The Data Scientists Quietly Powering Hollywood’s Greenlight Machine

Summary

Podcast Summary

Podcast: The Town with Matthew Belloni
Episode: The Data Scientists Quietly Powering Hollywood’s Greenlight Machine
Date: July 17, 2026
Host: Matthew Belloni
Guest: Violine Roussel (Professor of Sociology, University of Paris 8, author of "Data Driven")

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Episode Overview

This episode dives deep into the growing—yet little understood—role of data scientists in Hollywood. Host Matthew Belloni interviews sociologist Violine Roussel, author of "Data Driven: The New Data Professionals in the Age of Streaming," to discuss how data, algorithms, and analytics now shape what gets greenlit, made, and renewed at studios and streamers. They explore the tension and collaboration between traditional creative producers and the emergent data teams, how projects are valued and selected, the myth versus reality of algorithmic hit-making, and the looming impact of AI on Hollywood’s workforce.

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Key Discussion Points & Insights

The Rise of Data Science in Hollywood

• Background: Hollywood has always drawn on data (box office numbers, Nielsen ratings, Q ratings), but since the 2010s and the streaming boom, data’s influence has exploded, infiltrating the very start of the content development pipeline ([02:30–06:30]).
• Role of Data Scientists: Thousands now work inside studios and streamers, forming a silent, powerful class that influences content strategies, greenlighting, talent evaluation, and pricing.

Internal Power Dynamics

• Evolving Influence: Early on, creative execs held sway; now data teams negotiate power, increasingly setting the production agenda ([06:14–10:20]).
• Two Big Groups:
  • "Hard science" data scientists/engineers—build models and algorithms.
  • Business/data strategists—act as go-betweens, translating tech for producers and creative ideas for data teams ([07:13]).
• “New Production Narrative”: Data specialists are pushing a new legitimacy, reframing how decisions are justified.

Demographic Shift & Initial Resistance

• Nontraditional Gatekeepers: Data specialists are more diverse—immigrants, women, PhDs with global backgrounds—not the typical “old Hollywood club” ([11:07–13:15]).
• Producer Pushback: There was skepticism—classic Hollywood execs claimed to know “what’s good” based on intuition, not data. (“I don’t believe in data. ...I have the relationship, I have the connection, I have the experience, and I have an eye for quality.” — Roussel quoting execs, [11:53]).
• Erosion of Resistance: That pushback is fading; now “even the top producers want to see the data,” even if they don’t always follow it ([11:23]).

How Data Really Decides What Gets Made

• Internal & External Data: Studios primarily use internal user data (what audiences watch, how, when), supplementing with external info (IMDb, Google, social) for incomplete scenarios or trend forecasting ([14:25]).
• The “Oracle Function”: Data teams try to predict hits, but their predictive power is better at repeating past successes than finding truly original blockbusters ([14:57–15:57]).
  • Memorable quote: “These hits that are... most famous... are not necessarily projects that were backed by data.” — Roussel, [15:34]
• Taste Clusters:
  • Netflix famously uses ~2,000 “taste clusters” based on hyper-granular content labels (“movie with strong female lead set in France in the 60s”) and nuanced viewing behaviors ([17:02–18:36]).
  • Clusters are defined by actions, not demographics (“I don’t know who you are; I only know what you do on the platform.” — Belloni quoting Ted Sarandos, [19:02]).

Data’s Role in Project Valuation & Talent Assessment

• Negotiation, Not Dictate: Data teams now set the terms for internal negotiations—evaluating pitches, pricing projects, setting priorities ([09:39]).
• Assigning Value: Project value comes from a blend of audience metrics (engagement, completion rates, drop-off/decay), content trends, and cost analysis ([23:01–24:17]).
• No Secret Sauce: Netflix’s data edge is more about “being ahead and taking risks” than having a magical hit-predicting algorithm ([24:17–24:46]).

The Limits and Myths Around Data-Driven Hits

• Legend vs Reality: The “House of Cards was greenlit entirely by data” story is largely myth—those early decisions were not as data-driven as legend claims ([22:29]).
• True Originals: Genuine out-of-the-box hits (e.g., Stranger Things, Squid Game) are rarely “predicted” by data; algorithms reinforce past successes, not originality ([15:57–16:03], [28:36–29:27]).

