INTRODUCTION
We are currently living through a synergistic
cultural moment of the rapid birth, maturity and dominance of algorithmic
software, touted as “artificial intelligence”. Many of its uses can be
subjectively perceived as more benevolent than others. The use in the
Healthcare industry in screening for earlier cancer detection seems more universally embraced;
while its use in screening job applicants less so due to the reinforcement of
racist and misogynistic stereotypes (Noble 2018, Bates 2025). In film, and the process of filmmaking, there
is this same dichotomy of acceptable and unacceptable uses throughout every
stage of the filmmaking process (pre-production, production, and post). Yet unsurprisingly,
that embrace or rejection has been typically divided across social class status,
and whether someone has the ability to hold and exercise power.[1] The higher the status,
which correlates to an increased likelihood that an individual has the ability
to hold and exercise power in some form or another (aided by their other intersecting
privileges of whiteness or maleness) tends to embrace the algorithm as a tool
to increase profit, productivity and (perceived) convenience, and reducing
cost. While those that reject it and see potential calamity in its use are
those without either status, wealth or power. In the filmmaking industry, this
divide is typically oversimplified between the financiers (studio heads, producers,
other corporate executives) and the creatives (filmmakers: actors, directors, production
designers, cinematographers, key grips, boom operators etc.). While there is
some validity to this clean bifurcation, the reality is a bit more muddled.
This paper will briefly go over the history of using technology in films, and the
cultural shifts that happened because of them. Then, contemplate the many
social ills that lead to AI’s generation and others with it at the epicenter.
HISTORICAL
CONTEXT
Filmmaking
is an artform made possible through technology. This can be said of every artistic
expression regardless of time-period, and like those other art forms, film builds
around it a culture and a lucrative identity steeped in the norms and rituals
of the time. As these technologies change, often falsely framed as strictly an
improvement, the culture reforms around it. Every era of film saw this
transition. Silent film finally spoke with “The talkies”, black and white
screens became Technicolor*, and filmstock transformed into digital.
These are periods of transition that brought
with it monumental change. Subjectively, each of these periods in film history
were met with scoff, skepticism, and outright disdain; some founded and others
not. Granted, things always got lost in the interim. Several silent movie
actors found that they could not easily make the transition into spoken film.
When film started presenting in color- Cinematographers and Costume designers
lost the ability to light and use darkness. Digital films lose texture and a sense
of verisimilitude to the image because it is shot and presented at a higher
frame rate than our eyes take in and our brain can process information. Whether
you mark these losses as trivial, a travesty or triumph of technology, there
are those skills and people that will always be left behind when technology
leaps forward. The difference with algorithms, and using AI software for a
creative outlet, is there is a relinquishing of agency and autonomy in its use.
Like
any of the other gorge leaping feats of technological advancement, AI has been
embraced and reviled. Currently, algorithmic software is being used in all part
of the film production process. In pre-production, many types of
software are now being used in brainstorming script ideas, make box office predictions,
story boarding, location scouting and 3-D modeling. In production: algorithmic
software is used in art departments and production design. Shots are being
built by computer models with only text-based prompts. In post-production, the
use of algorithmic software in visual effects reduces both labor costs and time.
These are already being used in films with bigger budgets; and regardless of the
(justifiably understandable) vitriol films receive who use “AI” (in this way),
it does not stop it forward momentum from implementation to integration. But
before we can look at the social ramifications of this, we need to understand
the Sociological context.
Some
Relevant Sociological Basics:
Sociologically,
the social relationship/dependence on a particular piece of technology can be
measured through the understanding of cultural lag and cultural diffusion. Cultural
lag is the difference in time between when a piece of technology is
invented and when it is integrated into social norms and the social order (Jary
and Jary 1991). Basically, technology is always created before the behaviors
and cultural rituals that allow it to become a necessity for daily living.
Every piece of technology goes through a
cultural lag process. It is that which determines its broader use. Sometimes
that use becomes something way beyond its original scope, such as the internet.
This is because due to our (US) lopsided value system (motivated by profit
driven capitalism) much of our technology is developed for governmental
and military application
first, and then redesigned for civilian use after for the purposes of profit.
