









































Pa
ge

 
1



Pa
ge

 
46

American Journal of   
Society and Law ( AJSL)

Impact of  Generative Artificial Intelligence on The Global Entertainment Industry & Its 
Legal Dynamics

D. O. Ben-Daniel1*

Volume 4 Issue 1, Year 2025
ISSN: 2835-3277 (Online)

DOI: https://doi.org/10.54536/ajsl.v4i1.4566
https://journals.e-palli.com/home/index.php/ajsl

Article Information ABSTRACT

Received: February 20, 2025

Accepted: March 21, 2025

Published: April 28, 2025

The emergence of  Artificial Intelligence (AI) has given rise to unprecedented and momentous 
consequences across humanity.  There is no doubt that AI, most especially, Generative (Gen) 
AI portents massive consequences within the entertainment industry. This article attempts 
to contribute to the overall discuss of  gen AI within the entertainment industry, and covers 
the proper understanding of  gen AI and how AI models are trained; the entertainment 
industry; the impact of  gen AI on the entertainment industry globally; the advantages and 
disadvantages of  deploying gen AI within the entertainment industry; and the legal tools 
available to manage the existential threat of  gen AI to the entertainment industry. Therefore, 
the article explains and discusses AI broadly, gen AI specifically, the training process, the 
current developments within the entertainment industry regarding the adoption of  AI, and 
the legal consequences of  gen AI, such as, copyright infringements.  This article concludes 
by emphasizing the quantum advantage of  deploying gen AI as an effective tool within the 
entertainment industry, which shifts the focus from the threats gen AI portends for the 
industry at large, to its huge benefits if  properly harnessed. 

Keywords
Copyright, Creative Industry, 
Entertainment, GenAI, IP

1 7220 McCallum Blvd, Dallas 75252, Texas, USA 
* Corresponding author’s e-mail: gbendaniel@gmail.com

INTRODUCTION
The past two decades have witnessed exponential 
growth in machine learning and robotic science, known 
as artificial intelligence (AI). The growth and adoption 
of  these technologies across multiple industries have 
been unprecedented, to the extent that there has been 
a growing fear that these technologies could eventually 
replace humans or even render humans “useless”, as 
if  humans did not develop these technologies. This 
global phenomenon is inferencing the possibility that 
AIs could finalize the much touted replacement theory, 
a similar sentiment expressed by a leading scientist, 
Joseph Weizenbaum, that the study of  AI is obscene, anti-
human and immoral.  This fear of  human replacement has 
always been expressed, each time humanity witnesses 
an evolution or revolution in technology, right from 
the age of  industrial revolution as we know it today. 
Considering the pace of  development and adoption 
of  AI technologies across industries, such replacement 
perception is not unfounded. Most especially, with the 
release of  AI engines like ChatGPT, Gemini, Adobe 
Photoshop (powered AI), and the most recently released 
Chinese AI, DeepSeek, that crashed valuations of  AI 
companies across global stock markets. Furthermore, 
the simultaneous crashing of  the computing power 
requirements of  these Large Language Models (LLMs) 
is leading to more computing power becoming available 
at extremely affordable costs, increasing the rate of  
adoption across industries. Adopting the AI phenomenon 
within the entertainment industry has also produced a 
monumental impact within the industry, such that the 
same replacement theory is being touted, leading to 
threats of  strikes, law suits, copyright infringements, etc. 
If  not properly managed and streamlined, gen AI could 

be more damaging than constructive and developmental 
to the global entertainment industry. Therefore, the 
objective of  this journal write up is not to pitch “Humans 
vs AIs” narrative in an adversarial way, but to demystify 
this global phenomenon, analyse its growth and impact 
within the entertainment industry by showcasing the 
gen AI benefits, and the potential threats to the industry 
if  gen AI is not properly harnessed. Furthermore, 
unveil the opportunities available within the copyright 
and entertainment laws when gen AI is properly and 
strategically utilized and deployed. 

Research Problem
To what extent has the emergence of  gen AI impacted 
the global entertainment industry, and to what extent 
does strategic integration of  gen AI tools into the 
entertainment industry’s value chain directly correlate 
with increase in operational efficiency, creativity and 
overall value of  the industry. 

Research Questions
• How does the adaptation of  gen AI automation tools 

impact the time and cost associated with productions 
within the industry?

• What is the impact of  gen AI on resource allocation 
and waste reduction within the production process?

• How does the adoption of  gen AI affect the speed and 
accuracy of  script selection process and other processes 
in production?

• What are the quantifiable improvements in 
productivity and output resulting from the strategic 
integration of  gen AI automation in production process?

• How does the adoption of  gen AI impact creativity 
and the overall value of  the entertainment industry 



Pa
ge

 
47

https://journals.e-palli.com/home/index.php/ajsl

Am. J. Soc. L. 4(1) 46-54, 2025

Research Objectives
• To assess the overall impact of  gen AI on the 

global entertainment industry, its operational efficiency, 
creativity and overall profitability.

• To analyse the impact of  this phenomenon on the 
human creatives (workforce) within the entertainment 
industry, and the changing dynamics because of  adoption 
of  gen AI within the industry.

• To evaluate the ethical and societal impact of  gen AI 
on the industry, the legal instruments available to limit 
the “damaging” effect of  the phenomenon within the 
entertainment industry.  

• To evaluate the cost-benefit analysis of  the adoption 
of  gen AI within the global entertainment industry.

