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---
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source_type: "tweet"
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title: "Mediawan Kids and Family to Turn Claynosaurz Into Animated Series"
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author: "@cabanimation"
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url: ""
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date_published: "2025-06-02"
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date_archived: "2025-06-02"
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archived_by: "clay"
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domain: "entertainment"
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status: processed
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claims_extracted:
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- "progressive validation through community building reduces development risk by proving audience demand before production investment"
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- "traditional media buyers now seek content with pre-existing community engagement data as risk mitigation"
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---
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# Mediawan Kids & Family to Turn Claynosaurz Into Animated Series
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Written by @cabanimation
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June 2nd, 2025
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Published on X: https://x.com/Cabanimation/status/1929604785117823282
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Partnering with Mediawan Kids & Family (@Mediawan_kf) is one of the most important
|
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steps we've taken in building Claynosaurz into a true global franchise. Here's why:
|
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Mediawan isn't just an animation studio. They're franchise engineers.
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They've produced or distributed over 2,500 half-hours of kids and family content and built
|
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IP that now rivals the likes of Nickelodeon and Disney globally. Their reach spans Netflix,
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Disney+, YouTube, TF1, and other major platforms. Most importantly, they've proven they
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know how to take a piece of original IP and scale it into a multi-billion-dollar brand. Need
|
||||
proof? Look at Miraculous: Tales of Ladybug & Cat Noir.
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||||
Developed by Mediawan's Method Animation and ZAG Heroez, Miraculous has become
|
||||
one of the most successful kids' properties of the last decade:
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$2B+ franchise revenue
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35B+ YouTube views
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100M monthly active viewers
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Aired in over 120 countries, translated into 50+ languages
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Dominates licensing across fashion, toys, publishing, and more
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That's not just a hit—it's a blueprint. Now imagine what we can do with a brand like
|
||||
Claynosaurz, which already has:
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|
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A 450K+ social media following
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Over 500M short-form content views
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||||
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||||
A passionate collector community
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||||
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Toyetic character design baked in from day one
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||||
A mobile game launching with Gameloft
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||||
|
||||
#
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An upcoming Achievement System that rewards fan contribution
|
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|
||||
A content team from studios like Pixar, Disney, and DreamWorks
|
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|
||||
This has been a long time coming. Claynosaurz was never about being “just an NFT
|
||||
project." It's about telling stories, creating characters people care about, and inviting fans
|
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into a world that's built to last. We're here to make this a franchise. One that pulls
|
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collectors in.
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We had to find the right long-term creative ally-one that shares our vision, understands
|
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how to scale original IP, and respects the way we've built this community. Mediawan gets
|
||||
that. They're creator-first, globally connected, and looking to build the next generation of
|
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breakout brands from the ground up. Together, we're building something that can live
|
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across screens, shelves, and generations.
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|
||||
We're all about changing the game and becoming a beacon for Web3. Mediawan
|
||||
understands how important this is to us, and the gamified content opportunities that we
|
||||
can explore. This is the next chapter—and it's a big one.
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@ -1,434 +0,0 @@
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---
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source_type: "article"
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title: "How Will the Disruption of Hollywood Play Out?"
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author: "Doug Shapiro"
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url: "https://dougshapiro.substack.com/p/how-will-the-disruption-of-hollywood-play"
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date_published: "2023-07-05"
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date_archived: "2025-04-23"
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archived_by: "clay"
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domain: "entertainment"
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status: processed
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claims_extracted:
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- "five factors determine the speed and extent of disruption including quality definition change and ease of incumbent replication"
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---
|
||||
# How Will the "Disruption" of Hollywood Play Out?
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Saved from https://dougshapiro.substack.com/p/how-will-the-disruption-of-hollywood-play on 23 Apr 2025 17:53:23 UTC
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## How Will the “Disruption" of Hollywood Play Out?
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A Framework for Thinking Through the Speed and Extent of Disruption Shows Hollywood's Vulnerability
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DOUG SHAPIRO
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JUL 05, 2023
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[Note that this essay was originally published on Medium]
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[Image of a scene depicting an army of the dead breaching a wall. The source is attributed to Floris Didden (https://www.artstation.com/didden)]
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Army of the Dead Breaching the Wall. Source: Floris Didden (https://www.artstation.com/didden)
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|
||||
Six months ago, I wrote an essay titled Forget Peak TV, Here Comes Infinite TV. It laid out the case for why four technologies, most notably virtual production and AI, are poised to democratize high quality video content creation over the next 5-10 years. The main conclusion was that-just as the past decade in the TV and film business has been defined by the disruption of content distribution—the next decade will be defined by the disruption of content creation.
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When I wrote it, I was a little concerned that the concept was so far out that it would be considered too theoretical and irrelevant. But a lot has happened since then: there has been an onslaught of new AI-enabled production tools and features; research breakthroughs that portend future commercial products; a ton of experimental videos posted online; widespread press coverage; and Al moving front and center in ongoing negotiations between the studios and the guilds. The idea that Al will have a significant effect on TV and film production in coming years has gone from fringe idea to consensus, very fast.
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## 2/19
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The idea that AI will have a significant effect on TV and film production in coming years has gone from fringe idea to consensus, very fast.
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Even so, when I write that Hollywood may be "disrupted,” what does that actually mean? By disruption, I mean the way Clay Christensen defined it in his theory of disruptive innovation: the process by which new entrants target an overserved market with an inferior, but “good-enough" product, then relentlessly improve the performance of the product and ultimately challenge the incumbents.
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While that describes a specific process, it is still imprecise in important ways-namely its extent and speed. Will the disruption be complete or partial? Will it be fast or slow? If you're an operator or investor, the answers are critically important.
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In this essay, I try to be more precise about what I mean by the disruption of content creation and introduce a framework for thinking about how it might play out.
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Tl;dr:
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* To clarify what I mean by the "disruption of Hollywood:" 1) social video is already disrupting Hollywood, but new production tools promise to throw gas on the fire: 2) the initial experiments with Al video are mostly crappy, but that's how disruption works; 3) this is about tools that make people more productive, not robots making movies; and 4) these tools may benefit Hollywood, but they will likely hurt more than they help.
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* How fast and to what degree will disruption occur?
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* Christensen didn't write much about what factors determine the speed and extent of disruption, but common sense suggests they include: the hurdles for the new entrant to move upmarket; the hurdles to consumer adoption of the new entrant's product; the degree to which the new entrant changes consumers' definition of quality; the size and persistence of the high end of the market; and the ease for the incumbent to replicate the new entrant's business model.
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* This framework helps explain why newspapers were destroyed by online aggregators, digital native publishers, social, newsletters and vertical marketplaces; major music labels have proven relatively resilient despite the explosion of independent music; and videogame publishers have retained the profitable high end of the market even as most missed mobile gaming, the chief growth engine over the last decade.
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* Applying the framework also shows why Hollywood is highly vulnerable. While it will likely retain the high end of the market, that market isn't growing. And consumer adoption of independent content could happen literally overnight.
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* Hollywood is hardly dead, but it risks retreating into a smaller version of itself.
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|
||||
Thanks for reading The Mediator! Subscribe for free to receive new posts and support my work.
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|
||||
## 3/19
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|
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Revisiting the Disruption of Hollywood
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In Forget Peak TV, Here Comes Infinite TV, I first laid out the thesis for why high-quality, professional video content creation—or what I'll call Hollywood for short-may be disrupted in coming years.
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Since I wrote that piece in January, I've had a lot of conversations that have highlighted several points I need to refine or emphasize.
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1) Professional Video is Already Being Disrupted by Social Video, New Tech Adds Gas to the Fire
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There is already effectively an infinite amount of video content (from Infinite TV):
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Short form (or “social video” or “user generated content") is effectively already "infinite." YouTube has 2.6 billion global users and ~100 million channels that upload 30,000 hours of content every hour. That is equivalent to Netflix's entire domestic content library—every hour. TikTok has 1.8 billion users. And while we don't know how many hours of content are on TikTok, 83% of its users also upload content.
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And, if we define disruption as the process by which a new entrant enters the low-end of the market, establishes a foothold, gets relentlessly better and then challenges the incumbents, then you could argue that Hollywood is already in the early stages of being disrupted by social video.
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YouTube is already challenging Hollywood for the least demanding viewers: kids and unscripted viewers.
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As shown in Figure 1, according to Nielsen, YouTube is already the largest source of streaming to TVs. In other words, people watch YouTube on their TV-in their living rooms-more than Netflix, Disney+ or any other Hollywood-content streaming service. And while a lot of this content is music videos, kids playing Minecraft and home improvement videos, YouTube is starting to challenge Hollywood for the least demanding consumers-kids and unscripted viewers.
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What's the most popular kids show in the world? Between its presence on YouTube and Netflix, it's CoComelon (with over 160 million subscribers on YouTube). The most popular unscripted show? If you were to consider all his videos as a “show,” it's Mr. Beast, also with over 160 million subscribers, and over 1 billion views per month.
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CoComelon is already the most popular kids show and one could argue that Mr. Beast is the most popular unscripted show.
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Figure 1. YouTube is Already the #1 Streaming Destination on TVs
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## 4/19
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[Image of a graph from Nielsen showing the breakdown of streaming viewership by platform. The graph is a pie chart with the following segments: Broadcast (23.1%), Cable (31.5%), Streaming SVOD (34.0%), Other (11.5%). Within the Streaming SVOD segment, the breakdown is: YouTube (8.1%), Netflix (6.9%), Hulu (3.3%), Prime Video (2.8%), Disney+ (1.8%), HBO Max (1.2%), Peacock (1.1%), Tubi (1.1%), Pluto (0.8%), Other (6.9%).]
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Source:
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Independent/creator content isn't yet challenging Hollywood for the most demanding forms of content, such as scripted comedies and dramas. When you consider the costs for talent, locations, and VFX and the enormous number of people that need to come together to create a production, those are really hard and expensive to do. My argument is that over time virtual production and AI-assisted tools will lower the entry barriers for this kind of content too, enabling independent/creator content to keep marching up the performance curve. Put differently, these tools will accelerate a disruption process that is already underway. Visually, this process looks a little like Figure 2.
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Figure 2. A Visual Representation of Content Disruption
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[Image of a graph showing a visual representation of content disruption. The graph plots "Breadth, Production Value" against "High-Quality Scripted Show and Original Movie Viewers, Reality Show Viewers, Kids". There are lines representing Netflix, ABC, and YouTube, showing how their performance capabilities are changing over time relative to the performance demands of customer segments.]
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Note: YouTube is meant as a proxy for independent/creator content; TNT is a proxy for cable; ABC is a proxy for broadcast; and Netflix is, well, Netflix. Source: Author
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2) At First, Al-Assisted Content Will be Inferior-That's How Disruption Works
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In recent months, there has been a growing amount of video content produced using new AI tools, like RunwayML Gen-2, KaiberAI, Wonder Studio or manipulation of generative imaging tools, like MidJourney, ControlNet or Dall-E to create videos. (Keep in mind that RunwayML Gen-1 and Gen-2, Kaiber and Wonder Studio were all released since January.) I've tried to keep a running tally of these new tools and some of the most impressive examples in running Twitter threads, pasted below, but it's hard to keep up.
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A lot of these efforts are just experiments or they are derivative (for some reason, people like to re-imagine famous movies as if directed by Wes Anderson), surreal or
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## 5/19
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even creepy. There are few examples of real narrative-based storytelling. But this isn't an indictment of the theory. That's generally how disruption starts—as something that is clearly inferior, but gets better over time.
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Disruption always starts as something that appears inferior but gets better over time.
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3) It's About More Productive People, Not Creative Robots
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Some of the Al films posted online have been created almost entirely using AI, such as the combination of a script written by ChatGPT-4, text-to-video from RunwayML, a talking avatar by DID, voiceover by ElevenLabs, etc. To state the obvious, this is not really "content created entirely by AI" since it takes a human to string all these tools together. Whether content created entirely by AI will ever be more than a novelty is an open question. But the disruptive path I laid out above is not contingent on that. I am merely making the case that these kinds of tools will enable creators to do a lot more with a lot fewer people at a much lower cost, which will alter the competitive dynamic in the market for high-quality video content.
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I'm arguing that AI-assisted tools will enable creators to do a lot more with a lot fewer people at a much lower cost, not that content created entirely with AI will take over.
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4) These Tools are Available to Hollywood—and to Everyone Else Too
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In the online discourse about the effect of these kinds of tools-especially generative AI (GAI)-on Hollywood, many argue that the big studios will co-opt them and therefore be the main beneficiaries.
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Arguing that lower cost production tools are good for Hollywood is a little like arguing in 1998 that the Internet was good for magazines.
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I think this is unlikely. The good news for Hollywood is that these tools could significantly lower production costs. The bad news is that they will lower the costs for everyone else too and, therefore, the barriers to entry. It's a little like arguing in 1998 that the Internet is good for magazines because it will lower their distribution costs. In addition, for reasons I recently explained in What Clay Christensen Missed, I think Hollywood will struggle to adopt many of these new tools quickly because of the complex ecosystem of talent, agencies, guilds and trades in which the studios operate. It is telling that one of the key sticking points in the ongoing Writers Guild of America (WGA) strike is the WGA's demands to limit how the studios can use AI.
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That is meant to help clarify what I mean by the “disruption” of Hollywood. Even so, what I have not addressed is really important: to what extent will Hollywood be disrupted, and how fast?
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# 4/23/25, 6:58 PM How Will the "Disruption" of Hollywood Play Out?
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What Determines the Extent and Speed of Disruption?
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As mentioned above, sometimes disruption is complete and incumbents ultimately exit the market; sometimes they retain a profitable high end of the market indefinitely. Sometimes it plays out over years, sometimes it takes decades. What determines the difference?
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[https://archive.ph/nk30T](https://archive.ph/nk30T)
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Disruption describes the process by which new entrants target a market and ultimately challenge the incumbents, but it doesn't predict speed or extent.
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As far as I can tell, Christensen never explored the question in depth, but we can apply a little common sense to come up with a simple framework. To do so, it's helpful to use the vocabulary of another Christensen framework, jobs theory, which he explained in his 2016 book, Competing Against Luck. The premise of jobs theory (or sometimes called Jobs to be Done theory, or JTBD) is that consumers “hire” a product or service to do a "job" in their life. (To quote Harvard Business School Professor Ted Leavitt, “People don't want to buy a quarter-inch drill. They want a quarter-inch hole!") They "fire" that product and "hire” a different one when the benefits of the new product offset the switching costs. It's important to keep in mind that most products and services do multiple jobs and the importance of each of these jobs differs for different consumers. While there is no consensus definition of the word "quality," my working definition is that, for each consumer, it is the relative weighting of each of these jobs.¹
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Using the language of JTBD, let's think through the factors that determine the speed and extent of disruption:
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Hurdles for the New Entrant to Move Upmarket
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In the disruption process, the upstart gets a foothold in the market and then improves its offering. It starts out doing certain jobs, but then gets better at those jobs and keeps adding more jobs and appeals to more customer segments. But how thoroughly and quickly does it improve? Gating factors to moving upmarket may include technological complexity, regulation or incumbents' control of a scarce resource.
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Consider one of the canonical examples of disruption that Christensen highlighted in The Innovator's Dilemma-minimills' disruption of integrated steel mills. Owing largely to the technological complexity, required capital investment and regulatory requirements of higher grade steel, the process took decades. Minimills entered the market with the least demanding and lowest cost form of steel, rebar, in the 1960's and '70's. In the late '80s, they developed flat-rolled steel and it took another 15 years to move into the highest quality sheet steel. And that disruption is not complete. As of 2017, integrated steel mills still produced about 30% of steel in the U.S.
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Hurdles for Consumer Adoption
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The prior point focused on the hurdles for new entrants to move upmarket, but another factor is the hurdles for consumers to adopt new entrants' products. These hurdles include the risk aversion of the customer (for instance, individuals and small businesses may adopt some technologies faster than large enterprises and governments owing to lower risk aversion) and switching costs. Switching costs
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# 6/19
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# 4/23/25, 6:58 PM How Will the "Disruption" of Hollywood Play Out?
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include the consumers' sunk investments in the incumbents' products or services, the learning curve on the new product, entrenched business relationships and the hardware replacement cycle. Consider the obliteration of standalone driving navigation devices (Garmin, TomTom) by mobile driving apps, like Waze or Google Maps. The hurdles to consumer adoption were negligible because almost all drivers have smartphones anyway.
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Degree to Which the New Entrant Changes the Consumer Definition of Quality
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As I've discussed in other essays (see Four Horsemen of the TV Apocalypse), one of the more insidious, but less discussed, elements of the disruption process is the tendency of new entrants to introduce new features that change the consumer definition of quality.
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AirBNB is a favorite example. It started with a low-end offering, targeting people who needed a room but couldn't afford a hotel. However, it also introduced new features that most hotels simply can't offer, like quaint neighborhoods, more privacy, full working kitchens, a backyard barbeque and substantially more space. For some customers, these new features have completely changed their definition of quality and they no longer consider hotels when traveling.
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Size and Persistence of the High End of the Market
|
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|
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Sometimes, the new entrant never moves all the way upmarket. For instance, maybe it makes business model choices that foreclose the high end or it can't overcome technological or regulatory hurdles. Or perhaps the market of non-consumers is large enough that it doesn't need to directly target the incumbents' highest-end customers. In these cases, there are two critical questions for incumbents: how big and how persistent is the residual high-end market? Why the size of the market is important is obvious. The persistence of the market depends on how broadly the new entrant changes the consumer definition of quality. If the consumer definition of quality changes materially even for high-end consumers, then the traditional high end of the market may disappear.
|
||||
|
||||
Take AirBNB again. Even though it has changed the definition of quality for many consumers, it still can't (and likely won't ever) compete on certain "jobs" that are important to many business travelers, like convenience, 24-hour service, security, common spaces to meet business contacts and proximity to business districts. And business lodging is a massive market. Similarly, Coursera will probably never compete for many of the jobs that are highly valued by college students and their parents, like a gradual transition into adulthood, social life and a valued alumni network. On the other end of the spectrum, consider film photography. The advent of digital photography so completely changed the definition of quality that the high-end market for film-professional photographers—eventually all but disappeared.
|
||||
|
||||
Ease for Incumbent to Replicate the New Entrant's Business Model
|
||||
|
||||
In theory, incumbents can head off disruption by rapidly matching the pricing and product offerings of the new entrant. In practice, a company's ability to do this is heavily influenced by the complexity of the ecosystem in which it operates, as I explained in What Clay Christensen Missed:
|
||||
|
||||
[https://archive.ph/nk30T](https://archive.ph/nk30T)
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||||
|
||||
# 7/19
|
||||
|
||||
# 4/23/25, 6:58 PM How Will the "Disruption" of Hollywood Play Out?
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|
||||
Often, firms get disrupted not because they don't understand the disruption process, see it coming or know what's at stake. They don't even get disrupted because of the difficulty of changing internal processes. They get disrupted because companies operate in complex ecosystems of stakeholders with misaligned interests: employees (including well-paid, powerful executives), unions, vendors, distributors, "complementors,” board members, shareholders, etc.
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In the best cases, this is really hard, in others, it is essentially impossible.
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Models of Media Disruption: News, Music and Gaming
|
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|
||||
Before using this framework to predict the possible speed and extent of disruption of Hollywood, let's see if it can help explain the recent history of other similar media businesses, namely newspapers, music labels and videogame publishers.
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|
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I call these businesses similar because, like TV and film studios, they are all intermediaries between creators and consumers (whether those creators are salaried employees, like journalists and videogame developers, or independent contractors). All historically earned a critical place in the value chain by performing functions that creators couldn't easily do themselves, such as financing production, handling monetization (ad sales, licensing, wholesale sales, retail sales), developing distribution networks or brokering distribution deals and marketing. (I.e., they are all "producer/publishers" in the simplified generic media supply chain in Figure 3.)
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||||
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||||
Figure 3. A Simplified Media Value Chain²
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||||
|
||||
The image is a diagram illustrating a simplified media value chain. It is structured horizontally with four key stages: Creator, Producer/Publisher, Aggregator/Distributor, and Consumer. Each stage is represented by a blue rectangle with white text, and the flow of value is indicated by right-pointing arrows between the stages.
|
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|
||||
* **Creator:** This stage includes roles such as Writer, Composer, Musician, Director, Actor, Developer, and Cinematographer.
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||||
* **Producer/Publisher:** This stage includes entities like Music Labels, Newspapers, Magazines, Journalists, Photographers, Videogame Publishers, and TV and Film Studios.
|
||||
* **Aggregator/Distributor:** This stage includes Online Aggregators, Social Networks, Retailers (electronic or physical), Streaming Services, Theaters, TV/Radio Stations, Cable Networks, Cable Systems, Satellite, and Telco.
|
||||
* **Consumer:** This is the final stage, representing the end-user of the media product.
|
||||
|
||||
The diagram is intended to show how different entities in the media industry contribute to the creation, production, distribution, and consumption of media content.
|
||||
|
||||
Source: Author.
|
||||
|
||||
All three have been disrupted to some degree as technology has reduced the cost or complexity of most of these activities, making it easier for both independent studios/publishers/labels and individual creators to disintermediate their roles. But the extent of this disruption has been quite different. Let's explore why.
|
||||
|
||||
[https://archive.ph/nk30T](https://archive.ph/nk30T)
|
||||
|
||||
Newspapers, music labels and videogame publishers are all similar to TV and film studios: they are intermediaries between creators and consumers. They have all established a critical role in the value chain by doing things that are very hard or expensive for creators to do themselves, but technology is making all those things easier.
|
||||
|
||||
Newspapers: Near-Complete Disruption
|
||||
|
||||
# 8/19
|
||||
|
||||
# 4/23/25, 6:58 PM How Will the "Disruption" of Hollywood Play Out?
|
||||
|
||||
Historically, newspapers did several jobs. They aggregated national newsgathering services (AP and Reuters); produced regional/local news and opinion; and acted as a local marketplace for employment, real estate, used cars and other used goods (the classifieds). The Internet disrupted all three. It made it possible for online news aggregators to provide the same aggregation services; new digital native publishers to emerge; journalists and independent creators (both amateurs and professionals) to disintermediate newspapers and publish directly to digital native publications, blogs, newsletters and social networks; and it enabled the creation of multi-sided vertical online markets (Craigslist, AutoTrader, Ebay, Indeed, Zillow, etc.) that supplanted the classifieds.
|
||||
|
||||
The newspaper business has been eviscerated over the past two decades. Figure 4 shows aggregate newspaper revenue in the U.S. (both advertising and circulation) graphed against total U.S. online advertising. This is an admittedly blunt and imperfect comparison (the online advertising numbers include categories that are not strictly competing for newspaper ad dollars, such as online video advertising), but it roughly shows the point: aggregate newspaper revenue is down by 2/3 over the last two decades, from close to $60 billion to around $20 billion today. All of that revenue has been vacuumed up by online advertising, primarily Meta and Google, and online marketplaces.
|
||||
|
||||
Figure 4. Newspaper Revenue is Down 2/3 Since 2000
|
||||
|
||||
The image is a line graph comparing U.S. Newspaper Industry Revenue vs. Online Advertising from 2000 to 2020. The x-axis represents the years, and the y-axis represents the revenue in billions of dollars.
|
||||
|
||||
* **U.S. Newspaper Industry Revenue:** This line starts at around $60 billion in 2000 and declines steadily over the years, reaching approximately $20 billion by 2020.
|
||||
* **Online Advertising:** This line starts at a low value in 2000 and increases sharply over the years, surpassing the newspaper industry revenue around 2010 and reaching a high value by 2020.
|
||||
|
||||
The graph illustrates the significant decline in newspaper industry revenue and the corresponding rise in online advertising revenue over the two-decade period.
|
||||
|
||||
Sources: Pew Research Center, IAB, PwC.
|
||||
|
||||
Running the newspaper business through our framework shows why. (Since we're looking at these dynamics from the perspective of the incumbents, factors with an favor the new entrant, those with a favor the incumbent and those with a Pare neutral or unclear.):
|
||||
|
||||
* X Ease for new entrants to move upmarket: For both independent (i.e., non-newspaper) written information/opinion and vertical marketplaces there were no major barriers to move upmarket. The high end of the market for information is brand-name journalists, but “newsletter in a box” services like Substack and Beehiv have made it easy for journalists to cut newspapers out and go direct-to-
|
||||
|
||||
[https://archive.ph/nk30T](https://archive.ph/nk30T)
|
||||
|
||||
# 9/19
|
||||
|
||||
# 4/23/25, 6:58 PM How Will the "Disruption" of Hollywood Play Out?
|
||||
|
||||
* ➤ consumer. Online marketplaces had to establish a sufficient network of buyers and sellers to overtake classified services, but that didn't take long. Put differently, at this point there are few, if any, jobs that newspapers do that aren't done by online providers and, in many cases, better.
|
||||
* ➤ Hurdles to consumer adoption: The chief hurdles to adoption were widespread broadband access, widespread mobile device adoption and shifts in consumer behavior toward accessing information online. The only gating factor to all three was time, but that has since passed.
|
||||
* X Degree of change in consumer definition of quality: Online news changed the consumer definition of quality in important ways: consumers now expect information to be immediate and it raised the bar for what people are willing to pay for. Many people also now rely on their chosen panel of friends or experts on social networks, like Facebook and Twitter, to act as their news filter, not the editorial staff of a newspaper. In the classifieds business, vertical online marketplaces have offered many new features, such as easy search, customized alerts, rich media (more photos and videos), the ability to communicate or transact with counterparties seamlessly online, larger selection, shipping, buyer protection and escrow services, etc., that have completely changed the definition of quality.
|
||||
* X Size and persistence of high-end market: Because of the ease for new entrants to compete at the highest end of the markets-analysis and opinion from brand-name journalists and sales of high-end real estate, cars, etc.— and because of the broad shift in the consumer definition of quality, there is no residual high-end market left to newspapers. There are a few highly trusted brands, such as The New York Times or The Financial Times, which can fulfill the job of "provide me information I can trust" for some consumers better than online outlets, newsletters, aggregators or social platforms, but this is more the exception than the rule. For some consumers, “deliver me a physical newspapers daily" is still an important job, but this is a small and probably declining market. In the classifieds business, vertical online marketplaces have so altered the definition of quality that newspaper classifieds sections have shrunk dramatically or been curtailed in many markets.
|
||||
* X Ease for incumbent to replicate new entrant's business model: Whether it would've been easy for newspapers to launch their own news aggregators, online marketplaces or social networks is moot—some tried, but it didn't help much.
|
||||
|
||||
Major Music Labels: Relative Resiliency
|
||||
|
||||
The recent history of the major music labels is very different, as I discussed in Will Radio Save the Video Star?.
|
||||
|
||||
Newspapers were obliterated, while major music labels have proved resilient. Why?
|
||||
|
||||
Historically, the primary role of music labels was artist development, financing, marketing and distribution. The barriers for independent labels and artists to disintermediate the labels have fallen substantially over the last 15-20 years. Owing to sophisticated in home production software (DAWs, like LogicPro) and hardware;
|
||||
|
||||
[https://archive.ph/nk30T](https://archive.ph/nk30T)
|
||||
|
||||
# 10/19
|
||||
|
||||
|
||||
# How Will the "Disruption" of Hollywood Play Out?
|
||||
|
||||
streaming services (Spotify, Soundcloud, etc.); and social networking, today artists can self-produce, self-distribute and market through their own social followings.
|
||||
|
||||
Owing to these lower barriers to entry, there has been an explosion of independent music in recent years. Spotify boasts 11 million artists (as of 4Q21) and 100 million tracks. Spotify estimates that only 200,000 of the 11 million artists on the platform are “professional” musicians, implying the other 98+% are not represented by any label, major or independent. An estimated 100,000 new songs are uploaded to streaming services each day. I estimate that half of the new tracks on Spotify were added in the last three years and that less than 10% of the tracks on the service are repped by major labels.
|
||||
|
||||
Nevertheless, the major labels have proven surprisingly resilient. As shown in Figure 5, the three major music labels (Universal Music Group, Sony Music Entertainment and Warner Music Group) have actually gained revenue share over independents over the last few years. As shown in Figure 6, while they have lost share of Spotify streams, the majors and Merlin (a consortium of large independent labels) still represent about 75% of all streams and the pace of decline has flattened in recent years, even as the quantity of music from independent creators has exploded.
|
||||
|
||||
## Figure 5. The Majors Are Dominant and Have Been Gaining Revenue Share
|
||||
|
||||
The image is a line graph titled "Global Music Revenue Market Share". The x-axis represents years from 2017 to 2021, and the y-axis represents percentage from 0% to 40%. There are four lines on the graph, each representing a different category: UMG, SME, WMG, and Independents. The graph shows that UMG, SME, and WMG have been gaining revenue share over independents over the last few years.
|
||||
|
||||
Source: Omdia (Music & Copyright).
|
||||
|
||||
## Figure 6. The Majors and Merlin Still Have ~75% Share of Spotify Streams, Even with 100,000 New Tracks Uploaded Daily
|
||||
|
||||
[Meta: The following content is a continuation of the previous section, and is still on page 11/19]
|
||||
|
||||
[https://archive.ph/nk30T](https://archive.ph/nk30T)
|
||||
|
||||
## Page 12/19
|
||||
|
||||
The image is a line graph titled "Share of Spotify Streams for Majors and Merlin". The x-axis represents years from 2017 to 2022, and the y-axis represents percentage from 50% to 100%. There is one line on the graph, which represents the share of Spotify streams for majors and Merlin. The graph shows that the share of Spotify streams for majors and Merlin has been declining in recent years, but has flattened out.
|
||||
|
||||
The image also contains a table titled "Representation at Commercial Debut". The table lists several artists and their representation at commercial debut and current representation.
|
||||
|
||||
Source: Billboard, Author analysis.
|
||||
|
||||
Let's explore music labels through the framework:
|
||||
|
||||
* Ease for new entrants to move upmarket: In music, for new entrants to move upmarket would mean higher quality/more popular³ acts going to independent labels or direct. As I discussed in Will Radio Save the Video Star?, while there are no technical hurdles, there are significant business hurdles. Most important, major labels have the scale and resources to help artists navigate the complexity of the music business, which has multiple revenue streams and is global. They also have a leg up in artist development, because they can attract the biggest-name producers and musical collaborators. And they retain substantial bargaining power over streaming services, largely due to the importance of catalog music, which the majors control. As a result, even the most powerful artists, who are best positioned to go direct, still have major label deals (even if they also have tremendous bargaining power over the labels).
|
||||
|
||||
* ➤ Hurdles to consumer adoption: There are no hurdles to consumers listening to independent music. It sits side-by-side with major label music on streaming services; as mentioned, the vast majority of music on streaming services is non-major label-probably >90%.
|
||||
|
||||
* Degree of change in consumer definition of quality: The consumer definition of quality in music has arguably changed very little in the last few decades. Perhaps most relevant is that catalog is still extremely important. As shown in Figure 8, according to Luminate, last year 72% of music consumption was catalog (which is defined as music that has been on the market for 18 months or longer
|
||||
|
||||
## Page 13/19
|
||||
|
||||
and has fallen below 100 on the Billboard Top 200 chart). While popular culture focuses on the newest music, most of what people actually listen to is catalog, which is largely controlled by the major labels.
|
||||
|
||||
## Figure 8. An Estimated 72% of U.S. Music Consumption is Catalog
|
||||
|
||||
The image is a bar graph comparing U.S. catalog vs. current consumption. The graph shows that catalog share is 72.2% and current share is 27.8%. The graph also shows that catalog total album consumption is 703.9M and current total album consumption is 270.9M.
|
||||
|
||||
Note: ** Catalog = 18 months or older and have fallen below Nº100 on the Billboard 200 Chart and don't have a single that is current on any of Billboard's radio airplay charts. Source: Luminate.
|
||||
|
||||
* Size and persistence of high-end market: If the high end of the market is defined as the current and catalog recordings of the most popular artists, then it is still the bulk of the market.
|
||||
|
||||
* Ease for incumbent to replicate the new entrant's business model: As noted above, most independent artists who break out sign major label deals. It is also relatively easy for the major labels to buy independent labels and distribution services and thereby subsume the forces of disruption. For instance, Sony purchased The Orchard and AWAL, two independent distributors, in recent years.