Thus, the cultural lag is motivated by profit; private companies determining
how they can get civilians to consume their (usually, at the time, unnecessary)
gadget by convincing/conditioning them through marketing that their contraption
will make consumers’ lives convenient. With that, the piece of technology
becomes more ubiquitous, through the process of cultural diffusion.
Cultural Diffusion is the spread of
cultural traits or social practices from one society or group to another. Globalization
and particularly imperialism often causes the spread of these cultural traits.
For over a century the Unites States’ main export was our culture through the
creation of the globalized marketplace. In the case of technology, the more
available that technology is the more likely people are going to use it.
Additionally, if you make that piece of tech necessary by changing the way necessary
social behaviors are performed (applying for a job, acquiring necessary
government documentation etc.); you have both a fundamental consumer base and a
captive audience.
This
diffusion and integration are still capitalist forward with the understanding
of consumer engineering and planned obsolescence which perpetuates the cycle of
cultural lag and diffusion. By making products with a designed shelf life and
then constructing a barrier to object repair through the legalese of “proprietary
technology”, allows Corporations that control this technology to update, change
and (inevitably) make worse the technology and service you are using while
discontinuing the diffused product in favor of the new one. So, we engineer
consumers into a product’s planned obsolescence- effectively dragging people across
the cultural threshold of new technology; and making a profit while doing
it.
Those that have the money, ability and cultural context to shift with the cycle of lag and diffusion have an easier time existing in this social order. Those with money can purchase the knowledge or hire someone to keep up with the cycle as it happens. Younger generations who are digital natives to the newer forms of technology than the older generation’s digital immigrants will have an easier time integrating and adapting; and those who understand the cycle’s context shift are more likely to have an easier transition. The rest, however, may be dragged kicking and screaming[2] over that cultural threshold.
The Arbitrary beginning (of the End):
George Lucas, Peter Jackson and Dominance of CGI
As
stated earlier, the filmmaking artform is made possible only through the advent
of both photography and the development of “moving pictures”. The ability to
record movement through the capturing of consecutive still frames that, when
played back at a particular speed (24 frames per second) gives the illusion of
movement. Since there has been a variety of advance technologies that have
fundamentally reshaped how the filmmaking process works, especially in the US,
it is relatively arbitrary where a researcher stakes their claim as to what is
the source of our current predicament with algorithmic “AI”. This paper could
have easily gone back to the invention of the camera and moved forward; but in
the interest of time, and the author’s own specialty and interest; This paper
is arbitrarily starting with two directors, George Lucas and Peter Jackson and
their impact on the subgroup of visual effects within the filmmaking industry.
There
is a lot that could be (and has been) said/written about George Lucas: unjustifiably
deified “God” of a Sci-fi Universe, Independent filmmaking renegade turned vapid power-hungry megalomaniacal
CEO in his older age.
And an innovator of visual and special effects. Focusing on the latter, being a
youthful cinephilic brigand, George Lucas created companies that specialized in
parts of the filmmaking process with which he wanted to experiment. By
controlling his own companies there was less resistance to his ideas, thereby
less rejection, and no one to tell him “No.” (This became a problem later in
his career). In the 1970’s Lucas either singularly founded or Co-founded several
companies specializing in one or more aspects of the filmmaking process.
The
companies (and it’s scions) that George Lucas founded which served as a
precursor to the current algorithmic apocalypse in filmmaking are Lucasfilm, and
its subsidiaries: Industrial Light and Magic (ILM) and Skywalker Sound. Through these companies, Lucas
could experiment and push the boundaries of the filmmaking process;
transforming the theatrical experience and how films are made.[3]
On
the heels of Lucas, Peter Jackson was another principled indie horror director
that became the head of his own studio, and a significant data point in the
genealogy of AI use in film. Jackson’s historical significance to this
development is Weta Digital, a company he co-founded to create the visual
effects on his feature Heavenly Creatures in 1994; and would later gain
recognition in the late 90’s early 2000’s for the company’s work on Jackson’s
adaptation of The Lord of the Rings(LOTHR). It was through the production
process that the Weta team developed the software to create a computer-generated
background actor. This allowed the battle sequences to achieve an epic scale.
