While Hollywood has fretted, fumed and largely failed to respond to either the promises or the perils of the AI revolution, a small, rotating group of A-list filmmakers and technologists from Hollywood, Silicon Valley and beyond has been meeting in private for a series of off-the-record conversations about one of the industry’s most divisive questions: How AI can enter the filmmaking process without replacing the humans who make the films.
Now, after more than three years of secret meetings, the group is going public with its first major proposal: Hollywood needs to stop treating all AI as the same thing.
The conversations, which started in 2023, have been organized by legendary producer Kathleen Kennedy and Susan Ruskin, the dean of the American Film Institute. Their existence has been kept out of the public eye — remarkably, without leaking — and I’ve attended these meetings alongside my colleague, The Ankler’s editor-in-chief Janice Min, under an off-the-record stipulation.
Although I’m not permitted to reveal the full list of participants, the rooms included some of the people with the most power to shape what happens next: Oscar-winning filmmakers, celebrated actors, Hollywood C-suite executives and major names from Silicon Valley.
They came together around a problem that has increasingly paralyzed Hollywood’s AI debate: The term itself has become so broad that it can mean everything from a familiar post-production tool to a machine-generated performance replacing an actor.
Their proposed solution is a new framework meant to distinguish between those uses — including what they call “Human Generative Workflows,” in which generative technology remains under granular human creative control.
This weekend, the dialogue grew into a much larger event with 120 participants and produced the group’s first public statement. Today, The Ankler is publishing that document for the first time.
How did some of Hollywood’s most powerful people get from the fear and fury of 2023 to an attempt at common ground?
Before I reveal the document, some context about the thought process and what was said in the room.
Setting the Table
The meetings were organized to ensure the future of filmmaking is shaped “intentionally, not accidentally.”
That meant getting people who often regarded one another with suspicion into the same room — and, crucially, getting specific about what they actually meant when they said “AI.”
The group’s conversations focused on, in the words of the organizers, “building a shared understanding, a common vocabulary, and a collective responsibility for how AI is integrated into the creative process.” Part of each meeting was devoted to demos from the farthest reaches of AI — not to show off whiz-bang technology for its own sake, but as examples of artist-driven creative works guided entirely by human hands.
Kennedy drew inspiration from the work of artist Refik Anadol, who “locates creativity at the intersection of humans and machines” and who has been featured at the Museum of Modern Art in New York, the Serpentine Galleries in London and the Guggenheim Bilbao in Spain, to name a few. Kennedy met Anadol after his MoMA show, at a time when much of Hollywood approached generative AI primarily as a threat.
“She is a very brave person,” Anadol tells me of Kennedy. “And truly, she’s a very open-minded person, and she prefers, I believe, to create those sessions to understand AI much better.”
Anadol’s studio in Los Angeles was the location of the first meeting — which took place in the shadow of the contentious labor strikes — in November 2023.
“November of 2023, as you remember, was after a long and very hard period in Hollywood,” says Jessica Sittig, a creative technologist who worked at Google and Apple and introduced Kennedy to Silicon Valley and the idea of bringing both sides together to discuss AI. “It started as a conversation between these two neighboring tribes who clearly shared a future, but knew very little about each other.”
The meetings then moved to the American Film Institute campus, where Ruskin helped curate the conversation.
“We realized that the American Film Institute is a really unique place. It has all the heads of the big studios, all the guilds are part of the board of trustees — so it ends up being a really great neutral gathering place,” says Mira Lane, Google’s VP of technology and society, who was brought into the fold by Ruskin. “A lot of people think AI is happening to them, and what we wanted to do was create forums for people to seize control of the narrative and build from within.”
The neutrality mattered. Studios, labor, filmmakers and technology companies were already approaching the subject from very different — and frequently antagonistic — positions.
Bryn Mooser, a filmmaker whose Echo Park-based XTR production company has explored the possibilities AI can unlock, says the sessions aimed to educate. “For filmmakers, it’s optional whether you use AI, but it’s not optional whether or not to understand it.”
What eventually emerged was less a consensus about AI than a consensus about the problem with the debate itself.
Sittig recalls one participant saying, “We are dealing with a Tower of Babel. Nobody can talk about AI because the word carries too much meaning, too much ambiguity, way too much fear. One word is doing all the work.”
That became the animating idea behind what the group is now proposing publicly: stop treating every use of AI as equivalent.
After years of meetings and conversations, Ruskin felt it was time to break this discussion out of the small group. Last weekend, that meant expanding the room to roughly 120 people and testing the ideas with a much broader cross-section of the industry.
“It was time for us to start really thinking about how the technology can be in service of artist-controlled, intentional creative decisions, not replacing them,” Ruskin says.
Navigating the Future
The AFI event opened with a blunt Q&A session with the organizers, giving participants time to vent their apprehensions before moving into discussions and breakout sessions on authorship, standards, transparency and control.
