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.
DOI:
https://dx.doi.org/10.51244/IJRSI.2025.1210000295
This study investigates the influence of artificial intelligence (AI) on Hollywood’s creative production processes,
with a particular focus on how technological adoption is reshaping roles, workflows, and notions of authorship.
Drawing on an interpretivist paradigm and a qualitative research design, the study employed document analysis
of industry reports, union statements, scholarly publications, and media sources published between 2018 and
2024.
The findings reveal that AI has been incorporated into multiple stages of filmmaking, including concept
development, screenwriting, casting, visual effects, editing, and production planning. While these tools primarily
function as supplementary aids that enhance efficiency and expand creative options, concerns persist about
reduced human agency, job security, and cultural originality. Stakeholder perceptions vary: producers and
executives often emphasize efficiency and cost reduction, whereas writers, performers, and technical staff
express unease about the erosion of creative sovereignty and skill development. Labor unions such as the Writers
Guild of America (WGA) and SAG-AFTRA have begun to push for contractual safeguards addressing issues of
authorship, copyright, and likeness protection.
The study concludes that AI’s integration into Hollywood is negotiated rather than uniform, marked by tensions
between innovation and creative integrity. It highlights the need for regulatory frameworks, ethical oversight,
and skill development initiatives to ensure that technological progress does not compromise artistic expression,
labor conditions, or cultural diversity. By situating these findings within broader theoretical and empirical
debates, the research contributes to ongoing discussions on the future of creativity in the age of intelligent
machines.
Artificial intelligence (AI) is transforming the creative process of the film industry, indicating a revolutionary
change in modern media production (Anantrasirichai & Bull, 2022). Today, AI is incorporated into almost every
stage of the filmmaking process -from pre-production to post-production- through deep learning algorithms,
generative models, and neural networks capable of performing tasks that human experts conventionally complete
(Liu, 2024). For instance, The Irishman (2019) showcased the growing sophistication of these techniques with
AI-driven visual effects, including deepfake-based face replacements and facial de-ageing (Sun, 2024). These
technological advancements not only impact production efficiency and realism but also modify creative results
and economic dynamics. However, this progress introduces many tensions in Hollywood's creative ecosystem.
Jasim and Awqati (2025) argue that AI systems’ capacity to mimic human-generated work has highlighted
apprehensions regarding creative authorship, ownership, and authenticity. Writers, performers, and directors
have increasingly expressed concern over losing human agency in narrative and creative decision-making
(Bristol Creative Industries, 2023). Additionally, the legal uncertainties surrounding copyright, consent, and the
use of proprietary creative material to train AI models raise complex regulatory challenges (Lucchi, 2024). As a
result of these evolving dynamics, the Writers Guild of America (WGA) and SAG-AFTRA identified AI usage
as their top concern during the 2023 Hollywood labor disputes (Askar, 2024). In response, contractual
safeguards were implemented to protect human creators from being coerced into AI-assisted collaboration and
to establish preliminary frameworks for the ethical use. According to Fukuda‐Parr and Gibbons (2021) and Siala
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and Wang (2022), these measures represent an underlying step towards balancing innovation with the protection
of rights. Nevertheless, given how quickly AI technologies are developing, the moral and regulatory environment
is changing, requiring constant research.
The growing use of AI in Hollywood filmmaking has sparked much controversy, primarily because of the
implications for ethical governance, labor relations, and creative sovereignty. Fukuda‐Parr and Gibbons (2021)
note that while AI can potentially enhance creative innovation and operational efficacy, its unbridled application
may compromise fundamental aspects of authorship, job security, and legal accountability. The displacement of
human creative labor is one of the main issues. As AI systems produce scripts that mimic humanlike story
patterns, writers risk receiving less payment and creative input (Biermann, Ma & Yoon, 2022). Similarly, actors
are increasingly at risk of having their voices and likenesses replicated by AI without copyright approval or
reasonable compensation, raising significant concerns about consent and ownership. These developments
challenge the traditional frameworks of artistic authorship and individual consent, as highlighted by Lucchi
(2024) and Murdoch (2021). In response, the WGA’s 2023 Minimum Basic Agreement addresses these concerns
by clarifying that AI cannot be credited as a writer and restricting the classification of AIgenerated material as
literary work. Furthermore, using AI in content creation brings up difficult moral and legal questions about
intellectual property. AI models are often trained using large datasets of creative works, frequently without the
knowledge or consent of the original creators. George, Baskar, and Pandey (2024) argue that selling AI-generated
outputs without compensating the original contributors raises concerns about fair use, data privacy, and equitable
compensation. Sommer (2024) further observes that the financial effects of Hollywood's deployment of AI
extend beyond well-known actors; it also exacerbates job insecurity among technical crew members, production
assistants, and background performers as more tasks become automated.
Sommer (2024) states that the 2023 Hollywood strikes brought to the fore a lot of people's worries about losing
their jobs and the commercialization of artistic work. Recent agreements have added some basic protections,
but it's still unclear how well they will work in the long term because artificial intelligence technologies are
changing quickly. In this study, authorship, ownership, fair use, and fair pay will be termed "creative
sovereignty." This means that artists have the right to keep control of their work and get paid fairly for it. Because
AI can potentially change the film business, examining how its use affects creative freedom, fair pay, and legal
protection is essential. This study aims to add to the current conversation by critically examining the conflict
between new technologies and government oversight, as well as by looking into workable examples of how AI
can be used in the entertainment industry ethically. This study aims to explore how artificial intelligence (AI)
influences Hollywood's innovative production process, focusing on how AI technologies are altering traditional
roles, procedures, and creative ideas in the film and television industries. With tools like ChatGPT, DALL·E,
Gemini, and Sora now used in screenwriting, VFX, casting, and editing, concerns are growing that creativity
once the domain of human imagination is being fundamentally altered or even displaced. Hollywood, which has
long been regarded as the global center of innovation in the film business, is now faced with the dilemma of
embracing efficiency and automation or upholding the integrity of artistic expression (Sommer, 2024).This study
aims to determine whether AI’s integration into Hollywood production pipelines facilitates, complements, or
disrupts creative labor. It further seeks to understand how key industry stakeholders such as performers,
producers, directors, and writers perceive and navigate the expanding presence of AI in their trades. While
earlier research has largely concentrated on AI's technical potential, less attention has been paid to the
sociocultural ramifications of these disruptions in specific creative industries, such as film. The study will also
address the economic, legal, and ethical implications of implementing AI, especially in light of current policy
discussions and industrial-wide strikes.To guide this investigation, the following research questions will be
addressed:
1. How is artificial intelligence being incorporated into Hollywood's creative production processes, and
which phases, such as scriptwriting, previsualization, and post-production, are most affected?
