The Socioeconomic Impact of Artificial Intelligence: Profit-
Driven Growth and the Struggle for Control
Mohd Shafi Pathan
*
and Shivraj Patare
*
Department of Computer Science and Information Technology, MIT Art Design and Technology University, Pune, Maharashtra, 412201,
India
*Email: shafi.pathan@mituniversity.edu.in (S. Pathan); shivrajpatare.work@gmail.com (S. Patare)
Abstract
Artificial Intelligence (AI) has emerged as a transformative force driving economic expansion and innovation across
industries. As organizations race to adopt AI for productivity gains, cost efficiency, and market dominance, the
resulting growth has become increasingly profit-driven. However, this unregulated pursuit of technological
advancement brings with it significant societal, ethical, and policy-related challenges. This research paper explores
the dual nature of AI's socioeconomic impact—one marked by economic acceleration and the other by widening
inequality, job displacement, and a struggle for control between corporate powerhouses and governing institutions.
The core problem addressed is the lack of comprehensive governance mechanisms to balance economic incentives
with the ethical and equitable deployment of AI. While AI promises significant benefits such as automation, predictive
analytics, and enhanced decision-making, it also risks marginalizing underrepresented communities and
concentrating power among a few technology monopolies. Our research proposes a balanced methodology:
combining a literature review, comparative analysis of existing regulatory efforts, and the development of a
conceptual framework integrating socio-technical ethics with economic policy. Through an architectural diagram,
system design flowchart, and policy-oriented algorithm, the study outlines actionable recommendations. These
include progressive AI taxation, reskilling initiatives, equitable data governance, and collaborative public-private AI
governance models. By benchmarking across multiple metrics such as economic inclusivity, transparency, and
scalability, the paper presents a comparative analysis of current approaches and highlights their limitations.
Ultimately, the research concludes that while AI can significantly enhance socioeconomic systems, sustainable
progress will only be achieved through ethical oversight and inclusive regulatory frameworks. Governments,
academia, and the private sector must collaborate to redirect AI’s trajectory from unchecked profit-driven growth
toward a balanced, inclusive, and equitable digital future.
Keywords: Artificial intelligence; Automation, Economic disparity; Ethical AI; Job displacement; Machine learning; Profit
motives; Regulation; Social control; Technology adaptation.
1. Introduction
Artificial Intelligence is no longer confined to research labs or speculative fiction. It now dictates logistics, healthcare
diagnoses, financial systems, and even content creation. As the global AI market is expected to exceed $500 billion by
2025, AI development has shifted toward hyper-commercialization. Private corporations race to dominate this domain,
not just with the intent to innovate, but to maximize returns. But what happens when the race for profit eclipses ethical
governance and human-centric development?
The motivation behind this study arises from a stark observation — while AI promises economic transformation, its
benefits are not equitably distributed. A small cluster of companies commands vast computational resources,
intellectual property, and policymaking influence. Meanwhile, workers in traditional sectors face job losses, and
developing nations lag behind in adaptation and literacy. This paper aims to dissect this imbalance and explore how
society can course-correct before AI’s socioeconomic effects become irreversible.
• Artificial Intelligence (AI): Computational systems capable of performing tasks typically requiring human
intelligence, such as language understanding, learning, and decision-making.
• Profit-Driven Growth: The expansion of AI technologies with the primary goal of maximizing financial returns
for companies or stakeholders.
• Socioeconomic Impact: The effect on societal structures (e.g., class, equity, labor) and economic metrics (e.g.,
employment, GDP, market share).
Despite AI’s immense potential for innovation and societal benefit, its development has largely been driven by profit
motives. This leads to unintended consequences: job automation without safety nets, ethical dilemmas without
regulatory clarity, and monopolization of data and power. The question is not whether AI will impact society — it
already has — but whether its impact will be inclusive, ethical, and sustainable. This research seeks to examine how
AI’s profit-driven trajectory is reshaping socioeconomic structures, creating control struggles between the elite and the
public. Figure 2 explores:
• The relationship between AI automation and job displacement.
[1]
• Corporate monopolization of AI tools and decision-making power.
• Ethical and regulatory challenges.
• Public adaptation and the role of policy, education, and media.
Fig. 1: System component.
