Hollywood’s ongoing debate over the role of artificial intelligence in film production has found a thoughtful voice in media analyst Doug Shapiro. Writing extensively on AI’s potential impact, Shapiro explains why AI is not yet ready for the big screen in a lengthy essay, “Fear and Loathing (and Hype and Reality) in Los Angeles.”
“For Hollywood, using GenAI models that were trained on copyrighted material is lose-lose legally: either they are infringing others’ copyrights or implicitly undermining their own,” he contends, naming three major obstacles preventing widespread adoption: labor relations, unresolved legal issues, and technical limitations.
These challenges, Shapiro argues, will keep AI video generators from becoming a staple in Hollywood’s production toolkit for the foreseeable future.
There’s no denial that GenAI holds significant promise for Hollywood, offering enticing new tools and capabilities that could provide efficiencies throughout the production chain.
These AI technologies can assist in scriptwriting, concept art, and even automating visual effects processes. The most transformative potential, however, lies in AI video generators like Sora, Veo and Runway Gen-3, that can create high-quality video content from simple text or image prompts.
“AI won’t replace Hollywood, but the bigger risk is that it disrupts it,” Shapiro cautions. “Disruption requires two main ingredients: a disruptive innovation that lowers barriers to entry and incumbents who can’t respond. This has both.”
Labor Relations: A Major Hurdle
While the potential applications of GenAI are vast, the major studios face significant hurdles that will slow widespread adoption. One of the most significant barriers is labor relations.
The industry relies heavily on its talented workforce, including writers, directors, actors, and crew members, all of whom have a vested interest in how new technologies might impact their jobs. Recent labor disputes — including last year’s WGA and SAG-AFTRA strikes, and this year’s narrowly avoided IATSE strike — have underscored the sensitivity around AI in the production process.
Shapiro encapsulates the industry’s trepidation: “With a bad taste still lingering from the WGA and SAG-AFTRA strikes last year and IATSE negotiations underway, anything AI is ‘third rail’ for the studios right now.”
A Legal Quagmire
Beyond labor relations, unresolved legal issues pose a significant barrier to the widespread adoption of AI video generators in Hollywood. The primary concerns revolve around copyright infringement and intellectual property rights, creating a complex legal landscape that studios must navigate carefully.
Copyright law, particularly the concept of “fair use,” is central to this issue. Fair use allows for limited use of copyrighted material without permission, but its application to AI training data is unclear.
Studios face the risk of lawsuits if they use AI-generated content derived from copyrighted materials without proper licensing. This uncertainty makes it difficult for major studios to adopt AI video generators without risking legal repercussions.
Examples include scenarios where AI models generate content similar to existing copyrighted works, Shapiro notes. For instance, if an AI video generator produces a scene reminiscent of a famous movie, determining whether this constitutes infringement can be highly contentious.
Studios must also consider the implications of using AI to create content that may inadvertently mimic the style or elements of copyrighted works, leading to potential IP conflicts.
Additionally, there is the question of who owns the rights to content created by AI. The US Copyright Office has stated that only material produced through human creativity can be copyrighted. This means that works generated entirely by AI may not be eligible for copyright protection, leaving studios vulnerable to even further legal disputes.
Generative AI Goes to Film School
While the advancements in AI video generators are impressive, technical limitations prevent them from meeting the high standards required by Hollywood productions. These are significant enough, says Shapiro, to deter Hollywood studios from fully embracing generative video in the near future.
“Despite the amazing quality improvements in these models, they won’t meet the needs of the most demanding directors/showrunners/cinematographers, probably for years.”
AI video generators lack a deep understanding of complex cinematic elements such as lighting, camera movements, and the subtleties of human emotions, Shapiro points out.
These tools also fall short in replicating the nuanced performances of actors, and lack an understanding of cinema history. In other words, for GenAI to be effective, it needs to go to film school.
Another significant limitation is the control over the final output. Directors and cinematographers rely on precise control over every aspect of a scene, from camera angles to color grading.
Current AI models do not offer the level of fine-grained control needed to meet these exacting standards. The process of integrating AI-generated content with live-action footage also presents challenges, as seamless integration requires a level of technical sophistication that AI tools have yet to achieve.
The Future of GenAI in Content Creation
Looking ahead, there are several potential scenarios where GenAI could become more integrated into Hollywood workflows, Shapiro says. One possible future is the gradual adoption of AI tools in pre- and post-production processes. However, the most immediate and impactful use cases for GenAI, he argues, will take place on the sidelines.
“The earliest adoption and most important use cases will come outside of the major Hollywood studios. These include empowering the massive creator class and independent producers.”
By democratizing access to advanced production tools, he says, GenAI could pave the way for a more diverse and innovative media landscape. Independent creators, small production teams, and other sectors such as advertising, music videos, and educational content are poised to benefit from AI advancements.
These groups can leverage AI tools to produce high-quality content more efficiently and at a lower cost, breaking down barriers to entry in the industry.
