1 00:00:05,440 --> 00:00:08,360 Speaker 1: Welcome to Daily Variety, your daily dose of news and 2 00:00:08,400 --> 00:00:13,480 Speaker 1: analysis for entertainment industry insiders. It's Thursday, October sixteenth, twenty 3 00:00:13,520 --> 00:00:16,920 Speaker 1: twenty five. I'm your host, Cynthia Littleton. I am co 4 00:00:17,079 --> 00:00:20,880 Speaker 1: editor in chief of Variety alongside Ramin Setuda. I'm in 5 00:00:21,079 --> 00:00:23,920 Speaker 1: La He's in New York, and Variety has reporters around 6 00:00:23,960 --> 00:00:27,840 Speaker 1: the world covering the business of entertainment. On today's episode, 7 00:00:27,920 --> 00:00:31,200 Speaker 1: we'll talk with Variety's Gene Modis about his eye opening 8 00:00:31,320 --> 00:00:34,960 Speaker 1: article on AI being used in the front lines of 9 00:00:35,000 --> 00:00:40,160 Speaker 1: creative development, reading and evaluating scripts. Is the script reader 10 00:00:40,280 --> 00:00:43,800 Speaker 1: rung of the production latter about to go away? Gene 11 00:00:43,880 --> 00:00:47,440 Speaker 1: unpacks his reporting and we'll conclude our week on the 12 00:00:47,440 --> 00:00:51,559 Speaker 1: French Riviera with the roundtable with my three Variety colleagues 13 00:00:51,840 --> 00:00:56,440 Speaker 1: who joined me in covering the mipcom content market, Elskis Lassi, 14 00:00:56,720 --> 00:01:00,840 Speaker 1: John Hopewell, and Leo Baraclough. Before we get to that, 15 00:01:01,240 --> 00:01:03,920 Speaker 1: here are a few headlines just in this morning that 16 00:01:04,000 --> 00:01:07,400 Speaker 1: you need to know. Warner Brothers Discovery has struck a 17 00:01:07,440 --> 00:01:11,560 Speaker 1: streaming distribution pact with Korea's cj E and M. There's 18 00:01:11,800 --> 00:01:16,560 Speaker 1: HBO Max will distribute CJE and m's t ving streaming 19 00:01:16,600 --> 00:01:22,200 Speaker 1: platform in seventeen markets across Asia Pacific, including Southeast Asia, Taiwan, 20 00:01:22,319 --> 00:01:25,880 Speaker 1: and Hong Kong. CBS News Head of Standards Claudia Milne 21 00:01:26,120 --> 00:01:28,840 Speaker 1: has exited amid the shakeup in the division and the 22 00:01:28,920 --> 00:01:32,160 Speaker 1: arrival of Barry Weiss as editor in chief. My colleague 23 00:01:32,200 --> 00:01:37,119 Speaker 1: Brian Steinberg has the scoop rip to actor Penelope Milford. 24 00:01:37,280 --> 00:01:40,200 Speaker 1: She was so good in nineteen seventy eight's Coming Home, 25 00:01:40,640 --> 00:01:44,440 Speaker 1: for which she earned an Oscar nomination. She was seventy seven. 26 00:01:45,200 --> 00:01:47,800 Speaker 1: You can find all of these stories and so much 27 00:01:47,840 --> 00:01:55,240 Speaker 1: more on Variety dot com. Right now. Now we turn 28 00:01:55,280 --> 00:01:58,200 Speaker 1: to conversations with Friday journalists about news and trends in 29 00:01:58,240 --> 00:02:03,520 Speaker 1: show business. Gene, Variety Senior media reporter, has the details 30 00:02:03,560 --> 00:02:06,600 Speaker 1: about a momentous test that was recently staged by the 31 00:02:06,720 --> 00:02:11,360 Speaker 1: Editors Guild. Who does the job better AI software or 32 00:02:11,400 --> 00:02:14,720 Speaker 1: professional script readers? Gene, Mattis, thank you for joining me. 33 00:02:15,680 --> 00:02:16,399 Speaker 2: Happy to be here. 34 00:02:16,600 --> 00:02:18,919 Speaker 1: Gene, you have been on a mission to look really 35 00:02:18,960 --> 00:02:22,800 Speaker 1: deeply at AI and look at concrete ways in which 36 00:02:23,240 --> 00:02:27,680 Speaker 1: this revolutionary technology is changing the business, and you've delivered 37 00:02:27,760 --> 00:02:29,840 Speaker 1: us a story this week. It's in the Variety's print 38 00:02:29,960 --> 00:02:34,440 Speaker 1: edition as well as online, a great, deeply reported story 39 00:02:34,600 --> 00:02:38,960 Speaker 1: about a very specific use case for AI that definitely 40 00:02:39,000 --> 00:02:42,680 Speaker 1: involves human activity. Tell us the basic concept of what 41 00:02:42,720 --> 00:02:44,800 Speaker 1: you wrote about, and tell us what was the spark 42 00:02:44,919 --> 00:02:46,760 Speaker 1: for you to pursue this story. 