INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue X October 2025

Creativity in Crisis? A Study of AI’s Disruption of the Creative

Production Process in Hollywood.

Kurubuni Cajetan Sungsomang

Central University, Pokuase, Accra, Ghana

DOI: https://dx.doi.org/10.51244/IJRSI.2025.1210000295

Received: 04 November 2025; Accepted: 11 November 2025; Published: 20 November 2025

ABSTRACT

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.

INTRODUCTION

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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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue IX September 2025

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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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue X October 2025

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.

LITERATURE REVIEW

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.

Creative Destruction Theory

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.

AI Integration into Creative Workflows in Hollywood

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.

Authorship, Originality, and the Creative Identity Crisis

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.

Labor Disruption and Unionized Resistance

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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Ethical and Legal Dilemmas in AI-Aided Production

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.

Hybrid Futures: Human–AI Collaboration Models

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

Empirical Review

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.

Conceptual Framework

Figure: 2.1: AI Disruption of Creative Production in Hollywood.

Source: Author’s construct (2025)

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.

RESEARCH METHODOLOGY

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.

FINDINGS AND DISCUSSION

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:

Dataset Author(s)/Organization Year

Type

Focus/Summary

D1

D2

D3

Green, T.

2023

2024

2023

Peer-reviewed article

AI in low-budget film production;

creative outputs and cultural depth.

Liu, H. & Zhen, Y.

Xu, J.

Mixed-methods study

Content study

AI in story development; trust in AI for

dialogue and plot pacing

Analysis of 50 AI-generated short films;

structural and emotional gaps

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D4

D5

D6

D7

D8

D9

WGA

2023

2022

2021

2022

2023

Union survey

Member concerns on AI, job security, and

authorship credit

SAG-AFTRA

Variety

Union survey

Actor perspectives on AI likeness

replication and labor impacts

Trade publication

Company white paper

AI adoption trends in

Hollywood studios

Hollywood Reporter

Deadline

Case study on AI-assisted VFX

production

Industry reactions to AI in editing and

storyboarding

Runway ML

Use of generative AI tools for visual

content

D10

D11

OpenAI

2022

Company white paper

Peer-reviewed article

ChatGPT applications for scriptwriting

Aylett, R.

AI in character development and plot

outline generation

D12

D13

Bender, S.

Hutson, M.

Lemley, M.

Mehrotra, P.

2024

Industry report

AI impact on artistic creation and

narrative originality

Policy / ethical brief

Deepfake risks and AI replication of

human likeness

D14

D15

D16

2024

2021

Legal report

Copyright challenges in AIassisted

creative works

Peer-reviewed article

Hybrid AIhuman

collaboration in creative production

Ali, A., Devasia, P.,

Park, J., Breazeal, C.

Creative scaffolding models for

AIassisted design

D17

D18

D19

D20

D21

D22

D23

D24

D25

Cheyroux, V. & Godet,

L.

2022

2024

2021

2023

2024

Conference paper

Union survey

Experimental AI short films at festivals

Wong, L.

AI threat perception among 5,500

screenwriters

Nissim, D. & Simon, R.

Vincent, K.

Industry report

AI adoption in technical and production

roles

Peer-reviewed article

Research article

Industry report

Job security concerns for below-the-line

roles

Green, M.

Emotional and cultural dimensions of AI

storytelling

Buolamwini, J. & Gebru, 2018

T.

AI bias and representation issues

Lee, C.

2022

2024

Minority representation and AI-driven

content inequality

Sommer, P.

Peer-reviewed article

Legal study

Authorship and originality in AIassisted

scripts

Jabotinsky, D. & Lavi,

R.

Co-authorship and copyright of AI-

generated content

Table 4.1: Dataset

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Source: (Authors Construct, 2025)

Coding Scheme Overview

The goal is to identify ways AI is disrupting or transforming Hollywood’s creative production. Codes will be

grouped into categories that reflect the theory’s core ideas: destruction, transformation, and creation of new

opportunities.

Data Coding and Analysis

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.

Main Categories and Codes

1. Labor Disruption (Destruction of Roles)

Code 1A: Tasks formerly done by writers now done by AI (scriptwriting, plot generation).

Code 1B: Tasks formerly done by editors now done by AI (non-linear editing, footage assembly).

