ARTIFICIAL INTELLIGENCE AND THE FUTURE
OF US COMPETITIVENESS: SECTORAL IMPACTS,
WORKFORCE TRANSITIONS, AND POLICY
CHALLENGES

Satyadhar Joshi

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Satyadhar Joshi. ARTIFICIAL INTELLIGENCE AND THE FUTURE OF US COMPETITIVE-NESS: SECTORAL IMPACTS, WORKFORCE TRANSITIONS, AND POLICY CHALLENGES. International Journal of Research in Commerce and Management Studies, 2025, 07 (04), pp.76-110. 10.38193/IJRCMS.2025.7405 . hal-05212339v2

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To cite this article: Satyadhar Joshi (2025). ARTIFICIAL INTELLIGENCE AND THE FUTURE OF US COMPETITIVENESS: SECTORAL IMPACTS, WORKFORCE TRANSITIONS, AND POLICY CHALLENGES, International Journal of Research in Commerce and Management Studies (IJRCMS) 7 (4): 76-110 Article No. 440 Sub Id 801

ARTIFICIAL INTELLIGENCE AND THE FUTURE OF US COMPETITIVENESS:
SECTORAL IMPACTS, WORKFORCE TRANSITIONS, AND POLICY CHALLENGES

Satyadhar Joshi

Independent Researcher
Alumnus, International MBA, Bar-Ilan University, Israel Alumnus, Touro College MSIT, NY, USA
satyadhar.joshi@gmail.com

DOI: https://doi.org/10.38193/IJRCMS.2025.7405

ABSTRACT

This paper provides a comprehensive analysis of Artificial Intelligence's impact on U.S. economic competitiveness through six key dimensions. First, we examine AI's macroeconomic effects, synthesizing projections that estimate potential contributions of $4.8-$19.9 trillion to global GDP by 2030, with annual productivity growth ranging from 0.5-1.3%. Second, we analyze labor market transformations, where 20-40% of jobs may be affected, creating both displacement risks and opportunities for workforce augmentation. Third, we investigate the intensifying geopolitical competition in AI, particularly between the U.S. and China, where military AI markets are projected to reach $38.5 billion and $32 billion respectively by 2030. Fourth, we evaluate sector-specific impacts, highlighting manufacturing efficiency gains of 15-30% and small business productivity improvements up to 25%. Fifth, we compare different AI paradigms - narrow AI, agentic AI, and AGI - with their distinct economic implications, from incremental task automation to potential exponential growth scenarios. Finally, we assess policy frameworks needed to balance innovation with risk mitigation, emphasizing digital infrastructure investment, workforce reskilling, and international governance cooperation. Our analysis synthesizes findings from over 100 recent sources to present a holistic view of AI's transformative potential and challenges. The paper concludes with strategic recommendations for maintaining U.S. competitiveness while addressing inequality risks and ensuring sustainable AI integration across economic sectors. Key findings suggest that while AI offers substantial economic opportunities, its benefits are not automatic and require proactive policy interventions to achieve equitable distribution and long-term growth. We examine the policy interventions required to harness AI's transformative benefits while mitigating its associated risks. The analysis highlights that while AI opportunities for innovation across sectors, its equitable and sustainable integration necessitates proactive governance, investment in digital infrastructure, workforce reskilling, and international collaboration. We examine the strategic policy interventions required to harness AI's transformative benefits while mitigating its associated risks. The analysis highlights that while AI offers opportunities for
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efficiency gains and innovation across sectors, its equitable and sustainable integration necessitates proactive governance, investment in digital infrastructure, workforce reskilling, and international collaboration.

**KEYWORDS:** Artificial Intelligence, Economic Growth, Competitiveness, Automation, Productivity, Labor Markets, Geopolitics, Policy, Digitalization, Innovation.

## INTRODUCTION

Artificial Intelligence (AI) represent a paradigm shift comparable to past industrial revolutions, with profound implications for economic structures, societal well-being, and geopolitical power dynamics [1]–[3]. As AI technologies mature and become increasingly integrated into various sectors, policymakers, businesses, and researchers are grappling with understanding and harnessing their immense potential while simultaneously addressing the associated challenges [4], [5]. This paper aims to provide a comprehensive overview of AI’s transformative impact on economic competitiveness and global dynamics, synthesizing insights from a wide array of recent publications.