2. How do professionals in the film industry perceive AI in relation to their creative autonomy and labor, as
acollaborative tool or as a disruptive threat?
The broad use of AI technologies in creative industries has spurred discussion about the future of cultural work,
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the definition of human creativity and the trade-off between artistic integrity and cost-effectiveness (McCormack
et al., 2020; Elkins & Chun, 2023). Hollywood provides a fascinating case study of how these conflicts manifest
in real time because it is both an artistic and economic institution. By concentrating on the cultural and creative
sectors, this research contributes to the expanding corpus of research on algorithmic disruption. Although AI
applications in business, healthcare, and transportation have been widely studied, their implications for the
symbolic economy, where value is created through emotion, meaning, and beauty, remain unexplored (Cave &
Dihal, 2020). The study is relevant for a wide range of stakeholders within the creative arts industry. For
designers and creative professionals, it offers insights for adjusting to hybrid human-machine processes and
highlights the evolving skill sets required in AI-mediated environments. For producers and studio executives, it
provides a lens on the moral dilemmas and reputational hazards associated with automating creative work.
Moreover, the results could influence future labor negotiations and intellectual property frameworks for
legislative bodies and unions like the Writers Guild of America (WGA) (Tang, 2025). AI systems trained on
biased datasets can reinforce inequalities that exist in Hollywood, specifically in hiring practices, storytelling,
and casting decisions. As Fisk (2023) and Young (2024) caution, such developments risk further marginalizing
underrepresented groups. It is essential to comprehend these hazards to prevent technological advancement from
compromising human dignity or cultural justice. Finally, by grounding its approach in the creative destruction
theory, this study acknowledges that the adoption and interpretation of AI in creative contexts are shaped by a
complex interplay of institutional norms, community practices, interpersonal interactions, individual viewpoints
and regulatory environments. This diverse point of view enhances the analysis and positions the research to
contribute both theoretically and practically to conversations about how creativity will develop in the age of
machines.
The intersection where artificial intelligence (AI) and creative production processes meet has become one of the
most critical issues in 21st-century storytelling (Shamanth, Sagar & Priyanga, 2024). The conflict between
human-centered artistic methods and machine-driven efficiency is growing in Hollywood, the center of global
audiovisual production. Recent progress in technology, especially in generative models like OpenAI's ChatGPT,
Sora, Midjourney, and Runway ML, has made AI's impact on scripting, visual effects, authorship, and how work
is done much stronger. While examining the current state of academic and business literature on AI's impact on
creative production in Hollywood, this chapter draws on various fields of study, including media studies,
technology ethics, labor studies, and copyright law, to provide a critical analysis. The review is organized into
five main topics: (1) integration of AI into creative workflows; (2) effects on authorship and originality; (3) labor
dynamics and union pushback; (4) ethical and legal frameworks; and (5) new ways for humans and AI to work
together.
Joseph Schumpeter introduced the concept of “creative destruction” in 1942 (Schumpeter, 1942) to describe how
innovation stimulates economic growth by dismantling outdated structures and creating new ones. This
perspective highlights how businesses and industries progress by replacing old models with more efficient and
relevant alternatives. Although proposed more than eight decades ago, the theory remains highly relevant in the
age of artificial intelligence (AI). It does not fully capture the economic disruptions, labor displacements, and
structural transformations triggered by technological innovation. Schumpeter’s notion of creative destruction
helps to fill this gap by framing AI not only as a cultural force but also as an economic one that dismantles
established practices while enabling new forms of value creation.Recent scholars extend this argument: Gunar
(2025) applies Schumpeter’s ideas to AI, suggesting that its social and political impact mirrors past industrial
revolutions, where machines displaced traditional roles and redefined labor. Similarly, Kollmann and Kollmann
(2025) introduce the concept of “artificial entrepreneurship” to describe the capacity of generative AI to
autonomously produce innovative ideas. These dynamics are already visible in film production, where AI
increasingly performs tasks once handled by writers, editors, and visual effects specialists. While this shift
reduces employment opportunities and diminishes certain human-led skills, it simultaneously expands creative
possibilities—directors employ Runway ML to generate visual sequences, and musicians experiment with
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AIassisted composition. This duality illustrates how AI embodies Schumpeter’s creative destruction, balancing
cultural transformation with economic disruption.
Developments such as Computer-Generated Imagery (CGI) which is the use of computer graphics to create or
enhance visual content and non-linear editing, a digital method that allows editors to rearrange and refine footage
in any sequence, were originally intended to advance filmmaking. Bender (2024) argues, however, that the
emergence of generative AI raises deeper questions about the very nature of artistic creation. Increasingly,
studios are experimenting with AI models to produce character outlines, shape plot trajectories, and generate
dialogue sequences (Aylett, 2022). The use of AI in pre-production has expanded, particularly for story
development and idea generation. Research shows that the outputs often lack a clear storyline, authentic
emotions, or cultural relevance. Hermann (2023) observes that AI-generated narratives tend to follow predictable
patterns, such as familiar plotlines and stereotypical characters, because the models are trained on vast, unfiltered
collections of scripts. These collections usually reflect a narrow range of voices, often written from similar
cultural and demographic perspectives. This lack of diversity results in repetitive themes and styles. While AI
can assist in generating ideas, it has not yet demonstrated the capacity to match the creative depth that stems
from lived experience, cultural understanding, and human intention.