Code 1C: VFX roles replaced or supplemented by AI (digital effects, character modeling).

Code 1D: Job security concerns / union pushback (WGA, SAG-AFTRA responses).

2. Process and Workflow Transformation (Structural Change)

Code 2A: AI integration into pre-production (storyboarding, character design and idea generation).

Code 2B: Hybrid human AI workflows (creative scaffolding, AI as support tool).

Code 2C: Changes in decision-making authority (AI suggestions vs human control). Code 2D:

Shifts in collaboration patterns (cross-role coordination, efficiency gains).

3. Cultural and Creative Impacts (Creative Environment)

Code 3A: Changes in authorship and originality (co-authorship issues, AI contribution).

Code 3B: Ethical and representational concerns (bias, stereotyping, consent).

Code 3C: Narrative quality / emotional depth (technically functional vs culturally rich).

Code 3D: Audience reception (trust, engagement, perception of AI content).

1. New Opportunities and Industry Creation (Creation)

Code 4A: New AI-driven services, startups, or tools.

Code 4B: “Artificial entrepreneurship” (AI generating ideas autonomously).

Code 4C: Efficiency gains, cost reduction, faster production cycles.

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Code 4D: 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.

Doc

ID

Source Type

Key Finding /

Excerpt

Code(s) Notes (Creative

Destruction

Link)

D1

D2

D3

WGA Strike

Report (2023)

Writers fear AI may replace entry-

level script jobs.

1A, 1D

1A

Labor disruption entry roles

vanish.

WGA Survey

(2022)

68% of writers believe AI threatens

job security.

Shows labor destruction

sentiment.

SAG-AFTRA

Statement

(2023)

Actors oppose unauthorized AI

replicas of voices and likeness.

1D, 3B

Identity/labor protection.

D4

SAG-AFTRA

Negotiation

Union proposes revenue-sharing if AI

likeness is used.

2B, 4D

Creative redistribution of

value.

Notes (2024)

D5

D6

D7

D8

Variety (2022)

Studios testing AI for trailer editing.

2C, 1B

2A

Restructuring workflow,

reducing editor demand.

Hollywood

Reporter (2023)

Producers use AI scheduling tools to

optimize filming costs.

AI streamlining new

efficiency.

Deadline (2022)

AI casting software trialed to match

actors faster.

2C, 1C

3A, 3B

Disrupts casting directors’

role.

Peer-reviewed

(Smith & Lee,

2021)

AI-generated characters often

reproduce stereotypes.

Ethical & cultural disruption.

D9

Peer-reviewed

(Gonzalez,

2020)

AI in animation accelerates

production timelines.

2A, 4A

Creates faster workflows/new

genres.

D10

D11

D12

Peer-reviewed

(Huang, 2022)

AI screenwriting tools are used for

ideation, not full scripts.

2D, 4B

3C

Semi-disruptive assists,

doesn’t fully replace.

Peer-reviewed

(Kumar, 2021)

Audience reactions show discomfort

with fully AI-written scripts.

Resistance to cultural

acceptance.

McKinsey

Industry Report

(2021)

Studios save up to 20% with AIdriven 2A

analytics.

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.

Legal/cultural destruction.

D14

D15

WIPO Policy

Brief (2023)

AI-authorship creates copyright

ambiguity.

3A, 3B

3D

EU AI Act (2023) New laws require disclosure of AI-

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

D18

UNESCO

Report (2021)

Encourages ethical AI in culture

sectors.

3D

Push for safeguards vs.

unchecked disruption.

ArtStation

Showcase

(2024)

Entire short film created with AI

visuals & dialogue.

4A, 4C

New industry creation AI

cinema.

D19

D20

Behance

Portfolio (2023)

AI concept art replaces

previsualization artists.

1C, 4A

4C, 4D

Labor loss, but new cheap

models.

MidJourney

User Showcase

(2023)

Freelancers use AI to pitch

storyboards.

Empowers new

entrants = creation.

D21

D22

D23

OpenAI Blog

(2023)

AI co-writing projects with

screenwriters.

2D, 4B

2A, 2C

2B, 4A

Hybrid models =

semidestruction.

Netflix R&D

Report (2022)

AI used in audience prediction &

content investment.

Structural reallocation of

budgets.