43 00:02:47,600 --> 00:02:51,079 Speaker 2: And we've been covering AI certainly intensely since the strikes, 44 00:02:51,520 --> 00:02:54,280 Speaker 2: and the writers and the actors both had their own 45 00:02:54,360 --> 00:02:57,720 Speaker 2: unique concerns about what could happen if AI came into 46 00:02:57,800 --> 00:03:01,480 Speaker 2: their domains and started writing script and started acting in movies, 47 00:03:01,560 --> 00:03:05,640 Speaker 2: right and this I was more interested in this case 48 00:03:05,760 --> 00:03:09,400 Speaker 2: in what is already happening, not what threats are sitting 49 00:03:09,480 --> 00:03:11,840 Speaker 2: on the horizon and could be in the future happening, 50 00:03:11,919 --> 00:03:14,760 Speaker 2: but what is actually happening now. This one was a 51 00:03:14,800 --> 00:03:18,360 Speaker 2: really concrete one of what can AI do that we 52 00:03:18,520 --> 00:03:22,320 Speaker 2: know it can do now today? It can absolutely summarize 53 00:03:22,400 --> 00:03:25,400 Speaker 2: written material. At everybody who's googled something or looked at 54 00:03:25,440 --> 00:03:28,200 Speaker 2: chat GPT or asked for a quick summary of something 55 00:03:28,280 --> 00:03:30,360 Speaker 2: knows that it can do that. And so there are 56 00:03:30,360 --> 00:03:32,960 Speaker 2: people in Hollywood, obviously who are paid to read scripts 57 00:03:33,000 --> 00:03:36,080 Speaker 2: and summarize them, who would be at the front lines 58 00:03:36,160 --> 00:03:38,880 Speaker 2: of people who are impacted by AI if in fact, 59 00:03:38,960 --> 00:03:41,480 Speaker 2: that becomes like a standard thing in the industry. So 60 00:03:41,520 --> 00:03:42,960 Speaker 2: that's what I wanted to look at. What are those 61 00:03:42,960 --> 00:03:44,440 Speaker 2: folks worried about and what. 62 00:03:44,400 --> 00:03:45,200 Speaker 3: Are they doing about it? 63 00:03:45,640 --> 00:03:48,040 Speaker 1: What is the specific program or platform that is the 64 00:03:48,080 --> 00:03:49,200 Speaker 1: focal point of your story. 65 00:03:49,760 --> 00:03:52,280 Speaker 2: So there are a few of them, and they're all 66 00:03:52,600 --> 00:03:56,800 Speaker 2: built sort of on top of the standard lms that 67 00:03:56,840 --> 00:04:00,440 Speaker 2: everybody knows about, you know, chet GPT or LAMA or 68 00:04:00,640 --> 00:04:04,760 Speaker 2: CLAUDE that's available. And what these things are is they're 69 00:04:04,880 --> 00:04:07,520 Speaker 2: very small teams of people three four people who can 70 00:04:07,600 --> 00:04:11,760 Speaker 2: program and an interface that's geared specifically for screenplays based on 71 00:04:11,880 --> 00:04:16,360 Speaker 2: those language models. So there's a couple. There's script Sense 72 00:04:16,520 --> 00:04:18,840 Speaker 2: is a very popular one. There's one called green Light, 73 00:04:19,200 --> 00:04:22,039 Speaker 2: there's a Veil, and we talk about Screenplay IQ in 74 00:04:22,080 --> 00:04:24,360 Speaker 2: the story. But there's a number of these that are 75 00:04:24,400 --> 00:04:28,760 Speaker 2: out there that are using AI technology specifically to give 76 00:04:28,800 --> 00:04:32,920 Speaker 2: feedback and notes on screenplays, to summarize screenplays. And that's 77 00:04:32,920 --> 00:04:35,760 Speaker 2: see exactly what a story analyst in Hollywood does is 78 00:04:35,800 --> 00:04:39,239 Speaker 2: write a coverage report based on a screenplay and tell 79 00:04:39,240 --> 00:04:41,400 Speaker 2: the bosses, here's what's in this and here's whether you 80 00:04:41,400 --> 00:04:43,400 Speaker 2: should make it or not. And that's what these programs 81 00:04:43,400 --> 00:04:44,240 Speaker 2: are reporting to do. 82 00:04:44,680 --> 00:04:48,840 Speaker 1: The proponents of AI say that the AI tools are 83 00:04:48,880 --> 00:04:51,400 Speaker 1: going to do the drudge work, the labor intensive stuff 84 00:04:51,440 --> 00:04:54,400 Speaker 1: that nobody really likes doing but is essential to the process. 