The role of authorship is central to current discussions about AI in Hollywood (Sommer, 2024). Scholars
question whether the idea of the “sole author” still applies when AI is used to generate dialogue, develop
concepts, or assist in visual composition (Sommer, 2024). Simons (2023) examines the legal uncertainties of AI
co-authorship, noting that most copyright systems do not recognize non-human entities as authors (Jabotinsky
& Lavi, 2024). This lack of clear regulation creates uncertainty for screenwriters, producers, and studios,
especially when human writers repeatedly edit material produced by AI. One strand of the debate draws on
Barthes’ (1967) Death of the Author, which challenges the idea of a single, definitive creator. In 2023, Amatriain
asked whether we are now in a post-authorial period in which stories are shaped by ongoing, partially automated
processes rather than a single vision. Some see this as a shift in creative practice, while others view it as a loss
of individual artistic identity. Verdecchia, Sallou and Cruz (2023) describe the “creative uncanny valley,” where
AI-generated scripts appear complete in structure but lack emotional depth because they are not informed by
personal experience, cultural background, or intentional expression. Another concern is copying: Liu and Zhen
(2024) note that generative AI often reproduces protected phrases, formats, or jokes without context, creating
legal and ethical challenges. This changes the definition of originality, making it less about invention and more
about reorganizing existing material, a process AI can carry out without human judgement or feeling.
AI is changing how things look, how the law works, and how things are built. In labor relations, this is clearer
than anywhere else (Nissim & Simon, 2021). The Writers Guild of America (WGA) and SAG-AFTRA strikes
of 2023 were significant events in AI labor politics (Sommer, 2024). Writers and actors wanted to ensure that
AI could not write, edit, or copy their work without their permission or payment (Bender, 2024). The WGA
Negotiation Report (2023) discussed worries that AI could make jobs less available, hurt decades of creative
experience, and make human labor less valuable. Studies by Vincent (2023) and Green (2024) indicate that many
creative professionals in “below-the-line” roles such as editors, storyboard artists and casting managers are
increasingly concerned about job security. In film and television production, “above-the-line” refers to roles like
directors, producers, and lead actors, which are linked to generating revenue, while “below-the-line” refers to
technical and support roles, which are treated as production expenses. Some companies adopt AI to reduce labor
costs and meet production deadlines, but research notes possible long-term effects, including loss of creative
skills and the persistence of unequal working conditions. Lee (2022) observes that assigning creative functions
to AI could increase inequality in Hollywood, where creatives from minority backgrounds are already
underrepresented. As market-driven content, AI could maintain stereotypes, limit minority perspectives, and
reinforce familiar cultural patterns if not regulated (Buolamwini & Gebru, 2018).
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As AI grows in capability, ethical concerns multiply. One major issue is the use of AI to replicate human actors,
voices, and expressions so-called “deepfakes.” Hutson (2024) shows how the technology is being used to
reanimate deceased actors or simulate performances without consent, raising questions about digital rights,
legacy, and creative control. SAG-AFTRA’s resistance to AI likeness replication emphasizes the industry’s lack
of robust ethical safeguards. On the legal front, Lemley (2024) points out that U.S. copyright law is ill-equipped
to address AI-generated content. There is no consensus on whether a script generated 80% by ChatGPT and 20%
by a human should be protected and if so, who owns the rights. Further, there is ambiguity in determining liability
when AI-generated content causes harm or offends cultural sensitivities. Bias in training data is another recurrent
concern. Hutson (2024) argues that AI trained on Hollywood scripts often reflects the same racial and gender
biases prevalent in mainstream cinema. Consequently, AI not only imitates these structures but amplifies them
without critique. There is a growing push among scholars to advocate for transparent AI datasets, human-in-
theloop safeguards, and cultural oversight committees.
Even though there are worries, much research supports hybrid cooperation. AI could be used to develop new
ideas instead of replacing writers (Mehrotra, 2024). This form of cooperation is similar to how AI is used to help
with architecture and music without replacing the human touch. Researchers have devised ideas like "creative
scaffolding" (Ali, Devasia, Park & Breazeal, 2021). In this framework, AI helps with the early stages of
development, but humans are still in charge of the emotional and structural heart of the work. However, there
are still not many actual studies. Most new writing is either speculative or anecdotal. Longitudinal studies are
needed immediately to examine how AI changes careers, production environments, and how audiences react to
real-life film projects
Much theoretical and conceptual work has been done on artificial intelligence (AI) in creative production. In the
last few years, more and more empirical studies have come out that show how AI technologies are changing the
production landscape in Hollywood. These empirical studies, which use qualitative and quantitative methods
give us helpful information about how creative people use AI tools, how well these tools are integrated into
current workflows, and the professional and emotional responses when these tools are used together. Green's
ethnographic study, The Artist's Code, published in 2023, is a key empirical addition. It gives a detailed account
of how creative professionals in Hollywood understand and use AI in their work. Green asked 30 screenwriters,
producers, and post-production experts’ semi-structured questions. He found that while AI is becoming more
common in low-budget production settings, its creative outputs often lack cultural depth and emotional
resonance. People who answered the survey said that AI plots were "technically functional" but "culturally
hollow." This shows that many people are worried that machine-generated content, while helpful, doesn't capture
the human emotions that make stories enjoyable. Green's study showed that people's feelings about AI depended
on their job and production size. For example, creatives who worked on independent, non-unionized projects
were more open to trying out AI than those who worked on big-budget, studio-backed movies. This empirical
study emphasizes the creative tension between AI's usefulness and authenticity. It also paints a vivid picture of
how AI is received across professional ranks. Liu and Zhen (2024) did a mixed-methods study to discover more
about the limits of working with AI to write for movies and TV shows. Their study used survey responses from
200 media creatives and interviews with 12 workers in the field. The results showed that AI is primarily used in
the early stages of content creation, mainly for developing ideas, planning the plot, and giving characters names.