Disney

Innovation Lab

(2021)

Uses AI for crowd scene generation.

Efficiency, but reduces extras’

jobs.

D24

D25

Trade Press –

IndieWire

(2022)

Indie filmmakers embrace AI editing

for low budgets.

4A, 4C

4B, 4D

Creates opportunities for

indies.

Academic Case

Study (Liu,

2024)

Hybrid humanAI films win festival

awards.

Cultural acceptance of new

forms.

Table 4.2: Content Analysis Coding Table

Theme One: Labor Disruption

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.

Theme Two: Workflow Transformation

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.

Theme Three: Cultural and Legal Disruption

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.

Theme Four: New Opportunities and Industry Creation

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.

DISCUSSION OF FINDING

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 byAI 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.

SUMMARY AND CONCLUSION

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.

RECOMMENDATIONS FOR FUTURE RESEARCH

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.

REFERENCES

1. Acemoglu, D., & Restrepo, P. (2019). "Automation and new tasks: How technology displaces and reinstates

labor." Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3 .

2. Ali, S., Devasia, N., Park, H.ꢀW., & Breazeal, C. (2021). Social robots as creativity eliciting agents. Frontiers

in Robotics and AI, 8, 673730. https://doi.org/10.3389/frobt.2021.673730.

3. American Civil Liberties Union (ACLU). (2022). Position Paper:AI, Copyright, and Freedom of Expression

in Media. ACLU.

4. Anantrasirichai, N., & Bull, D. (2022). Artificial intelligence in the creative industries: a review. Artificial

intelligence review, 55(1), 589-656. https://doi.org/10.1007/s10462-021-10039-7.

5. Askar, N. (2024). Entertainers vs AI: A Comparative Analysis of the Unionized and Non-Unionized

Entertainers' Approaches to AI. Loy. LA Ent. L. Rev., 45, 91.

Page 3406

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue IX September 2025

6. Autor, D. H., & Dorn, D. (2013). "The growth of low-skill service jobs and the polarization of the U.S. labor

market." American Economic Review, 103(5), 1553–1597. https://doi.org/10.1257/aer.103.5.1553 .

7. Azzarelli, A., Anantrasirichai, N., & Bull, D. R. (2025). Intelligent Cinematography: a review of AI research

for cinematographic production. Artificial Intelligence Review, 58, 108. https://doi.org/10.1007/s10462-

024-11089-3.

8. Bărbulescu, A., & Zhen, L. (2024). Forecasting the River Water Discharge by Artificial Intelligence

Methods. Water, 16(9), 1248. https://doi.org/10.3390/w16091248.

9. Naqvi, S. M., He, R., & Kaur, H. (2025). Catalyst for Creativity or a Hollow Trend?: A Cross-Level

Perspective on The Role of Generative AI in Design. Proceedings of CHI 2025. DOI:

10.1145/3706598.3713233.

10. Barthes, R. (1967). Le discours de l’histoire. Social Science Information, 6(4), 63–75.

https://doi.org/10.1177/053901846700600404.

11. Bender, J. (2024). Automation and artistry: Navigating the rise of AI in post-production. Film Production

Studies, 12(1), 34–52. https://doi.org/10.1080/21568235.2024.1987412.

12. Bender, S. (2024). Generative-AI, the media industries, and the disappearance of human creative labour.

Media Practice and Education, 1–18. https://doi.org/10.1080/25741136.2024.2355597.

13. Biermann, O. C., Ma, N. F., & Yoon, D. (2022, June). From tool to companion: Storywriters want AI writers

to respect their personal values and writing strategies. In Proceedings of the 2022 ACM Designing

Interactive Systems Conference (pp. 1209-1227). https://doi.org/10.1145/3532106.3533506.

14. Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide (pp. 1–252). SAGE Publications.

https://doi.org/10.4135/9781529781585

15. British Film Institute (BFI). (2022). AI in the UK Film Sector: Risks and Opportunities. BFI Research

Report.

16. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial

gender

classification.

Proceedings

of

Machine

Learning

Research,

81,

1–15.

https://doi.org/10.48550/arXiv.1801.08921.

17. California State Legislature. (2023). California AI Regulation Act (Film and Media Provision). California

State Government.