The Data Arms Race and Information Asymmetry

• Agencies Fight Back: Talent agencies are building their own data shops to negotiate against streamers—but remain at an information disadvantage because platforms withhold the best data ([24:46–25:53]).
  • “They give very little information even to people that they work with... their data is their power and their negotiating power.” — Roussel, [25:20]

The Future: Can AI Out-Data the Data Scientists?

• Coming Upheaval: Roussel warns that AI’s rise could threaten not just data specialist jobs, but creative and crew jobs as well ([29:27–30:38]).
• No Crystal Ball Yet: The guest won’t predict specifics, but says the AI/data revolution is already reshaping China’s content industry and will soon transform Hollywood ([30:38–31:06]).

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Notable Quotes & Memorable Moments

• On the new production narrative:
  “...they are pushing their new production narrative that says that basically it's important to look at the data.” — Violine Roussel, [10:20]
• On creative resistance:
  “At the beginning ...I could hear a lot of producers, studio heads... tell me, ‘Oh, I don't believe in data.’ ...I have an eye for quality. All things that data specialists don't consider to be a skill.” — Roussel, [11:53]
• On diversity among data scientists:
  “They are much more female than traditional Hollywood producers. They're non-white. They are largely immigrants. They are people that are not the traditional Hollywood gatekeepers.” — Belloni, [11:07]
• On taste clusters:
  “I don't know who you are. I only know what you do on the platform.” — Ted Sarandos (via Belloni), [19:02]
• On the limits of prediction:
  “Hits that are the most famous... are not necessarily projects that were backed by data or suggested...following what the algorithm was saying.” — Roussel, [15:34]
• On power imbalance in data:
  “It's really difficult to beat the platforms, the streamers, because they also are very protective of the confidential nature of their data.” — Roussel, [25:20]
• On data’s future in Hollywood:
  “The power balance is a little different and has shifted in favor of the data specialist. I don't know if it's... more accurate. It's just based on different skills.” — Roussel, [26:24–28:01]
• On AI’s disruption:
  “[The data professionals] are definitely worried... there is a risk that AI be used even for producing content. Maybe you don't need as many data specialists, but you also don't need a lot of crew people and even actors and actresses anymore.” — Roussel, [29:48–30:38]

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Important Timestamps

• [04:36] Defining data’s role in greenlighting decisions
• [07:13] Two big categories of data specialists
• [09:39] How data teams and creative teams interact
• [11:07] Diversity of data specialists, demographic implications
• [13:42] Combining internal/external data for valuation
• [15:34] Limits of data in predicting blockbuster hits
• [17:02] The concept of taste clusters
• [19:02] Behavior-based clusters, not demographic
• [22:29] The “House of Cards” data myth
• [23:01] Which engagement metrics matter most
• [24:46] Data vs data: Agencies vs streamers
• [29:27] Will AI “blow up” the data science class?
• [30:38] What’s happening in China and the future of AI in Hollywood

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Tone & Takeaways

• The tone is revealing, somewhat skeptical, and practical. Both Belloni and Roussel are clear-eyed about data’s power—enormous, but not omnipotent—and its cultural impact (shifting who holds sway in Hollywood).
• The episode demystifies how data-driven decisions work—debunking the myth that algorithms alone pick winners and losers.
• Urges listeners, especially those pitching to streamers, to understand the new behind-the-scenes power players—data professionals—not just creative execs.
• Leaves unresolved whether AI will soon upend the role of human data scientists, creatives, or both—underscoring that Hollywood’s true disruptors are often invisible.

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For Further Learning

• Book: "Data Driven: The New Data Professionals in the Age of Streaming" by Violine Roussel
• Recommended action: “All producers and people who are selling projects to the streamers should read your book and at least understand what you’re up against when these companies are evaluating projects.” — Belloni, [31:06]

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This summary captures the full sweep of the discussion—how much data runs Hollywood today, who these data scientists are, how they make (and limit) decisions, and fundamental challenges on the horizon.