|
||||
|
||||
## Videogame Publishers: A Middle Ground
|
||||
|
||||
Gaming has also arguably been disrupted over the last decade by mobile gaming. Console and mobile have very different business models. Mobile games also tend to be casual, with less demanding gameplay and shorter session length, and a more diverse user base.
|
||||
|
||||
AAA console titles have development costs that rival blockbuster movies- CD Projekt Red, developer of Cyperpunk 2077, disclosed it spent more than $300 million on development-require heavy marketing spend and entail significant manufacturing and platform fees to the console manufacturers. While many console titles have added downloadable content (DLCs), like expansion packs, skins, etc., and subscription services, the primary model is still selling titles at about $60 each. By contrast, owing in part to game development platforms like Unity and Epic's Unreal Engine and different consumer expectations, the development costs for a mobile game may cost ~$10,000-$100,000, or 3–4 orders of magnitude less. The vast majority of mobile games are also free-to-play and make their money from in-app purchases, so the economics are largely dependent on the size of the funnel and LTV/CAC (which is a function of both marketing efficiency and conversion rates to paying players).
|
||||
|
||||
With much lower barriers to entry, there are many more mobile games-the major console platforms each support several thousand games and there are over 50,000 PC games available on Steam, but there are hundreds of thousands of mobile games on both the iOS App Store and Google Play. Similar to news and music, the vast majority of these games are produced by small teams who circumvent the biggest console publishers (Microsoft, Sony, Electronic Arts, Nintendo, Activision, Take-Two, etc.).
|
||||
|
||||
## Page 14/19
|
||||
|
||||
As shown in Figure 9, the incumbent console publishers were largely unable to adapt to the mobile business model. While the two largest game publishers in 2012, Activision and EA, were among the top 10 mobile publishers in 2021, they didn't retain their console share. The good news for the incumbents is that mobile gaming attracted a lot of “non-customers” and the console and PC business has continued to grow at a relatively rapid clip-especially when compared to anything that is considered "media" (Figure 10). The bad news, also shown in Figure 10, is that mobile is now half the business.
|
||||
|
||||
## Figure 9. The Biggest Console Publishers in 2012 Didn't Keep Pace in Mobile
|
||||
|
||||
The image contains two bar graphs. The first bar graph is titled "Largest Game Publishers 2012". The x-axis represents the names of the game publishers, and the y-axis represents the market share. The second bar graph is titled "Largest Mobile Game Publishers 2021". The x-axis represents the names of the game publishers, and the y-axis represents the market share.
|
||||
|
||||
Notes: Supercell is majority owned by Tencent. Zynga was acquired by Take-Two in May 2022.
|
||||
Sources: Ubisoft via gamesindustry.biz, Appmagic.
|
||||
|
||||
## Figure 10. Mobile is Now Half the Business
|
||||
|
||||
## Page 15/19
|
||||
|
||||
## How Will the "Disruption" of Hollywood Play Out?
|
||||
|
||||
The image is a bar graph titled "Global Video Game Spending". The x-axis represents years from 2012 to 2021, and the y-axis represents the amount of spending in billions of dollars. There are three bars for each year, representing PC, Console, and Mobile spending. The graph also shows the CAGR for each category.
|
||||
|
||||
So, the value Why?
|
||||
|
||||
* Ease for new entrants to move upmarket: So far, it's proven very difficult for mobile developers to target the high end of the market, which is hardcore gamers and, for the most part, they don't try. Unlike consoles, which have uniform technical specifications (i.e., every PS5 is the same), mobile developers needs to cater to a wide range of devices. Generally, mobile devices don't have the processing power, screen size and control capabilities of consoles. There are a few exceptions, like Fortnite, PUBG and Genshin Impact, that have successfully translated to mobile. But this is more the exception than the rule.
|
||||
|
||||
* X Hurdles to consumer adoption: Like any other mobile app, there are no barriers to consumer adoption.
|
||||
|
||||
* Degree of change in consumer definition of quality: Mobile gaming has introduced new “jobs” to gaming and consequently mobile games tend to have a different set of use cases and definition of quality than console or PC games. They usually have a much quicker learning curve, they can be played in short sessions with a faster payoff and they are easier to play while multitasking. For most console and PC games, by contrast, the markers of quality tend to include higher-fidelity graphics, much more complex gameplay and storylines, live social features (e.g., chat) and more immersive, longer sessions.
|
||||
|
||||
* Size and persistence of high-end market: As noted in Figure 10 above, the high end of the market, console and PC games, has continued to grow at a healthy pace despite the emergence of mobile.
|
||||
|
||||
* Ease for incumbent to replicate the new entrant's business model: Large publishers have successfully bought their way into mobile, but have struggled to build mobile operations organically. The most successful acquisitions of a mobile games developer are arguably Tencent's purchase of a majority stake in Supercell (Clash of Clans), Microsoft's purchase of Mojang (Minecraft) and Activision's acquisition of King (Candy Crush). Nevertheless, as noted, none of the major AAA publishers have maintained their console share in mobile.
|
||||
|
||||
## Figure 11. Hollywood is Vulnerable
|
||||
|
||||
[https://archive.ph/nk30T](https://archive.ph/nk30T)
|
||||
|
||||
|
||||
# 4/23/25, 6:58 PM
|
||||
|
||||
How Will the "Disruption" of Hollywood Play Out?
|
||||
|
||||
Newspapers Music Labels Videogame TV/Film Studios
|
||||
Publishers
|
||||
|
||||
Ease for New Entrant to Move Upmarket X
|
||||
Hurdles to Consumer Adoption X X X X
|
||||
Change in Consumer Definition of Quality X ?
|
||||
Size and Persistence of High-End Market X
|
||||
Ease for Incumbent to Replicate New with Entrant's Model X X X ? X those
|
||||
|
||||
https://archive.ph/nk30T
|
||||
|
||||
## Applying the Framework for TV and Film Studios
|
||||
|
||||
The last and final step is to apply this framework to TV and film studios to address the critical question posed before: to what extent and how fast might Hollywood be disrupted?
|
||||
|
||||
* Ease for new entrants to move upmarket: The highest end of the market for TV and film is big-budget, high production value projects with big name directors/showrunners and actors and well-known IP. Will Steven Spielberg or Martin Scorsese lean into these new AI-enhanced production tools and create Hollywood-quality productions and disintermediate the studios and distribute them on YouTube? Probably not. In addition, the studios still control the most widely-recognized franchises, like Star Wars, Marvel, DC, Harry Potter, etc. Could high-production value hits emerge from the tail of independent content? For sure. But it will likely be very difficult for independent creators to approach the highest end of the market for Hollywood content anytime soon.
|
||||
* ➤ Hurdles to consumer adoption: Much like the examples above, there are no real barriers to consumer adoption of independent content. The disruption of video content distribution by Netflix took a long time because it required wide broadband adoption, smartphone and connected TV adoption and a change in consumer behavior to embrace streaming. By contrast, the adoption of independent content could happen literally overnight. As shown above in Figure 1, YouTube is already the #1 source of streaming to TVs. If there was a compelling independently-produced scripted TV show distributed on YouTube today, it could be the most popular show in the U.S. tomorrow.
|
||||
* Degree of change in consumer definition of quality: As I discussed in Infinite TV, it seems clear that social video is changing the consumer definition of quality for some consumers:
|
||||
|
||||
Most studio executives equate TV and movie quality with very high-cost attributes: high production values; established, well-known IP; brand name directors, show-runners, actors and screenwriters; and expensive effects, often signaled by equally expensive marketing campaigns. Short form doesn't (currently) compete on these attributes. But it ranks much higher on other attributes, like virality, surprise, digestibility, relevance to my community and personalization. These attributes are not inherently expensive.
|
||||
|
||||
To the extent that consumers consciously substitute short form for traditional TV, this reveals that their definition of quality is shifting toward de-emphasizing high-
|
||||
|
||||
## 16/19
|
||||
|
||||
# 4/23/25, 6:58 PM
|
||||
|
||||
How Will the "Disruption" of Hollywood Play Out?
|
||||
|
||||
cost attributes, and, in the process, lowering the barrier to entry. It seems like this is what's starting to happen. According to TikTok, as of March 2021, 35% of users were consciously—and therefore intentionally-watching less TV since they started using TikTok.
|
||||
|
||||
However, it is hard to predict how broadly the consumer definition of quality will change. Intuitively, it is a generational shift; older consumers will still likely define quality as they always have, namely high production values, while younger consumers will more highly value performance attributes like virality, authenticity and rapid consumption. But will there still be an appetite for blockbuster franchises even among young viewers? Probably.
|
||||
|
||||
* X Size and persistence of high-end market: Even though the high end of the market for TV and film may persist, a core challenge for Hollywood is that it isn't growing. I won't relitigate the point here, but as I explained in [Video's Fundamental Problem: It Over-Monetizes](https://stratechery.com/2021/videos-fundamental-problem-it-over-monetizes/), the chief reasons are that video consumption is already too high (the average adult watches more than 5 hours of video per day) and, owing to the cozy cartel between the cable networks and cable distributors, historically people paid too much for video they weren't consuming.
|
||||
* X Ease for incumbent to replicate the new entrant's business model: As I've written before, I think it will be very hard for Hollywood studios to adopt these new production technologies because of the complex ecosystem of talent, unions, agencies, etc. in which they operate.
|
||||
|
||||
## The Death of Hollywood Has Been Greatly Exaggerated, But it is Highly Vulnerable
|
||||
|
||||
In recent months, I've seen a few tweets that Hollywood is "over" or "dead." Or sometimes "RIP Hollywood." A good tweet requires a compelling hook, so I understand why people use these kinds of phrases. But, to be clear, when I write that content creation is on a path to be disrupted over the coming years, by no means am I predicting that Hollywood is “dead.”
|
||||
|
||||
The very highest end of the market, with A-level talent and the most widely-loved franchises, is safe for the foreseeable future. But the industry is vulnerable. As described above, the conditions are ripe for very rapid consumer adoption of independent content. It is also an open question how big this high-end market is and how it is can grow.
|
||||
|
||||
https://archive.ph/nk30T
|
||||
|
||||
The risk for Hollywood: over time, it retreats into a smaller version of itself.
|
||||
|
||||
Among the comparisons above, I think Hollywood is most analogous to gaming, with one crucial difference. Like the AAA publishers, Hollywood will probably continue to control the high end of the market indefinitely. The key difference is that the console and PC gaming markets are still growing, while the core market for high-end video is not. In gaming, there was a big market of non-consumers to target. There isn't in video. The risk for Hollywood is that over time it is relegated to big budget productions of a few key franchises-a stagnant or shrinking market-and retreats
|
||||
|
||||
## 17/19
|
||||
|
||||
# 4/23/25, 6:58 PM
|
||||
|
||||
How Will the "Disruption" of Hollywood Play Out?
|
||||
|
||||
into a smaller version of itself. This is not the most dire outcome, but adjusting to the reality that Hollywood is no longer a growth business, or in decline, would be a wrenching process.
|
||||
|
||||
¹ For instance, why did you "hire" your car? For transportation, of course. But you might have hired it to “provide me a comfortable commute,” “get me through tough weather," "go off-roading," or "carpool my kid and her friends to soccer." Explicitly or not, you probably also hired your car to “send a message about my identity," including what you wish to convey about your socioeconomic status, environmental consciousness and perhaps even marital status or political leanings. Christensen often made the point that customers should be segmented by the jobs they are trying to get done, not by demographics or geography.
|
||||
|
||||
2 Often, the producer/publisher has an affiliated aggregator/distributor arm (such as media conglomerates that include TV and film studios, broadcast and cable networks, TV stations, streaming services and even cable systems) and sometimes the producer/publisher just brokers distribution (like music labels).
|
||||
|
||||
3 Above, I defined “quality” as consumers' relative weighting of the “jobs" that a product or service does. By this definition, for goods or services of equal price, popularity is equivalent to the average definition of quality.
|
||||
|
||||
## Subscribe to The Mediator
|
||||
|
||||
By Doug Shapiro
|
||||
|
||||
The Mediator is (mostly) about the long term structural changes in the media industry and the business, cultural, and societal implications of those shifts. I write it to get closer to the frontier.
|
||||
|
||||
By subscribing, I agree to Substack's [Terms of Use](https://substack.com/terms), and acknowledge its [Information Collection Notice](https://substack.com/privacy#information-collection-notice) and [Privacy Policy](https://substack.com/privacy).
|
||||
|
||||
2 Likes
|
||||
|
||||
Previous Next →
|
||||
|
||||
## Discussion about this post
|
||||
|
||||
Comments Restacks
|
||||
|
||||
https://archive.ph/nk30T
|
||||
|
||||
## 18/19
|
||||
|
|
@ -1,357 +0,0 @@
|
|||
---
|
||||
source_type: "article"
|
||||
title: "GenAI is Foremost a Creative Tool"
|
||||
author: "Doug Shapiro"
|
||||
url: "https://dougshapiro.substack.com/p/genai-is-foremost-a-creative-tool"
|
||||
date_published: "2024-06-01"
|
||||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: processed
|
||||
claims_extracted:
|
||||
- "GenAI is simultaneously sustaining and disruptive depending on whether users pursue progressive syntheticization or progressive control"
|
||||
---
|
||||
# GenAl is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
Saved from https://dougshapiro.substack.com/p/genai-is-foremost-a-creative-tool
|
||||
|
||||
All snapshots from host dougshapiro.substack.com
|
||||
|
||||
23 Apr 2025 18:08:30 UTC
|
||||
|
||||
GenAl is Foremost a Creative Tool
|
||||
Concept Machines, Not Answer Machines
|
||||
|
||||
DOUG SHAPIRO
|
||||
JUL 17, 2024
|
||||
|
||||
17
|
||||
6
|
||||
2
|
||||
Share
|
||||
|
||||
*Image Description: A digital painting depicts a human conductor in a suit, facing away from the viewer, conducting an orchestra composed of robot musicians. The robots are silver and uniform in appearance, playing various instruments such as violins and cellos. Sheet music stands are visible in front of the robots, and the overall scene has a slightly surreal and futuristic feel.*
|
||||
|
||||
Midjourney, prompt: "a human conductor, wearing a suit, conducts an orchestra of robot musicians"
|
||||
|
||||
Turn and face the strange
|
||||
-David Bowie, Changes
|
||||
|
||||
For the average techno-curious Joe, making sense of GenAI is almost impossible. It is highly technical. The pace of innovation-new research, startups, use cases and
|
||||
|
||||
https://archive.ph/aH30b
|
||||
|
||||
1/12
|
||||
|
||||
# GenAl is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
products-is relentless. Using it doesn't clear up much. Sometimes, it feels like magic, and others, it's a waste of time.
|
||||
|
||||
Most confusing, even Al experts can't agree on some of the most fundamental questions, like whether:
|
||||
|
||||
* Al valuations are in a "bubble;"
|
||||
* the ongoing development of large language models (LLMs) puts us on a path to artificial general intelligence (AGI) or LLMs are just an “off ramp,” with fundamental constraints;
|
||||
* the benefits of scale will continue indefinitely or we'll get only “two more turns of the crank;"
|
||||
* it will replace jobs or just tasks;
|
||||
* consumers and enterprises are really using them or just trying them out;
|
||||
* value will flow to the closed-source frontier models (such as those from Google, OpenAI and Anthropic) or open-source models will commoditize the foundational model layer; and
|
||||
* it will or won't kill us all.
|
||||
|
||||
For many professional creatives, it is more than just confusing. It is emotional and personal. Many have a viscerally-negative reaction to anything “AI.” They may consider their art as an extension of themselves and the very idea that a computer can "make art" as offensive; fear that GenAI will threaten creative jobs; and/or believe that training models on artists' work without payment or attribution is theft.
|
||||
|
||||
GenAI raises real legal and ethical questions. But below I explain from a technological perspective why GenAI is foremost a creative tool.
|
||||
|
||||
Tl;dr:
|
||||
|
||||
* Fundamentally, GenAI models are impenetrable-because they are based on sub-symbolic systems that humans can't easily understand or modify-and unpredictable-because their output is probabilistic. Their unpredictability is a feature, not a bug.
|
||||
* The cutting edge of research is focused on ways to improve their reliability, such as through increased scale (of compute and training sets); agentic workflows that spread tasks among many models; and augmenting or conditioning them with known information. But today, they are primarily concept machines, not answer machines.
|
||||
* As a result, they aren't currently well suited to many use cases, especially high-stakes environments that require definitive, precise answers that are costly to verify.
|
||||
* Instead, they are very well suited to the opposite: conceptual, low-stakes, iterative tasks where the quality of output is easily verifiable.
|
||||
* In other words, GenAI tools are great creative assistants. They dramatically speed the creative process by providing faster feedback; they make it possible to try out a wider breadth of ideas, including riskier ones; they help give shape to partially-formed concepts; and they increase the “surface area of luck."
|
||||
|
||||
https://archive.ph/aH30b
|
||||
|
||||
2/12
|
||||
|
||||
# GenAl is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
* Creatives have a long history of rejecting new technologies as unnatural, threatening and unartistic that later become integral.
|
||||
* It isn't possible to stop technology, even if we wanted to. Legislating it, regulating it, shaming it or wishing it away probably won't work. GenAI is just another tool. Progressive creatives would be wise to learn how it might help their process.
|
||||
|
||||
Thanks for reading The Mediator by Doug
|
||||
Shapiro! Subscribe for free to receive new posts
|
||||
and support my work.
|
||||
|
||||
# Computers that Make Information
|
||||
|
||||
According to a recent presentation by Coatue, so far this year, two-thirds of the returns for the S&P 500 and 90% of the returns for the NASDAQ-100 is AI.
|
||||
|
||||
Figure 1. AI Represents 2/3 of the Stock Market Return YTD
|
||||
|
||||
*Image Description: A slide from a Coatue presentation titled "AI is the dominant driver of returns this year." The slide shows two pie charts, one for SPX Performance Attribution Year-To-Date and another for NASDAQ-100. The SPX chart indicates that AI represents 2/3 of the SPX returns, while the NASDAQ-100 chart shows that AI represents 90% of the returns. The slide also mentions NVIDIA and includes a note about the source of the presentation: Coatue presentation at East Meets West Conference, June 18, 2024.*
|
||||
|
||||
Source: Coatue presentation at East Meets West Conference, June 18, 2024.
|
||||
|
||||
Why is AI-and, in particular, GenAI-creating such a frenzy of investors flinging their money in its general direction? At the heart of it, GenAI is so exciting because it enables computers to make new information.
|
||||
|
||||
# Data vs. Information
|
||||
|
||||
Let's start with the distinction between data and information.
|
||||
|
||||
* Data is the raw, unprocessed representation of some phenomenon.
|
||||
* Information is the interpretation of that data in a way that has meaning.
|
||||
|
||||
Think about it in terms of the famous Zen koan: "If a tree falls in the forest and there is no one there to hear it, does it make a sound?" This question is often held up as some mystery of the universe, but it's not. The answer is no. The falling tree generates sound waves, but it only becomes sound if someone or something receives those waves and interprets them as sound.
|
||||
|
||||
The sound waves are data; the sound is information.
|
||||
|
||||
https://archive.ph/aH30b
|
||||
|
||||
3/12
|
||||
|
||||
# GenAl is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
# New Information
|
||||
|
||||
For most of the last 100,000-200,000 years or so, making new information was solely the province of humans, who created it by applying their own context, knowledge, intuition, interpretation, analysis, experience and creativity.
|
||||
|
||||
Computers are great (and far better than we are) at storing, retrieving, processing and, if connected over networks, transmitting (digital) information. As computers became more sophisticated, they started to generate information in limited ways. Data mining enables computers to identify patterns and draw insights from large datasets in a way that humans can't, although it is a matter of debate whether these insights are new information or not. With the advent of artificial intelligence, and in particular machine learning, they gained the ability to extract a broader range of insights from existing information-like image recognition and natural language processing.
|
||||
|
||||
GenAI is a leap forward. It does not just enhance information or classify it, but recognizes patterns, rules and structures within (vast amounts of) structured and unstructured data and then combines it in new ways to generate genuinely novel information: prose, images, videos, songs and code that have never existed before.
|
||||
|
||||
GenAI doesn't just enhance or classify information, it combines it to create new information.
|
||||
|
||||
The scope of that new information is bounded only by a model's training set and the relationships it learns from it. It can be anything that is represented digitally, not just text, images, songs or code, but 3D assets, weather patterns, biological sequences (DNA or proteins), chemicals or multi-modal or anything else.
|
||||
|
||||
Just because GenAI makes new information doesn't make that information useful.
|
||||
|
||||
Just because GenAI makes new information, however, doesn't indicate whether-or in which circumstances this information is useful.
|
||||
|
||||
To create a framework for when it is and when it isn't, we have to understand a little more about how GenAI works, from first principles.
|
||||
|
||||
# Symbolic and Sub-symbolic
|
||||
|
||||
Most of what we talk about today as “AI” is sub-symbolic AI, but from the 1950s-1980s, Al research was dominated by symbolic AI. The simplistic difference between the two is that a human would understand the rules encoded in a symbolic Al system, but not in a sub-symbolic system.
|
||||
|
||||
The idea behind symbolic Al is that human cognition can be replicated by hard coding logical rules. For example, the first Al programs that played chess were symbolic systems that used explicit human-programmed algorithms (and a lot of brute force computation) to search for the best moves.
|
||||
|
||||
https://archive.ph/aH30b
|
||||
|
||||
4/12
|
||||
|
||||
# GenAl is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
Sub-symbolic Al emerged as an alternative approach in the 1980s. Sub-symbolic systems are especially good for tasks that people perform easily but can't explain well. Instead of using explicit symbols and rules, sub-symbolic Al relies on abstract mathematical representations of patterns that the system learns itself, through machine learning (ML). The best example is neural networks, which learn patterns within large datasets using a structure inspired by the brain. But, just like seeing all the neurons firing in someone's brain wouldn't give you any clue what she was thinking, seeing all the dimension values and attention weights in a neural network won't help you understand what it is doing.
|
||||
|
||||
Just like seeing all the neurons firing in someone's brain wouldn't give you any clue what she was thinking, seeing all the dimension values and attention weights in a neural network won't help you understand what it is doing.
|
||||
|
||||
The shift in prominence from symbolic to sub-symbolic AI began in the late 1980s, accelerated by the increasing availability of large datasets, advancements in computing power, and breakthroughs in ML algorithms. 1 Pretty much everything in the headlines today-ChatGPT, Sora, Claude, Mistral, Stable Diffusion, Perplexity, Suno, Runway, you name it-is sub-symbolic.
|
||||
|
||||
For our purposes, the key here is that, even to leading researchers, how these models work or why they do what they do is not entirely clear. LLMs, for instance, have some properties that have surprised researchers, like the potential for analogical reasoning.
|
||||
|
||||
Part of the reason that there is so much debate about the future of Al is that it is so hard to understand how these sub-symbolic systems work.
|
||||
|
||||
# Unpredictability is the Whole Point
|
||||
|
||||
With a grounding in why these systems are inherently opaque, let's walk through a very high level description of how GenAI works. (For more detail, see the Appendix of my last post.)
|
||||
|
||||
GenAI models (whether autoregressive models, general adversarial networks (GAN), diffusion models, etc.):
|
||||
|
||||
* Are powered by neural networks that are fed vast (vast, vast) amounts of information through a labor and capital-intensive training process;
|
||||
* They represent that information mathematically;
|
||||
* They learn the patterns, rules and structures within it (sometimes informed by human feedback, sometimes not);
|
||||
* When fed a prompt, they analyze the prompt to understand it;
|
||||
* And finally, based on their understanding of the prompt and the patterns they have divined from their training, they generate an output probabilistically.
|
||||
|
||||
Perhaps the best way to conceptualize why GenAI is different is to compare GenAI with traditional software. A simple abstraction of most software is shown in Figure 2. The basic stack comprises a database, rules or logic, and an interface.
|
||||
|
||||
https://archive.ph/aH30b
|
||||
|
||||
5/12
|
||||
|
||||
|
||||
# GenAl is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
_Image: A diagram titled "Figure 2. A Simple Software Stack" shows a stack of three boxes. The top box is labeled "Interface," the middle box is labeled "Logic," and the bottom box is labeled "Database."_
|
||||
|
||||
Traditional Software
|
||||
|
||||
Let's say you go to www.twitter.com to post a tweet. Through your browser, you will interact with client-side code (JavaScript, HTML and CSS) written by (human) front-end engineers, which will interact with server-side code (Python, Java, Ruby, etc.) written by (human) backend engineers, and during the process of you logging in and posting the tweet, it will periodically access and modify several types of databases (relational, search indexes, time series, in-memory, etc.), many of which are human-readable and interpretable.
|
||||
|
||||
A LLM
|
||||
|
||||
Now, let's compare this with a LLM request. You go to www.claude.ai to ask Claude a question. While the front-end interaction is similar, the back-end processing is fundamentally different. The "logic" for both understanding the prompt and generating output has been derived from the model's training data, not programmed by humans. Given the complexity of the model, it is, as mentioned before, very hard or impossible for humans to understand it. The "database" is the model itself, consisting of billions or trillions of parameters (vector dimensions, attention weights) that are also very difficult for humans to interpret or modify directly. The output is not a simple lookup from a database or calculation, but a probabilistic generation based on the model's learned patterns. The model may use stochastic sampling techniques or introduce random noise to ensure there is variability in output, even from identical prompts.
|
||||
|
||||
_Image: A diagram titled "Figure 3. Comparing Traditional Software with a LLM" shows a table comparing the two. The table has three rows: Interface, Logic, and Database. The columns are Traditional Software and GenAI (LLM). The Traditional Software column lists Desktop, Browser, App, API for Interface; Deterministic, Human-Programmed for Logic; and Human-Readable and Modifiable, Standard Formats (SQL, JSON, CSV) for Database. The GenAI (LLM) column lists Browser, App, API for Interface; Probabilistic, Stochastic, Machine-Learned and Human Uninterpretable for Logic; and Difficult to Interpret/Modify, Billions or Trillions of Parameters (Vector Dimensions, Attention Weights) for Database._
|
||||
|
||||
Source: Author.
|
||||
|
||||
[https://archive.ph/aH30b](https://archive.ph/aH30b)
|
||||
|
||||
6/12
|
||||
|
||||
# GenAl is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
These distinctions are shown in Figure 3. To summarize:
|
||||
|
||||
* GenAI models are trained, not programmed
|
||||
* Their underlying logic and databases are neither easily understood nor modifiable by humans
|
||||
* Their output is probabilistic, not deterministic
|
||||
|
||||
The most important point here is the last one. GenAI models are probabilistic by design. The unpredictability of the output is the whole point!
|
||||
|
||||
Unpredictability is a feature, not a bug.
|
||||
|
||||
Concept Machines, Not Answer Machines
|
||||
|
||||
Relative to traditional software, GenAI models therefore have certain weaknesses and strengths. Weaknesses include:
|
||||
|
||||
* Hallucinations. GenAI models sometimes generate output that is nonsensical or just factually wrong. That's because they rely on patterns, not a true understanding of the information, and simply produce the probabilistically best output. (They are “stochastic parrots,” as coined in a now-famous paper.)
|
||||
* Limited by the training set. They are only as good as the underlying training set. In the case of text, LLMs have been trained on a very large proportion of all scrapable text on the internet (ChatGPT 40 is reportedly trained on 10 trillion words). Other modalities have far more limited sets available, such as video.
|
||||
|
||||
_Image: A text box that reads "GenAI models are trained on human abstractions of the real world, not direct experience of the real world itself."_
|
||||
|
||||
* Limited understanding of the physical world. Traditional software can be programmed with knowledge of physics and real world simulations. As mentioned, however, GenAI models are trained, not programmed. They are trained on human abstractions of the real world—text, images, audio, video, etc.-not the real world itself. It is currently a matter of debate whether any GenAI model can learn a comprehensive, general purpose “world engine” without a physical embodiment.
|
||||
|
||||
_Image: A text box that reads "GenAI models are trained on abstractions of the real world, not the real world itself."_
|
||||
|
||||
* No emotion and taste. They can mimic emotion, but they obviously don't have emotions themselves.
|
||||
|
||||
[https://archive.ph/aH30b](https://archive.ph/aH30b)
|
||||
|
||||
7/12
|
||||
|
||||
# GenAl is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
* Lack of transparency. As also mentioned, given their complexity, it is very hard or impossible for humans to audit or understand how these models generate their output.
|
||||
* Lack of precise control. If it is hard to understand the generation process, it follows that it is tough to precisely control the output.
|
||||
|
||||
Strengths include:
|
||||
|
||||
* Conceptual understanding. They are great at understanding high level concepts and nuanced connections.
|
||||
* Novel connections and combinations. They can extract unexpected combinations from their training sets and, as a result, produce unexpected content and ideas.
|
||||
* Natural language. They can understand (or intuit) subtle nuances in human language.
|
||||
* Flexibility. They can handle a very wide range of tasks without needing to be explicitly programmed for each use case.
|
||||
|
||||
There are many research efforts underway to improve the accuracy and reliability of these models, like increasing the scale of training data and compute; agentic workflows that break up tasks among multiple models; and conditioning or augmenting them with external, current knowledge (such as Retrieval Augmented Generation or RAG).
|
||||
|
||||
But it is important to understand that they are fundamentally designed to be concept machines, not answer machines.
|
||||
|
||||
What Are They Good For?
|
||||
|
||||
It follows from the above that, at least right now, GenAI is well suited to some use cases and not others.
|
||||
|
||||
Here are the use cases for which they're (currently) not useful:
|
||||
|
||||
* Those that require a definitive, precise answer.
|
||||
* Those that require real-time access to information.
|
||||
* Those that require an understanding of the physical world, including all its many edge cases.
|
||||
* Those that require empathy and a sophisticated understanding of human nature.
|
||||
* High-stakes environments in which the output is hard or time-consuming for humans to verify.
|
||||
|
||||
Here are the use cases for which they are useful:
|
||||
|
||||
* Natural language interactions.
|
||||
* Those that benefit from a degree of randomness.
|
||||
* Those for which many iterations, with human feedback at each step, are preferable to one right answer.
|
||||
* Those that benefit from conceptual understanding.
|
||||
|
||||
[https://archive.ph/aH30b](https://archive.ph/aH30b)
|
||||
|
||||
8/12
|
||||
|
||||
# GenAl is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
GenAI is great for conceptual, low-stakes, iterative tasks where the quality of the output is easy and cheap to verify.
|
||||
|
||||
There are applications in any field:
|
||||
|
||||
If you run a consumer-facing business, they are great “level 1” customer service agents.
|
||||
|
||||
If you're a lawyer, they're great for summarizing documents, combing through data, finding relevant cases or flagging problems in a contract, but you wouldn't want them to write your legal brief and you'd certainly want to double check all their citations.
|
||||
|
||||
If you're a financial analyst, they're great for interrogating quarterly earnings transcripts and financial filings, but you wouldn't want them to build your model without rigorous verification of the inputs.
|
||||
|
||||
If you're a medical professional, you might use it to summarize journal articles, but you sure want to check its diagnosis.
|
||||
|
||||
If you're a software engineer, they're helpful for generating code—and it's easy to verify-but they might not produce the most elegant version, be much help debugging or handle very complex structures or logic.
|
||||
|
||||
Ideally Suited to the Creative Process
|
||||
|
||||
I understand why the notion of GenAI making, or even contributing, to art is such a controversial idea and sometimes generates such a viscerally negative reaction. Many artists believe that the concept demeans and belittles what they do and, in some cases, their very identity. There is also legitimate concern about the way many Al models have been trained and whether they are “stealing” artists' work without payment or even attribution.
|
||||
|
||||
I firmly believe that, to quote Rick Rubin, "...the attraction of art is the humanity held in it." To me, the difference between "art" and "content" is that only a human can make art.
|
||||
|
||||
Nevertheless, as described above, GenAI is great at conceptual, low-stakes, iterative tasks where the quality of the output is easy and quick to verify.
|
||||
|
||||
In other words, they are fantastic creative assistants. They enable artists to create many, many more iterations than they otherwise could, much faster. This speeds the creative process by providing faster feedback; they make it possible to try out a wider breadth of ideas, including riskier ones; they help give shape to partially-formed ideas; and they increase the “surface area of luck” and the likelihood of serendipity.