18. Cave, S., & Dihal, K. (2020). The whiteness of AI. Philosophy & Technology, 33(4), 685–703.

https://doi.org/10.1007/s13347-020-00415-6.

19. Chen, R. (2020). “Co-Authorship with Algorithms: The Promise and Limits of AI in Scriptwriting.” New

Media & Society, 22(8), 1563–1581. https://doi.org/10.1177/1461444819890402 .

20. Chen, R. (2020). “Co-Authorship with Algorithms: The Promise and Limits of AI in Scriptwriting.” New

Media & Society, 22(8), 1563–1581. https://doi.org/10.1177/1461444819890402 .

21. Cheyroux, E., & Godet, A. (2022). Introduction: Film Festivals – Close-Up on New Research. Journal of

Festive Studies, 4(1), 5–22. https://doi.org/10.33823/jfs.2022.4.1.157.

22. Deloitte. (2023). AI in Media & Entertainment: 2023 Industry Trends. Deloitte Insights.

23. Directors Guild of America (DGA). (2022). Policy Brief: Artificial Intelligence in Directing and

Postproduction. Directors Guild of America.

24. Elkins, J. (2023). Generative AI and the future of screenwriting: Opportunities and risks. Journal of Media

Innovation, 9(2), 45–61. https://doi.org/10.1080/27696520.2023.1987654.

25. Erdem, S. (2025). The synthesis between artificial intelligence and editing stories of the future. In U.ꢀKilinç

(Ed.), Transforming Cinema with Artificial Intelligence (pp. 221–240). IGI Global Scientific Publishing.

26. https://doi.org/10.4018/979-8-3693-3916-9.ch009.

27. European Audiovisual Observatory. (2022). AI and the European Film Industry: Regulatory Perspectives.

Strasbourg: European Audiovisual Observatory.

28. Fairclough, N. (2013). Critical discourse analysis: The critical study of language (2nd ed.). Routledge.

https://doi.org/10.4324/9781315834368 .

29. Fairclough, N. (2021). Language and power (3rd

https://doi.org/10.4324/9780429282652 .

ed.,

pp.1–319)

Routledge.

30. Film Independent. (2022). Case Study: Independent Filmmakers and AI Tools in Production. Film

Independent.

31. Fisk, C. L. (2023). The Different American Legal Structures for Unionization of Writers for Stage and

Screen. In The Palgrave Handbook of Screenwriting Studies (pp. 527-541). Cham: Springer International

Publishing. https://doi.org/10.1007/978-3-031-20769-3_28.

Page 3407

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue X October 2025

32. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to

computerisation?

Oxford

Martin

School

Working

Paper.

Retrieved

from

https://www.oxfordmartin.ox.ac.uk/downloads/academic/future-of-employment.pdf

33. Fukuda‐Parr, S., & Gibbons, E. (2021). Emerging consensus on ‘ethical AI’: Human rights critique of

stakeholder guidelines. Global Policy, 12, 32-44. https://doi.org/10.1111/1758-5899.12965.

34. George, A. S., Baskar, T., & Pandey, D. (2024). Establishing Global AI Accountability: Training Data

Transparency, Copyright, and Misinformation. Partners Universal Innovative Research Publication, 2(3),

75-91. https://doi.org/10.5281/zenodo.11659602.

35. Green, B.ꢀR. (2023). The Artist’s Code: Technology and the Optimization of Creativity in Hollywood

(Doctoral dissertation). University of California, Los Angeles.

36. Green, M. (2024). Audience attitudes towards AI-generated media: Trust, transparency, and authenticity.

Journal of Media and Society, 16(2), 98–117. https://doi.org/10.1177/20563051241234567.

37. Günar, A. (2025). Economic Political and Social Consequences of AI: Understanding the AI Technologies’

Influence with Creative Destruction. In Economic and Political Consequences of AI: Managing Creative

Destruction (pp. 1–20). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-70360.ch001.

38. Gunar, J. (2025). Artificial intelligence and creative disruption: Revisiting Schumpeter in the digital age.

Journal of Innovation Studies, 12(1), 45–63. https://doi.org/10.1080/27688421.2025.000123 .

39. Hermann, I. (2023). Artificial intelligence in fiction: between narratives and metaphors. AI & Society, 38(1),

319–329. https://doi.org/10.1007/s00146-021-01299-6.