Transcript

[A]

Foreign. This episode is presented by AMC Network. A new chapter in Anne Rice's immortal universe begins with AMC's the Vampire Lestat. Get a backstage pass to the iconic frontman who Pace Magazine calls a Bowie inspired rocker that will have fans screaming. Don't miss the legendary vampire Lestat de Liancourt in his own electrifying rock saga. Watch the Vampire Lestat Sundays only on AMC and AMC Plus. Learn more@AMC Plus.com this episode is brought to you by LinkedIn Ads. Ever invest in something that seemed incredible at first but didn't live up to the hype? Marketers know that feeling. They optimize for the numbers that look great, impressions reach and reacts. But when they don't show revenue, well, that's a not so great conversation with the CFO. LinkedIn has a word for that bull spend. Instead, why not invest in what looks good to your CFO? LinkedIn Ads generates the highest roas of all major ad networks. Reach the right buyers with LinkedIn ads you can target by company, industry, job title and more. So cut the bull. Spend. Advertise on LinkedIn, the network that works for you. Spend $250 on your first campaign on LinkedIn ads and get a $250 credit for the next one. Just go to LinkedIn.com thetown that's LinkedIn.com thetown Terms and conditions apply. Com it is Thursday, July 16th it's no secret that Hollywood is increasingly dominated by large global streaming services and that those streaming platforms are governed by data. In fact, Netflix often likes to describe itself as having one foot in Hollywood and one in Silicon Valley. But people inside Hollywood tend to know a lot about how the Hollywood side of the company works. Yet they know a lot less about the tech side. The data side that influences so many decisions and when it's working, can help predict how a project will perform. Data has always informed what films and TV shows get made from the box office track record of stars, maybe their Q rating to the Nielsen viewership and demographic information. But since the 2010s and the rise of direct to consumer digital platforms like Prime Video, Apple tv, data has become much more powerful, used to define the content strategies and influence production choices and identify promising genres or even storylines, evaluate talent and effectively price projects for the platforms. And along with that has come an army of statisticians, data scientists, machine learning specialists, thousands of them across the industry occupying key roles at the streamers. They're not at the movie premieres or thanked in Emmy speeches, but these data scientists are the quiet powers of the Streamers. And few people in town know much about them, or more importantly, what they actually do. That's why a new book caught my eye. It's called Data Driven the New Data Professionals in the Age of Streaming. It's by Violin Roussel, a professor of sociology at the University of Paris 8. I read it and it's really about how the, quote, creative choices have been reinvented over the past 15 years thanks to data. For better and worse. Violin Talked to about 75 data scientists at the streamers and studios with titles like VP and Head of Science and Algorithms at Netflix. And she chronicles the rise of the data nerds at the streamers, the tension with traditional producers and creative execs. So, of course, I had to get her on the town. Today. It's the data that ate Hollywood and how streamers decide what to make and predict what viewers will love from the ringer and puck. I'm Matt Bellany and this is the.

[B]

Okay.

[A]

We are here with Violin Roussel, who is professor of sociology at the University of Paris 8 and an author of a very interesting new book. Welcome.

[C]

Thank you. Thank you for having me.

[A]

Everyone in Hollywood knows that data is informing most decisions at the streamers and increasingly the studios. Data, data, data. We hear it all the time. We see the engagement report. In fact, Netflix's engagement report is coming out today, and we'll get to see the result of their efforts over the last six months. But we don't know exactly how this works. And you wrote a whole book about how data and data scientists are being used at the streamers. So first of all, how much is data informing every decision at these companies, from what shows to develop to which stars to hire.

[C]

So what I discovered conducting investigation in Los Angeles and in Hollywood regarding data scientists who work for the streamers, and now as you also mentioned it for the studios, and really the distinction between the two is not as relevant as it used to be because the studios have also their own streaming platforms and the streamers are also studios. But the difference is really how much data is used. And it's happening, in fact, increasingly as we speak. It continues to develop, in fact, and now also with AI, which is a new development that I need to do a new book about, to write a new book about and your research. But from the the mid 2010s on, it's been a process of having data and data specialists more and more involved in not only producing numbers that can help for marketing or that can help producer to justify their choices in retrospect, but really involved at the very early stage of deciding what should be made. And that means both the general content strategy of the streamer of the platform, as well as more specific decisions on categories of projects or on one specific project that, you know, that would be expensive, for instance.