In addition to human extras in suits, makeup and prosthetics, there were
additional “background extras” that were painted in in post. Digital people
moving as determined by a programmable algorithmic sequence. The existence of
which would grow in contention for the next 25 years.
After LOTR, Jackson guided Weta to
continue to push the boundaries of creative algorithmic software. When the
Imperial War Museum approached him to create a tribute to WWI veterans by using
hours of archived footage from the time, Jackson, through Weta, slowed down the
film, colorized it, and reconstructed audio from the
archive approximating what these soldiers might have sounded like. The documentary They Will
Never Grow Old was met with high praise, which encouraged Jackson to go
further. In his latest Documentary about The Beatles, Jackson apocryphally
“brings the band back together” (read as from the dead) for a new song: “Now
and Then”. This was achieved by Algorithmic software. The AI scoured through demo tapes of John
Lennon to extract his vocals and place them with the rest of the Beatles who
recorded the song back in the 90’s for their Anthology album. This calls
into question the value of entertainers, legacy, agency and autonomy. An
argument that came to a head in 2023.
The
2023 WGA and SAG-AFRA Strike
In 2023, after the Writer’s Guild of America
(WGA) went on strike, contract negotiations also deteriorated between The
Screen Actors Guild (SGA) and the Alliance of Motion Picture and Television
Producers (AMPTP). Among the contested points of negotiation was “digital
likeness” rights and the use of AI in film. Writers and Actors in solidarity
clashed with Studio heads. Disney CEO Bob Igar called the demands “ non-realistic, disruptive and
dangerous.”. This
is after Disney allegedly scanned background actors in their show WandaVision
so that they could be used again without having to rehire the actors. Much
of this was done with the air of expectation with little communication between
production and the actors. Additionally, many of the actors not
only did not give consent to this process, but they were never told how or if
this digital avatar would ever be used on screen. If it's used, they might
never know. No matter what happens with it, they’d never see any payment for it.
During
the nearly 4-month strike from July to November 2023, the division and rhetoric
used got ugly. One unnamed executive was alleged to have revealed the studio’s
tactic: to draw out the strike long enough
until writers and actors start losing their homes to force more favorable
negotiating positions. The struggle became real for many writers and actors, as
many who were not award winning or “on the A list” struggled to pay rent. Yet, through a commitment to
solidarity, the Writers and Actors, with support from the Directors and
Producers Guild and many high-profile politicians, transformed the industry in
regard to the use of Generative AI.
The
deal that was ratified by the union in November 2023 includes the
following AI provisions:
·
Consent
must be given for any digital replication or alteration of performers. Consent
must be clear and conspicuous and may be obtained through an endorsement or statement
in the performer’s employment contract that is separately signed or initialed
by the performer or in separate writing that is signed by the performer.
·
48
hrs. notice before being scanned
·
A
separate written agreement in all contracts regarding digital likeness creation
and use
·
The
process of scanning is to be considered work, and they need to be compensated
·
Must
include a reasonably specific description of how the digital likeness will be
used
· Consent is obtained per job. If the likeness is used in another project a separate consent must be given.
This was a
landmark negotiation that closes a gap in Copyright Law that did not protect actor’s face
or voice. Thus, in 2024, using the 2023 strike as a foundation, voice actors
struck against the videogame industry for some of the same provisions. Yet,
even with these provisions to make the integration of AI more equitable, it
does not thwart the use of AI in filmmaking and the dangers it poses.
SOCIAL
ANALYSIS
The sad irony of the continued expansion of AI is that
through its learning process, and the more it is fed already published content,
the software becomes a cultural and social mirror that can be held up to
society. According to Bates (2025) AI lifts the veil on institutional
inequality of every variety as these Language Learning Models (LLM) are not
deliberately trained with a racist/sexist/ableist/classists agenda. Instead,
the sexism, racism and anti-humanism displayed by AI algorithmic software
content exposes the deeply inequitable discrimination the permeates every
aspect of American culture and every institutional mechanism it uses.