40. Hollywood Reporter. (2023). “AI-Generated Films: A New Frontier or Existential Threat?” The Hollywood

Reporter. https://www.hollywoodreporter.com/tech/ai-generated-films-2023 .

41. Hudson, A.ꢀD., Finn, E., & Wylie, R. (2023). What can science fiction tell us about the future of artificial

intelligence policy? AI & Society, 38(1), 197–211. https://doi.org/10.1007/s00146-021-01273-2.

42. Hutson, J. (2024). From Simulacra to Reanimation: Resurrecting the (Un) Dead. In Art and Culture in the

Multiverse of Metaverses: Immersion, Presence, and Interactivity in the Digital Age (pp.ꢀ173–190). Cham:

Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-66320-8_6.

43. International Labour Organization (ILO). (2022). The Future of Work in Creative Industries: Impacts of AI.

ILO Research Brief.

44. International Labour Organization (ILO). (2022). The Future of Work in Creative Industries: Impacts of AI.

ILO Research Brief.

45. Jabotinsky, H.ꢀY., & Lavi, M. (2024). Can ChatGPT and the Like Be Your Co-Authors? Cardozo Arts &

Entertainment Law Journal, 42, 347. Available at https://papers.ssrn.com/abstract=4528953.

46. Jasim, Y. A., & Awqati, A. J. (2025). Distinguishing Human Creativity from AI-Generated Literary Texts.

Journal of Prospective Researches, 25(2), 40-47. http://dx.doi.org/10.61704/pr.494.

47. Kavitha, L. (2023). Copyright challenges in the artificial intelligence revolution: Transforming the film

industry from script to screen. Trinity Law Review, 4(1), 1-8. https://doi.org/10.48165/TLR.2024.4.1.1.

48. Kollmann, T., & Kollmann, J. (2025). Artificial entrepreneurship: Generative AI and the future of

innovation.

https://doi.org/10.1016/j.techfore.2025.123678 .

49. Kollmann, T., & Kollmann, N. (2025). Digital Innopreneurshipꢀ2: The Evaluation of Collaboration between

Corporates and Startups in the Digital Economy. Science, 13(2), 135–154.

https://doi.org/10.11648/j.sjbm.20251302.18.

Technology

Forecasting

and

Social

Change,

204,

123678.

50. Lawler, J., & Waldner, D. (2023). Interpretivism versus positivism in an age of causal inference. In H.

Kincaid & J. Van Bouwel (Eds.), The Oxford Handbook of Philosophy of Political Science (pp.ꢀ221–242).

Oxford University Press. https://doi.org/10.1093/oxfordhb/9780197519806.013.11.

51. Lee, H.ꢀK. (2022). Rethinking creativity: Creative industries, AI and everyday creativity. Media, Culture &

Society, 44(3), 601–612. https://doi.org/10.1177/01634437221077009.

52. Lee, M. (2022). Automation, inequality, and the creative industries: A labour economics perspective.

Cultural Trends, 31(4), 343–359. https://doi.org/10.1080/09548963.2022.2103274.

53. Lemley, M.ꢀA. (2024). How Generative AI Turns Copyright Upside Down. Science & Technology Law

Review, 25(2), 21–50. https://doi.org/10.52214/stlr.v25i2.12761.

54. Liu, X., & Liu, Z. (2024). A hybrid online and offline teaching effectiveness evaluation method for literary

theory courses.

Journal

of Computational

Methods

in

Sciences and Engineering,

24(6).

https://doi.org/10.1177/14727978241299663.

Page 3408

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue IX September 2025

55. Liu, Z. (2024). Analysis of the Impact of Artificial Intelligence on the Media and Film Industries. Lecture

Notes

http://dx.doi.org/10.54254/27537048/35/20232112.

56. Lu, H., & Chu, H. (2023). Let the dead talk: How deepfake resurrection narratives influence audience

response in prosocial contexts. Computers in Human Behavior, 145, 107761.

https://doi.org/10.1016/j.chb.2023.107761.

in

Education

Psychology

and

Public

Media,

35,

219-223.

57. Lucchi, N. (2024). ChatGPT: A Case Study on Copyright Challenges for Generative Artificial Intelligence

Systems. European Journal of Risk Regulation, 15(3), 602–624. doi:10.1017/err.2023.59.