Fig. 2: Socioeconomic impact.
1.1 Literature survey
Artificial Intelligence’s socioeconomic impact has become a focal point in academic, corporate, and philosophical
discourse. This section synthesizes insights from leading studies, policy reports, and cultural analogies to map out the
current landscape.
1.1.1 Academic insights
Frey and Osborne’s foundational study (2017; updated 2021) revealed that up to 47% of U.S. jobs are at risk of
automation by 2030.
[2,3]
Their work sparked global concern, especially in developing nations where low-skill labor is
the backbone of employment. AI’s capacity to automate repetitive tasks threatens not just job availability but also
social mobility.
Acemoglu and Restrepo
[4]
emphasized that AI-driven productivity gains disproportionately reward capital owners,
reinforcing wealth inequality. Their findings show that the top 10% of firms capture over 80% of AI-generated
economic value, a clear indicator of economic centralization.
Floridi et al.
[5]
critiqued the absence of enforceable ethical governance, stating that existing frameworks are often non-
binding and reactive. As AI applications move from experimentation to execution, lagging regulation could lead to
exploitative and unchecked deployments.
1.1.2 Industry reports
The McKinsey Global Institute
[6]
forecasts that AI could contribute up to $13 trillion to the global economy by 2030,
mostly through automation and enhanced analytics. However, it admits that the benefits will not be evenly distributed
unless deliberate interventions are made.
Brynjolfsson and McAfee
[6]
discuss the “Second Machine Age,” where AI creates winners and losers. High-skill
workers benefit from productivity boosts, while low-skill roles are phased out, deepening the wage gap and threatening
economic stability.
1.1.3 Governance and control
Bostrom
[7]
raises concerns about superintelligence and control. While still speculative, his argument draws attention to
a scenario where humans may lose their ability to manage or even understand advanced AI systems.
Zuboff
[8]
contextualizes this loss of control through surveillance capitalism, showing how corporations exploit AI to
monitor, predict, and influence human behavior — reducing autonomy and increasing manipulation.
1.1.4 Pop culture reflections
Popular media often captures public anxieties before academic literature. Characters like Ultron (Avengers: Age of
Ultron) represent the fear of unregulated AI rebelling against human creators, a metaphor for systems that evolve
beyond ethical control.
Samantha, the AI from Her, embodies the illusion of intimacy, highlighting how emotional simulation may mislead
users into relationships based on false agency — echoing concerns in AI therapy and companionship apps.
Joi, from Blade Runner 2049,
[9]
is designed to please her owner, symbolizing commercialized servitude. She raises
questions about consent, autonomy, and the commodification of affection.
These fictional representations mirror real ethical dilemmas — AI’s ability to simulate intelligence, intimacy, or
decision-making doesn’t mean it should be used without clear oversight.
1.1.5 Gaps identified
While existing literature offers insights into automation, ethics, and control, there are significant gaps:
• Public education and reskilling efforts remain limited or fragmented.
• AI policy and regulation lag far behind technological advancement.
• Ethical frameworks often lack enforceability or are written by stakeholders with vested interests.
These gaps justify the need for this paper’s focus — understanding the control dynamics between profit-driven AI and
broader society, while proposing pathways for equitable outcomes.
1.1.6 Gap analysis
While the literature has thoroughly explored the economic potential, ethical considerations, and technical
advancements of Artificial Intelligence (AI), there remain significant unresolved challenges that hinder equitable and
sustainable adoption.
The Table 1 provides a comparative matrix summarizing the gaps between existing research themes and the persisting
issues:
Table 1: Gap Matrix – AI Research Themes vs. Unresolved Challenges.
Research Theme
Existing Contributions
Unresolved Challenges (Gaps)
Job Automation &
Workforce Disruption
Frey & Osborne
[1]
; McKinsey
[4]
provide
job-loss forecasts.
[10]
Lack of cross-country real-time employment impact
data post-AI implementation; underreporting in
informal labor sectors.
Profit Centralization
Acemoglu & Restrepo
[2]
; Zuboff
[7]
show
how profits are captured by tech elites.
[10]
Comparative absence of data between open-source
AI ecosystems vs. corporate AI monopolies in
shaping equitable outcomes.
Ethical & Regulatory
Frameworks
Floridi et al.