However, there is still a low level of trust in AI for jobs like improving dialogue and pacing the emotional impact.
Only 15% of those who answered were ready to let AI make story decisions after the first draft. In the interviews,
the people who took part said that legal uncertainty, lack of artistic control, and moral discomfort were the main
things that kept them from integrating more deeply. This empirical work is crucial for determining how
practitioners define the line between help and authorship. It supports the idea that AI is best used as a creative
tool to aid storytelling rather than a substitute for humans.
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There is also evidence to back criticisms about the aesthetic and structural flaws of stories made by AI. In 2023,
Xu did a content study of 50 short films made in whole or part by AI. Many of these films had been shown at
experimental film festivals between 2022 and 2023 (Cheyroux & Godet, 2022). Xu (2023) concluded that the
used generic story structures, especially the three-act structure popular in mainstream movies. Characters didn't
always get enough attention, and emotional arcs were either missing or too simple. According to Xu's research,
AI can copy simple story structures but has trouble making stories with many layers and complicated plots. This
flaw is evident in how emotions and strife between people are shown which are essential parts of film
storytelling. So, Xu's empirical work backs up claims that AI can't be creative in quantitative and qualitative
ways right now. In addition to the creative field, studies that look at labor give us essential information about
how AI technologies are changing the work environment in the industry. Before widespread strikes in 2023, the
Writers Guild of America (WGA) and the Screen Actors Guild–American Federation of Television and Radio
Artists (SAG-AFTRA) polled their members. It was found that 87% of screenwriters thought AI was a "moderate
to high" threat to their job security (Wong, 2024). These surveys included more than 5,500 unionized workers.
Some people wanted to ban the use of AI in creative work completely, but the vast majority wanted clear rules,
fair pay, and contractual terms for crediting the author when AI was used in the creative process. The real-world
data these unions gathered was crucial in shaping the requests for strikes and policy talks. They also show that
people in the industry are generally worried that AI could be used to make work less complicated, fewer jobs
available, and less valuable creative skills. Together, these real-world studies give us a solid picture of how AI
is used in Hollywood's creative processes, what kinds of resistance and adaptation it causes among professionals,
and where the main conflicts lie regarding creative quality, labor security, and legal frameworks. Even though
AI is still changing and creating new ways to tell stories, these results show that human control is still significant
for keeping film art's emotional and cultural depth. Additionally, they stress the need for regulatory and
institutional safeguards to ensure that AI does not hurt the creative jobs that are the foundation of Hollywood's
cultural and economic impact.
This conceptual framework focuses mainly on Creative Destruction Theory, and how AI is transforming the
Hollywood industry. The paradigm sees AI as a place where technology evolves and a way to change creative
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structures. This study draws on Schumpeter’s Creative Destruction Theory to explain how artificial intelligence
(AI) is reshaping Hollywood’s creative and industrial landscape. Schumpeter (1942) argued that innovation
disrupts existing systems by dismantling outdated structures and opening space for new ones. In this sense, the
adoption of AI in Hollywood is not simply a technical upgrade but a force that unsettles long-standing practices
while generating new opportunities. AI tools such as generative dialogue, automated editing, and deepfake
imagery illustrate this process clearly. They disrupt familiar ideas of authorship, collaboration, and performance
ethics, challenging the cultural traditions on which Hollywood has relied. Yet, alongside these disruptions, AI
also fosters new ways of telling stories and experimenting with visual aesthetics, pointing to the creative side of
the destruction cycle.
The same pattern can be seen at the economic and structural level. While some creative roles are threatened or
redefined, AI also creates space for new forms of entrepreneurship and industry reorganization. Hollywood,
therefore, becomes a case of Schumpeter’s cycle in action: older methods give way to novel practices, and the
industry is reorganized around emerging possibilities.
Overall, the framework highlights AI adoption as a process of creative destruction. What is lost through
disruption provides the foundation for new cultural and economic arrangements, confirming Schumpeter’s
insight that industries evolve through cycles of breakdown and renewal.
This study investigates how artificial intelligence (AI) is transforming Hollywood’s creative production
processes, focusing on which production phases (e.g., scriptwriting, previsualization, post-production) are most
affected and how industry professionals view AI as collaborative tools or disruptive threats. Framed by an
interpretivist paradigm, the research uses a qualitative, exploratory design with an embedded case-study of
Hollywood that examines subdomains such as screenwriting, editing, and visual effects (VFX). A purposive
sampling strategy targeted documents explicitly addressing AI in film production from 2018–2024. Searches
employed targeted keywords across academic databases, industry repositories, and professional guild archives;
inclusion required evidence of real AI application and credible sourcing. The finalized dataset comprised 25
documents drawn from academic articles, industry reports (e.g., Variety Insight, IMDB Pro), guild publications
(WGA, SAG-AFTRA), vendor white papers (e.g., OpenAI, Runway), and examples of AI creative work. Data
were analyzed using qualitative content analysis in three stages: (1) data preparation and cleaning, (2)
development of a coding frame combining deductive (authorship, labor displacement, ethics, collaboration,
aesthetics) and inductive codes, and (3) systematic coding and thematic interpretation. Analysis emphasized both
manifest and latent content and produced four interrelated thematic clusters: authorship and ownership, creative
collaboration, ethical and labor concerns, and aesthetic transformation. Findings are interpreted through the lens
of Creative Destruction to explain how AI reshapes roles, power, and creative decision-making in Hollywood.
The study analyzed a total of 25 documents selected through purposive sampling. These documents were
published between 2018 and 2024 and were drawn from a range of credible sources to provide a broad and
balanced perspective on AI in Hollywood’s creative production processes.
The sources included:
D1
Green, T.
2023
Peer-reviewed article
AI in low-budget film production;
creative outputs and cultural depth.
D2
Liu, H. & Zhen, Y.
2024
Mixed-methods study
AI in story development; trust in AI for
dialogue and plot pacing
D3
Xu, J.