Recent studies suggest AI could contribute $4.8 trillion to the global economy by 2030 [6], with significant implications for national competitiveness [7]. However, realization of these benefits depends on multiple factors including policy frame-works [4], workforce adaptation [8], and technological infras- tructure [9]. This paper examines these dimensions through the lens of: (1) AI- driven economic growth mechanisms, (2) Labor market transformations, (3) Geopolitical AI competition, (4) Policy frameworks, (5) Sectoral impacts, and (6) Strategic recommendations.

AI is poised to drive economic growth through enhanced productivity and innovation [10], [11], its complex and often uneven effects on labor markets and income distribution [12]–[15], and its critical role in shaping national security and global technology leadership [16]–[19]. It is important to understand the intensifying AI race between major global powers, notably the United States and China [20]–[23], and the strategic policies necessary to navigate this new era effectively [24]–[27]. The paper integrates perspectives on AI’s impact across diverse economic segments, from large enterprises to small businesses [28], [29], and its implications for specific industries like manufacturing [30]–[32].

The rapid advancement of Artificial Intelligence (AI) technologies has sparked intense debate about their potential economic impacts [12]. While some predict transformative productivity gains [33], others caution about modest effects. and potential disruptions [34]. This paper synthesizes current research to provide a balanced perspective on AI’s role in shaping economic competitiveness.

However, realization of these benefits depends on multiple factors including policy frameworks [4],
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workforce adaptation [8], and technological infrastructure [9].

## II. FIGURES: SCENARIO-BASED PROJECTIONS AND COMPARATIVE OUTCOMES

To illustrate the range of quantitative projections and scenario outcomes in the literature, this section presents key figures and tables summarizing the anticipated impact of AI on economic growth, labor markets, and national competitiveness.

## III. MAPPING OF PROJECTION DATA TO ACADEMIC SOURCES

### A. Interpretation Guide

* **GDP Projections:** The $4.8T-$19.9T range reflects methodological differences - UNCTAD focuses on measurable economic activity while IDC includes ecosystem effects

* **Growth Rates:** Higher estimates ([33]) assume AI augments innovation, while lower bounds ([12]) model only task automation

* **Job Impacts:** Varying percentages reflect different definitions of' affected" (displacement vs. transformation)

* **Geopolitical Data:** Military market sizes ([38]) correlate with broader economic competitiveness

### B. Projected Global GDP Impact of AI (2030)

*Description:* Figure 1 compares projections from various studies. Optimistic scenarios estimate up to $19.9 trillion in added global GDP by 2030, while more conservative models suggest gains as low as $4.8 trillion, depending on adoption rates and policy effectiveness.

### C. Annual GDP Growth Rate Attributable to AI

Table II summarizes various projections for annual GDP growth attributable to AI under different scenarios and sources, ranging from cautious to optimistic outlooks on AI's economic impact.

### D. Labor Market Impact Scenarios

*Description:* Figure 2 illustrates that, depending on the scenario, AI could impact 20-40% of global jobs, with varying degrees of displacement, augmentation, and new job creation.

### E. National Competitiveness: U.S. vs. China AI Investment

Table III presents the projected AI-related military market size and annual growth rates for the United States and China by 2030, highlighting the competitive investment landscape in AI
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technologies.

F. Comparative Scenarios:

Narrow AI, Agentic AI, and AGI Description: Figure 3 summarizes projected economic impacts by AI type. Narrow AI drives incremental sectoral gains, agentic AI could double the impact with end-to-end automation, and AGI is projected to have potentially exponential effects, but with high uncertainty and risk.

G. Summary Table: Key Scenario Outcomes

Note: Figures are based on synthesis of available projections; actual outcomes depend on adoption, policy, and technological breakthroughs.

H. Visualizing AI Impact Projections

Figure 4 illustrates the projected GDP impact by different AI types, showing both optimistic and conservative estimates. The economic effects distribution across productivity gains, labor displacement, and new job creation is visualized in Figure 5. Sectoral exposure to AI automation varies significantly, with Finance and Manufacturing sectors having the highest potential. Lastly, the geopolitical competition timeline in Figure 7 depicts the relative AI competitiveness of the US, China, and the EU through 2035.