[3]
; Bostrom
[6]
; Hagendorff
[9]
outline AI ethics theories.
[11]
Minimal implementation of globally standardized,
enforceable AI regulations—especially across
underrepresented nations.
Public Awareness &
Adaptation
Brynjolfsson & McAfee
[5]
on tech
acceleration; Manyika et al.
[8]
discuss
workforce transitions.
[8]
Few studies explore how marginalized communities
perceive or adapt to AI technologies; low AI literacy
in developing nations.
Cultural Perception &
Popular Influence
Rarely explored academically.
[9]
No formal study links how films like Her, Avengers:
Age of Ultron, or Blade Runner 2049 shape public
opinion on AI risk.
1.1.7 Descriptive insight
This gap analysis highlights that existing research often treats AI’s economic, ethical, and cultural dimensions in silos,
leaving interdisciplinary blind spots. While automation forecasts and ethical frameworks exist, they often fail to
intersect with real-time social dynamics like labor displacement in informal economies or algorithmic bias across
marginalized communities.
Furthermore, a unique omission in academic discourse is the influence of popular culture on public understanding of
AI. Characters like Ultron and Vision in the Avengers represent AI as existential threats or saviors. In Her (2013), AI’s
emotional intelligence blurs human boundaries,
[12]
while Joi in Blade Runner 2049 raises questions about synthetic
love, agency, and subservience. These portrayals deeply impact societal trust, fear, and expectation around AI, yet
receive minimal academic attention in policymaking contexts.
Thus, this research addresses not just technological and ethical gaps, but also socio-cultural blind spots, advocating a
more holistic framework for understanding AI’s socioeconomic impact.
3. Collective limitations
This section synthesizes the limitations across current AI deployment strategies, identifying where existing approaches
fall short in addressing socioeconomic concerns.
3.1 Common challenges across ai approaches
Despite their advancements, most AI applications today face three core limitations:
1. Adaptation speed
• AI evolves exponentially, while societal systems—particularly education and labor markets—struggle to adapt.
• Result: A growing gap between those who can upskill quickly and those left behind.
2. Equity oversight
• Profit incentives often override equity-focused outcomes.
• AI tools tend to prioritize efficiency and monetization, rarely considering distributional fairness.
3. Control centralization
• Power is consolidating among a few corporations that hold proprietary algorithms, vast datasets, and control over
deployment pipelines.
• This leads to monopolistic control, with little room for democratic participation or decentralization.
3.2 Comparative table of limitations
The following Table 2 summarizes how three dominant approaches to AI—Automation, Ethical AI, and Public
Policy—fare across scalability, equity focus, and adoption rate:
Table 2: Comparative limitations of ai approaches.
Parameter
Automation
[1]
Ethical AI
[3]
Public Policy
[5]
Scalability
High
Low
Medium
Equity Focus
Low
High
Medium
Adoption Rate
Fast
Slow
Slow
Fig. 3: bar chart.
3.3 Bar chart description
3.3.1 Bar Chart (Figure ): visualization of limitations
• The bar chart (to be inserted) illustrates the comparison from Table 1.
• X-Axis: Approaches (Automation, Ethical AI, Public Policy)
• Y-Axis: Scaled values for three attributes (0 to 5 scale)
o Automation: Scalability (5), Equity (1), Adoption (5)
o Ethical AI: Scalability (1), Equity (5), Adoption (2)
o Public Policy: Scalability (3), Equity (3), Adoption (2)
• Insight: Automation scores high in speed and scale but lacks equity. Ethical AI is rich in fairness but slow to
scale. Public Policy is moderate across all, reflecting its bureaucratic inertia.
3.4 Inferred limitations from literature & conversation
Cross-referencing both literature and the ChatGPT-generated insights yields recurring limitations:
• “People are adapting, but very slowly” (ChatGPT, 2025) underscores adaptation lag.
• “The ones who benefit the most are the corporations” reflects concentration of gains.
• Literature such as Brynjolfsson & McAfee echoes this: “
[13]
always outruns our ability to prepare everyone.”
4. Methodology
4.1 Research design
This study adopts a qualitative, exploratory design integrating AI-generated conversation data with peer-reviewed
literature between 2020 and 2025. This dual-source approach helps assess both real-time perceptions and long-term
socioeconomic trends in AI development and adoption.