85 00:04:55,120 --> 00:04:57,520 Speaker 1: But here we go right to something that there's no 86 00:04:57,640 --> 00:05:01,560 Speaker 1: question reading a script is a subjective thing. They're looking 87 00:05:01,640 --> 00:05:03,919 Speaker 1: for voice and nuance and does a person have a 88 00:05:03,920 --> 00:05:04,920 Speaker 1: flair for dialogue. 89 00:05:05,080 --> 00:05:07,359 Speaker 2: What's interesting is the people who make these platforms, I mean, 90 00:05:07,440 --> 00:05:10,520 Speaker 2: part of their purpose in making them is they feel 91 00:05:10,520 --> 00:05:14,000 Speaker 2: like that subjectivity is a problem, right, And wouldn't it 92 00:05:14,000 --> 00:05:17,880 Speaker 2: be great if there were computers that could objectively analyze 93 00:05:17,880 --> 00:05:19,880 Speaker 2: whether a script is good or not and then sort 94 00:05:19,920 --> 00:05:21,719 Speaker 2: of the best ones flow to the top. And so 95 00:05:21,760 --> 00:05:25,159 Speaker 2: that was the motivation for creating this particular program. And 96 00:05:25,200 --> 00:05:27,719 Speaker 2: so the people who are supportive of AI sort of 97 00:05:27,760 --> 00:05:31,640 Speaker 2: see it as like leveling the playing field, right, opening 98 00:05:31,720 --> 00:05:35,000 Speaker 2: up opportunities and allowing you to focus on things that 99 00:05:35,040 --> 00:05:38,000 Speaker 2: you might not have otherwise seen. But the script readers 100 00:05:38,040 --> 00:05:40,440 Speaker 2: themselves wanted to know, like, what are we up against 101 00:05:40,839 --> 00:05:44,400 Speaker 2: and can this thing actually do my job? And so 102 00:05:44,440 --> 00:05:47,640 Speaker 2: they investigated that and they did a study of it 103 00:05:47,960 --> 00:05:50,760 Speaker 2: to find out what is the difference between AI generated 104 00:05:50,800 --> 00:05:53,960 Speaker 2: script coverage and human generated script coverage. 105 00:05:53,560 --> 00:05:55,440 Speaker 1: And what did they find, Gene. 106 00:05:56,040 --> 00:05:58,800 Speaker 2: It's parallel to what people have found about playing around 107 00:05:58,800 --> 00:06:04,000 Speaker 2: with AI and many others. When it's tasked with just 108 00:06:04,520 --> 00:06:07,920 Speaker 2: distilling the content, it does a pretty good job of that. 109 00:06:08,839 --> 00:06:10,719 Speaker 2: It can write a log line just as well as 110 00:06:10,760 --> 00:06:13,120 Speaker 2: a human being can write a log line, and maybe better. Right, 111 00:06:13,720 --> 00:06:16,919 Speaker 2: it doesn't have maybe the idiosyncratic problems that a human 112 00:06:17,120 --> 00:06:21,280 Speaker 2: would would introduce. It can summarize, so that's a little 113 00:06:21,279 --> 00:06:24,919 Speaker 2: bit longer, but it can do a summary that's pretty good, 114 00:06:25,680 --> 00:06:29,200 Speaker 2: but maybe not as good as a professional script reader 115 00:06:29,440 --> 00:06:33,800 Speaker 2: doing it, but passable. It's when it gets to notes 116 00:06:34,040 --> 00:06:40,240 Speaker 2: that the real problem begins and evaluating critically, is this 117 00:06:40,320 --> 00:06:42,479 Speaker 2: a good script does have something new to say? Is 118 00:06:42,520 --> 00:06:45,520 Speaker 2: it just regurgitating what we've already seen a thousand times. 119 00:06:46,040 --> 00:06:49,120 Speaker 2: That's where it just cannot do the job. That's what 120 00:06:49,160 --> 00:06:51,360 Speaker 2: they found. Now, obviously this is coming from the point 121 00:06:51,360 --> 00:06:54,320 Speaker 2: of view of people who do this professionally, but they 122 00:06:54,480 --> 00:06:57,320 Speaker 2: put it up against human script readers versus all these 123 00:06:57,360 --> 00:07:00,760 Speaker 2: AI programs that we talked about and matched them across 124 00:07:00,800 --> 00:07:04,560 Speaker 2: all these different dimensions and found that the notes are 125 00:07:04,560 --> 00:07:07,880 Speaker 2: where the humans still beat the AI hands down. 126 00:07:08,480 --> 00:07:11,440 Speaker 1: Is there any union or any organization out there that 127 00:07:11,560 --> 00:07:14,440 Speaker 1: is really up in arms about this? On behalf of 128 00:07:14,480 --> 00:07:17,320 Speaker 1: the platoons of script preaders that work in Hollywood. 