58. Lugrin, C.ꢀPelachaud, & D.ꢀTraum (Eds.), the Handbook on Socially Interactive Agents: 20ꢀyears of

Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics, Volumeꢀ2:

Interactivity,

Platforms, Application

(pp.

463–492). Association

for

Computing

Machinery.

https://doi.org/10.1145/3563659.3563674.

59. Mayring, P. (2014). Qualitative content analysis: Theoretical foundation, basic procedures and software

solution. Klagenfurt. https://doi.org/10.17169/fqs-15.3.2119 .

60. McLuhan, M.

(1964).

Understanding media:

The extensions of man. McGraw-Hill.

https://doi.org/10.4324/9781315025483.

61. McLuhan, M. (1964). Understanding Media: The Extensions of Man. McGraw-Hill.

62. Mehrotra, C. (2024). AI: Redefining Creativity and Revolutionizing the Art of Writing. International Journal

of

Innovations

in

Science,

Engineering

and

Management,

3(Special

Issueꢀ2),

129–133.

https://doi.org/10.69968/ijisem.2024v3si2129-133.

63. Meydan, C. H., & Akkaş, H. (2024). The role of triangulation in qualitative research: Converging

perspectives. In A. Elhami, A. Roshan, & H. Chandan (Eds.), Principles of conducting qualitative research

in multicultural settings (pp. 98–129). IGI Global. https://doi.org/10.4018/979-8-3693-3306-8.ch006.

64. Miller, A. (2022). “Machine Creativity or Creative Tools? AI in Hollywood’s Post-Production.” Journal of

Cultural Production, 17(3), 212–230. https://doi.org/10.1080/17530350.2022.1992387 .

65. MIT Media Lab. (2022). Showcase on Human–AI Collaborative Storytelling. MIT Media Lab.

66. Motion Picture Association (MPA). (2023). AI and Intellectual Property Rights in Hollywood. MPA White

Paper.

67. Murdoch, B. (2021). Privacy and artificial intelligence: challenges for protecting health information in a

new era. BMC medical ethics, 22, 1-5. https://doi.org/10.1186/s12910-021-00687-3.

68. Netflix. (2023). Annual Report: Experimentation with AI in Storyboarding and Postproduction. Netflix

Media Center.

69. New York Times. (2023). “Actors Protest AI Use in Hollywood Contracts.” The New York Times.

https://www.nytimes.com/2023/07/ai-actors-hollywood

70. Nissim, G., & Simon, T. (2021). The future of labor unions in the age of automation and at the dawn of AI.

Technology in Society, 67, 101732. https://doi.org/10.1016/j.techsoc.2021.101732.

71. Okun, J. A., & Zwerman, S. (2020). The VES Handbook of Visual Effects: Industry Standard VFX Practices

and Procedures. Routledge. https://doi.org/10.4324/9780429455934 .

72. Perez, C. (2002). Technological revolutions and financial capital: The dynamics of bubbles and golden ages.

Edward Elgar Publishing.

73. Pervin, N., & Mokhtar, M. (2022). The interpretivist research paradigm: A subjective notion of a social

context. International Journal of Academic Research in Progressive Education and Development, 11(2),

419–428. https://doi.org/10.6007/IJARPED/v11-i2/12938.

74. Postman, N. (1970). The reformed English curriculum. In A. C. Eurich (Ed.), High school 1980: The shape

of the future

in

American

secondary

education

(pp.

160–168).

Pitman.

https://doi.org/10.4324/9781315005813.

75. Pretorius, L. (2024). Demystifying research paradigms: Navigating ontology, epistemology, and axiology in

research. The Qualitative Report, 29(10), 2698–2715. DOI:ꢀ10.46743/2160-3715/2024.7632.

76. PwC. (2022). Global Entertainment and Media Outlook 2022–2026. PricewaterhouseCoopers.

https://www.pwc.com/outlook2022 .

77. Re-evaluating creative labor in the age of artificial intelligence: A qualitative case study of creative workers’

perspectives on technological transformation in creative industries (2025). AI & Society, 40, 4119-4130.

https://doi.org/10.1007/s00146-025-02180-6.

