1 00:00:00,080 --> 00:00:07,040 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:07,920 --> 00:00:10,680 Speaker 2: Hey everybody, it's Max Chafkin. I just want to let 3 00:00:10,760 --> 00:00:13,360 Speaker 2: you know that you can now subscribe to Everybody's Business 4 00:00:13,400 --> 00:00:15,920 Speaker 2: in its new podcast feed. Click the link in the 5 00:00:15,960 --> 00:00:19,080 Speaker 2: show notes, or just type Everybody's Business into the search 6 00:00:19,160 --> 00:00:22,720 Speaker 2: bar wherever you listen and you'll hear the trailer. Everybody's 7 00:00:22,720 --> 00:00:24,840 Speaker 2: Business is still going to be available in the elon 8 00:00:24,920 --> 00:00:27,040 Speaker 2: ink feed for a few more weeks, but we hope 9 00:00:27,080 --> 00:00:30,040 Speaker 2: you'll join us on our new feed, the new Everybody's 10 00:00:30,080 --> 00:00:35,000 Speaker 2: Business Feed, next Friday, May sixteenth, for our first official episode. 11 00:00:36,920 --> 00:00:39,600 Speaker 3: This is Everybody's Business. I'm Stacy Manni Smith. 12 00:00:39,800 --> 00:00:43,240 Speaker 2: I'm Max Schafkin. Hey Stacy, Hello Max. 13 00:00:43,360 --> 00:00:45,280 Speaker 3: It is the end of an era. 14 00:00:45,479 --> 00:00:47,720 Speaker 2: Are you talking about the fact that the Who are 15 00:00:47,760 --> 00:00:48,560 Speaker 2: giving up touring? 16 00:00:49,159 --> 00:00:50,960 Speaker 3: I did not know that, and it's very sad to hear. 17 00:00:51,040 --> 00:00:53,760 Speaker 3: But no, I am talking about our incubation period in 18 00:00:53,840 --> 00:00:57,120 Speaker 3: the elon inc Podcast feed coming to an end. They're 19 00:00:57,160 --> 00:00:59,720 Speaker 3: pushing us out of the nest. But we get our radios, 20 00:01:00,200 --> 00:01:03,280 Speaker 3: we get our own nest. It's true new beginnings next week, 21 00:01:03,320 --> 00:01:08,440 Speaker 3: but this week all about endings, for instance, the end 22 00:01:08,440 --> 00:01:09,120 Speaker 3: of friendship. 23 00:01:09,760 --> 00:01:13,360 Speaker 2: Yeah, we're going to talk about this idea that some 24 00:01:13,560 --> 00:01:15,959 Speaker 2: of the big social media companies have that they're going 25 00:01:16,000 --> 00:01:19,640 Speaker 2: to replace your regular friends with AIS. And we have 26 00:01:19,840 --> 00:01:24,360 Speaker 2: our real friend, a human friend, Sarah Fryar, who is 27 00:01:24,800 --> 00:01:27,920 Speaker 2: my favorite Facebook reporter, one of the people who knows 28 00:01:27,959 --> 00:01:30,440 Speaker 2: the most about this company and this issue, to come 29 00:01:30,560 --> 00:01:31,520 Speaker 2: and talk to us about it. 30 00:01:31,640 --> 00:01:33,880 Speaker 3: We've also got the end of the movie business, at 31 00:01:33,959 --> 00:01:37,360 Speaker 3: least the tariff free movie business. Trump Is announced the 32 00:01:37,400 --> 00:01:41,000 Speaker 3: possibility of putting huge tariffs on movies that are filmed 33 00:01:41,040 --> 00:01:44,720 Speaker 3: outside the US. We have our also real friend, Lucas 34 00:01:44,720 --> 00:01:45,960 Speaker 3: Shaw on to talk about that. 35 00:01:46,440 --> 00:01:48,880 Speaker 2: And then in the department of unreal, we have our 36 00:01:49,000 --> 00:01:52,200 Speaker 2: underrated story the week, which is about cryptos. 37 00:01:52,400 --> 00:01:56,640 Speaker 3: Crypto ever underrated? I feel like crypto is always overrated. 38 00:01:56,720 --> 00:01:59,760 Speaker 2: No, but this one has everything. You got. Diplomacy, you got, bribery, 39 00:01:59,800 --> 00:02:01,640 Speaker 2: got world leaders. You're gonna like it. 40 00:02:01,720 --> 00:02:04,280 Speaker 3: I'll trust me, all right, I trust you. Let's do it, 41 00:02:07,600 --> 00:02:10,160 Speaker 3: so Max. There's been a lot of buzz this week 42 00:02:10,200 --> 00:02:14,440 Speaker 3: about human robot relations. If you know, if you've heard this. 43 00:02:14,520 --> 00:02:17,320 Speaker 2: Yeah, I mean AI every week, big deal. The big 44 00:02:17,360 --> 00:02:20,120 Speaker 2: news this week is that open Ai and Elon Musk 45 00:02:20,160 --> 00:02:24,240 Speaker 2: they're still fighting over how open ai should be owned. 46 00:02:24,520 --> 00:02:26,760 Speaker 2: The news is that open ai sort of back down 47 00:02:26,800 --> 00:02:28,880 Speaker 2: from this plan to spin off as a for profit. 48 00:02:29,120 --> 00:02:33,240 Speaker 2: It's going to stay a nonprofit. But honestly, Stacy, the 49 00:02:33,280 --> 00:02:38,519 Speaker 2: real story about AI, it's friendship. It is the human connections. 50 00:02:38,840 --> 00:02:41,360 Speaker 2: I didn't mean human. Actually, just let's listen to this 51 00:02:41,520 --> 00:02:44,480 Speaker 2: comment from Mark Zuckerberg on a podcast about a week ago. 52 00:02:44,800 --> 00:02:47,919 Speaker 4: There's the stoff that I think is crazy. The average American, 53 00:02:48,000 --> 00:02:51,160 Speaker 4: I think has I think it's fewer than three friends. 54 00:02:52,000 --> 00:02:54,680 Speaker 4: Three people have big consider friends, and the average person 55 00:02:54,680 --> 00:02:58,840 Speaker 4: has demand for meaningfully more. I think it's like fifteen 56 00:02:58,880 --> 00:02:59,600 Speaker 4: friends or something. 57 00:02:59,680 --> 00:02:59,840 Speaker 2: Right. 58 00:03:00,040 --> 00:03:01,760 Speaker 4: I guess there's probably some point where you're like, all right, 59 00:03:01,760 --> 00:03:03,720 Speaker 4: I'm just too busy. I can't deal with more people. 60 00:03:04,040 --> 00:03:08,120 Speaker 4: But the average person wants more connectivity, connection than they 61 00:03:08,120 --> 00:03:09,679 Speaker 4: have chilling, right. 62 00:03:09,800 --> 00:03:12,240 Speaker 3: I mean, there's just something about getting this data, like, 63 00:03:12,400 --> 00:03:14,680 Speaker 3: how do I assess that I have demand for thirteen 64 00:03:14,680 --> 00:03:15,080 Speaker 3: more friends? 65 00:03:15,120 --> 00:03:19,440 Speaker 2: Their studies? Stacy? Okay, from who? But it's just kind 66 00:03:19,440 --> 00:03:23,480 Speaker 2: of crazy that, you know, Mark Zuckerberg is framing himself 67 00:03:23,480 --> 00:03:25,880 Speaker 2: as an authority. I think we've all seen that last 68 00:03:25,880 --> 00:03:28,000 Speaker 2: scene in the Social Network when he's all alone about 69 00:03:28,000 --> 00:03:30,880 Speaker 2: to click that yeah, friendship button, but I also think 70 00:03:30,919 --> 00:03:33,720 Speaker 2: there's a real business story behind this. Yes. 71 00:03:33,760 --> 00:03:36,360 Speaker 3: Actually, one of the most interesting things Zuckerberg said on 72 00:03:36,360 --> 00:03:39,360 Speaker 3: this topic has been that one of the things he's 73 00:03:39,400 --> 00:03:42,720 Speaker 3: noticed the most demand for an AI is actually not facts, 74 00:03:43,080 --> 00:03:47,200 Speaker 3: but people asking for advice about relationships and asking for 75 00:03:47,240 --> 00:03:49,320 Speaker 3: advice like you would from a friend, which I did 76 00:03:49,320 --> 00:03:52,440 Speaker 3: find really interesting. There is a show right now on 77 00:03:52,520 --> 00:03:54,280 Speaker 3: Broadway that I saw a couple of weeks ago and 78 00:03:54,360 --> 00:03:57,120 Speaker 3: really loved. It is called Maybe Happy Ending, but it's 79 00:03:57,160 --> 00:03:59,200 Speaker 3: about two androids who fall in love. 80 00:03:59,360 --> 00:04:01,000 Speaker 2: Oh that's notice, It's really sweet. 81 00:04:01,480 --> 00:04:02,960 Speaker 3: So I was like, I'm going to go down to 82 00:04:03,000 --> 00:04:05,040 Speaker 3: the show and I'm going to ask the people who 83 00:04:05,120 --> 00:04:08,200 Speaker 3: are going in to see the show if they think 84 00:04:08,280 --> 00:04:10,560 Speaker 3: that humans and robots can really be friends, if a 85 00:04:10,640 --> 00:04:12,160 Speaker 3: robot could really fill that role. 86 00:04:12,680 --> 00:04:14,080 Speaker 5: Do you think. 