129 00:07:18,120 --> 00:07:22,480 Speaker 2: Yes, so full time story analysts, I should say that 130 00:07:22,640 --> 00:07:25,920 Speaker 2: is the term. Full time story analysts are represented by 131 00:07:25,960 --> 00:07:31,200 Speaker 2: the Editors Guild, which is obviously part of myozzi. Freelance 132 00:07:31,400 --> 00:07:35,440 Speaker 2: story analysts are not. This study was actually done under 133 00:07:35,480 --> 00:07:39,600 Speaker 2: the aegis of the Editor's Guild. And what's interesting about 134 00:07:40,040 --> 00:07:43,960 Speaker 2: that is the Editors Guild is actually pretty pro technology. 135 00:07:43,960 --> 00:07:45,600 Speaker 2: When you think about editors, you know, these are people 136 00:07:45,640 --> 00:07:48,320 Speaker 2: who are pretty comfortable with, you know, learning the next 137 00:07:48,400 --> 00:07:53,120 Speaker 2: software program. The Writer's Guild is obviously very concerned about 138 00:07:53,160 --> 00:07:57,520 Speaker 2: any AI touching scripts at any point and certainly need 139 00:07:57,560 --> 00:08:01,840 Speaker 2: notification if AI is being used to evaluate their scripts. 140 00:08:01,880 --> 00:08:03,360 Speaker 2: And so as of now, this is not really a 141 00:08:03,360 --> 00:08:06,240 Speaker 2: thing that's being used in any kind of formal way, 142 00:08:06,320 --> 00:08:11,280 Speaker 2: certainly at the studios, but independent producers agencies other places, 143 00:08:11,360 --> 00:08:13,800 Speaker 2: they are definitely using this kind of thing. The AI 144 00:08:13,840 --> 00:08:16,440 Speaker 2: companies will tell you that story analysts are using this 145 00:08:16,560 --> 00:08:19,239 Speaker 2: right now on the sly whether their employer is okay 146 00:08:19,240 --> 00:08:19,800 Speaker 2: with it or not. 147 00:08:20,000 --> 00:08:23,440 Speaker 1: What was your sense of talking to industry executives about 148 00:08:23,480 --> 00:08:26,160 Speaker 1: the use of this technology and whether they were concerned 149 00:08:26,320 --> 00:08:31,560 Speaker 1: about a loss of specificity and a loss of finding 150 00:08:31,640 --> 00:08:35,880 Speaker 1: the absolute gem of a writer. In a specscript, if you. 151 00:08:35,840 --> 00:08:39,760 Speaker 2: Talk to the top top people who do story analysis 152 00:08:39,920 --> 00:08:43,479 Speaker 2: at the studios, they will tell you that the executives 153 00:08:44,760 --> 00:08:48,560 Speaker 2: really do rely on them and value their input and 154 00:08:49,400 --> 00:08:51,840 Speaker 2: see it as a vital, essential part of their process 155 00:08:52,679 --> 00:08:57,559 Speaker 2: and would be pretty unhappy to have that person replaced 156 00:08:57,640 --> 00:09:02,440 Speaker 2: by a computer. The concern people have is, you know, 157 00:09:02,520 --> 00:09:05,679 Speaker 2: to what degree does this become kind of normalized over time, 158 00:09:06,400 --> 00:09:10,160 Speaker 2: and when a younger generation comes up that has spent 159 00:09:10,200 --> 00:09:12,719 Speaker 2: their whole school years using AI to help with their 160 00:09:12,720 --> 00:09:15,679 Speaker 2: studying and help write their college essays or whatever, are 161 00:09:15,760 --> 00:09:17,960 Speaker 2: those folks going to be much more comfortable with this 162 00:09:18,120 --> 00:09:20,079 Speaker 2: kind of thing and the way that they do things 163 00:09:20,120 --> 00:09:24,400 Speaker 2: now will just be completely outmoded. That's that's really the fear. 164 00:09:24,800 --> 00:09:27,720 Speaker 1: Well, I think we've probably raised enough nail biting concerns 165 00:09:27,760 --> 00:09:31,360 Speaker 1: for our industry for one segment. Gene, thanks for your 166 