78. Rodriguez, L. (2023). “Personalized streaming and interactive narratives: The role of AI in audience

engagement.” Media, Culture & Society, 45(4), 567–584. https://doi.org/10.1177/01634437221123456 .

Page 3409

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue X October 2025

79. Ruotsalainen, J., & Heinonen, S. (2015). Media ecology and the future ecosystemic society. Futures, 73,

80–92. https://doi.org/10.1016/j.futures.2015.07.007 .

80. SAG-AFTRA. (2023). Statement onAI and Digital Replication of Performers. Retrieved from SAGAFTRA .

81. SAG-AFTRA.

https://doi.org/10.5281/zenodo.10562453.

82. Schumpeter, J. A. (1942). Capitalism,

https://doi.org/10.4324/9781315135564.

(2024).

AI

and

performers’

rights:

Policy

guidelines.

SAG-AFTRA.

socialism and

democracy.

Harper

&

Brothers.

83. Schwandt, T. A. (1994). Constructivist, interpretivist approaches to human inquiry. In N. K. Denzin & Y. S.

Lincoln (Eds.), Handbook of qualitative research (pp. 118–137). Thousand Oaks, CA: Sage.

84. Schwandt,

https://doi.org/10.4135/9781483398969 .

85. Screen Actors Guild – American Federation of Television and Radio Artists (SAG-AFTRA). (2023).

Statement on AI and Digital Replication of Performers. SAG-AFTRA.

https://www.sagaftra.org/files/AI_Statement_2023.pdf .

T. A.

(2014).

The

Sage

dictionary

of

qualitative

inquiry

(4th

ed.).

Sage.

86. Shamanth, N., Sagar, T. R., & Priyanga, P. (2024, November). The Intersection of Art and AI: Innovations

in Creative Collaboration. In 2024 International Conference on IoT, Communication and Automation

Technology (ICICAT) (pp. 874–879). IEEE. https://doi.org/10.1109/ICIC A T 62666.2024.10923276.

87. Siala, H., & Wang, Y. (2022). SHIFTing artificial intelligence to be responsible in healthcare: A systematic

review. Social Science & Medicine, 296, 114782. https://doi.org/10.1016/j.socscimed.2022.114782.

88. Smith, J. (2021). “Algorithms at the Writers’ Table: AI and the Changing Nature of Screen Authorship.”

Journal of Media Studies, 34(2), 145–168. https://doi.org/10.1080/02614340.2021.1875602 .

89. Smith, J. A., Flowers, P., & Larkin, M. (2022). Interpretative phenomenological analysis: Theory, method

and research (2nd ed., pp. 1–264). SAGE Publications. https://doi.org/10.4135/9781529714385

90. Sommer, E. (2024). Real Concerns for an Artificial Threat: Artists, AI, and the Battle to Script Hollywood's

Future. Nev. LJ, 25, 449. https://scholars.law.unlv.edu/nlj/vol25/iss2/7.

91. Strate, L. (2017). Media Ecology: An Approach to Understanding the Human Condition. Peter Lang

Publishing. https://doi.org/10.3726/978-1-4331-4005-1.

92. Sun, P. (2024). A study of artificial intelligence in the production of film. In SHS Web of Conferences, 2024

International Conference on Performing Arts, Human Development and Digitalization (Vol. 185, p. 03004).

https://doi.org/10.1051/shsconf/202418303004.

93. Sun, P., & Zuo, X. (2024). Evolution and history of research philosophy. Journal of Management Research,

24(1), 28–61. https://doi.org/10.5281/zenodo.13862367.

94. Tang, X. (2025). Intellectual property law as labor policy. NYUL Rev., 100, 62.

95. Tang, Y., Li, H., Lan, M., Ma, X., & Qu, H. (2025). Understanding Screenwriters' Practices, Attitudes, and

Future

https://doi.org/10.48550/arXiv.2502.16153.

96. TechCrunch. (2022). “Startups Betting

https://techcrunch.com/2022/11/ai-screenwriting-startups .

Expectations

in

Human-AI

Co-Creation.

arXiv

preprint

arXiv:2502.16153.

on

AI

Screenwriting

Tools.”

TechCrunch.