87 00:04:13,920 --> 00:04:16,440 Speaker 3: People can have a true friendship with AI? 88 00:04:17,240 --> 00:04:17,480 Speaker 6: No. 89 00:04:18,080 --> 00:04:21,760 Speaker 3: I'm as software developer. I basically code, so I think 90 00:04:21,800 --> 00:04:23,320 Speaker 3: it could be a great tool. 91 00:04:24,120 --> 00:04:26,360 Speaker 1: But I think that's just that it's a tool and 92 00:04:26,480 --> 00:04:29,040 Speaker 1: there's no emotion behind this machine. 93 00:04:29,120 --> 00:04:32,080 Speaker 7: I certainly use CHATGBT, but I don't find the same 94 00:04:32,480 --> 00:04:36,760 Speaker 7: heart that humans have. I privasally think no, because there's 95 00:04:36,800 --> 00:04:40,279 Speaker 7: nothing that can replace real human interaction and connection and empathy. 96 00:04:40,640 --> 00:04:44,320 Speaker 6: I'd say in an age where people use the internet 97 00:04:44,480 --> 00:04:47,599 Speaker 6: to find solace in friendship, people can find that same 98 00:04:48,040 --> 00:04:52,200 Speaker 6: level of like meaningful interaction and relationship through AI. 99 00:04:52,360 --> 00:04:54,920 Speaker 3: So you think maybe, yes, for sure, I don't. 100 00:04:54,680 --> 00:04:57,279 Speaker 5: Think you could have a real relationship with an A. 101 00:04:57,400 --> 00:04:59,440 Speaker 5: I mean I think you can believe that you can. 102 00:05:00,120 --> 00:05:00,839 Speaker 2: It's not real. 103 00:05:01,440 --> 00:05:02,600 Speaker 3: What's not real about it? 104 00:05:02,680 --> 00:05:04,120 Speaker 5: I mean AI is a machine. 105 00:05:04,520 --> 00:05:07,200 Speaker 3: Yeah, but if you think it's real, then is it 106 00:05:07,360 --> 00:05:07,840 Speaker 3: not real? 107 00:05:08,120 --> 00:05:11,520 Speaker 5: I mean that's very meta. 108 00:05:12,000 --> 00:05:12,600 Speaker 3: That's true. 109 00:05:13,720 --> 00:05:14,839 Speaker 2: Holy, it's Stacy. 110 00:05:15,200 --> 00:05:17,520 Speaker 3: I like woke up thinking about this like three in 111 00:05:17,560 --> 00:05:19,880 Speaker 3: the morning. I was like, what is real? What is friendship? 112 00:05:19,880 --> 00:05:22,200 Speaker 3: What are relationships? There's a lot of questions, all. 113 00:05:22,160 --> 00:05:24,880 Speaker 2: Right, So I want to bring in who I think 114 00:05:24,920 --> 00:05:27,840 Speaker 2: really is a perfect person to talk about this. Sarah Fryar, 115 00:05:28,279 --> 00:05:31,520 Speaker 2: who leads Bloomberg's big tech coverage and I think probably 116 00:05:31,560 --> 00:05:36,200 Speaker 2: more importantly, has covered this world for a very long time. 117 00:05:36,320 --> 00:05:39,920 Speaker 2: Sarah's book No Filter, The inside story of Instagram is 118 00:05:40,720 --> 00:05:42,919 Speaker 2: You're going to find out why it's related to this story, 119 00:05:42,920 --> 00:05:46,080 Speaker 2: but it is. Sarah. Thanks for joining us on everybody's business. 120 00:05:46,640 --> 00:05:48,240 Speaker 5: Thanks for having me love this topic. 121 00:05:48,600 --> 00:05:51,480 Speaker 2: So Stacy and Sarah, I want to come back to 122 00:05:51,520 --> 00:05:53,120 Speaker 2: the friendship, but I want to start someplace else. Do 123 00:05:53,160 --> 00:05:56,719 Speaker 2: you know what the word glazing means to glaze someone? 124 00:05:57,360 --> 00:06:00,400 Speaker 3: I just heard about this, but I bet Sarah knows deeply. 125 00:06:00,800 --> 00:06:04,600 Speaker 7: It's the issue that we're getting in these AI chats 126 00:06:04,640 --> 00:06:08,159 Speaker 7: of syncopancy, where where the chats are just basically telling 127 00:06:08,200 --> 00:06:11,039 Speaker 7: you what you want to hear. They're being highly complimentary. 128 00:06:11,800 --> 00:06:15,960 Speaker 7: They're backing you up on your crazy ideas, and that 129 00:06:16,040 --> 00:06:17,719 Speaker 7: makes you want to use them more and gives you 130 00:06:17,760 --> 00:06:21,039 Speaker 7: all those fuzzy warm feelings about how the chatbot might 131 00:06:21,080 --> 00:06:21,839 Speaker 7: be your friend. 132 00:06:22,120 --> 00:06:26,159 Speaker 2: Oh my god, Sarah, that was so insightful. You are 133 00:06:26,320 --> 00:06:29,560 Speaker 2: just so unique and spectacular in the way you just 134 00:06:29,600 --> 00:06:32,840 Speaker 2: put that together. Okay, so that was glazing. I'm glazing 135 00:06:32,880 --> 00:06:34,920 Speaker 2: you right now. And I sound like a chatbot when 136 00:06:34,960 --> 00:06:35,640 Speaker 2: I say is it. 137 00:06:35,520 --> 00:06:37,400 Speaker 3: Glazing from like when you glaze a doughnut and you 138 00:06:37,440 --> 00:06:38,240 Speaker 3: cover it with sugar. 139 00:06:38,400 --> 00:06:38,640 Speaker 1: Yes. 140 00:06:39,760 --> 00:06:42,040 Speaker 2: So Sarah hinted at this. But a little over a 141 00:06:42,040 --> 00:06:45,520 Speaker 2: week ago, Sam Altman talks about this new version of 142 00:06:45,600 --> 00:06:51,200 Speaker 2: chat Ept, and he says it has improved intelligence, improved personality. 143 00:06:51,520 --> 00:06:54,640 Speaker 2: Random guy on Twitter says it's been feeling very yes 144 00:06:54,720 --> 00:06:58,039 Speaker 2: Man lately, and Altman says, yeah, it glazes too much, 145 00:06:58,120 --> 00:07:01,240 Speaker 2: will fix. So there is this sense that like these 146 00:07:01,279 --> 00:07:05,360 Speaker 2: like chat GBT users are having that like it's too nice. 147 00:07:05,400 --> 00:07:07,880 Speaker 2: But I do think and this is why I want 148 00:07:07,920 --> 00:07:09,640 Speaker 2: to talk about this in the contact of friendship. Like 149 00:07:09,760 --> 00:07:12,920 Speaker 2: part of the reason it's so affirming is because this 150 00:07:13,040 --> 00:07:15,360 Speaker 2: is how people are using chat GPT to some extent. 151 00:07:15,800 --> 00:07:19,160 Speaker 7: Well, yeah, they're using it for relationship advice, Zuckerberg said, 152 00:07:19,160 --> 00:07:21,560 Speaker 7: I mean that's the use case you's seeing. They're going 153 00:07:21,560 --> 00:07:23,880 Speaker 7: to chat GBT and say, how do I talk to 154 00:07:23,960 --> 00:07:26,800 Speaker 7: my boss about this? How do I ask for what 155 00:07:26,880 --> 00:07:31,800 Speaker 7: I want getting life advice? In social advice? And they're 156 00:07:31,840 --> 00:07:36,440 Speaker 7: also trying to find information and maybe reinforce some of 157 00:07:36,480 --> 00:07:39,280 Speaker 7: their beliefs that nobody else will back them up on. 158 00:07:39,600 --> 00:07:41,720 Speaker 7: Then you can see how it gets dangerous. We've been 159 00:07:41,760 --> 00:07:44,920 Speaker 7: talking about social media echo chambers for so long, the 160 00:07:44,960 --> 00:07:47,560 Speaker 7: idea that you surround yourself with people who have the 161 00:07:47,640 --> 00:07:49,880 Speaker 7: same belief as you do, and then you just get 162 00:07:49,880 --> 00:07:52,120 Speaker 7: more entrenched in that belief and it's very hard to 163 00:07:52,120 --> 00:07:55,280 Speaker 7: pull you out of it. Imagine a world where those 164 00:07:55,280 --> 00:07:59,080 Speaker 7: conversations are just one on one with a chatbot, no 165 00:07:59,800 --> 00:08:03,200 Speaker 7: ch and balances even from the wider group of people 166 00:08:03,200 --> 00:08:06,080 Speaker 7: who might have that feeling, and you're just being sucked 167 00:08:06,120 --> 00:08:08,040 Speaker 7: deeper into that worldview. 