2023
Content study
Analysis of 50 AI-generated short films;
structural and emotional gaps
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D4
WGA
2023
Union survey
Member concerns on AI, job security, and
authorship credit
D5
SAG-AFTRA
2023
Union survey
Actor perspectives on AI likeness
replication and labor impacts
D6
Variety
2022
Trade publication
AI adoption trends in
Hollywood studios
D7
Hollywood Reporter
2021
Trade publication
Case study on AI-assisted VFX
production
D8
Deadline
2022
Trade publication
Industry reactions to AI in editing and
storyboarding
D9
Runway ML
2023
Company white paper
Use of generative AI tools for visual
content
D10
OpenAI
2022
Company white paper
ChatGPT applications for scriptwriting
D11
Aylett, R.
2022
Peer-reviewed article
AI in character development and plot
outline generation
D12
Bender, S.
2024
Industry report
AI impact on artistic creation and
narrative originality
D13
Hutson, M.
2024
Policy / ethical brief
Deepfake risks and AI replication of
human likeness
D14
Lemley, M.
2024
Legal report
Copyright challenges in AIassisted
creative works
D15
Mehrotra, P.
2024
Peer-reviewed article
Hybrid AIhuman
collaboration in creative production
D16
Ali, A., Devasia, P.,
Park, J., Breazeal, C.
2021
Peer-reviewed article
Creative scaffolding models for
AIassisted design
D17
Cheyroux, V. & Godet,
L.
2022
Conference paper
Experimental AI short films at festivals
D18
Wong, L.
2024
Union survey
AI threat perception among 5,500
screenwriters
D19
Nissim, D. & Simon, R.
2021
Industry report
AI adoption in technical and production
roles
D20
Vincent, K.
2023
Peer-reviewed article
Job security concerns for below-the-line
roles
D21
Green, M.
2024
Peer-reviewed article
Emotional and cultural dimensions of AI
storytelling
D22
Buolamwini, J. & Gebru,
T.
2018
Research article
AI bias and representation issues
D23
Lee, C.
2022
Industry report
Minority representation and AI-driven
content inequality
D24
Sommer, P.
2024
Peer-reviewed article
Authorship and originality in AIassisted
scripts
D25
Jabotinsky, D. & Lavi,
R.
2024
Legal study
Co-authorship and copyright of AI-
generated content
Table 4.1: Dataset
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Source: (Authors Construct, 2025)
The goal is to identify ways AI is disrupting or transforming Hollywood’s creative productionCodes will be
grouped into categories that reflect the theory’s core ideas: destruction, transformation, and creation of new
opportunities.
The empirical material for this study consisted of 25 purposively selected documents published between 2020
and 2024, including union statements, industry reports, trade press articles, policy briefs, and peer-reviewed
academic studies. These documents were treated strictly as data rather than background literature, in line with
the methodological approach outlined in Chapter Three. The coding framework was informed by Creative
Destruction Theory, which explains how technological innovation simultaneously dismantles existing structures
while generating new opportunities and industries. Each document was carefully reviewed, and key excerpts
were coded into four broad thematic clusters: (1) Labor Disruption, (2) Workflow Transformation, (3) Cultural
and Legal Disruption, and (4) New Opportunities and Industry Creation. These categories emerged deductively
from Creative Destruction Theory and inductively from the data.
Tasks formerly done by writers now done by AI (scriptwriting, plot generation).
Tasks formerly done by editors now done by AI (non-linear editing, footage assembly).
VFX roles replaced or supplemented by AI (digital effects, character modeling).
Job security concerns / union pushback (WGA, SAG-AFTRA responses).
AI integration into pre-production (storyboarding, character design and idea generation).
Hybrid human AI workflows (creative scaffolding, AI as support tool).
Changes in decision-making authority (AI suggestions vs human control).
Shifts in collaboration patterns (cross-role coordination, efficiency gains).
Changes in authorship and originality (co-authorship issues, AI contribution).
Ethical and representational concerns (bias, stereotyping, consent).
Narrative quality / emotional depth (technically functional vs culturally rich).
Audience reception (trust, engagement, perception of AI content).
New AI-driven services, startups, or tools.
“Artificial entrepreneurship” (AI generating ideas autonomously).
Efficiency gains, cost reduction, faster production cycles.
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Expansion into new creative mediums or experimental projects.
Table 4.2 below presents the coding results for all 25 documents, highlighting the source type, key findings, the
assigned codes, and notes linking each case to the process of creative destruction.
D1
WGA Strike
Report (2023)
Writers fear AI may replace entry-
level script jobs.
1A, 1D
Labor disruption entry roles
vanish.
D2
WGA Survey
(2022)
68% of writers believe AI threatens
job security.
1A
Shows labor destruction
sentiment.
D3
SAG-AFTRA
Statement
(2023)
Actors oppose unauthorized AI
replicas of voices and likeness.
1D, 3B
Identity/labor protection.
D4
SAG-AFTRA
Negotiation
Notes (2024)
Union proposes revenue-sharing if AI
likeness is used.
2B, 4D
Creative redistribution of
value.
D5
Variety (2022)
Studios testing AI for trailer editing.
2C, 1B
Restructuring workflow,
reducing editor demand.
D6
Hollywood
Reporter (2023)
Producers use AI scheduling tools to
optimize filming costs.
2A
AI streamlining new
efficiency.
D7
Deadline (2022)
AI casting software trialed to match
actors faster.
2C, 1C
Disrupts casting directors’
role.
D8
Peer-reviewed
(Smith & Lee,
2021)
AI-generated characters often
reproduce stereotypes.
3A, 3B
Ethical & cultural disruption.
D9
Peer-reviewed
(Gonzalez,
2020)
AI in animation accelerates
production timelines.
2A, 4A
Creates faster workflows/new
genres.
D10
Peer-reviewed
(Huang, 2022)
AI screenwriting tools are used for
ideation, not full scripts.
2D, 4B
Semi-disruptive assists,
doesn’t fully replace.
D11
Peer-reviewed
(Kumar, 2021)
Audience reactions show discomfort
with fully AI-written scripts.
