AI for Film Making: Some Parts of the Value Chain Gain, Others Lose

Artificial intelligence angst in the content creation industry is understandable: history suggests there will be significant disruption.

AI in movie-making might represent a disruptive force akin to the introductions of synchronized sound (talkies in the late 1920s), color film (Technicolor in the 1930s), and CGI (exploding in the 1990s), according to McKinsey. 

 

Each innovation fundamentally altered filmmaking workflows, aesthetics, and economics while reshaping the roles of actors, directors, producers, and distributors. 

Past technologies also faced initial resistance, high costs or technical hurdles, and job disruptions. They also expanded creative possibilities, audience appeal, and industry scale. 

AI builds on this pattern.

Historians might argue that every transformative technology in Hollywood has followed a similar arc:

• initial chaos and career destruction

• followed by new creative possibilities

• ultimately a reshuffling of economic power

• with value flowing to whoever controls distribution.

In every case, realism was enhanced but with implications for cost, storytelling and aesthetics. The transition from black-and-white to color enhanced realism, but also added production cost.

CGI, we might agree, also enhances realism or immersion, arguably enabling some scenes at less cost than any other method allows. 

As always, there will be efforts to limit the new technology’s scope. But those efforts seemed doomed to fail, longer term. That is what always has happened. 

For example:

• Sound killed the careers of actors with weak voices or accents

• Technicolor's monopoly bled producers

• CGI consolidated power with tentpole studios and squeezed out mid-budget films.

AI could be unique and more disruptive than any predecessor:

• Speed: The transition to sound took roughly five years; color, two decades. AI might propagate faster

• Breadth: Sound threatened actors. Color threatened art departments. CGI threatened stunt performers and practical effects crews. AI threatens all simultaneously, plus writers, composers, voice artists, and editors

• Uncertainty about the ceiling. With sound, everyone could at least imagine what the end state looked like. With AI, no one can do so.

The closest historical analogy might be the introduction of sound itself. That revolution was so rapid that it wiped out entire categories of talent while simultaneously creating new genres, new stars, and a vastly larger global audience.

Innovation	Actors Impact	Directors Impact	Producers Impact	Distributors Impact
Sound (Talkies, ~1927–1930s)	Many silent stars displaced (voice/accent issues); shift to dialogue-driven, naturalistic performance; new stage talent influx.	Lost real-time on-set direction; camera restrictions initially; later gained editing freedom via postsynchronization for montage/experiments.	Massive investments in soundproofing, theater wiring, and dialogue scripts; enabled genre expansion but high transition costs.	Theaters upgraded en masse; new revenue from immersive "talkies" but initial infrastructure overhaul.
Color (Technicolor, 1930s)	Makeup/lighting/costume adjustments for vibrant looks; some stars benefited visually; no widespread displacement.	New aesthetic rules (color as narrative tool); interference from consultants (e.g., Kalmus); bolder visual storytelling.	High costs limited to big-budget films; prioritized color for spectacle and audience demand.	Premium appeal for color films boosted theatrical draw and perceived quality.
CGI (~1990s onward)	Green-screen acting (detached from environments/co-stars); de-aging possible but performances can feel less grounded.	Greater post-production flexibility but dependency on VFX teams; "armchair" directing risks reduced creativity.	Budget shifts to post/VFX; enabled ambitious projects but crunches and overruns common.	Enabled bigger blockbusters and global spectacles for wider distribution.
AI (Generative)	Deepfakes/synthetic performances; likeness rights sales; job threats from virtual actors; consent, deepfake risks.	AI tools for generation/editing assist but challenge authorship/control; potential for new storytelling paradigms.	Lower costs/faster cycles democratize production; more projects possible but IP/protection needs rise.	Efficiency gains and personalization; risk of content flood/saturation.

AI is likely to feature its own pros and cons, across the value chain, 

Among the pros:

• Dramatically lowers costs and speeds workflows (productivity gains in pre-production; virtual sets reduce reshoots; AI for script analysis, VFX acceleration, dubbing, localization)

• Democratizes high-end filmmaking for indies/smaller producers, enabling more content and new formats (personalized/immersive stories)

• Expands creativity: AI assists with ideas, consistent world-building, de-aging, or generating complex visuals beyond traditional CGI limits

• Benefits distributors by efficiency, hyper-personalization, and higher margins on scalable content.

Among the cons:

• Job displacement (actors; writers; VFX artists)

• Possible loss of human authenticity, emotional depth in storytelling

• Ethical/IP issues (unauthorized likeness use, consent for digital doubles, training data lawsuits; potential for deepfake misuse)

• Market saturation from increased content supply.

The point is, most parts of the value chain might benefit (lower costs, faster production, independent producer projects) even if actors face demand issues.