168 00:08:08,440 --> 00:08:11,240 Speaker 3: That does present sort of a compelling counter to a 169 00:08:11,280 --> 00:08:14,080 Speaker 3: real friend, who is a human being, who might be 170 00:08:14,320 --> 00:08:17,560 Speaker 3: busy when you need advice from them, who might disagree 171 00:08:17,560 --> 00:08:19,960 Speaker 3: with you in a way that irritates you, who might 172 00:08:20,000 --> 00:08:22,280 Speaker 3: give you information you don't want, but that is what 173 00:08:22,320 --> 00:08:24,440 Speaker 3: a true friend does. I feel like we always see 174 00:08:24,440 --> 00:08:26,080 Speaker 3: this with like world leaders and people who have a 175 00:08:26,120 --> 00:08:28,600 Speaker 3: lot of power, or big celebrities who get surrounded by 176 00:08:28,600 --> 00:08:30,840 Speaker 3: too many yes people and then begin to really go 177 00:08:30,880 --> 00:08:33,920 Speaker 3: off the rails. And it's interesting that now just regular 178 00:08:34,040 --> 00:08:37,080 Speaker 3: people can have that important and I can see the 179 00:08:37,080 --> 00:08:37,920 Speaker 3: seduction of it. 180 00:08:38,040 --> 00:08:41,040 Speaker 2: We were talking about open aye glazing, but like all 181 00:08:41,080 --> 00:08:43,920 Speaker 2: of these big tech companies have chatbots, and Zuckerberg in 182 00:08:43,960 --> 00:08:46,800 Speaker 2: that clip we heard earlier, he talked about why he 183 00:08:46,880 --> 00:08:48,800 Speaker 2: thinks this is real. Let's just listen to a tiny 184 00:08:48,840 --> 00:08:52,200 Speaker 2: bit more of Zuckerberg trying to make the case that 185 00:08:52,280 --> 00:08:54,800 Speaker 2: you are going to be friends with your chatbot. 186 00:08:55,400 --> 00:08:57,920 Speaker 4: So I think that a lot of these things that 187 00:08:58,240 --> 00:09:00,720 Speaker 4: today there might be a little bit of a stick around. 188 00:09:01,559 --> 00:09:05,160 Speaker 4: I would guess that over time we will find the 189 00:09:05,240 --> 00:09:08,880 Speaker 4: vocabulary as a society to be able to articulate why 190 00:09:08,920 --> 00:09:10,920 Speaker 4: that is valuable and why the people who are doing 191 00:09:11,000 --> 00:09:14,160 Speaker 4: these things are like, why they are rational for doing it, 192 00:09:14,480 --> 00:09:17,440 Speaker 4: and like and how it is adding value for their lives. 193 00:09:17,920 --> 00:09:18,679 Speaker 2: Sarah, what do you. 194 00:09:18,640 --> 00:09:21,079 Speaker 3: Think about that? I'm so curious. Do you think this 195 00:09:21,160 --> 00:09:23,120 Speaker 3: is like leading us down a dangerous path? Do you 196 00:09:23,120 --> 00:09:25,520 Speaker 3: think this is good? That maybe it's helping people who 197 00:09:25,600 --> 00:09:28,119 Speaker 3: are in deep isolation or struggling. 198 00:09:28,559 --> 00:09:32,720 Speaker 7: You can look at it in many different ways, because 199 00:09:32,880 --> 00:09:35,080 Speaker 7: people definitely do need someone to talk to you. There's 200 00:09:35,080 --> 00:09:39,160 Speaker 7: often ways to use chatbots therapeutically, but if you want 201 00:09:39,200 --> 00:09:41,400 Speaker 7: it to be critical of you and you wanted to 202 00:09:41,440 --> 00:09:44,320 Speaker 7: give you constructive feedback, you have to really give it 203 00:09:44,600 --> 00:09:47,480 Speaker 7: that that feedback in the conversation and say I want 204 00:09:47,520 --> 00:09:50,600 Speaker 7: you to tell me what I'm wrong, what I'm getting wrong, 205 00:09:50,679 --> 00:09:54,760 Speaker 7: wanting to know about give me critical feedback. The analogy, 206 00:09:54,800 --> 00:09:59,400 Speaker 7: in my mind really is porn. People have, oh they're 207 00:09:59,480 --> 00:10:03,400 Speaker 7: porn prep diferences, and they look at that world and 208 00:10:03,440 --> 00:10:08,800 Speaker 7: it gives them this distorted view of what relationships can 209 00:10:08,840 --> 00:10:09,280 Speaker 7: be like. 210 00:10:10,040 --> 00:10:12,559 Speaker 5: And then that might affect their real world relationships. 211 00:10:12,640 --> 00:10:15,440 Speaker 3: It's like the girlfriend experience, but now it's the friend 212 00:10:15,520 --> 00:10:17,400 Speaker 3: experience from robots. 213 00:10:17,360 --> 00:10:18,160 Speaker 5: Right exactly. 214 00:10:18,240 --> 00:10:21,600 Speaker 7: So that can give people this distorted experience with how 215 00:10:21,600 --> 00:10:25,800 Speaker 7: they feel that romance should go, and then they might 216 00:10:26,240 --> 00:10:29,960 Speaker 7: not be satisfied in their regular dating life because they 217 00:10:30,000 --> 00:10:33,120 Speaker 7: have this misconception about how it should feel. We could 218 00:10:33,120 --> 00:10:37,760 Speaker 7: see that a similar effect in the world of AI conversation. 219 00:10:38,240 --> 00:10:41,559 Speaker 2: Yeah, and on the subject of porn, we're even seeing 220 00:10:41,559 --> 00:10:44,400 Speaker 2: this come up with chatbots. You know. The Wall Street 221 00:10:44,440 --> 00:10:48,880 Speaker 2: Journal did a story recently about these meta chatbots essentially 222 00:10:48,960 --> 00:10:54,240 Speaker 2: being induced into sort of creating sexual scenarios with minors. 223 00:10:54,040 --> 00:10:59,319 Speaker 2: It's doubly strange because Meta has licensed the voices of 224 00:10:59,360 --> 00:11:03,360 Speaker 2: a bunch of celebrit including the wrestler John Cena, like 225 00:11:03,679 --> 00:11:06,960 Speaker 2: super Icky, and it really gives you the sense that 226 00:11:07,240 --> 00:11:11,400 Speaker 2: these kind of like personalized bots could take people to 227 00:11:11,520 --> 00:11:15,600 Speaker 2: some weird and like genuinely troubling places. Sarah, you kind 228 00:11:15,600 --> 00:11:17,760 Speaker 2: of wrote the book on this, and I'm not just glazing. 229 00:11:17,920 --> 00:11:21,439 Speaker 2: There's a business problem here, which is these apps, whether 230 00:11:21,480 --> 00:11:24,280 Speaker 2: we're talking about Facebook and like social media, or we're 231 00:11:24,280 --> 00:11:27,560 Speaker 2: talking about open AI, they're trying to maximize time spent 232 00:11:27,760 --> 00:11:29,680 Speaker 2: on the app. And we sort of saw this play 233 00:11:29,679 --> 00:11:32,960 Speaker 2: out with social media right like where where companies were 234 00:11:33,040 --> 00:11:35,160 Speaker 2: essentially trying to find ways to keep users and I 235 00:11:35,200 --> 00:11:39,760 Speaker 2: guess still trying ways to keep users inside of their smartphones. 236 00:11:39,840 --> 00:11:42,680 Speaker 7: Essentially, it really is about giving the people what they want. 237 00:11:43,080 --> 00:11:49,160 Speaker 7: I think that what Facebook has historically done in Zuckerberg 238 00:11:49,200 --> 00:11:51,920 Speaker 7: has down. I should say now that it's company's called Meta. 239 00:11:52,040 --> 00:11:54,720 Speaker 7: They've looked at the data about what will get people 240 00:11:54,760 --> 00:11:58,280 Speaker 7: to use their products more and to the product to 241 00:11:58,360 --> 00:12:01,160 Speaker 7: that behavior as opposed to what people say they want. 242 00:12:01,480 --> 00:12:04,319 Speaker 7: So you may say you want good information, you may 243 00:12:04,360 --> 00:12:06,559 Speaker 7: say you want to hear from your friends and family, 244 00:12:06,840 --> 00:12:09,080 Speaker 7: but Meta looks at what you're actually clicking on, what 245 00:12:09,120 --> 00:12:12,760 Speaker 7: you're actually doing with your scroll, and they say, actually, 246 00:12:12,760 --> 00:12:16,080 Speaker 7: he wants more from these influencers. Actually, I think he 247 00:12:16,120 --> 00:12:20,000 Speaker 7: wants more video, and we're going to make him more 248 00:12:20,040 --> 00:12:21,920 Speaker 7: exposed to this stuff that we think would be better 249 00:12:21,920 --> 00:12:25,120 Speaker 7: for our business. So I may say I use Instagram 250 00:12:25,280 --> 00:12:27,760 Speaker 7: for keeping up with my friends and family, but when 251 00:12:27,760 --> 00:12:30,160 Speaker 7: I open the app, I'm just going to get sucked 252 00:12:30,160 --> 00:12:33,440 Speaker 7: in and it's going to become a passive experience. So 253 00:12:33,920 --> 00:12:37,600 Speaker 7: ultimately what's happening to the Internet at large is it's 254 00:12:37,679 --> 00:12:41,760 Speaker 7: becoming less of a critical thinking project where you're going 255 00:12:42,160 --> 00:12:44,760 Speaker 7: and you're seeking out what you want to know and 256 00:12:44,840 --> 00:12:48,079 Speaker 7: you're verifying it and you're saying, does this resolve my issue? 