaker 7: that's good for US for global soccer teams. Yeah, absolutely, 686 00:33:40,320 --> 00:33:40,760 Speaker 7: ball teams. 687 00:33:40,800 --> 00:33:45,440 Speaker 2: Sorry, Chris Moranji, thank you so much. He's president in 688 00:33:45,480 --> 00:33:50,240 Speaker 2: cos Cio over at the Gabelli Opportunities in Live in Sports. 689 00:33:50,720 --> 00:33:56,200 Speaker 1: This is the Bloomberg Business Week Daily podcast, available on Apple, Spotify, 690 00:33:56,360 --> 00:34:00,440 Speaker 1: and anywhere else you get your podcasts. Listen live weekday 691 00:34:00,440 --> 00:34:04,600 Speaker 1: afternoons from two to five pm Eastern on Bloomberg dot Com. 692 00:34:04,760 --> 00:34:08,560 Speaker 1: The iHeartRadio app tune In, and the Bloomberg Business app. 693 00:34:08,760 --> 00:34:11,600 Speaker 1: You can also watch us live every weekday on YouTube 694 00:34:11,840 --> 00:34:14,040 Speaker 1: and always on the Bloomberg terminal