00:09:31,400 --> 00:09:39,600 Speaker 1: labor as always, thanks for having me. Now we'll wrap 167 00:09:39,720 --> 00:09:43,280 Speaker 1: up our coverage of the MIPCOM Content Marketing con with 168 00:09:43,360 --> 00:09:47,600 Speaker 1: a lively conversation with my three Variety colleagues, Elsa Caslasi 169 00:09:47,800 --> 00:09:52,040 Speaker 1: International Editor who is based in Paris, John Hopewell, intrepid 170 00:09:52,120 --> 00:09:55,600 Speaker 1: correspondent and editor of our digital dailies franchise, who is 171 00:09:55,600 --> 00:09:59,520 Speaker 1: based in Madrid, and Leo Barakloff International Features Director who 172 00:09:59,559 --> 00:10:02,520 Speaker 1: is based in London. We are all running on fumes 173 00:10:02,559 --> 00:10:05,280 Speaker 1: after a busy market. But here's our best effort to 174 00:10:05,320 --> 00:10:07,640 Speaker 1: make some sense of the week that was. And we 175 00:10:07,720 --> 00:10:10,600 Speaker 1: all extend our gratitude to mipcom chief Lucy Smith and 176 00:10:10,640 --> 00:10:14,199 Speaker 1: her staff for treating Team Variety so well. Elsa Gaslasi, 177 00:10:14,400 --> 00:10:19,480 Speaker 1: John Hopewell and Leo Baraclough. We made it through mip Indeed, yes, 178 00:10:19,800 --> 00:10:21,640 Speaker 1: as we all call it a wrap. Here today on 179 00:10:21,679 --> 00:10:24,520 Speaker 1: Thursday October sixteenth, thought it would be fun to go 180 00:10:24,600 --> 00:10:27,840 Speaker 1: through some questions about things that stood out to us. 181 00:10:27,960 --> 00:10:30,840 Speaker 1: Appreciate you guys coming to play here. Okay, let me 182 00:10:30,840 --> 00:10:33,760 Speaker 1: start pretty broad. Most surprising moment of. 183 00:10:33,720 --> 00:10:37,160 Speaker 4: The week that I noticed how packed the session with 184 00:10:37,320 --> 00:10:40,920 Speaker 4: YouTube was. YouTube celebrates his twenty year anniversary this year, 185 00:10:41,400 --> 00:10:45,400 Speaker 4: and they made their first official presence at Midcom. And 186 00:10:45,480 --> 00:10:48,880 Speaker 4: you had Pedro Pina, who's the bus of EMEA for 187 00:10:49,000 --> 00:10:52,760 Speaker 4: YouTube we had a conversation with a BBC studio's executive 188 00:10:53,200 --> 00:10:56,520 Speaker 4: and the session was packed like no other that I've noticed. 189 00:10:56,600 --> 00:10:59,360 Speaker 1: At Midcomb, we had CEO Neil Mohan on the cover 190 00:10:59,440 --> 00:11:02,600 Speaker 1: in March of this year and really put their twenty 191 00:11:02,679 --> 00:11:04,360 Speaker 1: year milestone into perspective. 192 00:11:04,640 --> 00:11:05,400 Speaker 5: What about you, Leo. 193 00:11:05,640 --> 00:11:09,040 Speaker 3: I did a non stage interview with Robbie Brenner, who's 194 00:11:09,200 --> 00:11:14,400 Speaker 3: the head of Mattel Studios, and she was emphasizing the 195 00:11:14,400 --> 00:11:17,559 Speaker 3: fact that she wanted the projects to surprise, and certainly 196 00:11:17,559 --> 00:11:21,480 Speaker 3: one of her projects surprised me because it's a live 197 00:11:21,520 --> 00:11:26,120 Speaker 3: action movie based on Monster High and the director is 198 00:11:26,160 --> 00:11:29,400 Speaker 3: going to be Gerard Johnston, who's best known for the 199 00:11:29,440 --> 00:11:30,560 Speaker 3: horror hit Megan. 200 00:11:31,000 --> 00:11:36,840 Speaker 5: Mine was probably how happy people seemed, whilst at the 201 00:11:36,880 --> 00:11:41,599 Speaker 5: same time Guy Bison for Exauplet at Ampere Analysis was 202 00:11:41,640 --> 00:11:45,880 Speaker 5: saying we're still at seventy five percent of PEAKTV. In 203 00:11:45,920 --> 00:11:51,640 Speaker 5: other words, you have one hundred percent production sector spawned 204 00:11:51,679 --> 00:11:55,079 Speaker 5: by PEAKTV chasing seventy five percent of the market. If 205 00:11:55,080 --> 00:11:57,200 Speaker 5: it's sunny, people tend to forget that. 206 00:11:57,960 --> 00:12:01,680 Speaker 1: For me, the biggest surprise overall was how significant the 207 00:12:01,760 --> 00:12:06,040 