257 00:12:48,120 --> 00:12:49,040 Speaker 5: Okay, yes it does. 258 00:12:49,880 --> 00:12:53,240 Speaker 7: And instead of that kind of framework, you're going to 259 00:12:53,440 --> 00:12:55,240 Speaker 7: interact with an app and it's giving you what it 260 00:12:55,320 --> 00:12:59,719 Speaker 7: thinks you want, and our whole consumption experience is going 261 00:12:59,720 --> 00:13:04,320 Speaker 7: to become more passive, more algorithmically controlled, and less something 262 00:13:04,360 --> 00:13:05,880 Speaker 7: that we're thinking critically about. 263 00:13:06,040 --> 00:13:08,880 Speaker 3: I feel like the lines get really blurred here. Like 264 00:13:09,400 --> 00:13:12,240 Speaker 3: there have been like a couple of AI just total 265 00:13:12,280 --> 00:13:16,000 Speaker 3: AI pop stars, like people who are just AI generated. 266 00:13:16,120 --> 00:13:19,560 Speaker 3: There's Hot Sune Miko from Japan. A man married her 267 00:13:20,559 --> 00:13:24,480 Speaker 3: or it and also Hot Sune performed with Lady Gaga. 268 00:13:24,760 --> 00:13:27,400 Speaker 3: I mean it's and has millions of social media followers. 269 00:13:27,400 --> 00:13:29,240 Speaker 3: I don't even know how to really talk about this, but. 270 00:13:29,520 --> 00:13:33,680 Speaker 2: I have a suggestion. It's called chat GPT psychosis. I 271 00:13:33,720 --> 00:13:36,400 Speaker 2: said chat GPTs psychosis as a joke, but there are 272 00:13:36,400 --> 00:13:40,080 Speaker 2: some actual studies here showing that when you spend a 273 00:13:40,120 --> 00:13:43,640 Speaker 2: lot of time in these apps, you get addicted. And 274 00:13:43,720 --> 00:13:45,800 Speaker 2: there's Also, Rolling Stone did a big story where they 275 00:13:45,880 --> 00:13:49,440 Speaker 2: talked about essentially people ignoring their loved ones to like 276 00:13:49,520 --> 00:13:52,160 Speaker 2: talk to their chatbots. I mean, I think we have 277 00:13:52,240 --> 00:13:55,280 Speaker 2: seen this movie before and we're still like living it. 278 00:13:55,360 --> 00:13:57,320 Speaker 2: I mean, there are all these stories about you know, 279 00:13:57,400 --> 00:14:02,080 Speaker 2: teenagers being driven to you know, deep mental health problems, 280 00:14:02,080 --> 00:14:05,040 Speaker 2: even suicide after spending like huge amounts of time inside 281 00:14:05,040 --> 00:14:08,079 Speaker 2: these apps. I mean, I think we really, like it's 282 00:14:08,160 --> 00:14:10,600 Speaker 2: very tempting to be like, oh, the fact that some 283 00:14:11,280 --> 00:14:15,800 Speaker 2: body like married their AI or whatever like that is validating, 284 00:14:15,800 --> 00:14:18,360 Speaker 2: but also there's the human reaction, which is like that 285 00:14:18,480 --> 00:14:20,360 Speaker 2: is crazy. Well. 286 00:14:20,440 --> 00:14:22,760 Speaker 3: I wrote a story years ago about how the military 287 00:14:22,920 --> 00:14:26,680 Speaker 3: was using AI therapists to talk to veterans who felt 288 00:14:26,720 --> 00:14:29,040 Speaker 3: like they couldn't open up to another person for fear 289 00:14:29,040 --> 00:14:33,720 Speaker 3: of judgment, but actually could let a lot of people 290 00:14:33,760 --> 00:14:36,360 Speaker 3: with PTSD kind of let a lot of these experiences 291 00:14:36,400 --> 00:14:40,720 Speaker 3: out with an AI, and I was very interested by that. Like, 292 00:14:40,760 --> 00:14:43,120 Speaker 3: I don't think it's quite as cut and dry as like, 293 00:14:43,240 --> 00:14:46,280 Speaker 3: human relationships are great, Robot relationships aren't. 294 00:14:46,320 --> 00:14:48,120 Speaker 5: But there's a place for you, No, I SA with you. 295 00:14:48,200 --> 00:14:50,640 Speaker 7: I feel like there's some people who feel like they 296 00:14:50,680 --> 00:14:54,240 Speaker 7: can't be even fully themselves with their therapists, because it 297 00:14:54,280 --> 00:14:58,320 Speaker 7: feels like like they're disclosing things that they will be 298 00:14:58,440 --> 00:15:01,360 Speaker 7: judged by. Even if oh, yes, you're paying you not 299 00:15:01,520 --> 00:15:06,320 Speaker 7: to judge you. There's certainly a place for this. Now, 300 00:15:06,440 --> 00:15:08,520 Speaker 7: what the companies might say is, who are we to 301 00:15:08,600 --> 00:15:12,520 Speaker 7: judge how people decide to have conversations with these products 302 00:15:12,560 --> 00:15:15,960 Speaker 7: we create. You know, whatever helps them is good, right, 303 00:15:16,520 --> 00:15:20,120 Speaker 7: they can decide. But what we don't understand, and what 304 00:15:20,160 --> 00:15:27,000 Speaker 7: the companies won't even understand maybe themselves, is what the 305 00:15:27,160 --> 00:15:31,240 Speaker 7: tendencies are of these chatbots, how they work, what kind 306 00:15:31,240 --> 00:15:34,840 Speaker 7: of patterns they fall into, because there's not a lot 307 00:15:34,840 --> 00:15:37,720 Speaker 7: of public data on this. The way we saw content 308 00:15:37,760 --> 00:15:40,800 Speaker 7: go viral on Facebook and we could say, oh, they're 309 00:15:40,880 --> 00:15:45,560 Speaker 7: you know, stop the steal is becoming a troubling trend 310 00:15:45,720 --> 00:15:49,480 Speaker 7: on Facebook. Maybe something's going to happen, and then, you know, 311 00:15:50,000 --> 00:15:52,720 Speaker 7: violence did happen the January sixth insurrection, and the people 312 00:15:52,760 --> 00:15:58,040 Speaker 7: were able to trace it back to what happened algorithmically 313 00:15:58,640 --> 00:16:02,600 Speaker 7: that convinced people to join a movement that then resulted 314 00:16:02,640 --> 00:16:04,120 Speaker 7: in real world harm. 315 00:16:04,240 --> 00:16:05,160 Speaker 5: I think it's going. 316 00:16:05,120 --> 00:16:08,320 Speaker 7: To be more difficult for us as observers of the 317 00:16:08,320 --> 00:16:12,640 Speaker 7: Internet to see those patterns before any sort of real 318 00:16:12,680 --> 00:16:16,680 Speaker 7: world harm happens. Like you're saying, Max, a lot of 319 00:16:16,680 --> 00:16:20,240 Speaker 7: this is just individual anecdotes, people saying, you know, I 320 00:16:20,280 --> 00:16:23,240 Speaker 7: had this experience, and you can see the chat logs later, 321 00:16:23,680 --> 00:16:26,760 Speaker 7: but in the moment, there's really not that much that 322 00:16:26,840 --> 00:16:30,560 Speaker 7: a company is doing to review those conversations, and people 323 00:16:30,640 --> 00:16:32,120 Speaker 7: might be creeped out if they did. 324 00:16:32,480 --> 00:16:35,240 Speaker 3: Right, because then you'd be really vulnerable potentially to manipulation 325 00:16:35,800 --> 00:16:38,600 Speaker 3: or if you've got, you know, a deep connection with 326 00:16:38,760 --> 00:16:41,120 Speaker 3: something that's potentially controlled by a company. 327 00:16:40,960 --> 00:16:44,600 Speaker 7: Suprivacy issue, if you're discussing your deepest, darkest secrets with 328 00:16:45,240 --> 00:16:48,320 Speaker 7: a chatbot. Not that I recommend anyone do that on 329 00:16:48,400 --> 00:16:53,480 Speaker 7: a public company's chatbot network. That could be something that 330 00:16:53,520 --> 00:16:56,080 Speaker 7: you wouldn't want facebooks to know about. 