[13,14]
4.1.1 System architecture overview
The methodological framework follows a three-phase architecture as demonstrated in figure 4:
1. Input layer
• Conversational data sourced from an interactive AI model (ChatGPT, April 2025).
• Literature from leading AI journals, economic studies, and regulatory frameworks.
2. Processing layer
• Thematic coding of the ChatGPT dialogue using narrative analysis techniques.
• Cross-verification with existing literature to validate themes.
3. Output layer
• Synthesized insights on the dominant motives (e.g., profit), job impacts, ethical voids, and societal responses.
• Actionable suggestions for regulation, public adaptation, and equitable AI deployment.
Fig. 4: System Architecture Description: Input → NLP & Theme Analysis → Verification → Insight Generation.
4.2 Proposed algorithm for analysis
The research applies a custom heuristic algorithm to extract patterns from AI-human dialogue and map them to known
literature patterns.
Algorithm: AssessSocioeconomicImpact
Input: DialogueData, LiteratureCorpus
Output: KeyThemes, ValidatedInsights
mathematica
CopyEdit
Begin
Themes ← CategorizeThemes(DialogueData)
For each Theme in Themes
CrossRef ← SearchLiterature(LiteratureCorpus, Theme)
If MatchFound
ValidateTheme ← True
Else
FlagTheme ← NewFinding
EndIf
EndFor
Insights ← Compile(ValidatedThemes, FlaggedThemes)
Return Insights
End
4.2.1 Complexity analysis
• Time Complexity: O(n), where n is the number of conversational tokens.
• Space Complexity: O(k), where k is the number of unique themes extracted.
• Optimization Strategy: Redundant phrases and non-informative utterances filtered using TF-IDF weightage.
4.2.2 Toolset and environment
• AI System: OpenAI GPT-based conversational model (ChatGPT-4, April 2025 snapshot).
• Data Coding: Manual thematic labeling + NLTK for preprocessing.
• Verification Tools: Zotero for citation tracking; Scopus & IEEE Xplore for peer-reviewed comparison.
• Software: Python 3.11, Jupyter Notebook, MS Word for final paper formatting.
4.2.3 Ethical considerations
No human subject data was used beyond the AI's publicly accessible interface.
All literature references are open-access or cited from public digital libraries.
The research maintains transparency, attribution, and academic integrity in data collection and synthesis.
5. Results and discussion
5.1 Dataset description
This study analyzed two primary data sources:
1. Conversational dataset
• AI model: ChatGPT (April 2025 version)
• Duration: 7,000+ tokens across multiple thematic dialogues
• Focus: Human-like reasoning about AI’s societal, economic, and ethical implications
2. Literature Dataset
• Timeframe: 2020–2025
• Scope: 25 peer-reviewed papers, global reports, and ethics guidelines
• Domains: AI ethics, automation, policy, corporate AI influence
5.2 Key findings
Thematic analysis revealed three major impact areas:
A. Profit-centric development
“Profit is the ultimate driving force in most AI ventures.” — ChatGPT (2025)
• Validation: Echoes findings by Acemoglu and Restrepo (2022) and McKinsey (2023), who report 80% of AI
patents and market share controlled by five corporations.
• Impact: Centralized financial benefit, primarily accruing to shareholders and investors.
• Example: Google's DeepMind and Microsoft-backed OpenAI have driven innovations with little public return
or policy transparency.
B. Job displacement and skill mismatch
“If people lose jobs faster than new opportunities emerge, frustration will build.” — ChatGPT
• Validation: Frey & Osborne (2021) predicted ~47% of U.S. jobs are vulnerable to automation.
• Result: Job loss across transportation, manufacturing, and customer service; insufficient reskilling programs in
developing nations.
• Case in Point: Amazon’s warehouse automation led to 25% reduction in human labor per center in under 3
years.
C. Control struggles & societal lag
“Common people are adapting, but at a slow rate.” — ChatGPT
• Validation: Brynjolfsson and McAfee (2020) highlight lagging AI literacy and unequal access to emerging tools.
• Implication: Risk of unrest, digital divide, and trust erosion in technology.