[A]

So I want to get into the kind of nitty gritty of how this works, because there are, from my experience in just talking to these companies, there are content strategists, there are content finance analysts that look at the economics behind each project and try to leverage data to say, okay, we should be placing more bets here, because this genre with this filmmaker actually makes more sense for us as a platform. There are content planners that look out into the future and say, okay, this is in the ballpark of where we should be placing our bets. We're getting our lunch eaten by Netflix in the podcast and daytime consumption space. So all of a sudden, a year after they notice that data Netflix is going full speed into podcasts. So explain a little bit to us about the interaction between these content creators, strategists, analysts, and the creative teams.

[C]

Yes. So, first of all, you mentioned different categories, in fact, of data specialists, and that's very true. And in particular, what I describe in my book and what I realized doing the field work is that there are two big categories of data specialists that play a different role. Some of them are data scientists, sometimes engineers, sometimes people with a PhD in computer science. And they are more on the tech side of things, creating the models that are going to be used for the algorithmic measurements of things later on. And then there are the people who work in the data strategy, content, data analysis, content planning departments. The names vary a little bit depending on each organization. It's streamer. But basically, these people act as middlemen, middlewomen between the kind of hard science data scientists and technicians on one hand, and on the other hand, the traditional type of producers that work with the creators, with the artists to produce, to make the content, who have been traditionally the ones that were really monopolizing the power to make the decision before, depending if they were high enough, studio heads and studio executives that had enough power, they were indeed very powerful in deciding what should be made. And now they have to negotiate in a way with those data scientists. And in particular, they do it through the content strategy teams and the content strategist, content analysts that have a business background and are more able in a way to translate the language of data in a way that is understandable by the traditional producers, but also the language of the traditional producers to the Data team or the data science team, rather, so that they can make the tools in a way that is more efficient for the goal. That is right.

[A]

So it's a back and forth. It's not just we should be making more comedies with Will Ferrell, it's the production team coming to them and say, listen, we've been pitched a comedy golf series with Will Ferrell. Give us your assessment. And these teams perform analysis based on who the audience is, who is potentially going to watch this, what the cost of the show would be, and then they come up with a price that they are willing to pay for this project if they decide it's something that's worthwhile.

[C]

Yes. I would say it's a negotiation, but the terms of the negotiation have changed over time.

[A]

An internal negotiation.

[C]

An internal negotiation, yes. And I would say at the start of this process that I study, which is in the mid-2010s and late 2010s, the traditional producers, the content people, had a lot of power in those negotiations still. And the data specialists were trying to push their own agenda or their own forms of legitimacy to establish their own forms of legitimacy, their own. I call this new way of defining how content should be selected, a new production narrative. So they are pushing their new production narrative that says that basically it's important to look at the data.

[A]

And a lot of pushback you describe in the book to that process. And it was not just, you know, the traditional, I'm, you know, the creative executive, I'm the producer, I know better than what the data says. But also you describe a lot of demographic differences. You know, these are PhDs, these are scientists. You say they are much more female than traditional Hollywood producers. They're non white. They are largely immigrants. They are people that are not the traditional Hollywood gatekeepers. And this was a challenge, but I think those days are over. I think these days, everyone accepts it. Even the top producers want to see the data. They may not listen, but they do want to see it.

[C]

Yes. And they have to. If not listen, at least they have to take these other key players that are the data specialists into account. Now, at the beginning of when I started doing this field work, it was in the mid 2010s, and I could hear a lot of producers, studio heads, people with power in Hollywood at the time, tell me, oh, I don't believe in data. I don't believe in data. I don't. Whatever they say, I don't care. I know the artist, I have the relationship, I have the connection, I have the experience, and I Have an eye for quality. All things that data specialists don't consider to be a skill. It's not their skill. And they come from, like you said, they come from a very different background. And they also. They don't come from a film school and they don't come from the background in which they would value making art or working in entertainment.

[A]

Well, I think there's still some. I think Taylor Sheridan would probably still take a ream of data and whip out a cigar, light it on fire and throw it out the window. But I think there are more and more that will.

[C]

Precisely. There is more and more. You said the key thing, which is that over time these two groups that are the traditional producers and the data specialists have come to share more common ground, to learn the way of the other, to build this negotiated order in which they can work together. Because it's absolutely true what you said, there is not as much friction as there used to be.