331 00:16:57,440 --> 00:17:01,040 Speaker 3: Although there are some very delightful thing from AI. There 332 00:17:01,080 --> 00:17:04,640 Speaker 3: is an AI bot who has a permanent place on 333 00:17:04,680 --> 00:17:10,080 Speaker 3: my workout playlist. It's new Norri, I guess from this 334 00:17:10,240 --> 00:17:13,760 Speaker 3: creator in Germany, but it is an AI chatbot. And 335 00:17:13,840 --> 00:17:17,360 Speaker 3: there is a song that New Norri it she sings 336 00:17:17,440 --> 00:17:20,439 Speaker 3: that I actually really like. I wanted to play it 337 00:17:20,480 --> 00:17:40,560 Speaker 3: for you. Max is dying now to the latest in 338 00:17:40,720 --> 00:17:44,240 Speaker 3: tariff news. President Trump announced this week a one hundred 339 00:17:44,320 --> 00:17:47,920 Speaker 3: percent tariff on movies produced outside the US. A lot 340 00:17:47,920 --> 00:17:50,840 Speaker 3: of this was on his platform of choice Truth Social Trump. 341 00:17:51,080 --> 00:17:52,359 Speaker 3: I guess is it truth out? 342 00:17:52,560 --> 00:17:54,359 Speaker 2: Truth truth, truth out? 343 00:17:54,920 --> 00:17:57,440 Speaker 3: That the American movie industry was dying quote a very 344 00:17:57,440 --> 00:18:00,840 Speaker 3: fast death because of other countries offering insteads and tax 345 00:18:00,880 --> 00:18:04,240 Speaker 3: breaks and also cheaper labor in other countries. President Trump 346 00:18:04,320 --> 00:18:08,320 Speaker 3: actually called this a national security threat and wrote truth 347 00:18:08,480 --> 00:18:11,399 Speaker 3: in all caps. We want movies made in America again. 348 00:18:11,760 --> 00:18:14,560 Speaker 2: Yeah, Stacey, this has, like I've been an issue for Trump. 349 00:18:14,720 --> 00:18:17,720 Speaker 2: You remember the film Parasite. Great movie? 350 00:18:17,920 --> 00:18:20,840 Speaker 3: Yes, yes, movie won Best Picture. 351 00:18:20,600 --> 00:18:22,119 Speaker 2: One Best Picture, really moving. 352 00:18:22,240 --> 00:18:23,760 Speaker 3: I thought True is a beautiful movie. 353 00:18:23,840 --> 00:18:26,879 Speaker 2: Not a fan of Parasite, You'll be surprised to hear 354 00:18:27,000 --> 00:18:30,199 Speaker 2: with Parasite, we got enough problems with South Korea with 355 00:18:30,280 --> 00:18:32,760 Speaker 2: trade on top of it, they give him the best 356 00:18:32,840 --> 00:18:34,720 Speaker 2: movie of the year. Was it good? I don't know, 357 00:18:35,640 --> 00:18:38,359 Speaker 2: you know, I'm looking for like where let's get Gone 358 00:18:38,359 --> 00:18:39,840 Speaker 2: with the Wind? Can we get like Gone with the 359 00:18:39,880 --> 00:18:40,360 Speaker 2: Wind back? 360 00:18:40,440 --> 00:18:40,760 Speaker 7: Please? 361 00:18:43,080 --> 00:18:48,000 Speaker 3: Sunset full of also appreciate that his movie knowledge seems 362 00:18:48,080 --> 00:18:51,120 Speaker 3: to like cease with the end of Technicolor. 363 00:18:51,359 --> 00:18:56,040 Speaker 2: Okay, but our movie review retrospective. Because Lucas Shaw, the 364 00:18:56,280 --> 00:18:59,040 Speaker 2: author of Bloomberg screen Time newsletter, is here with us 365 00:18:59,080 --> 00:19:00,000 Speaker 2: to talk about the story. 366 00:19:00,040 --> 00:19:02,760 Speaker 3: We're very lucky to have him. Hi, Lucas, welcome. 367 00:19:03,600 --> 00:19:05,879 Speaker 1: Hey, So, what is the problem that you two have 368 00:19:05,920 --> 00:19:08,679 Speaker 1: with movies that are seventy plus years old? 369 00:19:09,680 --> 00:19:13,280 Speaker 3: I have no problem with it. I was just interested 370 00:19:13,400 --> 00:19:16,359 Speaker 3: that those were the two movie references. But you know, 371 00:19:16,720 --> 00:19:19,360 Speaker 3: maybe the pictures got smaller and now they're not even 372 00:19:19,400 --> 00:19:23,879 Speaker 3: worth talking about. So, Lucas, you've been writing about this issue, 373 00:19:24,200 --> 00:19:27,119 Speaker 3: tell us, like, what exactly are the details here? What 374 00:19:27,119 --> 00:19:28,520 Speaker 3: would these tariffs be? 375 00:19:29,200 --> 00:19:31,480 Speaker 1: Well, you know as much as I do on that front. 376 00:19:31,640 --> 00:19:33,560 Speaker 1: I think you also know as much as the president 377 00:19:33,600 --> 00:19:35,679 Speaker 1: does on that front. He just sort of proposed one 378 00:19:35,720 --> 00:19:40,000 Speaker 1: hundred percent tariff on movies. But one of the challenges 379 00:19:40,080 --> 00:19:44,000 Speaker 1: here and why his posts so confused and alarmed people 380 00:19:44,000 --> 00:19:47,080 Speaker 1: who work in the entertainment business. First of all, it's 381 00:19:47,119 --> 00:19:49,359 Speaker 1: just hard to figure out how you how. 382 00:19:49,280 --> 00:19:50,280 Speaker 2: Do you terrifrom the movie? 383 00:19:50,320 --> 00:19:51,679 Speaker 1: But we can get there, we can get there in 384 00:19:51,680 --> 00:19:53,880 Speaker 1: a second. But then there's a question of like what 385 00:19:54,080 --> 00:19:57,199 Speaker 1: counts as an imported movie? 386 00:19:57,280 --> 00:19:57,480 Speaker 2: Right? 387 00:19:57,520 --> 00:20:00,240 Speaker 1: So parasite that one's pretty cut and dry. Right, that 388 00:20:00,359 --> 00:20:03,920 Speaker 1: is a movie financed and produced out of Korea. Yes, 389 00:20:04,000 --> 00:20:07,040 Speaker 1: it was ultimately sort of distributed in the US by 390 00:20:07,280 --> 00:20:10,760 Speaker 1: an American company, Neon, but that feels more like a 391 00:20:11,400 --> 00:20:13,480 Speaker 1: just straight import. And if you want to put taxes 392 00:20:13,480 --> 00:20:16,879 Speaker 1: on that, you can. That accounts for such a small 393 00:20:16,920 --> 00:20:19,680 Speaker 1: percentage of the US box office that it doesn't really 394 00:20:19,680 --> 00:20:22,680 Speaker 1: make any sense. And the real issue is if there 395 00:20:22,800 --> 00:20:25,800 Speaker 1: was in any way retaliatory tariffs, that would be pretty 396 00:20:25,800 --> 00:20:31,960 Speaker 1: devastating because US movie studios rely on international sales for 397 00:20:32,000 --> 00:20:34,240 Speaker 1: the majority of their money, which is not the case 398 00:20:34,320 --> 00:20:36,760 Speaker 1: for most movies made in other countries. But then there's 399 00:20:36,800 --> 00:20:40,240 Speaker 1: like the added wrinkle of okay, like what this all 400 00:20:40,280 --> 00:20:44,160 Speaker 1: star team of John Voyd and Mel Gibson and Sylvester Stallone, 401 00:20:44,200 --> 00:20:47,240 Speaker 1: these ambassadors are supposed to be working on. 402 00:20:47,280 --> 00:20:49,040 Speaker 3: Oh yes, these are the people who are like at 403 00:20:49,080 --> 00:20:51,080 Speaker 3: Marlozzo lobbing him on this issue, right. 404 00:20:51,320 --> 00:20:53,960 Speaker 1: Yeah, is like bringing production back to the US, which 405 00:20:54,000 --> 00:20:56,439 Speaker 1: is a separate issue. Right, Like a lot of Disney movies, 406 00:20:56,520 --> 00:20:58,560 Speaker 1: a lot of Warner Brothers movies, a lot of Universal 407 00:20:58,600 --> 00:21:02,080 Speaker 1: movies are produced somewhere else, but there's a lot of 408 00:21:02,119 --> 00:21:04,080 Speaker 1: work still done on them here, And it's like does 409 00:21:04,119 --> 00:21:04,720 Speaker 1: that count? 410 00:21:04,880 --> 00:21:07,560 Speaker 2: Can I just point out, like, this is weird because 411 00:21:08,200 --> 00:21:11,040 Speaker 2: Hollywood seems like the kind of industry that Donald Trump 412 00:21:11,040 --> 00:21:14,679 Speaker 2: would want to humiliate destroys, Like, isn't this part of 413 00:21:14,720 --> 00:21:18,480 Speaker 2: the same group of media academia that you would think 414 00:21:18,720 --> 00:21:20,280 Speaker 2: he wouldn't be like we need to give them a 415 00:21:20,280 --> 00:21:24,320 Speaker 2: bunch of tax breaks or create tax penalties on foreign competitors, Lucas. 