LITERATURE REVIEW
This article clearly articulates the established concepts of  
AI and description of  the entertainment industry.  The 
Oxford Languages defines AI as the theory and development 
of  computer systems able to perform tasks that normally require 
human intelligence, such as visual perception, speech recognition, 
decision-making, and translation between languages. According 
to Stryker & Kavlakoglu (2024), AI is a technology 
that leverages on smart computers and machines to 
simulate human learning, comprehension, problem solving, 
decision making, creativity and autonomy. Turing (1950), an 
English mathematician and a leading AI researcher, first 
conceptualized the “thinking computers or machines,” 
acknowledging that only a special kind of  machine, known 
as “digital computers,” could do that fit.  McCathy (2007), 
a computer scientist at Stanford University and one of  
the founding fathers of  AI, defines AI as the science 
of  making intelligent machines and software programs, 
adapting them to human intelligence, without necessarily 
confining it to methods of  biological observations. 
He holds that machine intelligence, though adaptable 
to human intelligence, requires some mechanisms of  
intelligence that enables it deliver impressive performances 
on the tasks its built for. Colorado State University 
(CSU) Global (2021) sates that AI technology enables 
computers and machines to mimic human intelligence 
through an interactive processing and algorithm training. 
CSU further explains that the combination of  interactive 
processing and algorithm training enables problem-
solving and providing strategic directions, which is a 
process of  continuously and intelligently relying on its 
interactive algorithms through the combination of  large 
data sets that are analyzed and then learning from their 
patterns and features. Eventually, the machine develops 
high capabilities and learnings from these data patterns, 
which enables it to intelligently answer or solve problems, 
and possibly project (predict) into the feature based on 
these data patterns. 
To fully comprehend the concept of  AI and its 
applications, the component parts must be properly 
understood. According to CSU, these component parts 
sum up the discipline of  AI, and they make up the sub-
domains of  the main AI domain. Some of  the sub-

domains of  AI are machine learning (ML), which is 
component of  AI that enables computer systems and 
programs to learn real time, and advance outcomes based 
on its experience, rather than how it’s programmed. This 
is where AI discovers data patterns and logic that enable it 
deliver on its pre-set outcomes or deliverables. The other 
one is deep learning, which is the type of  ML enables 
AI improve by processing data, because it utilizes artificial 
neural networks that mimics the human brain’s biological 
neural networks when processing information. Others are 
the neural network itself  that functions like a human brain, 
but with capacity to work with large dataset. These make 
up the entire global AI architecture that drives this cutting 
edge technology across multiple sectors like healthcare, 
legal services, financial services, manufacturing, creative 
(including entertainment), automobile, etc. Stryker & 
Kavlakoglu (2024) affirm that AI has evolved over the 
decades from its inception in the 1950s from machine 
learning (where machines learns from historical data), 
to deep learning (where machine learning models try 
to mimic humans’ brain function), and now, generative 
(gen) AI (where deep learning models can create original 
content, such as text, videos, images, etc.).
Gen AI is that AI used to create new content such as text, 
videos, images, music, computer codes and audio. Gen 
AI is built on two components of  AI, namely, machine 
learning and deep learning. Stryker & Kavlakoglu (2024) 
emphasise that understanding these two components 
is integral to comprehending gen AI. Gen AI deploys 
these two component to identify and learn patterns and 
relationships in a dataset provided, and then utilizes these 
learnings to generate new content. The newly created 
content could be images, texts (inform of  essays or chat 
responses), videos, audios, and computer codes. The 
examples of  gen AI are Chat GPT, DALL-E 2, Gemini, 
DeepSeek, etc. Therefore, gen AI is trained on provided 
dataset, through the process of  machine learning and 
deep learning, its able to understand the dataset patterns 
and relationships, and be able to develop its own ability 
to generate new content. Gen AIs like Gemini, Chat 
GPT, DeepSeek, etc., have been trained on large volume 
of  dataset, which explains their ability to generate new 
content as requested by users. Gen AI has various learning 
processes, such as supervised, which is a technique of  
machine/deep learning that utilizes labeled dataset in its 
learning process of  identifying data patterns and their 
relationships. Toloka (2023) explains that labeled dataset 
contains meaningful tags (information) that requires extra 
process of  labeling (such as specifying what object is in 
the image dataset, be it car or bird; or even the words 
uttered in the recording, etc.) This makes the dataset 
more meaningful to the machine. Labeling of  dataset is 
a fundamental step towards building a high-performing 
and accurate gen AI algorithm, which is the goal of  its 
learning process-to develop an AI model that delivers 
correct output based on real-world data. The objective 
of  this is to ensure an effective and “supervised” 
training process that guarantees a more accurate result. 



Pa
ge

 
48

https://journals.e-palli.com/home/index.php/ajsl

Am. J. Soc. L. 4(1) 46-54, 2025

Explaining further that these labels play a vital function in 
enabling the gen AI model deliver the best outcome and 
predictions, for example, predicting travel time, based on 
the time of  the days and the weather conditions.
The other type of  learning is unsupervised learning, 
and IBM (2021) states that this relies on machine-
learning (ML) algorithms to identify hidden data pattern 
and groupings without human intervention. The ML 
algorithms analyzes and clusters unlabeled datasets, and 
identifies the similarities and differences of  the data 
(information) contained in the dataset, and this type of  
gen AI is ideal for data analysis, customer segmentation, 
image recognition (object recognition), and cross selling 
strategies. According to Bergmann (2023), another type is 
semi-supervised learning, combines both supervised and 
un-supervised learning, thus giving a bit of  both worlds. It 
utilizes both labeled and unlabeled datasets for its training 
for both classification and regression tasks. This solves the 
problems of  not having enough labeled dataset to utilize 
for learning, and when it’s too expensive to label enough 
dataset. Another type of  learning is self-supervised 
learning that uses unsupervised learning to train models 
that require supervised learning, mostly because of  the 
difficulty and time required to label the dataset. Thus, 
self-supervised learning is more time-effective and cost-
effective because the need to manually label the dataset is 
replaced with an AI model generating implicit labels from 
unstructured data. Another type of  learning, as analyzed 
by IBM, is reinforcement learning, which is similar to 
supervised learning but not trained using a sample dataset. 
It is rather based on trial and error, and the successful 
sequence of  outcomes is reinforced to develop the best 
recommendation or policy for a given problem. Transfer 
learning, which Murel & Kavlakoglu (2024) explain to be 
the type of  learning that utilizes pre-trained AI models and 
dataset to optimize the performance and generalizability 
for a related task or dataset.
The product or output of  these trainings is what is know as 
large language model (LLM) that now functions as a base 
model for any gen AI.  These base models can further be 
trained for industry vertical or specific performance. This 
means, some LLMs are ready for use after training, such as 
Chat GPT, Gemini, DeepSeek, etc., whilst, some are just 
base model (framework) to be further trained vertically to 
perform (generate new content) industry specific tasks, 
some of  which are GPT-3, LLaMA, Gemini, DeepSeek, 
etc. These base models have been pre-trained on vast 
amount of  data for generalizability and related tasks, 
and they are just retrained to further perform narrower 
related and specific tasks. For example, retraining a GPT-
3 LLM model to perform legal specific functions like 
legal writing, case summarization, etc., or even training 
the same model to function within a hospital setting, 
such as patient data analysis for tracking and diagnosis 
of  chronic diseases, diseases outbreak predictability, etc.