It’s Capitalism’s Fault
When looking at the continued encroachment
of AI into creative spaces of film and popular culture, there needs to be an
acknowledgement of the driving force of its generation: Capitalism. Granted,
this is an easy thing to blame, due to capitalism’s basic function of dehumanization
and alienation of those that are trapped within it. Which is everyone. Marx (1992)
calls this commodification: the ability for anything in society, including
humans, to be bought, sold, traded or exchanged. Weber (2019) identified how maintenance
of routines results in complacency to the exploitation that people experience
allowing capitalism to continue unfettered. While conditioning individuals to
be a better fit for this exploitative bureaucratic capitalist system.
A
culturally learned behavior that contributes to capitalism’s continuation and
generational reproduction is the belief that advancing technology leads to
progress. Since the birth of the digital age, we have had this belief that technology
makes our lives “better”. This techno futurism has been in cultural lock step
with capitalism. Advances in technology are advanced by the profit motive, but often,
it is not for the betterment of people more than it is the deepening of
pockets. Thus, the intimacy of technology and capitalism may make our lives
more convenient (once we are integrated), but it also ushers us into a new age
of digital feudalism (Arditi 2023).
According to Arditi (2023):
The term digital
feudalism connects to the moment of primitive accumulation as feudalism
transitioned to capitalism. Just how peasants were kicked off the land to go to
work in new positions [in the factories] without a social safety net today
workers lose their jobs and go to work as gig workers (4)
Digital feudalism thrives on debt, precarious
labor and unending consumption (Arditi 2023). Unending Consumption is
the driving force that has motivated the development of AI and algorithmic
software. Arditi (2021) pinpoints the rise and dominance of streaming that
fundamentally changed how we consume content. The term “streaming” being an apt
description because it invokes a flow that is endless.
In the digital age, capitalism sped
up- expanding the means of consumption into the liminal space of the internet-which
promptly turned into a giant shopping mall (Agger 2004). But for companies,
consumers are the product. Not only do they provide these services that vie for
customer attention and their engagement. Now, even consumer information is
monetized under the paper-thin guise of free access. The internet and social
media provide companies with a marketplace of data on which to harvest. In this
space, we are then conditioned to endlessly consume: “…we subscribe to music,
video, software, or news services we provide companies with constant and
consistent consumption. It is unending because once we subscribe there is no
out.” (Arditi 2021:17). This ultimately makes transactions more transient. Therefore,
there is an unwillingness to connect, resulting in alienation.
Desires do not desire
satisfaction; desire, desires desire… The ideal consumer (Saarinen and Taylor:
1994)
Zygmunt Bauman (2007) discusses this
transition to transience in his critique of the consumer economy. Stating that nothing is
embraced by individuals in a consumer economy for very long. There is no ultimate desire, no point of full
satisfaction. What this means is that individuals in a consumer society are in
a state of perpetual un-fulfillment (Bauman 2007).
This creates
consumers who:
- Are Impatient, impetuous, restive and
excitable
- Lose
interest quickly
- Their momentary satisfaction does not require
learning or skill to obtain (Instant Gratification)
- The promise and hope of satisfaction precede
the need promise to be satisfied
- Are roped into consuming by the sensations and
experiences certain products promise. Which is why product acquisition
always seems hollow
- Believe that having no more desires is equated
to having no more prospects in the world
Now,
with these consumers, this unending consumption is liquified and so are our
identities (Arditi 2021, Binkley 2008).
Under traditional consumption
practices, there is a fetishizing of commodities as they are used to represent
identity (Marx 1992). Much of who we are is determined and represented by the
products that we consume. In the era of streaming, Liquid Consumption is
defined as the
consumption of the 21st century, one of liquid Modernity (the
contemporary social and cultural
condition of radicalized ambivalence) where a person’s identity is not
fixed by an imposed order or shared group affiliation.[4]
The practice of this is one of anti-consumerism in the traditional sense, and
yet they still consume. As the title of “liquid” implies, we have shifted to
consume an increased number of
experiences (the intangible) rather
than just things (tangible). But this is false. It is more accurate that through
streaming, we have “liquified” our culture transforming art, literature,
theater, and film into “content.”