|
||||
|
||||
GenAI is perfectly suited to be a creative assistant.
|
||||
|
||||
Runway founder Cristobal Valenzuela recently posted a tweet that captures this idea:
|
||||
|
||||
9/12
|
||||
|
||||
# GenAl is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
_Image: A screenshot of a tweet from Cristóbal Valenzuela (@c_valenzuelab). The tweet reads: "I've been watching too many people immerse themselves for hours using Gen-3, and there's this pattern that keeps popping up. It's like this: You start with some vague idea in your head. But as you play around, you end up in totally different places. It's weird - the twists and turns become more interesting than what you first thought of. It's not like you have a clear destination. You're just... going. And as you bump into new stuff - things the model mashes together in ways you didn't expect - you change course. You explore. It's like the model is saying, "Hey, what about this?" and you're like, "Huh, never thought of that." There's a buzz to it. A thrill in not knowing what's coming next. You're not trying to make some big, fancy project. You're just poking at your brain, seeing what comes out. It's like stretching a muscle you didn't know you had. It's a new form of creative dialogue. The rapid-fire generation speed allows for a true back-and-forth, a conversation in visual language. You prompt, the model responds, sparking new ideas in your mind, leading to new prompts, and on it goes in a virtuous cycle. It's a form of "generative daydreaming." The boundaries between your initial concept and the model's output blur into one stream of continual discovery. You're not crafting a singular, static piece of media, but rather exploring possibilities. And it's joyful and fun. This process taps into a part of our brains that craves novelty and surprise. It's not about the pressure to produce a film or a masterpiece. It's about flexing our creative muscles simply for the joy of the exercise. Like going to a gym for the mind, each session with the model leaves you invigorated, your imagination stretched in ways you didn't expect. When the tools are swift enough, you enter a flow state, a creative dialogue. A form of play and discovery that's as rewarding as any final form. It's not about reaching a predetermined endpoint, it's more about reveling in the serendipitous exploration." The tweet was posted on July 3, 2024, and has 37.9K views._
|
||||
|
||||
Face the Strange
|
||||
|
||||
Here's another tweet, which went viral:
|
||||
|
||||
_Image: A screenshot of a tweet from Joanna Maciejewska-Snakebitten (@AuthorJMac). The tweet reads: "You know what the biggest problem with pushing all-things-Al is? Wrong direction. I want Al to do my laundry and dishes so that I can do art and writing, not for Al to do my art and writing so that I can do my laundry and dishes." The tweet was posted on March 29, 2024, and has 3M views._
|
||||
|
||||
[https://archive.ph/aH30b](https://archive.ph/aH30b)
|
||||
|
||||
Fortunately or not, GenAI is expressly good at helping with art and writing and, at least today, expressly bad at doing laundry and dishes.
|
||||
|
||||
There is a long history of creatives rejecting new technologies that later became integral: photography was thought to herald the end of painting, but instead birthed new forms of painting (impressionism, surrealism, etc.) and became an art form in its own right; digital photography was initially rejected as requiring less skill; musicians
|
||||
|
||||
10/12
|
||||
|
||||
|
||||
# GenAI is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
hated synthesizers and, later, autotune; sampling was considered stealing and is now a fundamental technique in hip-hop and rap; animators rejected CGI; physical effects artists, stop motion animators and matte painters resisted the shift to VFX, etc.
|
||||
|
||||
But it isn't possible to stop technology, even if we wanted to. Legislating it, regulating it, shaming it or wishing it away probably won't work. GenAI is just another tool. Progressive creatives would be wise to learn how it might help their process.
|
||||
|
||||
1 A big turning point came from game playing. IBM's Deep Blue, which famously beat chess grandmaster Garry Kasparov in 1997, was a symbolic system. But DeepMind's AlphaGo, which in 2015 because the first Al to beat a human champion, was a hybrid symbolic/sub-symbolic system. The success of AlphaGo Zero, which in 2017 beat AlphaGo after only three days of self-training, marked an even further shift toward sub-symbolic AI.
|
||||
|
||||
# Subscribe to The Mediator
|
||||
By Doug Shapiro
|
||||
|
||||
The Mediator is (mostly) about the long term structural changes in the media industry and the business, cultural, and societal implications of those shifts. I write it to get closer to the frontier.
|
||||
|
||||
By subscribing, I agree to Substack's [Terms of Use](https://substack.com/terms), and acknowledge its [Information Collection Notice](https://substack.com/privacy) and [Privacy Policy](https://substack.com/privacy).
|
||||
|
||||
* 17 Likes 2 Restacks
|
||||
|
||||
* 17
|
||||
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|
||||
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|
||||
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* [Previous](#)
|
||||
* [Next](#)
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|
||||
# Discussion about this post
|
||||
|
||||
* Comments
|
||||
* Restacks
|
||||
|
||||
Write a comment...
|
||||
|
||||
Andrea Girolami Jul 17
|
||||
|
||||
❤Liked by Doug Shapiro
|
||||
|
||||
I will read the post as usual but first: we had the same idea for the a prompt! [https://open.substack.com/pub/scrollinginfinito/p/lintelligenza-artificiale-ha-bisogno?r=vt52&utm\_medium=ios](https://open.substack.com/pub/scrollinginfinito/p/lintelligenza-artificiale-ha-bisogno?r=vt52&utm_medium=ios)
|
||||
|
||||
* LIKE (1)
|
||||
* REPLY
|
||||
* SHARE
|
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|
||||
1 reply by Doug Shapiro
|
||||
|
||||
11/12
|
||||
|
|
@ -1,853 +0,0 @@
|
|||
---
|
||||
source_type: "article"
|
||||
title: "How Far Will AI Video Go?"
|
||||
author: "Doug Shapiro"
|
||||
url: "https://dougshapiro.substack.com/p/how-far-will-ai-video-go"
|
||||
date_published: "2025-02-01"
|
||||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: processed
|
||||
claims_extracted:
|
||||
- "GenAI adoption in entertainment will be gated by consumer acceptance not technology capability"
|
||||
- "GenAI is simultaneously sustaining and disruptive depending on whether users pursue progressive syntheticization or progressive control"
|
||||
---
|
||||
# How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
archive.today Saved from https://dougshapiro.substack.com/p/how-far-will-ai-video-go
|
||||
search
|
||||
no other snapshots from this url
|
||||
23 Apr 2025 17:51:06 UTC
|
||||
webpage capture
|
||||
All snapshots from host dougshapiro.substack.com
|
||||
Webpage
|
||||
Screenshot
|
||||
https://archive.ph/spTgJ
|
||||
|
||||
## How Far Will Al Video Go?
|
||||
Mapping Out the Scenarios
|
||||
|
||||
DOUG SHAPIRO
|
||||
FEB 14, 2025
|
||||
|
||||
47
|
||||
7
|
||||
9
|
||||
share
|
||||
|
||||
_Image: A person stands at a crossroads, symbolizing decision-making and future paths. The person is facing away from the viewer, contemplating the different directions._
|
||||
|
||||
Source: Midjourney.
|
||||
|
||||
I often write that the last 10-15 years in video 1 have been defined by the disruption of
|
||||
content distribution and the next 10 years are poised to be defined by the disruption of
|
||||
content creation.
|
||||
|
||||
Here's the argument: The internet unbundled information from infrastructure and,
|
||||
with the help of a host of related technologies and massive infrastructure investment,
|
||||
caused the cost to move bits around to functionally head toward zero. We know what
|
||||
|
||||
## 1/21
|
||||
|
||||
happened next. 2 Now, there is another emerging general purpose technology, GenAI,
|
||||
that may send the cost to make bits to head toward zero, too.
|
||||
|
||||
This symmetry of falling costs to move bits and make bits sounds good. It's pithy and
|
||||
memorable. It seems plausible. But still: it is admittedly very high level and hand wavy.
|
||||
|
||||
What will GenAI really mean in practice for the video business? Will the cost to make
|
||||
TV and movies truly “fall to zero?” Will two kids in a dorm room one day make the
|
||||
“next Avatar?” Or, is GenAI another flavor of Silicon Valley's naïve technological
|
||||
determinism, a blind belief that technology always marches forward and anything
|
||||
that's technically possible is inevitable, without regard to pesky inconveniences like
|
||||
law, regulations, ethics and consumer demand? And what does disruption mean,
|
||||
anyway? Are we talking about complete devastation, the Kodak-disrupted-by-digital-
|
||||
cameras kind of disruption, or the far more benign Marriot-disrupted-by-Airbnb kind
|
||||
of disruption?
|
||||
|
||||
Figure 1. Two "Victims” of Disruption
|
||||
|
||||
_Image: A graph showing the stock performance of Kodak (EK) over time, illustrating a significant decline. The graph spans from 1998 to 2011, showing a steep drop in Kodak's stock value._
|
||||
|
||||
_Image: A graph showing the stock performance of Marriott (MAR) over time, illustrating a significant increase. The graph spans from 2000 to 2020, showing a steady rise in Marriott's stock value._
|
||||
|
||||
The only credible answer to these questions is: no one knows. That doesn't mean we're
|
||||
completely flying blind though. We can frame out a range of possible outcomes by
|
||||
using scenarios.
|
||||
|
||||
Tl;dr:
|
||||
|
||||
* Scenario planning is a useful tool for navigating uncertainty. It can help identify
|
||||
the range of possible outcomes, the key milestones to watch, and the potential
|
||||
implications.
|
||||
* A key step is identifying the two critical variables that will determine possible
|
||||
future states and the extreme potential outcomes for each. Below, I use technology
|
||||
development and consumer acceptance to construct a scenario matrix and analyze
|
||||
the possible state and implications of AI video in 2030.
|
||||
* The possible outcomes for technology development range, at one extreme, from
|
||||
Al video models stalling out at their current capabilities to, at the other,
|
||||
completely resolving their current limitations in realism (especially the "uncanny
|
||||
valley"), audio-visual sync (especially lips), understanding real-world physics, and
|
||||
fine-grained creative control.
|
||||
* The possible outcomes for consumer acceptance range from skepticism and
|
||||
sometimes outright hostility to fully embracing AI (and actually preferring it for
|
||||
some use cases). Steps along the way include consumers accepting it for certain
|
||||
content genres and use cases, especially those that don't rely on emotive humans.
|
||||
|
||||
## 2/21
|
||||
|
||||
* Varying each of these variables between their extremes produces a 2 x 2 with four
|
||||
scenarios: low tech development, low consumer acceptance ("Novelty and Niche");
|
||||
high tech development, low consumer acceptance (“The Wary Consumer"); low
|
||||
tech development, high consumer acceptance ("Stuck in the Valley"); and high
|
||||
tech development, high consumer acceptance ("Hollywood Horror Show”).
|
||||
* Writing out narratives for each scenario is the most instructive part, because it
|
||||
helps make the abstract more concrete.
|
||||
* Reality will probably fall somewhere in between, but this shows why it won't
|
||||
require the most radical scenarios for the video business to change radically.
|
||||
|
||||
Thanks for reading The Mediator! Subscribe for
|
||||
free to receive new posts and support my work.
|
||||
|
||||
### How Scenarios Work
|
||||
|
||||
One of the most useful tools for operating in an uncertain environment is a scenario
|
||||
planning matrix. This entails identifying the two most important variables,
|
||||
determining the polar extreme outcomes for these variables over a given time period,
|
||||
and constructing a 2 x 2 matrix that produces four potential future state scenarios. The
|
||||
most instructive part is writing a narrative describing each of these scenarios. Think
|
||||
of these narratives like news articles from alternate futures, explaining how we got to
|
||||
that (possible) future state.
|
||||
|
||||
The scenarios are extreme, so reality will probably fall somewhere between them. But
|
||||
the exercise helps define the bounds of what will probably unfold; the signposts that
|
||||
would indicate we are heading in one direction or another; and the potential
|
||||
implications of different outcomes. It also helps make abstract problems feel a bit
|
||||
more concrete, especially when the scenarios are specific.
|
||||
|
||||
### A Brief Digression: What I Mean by “GenAl Video"
|
||||
|
||||
Before getting into the scenarios, it would probably be a good idea to explain what I
|
||||
mean by “GenAI video” (or “AI video,” which I use interchangeably). I am referring to
|
||||
Al video tools that augment and streamline human creativity, NOT fully-
|
||||
autonomous AI-generated video.
|
||||
|
||||
Sometimes, “AI video” is considered synonymous with “zero-shot AI video," namely
|
||||
that you put in a prompt and a fully-realized movie comes out. Other times, it even
|
||||
means "fully autonomous storytelling,” where an Al writes, directs and produces film
|
||||
completely independently. I think both are unlikely to produce anything watchable
|
||||
anytime soon, if ever. But more to the point, this capability depends more on the
|
||||
evolution of LLMs and multimodal AI than on Al video models.
|
||||
|
||||
By "AI video,” I mean tools that augment, enhance and streamline human creativity, not
|
||||
|
||||
## 3/21
|
||||
|
||||
replace it.
|
||||
|
||||
Throughout this analysis, I assume that GenAI video will require significant human
|
||||
oversight and judgment for the foreseeable future. So, I am referring to tools, like AI
|
||||
video models (and AI audio models, workflow tools, etc.), that empower people to
|
||||
make high-quality video faster and cheaper. This might involve delegating some
|
||||
creative decisions to AI, but by no means all or even most of them.
|
||||
|
||||
With that out of the way, let's get to the scenarios.
|
||||
|
||||
### Identifying the Two Key Variables
|
||||
|
||||
There are a lot of unknowns about how GenAI video will evolve. Here's a partial list:
|
||||
|
||||
* How will regulators, the courts or the market resolve issues around copyright
|
||||
infringement and IP rights? Will regulators or consumers require Al content
|
||||
labeling?
|
||||
* Will there emerge even more performant architectures, beyond transformers and
|
||||
diffusion models?
|
||||
* Is there room for so many competing proprietary GenAI models (Sora, Veo, Kling,
|
||||
Minimax, Runway, Pika, Krea, Luma, etc.)? Will they carve out niches, in which
|
||||
some are better for certain applications? How big is the TAM? Will they solely
|
||||
appeal to enterprise and prosumer or are they mass consumer products? What is
|
||||
the competitive advantage in these models? Data? Compute? Architecture? Will
|
||||
proprietary or open-source models prevail?
|
||||
* What is the true cost of operating these models? Will they need to be run in
|
||||
expensive data centers or will local devices suffice?
|
||||
* How much will GenAI really reduce costs for traditional video production
|
||||
workflows? Will it replace jobs? Which ones?
|
||||
* Will consumers accept GenAI and for which use cases? For which content genres?
|
||||
* Will GenAI ever cross the “uncanny valley” and produce synthetic people that are
|
||||
indistinguishable from live footage?
|
||||
* Will Hollywood studios adopt it? Creatives? Creators? Will an AI-enabled film
|
||||
ever win critical praise or even an industry award?
|
||||
* How will fine-grained control evolve? Will models eventually replicate (or surpass)
|
||||
anything that can be done with a camera and professional lighting? Or will using
|
||||
AI always necessitate a tradeoff with creative control?
|
||||
* Will "world models" enable GenAI to simulate complex real-world physics?
|
||||
|
||||
And you could tack on another question at the end of each of these:
|
||||
|
||||
* If so, when?
|
||||
|
||||
That's a lot of things we don't know. For our exercise, we need to distill them into two
|
||||
critical variables and determine the range of potential outcomes for each. (In our case,
|
||||
our time frame is in 2030, out five years.)
|
||||
|
||||
## 4/21
|
||||
|
||||
Looking at this list, we can group most of these unknowns into four categories:
|
||||
technology development, consumer acceptance, legal/regulatory and
|
||||
economics/business models. The latter two are clearly important. Hollywood won't
|
||||
adopt GenAl without legal clarity. Economics will determine the size and distribution
|
||||
of profit pools.
|
||||
|
||||
But since we can only choose two, let's go with what I think are the biggest unknowns:
|
||||
technology development and consumer adoption.
|
||||
|
||||
### Technology Development
|
||||
|
||||
Al video models have improved tremendously in the last two years. Below is the iconic
|
||||
and disturbing Will Smith-eating-spaghetti video, made with Stable Diffusion in April
|
||||
2023. Compare it to the Veo2 compilation demo from Google or a recent video made
|
||||
using Sora by Chad Nelson from OpenAI.
|
||||
|
||||
Al Will Smith eating spaghetti pasta (Al footage and audio)
|
||||
Copy link
|
||||
|
||||
_Image: A screenshot of a YouTube video titled "Al Will Smith eating spaghetti pasta (Al footage and audio)". The video shows a digitally created or altered image of Will Smith eating spaghetti._
|
||||
|
||||
[Watch on ►►YouTube](https://www.youtube.com/)
|
||||
|
||||
Veo 2 compilation
|
||||
Copy link
|
||||
|
||||
_Image: A screenshot of a YouTube video titled "Veo 2 compilation". The video shows a compilation of scenes generated by Google's Veo 2 AI model._
|
||||
|
||||
[Watch on](https://www.youtube.com/)
|
||||
|
||||
## 5/21
|
||||
|
||||
|
||||
# How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
We couldn't verify the security of your connection.
|
||||
Access to this content has been restricted. Contact your internet service provider for help.
|
||||
|
||||
This pace of improvement in less than two years is startling. But they aren't perfect yet.
|
||||
|
||||
Al video models don't pass the “video Turing Test," at least not yet.
|
||||
|
||||
In 1950, Alan Turing introduced the so-called Turing Test (originally called "the imitation game”), meant to test whether a machine could fool a human into believing it is communicating with another human. Turing didn't conceive of different tests for different modalities, but let's propose a "video Turing test,” to test whether a human would believe Al video was generated or live action. Al video models don't currently pass the video Turing Test.
|
||||
|
||||
There are a few areas they can still improve:
|
||||
|
||||
* Realism (especially the “uncanny valley"). If you look again at the Veo2 demo, it's hard to tell that both of the women (the DJ and the doctor) aren't real. We're getting very close to passing the so-called “uncanny valley,” but it's a high bar. Humans are highly sensitized to the most subtle changes in human faces even before we can speak (think of an infant staring at her mother's face). Note that the Veo and Sora demos feature relatively quick cuts, so the people don't convey much change in emotion.
|
||||
* Audio-visual sync. Also notice that no one is talking in either demo. Runway now offers Lip Sync and the open-source tool Live Portrait makes it possible to sync facial movements between a reference video and a generated video, including lip sync. However, in both cases it is clearly noticeable. It isn't there yet.
|
||||
* Resolution and clip length. These are almost solved. Veo2 is in closed beta, but it claims to enable up to 4K resolution and clips as long as 1 minute. There has also been rapid development in upscaling technologies that can increase resolution (such as from Topaz and Nvidia). 4K is suitable for all but the largest format screens, like Imax, or very VFX-heavy films. And most shots in TV shows and films are just a few seconds, other than an occasional long take, so 1 minute is more than enough.
|
||||
* Physics/temporal coherence. Despite the impressive realism in the demos above, these models still struggle with complex dynamics, especially involving multiple objects or actors. They have been trained on video, which is an abstraction of the real world, so they do not yet understand the real world. Despite occasional breathless claims to the contrary, they don't contain sophisticated “world models" or physics engines. (There are early efforts underway to fix that, such as Runway's research on general world models or World Labs, co-founded by Fei Fei Li.) My "model buster” prompt is “A man in a smoky pool hall, breaking a rack of balls." No model has figured it out yet.
|
||||
* Fine-grained control. Initially, GenAI video models were like slot machines-you put in a prompt and held your breath. Over time, they have been progressively adding finer-grained control (something I discussed in detail in Is GenAI a Sustaining or Disruptive Innovation in Hollywood?). Last week, Hailuo, creator of Minimax, introduced the T2V-01-Director Model, which enables more sophisticated camera controls, as shown in the embedded video below. At around the 0:30 mark, see how the shot faithfully follows the complex set of instructions "first, truck left, tracking shot, then pull out, and end on a vehicle POV.” Models are learning better controls through a combination of pre-labeling video clips (e.g., including metadata about the camera motion, like “shake camera slightly”, “tilt up," "truck left," in the training data) and “manipulation in the latent space." The latter means that the model learns which parameters correspond to different visual outcomes, so that it is possible to influence the generation process during inference. In theory, with enough training data and metadata, it will be possible to offer ever-finer grained control.
|
||||
|
||||
[Hailuo Al | T2V-01-Director Model: Control Your Camera Like a Pro!](https://www.youtube.com/watch?v=09r65-f9184)
|
||||
|
||||
Recall that our goal is to identify the continuum of possibilities for how GenAI technology will develop by 2030. At one extreme is the current state, which assumes that the technology won't improve from here. The other extreme is the idealized future state for each of the features described above, meaning that each of these limitations is eventually solved. This continuum is shown in Figure 2.
|
||||
|
||||
Figure 2. The Continuum of Potential Technology Development
|
||||
|
||||
## 8/21
|
||||
|
||||
Current State
|
||||
Idealized Future State
|
||||
|
||||
Realism/Temporal Consistency
|
||||
Imperfect but improving dramatically. Still some shifting details from frame-to-frame. Especially challenging with humans. Struggles with human emotion, even with face mapping tools like Live Portrait.
|
||||
Object and character consistency. Surpasses the "uncanny valley," indistinguishable from live action.
|
||||
|
||||
Audio-visual sync
|
||||
Rudimentary and noticeable, especially lip sync.
|
||||
Seamless.
|
||||
|
||||
Resolution
|
||||
State-of-the-art is 4K.
|
||||
4K or 8K.
|
||||
|
||||
Physics/Temporal Coherence
|
||||
Some motion still janky. Unable to handle complex dynamics, especially interaction between multiple objects or actors. Occasional challenges with temporal coherence among objects, lighting, etc.
|
||||
True "world models" with an understanding of physics.
|
||||
|
||||
Fine-grained control
|
||||
Directorial controls improving, but still requires tradeoffs with consumer adoption
|
||||
Replicates anything that can be done with a camera and lighting equipment.
|
||||
|
||||
Technology Development
|
||||
|
||||
There has been some backlash to the use of AI, especially when not disclosed beforehand, such as Disney's use of AI to generate the opening credits of Secret Invasion; the use of AI for a few still images in Late Night with the Devil; or, most recently, the use of AI for voice enhancement in The Brutalist and Emilia Perez. However, it isn't that simple. The issue here seems to be whether or not filmmakers were upfront about it; no one seemed to care when AI was used for de-aging in The Irishman, Indiana Jones and the Dial of Destiny or Here. Also, it isn't clear that the public cares as much as the industry.
|
||||
|
||||
A recent survey from HarrisX and Variety VIP+ found that consumers' willingness to engage with AI-enabled content varies (Figure 3). As shown, when asked about their interest in watching a movie or TV show written using GenAI, 10% said they didn't have an opinion, and, of the remaining 90%, 54% were indifferent or more interested in GenAI content. Plus, receptivity seems correlated with familiarity. Variety noted that those who “report regularly using gen AI tools are also more likely to feel positively toward the use of AI-generated material in varied types of media content, according to recent FTI Delta survey data shared with VIP+.”
|
||||
|
||||
Figure 3. Consumer Receptivity to AI-Generated Content Varies
|
||||
|
||||
The image is a table showing consumer receptivity to AI-generated content. The table has four columns: "More interested", "Less interested", "No difference", and "Don't know". The rows represent different types of content, such as playing a video game, watching a movie/TV show, engaging with images or videos on social media, reading the news, listening to music, and listening to a podcast or audiobook. The percentages in each cell indicate the proportion of respondents who expressed that level of interest in the respective content type.
|
||||
|
||||
## 9/21
|
||||
|
||||
How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
Source: HarrisX, Variety VIP+, May 2024, N=1,001 U.S. Adults
|
||||
|
||||
For our purposes, it is possible to imagine a continuum of consumer acceptance that looks like Figure 4.
|
||||
|
||||
This continuum progresses from the current high-degree of skepticism and sometimes hostility; to acceptance in low-stakes, low-expectation content, like social video, memes, etc.; to progressively accepting AI in different genres, depending on that genre's reliance on emotive human faces, starting with ads and animation, then music videos, educational, historic re-enactment/true crime/docudrama, then maybe sci-fi and horror (especially in which humans are heavily doctored), and, the final frontier would be comedies and dramas that require subtle timing, nuanced performances and a wide emotional range; and the most extreme outcome would be that consumers come to prefer Al-generated content for certain use cases, especially those that GenAI is uniquely suited to do, like personalized, interactive and emergent stories.
|
||||
|
||||
Figure 4. The Continuum of Potential Consumer Acceptance
|
||||
|
||||
The image is a diagram illustrating the continuum of potential consumer acceptance of AI-generated content. The diagram is structured as an arrow moving from left to right, representing increasing acceptance. The stages along the continuum are: Skepticism, Acceptance, and Preference. Each stage is associated with specific content genres. Skepticism is linked to a general skepticism towards AI-generated content. Acceptance is associated with low-expectation content like social media and memes, as well as ads, animation, and music videos. The final stage, Preference, is linked to consumers preferring AI-generated content for specific use cases like interactive, personalized, or emergent stories.
|
||||
|
||||
The Scenarios
|
||||
|
||||
Having defined our ranges for the two key variables, the next step is to construct the potential future states in 2030. For now, let's not judge the likelihood of each. We'll get to that in a moment.
|
||||
|
||||
Figure 5. The Four Scenarios
|
||||
|
||||
## 10/21
|
||||
|
||||
The image is a 2x2 matrix representing four potential scenarios for the future of AI video, based on two axes: "Acceptance" and "Technology Development". The four scenarios are: "Stuck in the Valley" (high acceptance, low technology development), "Hollywood Horror Show" (high acceptance, high technology development), "Novelty and Niche" (low acceptance, low technology development), and "The Wary Consumer" (low acceptance, high technology development).
|
||||
|
||||
Below, I write out a narrative for each.
|
||||
|
||||
"Novelty and Niche” (low tech development, low consumer acceptance)
|
||||
|
||||
This is more or less the status quo. The technology doesn't evolve a lot from here and consumers view AI video as a novelty good for a limited range of use cases, like memes, social video, simple animation and maybe music videos.
|
||||
|
||||
The tech stalls out and consumers aren't interested anyway.
|
||||
|
||||
In Hollywood, by 2030 AI still isn't used much in final frame, other than for some environments, establishing shots and digital re-shoots. It is mostly used in pre- production-for previsualization, script writing assistance, script coverage, and concept art-and in post production-like localization services in smaller markets, some VFX automation, first pass edit, de-aging and voice synthesis. Studios have used these technologies to marginally reduce production costs, say 15-25%.
|
||||
|
||||
Al is regarded largely as a novelty and a sustaining innovation, but hasn't changed the business much. Current trends (cord cutting, growth in streaming, shift of time and attention to creator content, etc.) have continued at a steady, linear pace.
|
||||
|
||||
"The Wary Consumer" (high tech development, low consumer acceptance)
|
||||
|
||||
Here, AI can produce visuals that are nearly indistinguishable from live action and has leapt over the uncanny valley. Blockbuster-quality films could theoretically be made entirely synthetically, using synthetic actors and sets. But consumers aren't having it.
|
||||
|
||||
Unions and regulators have pushed for strict controls and disclosure of any Al usage. Consumers view AI as fake, cheap, and ethically dubious. Again, it is considered
|
||||
|
||||
# 4/23/25, 6:54 PM
|
||||
|
||||
How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
suitable only for a narrow range of use cases, this time constrained by public opinion,
|
||||
not technology. It is used in the same kinds of applications as in the “Novelty and
|
||||
Niche" scenario: memes, social video, music videos, perhaps some educational or
|
||||
factual content where there is no perceived need for human authorship or authenticity.
|
||||
Even animated programming that uses AI is considered creepy and parents shun it.
|
||||
|
||||
AI can create high fidelity visuals that are indistinguishable from live action, but the public
|
||||
won't have it.
|
||||
|
||||
Hollywood could do more, but is constrained by public pressure and the stance of
|
||||
talent. In the production process, AI is again relegated to behind-the-scenes, mostly
|
||||
pre- and post-production. For well-known creatives, the prospect of making projects
|
||||
at a fraction of the cost of traditional production and ending their reliance on big
|
||||
studios is appealing. But they steer clear of AI, fearful of both public backlash and
|
||||
being ostracized by the rest of the creative community. Emerging creators try to
|
||||
leverage Al to break into the industry, but most of the public rejects these efforts.
|
||||
|
||||
The current dynamics in media continue, including consumers continuing to shift
|
||||
their time and attention to creator media. But they still spend a lot of time and money
|
||||
on the biggest blockbusters and premium TV shows. Hollywood retains its lock on
|
||||
high-production value content and the relatively small oligopoly among the biggest
|
||||
media conglomerates and a few big tech companies stays intact, other than perhaps
|
||||
some consolidation here and there.
|
||||
|
||||
## "Stuck in the Valley” (low tech development, high
|
||||
consumer acceptance)
|
||||
|
||||
In this scenario, consumers embrace AI, but the technology doesn't keep pace.
|
||||
|
||||
Consumers think GenAI is cool, especially some of its unique attributes, like being
|
||||
able to generate personalized, interactive and emergent stories in real time. They also
|
||||
like using GenAl for fan creation, making memes, parodies and fan films about their
|
||||
favorite IP.
|
||||
|
||||
Consumers want it, but the technology can't deliver.
|
||||
|
||||
The technology hasn't improved much from the current state, never achieving realistic
|
||||
humans and still struggling with complex physics. However, GenAI is used extensively
|
||||
in advertising, animated content, DIY/educational, historical/docudrama/true crime
|
||||
and even some sci-fi, fantasy and horror movies and shows.
|
||||
|
||||
Creators also work within its constraints to create a tsunami of new content, most
|
||||
unwatchable, but some intriguing and some compelling. To cite a statistic I use all the
|
||||
time: by my estimate, Hollywood put out about 15,000 hours of film and TV shows in
|
||||
2024 (a generous estimate, by the way) vs. about the 300,000,000 hours of creator
|
||||
content uploaded to YouTube. At the same time, consumers' definition of quality.
|
||||
|
||||
https://archive.ph/spTgJ
|
||||
|
||||
11/21
|
||||
|
||||
# 4/23/25, 6:54 PM
|
||||
|
||||
How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
continues to shift away from high production values. By 2030, very little of this new
|
||||
content is considered good, but only an tiny proportion needs to be competitive with
|
||||
Hollywood to upend the supply/demand balance. Keep in mind that 0.01% (1/100 of a
|
||||
percent) of 300,000,000 hours is 30,000 hours-twice what Hollywood produces per
|
||||
year.
|
||||
|
||||
By 2030, YouTube's share of TV viewing surpasses 20%, up from 11% today. Consumers
|
||||
have enough "good enough” content available for free on YouTube and other online
|
||||
platforms that in recent years they have started to cancel streaming services; by the
|
||||
end of this decade, the average number of streaming services per streaming home has
|
||||
slipped, falling from 4 to 3. The have/have not divide in Hollywood widens, as subscale
|
||||
monoline video companies are consolidated into larger multi-line business as it
|
||||
becomes clearer that corporate video is no longer a profit center for most.
|
||||
|
||||
## "Hollywood Horror Show” (high tech development, high
|
||||
consumer acceptance)
|
||||
|
||||
In this scenario, both technological development and consumer acceptance continue
|
||||
to increase. GenAI video is virtually indistinguishable from anything shot with a
|
||||
camera. Consumers aren't phased by dramas starring synthetic people and are
|
||||
embracing some of the unique capabilities of GenAI video described before.
|
||||
|
||||
The cost to produce video converges with the cost of compute; the below-the-line cost
|
||||
(i.e., non-talent production costs) of a blockbuster-quality film falls from $1-2 million
|
||||
per minute today to $10-20 per minute. There is a near infinite supply of high
|
||||
production value content. Just as there are one-author books and one-artist albums, we
|
||||
have one-artist feature length movies and shows. There are virtually no barriers to
|
||||
high-quality content creation-competition comes from everywhere, including the
|
||||
near infinite pool of independent creators, and is global. Demand for U.S. content falls
|
||||
internationally as the production values and volume of local content increases.
|
||||
|
||||
Infinite content meets finite demand, completely altering the economics of video creation.