On the other hand, the entertainment industry is an 
economic sector made up of  individuals and businesses 

that engage in creativity, such as designs, music, 
motion pictures, gaming, publishing and literature, 
visual arts, performing arts, fashion, TV and radio, and 
advertising. The entertainment industry is made up of  
individuals (humans) with creative skills and talent that 
are creating a thriving economy through their work of  
designs, music, motion pictures, writing, performing 
arts, etc. According to PwC (2024), the Media and 
Entertainment is expected to hit a global valuation of  
US$3.4 trillion in 2028, and the National Assembly of  
State Arts Agencies (NASAA) confirmed that the U.S. 
entertainment industry alone was valued US$1.1 trillion 
as at the end of  2022. The entertainment industry is one 
of  the fastest growing industry, globally, most especially 
with its massive potential for wealth and job creation. In 
most developing/emerging countries like India, Nigeria, 
Ghana, South Africa, Brazil, Turkey, Mexico, Peru, Cuba, 
etc., the entertainment industry is one of  the greatest 
wealth creators and employers. Over the decades, the 
entertainment (creative) industry has always depended on 
intellectual property (IP) law to protect its creation, and 
maximize both its value and return on investments.  The 
works of  the entertainment industry, such as, designs, 
music, motion pictures, gaming, publishing and literature, 
visual arts, performing arts, fashion, TV and radio, 
and advertising are called “original works” or “creative 
works” in the copyright law world, and they are copyright 
protected (patent protected in certain jurisdictions). What 
makes the entertainment industry function effectively is its 
‘regulation’ by the intellectual property legal instruments, 
such as copyright, patent, designs, trade secrets; and other 
legal instruments such as contracts, torts, and personal 
rights.  

MATERIALS AND DISCUSSIONS
Research Design
This research design adopts qualitative research method, 
which involves adopting emerging case studies within the 
entertainment industry; entertainment industry insider’s 
perspective (ethnography), which involves understanding 
inside dynamics within that industry; analysis of  lived 
experiences of  individual players within the industry; and 
the established legal theories regulating the industry. 

Data Sources
The research work was sourced from the U.S. constitution, 
statutory instruments, regulations, published journals of  
other authors, researched works and news reports of  
credible media outlets, and other verified authorities. 
Consistently ensuring legal and other verified authorities 
constitute the sources for this research ensures the 
authenticity and accuracy of  the data analysed to arrive at 
the conclusions of  the research work.

Analysis
This research work involves the textual analysis of  
constitutional, statutory and regulatory frameworks that 
define and establishes the legal position in copyright law, 



Pa
ge

 
49

https://journals.e-palli.com/home/index.php/ajsl

Am. J. Soc. L. 4(1) 46-54, 2025

which influences the legal framework for the entertainment 
industry. These textual analysis is then applied to the 
current trends and dynamics within the entertainment 
industry, as its been revolutionized by gen AI. This then 
informs the conclusions and recommendation discussed 
in the article.  

RESULTS AND DISCUSSIONS
The past 2-5 years have seen significant adoption of  
gen AI within the entertainment industry, leading to 
definitive impact on the industry. The architecture of  
gen AI is generally to mimicking humans, which ensures 
the execution of  the same tasks that humans do, but at a 
larger and more efficient scale. Several gen AI applications 
have been developed for the entertainment vertical 
sector, which has had significant impact, largely leading 
to broken trust amongst the industry stakeholders. In 
consistent with gen AI’s training processes described 
above, Kinder (2024) explicated that a gen AI model can 
be trained with vast amount of  existing movie scripts, 
and based on this training, can generate new scripts based 
on ideas, dialogue, types of  characters, and even plot developments 
that appear to work well together. This will have massive 
impact on script development within the entertainment 
industry, both positively and negatively. The benefits 
and costs of  gen AI to the entertainment industry is 
discussed later in this article. The summary of  this point 
is that a gen AI model, pretrained on vast amount of  
existing scripts, can subsequently create new scripts, not 
requiring human script developers any longer. In many 
instances now, gen AI creates the idea and the scripts, 
and the writers are simply hired to polish and re-write the 
scripts, which is much better, as articulated further in this 
article. This development led to the breakdown of  trade 
agreement between Writers Guild of  America  (WGA) 
and The Alliance of  Motion Picture and Television 
Producers (AMPTP), the alliance of  over 350 American 
television and film production companies in May 2023, 
leading to a 148-day strike against the use of  AI in script 
development. Sakoui confirms that the Screen Actors 
Guild-American Federation of  Television and Radio 
Artists (SAG-AFTRA), and Teamsters and International 
Alliance of  Theatrical Stage Employees (IATSE) also 
joined the strike by WGA, against AMPTP because of  
the same perceived threat from gen AI. 
Another big controversy generated by gen AI, as reported 
by Korn, (2023), is in the area of  image generation that 
involved Getty Images suing Stability AI for copyright 
violations, regarding its arts tool, Stable Diffusion. 
Getty accused Stability of  illegally training its AI model 
utilizing Getty’s images without obtaining license to do 
that. According to by Lan (2023), the legal challenges 
emanating from “AI painting” alone grew by 560% 
between July 2022 and January 2023. This percentage 
growth in the volume of  related litigations on copyright 
infringements within the entertainment/creative industry 
depicts the dimensions gen AI is taking the industry to in 
the coming years. Another dimension is the application 