Speaker 1: microdrama business is. I attended a really great presentation from 208 00:12:06,080 --> 00:12:08,959 Speaker 1: an analyst named Claire Thompson, and the size and scope 209 00:12:09,360 --> 00:12:13,040 Speaker 1: of this business is impressive, growing in the US. Most 210 00:12:13,080 --> 00:12:16,960 Speaker 1: surprising single fact you heard. We all moderated panels. We 211 00:12:17,040 --> 00:12:19,480 Speaker 1: sat through panels. There's a lot of facts and figures 212 00:12:19,520 --> 00:12:21,840 Speaker 1: talked about. I will start with this one. Back to 213 00:12:21,960 --> 00:12:25,160 Speaker 1: the microdrama panel. This blew my mind in tracing the 214 00:12:25,559 --> 00:12:29,200 Speaker 1: arc of microdramas starting in China in twenty eighteen. The 215 00:12:29,240 --> 00:12:33,720 Speaker 1: first big audience for them in that timeframe was older people, 216 00:12:33,880 --> 00:12:38,520 Speaker 1: people ages forty to sixty and completely counterintuitive. 217 00:12:38,000 --> 00:12:41,920 Speaker 5: It will be raphalike you've said microdramas. I talked to 218 00:12:41,960 --> 00:12:44,720 Speaker 5: the people who were making the first microdrama in the 219 00:12:44,800 --> 00:12:50,040 Speaker 5: Arab world. The revenue this year not in twenty thirty, 220 00:12:50,280 --> 00:12:55,320 Speaker 5: in twenty twenty five for reach eleven billion dollars, which 221 00:12:55,320 --> 00:12:59,880 Speaker 5: has already double the size of the revenues for fast channels. 222 00:13:00,440 --> 00:13:02,040 Speaker 5: That is huge. 223 00:13:02,240 --> 00:13:03,160 Speaker 1: What about you, Wilsa. 224 00:13:03,360 --> 00:13:07,160 Speaker 4: So for me, the biggest surprise was hearing Marco Bassetti 225 00:13:07,160 --> 00:13:10,120 Speaker 4: from Banije, which is a company best known for unscripted 226 00:13:10,160 --> 00:13:13,600 Speaker 4: format you know, like Master Chef. They're actually looking to 227 00:13:13,720 --> 00:13:17,559 Speaker 4: invest more in movies because Marcos seekings that people are 228 00:13:17,640 --> 00:13:20,560 Speaker 4: going to get tired of series repeating themselves with the 229 00:13:20,600 --> 00:13:23,280 Speaker 4: same plots, and so it thinks that there's going to 230 00:13:23,360 --> 00:13:25,600 Speaker 4: be a growing happetite for movies and he wants to 231 00:13:25,640 --> 00:13:26,160 Speaker 4: invest more. 232 00:13:26,320 --> 00:13:27,199 Speaker 1: Leo, what about you. 233 00:13:28,240 --> 00:13:31,560 Speaker 3: The fact that surprised me was in Junior, which is 234 00:13:31,600 --> 00:13:35,480 Speaker 3: for children's shows, and it's that sixty five percent of 235 00:13:35,559 --> 00:13:38,920 Speaker 3: eight to eleven year olds have their own social media accountant. 236 00:13:39,960 --> 00:13:43,400 Speaker 3: Amongst greet of eight year olds, they watch five and 237 00:13:43,440 --> 00:13:45,400 Speaker 3: a half hours day or more. 238 00:13:46,120 --> 00:13:51,120 Speaker 1: All eric most calm moment something funny, you overheard or observed, 239 00:13:51,240 --> 00:13:53,199 Speaker 1: something that would only happen in Can. 240 00:13:53,480 --> 00:13:56,840 Speaker 4: So for me, the most Can moment was having a 241 00:13:56,880 --> 00:14:00,400 Speaker 4: great launch on the beach with Cheryl l Azar, who 242 00:14:00,400 --> 00:14:04,360 Speaker 4: hosted the Digital up Fronts this year. And then I 243 00:14:04,400 --> 00:14:09,240 Speaker 4: saw Carolyn Benjo, my great friend, a great producer from Friends, 244 00:14:09,520 --> 00:14:12,199 Speaker 4: who actually was on the co production panel that she 245 00:14:12,320 --> 00:14:15,960 Speaker 4: hosted Cynthia, and it was so you know, interesting to 246 00:14:16,040 --> 00:14:21,360 Speaker 4: have a French producer and an American digital creator Shira. 247 00:14:21,600 --> 00:14:23,640 Speaker 4: You know, it was like the best of both worlds 248 00:14:23,720 --> 00:14:25,560 Speaker 4: coming together on the beach in Cahn. 249 00:14:25,680 --> 00:14:30,200 Speaker 1: Yes, she was so impressive. John, you are definitely a 250 00:14:30,280 --> 00:14:33,040 Speaker 1: veteran of the cosset this time around. What was your 251 00:14:33,120 --> 00:14:34,240 Speaker 1: most can moment. 