IV. AI and Economic Growth: Productivity and Innovation

Artificial Intelligence is widely touted as the next frontier for driving significant economic growth and productivity gains [45], [46]. Projections vary, but many indicate substantial contributions to global GDP. For instance, some reports suggest generative AI could inject $1 trillion into the U.S. economy over 10 years [36], [41], while others foresee AI contributing $19.9 trillion to the global economy through 2030, driving 3.5% of global GDP [35]. The UN Trade and Development highlights AI's $4.8 trillion future, emphasizing the need for action to prevent wider divides [6].

The mechanisms through which AI enhances productivity are multifaceted. AI can automate routine tasks, enhance decision-making by analyzing vast datasets, and foster innovation across various sectors [47]–[49]. This extends to areas such as financial analytics, where AI-powered tools are revolutionizing economic forecasting and risk management, contributing to national growth [50]. The integration of AI technologies, particularly in industrial settings, can lead to more sustainable and efficient operations, with projections suggesting AI could ensure a country's GDP growth by 1% by 2025 [51].

However, not all analyses are uniformly optimistic. Some research suggests that AI's macroeconomic effects, while non-trivial, might be more modest than commonly believed, with
<figure><img src="image_6.png" /></figure>

total factor productivity (TFP) gains perhaps less than 0.71% or even 0.55% over 10 years, particularly when considering the challenges of automating complex tasks [12]. Others argue that AI may not supercharge GDP growth as much as anticipated, due to factors like zero-sum games and consumer surplus not being fully captured in traditional GDP statistics [34].

<table><caption>TABLE I KEY-AUTHOR MAPPING FOR AI ECONOMIC PROJECTIONS</caption><thead><tr><td>Chart Keys (Python)</td><td>BibTeX Key</td><td>Source</td><td>Projection Description</td></tr></thead><tbody><tr><td>AIs$48Trillion2025</td><td>[6]</td><td>UNCTAD (2025)</td><td>Projects $4.8 trillion global economic impact by 2030, emphasizing developing country adoption challenges and digital divides</td></tr><tr><td>IDCArtificialIntelligence</td><td>[35]</td><td>IDC (2025)</td><td>$19.9 trillion cumulative GDP contribution forecast through 2030, covering all AI sectors and applications</td></tr><tr><td>CognizantImpactStudy</td><td>[36]</td><td>Cognizant/ Oxford (2024)</td><td>$1 trillion US-specific GDP impact focused on generative AI's productivity effects across industries</td></tr><tr><td>aghionAIGrowthWhere</td><td>[33]</td><td>Aghion & Bunel (2024)</td><td>0.8-1.3pp annual productivity growth using historical tech revolution parallels</td></tr><tr><td>acemogluSimpleMacroeconomicsAla</td><td>[12]</td><td>Acemoglu (2024)</td><td>0.55-0.71% TFP growth based on task-level automation limits</td></tr><tr><td>AIWillTransform2024a</td><td>[37]</td><td>IMF (2024)</td><td>40% global workforce exposure estimate with complementary job creation</td></tr><tr><td>soodMacroeconomicAnalysisImpact2024</td><td>[13]</td><td>Sood & Khanna (2024)</td><td>30% displacement risk emphasizing polarization and inequality effects</td></tr><tr><td>mitchellAIWillHave2024</td><td>[8]</td><td>Mitchell (2024)</td><td>20% US job transformation requiring reskilling investments</td></tr><tr><td>marketsArtificialIntelligenceAI2025</td><td>[38]</td><td>Research&Markets (2025)</td><td>Military AI market sizing ($38.5B US, $32B China)</td></tr><tr><td>singerStakesRisingUSChina2024</td><td>[22]</td><td>Singer (2024)</td><td>12.8% vs 13.5% growth rates in US-China AI competition</td></tr><tr><td>AIAgentsMove</td><td>[39]</td><td>KPMG (2025)</td><td>Agentic AI adoption timelines (24-month transformation)</td></tr><tr><td>newtonAITurnsCrossBorder2025</td><td>[40]</td><td>Newton (2025)</td><td>Supply chain optimization case studies</td></tr></tbody></table>
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<table><tr><td>GenerativeAI Economic</td><td>[41]</td><td>Cognizant (2024)</td><td>Generative AI's disproportionate service sector impacts</td></tr><tr><td>Aghion NBER WORKINGPA PERA</td><td>[2]</td><td>Aghion et al. (2024)</td><td>AGI's potential "singularity" economic models</td></tr></table>

<figure><img src="image_8.png" /><figcaption>Fig. 1. Projected AI Contribution to Global GDP by 2030 under Different Scenarios. Sources: [6], [12], [33], [35], [36]</figcaption></figure>