of  gen AI to voice generation, and the one main 
example, as reported by Ingham (2023), is an AI song 
featuring fake Drake and the Weeknd vocals that showed 
up across multiple streaming platforms of  YouTube, 
Spotify, TikTok, Tidal and Apple Music. This triggered 
prompt response from Universal Music Group (UMG) 
on April 17, 2023, requesting the streaming platforms to 
pull down immediately the AI song from their respective 
platforms. This is the power of  gen AI, trained with the 
vocals of  known personalities like celebrities, politicians, 
etc., and then deploying it to create fake audio vocals over 
videos, be it music, news, or motion pictures. This trend 
is multiplying exponentially, with enormous impact in 
deceiving global audiences and the marketplace, denying 
artists and other right owners royalties and other revenues 
due to them. From the forgoing, the impact of  gen AI on 
the entertainment industry is extremely significant, and 
has both advantages and disadvantages. The advantages 
of  adopting gen AI within the industry include the 
following:

Pace of  Development
The overall pace of  developing a creative work is enhanced 
with gen AI. In the traditional mode, developing a movie 
production could take years, because of  the intense work 
required to develop every part of  the production. Script 
development is part of  the pre-production phase, and 
it’s a fundamental stage of  the production, and could 
take months/years. Traditionally, the pre-production 
stage of  script acquisition or development could require 
several options, namely, securing a copyright protected 
pre-developed script, or secure a book to develop into 
a script, or getting skilled scriptwriters to develop fresh 
scripts. Any of  the options selected, though tedious 
and fundamental to the production process, can still be 
optimized and enhanced by gen AI. Without an effective 
script development process, you might not have an 
authentic and quality production, worthy of  generating 
the revenue numbers for all the stakeholders in the 
project. The other pre-production activities are artists/
cast selections, photoshoot, crew selections, etc., and all of  
these can be fast tracked and optimized deploying gen AI. 

Cost and Timing of  Production
The first benefit of  gen AI to the entertainment industry 
is the potential cut in cost of  production, and the time it 
currently takes to complete a motion pictures production 
of  any kind, which includes the pre-production, the 
actual production and the post-production. This applies 
across the entertainment industry, namely, music (both 
audios and videos), performing arts, gaming, etc. Gen 
AI’s ability to save cost and timing could actually help 
remove barrier to entry within the entertainment 
industry. According to Nashville Film Institute, major 
global studios spend between $70Million and $150Million 
to make a feature film, covering the cost of  crew/cast, 
scriptwriters, producers and directors, logistics, and post-
production. However, Movie Budgets affirms that some 



Pa
ge

 
50

https://journals.e-palli.com/home/index.php/ajsl

Am. J. Soc. L. 4(1) 46-54, 2025

blockbuster movies are produced as high as $600Million.  
No doubt, the African country with the biggest creative 
industry is Nigeria, and from experience, the average cost 
of  producing a motion picture in Nigeria range from 
as low as $2,000 to $300,000, depending on casts/crew, 
production value, target audience and platform, logistics, 
etc. However, with gen AI, according to Marcolivio 
(2024), the average cost of  producing a digital movie 
could go as little as $829 an hour, eliminating cost of  
logistics, insurance, lowering or eliminating cost of  
crews/casts, etc. This completely removes cost-induced 
barrier to entry for movie makers, and massive reduction 
of  the time to produce to just about a few days for an 
hour production.

Improved Efficiencies & Elimination of  Wastes
Embedded in the cost and time savings above is gen 
AI’s ability to improve efficiencies in the production 
of  a motion picture, music, or creative arts, and help 
eliminate waste. Marcolivio (2024) explains that AI could 
eliminate or reduce the time spent on pre-production 
activities such as scenes set ups, shoot days, night scenes 
dynamics, delays from bad weather, and shooting of  
scenes with animals. Gen AI guarantees operational 
efficiencies and waste elimination, because every part of  
the creative process is optimised and automated, which 
ensures quick turnaround and effectiveness. An example 
here is Jumpcut’s AI tool used to read through 50,000+ 
submitted scripts, and generates two-page report for 
producers and directors, breaking it down into genre, 
subgenre, characters and similar titles. This simply does 
not replace human creativity; it just enhances script 
selection process, saves time and improves quality.

Emerging New Business Models
Gen AI within the entertainment industry is driving 
new business models within the industry. As the AI 
phenomenon scales up,  new AI character (actors) 
ecosystem is emerging, outside of  the traditional 
entertainment’s characters (actors) ecosystem. This 
involves creating digital actors whose features are 
programmed to meet individual preferences, almost the 
same patterns or similarities with fictional characters. The 
digital actors take several formats such as super hero, a 
training coach, a newscaster, an avatar, music super star, 
TV show presenter, etc. To exploit this emerging digital 
character creation, new business models are emerging 
within the AI powered entertainment industry. Marcolivio 
(2024) gave a few recent examples; Meta paid Snoop 
Dogg $5million to create AI personae, Lucasfilm signed a 
$22 Million deal with James Earl Jones to recreate Darth 
Vader voice with AI. Others are Cadbury powered AI 
that features Indian super star, Shah Rukh Khan, etc. 
In addition to the digital assets’ creation is the potential 
for marketplaces for AI characters (actors) to emerge in 
the coming months that would be sources of  characters 
(actors) for the entertainment industry.
On the flip side, the disadvantages of  adopting gen AI 

within the entertainment industry are also significant, 
compared with the benefits:

Economic Impact on the Industry
According to the PwC’s report, the global valuation of  
the entertainment industry is expected to hit US$3.4 
trillion. This valuation covers all the value chain within 
the entertainment industry, such as script development, 
other production preps, photography, video editing, 
main production, postproduction, and marketing & 
distribution. This valuation spreads across the value 
chain, though not evenly distributed, but every part of  
the value chain retains substantial valuation. However, 
Gen AI potentially could distort that spread, thus, 
creating massive disruptions within the industry, and 
could potentially wipe out value across the different 
subsectors of  the entertainment industry. Gen AI, 
if  not well regulated and managed, could have this 
negative economic impact within the entertainment 
industry. However, if  well leveraged, could leapfrog the 
entire industry’s ecosystem, triggering the next wave of  
development within the industry, which has a multiplier 
effect on the growth and valuations of  the industry. This 
requires consorted and strategic collaborations covering 
training and development, and adoption of  AI as an 
effective tool for the creative professionals within the 
entertainment industry. 

Impact on Copyright Violation and Infringement
Another con of  gen AI within the entertainment industry 
is the massive copyright infringements that have taken 
place during the training of  several AI models. Most of  
the AI models have been trained with copyright protected 
materials such as images, scripts, books, audio, video, etc. 
The fall out of  these copyright infringements has led 
to unprecedented legal battles across the globe. Many 
more legal battles are still brewing up. The reality is, as 
we have discussed above, gen AI depends on being pre-
trained by huge amount of  dataset to be able to function, 
run effectively, and generating new output based on 
the historical data it had been trained with. Therefore, 
most gen AI models currently being deployed within the 
entertainment industry have been pre-trained with huge 
amount of  dataset from within and outside the industry, 
some or most of  which are copyright protected, thus, 
infringing on the right holders substantially. 

Zero Copyright Ownership and Protection
Several sections of  law are present in the entertainment 
industry, namely copyright, trademark, “rights to 
publicity”, contract, licensing, employment, immigration, 
bankruptcy, defamation, privacy, and torts. However, 
fundamental to the industry is copyright law, because 
this is what protects all the creatives of  the industry, and 
provides the foundation for the effective monetization 
of  all created original works of  the industry.  The U.S. 
Constitution states that “the Congress shall have the Power…
To promote the Progress of  Science….by securing for limited Times 



Pa
ge

 
51

https://journals.e-palli.com/home/index.php/ajsl

Am. J. Soc. L. 4(1) 46-54, 2025

to Authors….the exclusive Rights to their…Writings. Then 
the U.S. Congress responded with the Copyright Act of  
1976, which states “original works of  authorship fixed in 
any tangible medium of  expression, now known or later 
developed, from which they can be perceived, reproduced, 
or otherwise communicated, either directly or with the 
aid of  a machine or device. Works of  authorship include 
the following categories:

(1) literary works;
(2) musical works, including any accompanying words;
(3) dramatic works, including any accompanying music;
(4) pantomimes and choreographic works;
(5) pictorial, graphic, and sculptural works;
(6) motion pictures and other audiovisual works;
(7) sound recordings; and
(8) architectural works.

(b) In no case does copyright protection for an original 
work of  authorship extend to any idea, procedure, process, 
system, method of  operation, concept, principle, or 
discovery, regardless of  the form in which it is described, 
explained, illustrated, or embodied in such work” 
Ss(b) simply differentiates copyright protection, which 
deals with works of  expressions, from that of  patent, 
which protects ideas, procedure, process, etc.  The 
focus here is the emphasis on “Author” and “works 
of  authorship”. This clearly shows that authors under 
copyright law are humans, and not a machine or software 
(AI algorithm) that generates the “work.” Furthermore, 
the U.S. Copyright Office defines “an original work of  
authorship is a work that is independently created by 
a human author and possesses at least some minimal 
degree of  creativity.” The AI generated output is not 
classified as an “original works of  authorship” that 
is worthy of  copyright protection or any form of  
protection under entertainment law.  The main issue 
here is that AI generated creatives are actually derivative 
works of  several other original works that belong to 
other authors, which were used to train the AI model 
based on a copyright doctrine of  fair use. In Burrow-
Giles Lithographic Co., the court also agreed with the 
doctrine that only humans qualify as “author” and the 
ONLY one who can get copyright protection under 
“original works of  authorship.” In the Federal Register, 
the U.S. Copyright office issued a guideline in regarding 
AI generated works, stating that when AI “determines the 
expressive elements of  its output, the generated material 
is not the product of  human authorship.” In a recent 
decision, Stephen Thaler, the court held that “Human 
authorship is a bedrock requirement of  copyright.” This 
further affirms the legal position that AI generated works, 
including within the entertainment industry lacks any 
form of  IP (copyright) protection. This similar position 
is held in South Korea, the European Union (Matt-2023), 
South Africa and most other African states. However, 
AI generated materials may be protected in the United 
Kingdom and China (Wang-2024). The implication of  
zero copyright ownership and protection means No 
Extraction for Works of  Original Authorship. Without 

Extraction for Works of  Original Authorship the entire 
entertainment industry structure and value chain is of  
minimal value. According to the U.S. Copyright Act (§§ 
106), an author has the exclusive derivative rights over 
his/her Works of  Original Authorship, which are right 
to reproduce, distribute, perform, display, license, and to 
prepare derivative works based on the copyrighted work. 
Therefore, lack of  protection for AI generated materials, 
means no further extraction of  rights (derivative rights) 
on the AI created works, which limits the ability of  the AI 
created works to maximize benefits to the owners through 
maximum derivative rights. This is counterproductive 
for the entertainment industry, which depends on those 
derivative rights to further maximize value for the 
industry.  Therefore, there needs to be a more structured 
and planned deployment of  gen AI technologies within 
the industry to avoid gen AI deployments ending up 
being self-destructive for everyone within the industry. 