252 00:14:35,240 --> 00:14:39,360 Speaker 5: It's seeing people who know they're on to a great deal. 253 00:14:40,520 --> 00:14:45,280 Speaker 5: A company called Scene based in the Lebanon Rotana Media, 254 00:14:45,320 --> 00:14:47,960 Speaker 5: which is one of the biggest studios in the Arab world, 255 00:14:48,560 --> 00:14:53,200 Speaker 5: presenting that they were going to create the first late 256 00:14:53,360 --> 00:14:57,720 Speaker 5: ever of microdramas in the Arab world, and they knew, 257 00:14:57,800 --> 00:15:00,600 Speaker 5: you could see it from their faces of the press conference. 258 00:15:01,080 --> 00:15:05,000 Speaker 5: They knew that they were going to make a proverbial package. 259 00:15:05,480 --> 00:15:07,560 Speaker 3: I'm going to stretch your definition a little bit because 260 00:15:07,640 --> 00:15:09,640 Speaker 3: last night I went on a set visit to a 261 00:15:09,720 --> 00:15:13,360 Speaker 3: town down the coast called Casis, beautiful town, and that 262 00:15:13,680 --> 00:15:17,040 Speaker 3: there's a German production being shot there, a series called 263 00:15:17,080 --> 00:15:20,120 Speaker 3: West End Girl, and I met the cast and crew, 264 00:15:20,400 --> 00:15:24,800 Speaker 3: and one of them, Lucas Gogorowitz, is very well known 265 00:15:24,840 --> 00:15:28,120 Speaker 3: in Germany. He's in the German version of My Agent. 266 00:15:28,480 --> 00:15:31,600 Speaker 3: For example, he happens to know someone I lived with 267 00:15:32,120 --> 00:15:35,600 Speaker 3: while I was at the university, and I haven't seen 268 00:15:35,640 --> 00:15:38,800 Speaker 3: for decades. So that one of those kind of moments 269 00:15:38,840 --> 00:15:42,080 Speaker 3: where paths cross that you didn't expect. 270 00:15:42,280 --> 00:15:45,400 Speaker 1: That segues nicely into mine, which was I could not 271 00:15:45,600 --> 00:15:49,640 Speaker 1: believe I ran into Paul Siegel, who was in his 272 00:15:49,840 --> 00:15:52,400 Speaker 1: mid eighties now, and he was the owner of a 273 00:15:52,440 --> 00:15:56,680 Speaker 1: little company called All American, which gave the world Baywatch. 274 00:15:56,920 --> 00:15:59,760 Speaker 1: He and his brother sold All American thirty years ago, 275 00:16:00,360 --> 00:16:03,200 Speaker 1: but he's still in the business. He owns an animation 276 00:16:03,320 --> 00:16:07,480 Speaker 1: company in Mumbai. Because I think media entrepreneurs, I think 277 00:16:07,480 --> 00:16:10,920 Speaker 1: they have trouble stopping. Last one biggest questions you have 278 00:16:11,120 --> 00:16:14,040 Speaker 1: leaving this market, what concepts, what stories are you going 279 00:16:14,120 --> 00:16:14,560 Speaker 1: to chase? 280 00:16:14,880 --> 00:16:18,160 Speaker 4: You know, I'm just wondering about the future of the 281 00:16:18,240 --> 00:16:21,720 Speaker 4: creator economy and if we're going to see another platform 282 00:16:21,920 --> 00:16:26,200 Speaker 4: as powerful as YouTube emerging in the future, because right 283 00:16:26,240 --> 00:16:29,720 Speaker 4: now YouTube is in a very dominant position. And also 284 00:16:30,040 --> 00:16:33,640 Speaker 4: if that creator economy is really there to stay or 285 00:16:33,640 --> 00:16:34,720 Speaker 4: if it's a phase. 286 00:16:34,920 --> 00:16:37,560 Speaker 1: I will say that I have been extremely skeptical of 287 00:16:37,680 --> 00:16:41,160 Speaker 1: when this world of creators and social media video, when 288 00:16:41,200 --> 00:16:44,440 Speaker 1: would it amount to a real business for professional, high 289 00:16:44,520 --> 00:16:48,000 Speaker 1: end content producers. And I do think that moment is coming. 290 00:16:48,120 --> 00:16:51,480 Speaker 1: There's a whole world of AI and marketing and branding 291 00:16:51,640 --> 00:16:55,880 Speaker 1: infrastructure around the creator economy. John, what's the big idea, 292 00:16:55,960 --> 00:16:57,920 Speaker 1: the big concept that you're going to be thinking about. 