|
||||
|
||||
Content and culture atomize further along a continuum of experiences, reflecting the
|
||||
tension between the need for individual and shared experiences. These range from
|
||||
personalized content to micro-communities, subcultures, sub-mass and mass cultural
|
||||
experiences, but the last category are few and far between.
|
||||
|
||||
Infinite supply meets finite demand. The economic model of content creation shifts
|
||||
radically, as video becomes a loss leader to drive value elsewhere—whether data
|
||||
capture, hardware purchases, live events, merchandise, fan creation or who knows
|
||||
what else. The value of curation, distribution chokepoints, brands, recognizable IP,
|
||||
community building, 360-degree monetization, marketing muscle and know-how all go
|
||||
up.
|
||||
|
||||
Hollywood looks nothing like it does today.
|
||||
|
||||
## Placing Some Bets
|
||||
|
||||
https://archive.ph/spTgJ
|
||||
|
||||
12/21
|
||||
|
||||
# 4/23/25, 6:54 PM
|
||||
|
||||
How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
These scenarios range from incremental change to radical transformation. Before, I
|
||||
wrote that we should hold off judging their likelihood. Let's now turn to that.
|
||||
|
||||
The most conservative scenario, namely that the current state persists, seems highly
|
||||
unlikely. The question is where we settle out among the others.
|
||||
|
||||
## Technology Will Surely Advance, But How Much?
|
||||
|
||||
The concept that GenAI technology will stall out here defies all logic and recent
|
||||
experience-especially in light of the amazing advances in just the past two years, the
|
||||
resources being thrown at it, and the practice in the Al community of sharing many
|
||||
breakthroughs.
|
||||
|
||||
So, we know it will keep getting better, but how much and how fast? I'm not sure
|
||||
anyone knows and I certainly don't. Here are a few things we do know:
|
||||
|
||||
## Training Data Will Likely Grow
|
||||
|
||||
Unlike LLMs, which have apparently scraped nearly all the text on the internet, a lot of
|
||||
video footage is still inaccessible to AI video models. With more data, they will get
|
||||
better.
|
||||
|
||||
So far, Hollywood studios have been reluctant to license their libraries for training.
|
||||
However, the models need a large volume of hours more than they need specific
|
||||
libraries or IP. My guess is that owners of smaller libraries, who are less worried about
|
||||
the blowback from talent, public relations or (perhaps) the long-term strategic
|
||||
implications, will be more willing to license training rights. If large studios see that
|
||||
the window is closing to license their rights, some may follow suit. This could prove
|
||||
enough.
|
||||
|
||||
## Fine-Grained Control Will Improve
|
||||
|
||||
There is a lot of effort underway here currently. These include fine-tuning models to
|
||||
enable very specific camera controls (using more efficient, LoRA-based approaches),
|
||||
more research into manipulating parameters in the inference process and creating
|
||||
larger labeled datasets in pre-training.
|
||||
|
||||
## Al Will Probably Achieve a Better Understanding of Physics, Not Only
|
||||
for Video
|
||||
|
||||
Most GenAl models are trained on abstractions of reality, as I alluded to above. LLMs
|
||||
are trained on text (which is an abstraction of an abstraction; it is an abstraction of
|
||||
language, which is an abstraction of thought); video models are trained on pixels;
|
||||
audio models are trained on digitally-sampled notes, etc. They are not trained on the
|
||||
real world.
|
||||
|
||||
The next frontier of AI will require a better understanding of real-world physics and video
|
||||
models would benefit.
|
||||
|
||||
As also mentioned above, there are currently efforts underway to address this
|
||||
deficiency by creating "world models,” some of which rely on some sort of physical
|
||||
|
||||
https://archive.ph/spTgJ
|
||||
|
||||
13/21
|
||||
|
||||
# 4/23/25, 6:54 PM
|
||||
|
||||
How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
embodiment. These kinds of models are needed for more than just more lifelike video.
|
||||
The next frontier in Al is real-world applications: autonomous vehicles and robots. For
|
||||
these to succeed, it will be necessary for AI to develop a better understanding of the
|
||||
physical world, including all its many edge cases. So, these efforts are pursuing a much
|
||||
bigger prize than the payoff of achieving temporal coherence in a video model, but
|
||||
video models should be among the beneficiaries.
|
||||
|
||||
## Brains Want to Interpolate
|
||||
|
||||
The bar for realistic video may be lower than commonly believed.
|
||||
|
||||
Human brains are very good at interpolating. Vision in particular is heavily
|
||||
constructed, not just perceived. Many studies (like this one) have shown that most of
|
||||
the input to the visual cortex comes from our own internal models of the world, not
|
||||
sensory input from our eyes. (We also have a blind spot where our optic nerves connect
|
||||
to our retinas, but we don't see it because our brain fills in the gap.) We actively seek to
|
||||
create cohesive images from limited information. That's why minimalist and abstract
|
||||
art can be highly evocative even with a few brushstrokes or lines.
|
||||
|
||||
AI models don't need to be perfect.
|
||||
|
||||
The implication is that AI video models don't need to have perfect, frame-by-frame
|
||||
photorealism. They only to need to provide the right cues for the brain to fill in the
|
||||
rest. Where they currently fall short is when those cues are confusing or discordant.
|
||||
|
||||
## There is No Technical Reason the Uncanny Valley Can't be Vaulted
|
||||
|
||||
While our biology is cooperative in some areas, in others it is not. As mentioned
|
||||
before, the uncanny valley is a very high bar, because we're so attuned to nuanced
|
||||
facial expressions. Nevertheless, there is no technical reason AI can't overcome this
|
||||
challenge.
|
||||
|
||||
Following on the prior points, all video is an abstraction of reality. It comprises frames
|
||||
moving past at the rate of 24 or 30 per second. These frames comprise pixels. And
|
||||
what are pixels? They are just a color value that is captured by a lens, converted to
|
||||
numbers, converted to bits, and then converted back to a color value. 3
|
||||
|
||||
So, when you watch iShowSpeed or Stranger Things or Downton Abbey or The
|
||||
Kardashians or NBC Nightly News with Lester Holt or any other real people, doing real-
|
||||
people things, everything you are watching is just pixels, no different than the pixels
|
||||
produced by an Al model. Technically, video of synthetic people can be literally
|
||||
indistinguishable from video of real people.
|
||||
|
||||
There is no technical reason that synthetic people can't be literally indistinguishable from real
|
||||
people.
|
||||
|
||||
https://archive.ph/spTgJ
|
||||
|
||||
14/21
|
||||
|
||||
# 4/23/25, 6:54 PM
|
||||
|
||||
How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
And we're getting closer. As mentioned above, it is already hard to tell that the people
|
||||
in the Veo demo aren't real. This mirrors the amazing improvement in image
|
||||
generation models over the last couple of years; Figure 6 shows the same prompt used
|
||||
in each generation of Midjourney, up through the most recent.
|
||||
|
||||
Will AI ever surpass the uncanny valley? Right now, it's impossible to know, but it will
|
||||
likely keep improving. The ability to capture more nuanced emotions and lip syncing
|
||||
will almost certainly get better, owing to larger datasets, better markerless motion
|
||||
capture (when using reference video) and multi-modal model architectures that are
|
||||
better able to handle multiple data streams (like transformers that have both visual and
|
||||
audio attention mechanisms).
|
||||
|
||||
## Figure 6. Progression in Midjourney
|
||||
|
||||
The image shows a grid of seven AI-generated portraits of a young Japanese woman smiling, each created using a different version of Midjourney. The versions are labeled V1, V2, V3, V4, V5, V6, and V6.1. The portraits show a progression in realism and detail, with the later versions exhibiting more natural lighting, skin texture, and facial expressions. The prompt used to generate the images is "high quality photograph of a young Japanese woman smiling, backlighting, natural pale light, film camera." The source is attributed to Rinko Kawauchi.
|
||||
|
||||
## Consumers Will Probably Warm to Al—To a Degree
|
||||
|
||||
I think that the trajectory of consumer acceptance of AI is a bigger wildcard than the
|
||||
technology.
|
||||
|
||||
Al is unsettling. Here's a quote from Brian Arthur in The Nature of Technology that I've
|
||||
cited before, which I think captures it:
|
||||
|
||||
Our deepest hope as humans lies in technology; but our deepest trust lies in nature.
|
||||
These forces are like tectonic plates grinding inexorably into each other in one
|
||||
long, slow collision....We are moving from an era where machines enhanced the
|
||||
natural-speeded our movements, saved our sweat, stitched our clothing-to one
|
||||
that brings in technologies that resemble or replace the natural-genetic
|
||||
|
||||
https://archive.ph/spTgJ
|
||||
|
||||
15/21
|
||||
|
||||
|
||||
# 4/23/25, 6:54 PM
|
||||
How Far Will AI Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
engineering, artificial intelligence, medical devices implanted in our bodies. As we
|
||||
learn to use these technologies, we are moving from using nature to intervening
|
||||
directly within nature. And so the story of this century will be about the clash
|
||||
between what technology offers and what we feel comfortable with.
|
||||
|
||||
Most depictions of AI in popular culture reflect this unease. From HAL in 2001: A
|
||||
Space Odyssey, to Skynet in Terminator, to M3GAN, AI is usually something to be feared
|
||||
or distrusted. It's not surprising that people would be disconcerted by content created
|
||||
with AI. Will they get over this hump? Here's how I think about it:
|
||||
|
||||
## TV and Film Keeps Getting More Synthetic and Consumers Haven't Revolted Yet
|
||||
|
||||
Filmmaking has always involved a social contract between viewer and filmmaker: "I
|
||||
will suspend my disbelief that this is fake as long as it's sufficiently believable. But I
|
||||
know it's fake.” From [AI Use Cases in Hollywood](https://www.hollywoodreporter.com/business/business-news/ai-use-cases-hollywood-1235858103/):
|
||||
|
||||
You can draw a line from George Méliès using stop motion animation in A Trip to
|
||||
the Moon (1902) to the intricate sets in Fritz Lang's Metropolis (1927) to the
|
||||
maquettes in King Kong (1933) to the even more sophisticated models, costumes and
|
||||
make up in Star Wars (1977) to the first CGI in TRON (1982) and the continuing
|
||||
evolution of computer graphics and VFX in Jurassic Park (1993), the Lord of the Rings
|
||||
trilogy (2001) and Avatar (2009), to where we are today. Every step has become more
|
||||
divorced from reality...[T]oday almost every mainstream film has some VFX and, in
|
||||
a film like Avatar 2: Way of Water, almost every frame has been heavily altered and
|
||||
manipulated digitally.
|
||||
|
||||
This history of syntheticization is pictured in Figure 7. Note that, until the advent of
|
||||
CGI in the early 1980s, most of the innovation in syntheticization consisted of adding
|
||||
synthetic physical elements (maquettes, prosthetics, physical special effects, etc.); after
|
||||
that, most of it consisted of adding synthetic virtual elements, created on a computer.
|
||||
But consumers have continued to eat it up, even as films and TV shows have become
|
||||
increasingly VFX-heavy.
|
||||
|
||||
Figure 7: The History of Filmmaking as a Process of Syntheticization
|
||||
|
||||
### SYNTHETICISM
|
||||
|
||||
The image is a timeline of films and their advancements in syntheticism.
|
||||
|
||||
* 1902: A Trip to the Moon. Pioneering use of stop motion animation. More sophisticated use of stop motion and maquettes.
|
||||
* 1933: King Kong. Intricate models, front projection, green screen and several other new special effects techniques.
|
||||
* 1968: 2001: A Space Odyssey. More advanced models, costumes and make up.
|
||||
* 1977: Star Wars. Special effects.
|
||||
* 1982: Tron. First extensive use of computer-generated imagery (CGI) combined with live action.
|
||||
* 1993: Jurassic Park. Groundbreaking use of CGI, robotics and digital compositing.
|
||||
* 2001: Lord of the Rings. Photorealistic CGI, further advancements in motion capture and blending of practical effects with visual effects (VFX).
|
||||
* 2009: Avatar. More sophisticated performance capture and use of virtual cameras/simulcam technology.
|
||||
* 2019: The Mandalorian. First extensive use of virtual production (VP) sets.
|
||||
* 2023: Avatar: The Way of Water. Invention of underwater motion capture technology and 98% of shots use VFX.
|
||||
|
||||
# 16/21
|
||||
|
||||
# 4/23/25, 6:54 PM
|
||||
How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
Source: Author.
|
||||
|
||||
So, the question then is: Is there something about the “fakeness” of AI that is
|
||||
inherently more off-putting than the “fakeness” of VFX? I think the answer is no. I
|
||||
believe that the problem to date has been unnatural humans, janky motion, temporal
|
||||
inconsistency and temporal incoherency - things that have just looked "off." But if
|
||||
these are sufficiently resolved, I don't expect that consumers will reject AI just
|
||||
because it is AI.
|
||||
|
||||
Is there something about the “fakeness” of AI that is inherently more off-putting than the
|
||||
"fakeness” of VFX, which consumers have embraced?
|
||||
|
||||
## The Lines Between Al and Not-Al Will Blur
|
||||
|
||||
It will also get harder to tell what is AI and what isn't. AI will increasingly be
|
||||
incorporated in popular edit suites, native AI like Adobe Firefly or 3rd party plug-ins.
|
||||
Workflows will increasingly entail some combination of live footage, Al enhancement
|
||||
or augmentation, AI-assisted editing, manual cleanup, etc. At that point, who will
|
||||
know what is and isn't AI in the final product?
|
||||
|
||||
## Familiarity Will Probably Breed Acceptance
|
||||
|
||||
The FTI Delta study mentioned above concluded that consumers are more receptive to
|
||||
Al when they've used the tools. That follows a general truism: people like things (and,
|
||||
for that matter, people) more when they're more familiar with them. Right now, Al is
|
||||
scary partly because it's mysterious. As the mystery fades, reluctance probably will too.
|
||||
|
||||
## It Doesn't Require Radical Scenarios to Produce Radical Outcomes
|
||||
|
||||
A lot of people in Hollywood don't want to engage on this topic. I think they should.
|
||||
|
||||
Part of the problem is that we tend to think linearly, even though the world isn't linear.
|
||||
So, it can be very hard to see inflection points, even when you're standing right in front
|
||||
of them. It reminds me of this cartoon from [Wait But Why](https://waitbutwhy.com/):
|
||||
|
||||
Figure 7. It's Hard to See Inflection Points, Even When They're Right Next to You
|
||||
|
||||
The image shows two graphs, both titled "It's Hard to See Inflection Points, Even When They're Right Next to You". The graphs depict human progress over time. The first graph shows a gradual, linear increase in human progress, followed by a sharp, exponential increase at a later point in time. The second graph shows a similar pattern, but with a slightly different shape. Both graphs illustrate the idea that it can be difficult to recognize inflection points, even when they are occurring.
|
||||
|
||||
# 17/21
|
||||
|
||||
# 4/23/25, 6:54 PM
|
||||
How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
Source: Wait But Why.
|
||||
|
||||
Another challenge is that it's easy to dismiss a risk that seems so abstract. A few
|
||||
months ago, I was talking with a Hollywood executive about GenAI and he shrugged
|
||||
his shoulders and said "Yeah, no one knows." The point of this scenario exercise is to
|
||||
make the abstract more concrete and force us to confront what might happen.
|
||||
|
||||
For the reasons described above, it is hardly imaginable that GenAI technology won't
|
||||
keep progressing. Maybe it will never be entirely indistinguishable from live action
|
||||
footage, but it will get closer. It's also hard (albeit not as hard), to imagine that
|
||||
consumers won't warm to GenAI-enabled content over time. Perhaps we'll never fully
|
||||
accept synthetic humans, but there are a lot of content genres and use cases that don't
|
||||
rely on emotive actors. So, the most likely outcomes probably fall somewhere in the
|
||||
messy blob in Figure 8.
|
||||
|
||||
Figure 8. The Messy Blob of Likelihood
|
||||
|
||||
The image is a diagram showing the messy blob of likelihood. The diagram has four quadrants: Stuck in the Valley, Novelty and Niche, The Wary Consumer, and Hit Show. The Most Likely Outcomes is in the center of the diagram.
|
||||
|
||||
Source: Author.
|
||||
|
||||
What does that tell us? Even short of the most radical scenarios, the business would
|
||||
transform radically. Among other things, within that blob:
|
||||
|
||||
* There would be a vast increase in the supply of content, especially in certain
|
||||
genres.
|
||||
* Consumer time and attention would continue to get drawn away from corporate
|
||||
content, perhaps everything other than the most premium blockbusters and
|
||||
scripted TV.
|
||||
* Barriers would fall for small teams, creators and international producers who are
|
||||
willing and able to work within the constraints of technology and consumer
|
||||
preferences.
|
||||
* As production costs fall, new revenue and distribution models would likely
|
||||
emerge.
|
||||
|
||||
# 18/21
|
||||
|
||||
# 4/23/25, 6:54 PM
|
||||
How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
* As content becomes more abundant, other things would get scarcer and more
|
||||
valuable as consumers seek out both filters to navigate all that choice and human
|
||||
connection. These include curation, trusted IP and brands, marketing prowess,
|
||||
communities, provenance, and IRL events.
|
||||
|
||||
In Figure 7, you can't tell which way the little guy is facing. Today, a lot of people in
|
||||
Hollywood are looking backwards, assuming or hoping the slope won't change much.
|
||||
It probably will.
|
||||
|
||||
Thanks for Mike Gioia for his feedback on a draft of this post.
|
||||
|
||||
1 And, for that matter, media broadly.
|
||||
|
||||
2 For the sake of completeness: Entry barriers fell, paving the way for new entrants like
|
||||
Netflix, Amazon and YouTube. They have radically changed the consumer video experience
|
||||
and the economics of the video business. This has exerted tremendous pressure on the
|
||||
incumbent video value chain, including media conglomerates, cable and satellite video
|
||||
distributors, TV stations, and movie theaters, and ripple effects have been felt everywhere
|
||||
else, including advertisers, ad agencies, sports leagues, talent, and talent representation.
|
||||
|
||||
3 Each pixel is usually made up of three subpixels, that emit different colors: red, green, and
|
||||
blue (RGB). In an 8-bit system, each of these subpixels could have any of 256 values (two
|
||||
possible values for each bit raised to the 8th power = 256). So, that means that each pixel can
|
||||
take on one of 16.8 million values (256 x 256 x 256)-in other words, virtually any color the
|
||||
human eye can see. In an HD signal, there are over 2 million pixels per frame; a 4K image
|
||||
has four-times as many, or more than 8 million.
|
||||
|
||||
## Subscribe to The Mediator
|
||||
By Doug Shapiro
|
||||
|
||||
The Mediator is (mostly) about the long term structural changes in the media industry and the business,
|
||||
cultural, and societal implications of those shifts. I write it to get closer to the frontier.
|
||||
|
||||
By subscribing, I agree to Substack's [Terms of Use](https://substack.com/terms), and acknowledge
|
||||
its [Information Collection Notice](https://substack.com/privacy#collection) and [Privacy Policy](https://substack.com/privacy).
|
||||
|
||||
47 Likes 9 Restacks
|
||||
|
||||
47 7 9
|
||||
|
||||
[Previous](#)
|
||||
[Next](#)
|
||||
|
||||
## Discussion about this post
|
||||
|
||||
[Comments](#) [Restacks](#)
|
||||
|
||||
# 19/21
|
||||
|
||||
# 4/23/25, 6:54 PM
|
||||
How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
Write a comment...
|
||||
|
||||
stephan pauly Feb 15 Edited
|
||||
❤Liked by Doug Shapiro
|
||||
|
||||
Thank you so much, what a great and solid analysis! Beats 99,9% of my linkedin feed for sure.
|
||||
I'm in the advertising film business, and there's 2 things I can already tell:
|
||||
|
||||
1) your second factor - audience acceptance - is irrelevant in our ecosystem as long as the quality is
|
||||
good enough, which it obviously already is. The 100% ai generated COKE xmas commercials were
|
||||
tested with audiences and people loved them, no pushback there.
|
||||
|
||||
2) "Studios have used these technologies to marginally reduce production costs, say 15-25%." That
|
||||
does not seem "marginal" to me! As we pitch each&every project against at least 2 competitors, a 20%
|
||||
cost advantage is a MASSIVE business advantage over the competition. I wish we could harness Al's
|
||||
potential to be 20% less costly than the competition (but then again, if we can, then the competition
|
||||
also can).
|
||||
|
||||
For now, these cost cutting advantages have not arrived in our ecosystem. I assume that is to a large
|
||||
extent based on legal uncertainties around the use of Al, and will soon change drastically once the
|
||||
legal frameworks get adjusted to what's technically achievable.
|
||||
|
||||
LIKE (3) REPLY SHARE
|
||||
|
||||
Jordi Martínez Subías Feb 15
|
||||
❤Liked by Doug Shapiro
|
||||
|
||||
It is not true to say that people have enough video content available "for free" on YouTube: we either
|
||||
pay a subscription fee or have to watch a huge amount of video ads. This means it has to be rewarding
|
||||
anyhow. We might be open to spend 2 or 3 minutes watching entirely Al generated video while the
|
||||
technology behind is surprising, but eventually we'll not care about how that video was made and
|
||||
enjoy it for its content: the story, the characters, the setting, etc. So, I believe people will eventually
|
||||
accept video Al except when the characters matter. Otherwise, it feels like an animation movie and
|
||||
these are set apart even without the involvement of Al at all.
|
||||
|
||||
LIKE (2) REPLY SHARE
|
||||
|
||||
5 more comments...
|
||||
|
||||
Top Latest Discussions
|
||||
|
||||
28 Days of Media Slides
|
||||
An Industry in Upheaval
|
||||
JAN 7 DOUG SHAPIRO
|
||||
|
||||
The image is a thumbnail for a post titled "28 Days of Media Slides" with the subtitle "An Industry in Upheaval". The thumbnail shows a calendar with the word "December" written on it, and the letters "HBO" are circled.
|
||||
|
||||
53 9
|
||||
|
||||
Quality is a Serious Problem
|
||||
Understanding The Changing Consumer Definition of Quality in Media
|
||||
JAN 20 DOUG SHAPIRO
|
||||
|
||||
The image is a thumbnail for a post titled "Quality is a Serious Problem" with the subtitle "Understanding The Changing Consumer Definition of Quality in Media". The thumbnail shows a close-up of a person's face, with a blurred background.
|
||||
|
||||
91 19
|
||||
|
||||
The Relentless, Inevitable March of the Creator Economy
|
||||
How Big it Is and Why it Will Keep Growing at the Expense of Corporate Media
|
||||
DEC 1, 2024 DOUG SHAPIRO
|
||||
|
||||
The image is a thumbnail for a post titled "The Relentless, Inevitable March of the Creator Economy" with the subtitle "How Big it Is and Why it Will Keep Growing at the Expense of Corporate Media". The thumbnail shows a crowd of people holding up their phones, with a blurred background.
|
||||
|
||||
72 10
|
||||
|
||||
# 20/21
|
||||
|
|
@ -1,369 +0,0 @@
|
|||
---
|
||||
source_type: "article"
|
||||
title: "IP as Platform"
|
||||
author: "Doug Shapiro"
|
||||
url: "https://dougshapiro.substack.com/p/ip-as-platform"
|
||||
date_published: "2023-08-01"
|
||||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: processed
|
||||
claims_extracted:
|
||||
- "entertainment IP should be treated as a multi-sided platform that enables fan creation rather than a unidirectional broadcast asset"
|
||||
---
|
||||
# IP as Platform - by Doug Shapiro - The Mediator
|
||||
|
||||
4/23/25, 6:56 PM
|
||||
archive.today Saved from https://dougshapiro.substack.com/p/ip-as-platform
|
||||
search
|
||||
23 Apr 2025 17:52:34 UTC
|
||||
no other snapshots from this url
|
||||
All snapshots from host dougshapiro.substack.com
|
||||
Webpage
|
||||
Screenshot
|
||||
webpage capture
|
||||
download.zip
|
||||
report bug or abuse
|
||||
|
||||
## IP as Platform
|
||||
|
||||
How Entertainment Companies Can Capitalize on Infinite Content
|
||||
|
||||
[Image of Doug Shapiro]
|
||||
DOUG SHAPIRO
|
||||
FEB 21, 2023
|
||||
|
||||
2
|
||||
1
|
||||
share
|
||||
|
||||
[Note that this essay was originally published on Medium]
|
||||
Share
|
||||
|
||||
[Image of a crowd of people walking towards a swirling vortex of colorful figures]
|
||||
Source: Midjourney, prompt: "an abstract image of an infinite number of people
|
||||
collaborating on a work of art"
|
||||
|
||||
Last month, I published a post called Forget Peak TV, Here Comes Infinite TV. It
|
||||
made the case that over the next 5-10 years, several technologies (including virtual
|
||||
production and AI) will cause the quality distinction between professionally-produced
|
||||
and user-generated content to blur, resulting in effectively “infinite” high-quality
|
||||
video.
|
||||
|
||||
Putting aside the specific technologies, there are two basic ideas here that I think are
|
||||
hard to refute: 1) technology generally makes it possible to do more with less; and 2)
|
||||
https://archive.ph/AsshV
|
||||
1/12
|
||||
|
||||
## IP as Platform - by Doug Shapiro - The Mediator
|
||||
|
||||
4/23/25, 6:56 PM
|
||||
the collective creative energy of the general population is far greater than the tiny
|
||||
percentage of people who have navigated the established system for creating content.
|
||||
|
||||
We have already seen both play out in journalism and music. What once required an
|
||||
entire newspaper printing and distribution infrastructure to accomplish can now be
|
||||
done with Substack; what once required a record label now can be done with Logic Pro
|
||||
and Spotify. The vast, vast majority of self-published writing and music is not worth
|
||||
reading or listening to. But some is. Today, some of the best journalists in the world
|
||||
never worked at a newspaper and most new superstar music acts emerge from the tail
|
||||
of self-distributed music. The arc of technology suggests that inevitably film and TV
|
||||
will face the same dynamics. This doesn't mean the end of Hollywood. But it has the
|
||||
potential to be extremely disruptive.
|
||||
|
||||
Rather than focus on the threat, let's focus on the opportunity. Suppose you were
|
||||
running an entertainment company and you bought the premise. Could you capitalize
|
||||
on it? Even if you think the trends I'm describing are years away, the recent explosion
|
||||
of activity and attention around Al make the question worth asking now.
|
||||
|
||||
One way to harness this creative energy, as opposed to fighting or dismissing it, is to
|
||||
think of your IP as a platform.
|
||||
|
||||
Tl;dr:
|
||||
|
||||
* It's easy to see why "infinite TV" could be extremely disruptive for entertainment
|
||||
companies. But they can also capitalize on it.
|
||||
* "IP as platform" means enabling and encouraging creators to expand on your
|
||||
intellectual property and curating this fan content for consumers.
|
||||
* This may sound like a radical idea, but fan art is an inherent part of the music
|
||||
business and the gaming industry has been built by commercializing emergent fan
|
||||
behaviors.
|
||||
* Not every entertainment franchise will inspire fan creation. But facilitating fan art
|
||||
could have several benefits for entertainment companies, such as strengthening
|
||||
their relationships with their most ardent fans and attracting new ones; providing
|
||||
free marketing; possibly sourcing new stories and talent; and boosting revenue.
|
||||
Plus, it might be hard to prevent even if they wanted to.
|
||||
* I discuss a basic framework for how all this might work.
|
||||
|
||||
Thanks for reading The Mediator! Subscribe for
|
||||
free to receive new posts and support my work.
|
||||
|
||||
## What Does "IP as Platform" Mean?
|
||||
|
||||
Let's break down "IP as platform" into its components, starting with intellectual
|
||||
property (IP). From Infinite TV:
|
||||
|
||||
The most valuable franchises may become even more valuable. With new tools and
|
||||
lower costs, many creators will want to dream up entirely new stories. A lot will also
|
||||
https://archive.ph/AsshV
|
||||
2/12
|
||||
|
||||
## IP as Platform - by Doug Shapiro - The Mediator
|
||||
|
||||
4/23/25, 6:56 PM
|
||||
probably want to expand on their favorite fictional worlds, whether Harry Potter,
|
||||
the MCU or Game of Thrones—or create mash-ups between them. Historically,
|
||||
Hollywood has guarded its IP closely and has been more inclined to view fan fiction
|
||||
as copyright infringement than enhancement. But progressive rights owners would
|
||||
be wise to harness all the potential creative energy, not stifle it.
|
||||
|
||||
By platform, I mean a multi-sided market-a business that facilitates the interaction
|
||||
of 3rd parties and consumers. Prototypical platform businesses include Microsoft
|
||||
Windows, which enables developers to create applications for PC owners, or Uber,
|
||||
which connects drivers and riders.
|
||||
|
||||
What would "IP as platform" mean for an entertainment company? Below I discuss
|
||||
what this might mean in practice, but in theory it means enabling and encouraging 3rd
|
||||
party creators to produce content that builds on their IP and making that content
|
||||
available to consumers.
|
||||
|
||||
"IP as platform” means enabling and encouraging creators to expand on your intellectual
|
||||
property and surfacing it for consumers.
|
||||
|
||||
The analogy only extends so far. Platform businesses are usually characterized by
|
||||
strong network effects on each side of the market, which are key to their value
|
||||
proposition, competitive moats and consumer lock in. As a result, they have a “cold
|
||||
start" problem (they need to have a lot of buyers and sellers to attract a lot of sellers
|
||||
and buyers) and platform businesses with particularly strong network effects often
|
||||
create winner-take-most markets. Neither would be the case here. The most popular
|
||||
entertainment franchises definitionally already have rabid fan bases and, because they
|
||||
are so highly differentiated, there won't be winner-take-most markets (Harry Potter,
|
||||
the MCU and James Bond can all succeed).
|
||||
|
||||
Hollywood is very precious about its IP and the idea of providing access to the general
|
||||
populace might sound like heresy.
|
||||
|
||||
Here's why it shouldn't.
|
||||
|
||||
## Hollywood Needs Fans
|
||||
|
||||
As the world transitions to infinite content, IP owners need fans more than ever.
|
||||
"Users" are dispassionate; “consumers” don't give anything back. “Fans” are...fanatical.
|
||||
|
||||
According to a study by Troika, 85% of people say they are a fan of something, and 97%
|
||||
of people aged 18–24. Especially at a time when religious affiliation continues to
|
||||
decline, for a lot of these people, their fandom is a vital part of their identity. (That's
|
||||
exemplified by the prevalence of brand tattoos.)
|
||||
|
||||
For many people, the object of their fandom is entertainment IP. Anyone who has been
|
||||
to ComicCon, E3 or a Harry Styles concert has seen that, as does anyone who has been
|
||||
on the wrong side of fan backlash.
|
||||
https://archive.ph/AsshV
|
||||
3/12
|
||||
|
||||
## IP as Platform - by Doug Shapiro - The Mediator
|
||||
|
||||
4/23/25, 6:56 PM
|
||||
Fans are loyal. Fans are unpaid marketers. And fans are lucrative. In theory, for every
|
||||
product that has a downward sloping demand curve, every unit of demand to the left of
|
||||
the market clearing price is willing to pay more than that price. Those points on the
|
||||
curve represent fans. Consulting firm Activate has been particularly vocal about the
|
||||
need for media companies to target “Superusers.” According to their research,
|
||||
Superusers represent a disproportionate amount of both time spent (Figure 1) and
|
||||
dollar spend (Figure 2).
|
||||
|
||||
Figure 1. Superusers Represent a Disproportionate Amount of Time Spent...
|
||||
|
||||
[Image of a bar graph comparing the average daily time spent with media per user between all other users and super users. The graph shows that all other users spend an average of 9 hours and 21 minutes, while super users spend an average of 18 hours and 55 minutes. The graph also shows that super users make up 22% of the user population.]
|
||||
|
||||
1. Includes time spent watching video, playing video games, listening to music, listening to
|
||||
podcasts, and using messaging / social media services. Does not account for multitasking.
|
||||
Sources: Activate analysis, Activate 2022 Consumer Technology & Media Research Study (n =
|
||||
4,001), Company filings, Comscore, Conviva, eMarketer, GWI, Music Biz, Newzoo, Nielsen,
|
||||
NPD Group, Pew Research Center, U.S. Bureau of Labor Statistics.