[A]

I want to get into how these platforms assign comparative values to projects or even to individual actors. And it's a mix of the internal data that they have about their customers and what people are watching, but it's also third party data. They are getting outside information from Google, from Facebook. Amazon uses IMDb to see what people are searching for and what kind of stars are getting engagement, and they use that data to inform Prime Video. So take us into how this actually works internally at the streamers to figure out what to make.

[C]

So at the time, they used a lot of external data. Now all of the streaming platforms have their own internal data and they use, I think the external data mostly for information that you cannot get from your own data, which is what will work in the future that we don't yet have. Their internal data only give them information about what they already have. So if they want to try to guess, and that's these, I call that the oracle function, their role of predicting the future. Basically the future.

[A]

That's what this is all about, trying to predict what's going to be a hit, predicting hits. And you know, there's a long history in Hollywood of charlatans coming forward and claiming they have the magic secret to predict hits. I mean, Ryan Kavanaugh was the famous one at Relativity Media, and he ultimately was proved to be not that. But for as much as the data can inform, hit after hit at Netflix seems to butt up against that theory. Adolescence, Squid Game, Stranger Things are these projects that the data would have said are going to be hits.

[C]

That's the interesting fact. These hits that are the most Famous, of the big platforms such as Netflix, the one that you just mentioned, there are not necessarily projects that were backed by data or suggested created following what the algorithm was saying.

[A]

Every producer is now cheering in their car that I asked that question because they hate this stuff.

[C]

Yes, but that doesn't mean that the economic model of Netflix that made Netflix as successful as it is, is not at least party based on their use of data, but it's mostly to detect what is the most successful in what they already have and make more of the same. Because with the categorization of content, the big databases that they have built based on the titles that they already have in their library of content, and creating micro tags, micro jars, micro categories to classify those titles, and then trying to compare based on that, the new projects that they are offered. So basically with this system, there is a feedback loop that you can imagine, like the system is telling you, is suggesting that you're making more of the same that worked in the past.

[A]

And that's where we get to the taste clusters, right? Yes, because Netflix uses this system of taste clusters where you say it's about 2,000 separate little micro communities that they see in their data that tells them what would appeal to certain types of people and explain how that works.

[C]

So the taste clusters are basically made by combining the subcategories of content that have been created following the process that I mentioned before. These micro categories of content are connected to micro categories of behavior that the people who are on the platform, the subscribers, what they are doing on the platform, basically what they watch, when, how, in how many segments, everything that defines subcategories of behavior in how you consume the content.

[A]

Let's use Craig as an example. He watches a lot of NBA documentaries and high school comedies from the early 2000s. Is that a taste cluster? Like what? Like give us the categories they're using.

[C]

The categories of content can be what you just mentioned, you know, a movie with strong female lead set in France in the 60s, for instance.

[A]

So it gets that granular, that could

[C]

be a subcategory of content. Yes. The categories of content can be extremely granular, even smaller than this. And then you combine that with information about the behavior of people on the platform. So these subcategories of behavior, it's classes of behaviors, it's not groups. That's why I can't tell you. It's like white people that age, that level of education.

[A]

Oh, yeah, no, I've talked to Ted Saranis about this. He's like, I don't know who you are I only know what you do on the platform.

[C]

Yes, exactly. So basically these subcategories of behavior that are not one person or one group either are connected algorithmically with the subcategories of content to produce these taste clusters. But you understand that these taste clusters are very difficult to illustrate. I could show you. I'm not prepared to do it, but I could show you a diagram what it looks like, but it won't tell you who is in this taste cluster.

[A]

So then that is used to create the algorithm that predicts what you, Joe Blow is going to want to see next.

[C]

No, it's used to predict what the platform should make.

[A]