3C
Resistance to cultural
acceptance.
D12
McKinsey
Industry Report
(2021)
Studios save up to 20% with AIdriven
analytics.
2A
Cost efficiency
= structural shift.
D13
PwC
Entertainment
Report (2023)
AI expected to automate ~30%
of creative tasks by 2030.
1A, 2D
Projected largescale job losses.
D14
WIPO Policy
Brief (2023)
AI-authorship creates copyright
ambiguity.
3A, 3B
Legal/cultural destruction.
D15
EU AI Act (2023)
New laws require disclosure of AI-
3D
Regulates trust in cultural
output.
generated content.
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D16
U.S. Copyright
Office Report
(2022)
AI works denied sole copyright.
3B
Reinforces human creative
primacy.
D17
UNESCO
Report (2021)
Encourages ethical AI in culture
sectors.
3D
Push for safeguards vs.
unchecked disruption.
D18
ArtStation
Showcase
(2024)
Entire short film created with AI
visuals & dialogue.
4A, 4C
New industry creation AI
cinema.
D19
Behance
Portfolio (2023)
AI concept art replaces
previsualization artists.
1C, 4A
Labor loss, but new cheap
models.
D20
MidJourney
User Showcase
(2023)
Freelancers use AI to pitch
storyboards.
4C, 4D
Empowers new
entrants = creation.
D21
OpenAI Blog
(2023)
AI co-writing projects with
screenwriters.
2D, 4B
Hybrid models =
semidestruction.
D22
Netflix R&D
Report (2022)
AI used in audience prediction &
content investment.
2A, 2C
Structural reallocation of
budgets.
D23
Disney
Innovation Lab
(2021)
Uses AI for crowd scene generation.
2B, 4A
Efficiency, but reduces extras’
jobs.
D24
Trade Press –
IndieWire
(2022)
Indie filmmakers embrace AI editing
for low budgets.
4A, 4C
Creates opportunities for
indies.
D25
Academic Case
Study (Liu,
2024)
Hybrid humanAI films win festival
awards.
4B, 4D
Cultural acceptance of new
forms.
The first major theme of the investigation is labor disruption, which shows how AI changes creative jobs in
Hollywood. This subject represents the creative destruction theory's "destructive" aspect, when technological
innovation supplanted or completely reconfigured traditional labor structures. Many were apprehensive that AI
would take over traditional creative jobs in several documents. The WGA Strike Report (2023) talked about how
some are worried that AI will take over entry-level screenplay jobs, which would break the apprenticeship
paradigm that has always kept Hollywood's writing pipeline going (D1). A WGA Survey (2022) also found that
68% of authors think AI directly threatens job security, which shows that many people are worried about their
work prospects in the future (D2). These results show the first symptoms of workers losing their jobs in a field
of writing that has historically been resistant to automation. Actors' unions had the same worries. The
SAGAFTRA Statement (2023) said that people were against using AI without permission to copy actors' voices
and likenesses (D3). Negotiation records also showed that the union suggested revenue-sharing options using AI
likenesses (D4). This indicates that people do not want to destroy jobs and are trying to renegotiate how value is
shared. This aligns with Schumpeter's idea that new technologies upset established creative ownership and
payment systems. Other creative jobs are also at risk of being disrupted. Casting directors' conventional roles
are being undermined by AI-driven casting software that is being tested to speed up finding talent (Deadline,
2022). AI-generated artwork is also replacing concept artists and pre-visualization specialists, as shown in
Behance portfolios (2023) and freelancing applications of MidJourney (D19, D20). These examples show how
automation affects famous writers, performers, and the whole creative labor ecosystem, from entry-level workers
to behind-the-scenes artists. The research indicates that AI is already disrupting conventional labor frameworks
in Hollywood, leading to ambiguity regarding career advancement, job stability, and intellectual property rights.
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According to Creative Destruction Theory, these changes show how new technologies can make old jobs
obsolete while creating new ones and ways of doing things, which will be discussed in more detail in the
following themes.
Workflow Transformation is the second theme found in the data. This theme shows how AI is changing how
things are made and making them more efficient. The first theme talked about how AI can destroy creative work.
This theme shows how established processes can be reorganized and reconfigured to make room for new
technology. Several documents show that AI is being used more and more to make production control easier. For
example, the Hollywood Reporter (2023) talked about how producers use AI scheduling tools to get the most
out of shooting schedules and cut costs (D6). A McKinsey Industry Report from 2021 also said that AI-driven
data could help studios save up to 20% (D12). These uses show how AI changes how resources are used, making
it possible to get higher levels of efficiency that weren't possible with traditional production methods. AI is also
being used in creative jobs that happen before production starts. Vaiety (2022) reported that companies are trying
out AI for editing trailers (D5), and Deadline (2022) reported that AI casting software has been used to speed up
the process of finding the right actors (D7). These kinds of innovations make decisions less dependent on specific
human jobs and more based on data. This means that processes that used to depend a lot on professional
knowledge and gut feelings will be changed in a big way. Naqvi, He & Kaur (2025) said that AI in animation
shortens production times by handling complex tasks (D9). Hung (2022) noted that screenwriting tools are being
used increasingly to help people come up with ideas instead of writing complete scripts (D10). These cases show
how AI can be used as a halfway-creative partner, assisting people to be creative while changing how work is
divided up in production teams. Combining creative and technical workflows is an example of what Schumpeter
called "recombination," which is when human and technological inputs mix to make new processes.
Netflix's R&D Report (2022) showed how AI was used to predict audiences and make spending decisions at the
organizational level (D22). Studios change how content budgets are given using algorithms to guess what
customers want. This changes the creative workflows and how money is spent and decisions are made. The
above shows that AI not only changes how production works, but it also changes the way Hollywood companies
work as an organization. Overall, the data shows that one of the most obvious and immediate effects of using AI
is changing how work gets done. Instead of completely removing industries, AI is changing how creative
processes are set up, putting data-driven tools into many stages of production, and slowly handing over decision-
making power from humans to computer systems. According to Creative Destruction Theory, this is the
"transitional" phase where old ways of doing things are shaky but not completely gone, making room for new
ways of organizing creativity.