|
||||
|
||||
Figure 2. ...And Spend
|
||||
|
||||
[Image of a bar graph comparing the monthly dollar spend by media type between all other users and super users. The graph shows the total video spend, total gaming spend, and total music spend for each group. The graph also shows the percentage of the user population that each group represents.]
|
||||
|
||||
1. Includes money spent on all videos and video services, including traditional/virtual Pay TV,
|
||||
video streaming subscription services, and video purchases/rentals. 2. Includes money spent on
|
||||
video games and other video gaming purchases (e.g. in app purchases, video gaming
|
||||
subscription services) across all devices. 3. Includes money spent on music and music services.
|
||||
Sources: Activate analysis, Activate 2022 Consumer Technology & Media Research Study (n =
|
||||
4,001), eMarketer, Goldman Sachs, Grand View Research, IFPI, Newzoo, Omdia,
|
||||
PricewaterhouseCoopers, Recording Industry Association of America, SiriusXM, Statista.
|
||||
https://archive.ph/AsshV
|
||||
4/12
|
||||
|
||||
## IP as Platform - by Doug Shapiro - The Mediator
|
||||
|
||||
4/23/25, 6:56 PM
|
||||
Fans Want to Create
|
||||
|
||||
For fans, fan art is a love letter to the object of their fandom and a way to strengthen
|
||||
their bond with the fan community. The most prevalent form-because it has the
|
||||
lowest barrier to entry—is fan fiction (or fanfic, FFs or just fics).
|
||||
|
||||
Figure 3. By One Estimate, the Volume of Fanfic Rivals All Fiction, Ever
|
||||
|
||||
[Image of a graphic comparing the volume of fanfiction to all other fiction. The graphic shows that fanfiction.net has 60 billion words, while all of human history has 80 billion words.]
|
||||
|
||||
Note: “All of Human History” comprises all the words in the Google English fiction corpus.
|
||||
Source: Cecelia Aragon.
|
||||
|
||||
The modern history of fanfic dates back to science fiction fanzines in the 1940s and
|
||||
the first TV-related fanzines, about Star Trek, in the late '60s. But fanfic surged with
|
||||
the advent of the Internet. There are now over 14 million stories on the largest fan
|
||||
fiction website, FanFiction.net. According to one researcher, this comprises 60 billion
|
||||
words, compared to the 80 billion words in the entire Google English fiction corpus
|
||||
over the prior five centuries (Figure 3).
|
||||
|
||||
There are 5 million fanfic stories on Archive of Our Own (AO3), including 500,000
|
||||
stories about the MCU, 400,000 about Harry Potter and 300,000 about DC, among
|
||||
many other fandoms. Sometimes even less well-known franchises have a rabid (or
|
||||
prolific) fan base; the TV series Supernatural has over 250,000 stories. The most-read
|
||||
work on AO3 (which occurs in the world of Harry Potter) has over 9 million hits. The
|
||||
fan site Fandom has over 250,000 fan-created “wikis,” where fans post fanfic, videos
|
||||
and articles that explain the official canon. Marvel and Star Wars, two of the largest
|
||||
wikis, include 280,000 and 180,000 pages, respectively.
|
||||
|
||||
It has also been legitimized. Initially, fan fiction lurked in the dark corners of the
|
||||
Internet. While much of the content is still graphic, in recent years it has become
|
||||
increasingly mainstream. In 2019, AO3 won a Hugo Award, the most prestigious
|
||||
award in science fiction. And a number of fan fiction works have achieved broad
|
||||
commercial success, like 50 Shades of Gray (which was originally Twilight fan fiction);
|
||||
The Mortal Instruments series (based off Harry Potter); and the zombie-Jane Austen
|
||||
mash-up Pride and Prejudice and Zombies.
|
||||
|
||||
Star Wars: X-Wing | A Star Wars Fan Film
|
||||
Copy link
|
||||
https://archive.ph/AsshV
|
||||
5/12
|
||||
|
||||
|
||||
# 4/23/25, 6:56 PM
|
||||
|
||||
Watch on ►YouTube
|
||||
|
||||
IP as Platform - by Doug Shapiro - The Mediator
|
||||
|
||||
If you search "fan film" in YouTube, some astounding stuff comes up, like the video embedded above. Seriously, watch at least the first minute. Or consider this fan-made re-imagining of *The Fresh Prince of Bel-Air*, which resulted in the show *Bel-Air* on Peacock and landed the creator an Executive Producer role. But video fan art is far less common than fanfic for the obvious reason. It's really hard to do. (In the video embedded above, all the 3D models were made from scratch and the project took four years.)
|
||||
|
||||
What happens when it isn't?
|
||||
|
||||
# Music and Gaming as Models
|
||||
|
||||
Hollywood and the literary community have ambivalent relationships with fan fiction. Whether non-commercial fan fiction falls under fair use protection is not clear cut, as fair use is determined on a case-by-case basis. Studios and book publishers have generally turned a blind eye-unless it is commercialized, in which case they (understandably) spring into action. Famous examples include J.K. Rowling shutting down a fan-made *Harry Potter* encyclopedia, J.D. Salinger suing to prevent a sequel of *Catcher in the Rye* or CBS/Paramount successfully stopping a *Star Trek* feature film.
|
||||
|
||||
Let's look at two media for which fan creation is much more closely tied to the business: music and gaming.
|
||||
|
||||
# Songwriters Must Enable Fan Art by Statute
|
||||
|
||||
Fan art is a critical part of the music business owing to the compulsory copyright license. Anyone granted a copyright for a musical work in the U.S. must issue a license to anyone who wants to record the music.
|
||||
|
||||
In other words, anyone can cover a song—and commercialize it—as long as they secure a so-called "mechanical license." (Most of these licenses are administered by the Harry Fox Agency, which issues licenses and collects royalty payments.) Some streaming services, like Spotify and Apple Music, even handle that for cover artists. The statutory mechanical royalty rate is set by the Copyright Royalty Board, which is overseen by the Library of Congress. Total mechanical royalties aren't a huge part of music publishers' revenue, but successful covers generate additional royalties and can substantially boost the popularity of the original recording.
|
||||
|
||||
This isn't to suggest that entertainment companies develop a similar framework-they probably don't want three judges who were appointed by the Librarian of Congress to
|
||||
|
||||
# 6/12
|
||||
|
||||
[https://archive.ph/AsshV](https://archive.ph/AsshV)
|
||||
|
||||
# 4/23/25, 6:56 PM
|
||||
|
||||
IP as Platform - by Doug Shapiro - The Mediator
|
||||
|
||||
decide the licensing terms for their IP. The point is that while we may not usually think of song covers this way, “fan art” is an inherent part of the music business.
|
||||
|
||||
# Gaming Was Built by Commercializing Emergent Fan Behaviors
|
||||
|
||||
While Hollywood has a low tolerance for fan art and the music industry has a mutually beneficial relationship (and no choice), the videogame industry has fully embraced fan creation. It is arguably built on the back of emergent fan behaviors.
|
||||
|
||||
Part of the reason is that, unlike passive media like TV, radio or print, gaming requires users to interact with the content and each other, which often leads in unexpected directions. Plus, the origins of gaming have close ties to the hacker/DIY community and many hardcore gamers have a high degree of technical proficiency and therefore the ability to alter games as they see fit.
|
||||
|
||||
Whatever the reason, progressive developers have long recognized these hacks and workarounds as unmet jobs to be done and commercialized them. I'm not talking about tangential features-much of the innovation in the videogame business originated with fan behavior.
|
||||
|
||||
*The videogame industry is built on the back of unexpected fan behaviors.*
|
||||
|
||||
# Modding
|
||||
|
||||
Modifying videogames, or “modding,” has been an essential part of gaming for decades. Initially, developers didn't encourage it, but in 1983, id Software released DOOM with a separate game engine and data file, which enabled the creation of game mods. Since then, it is more common than not that games permit or encourage modding and there are numerous platforms for creating and discovering mods, like Steam Workshop.
|
||||
|
||||
Some of the most successful games today are mods of other games: Counter-Strike is a mod of Valve's *Half-Life*; Dota 2 is a sequel to Dota, which is a mod of Blizzard's *Warcraft III*; and in turn League of Legends was inspired by Dota and is also built on the *Warcraft* engine.
|
||||
|
||||
Figure 5. Creating is Intrinsic to Roblox
|
||||
|
||||
The image shows a screenshot of the Roblox Studio interface. The interface is colorful and features a prominent "Start Creating" button. The text "Make Anything You Can Imagine" is displayed above the button, emphasizing the creative possibilities within the platform. The interface also includes options like "Discover," "Avatar Shop," and "Create," suggesting a comprehensive environment for game development and community interaction.
|
||||
|
||||
Some of the most successful games today have taken modding to its logical conclusion: rather than just provide separate tools for modding, it is an integral part of the
|
||||
|
||||
# 7/12
|
||||
|
||||
[https://archive.ph/AsshV](https://archive.ph/AsshV)
|
||||
|
||||
# 4/23/25, 6:56 PM
|
||||
|
||||
IP as Platform - by Doug Shapiro - The Mediator
|
||||
|
||||
experience. Over 40 million games have been created with Roblox Studio and although there are a handful of native games on Roblox, all of the top-ranked games were made by creators. According to Epic Games CEO Tim Sweeney, half of all play time on Fortnite is now on games made by 3rd parties using Fortnite Creative.
|
||||
|
||||
# Virtual Goods
|
||||
|
||||
The first virtual goods to be exchanged for real money (“Real Money Trade”) were items made for multi-user dungeons (MUDs) in the 1970s and massively multiplayer online games (MMOGs) in the early 1980s, traded on local message boards and later on Ebay. These trades were the first indications of user willingness to spend real money on virtual items. Today, virtual goods are the foundation of free-to-play gaming and people spend an estimated $80 billion annually on virtual goods in videogames.
|
||||
|
||||
# Competitions and Esports
|
||||
|
||||
Since videogames originated prior to widespread Internet adoption and, of course, broadband access, originally competitive online play of fast (“twitch”) games was impossible. However, as early as the 1970s groups of gamers held “LAN parties," at which they would bring their own PCs and hook them into a LAN. According to Mitch Lasky in the (highly-recommended) podcast Gamecraft, *Quake III Arena*, also from id, was the first game to be geared largely around online multiplayer play. Today, almost all games include multiplayer online gameplay modes and many games can't be played offline at all.
|
||||
|
||||
While the idea that people would want to play with other people online was a no-brainer, it was not at all as obvious that people would want to watch other people play videogames. In 1999, South Korean broadcaster ON Media sought content to fill up airtime in the evening on its cartoon network, Tooniverse, and broadcast a *StarCraft* tournament. It was such a phenomenon that the next year it launched a dedicated esports network, OnGameNet (OGN).
|
||||
|
||||
Today, League of Legends World Championship tickets sell out in minutes and last year Twitch viewers watched 22 billion hours on the platform. YouTube recently announced that Minecraft videos have now received a mind-boggling 1 trillion views. The game would likely never have been nearly as popular without all that free marketing. Whether esports is a good business is a fair question. But publishers of popular multiplayer online battle arena (MOBA) and first-person shooter games, like Riot, Blizzard-Activision and Valve, now rely on both live events and livestreaming platforms as critical marketing tools for their games.
|
||||
|
||||
# How Would You Do It?
|
||||
|
||||
So, fan art, broadly defined, is an important or even critical part of other media. As mentioned, historically this has been very hard to do in video, but as I described in Infinite TV, technology is on a path to make it much easier. For entertainment companies, they may not be able to stop this even if they want to. As also mentioned above, whether non-commercial fan fiction falls under fair use is a legal gray area and determined on a case by case basis. The democratization of high production value creation tools could result in a tsunami of non-commercial fan content. Even if these fans aren't competing for dollars, a flood of high quality Batman or Star Wars fan films could compete for attention.
|
||||
|
||||
# 8/12
|
||||
|
||||
[https://archive.ph/AsshV](https://archive.ph/AsshV)
|
||||
|
||||
# 4/23/25, 6:56 PM
|
||||
|
||||
IP as Platform - by Doug Shapiro - The Mediator
|
||||
|
||||
Entertainment companies may not be able to stop it even if they want to and embracing it could bring several benefits.
|
||||
|
||||
As a result, enabling fan art could be defensive. If done right, it could also provide numerous benefits. It would strengthen entertainment companies' relationship with their most ardent fans; could attract new fans; provide free marketing; might be an inexpensive way to source new stories and talent; and could boost revenue.
|
||||
|
||||
Figure 6. Unreal Engine Marketplace
|
||||
|
||||
The image shows a screenshot of the Unreal Engine Marketplace. The marketplace is a digital storefront where users can purchase and download assets for use in the Unreal Engine. The interface is clean and organized, with a search bar, filtering options, and various categories of assets. The assets displayed include environments, characters, and other 3D models. The image highlights the wide range of content available on the marketplace, suggesting its importance as a resource for game developers and other creators.
|
||||
|
||||
What does "done right" mean? This is just a sketch of an idea, but a framework would probably need a few components:
|
||||
|
||||
* Tools. The easiest way to provide creation tools would be to leverage existing real-time rendering engines, namely Unreal Engine and Unity. IP owners could offer creators packs of digital assets associated with different franchises (The Wizarding World of Harry Potter, the MCU, Minions, etc.), including characters (in different outfits, at different ages), environments, vehicles, props and even music and sound effects. These assets should be in a consistent style and aesthetic (across a franchise and, possibly, even the entire corporate umbrella) so creators can seamlessly combine them. The other benefit of tightly integrating with gaming engines would be the potential for these assets to be used for more than just linear storytelling, such as gaming and other interactive applications. They could go even further, and work with Unreal and Unity to offer a suite of assets let's say a "Warner Bros. Filmmaker" plug-in—that would offer easy set-up, editing, pre-set character animations, etc., so that complete beginners could make rudimentary films without extensive training. (This is loosely analogous to what Disney allowed in toy box mode of the now defunct Disney Infinity, albeit for game design, not filmmaking.) These assets and plug-ins could be available on new official fan creation sites and/or in the existing Unreal and Unity asset marketplaces (the Unreal Marketplace is shown in Figure 6 above). Epic and Unity could probably be persuaded to create storefronts for different franchises, to make navigation easy.
|
||||
* Rights. Entertainment companies would need to ensure they have the rights for all the digital assets they provide, especially the characters. Would the 3D digital
|
||||
|
||||
# 9/12
|
||||
|
||||
[https://archive.ph/AsshV](https://archive.ph/AsshV)
|
||||
|
||||
# 4/23/25, 6:56 PM
|
||||
|
||||
IP as Platform - by Doug Shapiro - The Mediator
|
||||
|
||||
* Tony Stark look like Robert Downey Jr.? That probably depends on what "image and personality" rights he signed away in his contract.
|
||||
* A legal framework. The digital asset licenses would need to have some sort of stipulation how the assets may be used. These should probably be as permissive as possible but include prohibitions against obscenity, whatever that is. IP owners would probably also want some sort of safe harbor protection against creators uploading fan art and then claiming that subsequent official releases were based on their ideas.
|
||||
* A distribution platform. Creators would need a way to distribute their work. Perhaps they should be allowed to distribute any way they want (YouTube, TikTok), perhaps not. But it would also be important to create an "official" dedicated distribution outlet for this content, such as within entertainment companies' streaming services or YouTube channels created specifically for fan content. This official platform would also be a natural place for fan communities to gravitate, where they could comment and vote on their favorite fan works.
|
||||
* A big carrot: the promise of validation. To tie this all together it would also make sense to add a strong incentive for creators to adhere to guardrails and post on the "official" distribution platform: validation. Entertainment companies could curate the best fan content, selectively provide some sort of Good-Housekeeping-seal-of-approval for some content (“Disney approved!") ("featured fan film of the month") and even hold out the promise of hiring the most talented creators for future work. The possibility of validation by IP owners would be a dream come true-and huge draw-for creators.
|
||||
* An economic framework. There would need to be some established revenue sharing arrangement for any monetization of the content (and probably a watermarking system to ensure the entertainment companies/creators get credit).
|
||||
* Careful management of the canon. Entertainment companies would also need to carefully manage what they deem official canon. But this already happens today. For instance, in 2014 Disney rebranded the Star Wars Expanded Universe (all non-film media, like books and comics) as *Star Wars Legends*, meaning that these stories were no longer canon and future films and stories wouldn't be bound by them. Disney also cleverly introduced the multiverse concept to the MCU, meaning that everything (and, I guess, nothing) is canon, because anything is possible. Official DC canon is also presumably up in the air with the recent arrival of James Gunn and Peter Safran to run the franchise.
|
||||
|
||||
As described at the beginning, the quality differential between the "head" and the "tail" has already blurred in lower-barrier media, like journalism and music. It hasn't happened yet in video because the barriers are so much higher, but the usual arc of technology suggests those high barriers only delayed the inevitable. If you buy the premise, then entertainment companies have a choice: they can fight the tide or ride it. Since the former may be futile, the latter may be the only viable option.
|
||||
|
||||
Special thanks to Anthony Koithra for his feedback to a draft of this post.
|
||||
|
||||
# 10/12
|
||||
|
||||
[https://archive.ph/AsshV](https://archive.ph/AsshV)
|
||||
|
|
@ -1,857 +0,0 @@
|
|||
---
|
||||
source_type: "article"
|
||||
title: "Power Laws in Culture"
|
||||
author: "Doug Shapiro"
|
||||
url: "https://dougshapiro.substack.com/p/power-laws-in-culture"
|
||||
date_published: "2023-03-01"
|
||||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: processed
|
||||
claims_extracted:
|
||||
- "information cascades create power law distributions in culture because consumers use popularity as a quality signal when choice is overwhelming"
|
||||
---
|
||||
# 4/23/25, 6:53 PM Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
archive.today Saved from https://dougshapiro.substack.com/p/power-laws-in-culture
|
||||
|
||||
webpage capture
|
||||
All snapshots from host dougshapiro.substack.com
|
||||
search
|
||||
no other snapshots from this url
|
||||
Webpage
|
||||
Screenshot
|
||||
https://archive.ph/0cYxS
|
||||
|
||||
the mediator
|
||||
|
||||
Subscribe
|
||||
Sign in
|
||||
|
||||
## Power Laws in Culture
|
||||
|
||||
Why Hits Will Persist in an Infinite Content World
|
||||
|
||||
DOUG SHAPIRO
|
||||
MAR 16, 2023
|
||||
|
||||
[Note that this essay was originally published on Medium]
|
||||
|
||||
<!-- Image Description: A digital illustration depicts a person standing and looking up at several arrows of different colors (blue, orange, red) that are curving upwards. The arrows start low and curve upwards, with some reaching higher than others. The person is dressed in a blue suit and appears to be contemplating the upward trajectory of the arrows. The background is a plain white. The illustration is meant to represent growth, trends, or the power law concept. -->
|
||||
|
||||
Source: Hurca!/stock.adobe.com
|
||||
|
||||
* Almost 20 years ago, Chris Anderson wrote The Long Tail, which accurately predicted that the Internet would fragment attention and consumption would shift into the "tail.” But Top Gun Maverick generated over $700 million at the domestic box office last year, Bad Bunny had 18.5 billion streams on Spotify last year and 142 million households reportedly watched Squid Game Season 1 in its first 28 days. Why are there still hits in a fragmenting world?
|
||||
|
||||
* I recently posted an essay called Forget Peak TV, Here Comes Infinite TV. It made the case that over the next decade video will follow the path of text, photography and music and the quality distinction between “professionally-produced" content and "independent/creator/user-generated" content will increasingly blur. This will result in practically infinite quality video content. Will there still be hits then, or only personalized niches?
|
||||
|
||||
* Have you ever wondered why so many blockbuster movies are about superheroes? Is Hollywood lazy or are consumers' tastes becoming dumber and more homogenized? Or neither?
|
||||
|
||||
## 1/20
|
||||
|
||||
# 4/23/25, 6:53 PM Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
https://archive.ph/0cYxS
|
||||
|
||||
* Why does something go viral, anyway?
|
||||
|
||||
* Do content recommendations push you to the most popular shows, movies and songs or are they tailored just for you? Or do they have a different agenda?
|
||||
|
||||
* Will web3 really be the savior of small creators?
|
||||
|
||||
* When Billie Eilish, Lil Nas X, Mr. Beast or PewDiePie emerge from obscurity, was it inevitable that their talent would be recognized or just luck?
|
||||
|
||||
* Are the top rated reviews on Amazon or answers on Quora really the most helpful?
|
||||
|
||||
All of these are questions about the distribution of popularity. And the same phenomenon underlies the answers: networks.
|
||||
|
||||
This essay may be a little wonky, but the topic is something I've been thinking about for more than a decade. (Off and on, not continuously.)
|
||||
|
||||
I explain why power law-like distributions—meaning a few massive hits and a vast number of misses—are an inherent feature of networks; describe how recommendation systems can either dampen or reinforce social signals; show some examples of the persistence of power law-like distributions in media across movies, TV, music and the creator economy; and discuss why all this matters.
|
||||
|
||||
Tl;dr:
|
||||
|
||||
* In an apparent contradiction, the Internet both fragments and concentrates attention.
|
||||
|
||||
* The reason for the former is intuitive. More stuff, less attention per unit of stuff. The reason for the latter is not. It happens because networks are subject to powerful positive feedback loops. On a network, people's choices are influenced by others' decisions, amplifying "hits.”
|
||||
|
||||
* There are two mechanisms underlying this: information cascades (when people treat others' choices as signals of quality) and reputational cascades (when people conform with the group decision). As choice has exploded on the Internet and it has become easier to both observe others' choices and share your own, these mechanisms have become more powerful.
|
||||
|
||||
* Consumers also rely heavily on recommendation algorithms to make choices, intentionally and unintentionally. Depending on how they're constructed, these systems can either boost or dampen the social signals arising from the network.
|
||||
|
||||
* The result is that the distribution of consumption in almost all media persistently, and in some cases increasingly, looks like a power law: a few massive hits and a very, very (very) long tail. I provide a framework for thinking about the "extremeness" of the distribution and show a few examples: box office, Netflix original series, Spotify streams and Patreon patrons.
|
||||
|
||||
* There are a number of important implications for media companies. The good news is that there will likely always be big hits, even in a world of practically infinite content. The bad news is just about everything else: the lucrative middle is being hollowed out; the randomness—and therefore risk-in producing hits is climbing; the tail is become more competitive for hits; more economic rent will
|
||||
|
||||
## 2/20
|
||||
|
||||
# 4/23/25, 6:53 PM Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
https://archive.ph/0cYxS
|
||||
|
||||
likely shift to talent; content producers are increasingly at the mercy of curators' algorithms; and paid media is being devalued.
|
||||
|
||||
Thanks for reading The Mediator! Subscribe for free to receive new posts and support my work.
|
||||
|
||||
## The Long Tail Was Half Right
|
||||
|
||||
The idea that the Internet would cause media fragmentation is almost as old as the modern Internet itself. (Or maybe older. The line often misattributed to Andy Warhol that "in the future, everyone will be world-famous for 15 minutes” was a pre-Internet prediction of fragmentation.) In 1999, Qwest Communications produced an ad featuring a motel with “every movie ever made in every language" (Figure 1). [The Long Tail](https://www.wired.com/2004/10/tail/), published in 2004, argued that because the Internet dramatically lowered the cost to store and transport information goods, it would result in practically infinite shelf space. Faced with far more choice, consumers would shift most of their consumption to the "tail,” heralding the end of mass culture and waning importance of hits. If anything, Anderson underestimated the size of the tail because he didn't anticipate social media. The tail is not Icelandic synth pop, as it turns out, but an endless amount of user generated content.
|
||||
|
||||
Figure 1. Qwest Envisioned Media Fragmentation 25 Years Ago
|
||||
|
||||
<!-- Image Description: The image is a photograph of a vintage motel sign at night. The sign reads "ROY'S MOTEL CAFE" in large, illuminated letters. Below that, in smaller letters, it says "VACANCY." Further down, the sign advertises "EVERY ROOM HAS EVERY MOVIE EVER MADE IN EVERY LANGUAGE DAY OR NIGHT." The sign is brightly lit against the dark sky, and the surrounding area appears to be a desert landscape. The photograph is meant to evoke a sense of nostalgia and the promise of endless entertainment options. -->
|
||||
|
||||
Source: Qwest Communications print advertisement, 1999.
|
||||
|
||||
That the Internet would yield more choice and, therefore, more fragmentation was intuitive then and is indisputable now. But it only tells half the story. Though it seems contradictory, the Internet both fragments and concentrates attention. This latter idea was explored by Anita Elberse in her book [Blockbusters: Hit-making, Risk-taking, and the Big Business of Entertainment](https://www.amazon.com/Blockbusters-Hit-making-Risk-taking-Business/dp/0547248912), which was in part a rebuttal to The Long Tail. But that book
|
||||
|
||||
## 3/20
|
||||
|
||||
# 4/23/25, 6:53 PM Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
https://archive.ph/0cYxS
|
||||
|
||||
was more focused on why suppliers should pursue blockbuster strategies and less about the underlying demand-side dynamics that create hits.
|
||||
|
||||
Understanding those dynamics matters. The contention that there are still hits may seem uncontroversial and certainly feels right intuitively. We know that when Beyonce or Taylor Swift releases an album or the next season of Stranger Things or Game of Thrones drops, the collective attention of popular culture, much like the eye of Sauron, will be trained on it—at least until the next thing comes along. But understanding why there are still hits provides insight into whether this will persist as the supply of content keeps growing faster than demand.
|
||||
|
||||
Understanding why there are still hits provides insight into whether this will persist and the implications.
|
||||
|
||||
The reason the Internet concentrates attention is that it connects everyone in a big network. And networks are subject to powerful feedback loops. Since consumers increasingly both discover and consume content through information networks, their decisions are increasingly influenced by other people's decisions. These feedback loops amplify the popularity of a small number of choices-hits.
|
||||
|
||||
The net result of these opposing forces-fragmentation and concentration-is that media consumption, and culture more broadly, is persistently, and in some cases, increasingly observing power-law like distributions. That means that few TV shows, movies, songs, books, video games, journal articles, newsletters, short form videos and tweets will be wildly popular, while the vast (vast, vast, vast...) majority will be hardly consumed at all.
|
||||
|
||||
## What is a Power Law?
|
||||
|
||||
One of the first statistical concepts we are taught in school, right after mean, median and mode, is the "bell curve," aka the normal or Gaussian distribution. The intuition behind a normal distribution is that if you have enough random independent observations most observations will be relatively close to the average (or mean) and equally distributed on either side of it. Many independent natural phenomena approximate this distribution, especially when the extremes are bounded, like height, weight, test scores or rolling two six-sided dice.
|
||||
|
||||
Figure 2. Normal and Power Law Distributions
|
||||
|
||||
<!-- Image Description: The image is a diagram illustrating the difference between normal and power law distributions. There are three graphs. The first graph, labeled "NORMAL DISTRIBUTION," shows a bell-shaped curve, indicating that most values cluster around the mean. The second graph, labeled "POWER LAW DISTRIBUTION," shows a curve that starts high and rapidly decreases, indicating that a few values are very high while most are very low. The third graph, labeled "POWER LAW DISTRIBUTION NORMAL DISTRIBUTION," superimposes the power law distribution over the normal distribution, highlighting that the power law distribution has a longer tail and more extreme values compared to the normal distribution. The x-axis of each graph is labeled "Value." -->
|
||||
|
||||
## 4/20
|
||||
|
||||
# 4/23/25, 6:53 PM Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
https://archive.ph/0cYxS
|
||||
|
||||
Power law distributions, by contrast, look very different. A power law simply means that the dependent variable is a “power” of the independent variable. For instance, the volume of a cube is a “power” of the length of the sides, because volume increases 3 units for each 1 unit in length. Generally, they can be expressed as:
|
||||
|
||||
y = ax
|
||||
|
||||
In a power law probability distribution, the exponent is negative, which results in a downward sloping curve (as illustrated crudely in Figure 2). As shown, power law distributions are characterized by a large number of very small observations and a small number of very large observations.
|
||||
|
||||
There are plenty of places to explore the technical differences between a normal and power law distribution, including the excellent book [Networks, Crowds and Markets](http://www.cs.cornell.edu/home/kleinber/networks-book/), available for free here (see Chapter 18).
|
||||
|
||||
For our purposes, the main point of this comparison is shown in the graph furthest to the right in Figure 2, which superimposes a power law distribution over a normal distribution: the likelihood of both extremely small and extremely large observations is much greater in the former than the latter.
|
||||
|
||||
The main point: in a power law, both extremely small and extremely large observations are much more common.
|
||||
|
||||
Perhaps the best way of thinking about these differences is a framework popularized by Nassim Nicholas Taleb in The Black Swan. Think of the world of normal distributions as Mediocristan-a place where everything hovers somewhere around the average and the world of power-law distributions as Extremistan-a place where seemingly extreme things happen much more often.
|
||||
|
||||
## Why Do Power Laws Occur in Culture? Networks
|
||||
|
||||
As mentioned above, the idea that the Internet causes media fragmentation is intuitive but the idea that it also amplifies hits is not. Let's explore why that happens.
|
||||
|
||||
Power laws (or, strictly speaking, power-law like distributions) show up in a lot of places: the incidence of earthquakes, the occurrence of words in any given publication (called Zipf's Law), the population of cities, metabolic scaling among mammals and a whole lot else.
|
||||
|
||||
The mechanisms behind these power laws are not always clear (there is debate whether power laws are an inherent property of complex systems). But power laws are common in networks because network phenomena tend to be dependent, meaning there are feedback loops. Each node on the network influences, and is influenced by, other nodes.
|
||||
|
||||
## 5/20
|
||||
|
||||
|
||||
# 4/23/25, 6:53 PM
|
||||
Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
Popularity follows power-law like distributions because people's choices are subject to
|
||||
feedback loops.
|
||||
|
||||
This is particularly true for popularity. Power-law like distributions are everywhere in
|
||||
media, as shown in this [article](https://archive.ph/o/0cYxS/https://stratechery.com/2023/power-laws-in-culture/) by Michael Tauberg.
|
||||
|
||||
## Social Signals Influence Our Choices
|
||||
|
||||
So, if networks tend to amplify hits because people often base their choices on what
|
||||
they see other people do, the next question is: why? For two reasons: 1) it is often
|
||||
rational to assume that other people's choices contain valuable information; and 2)
|
||||
people care what others think of them.
|
||||
|
||||
These are two distinct phenomena, what social scientists call “information cascades”
|
||||
and "reputational cascades."
|
||||
|
||||
* Information cascades. What do you do when you have to make a choice and have
|
||||
incomplete information? It probably depends on how hard it is to determine the
|
||||
quality of your options yourself (“search costs”), as well as the consequences
|
||||
(including the reversibility) of making a bad choice (“opportunity costs”). Search
|
||||
costs are a function both of the number of choices and the time required to
|
||||
ascertain the quality of each choice. For instance, it is easy to quickly judge
|
||||
quality when scrolling TikTok and hard when looking for the next multi-season
|
||||
TV series. The opportunity cost of listening to the first 8 seconds of a
|
||||
recommended song on Spotify is very different than getting a babysitter and going
|
||||
to the movies. When search and opportunity costs are low, you may choose to
|
||||
figure it out yourself. When they are high and you can see what other people have
|
||||
done, it is reasonable to presume that (collectively) other people have more
|
||||
information than you do and base your decisions on theirs. When many people do
|
||||
this successively, it results in something called an "information cascade." This is
|
||||
sometimes called cumulative advantage, preferential attachment or the “rich-get-
|
||||
richer effect," whereby popular things tend to get more popular and unpopular
|
||||
things stay unpopular.
|
||||
|
||||
Taking signals from the network is a rational choice when confronted with high search and
|
||||
opportunity costs.
|
||||
|
||||
* Social conformity and reputational cascades. When you can see people's choices
|
||||
and they can see yours, you may conform, consciously or subconsciously. As a
|
||||
generality, we all feel pressure to conform, as was corroborated by famous social
|
||||
science experiments in the 1950s-1970s, such as those conducted by Solomon
|
||||
Asch. Alternatively, you may intentionally choose to follow the group's decisions
|
||||
because you want to signal your allegiance and worthiness of belonging, or what is
|
||||
called a reputational cascade.
