In the race to develop transformative artificial intelligence, a surprising hurdle has emerged for the titans of Hollywood: a scarcity of the very resource they are built on—data. Major studios, including Disney, are reportedly discovering that their vast archives of films and television shows are not enough to train the sophisticated, top-tier AI video generation models they envision. This paradox, where content kings find their proprietary libraries insufficient, is exposing deep technical, legal, and strategic challenges that are reshaping the entertainment industry's approach to AI, slowing down high-profile partnerships and leveling the competitive field between legacy media and agile tech startups.

The core of the issue lies in the immense and diverse data requirements for training a truly capable generative video model. While a studio like Disney possesses an enormous catalog, that content is stylistically specific and represents a fraction of the visual information needed to create a versatile, general-purpose AI. An effective model must learn from a dataset that encompasses a vast range of aesthetics, actions, environments, and physical interactions to generate novel, high-fidelity content on command. A source familiar with the matter noted that not only is the Lionsgate catalog too small to create a powerful model, but even Disney's extensive library would be insufficient for the task.[1][2] This limitation was a key factor in the slower-than-expected progress of the partnership between Lionsgate and the AI startup Runway, a deal initially touted as a way to create "cutting edge, capital efficient content."[1][3] The reality is that training a custom model on a single studio's assets, while useful for specific tasks like tweaking backgrounds, does not yield the broad capabilities needed for large-scale, ambitious projects.[3]

Beyond the sheer volume and diversity of data required, Hollywood studios are confronting a labyrinth of legal and ethical complications that make their archives a potential minefield.[3] The central question is whether owning the copyright to a film grants a studio the right to use it for training an AI model without further consent or compensation for the actors, writers, and directors involved. This is a thorny legal gray area.[2] Each production involves a multitude of rights holders, from actors with rights to their likeness and performance to writers who may retain certain ancillary rights to their original works.[1][3] Using these films to teach an AI could be seen as creating a derivative work for which the original contributors are not being compensated, leading to a host of potential legal challenges.[3] The Writers Guild of America (WGA) has already urged studios to protect writers' works from being used for AI training without authorization, citing the studios' fiduciary obligation to defend the copyrights they hold in trust.[4] This legal ambiguity has studio lawyers urging caution, significantly stalling efforts to weaponize their back catalogs for AI development and complicating deals like the one between Lionsgate and Runway.[1][3]

In response to these data and legal bottlenecks, the industry is pivoting toward alternative solutions, most notably the use of synthetic data. Synthetic video generation allows for the creation of entirely new, artificial datasets through computational methods.[1] This approach offers developers precise control over every element, from camera angles and lighting to object behavior, enabling them to build diverse and highly specific training material without the legal entanglements of copyrighted content.[1] By generating simulated scenes, companies can create vast amounts of data tailored to specific needs, such as training an AI to understand complex physical interactions or rare events, while mitigating the privacy and consent issues tied to real-world footage.[5][6] Furthermore, this strategy can be more cost-effective and faster than traditional data-gathering methods.[5] This shift also alters the competitive landscape, potentially giving an advantage to AI-native companies that can build models on legally "clean" synthetic data or on vast quantities of publicly available internet content, a practice that, while legally contested, is widespread.[7][8]

The realization that proprietary libraries are not a silver bullet is forcing a strategic evolution in Hollywood. Rather than attempting to build a single, all-powerful internal model, studios are increasingly looking to partner with multiple AI companies, leveraging a suite of specialized tools for different tasks within the production pipeline.[2][3] This approach acknowledges that different models excel at different things—from generating special effects to initial storyboarding. The data scarcity problem has demystified the idea that legacy content holders have an insurmountable advantage in the AI race. It has become clear that access to broad, diverse, and legally usable data is more critical than a library of even the most iconic films. This dynamic is fostering a more symbiotic, if still cautious, relationship between Hollywood and the tech sector, where collaboration and licensing become more crucial than isolated development. The future of AI in filmmaking will likely not be a single studio-owned super-intelligence, but a complex ecosystem of specialized tools trained on a combination of licensed, scraped, and increasingly, synthetic data.