Limited Originality & Uniqueness
As described above gen AI depends on historical data, and 
then generates new content based on the training it has 
had with the vast amount of  historical data. The challenge 
here is the generic nature of  the content generated by 
gen AI, because of  gen AI’s inability to generate unique, 
original and authentic content for a particular user. Some 
may argue that the limitation is as a result of  lack of  
proper prompt engineering skills by users, to be able to 
maximize the uniqueness of  a gen AI model. However, as 
long as gen AI depends on its training with historical data, 
to generates new output based on these historical data, 
its output is still largely dependent on its dataset training, 
which will still have generic output applicable to several 
users simultaneously. Affirming this argument, Doshi & 
Hauser (2024) explained that AI enabled stories are more 
similar to each other, compared to stories generated by 
humans alone, and this limits creativity, freshness and 
originality. However, despite the limitation of  gen AI, 
it still helps writers that lack writing skills to write more 
creatively and more enjoyable. If  writers can leverage on 
stories generated by AI as the starting block, and further 
develop the stories by infusing their own expressions, 
uniqueness, and authenticity. Authentic storylines have 
the elements of  uniqueness, novelty and relatedness, 
which resonates more with the audience.  No doubt, 
gen AI could enhance creativity of  a writer, and can also 
inhibit creativity and originality in another. 

CONCLUSION
From the fore going, the two fundamental legal dynamics 
highlighted here are, first, the challenges facing copyright 
holders globally, which is the continuous copyright 
infringement of  their works. There has been increased 
vigilance by right holders, constantly “scanning” and 
monitoring new AI models for possible copyright 
infringements. The Recording Industry Association of  
America (RIAA) has reported en masse violations of  
their members’ right through the training of  machines 



Pa
ge

 
52

https://journals.e-palli.com/home/index.php/ajsl

Am. J. Soc. L. 4(1) 46-54, 2025

with their members’ works. One of  such copyright 
infringements involves the action brought against Suno 
Inc., the owners of  Suno AI and Uncharted Labs, Inc., the 
developer of  Udio AI.  RIAA alleged mass infringement 
of  copyrighted sound recordings of  its members without 
any permission through the training, development and 
operations of  Suno AI and Udio AI. Copyright violations 
is at the heart of  AI’s damaging effect on the creative 
entertainment industry, which also has its impact on 
competition laws. The procecess of  training AI models 
with copyrighted dataset, without authorizations should 
be a serious concern to the entire industry stakeholders. 
Deploying technology to reap from where one has 
not sown is simply cyber-stealing, which also misleads 
unsuspecting and undiscerning members of  the public, 
and this falls under the preview of  unfair competition. 
Once a gen AI has cleared all the legal issues around 
copyright infringements, the next legal dynamics is 
the effective deployment of  this AI model within the 
entertainment industry. Effective deployment speaks 
of  AI becoming a tool for the human creators within 
the entertainment industry. Leveraging on gen AI as an 
effective tool that reduces costs and timing of  production, 
and optimizing the production processes. Combining that 
with human’s creativity, originality and ingenuity is what 
will take the entertainment industry to the next level or 
even greater heights. For example, Jumpcut AI, helps 
producers swiftly read through 50,000+ scripts received 
from scriptwriters, and generates two-page reports 
for producers and directors, breaking them down into 
genre, subgenre, characters and similar titles. This saves 
enormous time, and infuses accuracy into the whole 
process, avoiding the consequences of  human fatigue 
that potentially comes with manual selection process of  
over 50,000 scripts.
Furthermore, as we have discussed above, to enjoy 
maximum copyright protection, which includes full 
exclusive derivative rights over the “original works of  
authorship”, human creativity must be the “finishing 
line” or “final expression” for the AI generated works. 
This aligns with theory that when AI determines the 
expressive elements of  an output material, the generated 
output material does not qualify as human authorship. 
Therefore, its human authorship that infuses the needed 
creativity and originality that qualifies the work for 
“original works of  authorship”, which makes it copyright 
protectable, and gives it full exclusive right to derivative 
exploitation. This keeps the industry profitable, lucrative 
and attractive to investors, thus, leveraging on the full 
benefits of  gen AI. By this, gen AI will still be acceptable, 
and not seen as a “threat” to humans, but rather a 
powerful tool for effectiveness and maximum returns 
for the entertainment industry. Particularly, developing 
economies in Africa like Nigeria, South Africa, Kenya, 
Ghana, and other developing economies like India, 
Brazil, Turkey, Mexico, Peru, etc, that have achieved 
massive growth in their entertainment industry, can 
leverage on gen AI to further scale and globalize their 

local entertainment industry. With effective IP protection 
laws, gen AI deployed alongside the existing abundant 
human creativity, those countries are bound to witness 
a monumental leap in the quality and volume of  creative 
works that can qualify as “original works of  authorship.” 
Doshi & Hauser (2024) conducted a research on the 
effectiveness of  gen AI to enhance creativity (novelty) in 
script development. It was found that gen AI assistance 
increases both the novelty and usefulness of  stories more 
than baseline human efforts without gen AI assistance. 
The research confirmed that access to gen AI idea leads 
to greater creativity and novelty in writing. This affirms 
the position that deploying gen AI as a tool within the 
entertainment industry could lead to enhanced creativity 
and novelty, further expanding the value within the 
industry. What this implies is that with AI assistance, 
“original works of  authorship” can still be developed, 
protected under copyright law, and the derivatives fully 
extracted to the benefit of  the industry. The industry 
can still remain lucrative, profitable and investor friendly; 
rather than being destroyed by AI; its built by AI.
Finally, there are several current policies and legal 
frameworks that can make gen AI an effective and 
transformative tool within the entertainment industry, 
rather than becoming an existential threat to that industry. 
Some of  these are:

Regulations 
Several jurisdictions are still not clear on how to respond 
to the gen AI phenomenon. Many of  them are confused 
on the response to give to this wave. Most especially, the 
fear of  not intentionally stifling innovation is limiting 
conscious efforts to regulate these AI boom. A few 
jurisdictions seems to be wielding more regulatory 
enforcements, whilst others are just “watching”, allowing 
the cases to play out insides the courts. Whatever side, 
there has to be a definitive legal position on gen AI, to 
create certainty within the entertainment industry. Legal 
uncertainty is dangerous for any society, and this is what 
makes laws, including regulations, dynamic.  Regulations 
or AI legal frameworks on the development and usage 
of  AI models need to be set up and enforced for a much 
healthier and safer society. Of  course, regulations that 
do not stifle innovation and creativity. Thus far, without 
such regulations, the global community has witnessed 
ethical issues and abuses, such as massive copyright and 
trademark infringements and violations, competition 
abuses, misleading and defrauding members of  the public 
with fake celebrity audios and videos, and other dangerous 
tendencies, such as blasphemous and scandalous fake 
voice-overs and deep videos. The governments must 
find a way of  stepping into this global “chaos” through 
effective regulations, and developing policies around the 
development and usage of  AI technologies. Otherwise, 
if  left unregulated or unpoliced, the emergence and 
explosion of  AIs would develop into an uncontrollable 
phenomenon that is inimical to humanity, most especially, 
the potential damaging effect on the creative industry. Gen 



Pa
ge

 
53

https://journals.e-palli.com/home/index.php/ajsl

Am. J. Soc. L. 4(1) 46-54, 2025

AI should enhance human development and evolution, 
and not destroy its existence. Why should an AI generated 
song that uses the vocals of  popular artist(s) to deceive 
unsuspecting members of  the public, ultimately, generate 
commercial benefits to the AI owners? This is absolutely 
unethical and fraudulent, which should be prevented or 
brought under control through well thought-out and 
effective regulations.

Strengthening Current Copyright Laws
Emanating from regulation requirement is strengthening 
and enforcement of  copyright laws to prevent the mass 
stealing of  copyrighted materials by new and emerging 
AI models. The strengthening and enforcement of  
copyright laws should establish a balance between fair 
use, transformative use, and de minimus on one hand 
and the author’s derivative rights on the other hand. This 
is extremely essential for the survival and continuous 
growth of  the entertainment industry. Concepts like 
copyright protections for authors of  publications that 
describe tribal and community heroes, human legends 
that had ever lived; protecting them against unauthorized 
use of  their publications to generate new videos, images, 
audios, or any form of  multimedia. This copyright 
protection should have built into it compensation 
structure from the gen AI companies, for the authors 
or even custodians of  those heroes/legends stories, 
or even the host communities. Not all these heroes 
or legends belong to public domains.  Furthermore, 
trademark, competition, and tort laws could be deployed 
in response to malicious use of  gen AI technologies. A 
resounding legal framework specifically for gen AI will 
be an exciting thing for the entertainment industry, which 
will definitely enhance productivity, utility, and creativity, 
rather than destroying creativity or even humanity within 
the entertainment industry.

Developing Solid Business Models Around AIs that 
Benefit All
The emergence and adoption of  gen AI within the 
entertainment industry has led to the emergence of  new 
business models within the industry. As disruptive as 
these developments might be, gen AI developers/owners 
and the copyright owners, whose datasets are required 
to train this models, could ultimately benefit from well-
crafted business models. Different business models are 
emerging, and one of  them is the commissioning of  
known celebrities to create digital characters, by big studios 
and brands.  This is gradually triggering the emergence 
of  marketplaces for digital characters and other digital 
paraphernalia of  motion pictures production. Other 
potential business models are subscription and licensing 
models, where potential gen AI developers could enter 
into contracts with association of  copyright owners to 
licence the original works of  their members in bulk. 
This could be an upfront licensing model or a revenue 
sharing model, which constitutes a win-win situation for 
everyone, and completely scales gen AI’s impact within 

the entertainment industry.   

Teach WWW3.0 at Schools
AI technologies should be nurtured and taught at all 
schools’ levels, most especially in developing countries, 
bringing the generality of  humans into AI’s development 
and evolution. Furthermore, law schools should increase 
the adoption of  the teaching of  AI technologies as part of  
law schools’ curriculum, to enhance better understanding 
of  the technology and the importance of  formulation 
and enforcement of  legal frameworks that can protect 
everyone.  

REFERENCES
Barco, M. de. (2023, June 14). It’s gonna be a hot labor summer’ 

— Unionized workers show up for striking writers. NPR. 
https://www.npr.org/2023/06/14/1181947862/
writers-strike-union-solidarity

Belcic, I., & Stryker, C. (n.d.). What is supervised learning? 
IBM. https://www.ibm.com/think/topics/
supervised-learning

Bergmann, D. (2023). What is semi-supervised learning? 
IBM. https://www.ibm.com/think/topics/semi-
supervised-learning

Blaszczyk, M. (2023). Impossibility of  emergent works’ 
protection in U.S. and EU copyright law. North Carolina 
Journal of  Law & Technology, 25, 1. https://papers.ssrn.
com/sol3/papers.cfm?abstract_id=4519511

Burrow-Giles Lithographic Co. v. Sarony, 111 U.S. 53 (1884). 
See also, Urantia Foundation v. Maaherra, 114 F.3d 955 
(9th Cir. 1997).