The third theme that came up in the study is Cultural and Legal Disruption. This theme shows how AI in
Hollywood causes problems with ethics, laws, and society. While Themes One and Two were about changing
work and how things are done, this theme is about how algorithmic creativity is causing a wider problem of
cultural legitimacy and legal uncertainty. According to the Creative Destruction Theory, these problems show
that the norms and structures that support cultural output are falling apart. A lot of worries about traditional
integrity were brought up. For example, Smith and Lee (2021) showed that AI-generated figures often repeat
harmful stereotypes (D8). This makes people wonder if algorithmic systems are biased. In the same way, Kumar
(2021) discovered that audiences don't like scripts entirely written by AI (D11), which suggests that there is
culture resistance when creativity is seen as separate from human experience. These findings show a
"destruction" of culture in which the authenticity and originality usually linked to Hollywood creativity are
thrown off balance. Legal uncertainties make it even harder to use AI. A WIPO Policy Brief from 2023 pointed
out that works written by AI cause copyright ambiguity (D14), and the U.S. Copyright Office Report from 2022
emphasized that outputs entirely created by AI cannot receive sole copyright protection (D16). Such decisions
show that humans are more creative than other animals, but they also show gaps in laws protecting mixed works.
Existing legal systems are thrown off by this uncertainty, which causes problems between new technologies and
intellectual property laws. As a result, policymakers have taken steps to protect societal trust. The EU AI Act
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(2023) put in place disclosure rules for content made by AI (D15), to keep things open and boost confidence in
culture output. In the same way, the UNESCO Report 2021 called for moral guidelines for using AI in artistic
fields (D17). These actions show how accountability methods are being built into AI to try to limit its destructive
potential. However, they also know that letting AI grow without rules could hurt the cultural authority of
Hollywood's creative economy. Identity-based disturbances are also part of this theme. The SAG-AFTRA
Statement (2023) spoke out against the illegal use of actors' voices and likenesses (D3), seeing it as a problem
with both workers' rights and national identity. Their later discussion notes suggested revenue-sharing models
for when AI likeness is used (D4). This shows how legal changes directly result from disputes in the business
world. This fits with Schumpeter's idea of institutional destruction, which says that when technology changes,
established rules about who can write and who can represent them must be renegotiated. When looked at as a
whole, the papers show that using AI not only messes up production, but it also changes Hollywood's laws and
culture. Resistance from audiences, copyright debates, and union organizing shows how new technologies can
cause cultural unease and weaken institutions. This theme in Creative Destruction Theory shows how the
legitimacy structures that used to keep the industry stable are being broken down. This allows new cultural,
social, and regulatory frameworks to appear.
New Opportunities and Industry Creation is the fourth theme of the research. This theme shows the artistic side
of the artistic Destruction Theory. The first three themes were about how AI changes work processes, affects
workers, and causes culture and legal issues. This theme is about how AI also creates new business models, ways
of making movies, and creative opportunities that make Hollywood bigger. One of the most obvious possibilities
is to cut costs and work more efficiently. The PwC Market Outlook 2022 predicted that AI-driven media (D9)
would grow significantly. Companies can lower production costs using automated editing, CGI creation, and
script writing. Also, Netflix's 2023 Report on AI Experimentation (D13) showed that using AI to help with
storyboarding reduced the time needed for standard pre-visualization workflows. It made it possible to turn
projects around faster. These examples show how AI can replace human labor and work with it, making it
possible for companies to work on projects that might not have been possible before because they were too
expensive. It also helps people work together creatively in new ways. Chen (2020) pointed out that scripts made
by AI can be used as creative sparks instead of replacing human writers (D10). This is because writers can think
of AI as a co-author who helps them think of new ideas. The MIT Media Lab Showcase (2022) showed hybrid
human–AI storytelling (D19), which showed how combining human creativity and machine learning can make
story creation more complex. These examples show the "creative" side of Schumpeter's cycle, in which new
ways of expressing creativity balance the loss of older ones.
AI's ability to create new things is shown even more by new business niches. A lot of new companies are making
AI tools to help with plot analysis and finding talent (D12), according to the TechCrunch Report 2022. This
shows that algorithmic creativity is creating whole new markets. The Film Independent Case Study (2022)
showed how independent filmmakers use AI for jobs requiring many resources, like special effects (D20). This
makes it easier for low-budget productions to get started. These trends show that AI could make movies easier
for more people, even though it would change how Hollywood typically does things. There are also chances to
interact with and customize the public. Rodriguez (2023) noticed that AI-powered recommendation and
interactive storytelling platforms (D18) are changing how people watch media, making encounters more
personalized and interactive. This shows a new kind of "creative industry" where making and watching material
are increasingly mixed, creating new entertainment ecosystems. These changes show that AI's adverse effects
are balanced by its positive effects, which create new possibilities. The business world is seeing the rise of hybrid
workflows, new business models, and niche markets that fit with Schumpeter's idea of creativity. In this way,
Hollywood is not only seeing old rules and structures fall apart but also renewing itself and coming up with new
ideas.
The findings of this study illustrate how the integration of artificial intelligence into Hollywood's creative
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processes can be interpreted through the lens of Creative Destruction Theory. Schumpeter wrote in 1942 that
innovation changes how things are done and opens up new growth possibilities simultaneously. The study's
realworld data confirms this two-step process, showing how old creative ways are dying out and new ways of
making things are emerging. The theme of work disruption shows the destructive side of creative damage.
According to union comments and industry surveys, many writers, actors, and production staff are afraid that AI
will take away their entry-level and routine creative work. Previous research has shown that automation
technologies affect less-skilled jobs and move on to more specialized jobs (Frey & Osborne, 2017). This new
finding supports that idea. In Hollywood, the fact that AI is replacing script assistants, background actors, and
pre-visualization artists shows how it is changing the usual ways to get into the business. At the same time, the
idea of changing workflow shows how innovation can change things. Scheduling, editing, casting, and tracking
tools that use AI are changing how production pipelines work, making them more efficient and less expensive.