416 00:21:24,359 --> 00:21:27,520 Speaker 2: Do you have any idea, like what is happening here? 417 00:21:27,640 --> 00:21:30,600 Speaker 2: Is it just that John Voight is friends with President 418 00:21:30,600 --> 00:21:33,439 Speaker 2: Trump and John voightd is mad about this? Is it 419 00:21:33,480 --> 00:21:34,000 Speaker 2: that simple? 420 00:21:34,800 --> 00:21:35,040 Speaker 5: Well? 421 00:21:35,080 --> 00:21:39,800 Speaker 1: I think that the post on truth social is a 422 00:21:39,880 --> 00:21:43,639 Speaker 1: direct result of that, right, because John Voight and his manager, 423 00:21:43,680 --> 00:21:46,439 Speaker 1: Stephen Paul, they were at mar A Lago over the 424 00:21:46,440 --> 00:21:49,040 Speaker 1: weekend spending time talking about this issue. So I think 425 00:21:49,080 --> 00:21:52,720 Speaker 1: the specific we're going to do this now was definitely 426 00:21:52,760 --> 00:21:55,800 Speaker 1: a result of it. I think more broadly, everything about 427 00:21:55,880 --> 00:21:59,480 Speaker 1: Trump's sort of campaign is to like, bring America back 428 00:21:59,560 --> 00:22:02,680 Speaker 1: to the nineteen fifties, bring America back to the nineteen seventy. 429 00:22:02,760 --> 00:22:04,600 Speaker 1: You can kind of pick a decade, but something a 430 00:22:04,600 --> 00:22:08,000 Speaker 1: long long time ago. Gone with the Wind era, Yeah, 431 00:22:08,080 --> 00:22:12,639 Speaker 1: gone with the Wind's a boulevard era. The postwar packs 432 00:22:12,680 --> 00:22:16,440 Speaker 1: American in nineteen fifties. He loves Hollywood as this symbol 433 00:22:16,480 --> 00:22:20,520 Speaker 1: of like American greatness. The problem is is that it 434 00:22:20,760 --> 00:22:22,960 Speaker 1: still is, like it's so different from all these other 435 00:22:23,000 --> 00:22:25,040 Speaker 1: industries that he's going after, Like you want to bring 436 00:22:25,119 --> 00:22:28,600 Speaker 1: manufacturing back. Fine, Like, yes, we have surrendered manufacturing to 437 00:22:28,600 --> 00:22:30,840 Speaker 1: a lot of the other countries. And that's a conversation 438 00:22:30,920 --> 00:22:34,080 Speaker 1: you guys have had with other people. But Hollywood is 439 00:22:34,119 --> 00:22:37,520 Speaker 1: still the dominant business center when it comes to pop culture. 440 00:22:37,560 --> 00:22:40,600 Speaker 1: America is still the pre eminent power in global pop culture, 441 00:22:40,640 --> 00:22:42,800 Speaker 1: even if it's lost a little bit over the years. 442 00:22:43,359 --> 00:22:45,719 Speaker 1: Like we are a net exporter. So I don't know 443 00:22:45,800 --> 00:22:48,240 Speaker 1: what exactly he's trying to accomplish. Well. 444 00:22:48,320 --> 00:22:51,120 Speaker 3: One of the things that you pointed out in one 445 00:22:51,119 --> 00:22:53,720 Speaker 3: of the articles that you wrote was that part of 446 00:22:53,760 --> 00:22:57,280 Speaker 3: this is possibly because it has become a lot more 447 00:22:57,320 --> 00:23:01,119 Speaker 3: expensive to film movies in the US recently. Could you 448 00:23:01,119 --> 00:23:03,440 Speaker 3: talk a little bit about why that has happened and 449 00:23:03,480 --> 00:23:05,119 Speaker 3: how that might be affected by this. 450 00:23:05,960 --> 00:23:08,840 Speaker 1: Yeah, I mean the two biggest reasons why production has 451 00:23:08,840 --> 00:23:12,200 Speaker 1: gotten more expensive, which is why it's left, are unions 452 00:23:12,240 --> 00:23:16,199 Speaker 1: and tax incentives. Is this a strike, that's a symbolic 453 00:23:16,320 --> 00:23:20,640 Speaker 1: of Hollywood has very strong labor unions. They renegotiate their 454 00:23:20,720 --> 00:23:23,800 Speaker 1: deals with the studios every few years. Basically every year 455 00:23:23,840 --> 00:23:26,520 Speaker 1: there is some union renegotiating some part of their deal, 456 00:23:27,040 --> 00:23:29,120 Speaker 1: and most of the studio chiefs you talk to say 457 00:23:29,119 --> 00:23:32,320 Speaker 1: that that has made shooting in the US more expensive 458 00:23:32,320 --> 00:23:35,040 Speaker 1: because they keep getting better and better terms. It's one 459 00:23:35,040 --> 00:23:37,520 Speaker 1: of the kind of the rare areas of American business 460 00:23:37,560 --> 00:23:40,520 Speaker 1: where you still have very strong unions, but the even 461 00:23:40,560 --> 00:23:43,879 Speaker 1: bigger factor is just this incentive regime that has taken place. 462 00:23:43,880 --> 00:23:48,119 Speaker 1: It's one of the reasons why for many years, for decades, honestly, 463 00:23:48,200 --> 00:23:51,400 Speaker 1: California has lost production to other states within the US. 464 00:23:51,440 --> 00:23:53,879 Speaker 1: So there was, you know, a big boom in Atlanta 465 00:23:53,920 --> 00:23:56,920 Speaker 1: and New Mexico and New York and all these other places, 466 00:23:57,119 --> 00:23:59,359 Speaker 1: and then other countries have replicated this. So there's a 467 00:23:59,400 --> 00:24:02,199 Speaker 1: ton of production in Vancouver, there's a ton of production 468 00:24:02,280 --> 00:24:05,520 Speaker 1: in Toronto, there's a lot of production in London. There's 469 00:24:05,560 --> 00:24:08,080 Speaker 1: a decent amount of production in Australia and New Zealand. 470 00:24:08,280 --> 00:24:10,520 Speaker 1: And it's different from what we've seen with some of 471 00:24:10,520 --> 00:24:14,160 Speaker 1: these other industries where it's not like you're sending the 472 00:24:14,200 --> 00:24:16,879 Speaker 1: work to these super low cost places where people are 473 00:24:16,920 --> 00:24:20,480 Speaker 1: getting paid like a dollar a day to work for hours. 474 00:24:21,000 --> 00:24:24,040 Speaker 1: You know, Canada and the UK are very expensive, rich countries. 475 00:24:24,560 --> 00:24:27,320 Speaker 1: It's just that they are offering more incentives because they 476 00:24:27,359 --> 00:24:29,720 Speaker 1: see a reason to bring production over. 477 00:24:30,200 --> 00:24:33,000 Speaker 2: Gavin Newsom, the governor of California, is proposing this. I mean, 478 00:24:33,000 --> 00:24:37,160 Speaker 2: there is a version of subsidizing Hollywood that doesn't involve 479 00:24:37,320 --> 00:24:42,199 Speaker 2: taxing all these artsy for films that are doing like 480 00:24:42,760 --> 00:24:45,640 Speaker 2: two million bucks at the box office and winning awards 481 00:24:45,640 --> 00:24:47,640 Speaker 2: and just like compete with the tax incentives. 482 00:24:47,720 --> 00:24:52,320 Speaker 1: Right, Well, the central thrust of the John Voight plan 483 00:24:52,760 --> 00:24:56,600 Speaker 1: is incentives. It's not tariffs. But I just think Trump 484 00:24:56,800 --> 00:24:59,479 Speaker 1: likes tariffs. And you know, you made the point earlier 485 00:24:59,520 --> 00:25:02,560 Speaker 1: about does Donald Trump want to be you know, why 486 00:25:02,600 --> 00:25:05,040 Speaker 1: is he trying to help Hollywood. Donald Trump is not 487 00:25:05,240 --> 00:25:08,719 Speaker 1: a figure who generally likes to pay people to do 488 00:25:08,800 --> 00:25:11,199 Speaker 1: what he wants, right Like he prefers the stick to 489 00:25:11,240 --> 00:25:15,280 Speaker 1: the carrot, And so I think the notion of him 490 00:25:15,320 --> 00:25:19,040 Speaker 1: giving big tax incentives to these Hollywood studios, it seems 491 00:25:19,080 --> 00:25:22,639 Speaker 1: hard to imagine him doing that. But yes, you're right, 492 00:25:22,720 --> 00:25:24,639 Speaker 1: Gavin Newsom and others have proposed it. 493 00:25:24,840 --> 00:25:27,600 Speaker 2: So there is some counterteuitive here. But on the other hand, 494 00:25:28,480 --> 00:25:32,760 Speaker 2: there's been this like long running conversation in Hollywood about 495 00:25:33,280 --> 00:25:35,600 Speaker 2: China in particular, right, and of course China is a 496 00:25:35,640 --> 00:25:38,720 Speaker 2: huge issue of Donald Trump, the fact that the Chinese 497 00:25:38,840 --> 00:25:42,000 Speaker 2: film market is so important to Hollywood that you're seeing, 498 00:25:42,280 --> 00:25:45,520 Speaker 2: you know, Hollywood studios make choices, like the idea of 499 00:25:45,760 --> 00:25:50,080 Speaker 2: trying to impose some sort of nationalism on the movie industry. 