Oh, so that's on the production side. So if we are seeing a taste cluster forming around a particular style of content, we should make more yes, yes. This episode is brought to you by Accenture when your advertising operations fall out of sync, campaigns slow down, insights get buried and opportunities get missed. That's why Spotify and Accenture are working together to reinvent the rhythm of ad sales using automation, analytics and smarter workflows to simplify campaign delivery and access better data across the business. The result? Less time spent on operations, more time connecting brands with the moments and fandoms that matter most. To learn more, check out Accenture.com Spotify this episode is brought to you by Accenture when your advertising operations fall out of sync, campaigns slow down, insights get buried and opportunities get missed. That's why Spotify and Accenture are working together to reinvent the rhythm of ad sales using automation, analytics and smarter workflows to simplify campaign delivery and access better data across the business. The result? Less time spent on operations, more time connecting brands with the moments and fandoms that matter most. To learn more, check out Accenture.com Spotify this episode is brought to you by Holiday Inn by IHG Everyone knows Holiday Inn, right? Or they think they do. Because though they have the same name, they've got a whole new energy. They kept the global icon status and upgraded pretty much everything else. We're talking modern rooms with real reset mode vibes, spaces that feel like your living room, just a little more low key chic and dining done right from breakfast to dinner and drinks. Whether you're traveling for work or getting away for a minute, it's comfort that hits different. So yeah, Holiday Inn. It's a new day and a new stay. Book your next [e-mail] Spotify the example that always goes around Hollywood is the House of Cards example that you discuss in the book. Actually about How? The legend is that when Netflix decided to get into original content, the data people said we need a politics show set in Washington D.C. and Kevin Spacey should star in it. And all of a sudden this House of Cards adaptation from the UK became available and Netflix saw it and said, this is what our data people want us to make. Let's blow everybody out of the water with a double the bid that HBO is making. And then House of Cards became the first big Netflix show. I've also heard that story is bs. Is it bs?

[C]

Yes. Well, yeah, it's a, it's, it's a tale. It's a tale that people tell.

[A]

Right? Well, it doesn't mean I've asked Ted about this. He said yes, Kevin Spacey is, was a big star in the movies that we had on the platform with Kevin Spacey were, were big. So he was a good star for us. But I don't think it got that, that granular at that point.

[C]

No, especially because at that point the data teams were very, very small. It happened in the very early day of using data to decide what to make.

[A]

So producers, I think want to know what are the most important data metrics that Netflix and others use to judge their shows? Is it pure audience engagement? Is it completion rates? Is it the so called decay on the show, like the drop off rates, like what is it that they most care about or does it mix?

[C]

I think it's a mix. And it also depends on the moment, you know, when they feel that they have maxed out on how many new people they can get onto the platform. Do they feel this way or not? It depends on where in the US obviously I think it's the case, but elsewhere they still have room for growth. So basically the metrics that matter most depends on the moment and their strategy in the location where they are looking at the metrics. But it's very interesting. Also something that I observed during my, when I was doing fieldwork is that everyone else in the other platforms that are not Netflix wanted me to tell them something about Netflix because they believe that Netflix somehow, as a secret, a secret recipe to make you successful.

[A]

Well, you talked to 75 people, including a lot of people at Netflix. Do they have a secret?

[C]

No, I don't think so. I think they were smart and they were ahead of others and they took a lot of risks. They were in debts for a very long time and their strategy was successful.

[A]

Well, the market supported that debt.

[C]

Yes, eventually. But I don't think that they have a data based secret.

[A]

Yeah, but the Funny thing is, now everyone has data. The talent agencies have their own army of data scientists. And you describe in the book this funny thing where it's like it's now data versus data, whose data is better? So you've talked to people at the agencies who are obviously using the data to try to boost their clients and get more money for their shows and explain why their shows shouldn't be canceled. And yet they're going up against these big scale platforms that have, I would guess, better data. Whose data is better, the talent or the streamers?

[C]

It breaks my heart to say it because I studied the talent agencies for a long time and I really enjoyed it. But it's really difficult to beat the platforms, the streamers, because the streamers, they also are very protective of the confidential nature of their data. They give very little information even to the people that they work with, the external producers or creators, because they know that their data is their power and their negotiating power.

[A]

How dare you. Netflix says they're the most transparent platform out there and they release more data than anyone ever has.

[C]

Data is so precious. Can I tell you? Yeah, it's, it's, it's, it's very global. It's not just Netflix.

[A]

Sure. No, I get it. So you wrote this book, you researched this for many years. Are you convinced that the algorithm and the data knows best when it comes to what projects to greenlight and what shows to renew?