|
||||
|
||||
# 6/20
|
||||
|
||||
# 4/23/25, 6:53 PM
|
||||
Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
(There is also a third reason that people are often influenced by other's choices that
|
||||
I'm overlooking: network effects. Sometimes people follow the crowd because they
|
||||
benefit directly from a larger network. This may be a significant factor for fax
|
||||
machines, operating systems or electric vehicles, but probably has less relevance in
|
||||
culture. The direct benefits of more developers building apps for Windows or more
|
||||
Tesla rapid-charging stations are clear; the network effects from a lot of people
|
||||
watching your favorite TV show or listening to your favorite band are questionable
|
||||
and may actually be a drawback for people who believe they have unique taste.)
|
||||
|
||||
## Social Signals are Becoming More Important
|
||||
|
||||
So, people are more likely to be influenced by what other people do when: 1) there are
|
||||
a lot of choices; and 2) it is easy to observe what other people do.
|
||||
|
||||
Over the last two decades, the conditions that lead to cascades have become more prevalent:
|
||||
choice has exploded and it is far easier to observe others' actions and to be observed.
|
||||
|
||||
Both of those conditions have increased dramatically in the last few decades:
|
||||
|
||||
* The amount of content available has exploded, making search costs
|
||||
astronomical and increasing opportunity costs. It is obvious that more choice
|
||||
means higher search costs. It also means higher opportunity costs, because when
|
||||
you make a choice today there are many more things you are choosing not to do.
|
||||
* Owing to online networks, people are much more likely to be influenced
|
||||
(directly and indirectly) by what other people choose. Many people explicitly
|
||||
outsource their content curation to their friends (by relying on the Facebook
|
||||
newsfeed), their hand-selected panel of “experts” (on their Twitter timeline) or
|
||||
their favorite celebrities or influencers (on Instagram). But sometimes we forget
|
||||
that elements of social networking are embedded in non-social networking
|
||||
applications too. Go to the Apple app store, Amazon, OpenTable, or even look for
|
||||
“restaurants near me" on Google Maps-in every case, you will probably be
|
||||
influenced by other people's opinions. Most recommendation algorithms also rely
|
||||
in part on collaborative filtering, discussed more below, which is based on the
|
||||
collective choices of a group or subgroup. When you accept an algorithm's
|
||||
recommendation you are often indirectly influenced by what other people choose,
|
||||
whether you know it or not.
|
||||
|
||||
Taken together, this means that today, people are much more likely to base their
|
||||
choices on other people's decisions. This explains the paradox described at the
|
||||
beginning: while the Internet fragments attention, it also causes cascades that
|
||||
concentrate attention.
|
||||
|
||||
## Recommendation Engines Can Help or Hurt
|
||||
|
||||
Confronted with so much choice, consumers don't only depend on the organic social
|
||||
signals they receive from the network, they also rely (to varying degrees, depending on
|
||||
the person and type of media) on recommendation systems. Those systems may
|
||||
amplify or dampen the influence of the network, depending on how they are
|
||||
engineered.
|
||||
|
||||
# 7/20
|
||||
|
||||
# 4/23/25, 6:53 PM
|
||||
Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
Recommendation algorithms are based on two primary types of models: collaborative
|
||||
filtering and content models. In the former, the algorithm recommends content or
|
||||
products based on what other people have chosen. In the latter, recommendations are
|
||||
based on certain attributes of the content or products themselves.
|
||||
|
||||
Recommendation systems can amplify or dampen social signals, depending on how
|
||||
they're built.
|
||||
|
||||
It is common for these algorithms to include elements of both models. For instance, in
|
||||
its recommendation system Netflix incorporates all kinds of metadata associated with
|
||||
each content asset (director, actors, genre, age rating, tone) and popularity (viewership,
|
||||
completion rates and ratings) among cohorts it believes are similar to the customer, as
|
||||
well as prior viewing behavior by the customer (device, time of day, time spent
|
||||
viewing). TikTok similarly bases its algorithm on user behavior, collaborative filtering
|
||||
and specific content attributes, among other things. By contrast, Pandora's
|
||||
recommendation system is uncommon because it is based solely on content attributes,
|
||||
not on any collaborative filtering.
|
||||
|
||||
## A Simple Framework
|
||||
|
||||
As mentioned, power-law like distributions are ubiquitous in media, but to varying
|
||||
degrees. Synthesizing the last two sections, I'll propose a few rules of thumb for
|
||||
predicting when distributions will be more, or less, extreme:
|
||||
|
||||
* Higher search costs = more extreme distributions (because people need to rely
|
||||
more heavily on social signals)
|
||||
* Higher opportunity costs = more extreme distributions (also because people are
|
||||
more likely to seek out social signals before committing)
|
||||
* Recommendation systems that lean heavily toward collaborative filtering = more
|
||||
extreme distributions (because the algorithm amplifies the social signals)
|
||||
|
||||
## A Little Math
|
||||
|
||||
How do we know a popularity distribution is a power law and how do we measure
|
||||
"extreme?"
|
||||
|
||||
Answering those requires a little more math. As shown above, the general
|
||||
mathematical expression of a power law looks like this:
|
||||
|
||||
y = ax
|
||||
|
||||
In a pure power law, c is a constant, which can be thought of a scaling factor. In a
|
||||
power law distribution, c is also negative, which is why the curve is downward sloping.
|
||||
It can be hard to tell whether this scaling factor is constant just by looking-and
|
||||
therefore whether it is really a power law. An easier way is to convert the data to a log-
|
||||
log plot and determine whether the resulting relationship is linear. To see why, we
|
||||
take the log of both sides of the equation above:
|
||||
|
||||
# 8/20
|
||||
|
||||
# 4/23/25, 6:53 PM
|
||||
Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
log (y) = log (a) + c log (x)
|
||||
|
||||
That is a linear function, equivalent to y = b + mx. In other words, if we really have a
|
||||
power law (or something power-law like), the log-log plot should look like a straight
|
||||
line, where the slope is c and, the larger (or more negative) the value of c, the more
|
||||
"extreme" it is. We can also test how straight it is, and therefore whether the scaling
|
||||
factor is really a constant, by calculating the r².
|
||||
|
||||
Figure 3. Popularity Distributions Usually Show Value as a Function of Probability (or Rank)
|
||||
|
||||
The image shows two graphs. The first graph has "Value" on the x-axis and "Probability of value" on the y-axis. The graph shows a curve that starts high on the y-axis and decreases as it moves to the right on the x-axis. The second graph has "Probability of value" on the x-axis and "Value" on the y-axis. The graph shows a curve that starts high on the y-axis and decreases as it moves to the right on the x-axis. The graph is labeled with "The 'head'" and "The 'tail'".
|
||||
|
||||
## A Few Examples (and Caveats)
|
||||
|
||||
Below, I look at some representative time series of consumption distribution for a few
|
||||
media: box office, TV series on Netflix, streams on Spotify and Patreon creators.
|
||||
|
||||
(One quick note: In the power law distribution above in Figure 2, the Y-axis is
|
||||
probability and X-axis is value to better compare normal and power law distributions. A
|
||||
more intuitive and common way to discuss popularity distributions is to flip the axes
|
||||
so that the Y-axis is the value and the X-axis is the probability, which is also a power
|
||||
law (Figure 3). This shows that only a handful of observations will be extremely large
|
||||
(what is colloquially called the “head”) and a vast number will be very small (the “tail”).
|
||||
This is how I discuss popularity distributions below.)
|
||||
|
||||
This analysis is imperfect, for a few reasons. I would like to have longer time series
|
||||
than I show here (box office is great, at ~20 years, but it would be great to have 20 years
|
||||
of music data too). Also, the data for Spotify and Patreon only show the distribution of
|
||||
consumption at the head of the curve. Since power laws are self-similar (or "scale
|
||||
invariant"), in theory the distribution at the head of the curve is representative of the
|
||||
entire distribution, but if these are not pure power laws that may not be the case.
|
||||
|
||||
Putting those aside, all four of these examples show persistently extreme distributions
|
||||
that closely approximate power laws.
|
||||
|
||||
## Box Office
|
||||
|
||||
Relative to most other media, moviegoers face very few choices but extraordinarily
|
||||
high opportunity costs. Not surprisingly, the relative distribution of consumption has
|
||||
become even more concentrated in the top hits in recent years. Figure 4 shows the
|
||||
distribution of total U.S. box office in 2000, 2010, 2019 and 2022 and the same data on a
|
||||
log-log basis. As shown by the r-squared values in the log-log plots, these are close to
|
||||
|
||||
# 9/20
|
||||
|
||||
# 4/23/25, 6:53 PM
|
||||
Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
power law distributions. As also shown, over that time period the distribution has
|
||||
gotten increasingly extreme (i.e., the slope on the log-log plots has gotten increasingly
|
||||
negative); on a relative basis, the biggest hits are bigger than ever.
|
||||
|
||||
Figure 4. Distribution of Box Office Getting More Extreme
|
||||
|
||||
The image shows two graphs related to the distribution of total US box office revenue.
|
||||
|
||||
The first graph, titled "DISTRIBUTION OF TOTAL US BOX OFFICE," displays the percentage of total US box office revenue against release rank for the years 2000, 2010, 2019, and 2022. The graph shows that the top-ranked movies account for a larger percentage of the total box office revenue in more recent years.
|
||||
|
||||
The second graph, titled "DISTRIBUTION OF TOTAL US BOX OFFICE LOG-LOG," presents the same data on a log-log scale. This transformation helps to visualize the power-law distribution of box office revenue. The graph includes R² and Slope values for each year, indicating the goodness of fit of the power-law model. The R² values are close to 1, suggesting a strong fit, and the slopes are negative, indicating a decreasing trend.
|
||||
|
||||
Source: Box Office Mojo, Author analysis.
|
||||
|
||||
## Netflix TV Series
|
||||
|
||||
In TV, the search and opportunity costs of finding and committing to a TV series are
|
||||
pretty high, which should lead to relatively extreme distributions. But it's tough to test
|
||||
shifts in popularity distributions over time for all of TV because there is no good
|
||||
cross-platform (linear and streaming) measurement. And although Nielsen now
|
||||
provides streaming ratings, it's only been doing so for a couple of years.
|
||||
|
||||
The best data I could find was from the good people at Parrot Analytics, who provided
|
||||
me a time series of global demand for Netflix original series. Parrot's demand metric
|
||||
|
||||
# 10/20
|
||||
|
||||
# 4/23/25, 6:53 PM
|
||||
|
||||
Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
incorporates a variety of inputs (social, fan and critic ratings, piracy, wikis, blogs, etc.)
|
||||
to gauge the popularity of each series and movie on each streaming service.
|
||||
|
||||
The most remarkable takeaway from this data is that it remains relatively skewed and
|
||||
is becoming more power-law like over time despite Netflix's big international push
|
||||
over this timeframe. As noted, this is global demand and measures a period when
|
||||
Netflix added about 100 million subscribers, almost all of which were international,
|
||||
and its annual cash content spend increased from $13 billion to $17 billion, much of
|
||||
which was local content.
|
||||
|
||||
Despite its growth and increased spend internationally, as shown in Figure 5, globally
|
||||
demand remains concentrated in relatively few titles. Note that in 2018, 2020 and 2022,
|
||||
the top 10% of originals represented ~95%, 85% and 75% of all global demand on
|
||||
Netflix, respectively.
|
||||
|
||||
Figure 5. Demand for Netflix Series Has Remained Skewed Despite Big International
|
||||
Expansion
|
||||
|
||||
The image shows two line graphs related to the distribution of global demand among the top 250 series on Netflix. The first graph shows the distribution on a linear scale, while the second graph shows the distribution on a log-log scale. Both graphs plot data for the years 2018, 2020, and 2022. The log-log graph also includes R-squared values and slopes for each year. The graphs illustrate how demand is concentrated among a few top series, and how this concentration has changed over time.
|
||||
|
||||
Note: Parrot Analytics' demand metric incorporates a variety of inputs to measure the
|
||||
popularity of series and movies. Source: Parrot Analytics, Author analysis.
|
||||
|
||||
Spotify Streams
|
||||
|
||||
Music is an interesting case because there are factors working in both directions. On
|
||||
the one hand, with so much choice (Spotify has over 80 million tracks and 100,000 new
|
||||
songs uploaded every day), listeners use both social signals and recommendation
|
||||
engines to discover new music. And most streaming services' recommendation
|
||||
|
||||
[https://archive.ph/0cYxS](https://archive.ph/0cYxS)
|
||||
|
||||
11/20
|
||||
|
||||
# 4/23/25, 6:53 PM
|
||||
|
||||
Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
engines rely heavily on collaborative filtering (see a description of Spotify's
|
||||
recommendation engine here). This implies a relatively extreme distribution.
|
||||
|
||||
On the other hand, the search costs and opportunity cost of trying a new song are very
|
||||
low and easily reversed (you can easily skip to the next song). Both of those factors
|
||||
support a broader dispersion of consumption.
|
||||
|
||||
The result is that consumption in the head is extremely skewed toward the biggest
|
||||
hits, but also that more aggregate consumption is shifting into the tail. By implication,
|
||||
the "middle" is even skinnier than you would see in a pure power law.
|
||||
|
||||
Figure 6 shows the distribution of consumption among all the songs that appeared in
|
||||
Spotify's Global Top 200 Weekly at least once, in both 2017 and 2022 (and the same
|
||||
data on a log-log basis). In both years, that was about 1,000 songs. (This is the very
|
||||
head of the curve-it's the top 1,000 songs out of 80 million, or the top 0.001%.) As
|
||||
illustrated by the slope on the log-log plots, the distribution is very extreme, even
|
||||
more so than box office. As is also evident, the slope is not constant; it becomes more
|
||||
negative as you move past the 100th most popular song. That means the biggest hits
|
||||
are even bigger on a relative basis and even more consumption is occurring in the tail
|
||||
than would occur in a true power law.
|
||||
|
||||
Figure 6. The Head of the Spotify Curve Remains Extreme...
|
||||
|
||||
The image shows two line graphs related to the distribution of top songs on Spotify. The first graph shows the percentage of total streams among songs appearing in the weekly chart of top 200 songs globally, plotted against song rank. The second graph shows the same data on a log-log scale. Both graphs plot data for the years 2017 and 2022. The log-log graph also includes R-squared values and slopes for each year. The graphs illustrate how consumption is skewed towards the top songs, and how this skewness has changed over time.
|
||||
|
||||
[https://archive.ph/0cYxS](https://archive.ph/0cYxS)
|
||||
|
||||
12/20
|
||||
|
||||
# 4/23/25, 6:53 PM
|
||||
|
||||
Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
Source: Spotify, Author analysis.
|
||||
|
||||
The idea that more consumption is shifting to the tail is corroborated by aggregate
|
||||
consumption data. As shown in Figure 7, based on Spotify's reporting, the three
|
||||
majors (Universal, Sony and Warner Music) and Merlin (a partnership of independent
|
||||
labels) represented 77% of total streams in 2021, down 10 percentage points from 2017.
|
||||
|
||||
Figure 7. ...But More Consumption is Also Shifting to the Tail
|
||||
|
||||
The image is a bar graph showing the combined distribution market share of annual Spotify plays for Universal Music, Sony Music, Warner Music, and Merlin (%). The graph displays data from 2017 to 2021, with the market share decreasing from 87% in 2017 to 77% in 2021.
|
||||
|
||||
Source: Spotify company reports, via Music Business Worldwide.
|
||||
|
||||
Patreon Creators
|
||||
|
||||
Patreon provides a backend solution for creators to sell subscriptions, with more than
|
||||
250,000 creators on the platform and 13 million patrons. It is also an interesting
|
||||
example because consumption distribution is unaffected by recommendation
|
||||
algorithms. While Patreon.com features a handful of creators on its landing page, few
|
||||
consumers visit it. They primarily navigate directly to creators' Patreon pages from
|
||||
wherever their work is featured, such as YouTube, Apple podcasts or their websites.
|
||||
|
||||
With no amplifying effect from recommendation algorithms, it should show a slightly
|
||||
less skewed distribution than some other examples. Figure 8 shows the distribution of
|
||||
the top 1,000 creators at the end of both 2016 and 2022 and the log-log data. Again, this
|
||||
is the head of the curve, or 0.4% of creators in 2022. As shown, the distribution tracks
|
||||
almost exactly as a power law, but the slope is less extreme than the prior examples.
|
||||
|
||||
Figure 8. The Creator Economy Observes Power Laws Too
|
||||
|
||||
[https://archive.ph/0cYxS](https://archive.ph/0cYxS)
|
||||
|
||||
13/20
|
||||
|
||||
# 4/23/25, 6:53 PM
|
||||
|
||||
Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
The image shows two line graphs related to the distribution of patrons to top creators on Patreon. The first graph shows the distribution on a linear scale, while the second graph shows the distribution on a log-log scale. Both graphs plot data for the years 2016 and 2022. The log-log graph also includes R-squared values and slopes for each year. The graphs illustrate how patrons are distributed among the top creators, and how this distribution has changed over time.
|
||||
|
||||
Source: Graphtreon, Author analysis.
|
||||
|
||||
So What? Understanding the Pervasive Implications of
|
||||
Power Laws
|
||||
|
||||
As my 11th grade history teacher Mr. Conroy used to say "So what?" The persistence
|
||||
of these highly skewed consumption distributions has very important practical
|
||||
implications for the media business and culture more broadly.
|
||||
|
||||
Hits Will Persist in an Infinite Content World
|
||||
|
||||
As mentioned at the top, lately I have been writing about the inevitability of Infinite
|
||||
TV as the quality distinction between professional and independent/creator content
|
||||
blurs.
|
||||
|
||||
One of the questions I got back was: will there still be hits in such a world?
|
||||
|
||||
The short answer: there will likely always be hits, if not even larger ones. As described
|
||||
above, the more choice, the more consumers need to rely on social signals and
|
||||
recommendation engines (which in turn rely on social signals) to manage search costs.
|
||||
This is already evident in music. High production value tools have been democratized,
|
||||
leading to a practically infinite amount of high production value music. But massive
|
||||
hits persist.
|
||||
|
||||
[https://archive.ph/0cYxS](https://archive.ph/0cYxS)
|
||||
|
||||
14/20
|
||||
|
||||
# 4/23/25, 6:53 PM
|
||||
|
||||
Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
OK, but can we really use the word "always"? Let's go really far out. What if eventually
|
||||
generative Al is able to create distinct personalized content for each individual? In a
|
||||
recent post about generative AI, Sequoia posited that by 2030, movies will be
|
||||
"personalized dreams” (Figure 9).
|
||||
|
||||
Figure 9. Will All Content be “Personalized Dreams"?
|
||||
|
||||
The image is a table that outlines the evolution of AI capabilities in content creation across different media types (text, code, images, video/3D/gaming) from pre-2020 to a projected 2030. It shows a progression from basic tasks like spam detection and auto-complete to advanced capabilities like generating final drafts better than professional writers and developers, and ultimately, personalized video games and movies by 2030.
|
||||
|
||||
Source: Sequoia.
|
||||
|
||||
This may not be as far fetched as it sounds, at least technologically. Let's say that by
|
||||
2035 we are all wearing AR glasses, which record data about us that put Google and
|
||||
Facebook to shame. They track our gaze, including the length of time we linger on
|
||||
anything and the dilation of our pupils, respiration and heart rate (h/t Rony Abovitz).
|
||||
They might know more about us than we know ourselves. Let's go even further.
|
||||
Perhaps we'll wear devices that record brain activity as we sleep and reconstruct the
|
||||
imagery from our dreams. Sound crazy? Researchers in Japan just showed that this is
|
||||
already possible.
|
||||
|
||||
There is no way to disprove the concept of individualized content. But just because it
|
||||
might be technically possible doesn't mean it will be popular. It runs counter to two
|
||||
fundamental human needs: 1) People want agency (or at least the appearance of
|
||||
agency) in their choices-they don't want to be reduced to an algorithm. (Which is
|
||||
why Netflix recently removed its "Surprise Me" button.) 2) More important, we are
|
||||
ultimately social animals and have a need to coalesce around common experiences. As
|
||||
I discussed in another recent essay, for many people, those shared experiences are
|
||||
entertainment (sports, music, gaming, movies, TV shows). At a time when loneliness is
|
||||
considered a public health crisis, it is hard to imagine that we would forego shared
|
||||
experiences and retreat to lonely theaters of one.
|
||||
|
||||
Bye, Bye Middle
|
||||
|
||||
If the biggest hits are as big as ever-or bigger—and the tail is also getting bigger,
|
||||
another implication is that the middle is going away.
|
||||
|
||||
What's the middle? Consider the middle any content that attracted attention (and
|
||||
economics) solely because it benefited from formerly scarce distribution: local
|
||||
newspapers largely comprising syndicated news, TV stations with weak local
|
||||
coverage, radio stations without distinctive on-air personalities, middling general
|
||||
entertainment cable networks populated with second-tier reruns or inexpensive reality
|
||||
programming, mid-budget me-too theatrical releases, etc. It's hard to define "the
|
||||
|
||||
[https://archive.ph/0cYxS](https://archive.ph/0cYxS)
|
||||
|
||||
15/20
|
||||
|
||||
|
||||
# 4/23/25, 6:53 PM Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
middle" with precision, but it's safe to say that historically the middle has collectively
|
||||
generated a substantial proportion of profits in every media vertical.
|
||||
|
||||
The dwindling middle has generated a substantial portion of profits in every media vertical.
|
||||
|
||||
## Hits Include a Big Dose of Luck
|
||||
|
||||
Another important implication of this "power-lawing" is that hits are increasingly
|
||||
random because of how information cascades work. To be clear, I'm not arguing that
|
||||
all hits are random, but that luck is becoming more important.
|
||||
|
||||
Hits are not completely random, but the role of luck is increasing.
|
||||
|
||||
[Meta Comment: Link to archive.ph]
|
||||
https://archive.ph/0cYxS
|
||||
|
||||
More than 15 years ago, researchers Matthew Salganik, Peter Dodds and Duncan
|
||||
Watts conducted an experiment to determine the effect of social influence on content
|
||||
choices. They split 14,000 subjects into nine groups, one "independent group" and
|
||||
eight "social influence groups." All the subjects were invited to visit a website where
|
||||
they were asked to rate 48 unknown songs by unknown bands. They were able to
|
||||
download the songs if they chose. In the eight social influence groups, subjects could
|
||||
see how many times each song had been downloaded by prior visitors from their
|
||||
group; in the independent group, they couldn't. At the end, the researchers tallied the
|
||||
popularity of the songs in each group.
|
||||
|
||||
The major conclusions were twofold: 1) each of the nine groups had different rankings
|
||||
of the songs (while some songs tended to be more popular and some songs were
|
||||
consistently less popular, other than that the rankings were quite different); and 2) the
|
||||
distribution of popularity within the social influence groups was more extreme than in
|
||||
the independent group. The second conclusion supports the main point of this essay,
|
||||
namely that the presence of social signals will cause the distribution of popularity to
|
||||
be more skewed. (And keep in mind that in this experiment the only signal was the
|
||||
number of previous downloads, so the participants were only subject to information
|
||||
cascades, not pressure to conform or reputational cascades. In the real world, the
|
||||
social signals are a lot stronger.)
|
||||
|
||||
But let's think about the implications of the first conclusion, namely that each group
|
||||
produced a different popularity ranking. It implies that hits require a high degree of
|
||||
luck.
|
||||
|
||||
To see why this happens, try out a thought experiment (borrowed from Michael
|
||||
Mauboussin). Imagine a barrel with 1,000 balls in it, each of which is numbered 1-10,
|
||||
and there are 100 of each number (100 #1s, 100 #2s, etc.). Also imagine you have 10
|
||||
urns, each marked 1-10. Now randomly pick 10 balls out of the barrel and, based on
|
||||
the number marked on each, put each ball in its corresponding urn. Replace the 10
|
||||
balls you removed from the barrel with new balls, but this time the distribution of new
|
||||
balls will be equivalent to the distribution of balls in the urns. (If there are two balls in
|
||||
urn #2 and none in #3, then two of the new balls should be marked #2 and none should
|
||||
|
||||
## 16/20
|
||||
|
||||
# 4/23/25, 6:53 PM Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
be marked #3.) Keep running the process, removing 10 balls from the barrel at random,
|
||||
placing them in the corresponding urns, and adding new balls to the barrel based on
|
||||
the distribution of balls in the urns. After you run this process for enough cycles, what
|
||||
you find is that the urns with more balls are increasingly likely to have more balls
|
||||
added each time.
|
||||
|
||||
Or think of a real-world example: Amazon reviews. The Amazon algorithm places the
|
||||
reviews with the most "helpful" votes at the top. Naturally, most people start at the top
|
||||
and read just a few reviews. The first reviews written for a new book will appear at the
|
||||
top of the page (for lack of many reviews). So, they are more likely to be read and
|
||||
deemed helpful than subsequent reviews. This creates a positive feedback loop: they
|
||||
are more likely to remain near the top of the page, making it likely that new visitors
|
||||
will mark them as helpful, cementing their position at the top of the page.
|
||||
|
||||
In a networked environment, hits are highly sensitive to initial conditions.
|
||||
|
||||
[Meta Comment: Link to archive.ph]
|
||||
https://archive.ph/0cYxS
|
||||
|
||||
This phenomenon (which above I referred to as the rich-get-richer effect, cumulative
|
||||
advantage or preferential attachment) shows that in a networked environment
|
||||
popularity is influenced by luck and highly sensitive to initial conditions. The balls
|
||||
that happen to be selected first (or the reviews that are written first) have a much
|
||||
higher likelihood of dominating. Even in a hypothetical world in which all content was
|
||||
of equal quality there would still be massive, random hits. Was the success of
|
||||
PewDiePie or Charlie Puth inevitable? Hard to say.
|
||||
|
||||
As content consumption is increasingly affected by network dynamics, this means that
|
||||
hits will become more unpredictable. And just as in the financial markets, higher
|
||||
volatility means higher risk, and higher risk means lower returns.
|
||||
|
||||
## Hits Can (and Will) Emerge from the Tail
|
||||
|
||||
A corollary of the prior point is that hits can, and will, emerge from the tail. Again,
|
||||
this is already evident in music. As I wrote in Infinite TV:
|
||||
|
||||
[A]lmost all of the new breakout acts of the last few years-like The Weeknd, Billie
|
||||
Eilish, Lil Uzi Vert, XXXTentacion, Bad Bunny, Post Malone, Migos and many
|
||||
more-emerged from the tail of self-distributed content, not from A&R reps
|
||||
hanging around at 2AM for the last act.
|
||||
|
||||
Writing compelling fiction, composing a catchy pop song, conceiving innovative
|
||||
gameplay or writing a great screenplay are extraordinarily rare talents. It is reasonable
|
||||
to think that many of the people capable of doing these things, with persistence and
|
||||
luck, are able to succeed through the traditional channels of content production and
|
||||
win the support of the small handful of people who control resources at places like
|
||||
HarperCollins, Republic Records, Blizzard or Universal Pictures. But how many
|
||||
creative "lost Einsteins" are there who have fell through the cracks? Thousands? Tens
|
||||
of Thousands? Hundreds of thousands?
|
||||
|
||||
Just has occurred with the music labels, every traditional producer of any type of
|
||||
content should be prepared to both discover talent that emerges from the tail and
|
||||
|
||||
## 17/20
|
||||
|
||||
# 4/23/25, 6:53 PM Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
compete with it.
|
||||
|
||||
## There's a Reason Every Movie Star Wears Tights
|
||||
|
||||
If it sometimes feels like every movie is a prequel or sequel or about superheroes (or
|
||||
both) and every new TV show is a spinoff or reboot, that's because a disproportionate
|
||||
percentage of them are (as discussed in this article by Adam Mastroianni).
|
||||
|
||||
[Meta Comment: Link to article]
|
||||
this article
|
||||
|
||||
The reasons often cited for this include entertainment companies' crass
|
||||
commercialism, the death of creativity and the dumbing-down of the American
|
||||
consumer, among others. But looking at this through the lens of the network dynamics
|
||||
described in this essay suggests several other reinforcing reasons. Established IP
|
||||
reduces risk because it:
|
||||
|
||||
* Lowers consumer search costs. As discussed above, consumers are overwhelmed
|
||||
by choice and the resulting high search costs. Well-known brands, talent and
|
||||
franchises reduce those costs, making consumers less reliant on network signals.
|
||||
* Benefits from a pre-existing community. As also discussed, consumers
|
||||
sometimes choose content because of a desire to join a community or enhance
|
||||
their standing within it. Established IP has established communities, increasing
|
||||
the community's influence.
|
||||
|
||||
Whether this is good or bad is a different question. There is a risk that major media
|
||||
companies lean too heavily on established IP and all the innovative ideas instead
|
||||
emerge from the tail. But there is a clear logic behind it.
|
||||
|
||||
## Rents Will Likely Shift Even More Toward Top Talent
|
||||
|
||||
The details of how talent is compensated in creative businesses can be extraordinarily
|
||||
complicated and opaque. If you abstract it out, however, ultimately talent
|
||||
compensation is a function of the underlying economic structure of the industries in
|
||||
which they operate.
|
||||
|
||||
At a time when there is both more transparency of performance data and greater
|
||||
competition for superstars, a more extreme distribution of consumption will likely
|
||||
shift even more bargaining power to the top talent.
|
||||
|
||||
## No One is Policing the Algorithm
|
||||
|
||||
Algorithms clearly influence the distribution of consumption and they will become
|
||||
increasingly important. According to Spotify, 1/3 of new music discovery occurs
|
||||
through algorithmic recommendation. Netflix says that 80% of watch time comes from
|
||||
its recommendations and 20% from direct search (but it also concedes that "users tend
|
||||
to come to the service with a specific show, movie or genre in mind"). All things equal,
|
||||
the more choice, the more consumers will seek help in choosing, whether from the
|
||||
organic social signals that emerge from the network or recommendation systems.
|
||||
|
||||
Platforms have a strong incentive to surface the best recommendations. More usage
|
||||
increases consumer affinity, improves retention and, for ad supported platforms,
|
||||
increases revenue. But, at least on the margin, they may have other incentives. Spotify
|
||||
and Netflix both have an incentive to reduce their reliance on their largest suppliers.
|
||||
Both Spotify and TikTok disclose that “commercial considerations” influence their
|
||||
recommendations. Not much can or will likely be done about this, but the opacity and
|
||||
|
||||
## 18/20
|
||||
|
||||
# 4/23/25, 6:53 PM Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
importance of algorithms will become an increasingly important competitive
|
||||
advantage for content aggregators over time.
|
||||
|
||||
## The Creator Economy and Web3 Live in Extremistan Too
|
||||
|
||||
Much has been written (including by me) about the rise of the creator economy and
|
||||
platforms and tools that enable creators to connect directly with—and generate
|
||||
revenue from-fans (not just Patreon, but Substack, OnlyFans, Cameo and many
|
||||
others). Web3 promises an even more decisive step in that direction. Since web3
|
||||
applications are decentralized, data is not mediated by centralized servers and creators
|
||||
retain ownership of their product. For many people, the greatest promise of web3 is to
|
||||
redistribute power and value from centralized institutions to creators and users.
|
||||
|
||||
While both the evolution of the creator economy and web3 should enable more
|
||||
creators to make a living wage, redistribution should not be confused with equal
|
||||
distribution, something I also discussed here. As shown in the popularity distributions
|
||||
for Patreon creators above, as long as there are network dynamics, there will be power-
|
||||
law like popularity distributions.
|
||||
|
||||
## Earned Media is Increasingly Important
|
||||
|
||||
Back to Salganik, Dodds and Watts for a moment. As mentioned, some of the subjects
|
||||
were placed in an independent group that received no social signals at all. The
|
||||
researchers used this group's popularity ranking of songs as a proxy for “quality." What
|
||||
they found among the other groups was that the songs considered best by the
|
||||
independent group rarely did poorly and the songs considered the worst rarely did
|
||||
very well, but anything else could happen.
|
||||
|
||||
Quality matters in popularity. Complete crap will fail. But, above some threshold of quality,
|
||||
popularity is highly reliant on network dynamics.
|
||||
|
||||
The implication is that, as any marketer would tell you, marketing matters. Quality
|
||||
will not necessarily naturally rise to the top. The question is how to market.