500 00:25:50,160 --> 00:25:51,840 Speaker 2: As weird as it is to like help out a 501 00:25:51,840 --> 00:25:55,920 Speaker 2: bunch of Californians do it does like definitely make sense 502 00:25:55,960 --> 00:25:59,280 Speaker 2: in the context of Trump's like cultural project. 503 00:25:59,800 --> 00:26:02,760 Speaker 1: Yeah, the China part of it is, well, China is 504 00:26:03,000 --> 00:26:06,879 Speaker 1: less influential in the global movie business, or more specifically 505 00:26:06,880 --> 00:26:09,840 Speaker 1: in Hollywood's mind than it was kind of five to 506 00:26:09,880 --> 00:26:13,399 Speaker 1: ten years ago. China was seen as this big potential market, 507 00:26:13,440 --> 00:26:17,960 Speaker 1: and you had studios kind of rushing in to release 508 00:26:18,000 --> 00:26:21,240 Speaker 1: more movies there. You had Hollywood production companies raising a 509 00:26:21,240 --> 00:26:25,520 Speaker 1: lot of money from China. And that changed because at 510 00:26:25,520 --> 00:26:28,600 Speaker 1: a certain point the she administration decided that they didn't 511 00:26:28,680 --> 00:26:31,480 Speaker 1: like how much money these companies were sort of investing 512 00:26:31,560 --> 00:26:34,920 Speaker 1: in the US, and so Chinese money into the US 513 00:26:34,960 --> 00:26:38,520 Speaker 1: film business really stopped at the same time, they played 514 00:26:38,520 --> 00:26:40,280 Speaker 1: the long game of getting the US to come and 515 00:26:40,320 --> 00:26:42,520 Speaker 1: spend a lot of money in China help incubating a 516 00:26:42,560 --> 00:26:45,000 Speaker 1: local industry, and then just cut it off. And so 517 00:26:45,440 --> 00:26:48,920 Speaker 1: the Chinese movie business and ticket sales have continued to grow. 518 00:26:48,960 --> 00:26:51,439 Speaker 1: It's the second biggest film market in the world. But 519 00:26:51,800 --> 00:26:55,399 Speaker 1: the US share of box office has gone down and 520 00:26:55,440 --> 00:26:57,400 Speaker 1: down and down as local product has picked up. 521 00:26:57,840 --> 00:26:59,959 Speaker 2: Lucas, can we come back to where we started on 522 00:27:00,000 --> 00:27:03,160 Speaker 2: this conversation? You said something like, well, they could tearff parasite. 523 00:27:03,480 --> 00:27:05,719 Speaker 2: What does that actually look like? Where does the actual 524 00:27:05,760 --> 00:27:08,680 Speaker 2: tariff come in? Is it? Is it on the licensing fee? 525 00:27:08,760 --> 00:27:11,720 Speaker 2: Is it when I the consumer go to the box office, Like, 526 00:27:12,000 --> 00:27:14,200 Speaker 2: your ticket is fifteen dollars, but now it's going to 527 00:27:14,240 --> 00:27:17,160 Speaker 2: be thirty dollars because of the China teriffs? What would 528 00:27:17,160 --> 00:27:17,879 Speaker 2: the mechanism? 529 00:27:18,359 --> 00:27:20,280 Speaker 1: I mean, it could be any of those things, right 530 00:27:20,359 --> 00:27:23,320 Speaker 1: like when Neon bought the rights to Parasite at can 531 00:27:23,480 --> 00:27:25,360 Speaker 1: I guess you could make a rule that any US 532 00:27:25,480 --> 00:27:29,879 Speaker 1: company that buys a sort of a foreign movie must 533 00:27:29,920 --> 00:27:32,399 Speaker 1: pay you know, an extra twenty percent or whatever the 534 00:27:32,480 --> 00:27:34,240 Speaker 1: number is, or you could just pass it directly to 535 00:27:34,240 --> 00:27:35,280 Speaker 1: the consumer, like. 536 00:27:35,280 --> 00:27:40,480 Speaker 3: More expensive movie tickets or just more expensive tickets to watch. 537 00:27:40,280 --> 00:27:41,840 Speaker 2: A foreign film on Netflix. 538 00:27:43,720 --> 00:27:46,080 Speaker 1: Well, if you watch to foreign woman theaters, it's twice 539 00:27:46,119 --> 00:27:49,040 Speaker 1: as expensive then the Netflix part. 540 00:27:48,680 --> 00:27:49,640 Speaker 3: Porn is smaller. 541 00:27:50,160 --> 00:27:53,240 Speaker 1: Probably have to be at the licensing stage. But Netflix 542 00:27:53,280 --> 00:27:55,480 Speaker 1: is one of those companies where it gets so complicated 543 00:27:55,520 --> 00:27:59,199 Speaker 1: because they produce so much in other countries because they 544 00:27:59,200 --> 00:28:01,800 Speaker 1: are a global service, right, So like the majority of 545 00:28:01,840 --> 00:28:04,840 Speaker 1: their customers don't live in the US. Oh, so they 546 00:28:04,880 --> 00:28:07,600 Speaker 1: have all of this programming that they produce for those people, 547 00:28:07,600 --> 00:28:10,040 Speaker 1: but it is available in the US because they have 548 00:28:10,119 --> 00:28:13,000 Speaker 1: the same originals available everywhere. So are you going to 549 00:28:13,119 --> 00:28:15,800 Speaker 1: tax every single Netflix foreign language program? 550 00:28:16,359 --> 00:28:18,399 Speaker 3: I Mean one question I did have that's like to 551 00:28:18,440 --> 00:28:20,439 Speaker 3: back up from this a little bit, is about the 552 00:28:20,480 --> 00:28:22,919 Speaker 3: movie industry in general, because Max and I are in 553 00:28:23,119 --> 00:28:25,840 Speaker 3: New York and I've just been hearing for months and 554 00:28:25,880 --> 00:28:28,600 Speaker 3: months ever since the strike, really that the movie industry, 555 00:28:28,640 --> 00:28:31,480 Speaker 3: the TV industry is just not what it was. That 556 00:28:31,520 --> 00:28:33,960 Speaker 3: there are just way fewer jobs, there's way less work. 557 00:28:34,960 --> 00:28:38,800 Speaker 3: What kind of shape is the film and TV industry 558 00:28:38,840 --> 00:28:43,120 Speaker 3: in right now? In our pre tariff possible tariff world. 559 00:28:43,560 --> 00:28:46,240 Speaker 1: The movie business had a really tough go of it 560 00:28:46,320 --> 00:28:48,840 Speaker 1: with the pandemic because people stop going to movies right 561 00:28:48,880 --> 00:28:54,000 Speaker 1: theaters shut down, the habit stopped, and then you also 562 00:28:54,080 --> 00:28:57,160 Speaker 1: had the two labor stoppages, the writers and actors who 563 00:28:57,200 --> 00:29:00,360 Speaker 1: went on strike, which interrupted the flow of production. And 564 00:29:00,440 --> 00:29:04,000 Speaker 1: so the movie business has never fully recovered to where 565 00:29:04,040 --> 00:29:07,320 Speaker 1: it was in twenty nineteen. There's optimism that it will 566 00:29:07,360 --> 00:29:09,560 Speaker 1: continue to grow, but I think that most people who 567 00:29:09,560 --> 00:29:12,200 Speaker 1: work in the movie business have sort of accepted that 568 00:29:12,240 --> 00:29:15,000 Speaker 1: people just go to fewer movies now, partially because so 569 00:29:15,080 --> 00:29:18,640 Speaker 1: much it's available at home. The broader Hollywood ecosystem has 570 00:29:18,680 --> 00:29:21,640 Speaker 1: shrunk over the last few years just because there was 571 00:29:22,000 --> 00:29:24,280 Speaker 1: just too much being made and too much money being spent. 