[C]

I don't know if it knows best, but it knows or not. Algorithm and data, but the people, the people behind the algorithms and the data, because for me, it's very. I'm a sociologist of work and occupation, so it's very important to study people at work. It's not data or algorithms that do things, it's the people behind them. So I don't know if they know better, but they know differently. They have a different way of grasping what are the audiences and what they want and to contribute to defining it, to making it happen. And it's very. If you think of the way that producers, traditional producers before, were deciding what to make, it was mostly by focusing on their relationship with their artists, the people that they had a strong relationship with, that they believed in. And they thought that these artists had a connection with their audience and maybe can command some box office. But it was really in this duo between the producer and the talent that what was going to be made was decided. And now the power balance is a little different and has shifted in favor of the data specialist. I don't know if it's, it's not more accurate. It's just based on different skills.

[A]

Yeah, forget wining and dining the stars. These agencies and producers, they should be wining and dining the data guys.

[C]

Maybe they do.

[B]

I had one question. Are there any examples of, of hits that were primarily a data driven decision? You talked about. We talked about Stranger Things in Adolescence and Squid Game and the Taylor Sheridan shows. All shows that might not have worked, you know, because of the data. What about the opposite side? Are there shows that are huge hits that were primarily a data driven decision that creative executives would not have thought of?

[C]

I mean, there are shows that were supported. It's difficult to know exactly what is a show that is based on data versus not, because nowadays most shows, they have to be, you know, they have to be evaluated positively by the data teams to exist.

[A]

Right. The green light committees includes those people now.

[C]

Exactly. So you can always claim that it's because of the data, but a lot of shows that are unusual, that are very different, that you have never seen this type of show before, cannot be really based on algorithms to suggest that this is the best thing to do. Because there is this, you know, with, with the algorithmic logic. It is based on what worked before.

[A]

Okay, so last question. Is AI going to blow this entire thing up? Are the data scientists going to be replaced by AI?

[C]

And us too.

[A]

And us two. You and me both. Yeah. No, but that's a separate question. But let's focus on the massive data employee base that these companies have. Is that going to go away?

[C]

I can't really predict what the future is going to be.

[A]

Oh, but that's what this is all about.

[C]

But it's their role, not mine. But what I can tell you is that people who work in these data teams, they are definitely worried, because it's true. But it's not only the data specialists that have this kind of concern. It's in many, many sectors that there is a risk that AI be used even for producing content. Maybe you don't need as many data specialists, but you also don't need a lot of crew people and even actors and actresses anymore.

[A]

So some would argue that a lot of the content on Netflix is already AI generated, but I assure you it is not.

[C]

It's not supposed to at least. Yeah, no, I don't think it is. But now I'm working on China. I'm doing fieldwork in China and AI is even bigger and it's definitely changing. It's definitely a revolution and it will be a revolution in Hollywood as well. And it's an opportunity for me to do field work again and write another book. I can't really tell you more than that because I have not done this yet.

[A]

I get it. Well, I appreciate the insights here. It's fascinating. I think all producers and people who are selling projects to the streamers should read your book and at least understand what you're up against here and what you're dealing with when these companies are evaluating projects. So I appreciate you coming on the show.

[C]

Thank you so much, Matt.

[A]

We are back with the call sheet. Greg, you and I saw the Odyssey. On Monday night. We schlepped to Universal City. The premiere was in New York, so we went to the press screening. Chris Nolan will not allow us to see this movie in any theater other than his preferred Universal CityWalk IMAX 70 millimeter film theater. First off, before we get to the movie, what did you think of City Walk? You'd never been there.

[B]

I had never been to CityWalk. You know, kind of like a. Like a beaten down Downtown Disney.

[A]

Totally. It's one of the worst places in Los Angeles. And that says a lot.

[B]

Yeah, I, you know, more power to Christopher Nolan. I, I would not complain at all that we get to go see the movie in like the only 70 millimeter 143-movie in L. A. Like, that's. I know. I'm, I'm thrilled that we got to do that.

[A]

Yes. People are paying hundreds of dollars on stuff for the privilege of doing that. I just love this juxtaposition. It's the most Hollywood thing ever about the images that we create about the industry and the filmmakers and everything. I just love that Christopher Nolan, the top filmmaker in the world, this very erudite British American who is the head of the Director's Guild. I love that he parks in the Jurassic parking, parking lot and walks across the worst mall in Los Angeles and goes and sees his movies and other movies too, at the Cineplex at the CityWalk.