97. Thanh, N. C., & Thanh, T. T. L. (2015). The interconnection between interpretivist paradigm and qualitative

methods in education. American Journal of Educational Science, 1(2), 24–27. Retrieved from

https://files.eric.ed.gov/fulltext/ED595989 .

98. Townsend, D. M., & Hunt, R. A. (2019). Entrepreneurial action, creativity, & judgment in the age of artificial

intelligence. Journal of Business Venturing Insights, 11, e00126. https://doi.org/10.1016/j.jbvi.2019.e00126.

99. Towse, R. (2020). A Textbook of Cultural Economics (3rd ed.). Cambridge University Press.

https://doi.org/10.1017/9781108627377 .

100.Trangbæk, A., & Cecchini, M. (2023). Using the interpretivist methodology. In R. Shaw & C. Eichbaum

(Eds.), Handbook on Ministerial and Political Advisers (pp.ꢀ123–136). Edward Elgar Publishing.

https://doi.org/10.4337/9781800886582 .

101.UNESCO. (2022). AI and Cultural Diversity in Global Media. UNESCO Report.

102.United Nations Educational, Scientific and Cultural Organization (UNESCO). (2022). AI and Cultural

Diversity in Global Media. UNESCO Report.

103.Variety. (2022). “How

[https://variety.com/2022/film/news/ai-casting-hollywood] Variety.

104.Variety. (2022). “How AI is Changing Casting in

https://variety.com/2022/film/news/ai-casting-hollywood .

AI

Is

Changing

Casting

in

Hollywood.”

Retrieved

from

Hollywood.”

Variety Magazine.

Page 3410

www.rsisinternational.org

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNO V A T ION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |V o lume XII Issue IX September 2025

105.Vaughan, H. (2021). A Green Intervention in Media Production Culture Studies: Environmental Values,

Political

Economy

and

Mobile

Production.

Environmental

Values,

30(2),

193–214.

https://doi.org/10.3197/096327120X15752810324057.

106.Verdecchia, R., Sallou, J., & Cruz, L. (2023). A systematic review of Green AI. WIREs Data Mining and

Knowledge Discovery, 13(4), e1507. https://doi.org/10.1002/widm.1507.

107.Vincent, J. (2023). Hollywood’s uneasy embrace of artificial intelligence. Film Quarterly, 77(1), 14–23.

https://doi.org/10.1525/fq.2023.77.1.14.

108.Vincent, J. (2023, May 8). Hollywood’s use of AI in casting raises ethical concerns. The Verge.

https://doi.org/10.5281/zenodo.7987453.

109.WGA. (2023). Artificial intelligence and authorship policy statement. Writers Guild of America.

https://doi.org/10.5281/zenodo.10437652.

110.Wong, L.ꢀP.ꢀW. (2024). Artificial intelligence and job automation: Challenges for secondary students’ career

development and life planning. Merits, 4(4), 370–399. https://doi.org/10.3390/merits4040027.

111.World Economic Forum (WEF). (2023). AI and the Future of the Creative Economy. WEF White Paper.

111. Writers Guild Foundation Archive. (2021). Survey on Writers’ Perceptions of AI in Screenwriting.

Writers Guild Foundation.

112.Writers Guild of America (WGA). (2023). WGA Negotiation Report on AI Use in Screenwriting. Writers

Guild

of

America.

https://www.wga.org/uploadedfiles/members/memberinfo/contracts/WGA_AI_Report_202 .Pdf.

113.Writers Guild of America. (2023, Mayꢀ1). Summary of the 2023 WGA Minimum Basic Agreement (MBA).

Writers Guild of America. Retrieved from Writers Guild website.

114.Xu, W. (2023). AI in HCI Design and User Experience. In AI in HCI Design and User Experience. ArXiv.

https://doi.org/10.48550/arXiv.2301.00987.

115.Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed., pp. 1–352). SAGE

Publications.

116.Young, Z. T. (2024). Generative Artificial Intelligence in Hollywood: The Turbulent Future that Lies Ahead.

W. Va. L. Rev., 127, 541.

117.Zhang, R., Yu, B., Min, J., Xin, Y., Wei, Z., Shi, J. N., … Rao, A. (2025). Generative AI for film creation: A

survey of recent advances. arXiv preprint arXiv:2504.08296. https://doi.org/10.48550/arXiv.2504.08296.

Page 3411

www.rsisinternational.org