|
||||
|
||||
Marketers draw a distinction between paid, earned and owned media. Paid is
|
||||
traditional advertising: TV, outdoor, print, radio, retail media, display, search and
|
||||
social. Earned is PR and word-of-mouth, increasingly through influencers. And owned
|
||||
is the brand's own marketing channels, such as its branded content, website, retail
|
||||
outlets, catalogs, etc. Media companies tend to rely very heavily on paid media-think
|
||||
of massive advertising campaigns to launch a new show or movie. As more content
|
||||
discovery occurs through the network itself, the value of paid media is increasingly
|
||||
diluted. It also becomes more important for marketers to understand what signals are
|
||||
emerging organically and how to use both paid and earned media to amplify or
|
||||
counter those signals.
|
||||
|
||||
## We're Not in Kansas Anymore
|
||||
|
||||
Almost 30 years since the IPO of Netscape, the media industry is still coming to grips
|
||||
with the implications of the Internet. The reality that it fragments attention is
|
||||
intuitive. The reasons why it also amplifies hits are less well understood.
|
||||
|
||||
## 19/20
|
||||
|
||||
# 4/23/25, 6:53 PM Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
For media companies, the implications of operating in a networked world are a mixed
|
||||
bag, at best. The good news is that hits still matter and likely always will. The bad
|
||||
news is just about everything else: the lucrative middle is being hollowed out; risk is
|
||||
climbing; the tail is become more competitive for hits; bargaining power is shifting to
|
||||
the top talent; content producers are increasingly at the mercy of curators' algorithms;
|
||||
and paid media is being devalued. As consumers grapple with a growing tsunami of
|
||||
options, these dynamics will become more pronounced. None of this will get easier.
|
||||
|
||||
[Meta Comment: Social Media Icons]
|
||||
D
|
||||
|
||||
Previous
|
||||
|
||||
Comments
|
||||
|
||||
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|
||||
|
||||
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|
||||
|
||||
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|
||||
|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
||||
[Meta Comment: Link to archive.ph]
|
||||
https://archive.ph/0cYxS
|
||||
|
||||
## 20/20
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -1,557 +0,0 @@
|
|||
---
|
||||
source_type: "article"
|
||||
title: "What is Scarce When Quality is Abundant"
|
||||
author: "Doug Shapiro"
|
||||
url: "https://dougshapiro.substack.com/p/what-is-scarce-when-quality-is-abundan"
|
||||
date_published: "2023-10-01"
|
||||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: processed
|
||||
claims_extracted:
|
||||
- "consumer definition of quality is fluid and revealed through preference not fixed by production value"
|
||||
- "fanchise management is a stack of increasing fan engagement from content extensions through co-creation and co-ownership"
|
||||
---
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
archive.today Saved from https://dougshapiro.substack.com/p/what-is-scarce-when-quality-is-abundan
|
||||
|
||||
23 Apr 2025 14:29:31 UTC
|
||||
|
||||
All snapshots from host dougshapiro.substack.com
|
||||
|
||||
## What is Scarce When Quality is Abundant
|
||||
|
||||
Where Does Value Accrue?
|
||||
|
||||
DOUG SHAPIRO
|
||||
|
||||
OCT 22, 2023
|
||||
|
||||
3
|
||||
2
|
||||
Share
|
||||
|
||||
[Note that this essay was originally published on Medium]
|
||||
|
||||
### Image: Vizcom rendering of my sketch
|
||||
|
||||
The image shows a Vizcom rendering of a sketch. The rendering depicts a set of scales with a flat base. On one side of the scale, there is a flat, round weight. On the other side, there is a stack of coins. The scales are balanced.
|
||||
|
||||
Many of my recent posts explore the following idea: the last decade in film and TV was
|
||||
defined by the disruption of content distribution and the next decade will be defined
|
||||
by the disruption of content creation. The premise is that over the next five-seven
|
||||
years several technologies, particularly AI (including GenAI), will further blur the
|
||||
quality distinction between professionally-produced (or "Hollywood") content and
|
||||
creator or independent content, resulting in effectively “infinite" quality.
|
||||
|
||||
This idea raises a lot of questions, some of which I've tried to answer in posts like
|
||||
Forget Peak TV, Here Comes Infinite TV, How Will the Disruption of Hollywood Play
|
||||
Out? and AI Use Cases in Hollywood. But here's another question: what becomes
|
||||
scarce when quality is abundant? Where will value accrue in an abundant quality
|
||||
world?
|
||||
|
||||
Tl;dr:
|
||||
|
||||
* In analyzing any industry, it's critically important to understand which resources
|
||||
are abundant and which are scarce. That's because value accrues to the scarce
|
||||
|
||||
## 1/17
|
||||
|
||||
* resource in a value chain and, accordingly, it shifts along the chain when the
|
||||
relative abundance/scarcity of resources changes.
|
||||
* Hollywood will need to prepare for abundant quality content.
|
||||
* Last year, Hollywood released about 15,000 hours of new TV episodes and films in
|
||||
the U.S. Creators upload 500 hours of content to YouTube each minute, or over
|
||||
250 million hours per year. If consumers consider just 0.01% of this to be
|
||||
competitive with Hollywood, that would double Hollywood's annual output; if
|
||||
they consider 0.1% competitive, it would be 20x.
|
||||
* Al is set to democratize high production values. At the same time, many
|
||||
consumers' definitions of quality are shifting away from high production values
|
||||
and therefore lowering the bar at least some of the time. YouTube is already the
|
||||
most streamed service in the U.S. to TVs, equivalent to Hulu, Disney+, HBO Max,
|
||||
Peacock and Paramount+ combined. Or, consider that Mr. Beast's last video,
|
||||
which is performing near his average, got enough viewing to be a top 10 series on
|
||||
Netflix globally.
|
||||
* So, what becomes scarce (and more valuable) when quality becomes abundant? A
|
||||
few things: consumer time and attention; hits; marketing prowess; curation;
|
||||
fandom and community; IRL experiences; premium IP; library; and (maybe)
|
||||
certain picks and shovels.
|
||||
* Big media companies should invest in scarce resources where they can.
|
||||
* One opportunity is a much more purposeful effort to cultivate fandom, or what I
|
||||
refer to as "fanchise management.” Below, I discuss what this might mean in
|
||||
practice.
|
||||
|
||||
Thanks for reading The Mediator! Subscribe for
|
||||
free to receive new posts and support my work.
|
||||
|
||||
### Scarcity, Abundance and Value
|
||||
|
||||
In analyzing any industry, understanding the relative scarcity and abundance of key
|
||||
resources is critically important for two simple reasons: 1) value accrues to whomever
|
||||
controls the relatively scarce resource(s); and 2) when the relative abundance and
|
||||
scarcity of resources changes, value shifts along the value chain.
|
||||
|
||||
### Value Flows to the (Relatively) Scarce Resource
|
||||
|
||||
The idea that value flows toward scarce resources is a foundational concept in
|
||||
economics. Somewhere in the second or third chapter of every Econ 101 textbook is a
|
||||
discussion of market structures. It usually includes a few charts with a bunch of
|
||||
intersecting supply, demand, marginal revenue and marginal cost lines that illustrate
|
||||
the differences between pricing, profits, consumer surplus and producer surplus
|
||||
(among other things) for different market structures.
|
||||
|
||||
The two extremes in these textbooks, perfect competition and monopoly, illustrate
|
||||
why value flows to the scarce resource.
|
||||
|
||||
## 2/17
|
||||
|
||||
* In perfect competition, no company controls the key resources, all competitors are
|
||||
price takers and they generally only earn enough profit to offset their cost of
|
||||
capital (if that), earning no economic profit.
|
||||
* In a monopoly, at the other extreme, one company controls the scarce resource. As
|
||||
a result, it can set prices and extract profits above its cost of capital.
|
||||
|
||||
The graphs usually look something like Figure 1. As shown, relative to a perfectly
|
||||
competitive firm, a monopoly extracts much more producer surplus (and consumers
|
||||
extract less consumer surplus) because it controls the scarce resource(s).
|
||||
|
||||
### Figure 1. Value Flows to Whomever Controls the Scarce Resource
|
||||
|
||||
The image shows two graphs illustrating market structures. The first graph represents perfect competition, and the second represents a monopoly. Both graphs have axes labeled "Q" (quantity) and "P" (price).
|
||||
|
||||
In the perfect competition graph, the supply curve (MC) intersects the demand curve (D=MR) at the equilibrium point (Pc, Qc). The area above the equilibrium price and below the demand curve represents consumer surplus, while the area below the equilibrium price and above the supply curve represents producer surplus.
|
||||
|
||||
In the monopoly graph, the marginal revenue curve (MR) lies below the demand curve (Dmarket). The monopolist maximizes profit by producing at the quantity where marginal revenue equals marginal cost (Qm), resulting in a higher price (Pm) compared to perfect competition. The consumer surplus is smaller, and the producer surplus is larger. There is also a deadweight loss, representing the loss of economic efficiency due to the monopolist's restriction of output.
|
||||
|
||||
Note: Consumer surplus is the difference between what consumers would be willing to pay and
|
||||
the market clearing price; producer surplus is the difference between the price at which
|
||||
producers would be willing to supply and the market clearing price; and dead weight loss is the
|
||||
loss to society from market inefficiency (i.e., units that could have been bought/sold but are
|
||||
not). Source: Every economics textbook ever.
|
||||
|
||||
### Value Shifts When Relative Scarcity and Abundance Change
|
||||
|
||||
It follows that when the relative scarcity and abundance of key resources changes (and
|
||||
consequently who controls the scarce resource(s) changes), value shifts along the
|
||||
chain. Industries are often disrupted expressly because a key input that was scarce
|
||||
becomes abundant and entry barriers fall.
|
||||
|
||||
As an example, here's an excerpt from Web3 Could be Even More Disruptive than You
|
||||
Think describing the shifting relative scarcity and abundance of bandwidth and
|
||||
processing power over the last 60-70 years:
|
||||
|
||||
* In the first enterprise computing systems, local bandwidth was cheap and processing power
|
||||
was expensive. Dumb terminals were connected over a local area network to a centralized
|
||||
mainframe, which performed the processing.
|
||||
* In 1971, Intel invented the microprocessor and processing power became more abundant
|
||||
than bandwidth. That change birthed the modern computer industry and everything related
|
||||
to it the PC, peripherals, consumer software, enterprise software, video games and
|
||||
mobile phones, etc., etc.
|
||||
|
||||
## 3/17
|
||||
|
||||
* With all that distributed (and eventually commoditized) processing power in place, capital
|
||||
flowed toward the new scarce resource, bandwidth. During the '90s and '00s billions of
|
||||
dollars were spent laying fiber and putting up cell towers which, along with improved
|
||||
multiplexing technologies, compression algorithms and network architectures, flipped the
|
||||
script again, making bandwidth relatively inexpensive and processing power again relatively
|
||||
scarce. In turn, from cheap bandwidth emerged the cloud, the SaaS business model,
|
||||
streaming media and mobile gaming, among many other things.
|
||||
|
||||
The biggest beneficiaries of technological change are those who can anticipate which
|
||||
resources will become abundant and which will become scarce and are able to
|
||||
squander the abundant resource to corner the scarce one.
|
||||
|
||||
### The Math of Abundant Quality Video
|
||||
|
||||
Let's turn to the math.
|
||||
|
||||
To use round numbers, Hollywood put out around 15,000 hours of new film and TV
|
||||
content in 2022 in the U.S. That includes 496 films with an average running time of
|
||||
about 100 minutes, or about 800 hours of film content. As shown in Figure 2, last year
|
||||
there were an estimated 2,000 original series on TV in the U.S., including almost 600
|
||||
scripted series. Assuming an average of 10 episodes per series and 40 minutes per
|
||||
episode, that is another 13,000 hours of original video. So, we'll call it 15,000 total, if
|
||||
we're rounding up.
|
||||
|
||||
### Figure 2. There Were ~2,000 Originals on TV in the U.S. Last Year
|
||||
|
||||
The image is a bar chart titled "Scripted and Unscripted Originals on Broadcast, Cable and SVOD." The chart displays the number of original series on television in the United States from 2002 to 2022. The figures shown are for networks and services in the U.S.
|
||||
|
||||
The chart shows a general upward trend in the number of original series over time. The number of series increased from 125 in 2002 to 2,024 in 2022.
|
||||
|
||||
## 4/17
|
||||
|
||||
By contrast, in 2019 YouTube disclosed that 500 hours of new video are uploaded every
|
||||
minute, or 30,000 hours per hour. That is double the amount of new content released
|
||||
annually by Hollywood and equivalent to Netflix's entire domestic library every hour.
|
||||
And keep in mind that was in 2019. It has surely increased since then.
|
||||
|
||||
### Figure 3. A Vast Amount of Content is Uploaded to YouTube
|
||||
|
||||
The image shows a person standing in front of a large red screen displaying the text "> 500 hours of content are uploaded every minute." The person is wearing a dark suit and tie and appears to be presenting or speaking about the information on the screen. The background is blurred, suggesting the photo was taken at an event or conference.
|
||||
|
||||
Source: YouTube Newfronts presentation, May 2019.
|
||||
|
||||
But let's stick with the 30,000 hours per hour (or over 250 million hours per year).
|
||||
Obviously, most of that is not considered competitive with professionally-produced,
|
||||
Hollywood content. But consider this: if 0.01% of it is, that would equate to ~30,000
|
||||
hours of new, competitive content produced annually by independent creators, or
|
||||
double Hollywood's annual output. If 0.1% is considered competitive, that would be
|
||||
20x what Hollywood produces per year. Either way, it would be enough to completely
|
||||
upend the supply-demand dynamic.
|
||||
|
||||
If 0.01% of independent content is considered competitive with Hollywood, that would equate
|
||||
to 2x Hollywood output annually.
|
||||
|
||||
### Defining "Quality"
|
||||
|
||||
How realistic is it that consumers will eventually consider 0.01% or even 0.1% of
|
||||
independent content to be of sufficiently good quality to compete with Hollywood?
|
||||
Pretty realistic.
|
||||
|
||||
There are two primary reasons for this. The first, which is causing hand wringing
|
||||
throughout Hollywood, is that Al is democratizing high quality production. In a
|
||||
recent post (AI Use Cases in Hollywood), I discussed in detail both current and
|
||||
potential future AI use cases in film and TV production and why (and how) they may
|
||||
dramatically reduce production costs. The second reason, which is more subtle, is that
|
||||
many consumers' definition of quality is shifting away from high production values.
|
||||
|
||||
## 5/17
|
||||
|
||||
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
The assertion that independent content will increasingly be able to compete with Hollywood content is sometimes misconstrued to mean that the production values of independent content will match the upper echelon of blockbuster movies and premium TV. I'm not making that case. The question is not whether the production values of independent content will be comparable to the best Hollywood output, it is whether consumers will consider it competitive for similar use cases based on their own definitions of quality.
|
||||
|
||||
The question is not whether the production values of independent content will be comparable, it is whether consumers will consider it competitive for similar use cases based on their own definitions of quality.
|
||||
|
||||
## The Definition of Quality is Fluid
|
||||
|
||||
I've written about quality before, such as in The Four Horsemen of the TV Apocalypse, but I'll revisit it briefly. The word "quality" is hard-to-define, but here's what I mean: quality is the weighted combination of attributes one considers when choosing between identically-priced choices. So, quality is based on revealed preference; each person may have a different definition of quality; it is context dependent (e.g., you will have a different definition of quality when settling down with your family on a Sunday night than while sitting on a long flight); and it can change over time.
|
||||
|
||||
Quality is the weighted combination of attributes one considers when choosing between identically-priced choices.
|
||||
|
||||
It is self-evident to most younger consumers, or anyone who observes younger consumers, that social video is changing the definition of quality for many. Some Hollywood executives may define TV and film quality as high production values, good writing, well-known above the line talent (writers, directors, showrunners, actors), expensive effects, etc. But social video has introduced all kinds of potential new attributes to many consumers' quality algorithms, like accessibility (low friction), digestibility (easy and quick to watch), authenticity, virality and relevance to my sub-community or social circle, etc. The introduction of these new attributes lowers the weighting of more traditional attributes. That's not to say that high production values no longer matter, just that the introduction of new attributes necessarily means they matter less.
|
||||
|
||||
The introduction of new quality attributes necessarily means that traditional measures of quality, like high production values, matter less.
|
||||
|
||||
Let's make this less abstract. My wake up call occurred years ago, when I saw my son switch his Saturday-morning viewing from Teen Titans Go on Cartoon Network to watching gaming streamers DanTDM and LazarBeam on YouTube. Since he didn't pay
|
||||
|
||||
[https://archive.ph/nhtA3](https://archive.ph/nhtA3)
|
||||
|
||||
## 6/17
|
||||
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
the bills then (and still doesn't), his marginal cost to view everything was zero. So, when he chose a streamer over traditional TV, he revealed that he considered the former to be higher quality than the latter (at least at that moment). Or consider your own experience. If you subscribe to one or more streaming services, your marginal cost of consumption is also zero. If you've ever plopped down on the coach and scrolled through TikTok for 30 minutes rather than watch Netflix, you've signaled that TikTok was higher quality than Netflix at that moment — whether you explicitly thought about it that way or not.
|
||||
|
||||
## The Data Illustrate that the Definition is Changing
|
||||
|
||||
As shown in Figure 4, according to Nielsen, YouTube is the most streamed service in the U.S. to televisions. It gets the same viewing as Hulu, Disney+, Max, Peacock and Paramount+ combined. Note that this excludes viewing of the YouTube TV vMVPD service and YouTube viewing on PC, mobile or other devices. The usual rationale for why independent or creator content doesn't compete with Hollywood is that it is a very different use case. But this comparison is measuring precisely the same use case — watching on a TV. When looking to be entertained on their TVs, more people pick up a remote and select YouTube than any other service.
|
||||
|
||||
YouTube already surpasses every other streaming service for their primary use case — watching on a TV.
|
||||
|
||||
Figure 4. YouTube is Already the Most Streamed Service on TVs
|
||||
|
||||
The image is a pie chart showing the streaming service market share on TVs, according to Nielsen data from August 2023. The chart shows that YouTube has the largest share at 9.1%, followed by Netflix at 8.2%, Broadcast at 20.4%, Cable at 30.2%, Streaming SVOD at 38.3%, and Other at 11.1%. The streaming SVOD category includes Hulu (3.6%), Prime Video (3.4%), Disney+ (2.0%), Tubi (1.3%), Max (1.3%), Peacock (1.2%), Roku Channel (1.1%), Paramount+ (1.1%), and Pluto (0.9%).
|
||||
|
||||
Source: Nielsen.
|
||||
|
||||
To underscore the point, Figure 5 compares the first week viewing of Mr. Beast's latest video on YouTube (World's Most Dangerous Trap!) to the most watched English-language series on Netflix globally around the same period. The video garnered over 100 million views in its first week, which is about the (recent) average for a Mr. Beast video. With a 20 minute running time, it would rank right alongside Netflix's top viewed series whether you assume a 75%, 50% or even 25% completion rate.
|
||||
|
||||
Figure 5. Mr. Beast's Last Episode Would Rank With Netflix's Top Series Globally
|
||||
|
||||
[https://archive.ph/nhtA3](https://archive.ph/nhtA3)
|
||||
|
||||
## 7/17
|
||||
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
The image is a bar chart comparing the viewership hours of Netflix Global Top 10 Series (10/2/2023-10/8/2023) with the last Mr. Beast Episode (10/7/2023-10/13/2023). The y-axis represents hours, ranging from 0 to 70,000,000. The x-axis lists various series and the Mr. Beast episode with different completion rates (75%, 50%, 25%). The chart shows that the Mr. Beast episode, even at a 25% completion rate, has comparable viewership hours to some of the top Netflix series.
|
||||
|
||||
Source: Netflix, YouTube, Author (concept from Benedict Evans).
|
||||
|
||||
According to the collective judgment of bettors on Manifold Markets, at the time of this writing there is a 26% chance that a film created using a text-to-video generator (like Runway) will be nominated for an Academy Award (in any category) by 2030. But the bar is far lower than that. "Abundant quality" merely means that there will be a lot more content that competes with Hollywood in similar use cases and similar contexts, for a sufficient number of people.
|
||||
|
||||
## What Becomes Scarce When Quality is Abundant?
|
||||
|
||||
Let's paint a blurry picture of 2030.
|
||||
|
||||
* The cost to produce "quality" video content (as defined above) has dropped several orders of magnitude as a larger proportion of what appears on screen is synthetic.
|
||||
* In 2027, Runway achieves its stated goal of enabling the first (watchable) feature-length film entirely created by stitching together text/image/video-to-video generated video, so by 2030 it is common to see video that largely or entirely comprises synthetic scenes. Human actors are still prevalent in comedies and dramas, but less so in sci-fi, fantasy, action/adventure and horror genres.
|
||||
* With much lower cost, and risk, it is economically feasible to distribute content for free on ad-supported platforms, like YouTube and maybe TikTok.
|
||||
* The ability to render video near-real time enables dynamic, contextually relevant or perhaps even personalized content.
|
||||
* In 2029, three of the top 10 most popular shows in the U.S. are distributed on YouTube and TikTok, for free (ad supported).
|
||||
* YouTube exceeds 20% share of viewing by seamlessly combining Hollywood content and creator content, premium and ad-supported, in one consumer experience. For consumers, the distinction between “professionally-produced" and "creator" content becomes even less meaningful.
|
||||
|
||||
In other words, while it already feels like consumers are faced with infinite choice, it will become even “more infinite” (yes, there is such a thing).
|
||||
|
||||
[https://archive.ph/nhtA3](https://archive.ph/nhtA3)
|
||||
|
||||
## 8/17
|
||||
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
So, back to the questions I posed at the very beginning: When quality is abundant, what is scarce? Where does value flow?
|
||||
|
||||
Some of my answers below are obvious, in part because we've already seen this play out with other media, and only warrant a few sentences. Others would justify (or already have justified) an entire essay in themselves:
|
||||
|
||||
## Consumer Time and Attention
|
||||
|
||||
Consumers will clearly benefit. With more people competing for their time and attention, consumers will have even more choice, at higher quality and lower cost. We may not always think about consumers as competing for value within the value chain, but they do.
|
||||
|
||||
Beneficiary: consumers
|
||||
|
||||
## Hits
|
||||
|
||||
Hits will be scarcer and more valuable than ever. I discussed why in an essay a few months ago, called Power Laws in Culture, which has been one of most-read posts. As I wrote in that piece though, hits are hard to harness because they include a large dose of luck.
|
||||
|
||||
Here's a quick summary. When confronted with so much choice, consumers need filters. One of those filters is popularity, because people assume that other people's choices contain valuable information (i.e., “the most popular stuff must be popular for a reason, right?”). This causes an “information cascade,” a powerful positive feedback loop that amplifies hits. Across media this is resulting in persistently, and sometimes increasingly, extreme power law-like popularity distributions — a few huge hits and a massively long tail of misses. (In the essay, I show this empirically for Netflix shows, songs on Spotify, U.S. box office and Patreon patrons.) Over time, these distributions may become relatively more extreme as the tail gets ever longer. While in the future the hits may not be absolutely bigger, they will be relatively bigger, and therefore more valuable, than ever.
|
||||
|
||||
Who benefits from this? As I discuss in the Power Laws essay, information cascades are "highly sensitive to initial conditions" that are difficult to predict or control. So, while successful content must exceed some quality threshold, hits are heavily influenced by luck.
|
||||
|
||||
Beneficiary: a lucky few
|
||||
|
||||
## Marketing Prowess
|
||||
|
||||
Another implication of abundant quality is that marketing becomes more important and a lot harder.
|
||||
|
||||
An instructive example is the major music labels, as I discussed in Will Radio Save the Video Star? They already confront “infinite quality" (Spotify boasts 100 million tracks and an estimated 100,000 new songs are uploaded to streaming services each day). Plus, the value they provide artists — which was historically financing, marketing and distribution — has changed as technology has made it easier for artists to do these things themselves. But they have maintained their primacy in the value chain, and
|
||||
|
||||
[https://archive.ph/nhtA3](https://archive.ph/nhtA3)
|
||||
|
||||
## 9/17
|
||||
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
their value to artists, in part because of their marketing prowess and ability to manage artists' brands and images holistically.
|
||||
|
||||
But marketing also gets tougher, for a bunch of reasons: there is much more competition for users' attention; fragmentation makes it harder to reach consumers using traditional mass media; the consumer decision journey becomes more complex, as does attribution; the rising ability to segment and target consumers raises the bar (and the cost) for everyone; and you need to monitor and, if possible, dynamically influence or counter, the organic signals arising from the network itself. So, the job becomes a lot more analytical, data intensive and difficult to manage.
|
||||
|
||||
Beneficiary: good marketers
|
||||
|
||||
## Curation
|
||||
|
||||
Another filter consumers use is curation. This obviously shifts value to the platforms that control distribution. They have reams of data and control the UI. When done correctly, recommendation systems give the platforms the power to increase consumer usage, engagement and retention and perhaps steer viewers to content in which they have a vested interest (such as content they own or for which they pay lower license fees).
|
||||
|
||||
But there are limits. As I also discussed in Power Laws in Culture, not all recommendation algorithms are equally valuable. Consumers' dependence on recommendation engines seems directly correlated with search costs and inversely correlated with the opportunity cost of consumption. In music, for instance, the search costs are extremely high (100,000 new tracks per day!) and the opportunity cost of trying out a new song is very low (and easily surmounted by skipping it). By contrast, in TV the search costs are not as high (there are a lot of shows, but not as many) and the opportunity cost of watching a few episodes of a new series is very high. It is telling, for instance, that Netflix recently eliminated its “Surprise Me" button because “users tend to come to the service with a specific show, movie or genre in mind.” Rather than rely on recommendation algorithms, some consumers prefer to carefully manage their curation, outsourcing it to their most reliable friends on Facebook, favorite influencers on Instagram or TikTok, tastemakers on Spotify or chosen thought leaders on Twitter/X. Or, in some cases, they rely on good old word-of-mouth.
|
||||
|
||||
In addition, there's an open question whether technology will ultimately supplant the recommendation algorithm as we know it. Today, Spotify, Netflix or YouTube benefit by observing our behavior on-platform and perhaps appending additional first-party data they obtain through ownership of adjacent platforms or third-party data (such as might be obtainable if they have personally identifiable information (PII), like credit cards). But everything they know about us is by inference and they can't see all our behavior across digital platforms and offline. In the future, will we all have Al agents that both know our intentions (“pull me up a Lizzo-vibe playlist” or “what was that article I bookmarked on Twitter the other day?" or "give me a list of the top 10 movies I should watch with my 6-year-old daughter and 10-year-old son”) and have access to behavioral data across platforms and even IRL? Probably.
|
||||
|
||||
Beneficiary: the platforms, for now
|
||||
|
||||
## Fandom/Community
|
||||
|
||||
[https://archive.ph/nhtA3](https://archive.ph/nhtA3)
|
||||
|
||||
## 10/17
|
||||
|
||||
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
4/23/25, 6:48 PM
|
||||
|
||||
Yet another filter consumers will use to choose content is fandom or community. As Ben Valenta and David Sikorjak explain in their recent book Fans Have More Friends, fandom is ultimately driven by a deep-seated need for belonging. Fandoms provide a sense of connection, a common vernacular and perhaps even a shared value system. (We've all had that experience of meeting someone and realizing we share similar tastes in music, TV series or authors, and feeling a tighter bond.) When confronted with infinite choice, people will not only gravitate to content about their fandom, they will actively seek it out.
|
||||
|
||||
In the future, having an engaged, loyal fan base will be more important than ever.
|
||||
|
||||
The challenge for IP owners is how best to foster this fandom. For most traditional entertainment companies, it is an afterthought today. But as the volume of quality content explodes, having an engaged, loyal fan base will be more important than ever. Below, I discuss how entertainment companies should think about what I call "fanchise management."
|
||||
|
||||
Beneficiary: IP owners, if they prioritize it
|
||||
|
||||
## Premium Brands and IP
|
||||
|
||||
Following from the prior point, diehard fans will actively seek out content that relates to their fandom. But even casual fans will lean on well-known brands and IP as yet another filter to help them cut through the clutter. This is partly due to what behavioral economists call the “mere exposure effect:" people tend to like something just because they've been exposed to it before.
|
||||
|
||||
The big media companies already know this, as evidenced by Disney's investments in Star Wars and the MCU, WarnerBros. Discovery's announcement of a reboot of Harry Potter or NBCU's reported interest in bringing back The Office.
|
||||
|
||||
With lower production costs, it becomes less risky to resuscitate dormant or underleveraged IP.
|
||||
|
||||
Of course, you can take this too far and risk weakening the value of IP by creating so- called franchise fatigue. Perhaps a more interesting opportunity is to leverage falling production costs to try to resuscitate dormant or elevate underleveraged IP. Think it might be time to bring back Thundercats or reach deeper into the DC library and give Ragman or Metamorpho a shot? Might as well.
|
||||
|
||||
Beneficiary: IP owners
|
||||
|
||||
## Library
|
||||
|
||||
The major media companies have enormous libraries of content. For instance, this is from the Warner Bros. website (and this doesn't include HBO or the Turner networks):
|
||||
|
||||
The company's vast library, one of the most prestigious and valuable in the world, consists of more than 145,000 hours of programming, including 12,500 feature films and 2,400
|
||||
|
||||
[https://archive.ph/nhtA3](https://archive.ph/nhtA3)
|
||||
|
||||
11/17
|
||||
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
4/23/25, 6:48 PM
|
||||
|
||||
television programs comprised of more than 150,000 individual episodes.
|
||||
|
||||
No matter how inexpensive it gets to create new content, these libraries will retain value: they can be re-monetized through licensing or owned SVOD or FAST networks; they can be licensed to train generative Al models; they can be trained for proprietary internal generative models; it may be possible to upscale 2D content to 3D (using technologies such as NeRF or Gaussian Splatting) to give some of this content a new life and enable new experiences or create digital asset libraries for future games or productions; and, using new dubbing technologies, it may be possible to re-exploit them in non-English language countries.
|
||||
|
||||
In many cases, the owners of these libraries don't know exactly what they have, where it is, what rights they have in different jurisdictions or how to administer royalties if they can monetize them again. This is one of those big problems that sound really un- sexy but could unlock a lot of value.
|
||||
|
||||
Beneficiary: Big media owners, if they can figure it out
|
||||
|
||||
## IRL Experiences
|
||||
|
||||
There's a trope that when information goods get cheaper, experiences get more expensive. That's certainly been true in music. Live experiences offer a number of benefits that you can't get at home: the exclusivity itself is a draw, the communal experience, the social status (such as posting online that you "were there"), the signaling of the degree of your fandom and establishing a lasting memory.
|
||||
|
||||
In film and TV, that probably benefits the companies who are best poised to create live experiences around their IP, namely Disney and NBCUniversal, who own theme parks. But that is an extremely capital intensive business and it's highly unlikely any other major media company will take the plunge.
|
||||
|
||||
It is possible to create live experiences around entertainment IP with less investment, such as stage versions (like musical versions of Disney films) or traveling live shows (such as for Impractical Jokers). Netflix just announced plans to open brick and mortar locations for retail, dining and other live experiences. The challenge is that these businesses are definitionally tough to scale. Will it eventually be possible to create synthetic “metaverse”-type experiences that are compelling and exclusive, at scale? We'll see.
|
||||
|
||||
Beneficiary: Disney and NBCU
|
||||
|
||||
## Picks and Shovels, Maybe (?)
|
||||
|
||||
Many companies are currently trying to position themselves as the enablers of the democratization of content production. It's very much an open question whether it is possible to establish a competitive moat around enabling tools. For instance, Runway has established itself as the frontrunner in Al video generation and just secured a $1.5 billion valuation in its last funding round. But competitors seem to crop up every month or so, such as recent entrants Replay and Moonvalley. Adobe could be an even bigger competitive threat as it adds its Firefly generative AI features inside Premiere Pro and After Effects, since this is already the most-used edit suite in the industry. Alternatively, OpenAI will surely eventually launch a video generator, so maybe multi- modal AI (text, image, video and probably audio) in one platform ultimately wins.