[15]
5.3 Pop culture parallels
Ultron (Avengers: Age of Ultron): Symbol of unchecked AI autonomy and existential risk—mirrors Bostrom’s AI
governance concerns (2022).
Vision (Avengers): Represents the potential for ethical, harmonious AI—embodying the “ideal alignment” of AI goals
with human values.
Samantha (Her): Raises philosophical questions about AI consciousness, emotional manipulation, and romanticized
dependenc—relevant in discussions about AI companions and emotional labor.
Joi (Blade Runner 2049): An allegory of objectified, programmed empathy—parallels concern about gender, bias, and
emotional manipulation in AI systems.
These cultural examples resonate with real societal fears, emphasizing the blurred line between convenience and
control. Table 3 discusses Socioeconomic Impact with parametric notations
Table 3: Socioeconomic metrics.
Value (%)
Impact Description
80%
Concentrated among top 5 tech firms
47%
Mid- to low-skilled sectors hit hardest
Low
Limited public access and literacy
Fig. 5: Socioeconomic Impact Bar Chart (Description).
As stated in figure 5 three bars representing each metric:
• Profit Share: Highest (80%)
• Job Loss: Medium (47%)
• Adaptation: Lowest (marked "Low")
Insight: The bar chart reflects a sharp mismatch between AI benefits and public preparedness.
6. Discussion and interpretation
• The results support the hypothesis that profit motives dominate AI development, pushing equity and ethics to
the periphery.
• Job displacement and the public’s slow adaptation create a volatile environment where discontent could rise.
• Policy vacuums have failed to balance power, leading to corporate monopolization of AI benefits.
• The pop culture references function as metaphorical warnings—raising awareness of ethical design,
governance, and the consequences of emotional manipulation in AI systems.
7. Future scope
As AI continues to shape socioeconomic landscapes, this research identifies several opportunities and directions for
expansion:
A. Quantitative analysis integration
• Enhancement: Incorporating real-world statistics, job market analytics, and economic indicators can strengthen
future studies.
• Tooling: Use of data mining and NLP tools (e.g., Python, R, Tableau) to derive deeper insights from public
datasets.
B. Real-time public sentiment tracking
• Application: Analyzing social media sentiment on AI-related issues using large language models and sentiment
classifiers.
• Goal: Predict social unrest triggers and identify knowledge gaps in public understanding.
C. AI literacy and education programs
• Need: Propose national campaigns for AI literacy, especially in underserved communities.
• Implementation: Partnerships between governments, NGOs, and universities to provide affordable AI-skilling
bootcamps.
D. Ethical design frameworks
• Proposed Model: “Human-Centered AI Governance” – a framework ensuring profit-sharing, algorithmic
fairness, and public representation in development.
• Inspired By: Joi’s emotional manipulation in Blade Runner 2049, this framework would prevent emotional
exploitation by ensuring transparency and control in AI-generated emotional responses.
E. Creative and emotional ai research
• Opportunity: Study AI in storytelling, art, and emotional connection.
• Challenge: Develop ethical standards to prevent synthetic empathy exploitation (referencing Her and Joi).
6. Conclusion
Artificial Intelligence is a double-edged sword fueling economic growth while deepening social divides. This paper
explored how profit-centric AI development concentrates control in the hands of a few, risking widespread job
displacement, ethical oversights, and socioeconomic instability. Through a qualitative analysis of AI-generated
dialogue and contemporary literature, the study found strong evidence of corporate dominance over AI innovation and
the allocation of its benefits, a significant lag in societal adaptation and AI literacy, and a concerning level of ethical
ambiguity surrounding emotionally manipulative AI tools. Pop culture narratives from Ultron’s apocalyptic logic to
Joi’s emotionally programmed servitude mirror real-world fears about autonomy, dependency, and inequality. These
cautionary tales highlight the urgent need for inclusive policies, AI education, and ethical frameworks that ensure AI
serves all segments of society. Without intervention, we risk a future where technological intelligence outpaces human
values. But with responsible governance, AI can empower—not overpower—our collective future.
Conflict of Interest
There is no conflict of interest.
Supporting Information
Not applicable
Use of artificial intelligence (AI)-assisted technology for manuscript preparation
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing
or editing of the manuscript and no images were manipulated using AI.
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