The
internet has exacerbated this liquification of culture into “content” through
the decline of physical media. Endless consumption is easier, and possible,
because we no longer need tangible space to store it (Brutlag 2021). This
socially conditions the public to seamlessly transition into an unending liquid
consumption of content where we experience the content rather than acquire it. Because
we often do not see the spatial cost of the content we consume[5]. Digital
storage is amorphous. Arbitrary capitalist barriers are in place to allow for socially
constructed levels of exclusivity. How much storage you want is determined by
how much you are willing to pay. Once you pay, there is no lengthy home renovation
needed, you don’t have to purchase bookshelves or tubs for storage; you are
just granted access to more. Almost every digital storage service reinforces
the same nebulous imagery (e.g. “the cloud”) as if it doesn’t take up any
physical space at all. This too is false. The storage of data has tangible
consequences in the form of social and environmental impacts.
The Social
and Environmental Impact of Data Centers
The term
“The Cloud” purposefully evokes ethereal imagery. Designed to conjure a mental
picture of a liminal emptiness that is akin to the public perception of cosmic
space. However, as cosmic space is not the vacuous hollow many have perceived
(consisting most of dark matter and dark energy), “The Cloud” has a tangible
representation in the form of data centers. Data centers are buildings that house the
infrastructure needed to run computers, including servers, network equipment
and data storage drives. The more reliance we have on digital storage and
unending consumption through streaming, the more social and natural resources these
centers consume. Which results in outrageously dangerous environmental impacts
on land, water use and consumption of electricity.
“According to Lawrence Berkeley National Laboratory, [Data Centers] can range from smaller
centers—integrated into larger buildings for internal use by companies—that are
on average less than 150 square feet, to hyperscale centers which are operated
off-site by large tech companies to facilitate large-scale internet services. On
average, hyperscale data centers are 30,000 square feet, although the largest
of these data centers can reach sizes of well over one million square feet. As
of 2024, more than half of the world’s hyperscale data centers were owned by
tech giants Amazon, Microsoft and Google.” (Chen 2025).
There
are approx. 4,165 data
centers nationwide. These centers end up gluttonously consuming an
astronomical amount of resources. Land is used to store their massive servers
(often underground) and water is used to continuously cool the servers 24 hours
a day, seven days a week. Large data
centers can consume up to 5 million gallons per day, equivalent to the water
use of a town populated by 10,000 to 50,000 people. In
addition to this, in these areas, wastewater increases, often without
significant infrastructure to handle the volume. This leads to spills and leaks
that threaten land and ground water of a surrounding area.
According
to Nguyen and Green (2025):[6]
·
Data centers increase local electric utility rates
by driving up overall energy demand, which can strain grid capacity and force
utilities to invest in costly infrastructure upgrades. These costs are passed
on to residents through higher rates
·
A single data center can consume up to 2 megawatt
hours of power, equivalent to the power used by 2,000 homes
·
Tax breaks for data centers do not deliver the
promised economic benefits, such as high-paying jobs, and they reduce local tax
revenues, while shifting financial burdens onto communities and schools
·
Data centers’ massive energy demands are prolonging
the operation of fossil fuel plants and undermining state renewable energy
goals
·
While advanced cooling methods like liquid
immersion and direct-to-chip cooling offer energy efficiency improvements,
current technologies force a tradeoff between energy and water efficiency,
limiting sustainable solutions.
The construction of AI and algorithmic software
both makes these problems worse and is the next step in the logistical
exploitation of capitalism’s unending consumption.
A
standard Marxian analysis of capitalism is the understanding that as the
economic system expands there is a finite number of markets it can reach, which
is to say all of them. Once all the markets have been acquired, refined and synthesized
for efficiency, the source of exponential profit comes back to labor. Companies
can always reduce labor costs to increase profits. One mechanism by which to
reduce labor costs is automation. AI and algorithmic software are forms of
digital automation. AI and algorithmic software exploit labor in two keyways: job
replacement, and product theft. According to MIT, current AI has the
capacity to 11.7% of the workforce by the digital automation of customer service,
computer coding, data analysis and collection. AI is also changing the way that
jobs are traditionally done; skills are lost in favor of algorithmic analysis
and diagnostics; and those in the workforce have to prepare for the impending
transformation[7]
Secondly, already published material
whether that be entertainment products like art, film, music, and other forms
of popular culture, or Science based research and analysis, becomes food for
the algorithm. AI scours the internet for material to learn and copy from. It
is an unwilling digital apprenticeship that results in human creative
obsolescence. AI generated pop culture has become so ubiquitous that it is
increasingly difficult to spot.