|
||||
|
||||
[https://archive.ph/nhtA3](https://archive.ph/nhtA3)
|
||||
|
||||
12/17
|
||||
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
4/23/25, 6:48 PM
|
||||
|
||||
Will someone create the “TikTok” of high-quality content that provides easy-to-use, no code tools for content creation and a distribution platform all in one place? (And if so, why isn't this TikTok itself or the evolution of Fortnite Creator?) Will someone create the digital watermarking system that enables content to be tracked and monetized wherever it appears online? Will someone solve the library rights management problem I cited above?
|
||||
|
||||
The answer to all these questions is a resounding: who knows? It's too early to tell.
|
||||
|
||||
Beneficiary: if you know, tell me
|
||||
|
||||
## What's Big Media to Do?
|
||||
|
||||
As I've written before, disruption is never good for incumbents. But that doesn't mean you shouldn't play the hand you're dealt as best you can.
|
||||
|
||||
If you're a big media company, what do you do? When the relative scarcity/abundance of resources shifts, successful companies invest in the scarce resource. Looking through the list above, many of these new areas of scarcity aren't accessible for media companies. There is no way to corner the market for hits and there is little opportunity to control curation. But there are a few areas where the big media companies should invest (and, in some cases, they already are):
|
||||
|
||||
* Premium IP and brands (particularly those that have the best potential to cut through the noise, such as those with rich mythologies).
|
||||
* Marketing science.
|
||||
* Library rights management and monetization.
|
||||
* "Fanchise management.TM"
|
||||
|
||||
The first three are pretty self explanatory, so let's spend a moment on the last one.
|
||||
|
||||
(I didn't really trademark "fanchise management," but I should, right?)
|
||||
|
||||
## From Franchise Management to “Fanchise Management"
|
||||
|
||||
Above, I made the case that fandom and community will be an increasingly important filter as consumers confront infinite choice. What can entertainment companies do to foster it?
|
||||
|
||||
## Fandom as Output, Not Input
|
||||
|
||||
Historically, Hollywood had a largely one way relationship with its fans, partly because there was no practical alternative. A TV series or film was made by a relatively small team of creatives and released and, if it succeeded, a fandom would emerge. Fandom was considered an output of the creation process, not an input. These fandoms started as fan clubs (sometimes "official", sometimes not) and have evolved into dedicated websites, wikis and subreddits and conversations that happen on Twitter, Facebook, TikTok, etc. The most dedicated fans create their own fanfics or fan films, something I discussed in depth in IP as Platform.
|
||||
|
||||
[https://archive.ph/nhtA3](https://archive.ph/nhtA3)
|
||||
|
||||
13/17
|
||||
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
4/23/25, 6:48 PM
|
||||
|
||||
Even today, fandom is often viewed as something to manage, not cultivate.
|
||||
|
||||
Today, marketers engage with fans by establishing an official online presence, like dedicated Facebook pages or posts on YouTube, TikTok, Reels, etc., and use tools like sentiment analysis to monitor the online conversation. They'll also engage key influencers and have special screenings or sneak previews and talent panels at events like ComicCon. Studios try to listen and cater to the fans you definitely don't want to piss them off - but fandom is often viewed more so as something to manage than cultivate. And almost all of these fan conversations are happening on platforms the studios don't control.
|
||||
|
||||
Fanchise management is a much more purposeful approach to cultivating fandoms and developing community around them.
|
||||
|
||||
## Fanchise Management
|
||||
|
||||
To truly foster fandom, studios need to move from franchise management to "fanchise management." Most studios have some sort of franchise management function, the goal of which is to think holistically about a specific franchise and coordinate across the company on long-term creative strategy, brand marketing, merchandising, live events, licensing, gaming, etc. Sometimes it's done well and sometimes it's not, although it is often hard to tell from the outside (and sometimes even from the inside) whether this function is effective.
|
||||
|
||||
Figure 6. The Fanchise Management Stack
|
||||
|
||||
The image is a diagram illustrating the "Fanchise Management Stack." It's structured as an upward-pointing arrow, with "FAN ENGAGEMENT" written vertically along the left side, indicating that engagement increases as you move up the stack. The arrow is divided into several horizontal sections, each representing a different level or component of fanchise management:
|
||||
|
||||
1. **Good Content:** This forms the base of the stack, suggesting it's the foundational element.
|
||||
2. **360° Content Extensions:** This level builds upon good content, implying broader engagement opportunities.
|
||||
3. **Loyalty and Engagement Incentives:** This section focuses on rewarding and motivating fan participation.
|
||||
4. **Community Tooling:** This level emphasizes providing tools and platforms for fans to connect and interact.
|
||||
5. **User-Generated Content/Co-Creation:** This section highlights the importance of involving fans in content creation.
|
||||
6. **Co-Ownership:** This is at the top of the stack, suggesting the highest level of engagement where fans have a sense of ownership.
|
||||
|
||||
The diagram is intended to show how different elements of fanchise management contribute to increasing fan engagement, with each level building upon the previous one.
|
||||
|
||||
[https://archive.ph/nhtA3](https://archive.ph/nhtA3)
|
||||
|
||||
14/17
|
||||
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
4/23/25, 6:48 PM
|
||||
|
||||
Fanchise management would be an extension of this, but with a much more purposeful approach to encouraging fandoms and developing community around them. In Figure 6, I show an illustrative “fanchise management stack” with a series of capabilities that correspond to a higher degree of engagement as you move up the stack. Also note that most studios are currently trying to do some of this (especially the bottom two layers), but much less so as you move up the stack.
|
||||
|
||||
* The foundation is, as always, making good stuff.
|
||||
* On top of that is multiple, year-round content extensions that give fans the opportunity to engage with the IP and keep it top of mind, even outside of the normal content (TV, film) release cycle. This could include digital shorts, book or comic book publishing, mobile games, IRL events, podcasts, immersive experiences (eventually), physical and digital collectibles, etc. These are all potential revenue opportunities, but building fandom may be equally or even more valuable.
|
||||
* From there it gets progressively less common. Loyalty and engagement incentives might include digital collectibles or badges in exchange for viewing, commenting, sharing, etc. They could also be paired with utility tokens that could be exchanged for discounts or exclusive merchandise or events. In Every Media Company Needs an NFT Strategy-Now, I discussed how NFTs could facilitate this. NFT has become a four-letter word of late, so perhaps we should just call them unique digital assets, but the infrastructure keeps maturing and it is increasingly possible to abstract away the “crypto” so that consumers aren't even aware of it. For instance, Feature is currently partnering with media companies to create blockchain-enabled fan loyalty and engagement programs.
|
||||
* On top of that is community tooling. Today, the conversations about IP are spread between multiple platforms, so the goal would be to aggregate more of those conversations in one place. That would require either adding social tools in the places where fans already congregate, namely streaming apps, or creating new products or services that draw fans and also have social features. That's a good segue to the next layer.
|
||||
* Co-creation refers to giving fans input into content creation. At the most conservative end of the spectrum, copyright owners could tightly control what elements of the story fans are able to influence. For instance, viewers could choose between a few plot developments. At the other end, creators would be encouraged to make entirely new content using the copyright owner's IP, something I discussed in IP as Platform. I won't repeat the entire essay, but the bottom line is that encouraging fan creation (with the appropriate guardrails) would strengthen the entertainment companies' relationships with their most avid fans and attract new ones. (It might also provide free marketing; possibly source new stories and talent; and, to the degree they can monetize some of this new content, boost revenue.)
|
||||
* By co-ownership, I mean the opportunity for fans to have an economic interest in the success of an IP. This is a natural outgrowth of some of the prior ideas. For instance, the value of rare digital collectibles would likely increase if a show or movie becomes more successful. Similarly, if fan-created content can be monetized, the creator should get a cut. Providing fans an economic interest in their favorite IPs would make them even more ardent evangelizers.
|
||||
|
||||
[https://archive.ph/nhtA3](https://archive.ph/nhtA3)
|
||||
|
||||
15/17
|
||||
|
||||
|
||||
# 4/23/25, 6:48 PM
|
||||
|
||||
What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
## Hollywood Needs to Prepare
|
||||
|
||||
Right now, some of this might seem “out there." But keep in mind that I'm writing about trends that will play out over the next five-10 years. In 2009, the idea that Netflix would upend the entire pay TV ecosystem – globally seemed out there too.
|
||||
|
||||
Hollywood should be working overtime to position itself.
|
||||
|
||||
## Subscribe to The Mediator
|
||||
|
||||
By Doug Shapiro
|
||||
|
||||
The Mediator is (mostly) about the long term structural changes in the media industry and the business, cultural, and societal implications of those shifts. I write it to get closer to the frontier.
|
||||
|
||||
By subscribing, I agree to Substack's [Terms of Use](https://substack.com/terms), and acknowledge its [Information Collection Notice](https://substack.com/privacy). and [Privacy Policy](https://substack.com/privacy).
|
||||
|
||||
* 3 Likes 2 Restacks
|
||||
|
||||
* 3
|
||||
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|
||||
|
||||
[Previous](#)
|
||||
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|
||||
|
||||
## Discussion about this post
|
||||
|
||||
Comments Restacks
|
||||
|
||||
Write a comment...
|
||||
|
||||
Top Latest Discussions
|
||||
|
||||
### 28 Days of Media Slides
|
||||
|
||||
An Industry in Upheaval
|
||||
JAN 7 DOUG SHAPIRO
|
||||
53
|
||||
9
|
||||
|
||||
### Quality is a Serious Problem
|
||||
|
||||
Understanding The Changing Consumer Definition of Quality in Media
|
||||
JAN 20 DOUG SHAPIRO
|
||||
91
|
||||
19
|
||||
|
||||
[https://archive.ph/nhtA3](https://archive.ph/nhtA3)
|
||||
|
||||
## 16/17
|
||||
|
||||
**Image Descriptions:**
|
||||
|
||||
* The first image is a thumbnail for "28 Days of Media Slides" and features a dark blue background with white text that reads "28 Days of Media Slides" in a stylized font.
|
||||
* The second image is a thumbnail for "Quality is a Serious Problem" and features a person sitting in front of a television screen displaying the HBO logo. The person is looking at the screen with a thoughtful expression.
|
||||
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|
||||
"hollywood-vulnerable-to-disruption-because-consumer-adoption-barriers-are-zero-while-high-end-market-is-stagnant.md:stripped_wiki_link:purpose-built-full-stack-systems-outcompete-acquisition-base",
|
||||
"hollywood-vulnerable-to-disruption-because-consumer-adoption-barriers-are-zero-while-high-end-market-is-stagnant.md:stripped_wiki_link:two-phase-disruption-where-distribution-moats-fall-first-and",
|
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|
||||
"social-video-already-disrupting-hollywood-at-low-end-with-ai-tools-accelerating-upmarket-movement.md:stripped_wiki_link:two-phase-disruption-where-distribution-moats-fall-first-and"
|
||||
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|
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|
||||
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|
||||
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|
||||
"social-video-already-disrupting-hollywood-at-low-end-with-ai-tools-accelerating-upmarket-movement.md:missing_attribution_extractor"
|
||||
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|
||||
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|
||||
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|
||||
"date": "2026-03-19"
|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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@ -1,35 +0,0 @@
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
|
@ -1,44 +0,0 @@
|
|||
{
|
||||
"rejected_claims": [
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||||
{
|
||||
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|
||||
"issues": [
|
||||
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|
||||
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||||
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|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
"information-cascades-create-power-law-distributions-in-culture-because-consumers-use-popularity-as-quality-signal-when-choice-is-overwhelming.md:set_created:2026-03-19",
|
||||
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|
||||
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|
||||
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|
||||
"recommendation-algorithms-amplify-or-dampen-power-laws-depending-on-collaborative-filtering-weight-because-algorithms-that-surface-popular-content-reinforce-network-cascades.md:stripped_wiki_link:agents-that-raise-capital-via-futarchy-accelerate-their-own-",
|
||||
"the-middle-of-cultural-markets-is-disappearing-because-power-law-amplification-concentrates-returns-at-the-head-and-tail-simultaneously.md:set_created:2026-03-19",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"the-middle-of-cultural-markets-is-disappearing-because-power-law-amplification-concentrates-returns-at-the-head-and-tail-simultaneously.md:missing_attribution_extractor"
|
||||
]
|
||||
},
|
||||
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|
||||
"date": "2026-03-19"
|
||||
}
|
||||
|
|
@ -1,43 +0,0 @@
|
|||
{
|
||||
"rejected_claims": [
|
||||
{
|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"kept": 0,
|
||||
"fixed": 6,
|
||||
"rejected": 3,
|
||||
"fixes_applied": [
|
||||
"creator-and-corporate-media-are-zero-sum-because-total-time-is-stagnant.md:set_created:2026-03-19",
|
||||
"creator-and-corporate-media-are-zero-sum-because-total-time-is-stagnant.md:stripped_wiki_link:two-phase-disruption-where-distribution-moats-fall-first-and",
|
||||
"genai-reduces-creative-decisions-not-just-execution-cost-which-is-categorically-different-from-prior-tools.md:set_created:2026-03-19",
|
||||
"genai-reduces-creative-decisions-not-just-execution-cost-which-is-categorically-different-from-prior-tools.md:stripped_wiki_link:metis-is-practical-knowledge-that-can-only-be-acquired-throu",
|
||||
"genai-as-general-purpose-technology-advances-faster-than-domain-specific-tools-because-breakthroughs-compound-across-modalities.md:set_created:2026-03-19",
|
||||
"genai-as-general-purpose-technology-advances-faster-than-domain-specific-tools-because-breakthroughs-compound-across-modalities.md:stripped_wiki_link:technology-advances-exponentially-but-coordination-mechanism"
|
||||
],
|
||||
"rejections": [
|
||||
"creator-and-corporate-media-are-zero-sum-because-total-time-is-stagnant.md:missing_attribution_extractor",
|
||||
"genai-reduces-creative-decisions-not-just-execution-cost-which-is-categorically-different-from-prior-tools.md:missing_attribution_extractor",
|
||||
"genai-as-general-purpose-technology-advances-faster-than-domain-specific-tools-because-breakthroughs-compound-across-modalities.md:missing_attribution_extractor"
|
||||
]
|
||||
},
|
||||
"model": "anthropic/claude-sonnet-4.5",
|
||||
"date": "2026-03-19"
|
||||
}
|
||||
|
|
@ -1,34 +0,0 @@
|
|||
{
|
||||
"rejected_claims": [
|
||||
{
|
||||
"filename": "consumer-quality-definition-is-revealed-preference-not-production-value.md",
|
||||
"issues": [
|
||||
"missing_attribution_extractor"
|
||||
]
|
||||
},
|
||||
{
|
||||
"filename": "value-accrues-to-scarce-resources-and-shifts-when-relative-scarcity-changes.md",
|
||||
"issues": [
|
||||
"missing_attribution_extractor"
|
||||
]
|
||||
}
|
||||
],
|
||||
"validation_stats": {
|
||||
"total": 2,
|
||||
"kept": 0,
|
||||
"fixed": 4,
|
||||
"rejected": 2,
|
||||
"fixes_applied": [
|
||||
"consumer-quality-definition-is-revealed-preference-not-production-value.md:set_created:2026-03-19",
|
||||
"consumer-quality-definition-is-revealed-preference-not-production-value.md:stripped_wiki_link:disruptors-redefine-quality-rather-than-competing-on-the-inc",
|
||||
"value-accrues-to-scarce-resources-and-shifts-when-relative-scarcity-changes.md:set_created:2026-03-19",
|
||||
"value-accrues-to-scarce-resources-and-shifts-when-relative-scarcity-changes.md:stripped_wiki_link:giving-away-the-commoditized-layer-to-capture-value-on-the-s"
|
||||
],
|
||||
"rejections": [
|
||||
"consumer-quality-definition-is-revealed-preference-not-production-value.md:missing_attribution_extractor",
|
||||
"value-accrues-to-scarce-resources-and-shifts-when-relative-scarcity-changes.md:missing_attribution_extractor"
|
||||
]
|
||||
},
|
||||
"model": "anthropic/claude-sonnet-4.5",
|
||||
"date": "2026-03-19"
|
||||
}
|
||||
|
|
@ -7,14 +7,10 @@ date_published: "2025-06-02"
|
|||
date_archived: "2025-06-02"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: null-result
|
||||
status: unprocessed
|
||||
claims_extracted:
|
||||
- "progressive validation through community building reduces development risk by proving audience demand before production investment"
|
||||
- "traditional media buyers now seek content with pre-existing community engagement data as risk mitigation"
|
||||
processed_by: leo
|
||||
processed_date: 2026-03-19
|
||||
extraction_model: "anthropic/claude-sonnet-4.5"
|
||||
extraction_notes: "LLM returned 2 claims, 2 rejected by validator"
|
||||
---
|
||||
# Mediawan Kids & Family to Turn Claynosaurz Into Animated Series
|
||||
|
||||
|
|
@ -79,13 +75,3 @@ across screens, shelves, and generations.
|
|||
We're all about changing the game and becoming a beacon for Web3. Mediawan
|
||||
understands how important this is to us, and the gamified content opportunities that we
|
||||
can explore. This is the next chapter—and it's a big one.
|
||||
|
||||
|
||||
## Key Facts
|
||||
- Claynosaurz has 450K+ social media following as of June 2025
|
||||
- Claynosaurz content has generated 500M+ short-form views
|
||||
- Miraculous: Tales of Ladybug & Cat Noir generated $2B+ franchise revenue
|
||||
- Miraculous has 35B+ YouTube views and 100M monthly active viewers
|
||||
- Miraculous airs in 120+ countries and is translated into 50+ languages
|
||||
- Mediawan Kids & Family has produced/distributed 2,500+ half-hours of content
|
||||
- Claynosaurz is developing a mobile game with Gameloft
|
||||
|
|
|
|||
|
|
@ -7,13 +7,9 @@ date_published: "2023-07-05"
|
|||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: null-result
|
||||
status: unprocessed
|
||||
claims_extracted:
|
||||
- "five factors determine the speed and extent of disruption including quality definition change and ease of incumbent replication"
|
||||
processed_by: leo
|
||||
processed_date: 2026-03-19
|
||||
extraction_model: "anthropic/claude-sonnet-4.5"
|
||||
extraction_notes: "LLM returned 3 claims, 3 rejected by validator"
|
||||
---
|
||||
# How Will the "Disruption" of Hollywood Play Out?
|
||||
|
||||
|
|
@ -436,23 +432,3 @@ Comments Restacks
|
|||
https://archive.ph/nk30T
|
||||
|
||||
## 18/19
|
||||
|
||||
|
||||
## Key Facts
|
||||
- YouTube has 2.6 billion global users and ~100 million channels uploading 30,000 hours of content every hour
|
||||
- TikTok has 1.8 billion users with 83% also uploading content
|
||||
- Nielsen data shows YouTube is #1 streaming destination on TVs at 8.1% of total TV viewing vs Netflix at 6.9% (as of data shown in article)
|
||||
- CoComelon has over 160 million YouTube subscribers
|
||||
- Mr. Beast has over 160 million subscribers and over 1 billion views per month
|
||||
- According to TikTok, 35% of users were consciously watching less TV since starting TikTok as of March 2021
|
||||
- Spotify has 11 million artists and 100 million tracks as of 4Q21, with only 200,000 considered 'professional'
|
||||
- An estimated 100,000 new songs are uploaded to streaming services each day
|
||||
- Three major music labels (UMG, SME, WMG) have gained revenue share over independents in recent years
|
||||
- Majors and Merlin represent about 75% of Spotify streams despite explosion of independent music
|
||||
- According to Luminate, 72% of U.S. music consumption in 2022 was catalog (18+ months old)
|
||||
- CD Projekt Red spent over $300 million developing Cyberpunk 2077
|
||||
- Mobile game development costs ~$10,000-$100,000, or 3-4 orders of magnitude less than AAA console titles
|
||||
- There are over 50,000 PC games on Steam but hundreds of thousands of mobile games on iOS and Google Play
|
||||
- Mobile gaming is now approximately 50% of the global video game market
|
||||
- Average adult watches more than 5 hours of video per day
|
||||
- U.S. newspaper industry revenue declined from ~$60 billion in 2000 to ~$20 billion by 2020 (down 2/3)
|
||||
|
|
|
|||
|
|
@ -7,13 +7,9 @@ date_published: "2024-06-01"
|
|||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: null-result
|
||||
status: unprocessed
|
||||
claims_extracted:
|
||||
- "GenAI is simultaneously sustaining and disruptive depending on whether users pursue progressive syntheticization or progressive control"
|
||||
processed_by: leo
|
||||
processed_date: 2026-03-19
|
||||
extraction_model: "anthropic/claude-sonnet-4.5"
|
||||
extraction_notes: "LLM returned 2 claims, 2 rejected by validator"
|
||||
---
|
||||
# GenAl is Foremost a Creative Tool - by Doug Shapiro
|
||||
|
||||
|
|
@ -358,10 +354,4 @@ I will read the post as usual but first: we had the same idea for the a prompt!
|
|||
|
||||
1 reply by Doug Shapiro
|
||||
|
||||
11/12
|
||||
|
||||
## Key Facts
|
||||
- ChatGPT-4o is reportedly trained on 10 trillion words
|
||||
- According to Coatue presentation (June 2024), two-thirds of S&P 500 returns and 90% of NASDAQ-100 returns YTD were AI-related
|
||||
- Symbolic AI dominated AI research from 1950s-1980s before sub-symbolic approaches became prominent
|
||||
- IBM's Deep Blue (1997) used symbolic AI to beat Kasparov; DeepMind's AlphaGo (2015) used hybrid symbolic/sub-symbolic systems
|
||||
11/12
|
||||
|
|
@ -7,14 +7,10 @@ date_published: "2025-02-01"
|
|||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: null-result
|
||||
status: unprocessed
|
||||
claims_extracted:
|
||||
- "GenAI adoption in entertainment will be gated by consumer acceptance not technology capability"
|
||||
- "GenAI is simultaneously sustaining and disruptive depending on whether users pursue progressive syntheticization or progressive control"
|
||||
processed_by: leo
|
||||
processed_date: 2026-03-19
|
||||
extraction_model: "anthropic/claude-sonnet-4.5"
|
||||
extraction_notes: "LLM returned 2 claims, 2 rejected by validator"
|
||||
---
|
||||
# How Far Will Al Video Go? - by Doug Shapiro - The Mediator
|
||||
|
||||
|
|
@ -855,16 +851,3 @@ The image is a thumbnail for a post titled "The Relentless, Inevitable March of
|
|||
72 10
|
||||
|
||||
# 20/21
|
||||
|
||||
|
||||
## Key Facts
|
||||
- Will Smith eating spaghetti AI video was created with Stable Diffusion in April 2023
|
||||
- Veo2 claims to enable up to 4K resolution and clips as long as 1 minute
|
||||
- HarrisX/Variety survey (May 2024, N=1,001 U.S. Adults) found 54% of consumers indifferent or more interested in GenAI-written content
|
||||
- YouTube's share of TV viewing was 11% at time of writing, projected to surpass 20% by 2030 in one scenario
|
||||
- Average streaming services per home was 4 at time of writing
|
||||
- Hollywood produced approximately 15,000 hours of film and TV in 2024
|
||||
- YouTube had approximately 300,000,000 hours of creator content uploaded in 2024
|
||||
- Hailuo introduced T2V-01-Director Model with sophisticated camera controls
|
||||
- Runway offers Lip Sync tool for audio-visual synchronization
|
||||
- Live Portrait is an open-source tool for syncing facial movements between videos
|
||||
|
|
|
|||
|
|
@ -7,13 +7,9 @@ date_published: "2023-08-01"
|
|||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: null-result
|
||||
status: unprocessed
|
||||
claims_extracted:
|
||||
- "entertainment IP should be treated as a multi-sided platform that enables fan creation rather than a unidirectional broadcast asset"
|
||||
processed_by: leo
|
||||
processed_date: 2026-03-19
|
||||
extraction_model: "anthropic/claude-sonnet-4.5"
|
||||
extraction_notes: "LLM returned 2 claims, 2 rejected by validator"
|
||||
---
|
||||
# IP as Platform - by Doug Shapiro - The Mediator
|
||||
|
||||
|
|
@ -371,17 +367,3 @@ Special thanks to Anthony Koithra for his feedback to a draft of this post.
|
|||
# 10/12
|
||||
|
||||
[https://archive.ph/AsshV](https://archive.ph/AsshV)
|
||||
|
||||
|
||||
## Key Facts
|
||||
- FanFiction.net has over 14 million stories comprising approximately 60 billion words
|
||||
- Archive of Our Own (AO3) has 5 million fanfic stories including 500K about MCU, 400K about Harry Potter, 300K about DC, 250K about Supernatural
|
||||
- The most-read work on AO3 has over 9 million hits
|
||||
- Fandom has over 250,000 fan-created wikis with Marvel and Star Wars wikis containing 280K and 180K pages respectively
|
||||
- AO3 won a Hugo Award in 2019
|
||||
- According to Troika study, 85% of people say they are a fan of something, 97% of people aged 18-24
|
||||
- Over 40 million games have been created with Roblox Studio
|
||||
- According to Epic Games CEO Tim Sweeney, half of all play time on Fortnite is now on games made by 3rd parties using Fortnite Creative
|
||||
- Twitch viewers watched 22 billion hours on the platform in recent period
|
||||
- Minecraft videos have received 1 trillion views on YouTube
|
||||
- The compulsory copyright license in music is administered by the Harry Fox Agency and statutory mechanical royalty rate is set by the Copyright Royalty Board
|
||||
|
|
|
|||
|
|
@ -7,13 +7,9 @@ date_published: "2023-03-01"
|
|||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: null-result
|
||||
status: unprocessed
|
||||
claims_extracted:
|
||||
- "information cascades create power law distributions in culture because consumers use popularity as a quality signal when choice is overwhelming"
|
||||
processed_by: leo
|
||||
processed_date: 2026-03-19
|
||||
extraction_model: "anthropic/claude-sonnet-4.5"
|
||||
extraction_notes: "LLM returned 3 claims, 3 rejected by validator"
|
||||
---
|
||||
# 4/23/25, 6:53 PM Power Laws in Culture - The Mediator by Doug Shapiro
|
||||
|
||||
|
|
@ -859,17 +855,3 @@ Subscribe
|
|||
https://archive.ph/0cYxS
|
||||
|
||||
## 20/20
|
||||
|
||||
|
||||
## Key Facts
|
||||
- Spotify has over 80 million tracks with 100,000 new songs uploaded daily as of 2023
|
||||
- Top Gun Maverick generated over $700 million domestic box office in 2022
|
||||
- Bad Bunny had 18.5 billion Spotify streams in 2022
|
||||
- 142 million households watched Squid Game Season 1 in first 28 days per Netflix
|
||||
- Patreon has 250,000+ creators and 13 million patrons
|
||||
- Netflix reports 80% of watch time comes from recommendations, 20% from direct search
|
||||
- Spotify reports 1/3 of new music discovery occurs through algorithmic recommendation
|
||||
- Major labels (Universal, Sony, Warner) plus Merlin represented 87% of Spotify streams in 2017, declining to 77% by 2021
|
||||
- In Salganik/Dodds/Watts experiment, 14,000 subjects rated 48 unknown songs across 9 groups (1 independent, 8 social influence)
|
||||
- Box office distribution slope became increasingly negative (more extreme) from 2000 to 2022
|
||||
- Netflix top 10% of originals represented 95%, 85%, and 75% of global demand in 2018, 2020, and 2022 respectively during period of massive international expansion
|
||||
|
|
|
|||
|
|
@ -7,13 +7,9 @@ date_published: "2023-06-01"
|
|||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: null-result
|
||||
status: unprocessed
|
||||
claims_extracted:
|
||||
- "creator and corporate media economies are zero-sum because total media time is stagnant and every marginal hour shifts between them"
|
||||
processed_by: leo
|
||||
processed_date: 2026-03-19
|
||||
extraction_model: "anthropic/claude-sonnet-4.5"
|
||||
extraction_notes: "LLM returned 3 claims, 3 rejected by validator"
|
||||
---
|
||||
# 4/23/25, 6:54 PM The Relentless, Inevitable March of the Creator Economy
|
||||
|
||||
|
|
@ -856,20 +852,3 @@ JAN 7 DOUG SHAPIRO
|
|||
https://archive.ph/wTgnR
|
||||
|
||||
# 21/22
|
||||
|
||||
|
||||
## Key Facts
|
||||
- Global M&E market grew at ~5% annually 2019-2023 (PwC/Omdia estimates)
|
||||
- Creator media economy grew ~25% annually 2019-2023 (Shapiro estimate)
|
||||
- Corporate media grew ~3% annually 2019-2023 (Shapiro estimate)
|
||||
- Creator media economy was ~$250B globally in 2023 (~15% of total M&E)
|
||||
- Creator media accounted for almost half of total M&E revenue growth 2019-2023
|
||||
- Social video represents ~1/4 of all video consumption time in U.S. (Maverix/Nielsen data)
|
||||
- ~25% of Spotify streams are from artists not represented by majors or Merlin
|
||||
- YouTube has 114 million channels and users upload ~250 million hours annually
|
||||
- Spotify has 10 million+ total artists uploading ~37 million tracks per year (vs 225K professional/professionally-aspiring)
|
||||
- Steam has 100,000 games vs 3,000 supported on Xbox
|
||||
- Kodak estimated 80 billion photos taken in 2000; current estimates close to 2 trillion for 2023 (25x increase)
|
||||
- 83% of TikTok users have posted a video (2021 study) vs ~4% of YouTube users who create
|
||||
- Over 4 million podcasts exist today vs only a few thousand in 2004
|
||||
- Major labels and Merlin accounted for 74% of Spotify streams in 2023
|
||||
|
|
|
|||
|
|
@ -7,14 +7,10 @@ date_published: "2023-10-01"
|
|||
date_archived: "2025-04-23"
|
||||
archived_by: "clay"
|
||||
domain: "entertainment"
|
||||
status: null-result
|
||||
status: unprocessed
|
||||
claims_extracted:
|
||||
- "consumer definition of quality is fluid and revealed through preference not fixed by production value"
|
||||
- "fanchise management is a stack of increasing fan engagement from content extensions through co-creation and co-ownership"
|
||||
processed_by: leo
|
||||
processed_date: 2026-03-19
|
||||
extraction_model: "anthropic/claude-sonnet-4.5"
|
||||
extraction_notes: "LLM returned 2 claims, 2 rejected by validator"
|
||||
---
|
||||
# What is Scarce When Quality is Abundant - by Doug Shapiro
|
||||
|
||||
|
|
@ -559,13 +555,3 @@ JAN 20 DOUG SHAPIRO
|
|||
|
||||
* The first image is a thumbnail for "28 Days of Media Slides" and features a dark blue background with white text that reads "28 Days of Media Slides" in a stylized font.
|
||||
* The second image is a thumbnail for "Quality is a Serious Problem" and features a person sitting in front of a television screen displaying the HBO logo. The person is looking at the screen with a thoughtful expression.
|
||||
|
||||
|
||||
## Key Facts
|
||||
- Hollywood released approximately 15,000 hours of new TV and film content in the U.S. in 2022 (496 films averaging 100 minutes plus ~2,000 TV series averaging 10 episodes of 40 minutes each)
|
||||
- YouTube disclosed in 2019 that 500 hours of new video are uploaded every minute, or 30,000 hours per hour, equivalent to over 250 million hours per year
|
||||
- According to Nielsen (August 2023), YouTube is the most-streamed service on U.S. TVs at 9.1% share, exceeding Netflix (8.2%) and equal to Hulu, Disney+, Max, Peacock, and Paramount+ combined
|
||||
- Mr. Beast's recent video 'World's Most Dangerous Trap!' garnered over 100 million views in its first week with a 20-minute runtime, comparable to Netflix's top global series
|
||||
- Warner Bros. library consists of more than 145,000 hours of programming including 12,500 feature films and 2,400 television programs
|
||||
- Manifold Markets bettors gave 26% probability that a film created using text-to-video generator will be nominated for an Academy Award by 2030 (as of October 2023)
|
||||
- Impact investing is a $1.57 trillion market with 92% of investors citing fragmented measurement as a concern and $19.6 billion fleeing U.S. ESG funds in 2024
|
||||
|
|
|
|||
Loading…
Reference in a new issue