“That’s a lot of stuff to accomplish in one day. But everyone was there for it,” Ruskin says. “I was so heartened by the honesty of the film festival people and the Academy people, and the unions and production executives — they were all thinking about these things, and all thinking really seriously about (the questions): What are the standards? What does transparency really mean? What does a pipeline look like? How can we do this as an industry, rather than in isolation, in collaboration with the people building that tech?”
While Ruskin says she’s “not naive enough” to think tech will just follow Hollywood’s lead, she argues that refusing to engage would be worse. “Otherwise, that tech is just going to blow over us and we’re going to be left standing there saying, What do we do now?”
That people remain reluctant even to be identified as participants tells you how charged the subject still is. Having sat in these rooms, what struck me was less that everyone agreed — they didn’t — than that people who entered the debate from sharply different positions managed to get past the familiar finger-pointing long enough to define what they were actually arguing about.
The result is the document being released today. And its central proposition is deceptively simple: Not all AI use is the same.
The group’s document — entitled “Human Generative Workflows” — points the way to bringing this spirit out of the closed rooms and into the larger community.
One producer who attended the meetings describes HGW as an attempt to name the middle ground Hollywood’s binary AI debate has largely ignored: workflows that incorporate generative technology but remain directed, iterated and controlled by artists.
“That’s distinct from purely machine-generated content, which is what is causing much of our industry’s concern, isn’t copyrightable and is where the word ‘slop’ comes from,” the producer says.
The group’s argument — and it is an argument, not yet an industry standard — is that such workflows should be understood as an evolution of existing digital production rather than as machine substitution for human authorship.
“I believe that when audiences say they’re against AI, that’s what they mean,” the producer adds. “Right now, everything gets lumped together, and we need to change that. A lot of us think HGW will be looked back on as an evolution and extension of animation and CGI — requiring the same craft, precision, and skill as any other tools for filmmaking and storytelling in general.”
Adds Jon Avnet, who co-chairs the DGA’s AI Committee alongside Christopher Nolan and led one of the panel discussions last weekend: “The key thing was to start the conversation in light of day, and figure out what is happening, and what’s going to happen.”
“The more accurate information, the more you demystify what’s going on here,” he adds. “It’s really important not to be passive; be proactive.”
For Mooser, the stakes go beyond terminology.
“The industry’s not in a great place, and you have a moment where a powerful new technology is hurtling into it. It’s like a perfect storm.”
The existential question, he says, is whether Hollywood can “learn how to evolve with this technology and find ways that it can help us do what we want to do, which is entertain the world? Or are we destroyed by it?”
The document released today is the group’s first attempt at an answer, as it seeks to repair the broken conversation on AI and proposes a framework for a better discussion and understanding of the issues at stake.
Mooser credits Kennedy and AFI with becoming a “unifying voice” in exploring the question: How does the industry navigate the future of this?
As someone in the room, I can testify to that.
In introducing “Human Generative Workflows,” the group writes:
While much of the public conversation has focused on artificial intelligence itself, we believe the more important story is how an entire creative industry comes together to shape its future.
No single constituency can answer those questions alone. Studios, filmmakers, technologists, labor organizations, educators, investors, agents, and legal experts all have a role to play in defining the next generation of creative workflows. This discussion is intended as the beginning of that collaborative process through working together to establish a common language and shared principles that can guide the industry forward.
One framework that may help inform that conversation is Human Precision Generative Workflow (HPG). HPG is a proposed framework intended to stimulate industry discussion and provide a common vocabulary for thinking about authorship in an era of generative AI. Rather than asking simply whether AI was used, it encourages us to look at where human judgment, creative direction, editorial decision-making, and accountability exist throughout the filmmaking process. The objective is not to slow innovation, but to ensure that technology remains a precision instrument in the hands of artists, rather than becoming the force that determines the creative process.
Ultimately, this conversation is bigger than AI. It is about bringing the entertainment community together to shape a shared approach to the future of the creative process. The future of entertainment will be shaped not by AI alone, but by the choices we make about how AI is integrated into the creative process. Those choices will determine whether technology expands artistic possibility, strengthens authorship, and continues to serve storytelling.
Here is the first document from the AI working group, in its own words, for your consideration and, hopefully, to point the way toward a more productive conversation.
Human Generative Workflows
An invitation to conversation — 2026
Since November 2023, a small group of filmmakers and technologists has gathered with a simple objective: to ensure the future of filmmaking is shaped intentionally, not accidentally. Those conversations focused on building a shared understanding, a common vocabulary, and a collective responsibility for how AI is integrated into the creative process. This document is the result of those discussions and their valuable input.
The problem in plain language
One acronym is doing too much work.