Additionally,
AI has made all the social and environmental impacts of Data centers worse,
because they require vastly more resources than data centers were using prior. In
2024, NPR
reported
that each ChatGPT search uses ten times more electricity than a Google
search. In March 2024, Forbes
reported
that the water consumption associated with a single conversation with ChatGPT
was comparable to that of a standard plastic water bottle. This is due to
the training (feeding) of AI, and the required linguistic breakdown and
learning that has to happen in order for the algorithm to understand a query.
As our current administration leans into AI
and its use, we are seeing drastic impacts of land and housing. Most data
centers operate in rural areas. Therefore, the existence of a data center takes
up a lot of space which can affect the availability of housing. Last year, The
Trump Administration declared Abaline, TX
as the epicenter for AI’s future with a $500 billion infrastructure project to
build data centers for future AI products run by Stargate.[8] But as
thousands of construction workers descended on the town, rent prices increased the
Abilene average rent per month is $2,395, up $1,000 from the year before (2024).
Even before the data center, the city faced a housing shortage of about 5,600
units. Now even the Data center employees are living out of their cars, because
they had nowhere else to go. The more our unending consumption continues in
perpetuity, the more likely many of us won’t have a place to store ourselves
regardless of how many terabytes of storage we have access to.
AI is a Cultural Mirror of
Horrors
As
mentioned earlier, AI learns through the content it consumes. It acts as a
cultural mirror to society’s failings. In the United States, that means that AI
has learned to be misogynistic, racist and all around
hateful. There are
instances of AI predicting and increase in black
crime due to learned racist stereotypes. A Microsoft chat bot that learned to be a Nazi from reading
twitter. There
have even been chat bots that have encouraged
suicide; and
recently Twitter’s AI bot Grok has allowed users to “undress photos” using AI. This reinforces the way
that sexual abusers can use AI to stalk, harass, and violate their targets
(Bates 2025).
Outside
of a direct personal level of harm, and the use of micro aggressive language
used in social media, in a broader context, as these algorithms get ported
into various social institutions and put in charge of various organizational
tasks and distributions, their learned discrimination will perpetuate the
stereotypes they were built on. This could affect marginalized people’s
access to healthcare and job opportunities while also having the potential to
erase their existence entirely. Laura Bates (2025) points out that because AI
algorithms are taught to categorize sex and gender into a binary (and not the
properly understood spectrum) this will either cause people who don’t fit into
that binary be organized by their sex assignment rather than their preferred
gender identity. Thereby misgendering trans folk. While potentially erasing nonbinary
people as simply outliers. AI consumption and framing are organized by gender
to reinforce the patriarchy. The computer has a voice, and it sounds metaphorically
masculine (Faber,2020). Siri, Alexa and other LLM’s are
feminized because of the sexist culture of female assistants in business, and
the gender socialization that teaches girls to be mediators, placaters and
helpers for men. This trend will
continue so long as women remain 22 % of AI data professionals, only 18% of AI
users while 45% of AI Ph. D graduates are white and 80% are male (Bates 2025).
The continuation of this is more
than just a “reap-what-you-sow” reflection of the intersectionally vile ableist,
heterosexist, wealthy white supremacist capitalist patriarchy (hooks 2000).
There is a purpose for its reproduction, and that purpose is profit. Misinformation
is profitable, and political and economic institutions have been financially enriched
by trafficking in it (Noble 2018). Misinformation has been politically
weaponized to be a pathway to power; and currently used to implement
authoritarian style policies ( Project 2025,
NSPM-7). AI is not just regurgitating
conspiracies and falsities; they are also generating them. Recently, scholars
discovered that AI would fabricate academic articles by real
people. This got
so sophisticated that some scholars ended up citing the fake article as
evidence.