572 00:29:24,960 --> 00:29:27,560 Speaker 2: Lucas I wanted to ask about the future, where do 573 00:29:27,600 --> 00:29:29,080 Speaker 2: you think this proposal goes? 574 00:29:29,480 --> 00:29:32,600 Speaker 1: Look, I don't have a crystal ball. My prediction, though, 575 00:29:32,720 --> 00:29:35,520 Speaker 1: is that I don't see one hundred percent tariff on 576 00:29:35,680 --> 00:29:37,400 Speaker 1: any of this happening, because he's going to have a 577 00:29:37,400 --> 00:29:39,360 Speaker 1: conversation with all these studios. Aren't going to say, what 578 00:29:39,400 --> 00:29:41,840 Speaker 1: the fuck you thinking about this is not helpful? Whether 579 00:29:41,960 --> 00:29:45,160 Speaker 1: that leads to there being a real tangible outcome from this, 580 00:29:45,240 --> 00:29:48,400 Speaker 1: and like some kind of federal incentive regime. That's possible. 581 00:29:48,440 --> 00:29:50,760 Speaker 1: I think it's also equally likely that this is just 582 00:29:50,800 --> 00:29:52,760 Speaker 1: like a little flash in the pan and he picks 583 00:29:52,800 --> 00:29:53,800 Speaker 1: a new hobby horse in a. 584 00:29:53,800 --> 00:29:54,280 Speaker 5: Week or two. 585 00:29:58,040 --> 00:30:00,680 Speaker 3: Well, Lucas definitely stick around because because we are now 586 00:30:00,720 --> 00:30:03,480 Speaker 3: to our final segment of the show, which is our 587 00:30:03,680 --> 00:30:05,800 Speaker 3: Underreported Story of the Week. Every week we pick a 588 00:30:05,840 --> 00:30:10,240 Speaker 3: story that we think was underreported, like very important story 589 00:30:10,280 --> 00:30:11,760 Speaker 3: that we think just did not get the attention it 590 00:30:11,800 --> 00:30:15,240 Speaker 3: probably should have. This week, Max picked the story. Max, 591 00:30:15,320 --> 00:30:16,840 Speaker 3: what what did you see that you think is. 592 00:30:16,840 --> 00:30:20,240 Speaker 2: Writing this story already? Because it's so freaking complicated, But 593 00:30:20,520 --> 00:30:20,880 Speaker 2: just to. 594 00:30:21,320 --> 00:30:23,680 Speaker 3: Really, okay, what is it to say it laid out? 595 00:30:23,720 --> 00:30:25,520 Speaker 2: It's that the President of the United States is doing 596 00:30:25,600 --> 00:30:26,960 Speaker 2: all kinds of crap with crypto. 597 00:30:27,280 --> 00:30:30,480 Speaker 3: Oh now, crypto this is always a rabbit hole, but 598 00:30:30,560 --> 00:30:32,480 Speaker 3: it's important. This is the problem with crypto. 599 00:30:33,200 --> 00:30:36,040 Speaker 2: This one you might have caught because it was in 600 00:30:36,440 --> 00:30:39,440 Speaker 2: the New York Times on Monday and Bloomberg has been 601 00:30:39,440 --> 00:30:42,480 Speaker 2: writing about it. But it's so weird and wonky that 602 00:30:42,560 --> 00:30:44,360 Speaker 2: I feel like we need to pause in it. And 603 00:30:44,400 --> 00:30:48,760 Speaker 2: it is that Binance, which is a crypto company, has 604 00:30:48,920 --> 00:30:52,720 Speaker 2: formed a partnership with World Liberty Financial, which is one 605 00:30:52,720 --> 00:30:56,360 Speaker 2: of Donald Trump's crypto things. World Liberty Financial is starting 606 00:30:56,440 --> 00:30:59,280 Speaker 2: a stable coin. A stable coin is sort of like 607 00:30:59,320 --> 00:31:02,520 Speaker 2: a unlike since bank. It's it's basically like you give 608 00:31:02,560 --> 00:31:04,760 Speaker 2: it money and then it will agree to pay you back. 609 00:31:05,120 --> 00:31:10,240 Speaker 2: Donald Trump's unlicensed bank is doing a major financing, a 610 00:31:10,240 --> 00:31:12,800 Speaker 2: two billion dollar financing for a company called Binance. Binance 611 00:31:13,320 --> 00:31:16,120 Speaker 2: you may remember because it was accused of money laundering. 612 00:31:16,280 --> 00:31:19,160 Speaker 3: Well weren't they also part of the whole bankman freed. 613 00:31:20,400 --> 00:31:24,640 Speaker 2: There, They were in the mix and a lot of stuff. Ceo, 614 00:31:24,760 --> 00:31:26,640 Speaker 2: who I've met before, actually what you know, went to 615 00:31:26,720 --> 00:31:30,360 Speaker 2: prison pled guilty. The company has said that, you know, 616 00:31:30,480 --> 00:31:32,680 Speaker 2: although they pled guilty, they've essentially said they've cleaned up 617 00:31:32,720 --> 00:31:36,080 Speaker 2: their act. They're raising money and the investor in this 618 00:31:36,680 --> 00:31:41,920 Speaker 2: stable coin is a firm that is controlled by this guy, 619 00:31:42,040 --> 00:31:45,760 Speaker 2: Shaike Tannun bin Zayed al Nayan, who is head of 620 00:31:45,760 --> 00:31:48,760 Speaker 2: the UA's National Security Agency. So to recap, Trump is 621 00:31:48,800 --> 00:31:51,720 Speaker 2: starting an unlicensed bank. He's getting a two billion dollar 622 00:31:51,880 --> 00:31:54,520 Speaker 2: interest free loan from the guy who runs the uas 623 00:31:54,560 --> 00:31:57,880 Speaker 2: spy agency, and that money is going to this company 624 00:31:57,920 --> 00:32:00,600 Speaker 2: that until recently was accused of money laundering connection with 625 00:32:00,680 --> 00:32:02,719 Speaker 2: you know, all sorts of bad people, and I'm just wondering. 626 00:32:03,120 --> 00:32:07,280 Speaker 3: I don't stand any of this. Sorry, I don't know, Lucas, 627 00:32:07,320 --> 00:32:07,480 Speaker 3: are you? 628 00:32:07,760 --> 00:32:10,720 Speaker 1: That is why nobody cares. 629 00:32:11,760 --> 00:32:17,000 Speaker 2: What's broadly happening is remember the last Trump presidency when 630 00:32:17,040 --> 00:32:20,920 Speaker 2: Trump had like his hotel, and he has all these businesses, 631 00:32:21,280 --> 00:32:23,080 Speaker 2: and you had a bunch of figures who were hoping 632 00:32:23,120 --> 00:32:27,320 Speaker 2: to influence him by like booking hotel rooms or having 633 00:32:27,360 --> 00:32:29,760 Speaker 2: their conference at at Trump's hotel and DC or whatever. 634 00:32:30,000 --> 00:32:32,200 Speaker 2: I assume that's all going on. But there's this new 635 00:32:32,280 --> 00:32:37,000 Speaker 2: thing that's happening where Trump has cryptocurrencies and crypto Trump, 636 00:32:38,400 --> 00:32:40,680 Speaker 2: and now all of a sudden, if you want to 637 00:32:40,720 --> 00:32:42,960 Speaker 2: send some money Trump's way, it's gotten a lot easier. 638 00:32:43,000 --> 00:32:45,360 Speaker 2: You can. You can send huge amounts of money. I 639 00:32:45,400 --> 00:32:47,720 Speaker 2: don't know how many hotel rooms you would need to 640 00:32:47,760 --> 00:32:49,720 Speaker 2: book for two billion dollars. 641 00:32:49,840 --> 00:32:52,640 Speaker 3: So Donald Trump potentially make money off this. 642 00:32:53,360 --> 00:32:57,040 Speaker 1: Donald Trump is serving as an intermediary between the Amidis 643 00:32:57,880 --> 00:33:01,400 Speaker 1: and this crypto company, and let's presumably take some kind 644 00:33:01,400 --> 00:33:04,120 Speaker 1: of vig for serving as said intermediary. 645 00:33:04,760 --> 00:33:05,760 Speaker 3: I'm so confused. 646 00:33:09,960 --> 00:33:12,480 Speaker 2: This show is produced by Stacy Wong, with additional help 647 00:33:12,520 --> 00:33:16,520 Speaker 2: by Rachel Lewis Chriskey. Magnus Hendrickson is our supervising producer, 648 00:33:16,760 --> 00:33:20,000 Speaker 2: Amy Keen our editor, and Brennan Francis Newnham is our 649 00:33:20,040 --> 00:33:24,640 Speaker 2: executive producer. Sage Bauman. Welcome Backstage Heads Bloomberg Podcast. If 650 00:33:24,680 --> 00:33:26,360 Speaker 2: you have a minute, please rate and review this show. 651 00:33:26,480 --> 00:33:27,840 Speaker 2: It means a lot to us. And if you have 652 00:33:27,920 --> 00:33:30,760 Speaker 2: a story that should be our business, email us at 653 00:33:30,800 --> 00:33:34,240 Speaker 2: Everybody's at Bloomberg dot net. That's everybody with an s 654 00:33:34,280 --> 00:33:37,640 Speaker 2: APN at Bloomberg dot net. Thanks for listening and we 655 00:33:37,640 --> 00:33:42,120 Speaker 2: will see you next week