The term “AI” is being asked to describe everything from features like noise reduction that have lived inside post-production software for years, to the new capabilities of generative models that didn’t exist two years ago. It is used in the same breath to mean a color grading assist in DaVinci Resolve and a prompted synthetic performance that replaces an actor. It applies to both
the evolving CGI pipeline that artists spent a decade mastering and a deepfake made without consent.
That ambiguity is showing up in every contract negotiation, credit conversation, guild discussion, and audience disclosure question. It creates friction and confusion where the industry needs shared ground.
This paper deliberately focuses on one challenge: developing a shared language around forms of AI use in production workflow. It does not attempt to resolve equally important questions around training data, licensing, labor transition, consent, or economic policy. Those issues deserve their own discussion and will ultimately require legal, commercial and social solutions. A common vocabulary, however, is a necessary foundation for all of them.
A map, not a switch
The binary that AI is either “used” or “not used” doesn’t hold. The industry needs a map for the distinctly different forms of AI use in production, rather than conflating such different uses together. Over time, these categories could form the basis of a shared classification system: something productions use internally, in credits, and in guild-facing documentation.
An industry-built classification system that distinguishes between these distinct AI use cases could serve productions, crews, guilds, and eventually audiences.
Three distinct forms of AI usage are emerging, each of which can be used for concept development and exploration, as well as for producing final footage. This document focuses on the latter: where the following three uses are used for final production footage.
Utility Techniques & Embedded AI — Copyrightable
This foundational category encompasses utility-driven machine learning, as well as the AI tools already inside media software — Premiere, DaVinci Resolve, Avid, After Effects — and its ecosystem of plugins. Many of these types of AI-enabled features have existed for years.
Importantly, workflows in this category generally help to answer a question that has a defined correct answer and is not subject to much creative interpretation. The following is a non-exclusive example list of techniques that fit in this category:
Denoising: Removing noise/damage from an image or sound
Upscaling and Sharpening: Enhancing resolution of existing human-captured media
Audio Separation: Isolating distinct audio tracks from a mixed source
Rotoscoping: Automating matte generation for existing footage
Depth Information: Extracting spatial data from flat images
Markerless Mocap: Capturing human performance without physical tracking suits
Wire-removal & Inpainting: Erasing production artifacts from human-directed scenes
Most productions already use these techniques without flagging it as “AI use,” because until recently, no one needed to make the distinction. The outputs from these techniques are generally copyrightable when used as listed above
Human Generative Workflows (HGW) — Copyrightable
Artist-controlled pipelines that integrate specialized generative models inside VFX or production tools. These are human-authored, copyrightable, and the product of deliberate creative decisions. HGW works within professional tools like Nuke, Unreal, After Effects, Blender, ComfyUI, and DaVinci Resolve, and brings specialized generative models into that pipeline under the artist’s control, often alongside tightly trained “LoRA” (Low-Rank Adaptation) models, control nets, and structured inputs. The outputs are human-crafted and copyrightable.
Machine Generative (MG) — Not Copyrightable (likely not copyrightable)
Purely machine-generated likenesses, voices, performances, or stories that substitute for human creative work. A prompt-based generative tool is not precise enough and replaces human creative vision with a generative model. A prompt may include text, images, or other inputs. The key copyright question is not how creative those inputs are, but whether the user exercises sufficient control over the outputs specific expressive elements, which current generative systems may ultimately determine themselves. The distinction that these are exclusively “prompted generations” from machines is not a technicality: It is the difference between a set of tools and processes that serve human creative vision and a prompt-based generative tool that replaces it. These outputs are NOT copyrightable, as the machine solely generates the footage based on prompts.
What we mean by Human Generative Workflows
Human Generative Workflows are pipelines where, much like CGI, a human artist (author or artist) is making decisions. The generative models integrated into these workflows serve the artist’s creative vision by preserving, if not enhancing, precision and control. The artist defines the parameters, provides the inputs, and evaluates what comes back at every step of a process that often involves multiple traditional and modern tools working together.
HGWs take different shapes in different crafts. The walkthrough and worked examples that follow draw from visual production. The same logic applies in sound, music, editing, and the writing room: artist-led, granular control, and iteration. It will extend to crafts and ways of making work we haven’t yet named or even imagined.
A workflow walkthrough
An HGW production might begin with an artist or team hand-painting reference art that establishes the look. That reference work becomes training data for a small custom model, fine-tuned on the team’s own artwork and original IP rather than the open internet. The artists generate outputs using the trained model, then paint over what isn’t right and retrain. This custom-trained model can then be used to generate elements that integrate with the rest of the production pipeline in various ways. Scenes – or keyframes used in the animation process — are assembled with intentional camera, lighting, and composition. Traditional compositing brings everything into the final frame. Color and finishing happen as on any other production.
This walkthrough of an HGW behind a set of shots shows the human hand at every step.