[B]

Well, you know, this could be fixed if they just built another one. I know the CEO of IMAX was just interviewed talking about it. I don't know why there aren't more of these theaters in Los Angeles.

[A]

I know Rich Gelfond was on the red carpet and he said that basically they don't have more of these because they can't build them. There's no, there's no film projectors. Now that, that begs the question of why IMAX doesn't fund the creation of more film projectors for this format. I honestly think it's because they, outside of Chris Nolan movies. There aren't filmmakers making movies like this. Like they would have to spend millions and millions of dollars to build out the infrastructure for these movies. And like, other than Nolan and maybe some other, you know, top filmmakers, there's just not the product to do it. I think there should be. And, and maybe in the future that the future of movie going will be this event style, you know, see it in the biggest format possible. But there's gotta be a business case for it. And I'm not sure they see it right now.

[B]

I guess I feel like in the metropolitan hubs, your New York, Louisiana Chicago, there should be multiple in each of those cities. And I feel like that's the premium format thing, is where, where we're, we're headed and we'll be a big factor in how the Odyssey performs.

[A]

I agree. And that, and I think really that the audience taste is shifting because of the marketing of films like this. People want this more and they should be investing in more of these style theaters because the audience can see through the fake IMAX in Century City, which is not real imax, and all these other like premium formats, Infinity Vision, whatever that is that Disney's doing.

[B]

I mean there are, if you go online, you can see the difference between what a regular theater shows in the Odyssey, like what the aspect ratio looks like compared to what we saw saw and it's almost like double the screen, what we got to see.

[A]

I know, it's amazing. So obviously you and I really like the film. The tracking is sort of a challenge this weekend because some have it at 80, some have it as high as 115, 120. I'm going to set the line here at 100. That would be more than Oppenheimer, which opened to 82 in 2023. And remember that was boosted by the whole Barbenheimer Barbie phenomenon. It's an interesting one here because I worry that these focus on IMAX is actually going to depress the opening weekend because people are waiting to see it in that big format and maybe they could only get tickets for Thursday at, you know, 12 in the afternoon and they're not going to power this opening weekend.

[B]

I agree, but as we just discussed, aren't there there's not even enough of these premium format options for people to, for that to make a meaningful difference, don't you think?

[A]

Well, in New York and la, but I think in some of these smaller cities maybe they can see it at different times. I just worry that people are going to wait and that this will be like an avatar Type situation where it opens okay, but it just has legs. And legs. And legs.

[B]

The Nolan movies have legs. Word of mouth. Yeah. The premium format thing.

[A]

Oppenheimer and the reviews are great. Like 97% on Rotten Tomatoes. Like, it's. It's going to be huge.

[B]

It is a true spectacle. It's going to have great word of mouth. I'm still going to take the over on 100. I'm not going to.

[A]

Oh, you are.

[B]

Yeah, you are. I think. I think the IP is just way bigger than Oppenheimer. I think it's a much more global story. It has, like, every major movie star in this movie. Every relevant movie star is in this movie. The set pieces are way bigger than anything Oppenheimer did. I think it's going to be huge.

[A]

It's got a cyclops. How can you argue with a cyclops?

[B]

Yes.

[A]

All right, I'll take the. You've convinced me. I'll take the over. It's just hard, you know, if you go by the pre sales, this thing is the biggest movie of all time. But the pre sales are people like the film nerds. Is this going to play in the multiplex in Peoria? That is not a great experience. That's the question. Because it is. It is very much a dad movie.

[B]

It absolutely is. But I do think that Nolan. I think Barbenheimer helped a lot where Nolan is IP unto himself and he knew Spielberg in that regard. And people are going to go see the new Nolan movie because it's the new Nolan movie.

[A]

Yes. And he will be parking in Jurassic Parking and going to see his movies and others at the CityWalk.

[B]

I love when people record him just walking across the CityWalk to go to the theater. It's awesome.

[A]

It's my favorite. And I asked him about that at Cinemacon. I asked him if he's seen those videos and he said he has.

[B]

Well, if they build a new theater in la, he can go to that one.

[A]

I know, I know. It's amazing. All right, that's the show for today. I want to make my guests Violin Russell, producer Craig Horlebeck, artist Jesse Lopez and Stefano Sanchez. And I want to thank you. We'll see you one more time this week.

[D]

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