Desensitizing
the Masses
Unfortunately, as much as film and
popular culture has been negatively impacted by the creation and implementation
of AI into creative fields, it has also assisted in the desensitization of AI’s
threat through misdirection. For generations, there have been Sci-fi stories in
books, television and film that have depicted the integration of AI into human
societies as a catalyst for the apocalypse. At the core of this conflict is
usually a small band of human resistance fighting self-aware machines. We’ve seen versions of this same story for
the last 30-40 years. Blade Runner, The Terminator Franchise, and The Matrix all have presented the evolution
of AI to be a threat to human existence. A direct militaristic annihilation.
While these films as cultural products were talking about other eras and
drawing upon history and culture to say something about the human condition
(Faber, 2020). It also creates an explicit and hyperbolic threat of AI in the
public consciousness. Thus, every time someone discusses their fear of AI, they
invoke one of these well-known Sci-fi franchises: “Do you want Skynet? Because
this is how you get Skynet.” “This is how Ultron became a problem.” Did you even
see The Matrix?!”. The public fears AI only through the lens of
“malevolent humanoid machines taking over the world. As a result, we often tend
to think of the term AI as futuristic, distant and improbable.” (Bates
2025:230). We use film as a cultural touchtone, a lens by which we can
understand the world. Therefore, anything less than “The Rise of the Machines”
to overthrow humanity will seem bearable, manageable, and better than the
alternative. Which we will always use as a reference to how bad AI could be,
regardless of its fiction.
The
hyperbole around the fear of AI also conceals the real labor exploitation at
its core. The necessity of AI to have a reserve army of technicians and data
workers that collect annotate, curate and verify data sets (Bates 2025:265).
Classically, in the Marxian sense, this work is outsourced to call centers in
poorer developing countries of color. Unsurprisingly, the work is inconsistent,
it has grueling hours (12-20 hr. shifts), low wages, is tightly disciplined, involving
gender-based harassment, and psychological trauma. As with the textile
sweatshop labor before them, these workers are invisible to the western
consumers whose convenience is generated by their unseen labor.
CONCLUSION
Profit
driven capitalism has led us to this space of unending consumption fueled by a
techno-futurism that insists that LLM’s and algorithmic software will always be
considered progressive. Film and popular culture have been victims, accomplices,
and perpetrators in this venture that has led to the continued alienation and
isolation of the workforce; another way to disenfranchise and traumatize women
and people of color through this liminal digital space. Yet it cannot be
overlooked that convenience is used as a shield, a justification for this
exploitation that alienates us from the human rights abuses felt by others and
motivates us to be complacent uncritical lemmings intrenched in individualized
obedience.
REFERENCES
Agger, Ben 2004. Speeding Up
Fast Capitalism (1st ed) New York: Routledge
Arditi, David 2021. Streaming
Culture: Subscription Platforms and the Unending Consumption of Culture. United
Kingdom: Emerald Publishing
__________ 2023. Digital Feudalism:
Creators Credit, Consumption and Capitalism United Kingdom: Emerald
Publishing.
Bates, Laura 2025. The New Age
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[1]
Thus, due to an understanding of Intersectionality this also means that the
division is also across racial, gender, sexuality and disability lines as well
as social class
[2] It
should be noted that those with money, ability and generational context for one
type of technology; that does not translate to others. Somone may know howe to
navigate Spotify and download apps, but they do not know the application
apparatus of LinkedIn or People Admin
[4]
Our society, especially the current youth generation, has moved away from
defining the self as any one thing. Many in the youth culture use certain
phrases to illustrate this, such as: “I don’t like labels”, “Rebrand Yourself”
“You do You.” “Be a better You” and “Be who you want to be.”
[6]
This might be a great incentive to get back into physical media. Finite space
limits consumption and when you put a blu-ray on a shelf it does not threaten
to poison the water supply of an entire town.
[7]
The profession of teaching has changed as AI can provide test questions and
answers within seconds Making any actual learning immaterial.
[8] Again the name connotes media illiteracy