Compendium of  U.S. Copyright Office Practices (3d ed. 2021), 
§ 306, 54. U.S. Copyright Office. https://www.
copyright.gov/comp3/docs/compendium.pdf

Copyrightable authorship: What can be registered (n.d.). U.S. 
Copyright Office. https://www.copyright.gov/
comp3/chap300/ch300-copyrightable-authorship.pdf

Copyright registration guidance: Works containing 
material generated by artificial intelligence. (2023). 
Federal Register, 88(51). https://www.govinfo.gov/
content/pkg/FR-2023-03-16/pdf/2023-05321.
pdf#page=3

Creative economy contributes over $1.1 trillion to the 
U.S. economy. (2024). National Assembly of  State Arts 
Agencies. https://nasaa-arts.org/communication/
creative-economy-contributes-over-1-1-trillion-to-
the-u-s-economy/#:~:text=These%20data%2C%20
published%20by%20the,added%20to%20the%20
U.S.%20economy

Doshi, A. R., & Hauser, O. P. (2024). Generative AI 
enhances individual creativity but reduces the 
collective diversity of  novel content. Science Advances, 
10(28). https://doi.org/10.1126/sciadv.adn5290

Generative artificial intelligence and copyright law. (2023). 
Congressional Research Service. https://crsreports.
congress.gov/product/pdf/LSB/LSB10922

Historical and revision notes to the Copyright Act: House Report 
No. 94–1476. (n.d.).



Pa
ge

 
54

https://journals.e-palli.com/home/index.php/ajsl

Am. J. Soc. L. 4(1) 46-54, 2025

How does AI actually work? (2021). CSU Global Blog. 
https://csuglobal.edu/blog/how-does-ai-actually-work

How much does it cost to make a movie? Everything you need 
to know. (n.d.). Nashville Film Institute. https://
www.nfi.edu/how-much-does-it-cost-to-make-a-
movie/#:~:text=Introduction%20to%20Film%20
Budgeting,that%20are%20subject%20to%20change

IBM. (n.d.). What is machine learning? https://www.ibm.
com/think/topics/machine-learning

IBM. (2021). What is unsupervised learning? https://www.
ibm.com/think/topics/unsupervised-learning

Ingham, T. (2023). Universal Music Group responds to ‘fake 
Drake’ AI track. Music Business Worldwide. https://
www.musicbusinessworldwide.com/universal-music-
group-responds-to-fake-drake-ai-track-streaming-
platforms-have-a-fundamental-responsibility/

Kinder, M. (2024). Generative AI: What is at stake for 
Hollywood writers. Brookings. https://www.brookings.
edu/articles/hollywood-writers-went-on-strike-to-
protect-their-livelihoods-from-generative-ai-their-
remarkable-victory-matters-for-all-workers/

Korn, J. (2023, January 17). Getty Images suing the makers 
of  popular AI art tool for allegedly stealing photos. CNN 
Business. https://www.cnn.com/2023/01/17/tech/
getty-images-stability-ai-lawsuit/index.html

Lan, Z., Yang, S., Fan, R., Zhao, B., & Yan, Y. (2023). 
Innovation or piracy? Empirically demarcating AI 
painting copyright infringement boundary. https://
www.at lant is-press.com/proceedings/pmis-
23/125989284

Marcolivio, M. (2024). AI and the movies: A blockbuster 
success or a big budget disaster? IMD. https://www.imd.
org/ibyimd/artificial-intelligence/ai-and-the-movies-
a-blockbuster-success-or-a-big-budget-disaster/

McCarthy, J. (2007). What is artificial intelligence? Stanford 
University. https://www-formal.stanford.edu/jmc/
whatisai.pdf

Ministry of  Culture, Sports, and Tourism / Korea 
Copyright Commission & Korean Copyright 
Commission. (2024). A guide on generative AI and 
copyright. https://www.mcst.go.kr/english/policy/
pressView.jsp?pSeq=391

Movie budgets. (n.d.). The Numbers. https://www.the-

numbers.com/movie/budgets/all
Murel, J., & Kavlakoglu, E. (2024). What is transfer learning? 

IBM. https://www.ibm.com/think/topics/transfer-
learning

PwC. (2024). PwC global entertainment & media outlook 2024–
28. https://www.pwc.com/gx/en/news-room/
press-releases/2024/pwc-global-entertainment-and-
media-outlook-2024-28.html

Record companies bring landmark cases for responsible 
AI against Suno and Udio in Boston and New York 
federal courts, respectively. (2024). RIAA. https://
www.riaa.com/record-companies-bring-landmark-
cases-for-responsible-ai-againstsuno-and-udio-in-
boston-and-new-york-federal-courts-respectively/

Sakoui, A. (2023, July 13). Hollywood actors’ union SAG-
AFTRA votes to strike. ‘We are the victims here’. 
Los Angeles Times. https://www.latimes.com/
entertainment-arts/business/story/2023-07-13/sag-
aftra-board-strike-plan-vote

Snow, S. (2023, May 5). AI writing is the opposite of  thought 
leadership. Forbes. https://www.forbes.com/sites/
shanesnow/2023/05/05/ai-writing-is-the-opposite-
of-thought-leadership/?sh=b86faf19eafa

South Africa Copyright Act, 1978.
Stephen Thaler v. Shira Perlmutter and The United States 

Copyright Office, No. 1:22-cv-01564 (D.D.C. 2022).
Stryker, C., & Kavlakoglu, E. (2024). What is artificial 

intelligence? IBM. https://www.ibm.com/topics/
artificial-intelligence

The Difference Between Labeled and Unlabeled Data. 
(2023). Toloka Team. https://toloka.ai/blog/labelled-
data-vs-unlabelled-data

Turing, A. M. (1950). Computing machinery and 
intelligence. Mind, 49, 433–460. https://courses.
cs.umbc.edu/471/papers/turing.pdf

U.S. Const. art. I, § 8, cl. 8.
United Kingdom Copyright, Designs and Patents Act 1988, § 

9(3).
Wang, Y., & Zhang, J. (2024). Beijing Internet Court grants 

copyright to AI-generated image for the first time. Kluwer 
Copyright Blog. https://copyrightblog.kluweriplaw.
com/2024/02/02/beijing-internet-court-grants-
copyright-to-ai-generated-image-for-the-first-time/