293 00:16:58,120 --> 00:17:02,840 Speaker 5: I think business models which really monetize the collaboration which 294 00:17:02,840 --> 00:17:06,000 Speaker 5: you now see from the old economy and seen new 295 00:17:06,040 --> 00:17:09,760 Speaker 5: economy for example, and YouTube. 296 00:17:10,320 --> 00:17:11,160 Speaker 1: What about you, Leo. 297 00:17:11,920 --> 00:17:16,320 Speaker 3: My question is where is the next big, huge unscripted 298 00:17:16,359 --> 00:17:19,719 Speaker 3: format can come from? Because a lot of the legacy 299 00:17:19,760 --> 00:17:24,879 Speaker 3: formats like Got Talent, Idol, the family Feud, the price 300 00:17:25,000 --> 00:17:30,000 Speaker 3: is right, they are decades old. And so where's the 301 00:17:30,040 --> 00:17:32,720 Speaker 3: next one coming from? I mean, okay, You've got Traitors, 302 00:17:32,760 --> 00:17:37,520 Speaker 3: which is fantastic, and Fremantle have something called Pandora's Box 303 00:17:37,560 --> 00:17:41,120 Speaker 3: which they hope will be us Helper, which is owned 304 00:17:41,119 --> 00:17:44,240 Speaker 3: by John Demole. They've got something called The Floor which 305 00:17:44,280 --> 00:17:48,840 Speaker 3: is now in thirty territories, and ZEP so that is succeeding. 306 00:17:48,880 --> 00:17:49,960 Speaker 3: But these things are around. 307 00:17:50,400 --> 00:17:52,560 Speaker 1: John de mall is a Paul Siegel type. I think 308 00:17:52,640 --> 00:17:55,480 Speaker 1: he's easily on his fourth or fifth company of the 309 00:17:55,560 --> 00:17:58,400 Speaker 1: last twenty five years or so. Well, listen, all three 310 00:17:58,440 --> 00:18:01,000 Speaker 1: of you have worked really hard. Thank you for killing it. 311 00:18:01,040 --> 00:18:03,720 Speaker 1: I hope you worked in some good meals along the way. 312 00:18:03,960 --> 00:18:07,000 Speaker 4: We did have some great meals. Yes always. 313 00:18:07,000 --> 00:18:14,320 Speaker 1: Again, as we close out today's episode, here's a few 314 00:18:14,320 --> 00:18:17,240 Speaker 1: things we're watching. Its Power of Women's season again for 315 00:18:17,359 --> 00:18:21,080 Speaker 1: Variety We're getting ready to celebrate this year's West Coast 316 00:18:21,119 --> 00:18:24,760 Speaker 1: honorees at an event October thirtieth and Beverly Hills. Our 317 00:18:24,840 --> 00:18:28,720 Speaker 1: annual issue will be published October twenty ninth. As ever, 318 00:18:28,840 --> 00:18:31,679 Speaker 1: we will have five gorgeous covers, one for each of 319 00:18:31,720 --> 00:18:37,120 Speaker 1: our honorees Jamie Lee Curtis, Kate Hudson, Nicole Scherzinger, Sidney Sweeney, 320 00:18:37,600 --> 00:18:42,240 Speaker 1: and Wanda Sykes. Stay tuned before we go. Congrats to 321 00:18:42,320 --> 00:18:47,800 Speaker 1: Yoshinaga Sayuri, the legendary Japanese actress, will receive Lifetime Achievement 322 00:18:47,800 --> 00:18:51,840 Speaker 1: honors from the Tokyo International Film Festival. That festival runs 323 00:18:51,880 --> 00:18:56,120 Speaker 1: October twenty seventh through November fifth. Thanks for listening. This 324 00:18:56,160 --> 00:18:59,280 Speaker 1: episode was written and reported by me Cynthia Littleton, with 325 00:18:59,400 --> 00:19:04,159 Speaker 1: contribution from Gene Maddis, Elsi Caslasi, John Hopewell, and Leo Baraclough. 326 00:19:05,000 --> 00:19:08,080 Speaker 1: Stick's Next Hick Picks. Please leave us a review with 327 00:19:08,160 --> 00:19:11,080 Speaker 1: the podcast platform of your choice, and please tune in 328 00:19:11,119 --> 00:19:15,400 Speaker 1: Monday for another episode of Daily Variety. La Belle Franz 329 00:19:15,840 --> 00:19:20,200 Speaker 1: to MOUs Monc. Somewhere Louis B. Mayer is going, how 330 00:19:20,200 --> 00:19:22,600 Speaker 1: did I not think of this? We'll give him one minute. 331 00:19:22,600 --> 00:19:24,560 Speaker 1: At a time and charge him to keep going