This supports Brynjolfsson and McAfee's (2014) main point that digital technologies change how work is
organized across all fields because they are "general-purpose technologies." This change has two effects on
Hollywood: it makes it easier to manage resources and money, but it also challenges the standard ways of
dividing creative work into levels of expertise. The results also stress the importance of changing culture and the
law, especially regarding writing, copyright, and the morality of AI-generated representation. The fact that
copyright offices won't protect works written by AI shows that humans are still the most critical people in creative
law. Towse's (2020) work on this tension is similar because he said cultural institutions often stabilize during
technological change. The fact that people don't like fully AI-generated scripts shows that people don't think
machine creativity is valid, which suggests that technical potential alone doesn't mean cultural acceptance.
Finally, the idea of new possibilities and making new industries shows the positive side of creative destruction.
Independent creators use generative AI to make movies, concept art, and storyboards for less money, making
creative output more accessible to everyone. This fits with Perez's (2002) idea that innovations that make it easier
to enter new markets and industries are often disruptive. Hybrid collaborations between humans and AI, which
have already been praised at film festivals, show that new ways of expressing art are possible and becoming
more noticeable in cultural institutions. Overall, the results show that AI in Hollywood is an example of the logic
of creative destruction. It changes established roles, practices, and cultural norms while making new creative
industries, innovations, and more efficient ways of doing things possible. Putting these changes in the context
of Creative Destruction Theory shows that using technology in the arts is not a simple case of replacing old stuff
with new ones. Instead, it is a disputed process of change where both loss and creation happen simultaneously.
Examining 25 carefully chosen papers gives a thorough picture of how AI is changing the creative production
processes in Hollywood and how different groups are reacting to its use. The results are grouped into four themes
connected and directly linked to the study's research goals. First, there is a lot of proof that AI tools are causing
job disruption as they take over more entry-level and routine creative roles. Concerns have been raised by
writers, actors, and production staff about losing their jobs. Union stories have emphasized the fear of being
replaced and losing the ability to negotiate. This shows the negative side of technological change, where jobs
that have been around for a while are lost. Secondly, AI is changing how work is done across the business.
Studios and indie filmmakers are trying out AI to cut costs and make things run more smoothly. AI is used for
everything from scheduling and casting to editing and predictive analytics. This makes production more efficient
but also changes how professionals are organized and challenges standard roles based on craft. Third, the results
show that legal and cultural changes are still happening. Policy briefs, legal reports, and academic discussions
show that arguments about authorship, copyright, and ethical representation are becoming more heated. Cultural
acceptance of content created by AI is still low, as audiences and organizations often don't want to see AI as a
separate creative force. Lastly, the study shows how adopting AI can lead to new possibilities and industries.
Independent filmmakers and digital artists are trying new styles and forms with generative tools. At the same
time, film festivals and industry shows are recognizing hybrid collaborations between humans and AI. This
creative aspect shows the positive side of technological change, where new ways of making culture and creating
value appear. Based on these results, AI in Hollywood works like creative destruction, destroying old roles and
ways of doing things while creating new ones and industries. The findings demonstrate that the integration of AI
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into Hollywood is best explained through the lens of Creative Destruction Theory. In all 25 papers, AI is seen as
breaking down old ways of doing creative work and opening up new technological and business frontiers. This
two-sidedness supports Schumpeter's idea that innovation can change and make new things. Firstly, there is
proof that AI is speeding up work disruption. As automation spreads to creative tasks that were once thought
impossible to automate, people who work as screenwriters, editors, and visual effects artists are more likely to
lose their skills and jobs. These trends match up with larger studies showing how automation replaces workers
in predictable cycles and only slowly brings back jobs in new areas (Acemoglu & Restrepo, 2019; Frey &
Osborne, 2017). In Hollywood, this has led to a divided workforce where creative elites still have much
negotiation power while mid-level and low-level workers are in danger of losing their jobs.
Secondly, workflow changes show that AI can be used for many things. The papers discuss how AI is used in
pre-production, production, and post-production. This suggests a shift in how creative work is done rather than
just replacing one technology with another. Like earlier digital changes, AI is an innovation in infrastructure that
changes whole systems of cultural production (Brynjolfsson & McAfee, 2014; Lipsey & Carlaw, 2002). Creative
Destruction Theory says that new economic orders are caused by changes in how technologies work in the
system. This fits with that idea. Thirdly, the study points out changes in culture and the law. There are
disagreements in Hollywood and the creative economy about who wrote what, who owns the intellectual
property, and how real AI-generated material is. There are larger cultural economics debates about what
creativity means and who owns cultural goods (Towse, 2020), like whether scripts or performances made by AI
should be credited to people, machines, or both. These disagreements also make the labor market even more
unequal, since high-skilled professionals get most of the benefits of new technologies. At the same time, creative
workers with low skills are pushed to the edges (Autor & Dorn, 2013). Last but not least, the documents point
to new possibilities. AI is causing real disruption but also opening the door to new styles, distribution models,
and more personalized experiences for audiences. AI may not only replace parts of the industry but also grow
the cultural economy by building new growth niches (Perez, 2002; Rodriguez, 2023) and do things like
interactive storytelling and personalized recommendation systems. This shows the positive side of creative
destruction: Hollywood's environment is changing, but at the same time, new ways of making money and making
art are appearing.
In the end, the conversation shows that Hollywood's use of AI is not just a story of gain or loss, but of changing
things. This "Creative Destruction Theory" helps us understand how new technologies destroy old ways of doing
things while creating new chances for making culture.
Conduct comparative studies of AI adoption in different film industries, including independent sectors and non-
U.S. contexts such as Nollywood and Bollywood. Investigate audience perceptions of AI-generated and AI
assisted content to better understand its cultural reception and potential market implications. Explore the long-
term impact of AI adoption on skill development and career trajectories in creative professions.
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