An HGW example: Dear Upstairs Neighbors
Dear Upstairs Neighbors, the six-minute animated short directed by Connie He and produced by Márcia Mayer, working with a crew of dozens of artists and a broader production staff, and with support from engineers at Google DeepMind, premiered at Tribeca Festival in June 2026. It was made by a team of roughly forty-five people consisting of animation veterans from Pixar and DreamWorks working alongside DeepMind researchers and engineers. Every frame on screen is generated by a series of different fine-tuned machine learning models alongside traditional editing tools. This document advocates that every frame is also entirely human-authored.
The team hand-painted concept art that established the visual language of the film. They fine-tuned small custom models on their own original artwork. They developed video-to-video workflows in which animators provided rough animation as the structural backbone for the generated output. When the results didn’t match the team’s intent, they painted over, retrained, and iterated.
Connie and her team made this film using Human Generative Workflows. The pipeline was artist-led. Control was granular, built on custom training on the team’s own work. Iteration allowed for multiple cycles of creative exploration and discernment. Generative components contributed to a larger production process where human judgment governed every decision. Human Generative Workflows gave Connie language to describe her work and process. HGW proposes that creative authorship is human even when rendering is generative. The team’s artistic decisions are visible in every frame even though no frame was hand-drawn. This is another incremental step along the continual evolution of computer-generated imagery rather than a departure from it.
The more useful question is not “Did you use AI?” but “Where, and how, did you use AI in the process?” The moment a tool moves from exploration into the final pipeline is where the most consequential decisions get made, often quickly and informally. That transition point is where consent, credit, and copyright begin to apply.
What using Human Generative Workflows is not: a tool for replacing writers and actors, automating performances, or generating story without a human author.
Copyright law defines and protects HGW
The U.S. Copyright Office has published a document with interpretations and recommendations to help define human authorship, which could serve as a useful reference to distinguish between HGW and MG content. Here are a few notable excerpts:
- Copyright does not extend to purely AI-generated material, or material where there is insufficient human control over the expressive elements.
- Whether human contributions to AI-generated outputs are sufficient to constitute authorship must be analyzed on a case-by-case basis.
- Based on the functioning of current generally available technology, prompts do not alone provide sufficient control.
- Human-authored expression that remains perceptible in an AI-assisted output may be protected by copyright, as may sufficiently original human modifications or the human selection, coordination, and arrangement of human authored and AI-generated material; however, protection extends only to those human contributions, not to AI-generated
- The inclusion of elements of AI-generated content in a larger human-authored work does not affect the copyrightability of the larger human-authored work as a whole.
The market is already moving
The classification conversation is not hypothetical. Filmmakers and studios are already staking positions that will shape the formal frameworks to come. A24’s Heretic ended its 2024 credits with “No generative AI was used in the making of this film.” In 2025, Vince Gilligan’s Pluribus closed with “This show was made by humans.” DreamWorks’ Bad Guys 2 used its credits to prohibit AI training on the work. And yet, many subcontractors and production partners on projects like these are attempting to navigate the usage of new AI-enabled features in their standard tools (the “Embedded AI in Traditional Tools” category) and fast-emerging HGW on a project-by-project basis. There is significant ambiguity and inconsistency in both policy and parlance. Sales companies and rights advocates are beginning to push for industry-wide certification standards. We need more granularity and clear language.
The industry is still stuck in the binary question, “was AI used, or not?” HGW responds to a different and more useful question about its use: “Where in the process, by whom, and with what degree of control?” The future of entertainment will be shaped not by AI alone, but by the choices we make about how AI is integrated into the creative process.
What could happen next?
None of this requires waiting for a regulatory mandate. Several of these steps could begin now, in parallel, with the people already in the room.
- Publish this vocabulary and invite a response. A shared industry glossary, even a provisional one, gives everyone something concrete to react to. Reaction is how a draft becomes a standard.
- Start the consent conversation on Machine Generative (MG) outputs of actors. That’s where agreement is closest. Separating it from HGWs in early negotiations allows progress on both fronts simultaneously.
- Engage with market-driven labeling efforts. Filmmaker-led disclosures and emerging certification schemes reflect real buyer and audience demand. A shared classification framework should be legible alongside them, not in opposition.
- Think about governance as a living conversation. The tools will keep changing. A multi-stakeholder body, modest in scope and flexible enough to revisit its own definitions over time, may serve the industry better than any framework written to be permanent.
- How does this work across international co-productions? A framework developed around U.S. copyright rulings may not translate, subject to EU, U.K., or Chinese law. And, China’s regulatory approach to AI is moving on its own timeline.
- Extend the conversation to music, sound, and the writing room. Before those communities find themselves reacting rather than shaping.
“The way forward must involve collaboration, and the value of the human in the A.I. Age will be essential.”