<table><caption>TABLE II: ANNUAL GDP GROWTH RATE PROJECTIONS ATTRIBUTABLE TO AI</caption><thead><tr><td>Source</td><td>Scenario</td><td>Annual GDP Growth (%)</td></tr></thead><tbody><tr><td>Acemoglu & Restrepo [12]</td><td>Cautious</td><td>0.55-0.71</td></tr><tr><td>IDC [35]</td><td>Baseline</td><td>0.5-1.3</td></tr><tr><td>Generative AI Study [36]</td><td>Optimistic</td><td>1.0 (US)</td></tr><tr><td>UNCTAD [6]</td><td>Optimistic</td><td>3.5 (Global)</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>Fig. 2. Projected Percentage of Jobs Affected by AI by 2030. Sources: [8], [13], [37]</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 3. Comparative Economic Impact of Narrow AI, Agentic AI, and AGI. Sources: [24], [33], [41]</figcaption></figure>

Projected GDP Impact by AI Type
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 4. Comparison of GDP growth projections across AI capability levels from various studies [2], [12], [42]</figcaption></figure>

<table><caption>TABLE III ANNUAL GDP GROWTH RATE PROJECTIONS ATTRIBUTABLE TO AI</caption><thead><tr><td>Country</td><td>Military AI Market ($B)</td><td>Annual Growth (%)</td></tr></thead><tbody><tr><td>United States</td><td>38.5</td><td>12.8</td></tr><tr><td>China</td><td>32.0</td><td>13.5</td></tr></tbody></table>

<table><caption>TABLE IV SUMMARY OF KEY SCENARIO-BASED PROJECTIONS</caption><thead><tr><td>Scenario/Source</td><td>GDP Impact (2030)</td><td>Jobs Affected</td></tr></thead><tbody><tr><td>Optimistic (IDC, UNCTAD)</td><td>$19.9T, 3.5% growth</td><td>40%</td></tr><tr><td>Moderate (Acemoglu)</td><td>$4.8T, 0.7% growth</td><td>20-30%</td></tr><tr><td>Agentic AI (Projection)</td><td>$10-15T</td><td>30-50%</td></tr><tr><td>AGI (Speculative)</td><td>Exponential</td><td>50%+</td></tr></tbody></table>

Nevertheless, the consensus leans towards AI being a strategic imperative and a crucial driver of economic growth [52]. Early adoption of AI is already boosting U.S. economic growth [53], and proactive policy choices are essential for preparing the U.S. economy for an AI future [54]. AI technologies demonstrate significant potential to enhance economic productivity through multiple pathways:
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A. Productivity and Growth

The macroeconomic effects of AI are subject to ongoing research. Acemoglu [12] estimates modest total factor productivity (TFP) gains of 0.55% over 10 years, while Aghion and Bunel [33] project more optimistic annual growth increases of 0.8-1.3 percentage points. These disparities reflect differing assumptions about AI's task automation potential and complementarities.

PwC's global study [55] suggests AI could contribute $15.7 trillion to global GDP by 2030, with the US and China as primary beneficiaries. However, Trabelsi [48] notes that these gains may be unevenly distributed, potentially exacerbating existing inequalities.

B. Sectoral Impacts

AI's economic impact varies significantly across sectors:

*   Manufacturing: AI adoption is transforming production processes [30], with potential productivity gains of 20-30% [51].

*   Financial Services: AI-powered analytics are reshaping risk management and policy formulation [50].

*   Small Businesses: AI tools offer significant productivity potential but face adoption barriers [29].

C. Macroeconomic Projections

*   Generative AI could inject $1 trillion into the U.S. economy over 10 years [36], [41]

*   Global GDP could see 3.5% growth through AI contri-butions by 2030 [35]

*   Industrial AI integration may ensure 1% GDP growth by 2025 [51]

D. Productivity Mechanisms

AI enhances productivity through:

1) Task automation: Replacing routine human activities [12]

2) Data-driven decision-making: Enhancing analytical capabilities [50]

3) Cross-sector innovation: Revolutionizing manufacturing [30], finance [49], and R&D [47]

E. Contested Projections

Despite optimistic forecasts, several studies suggest caution:

*   Total Factor Productivity (TFP) gains may be limited to 0.55-0.71% over 10 years [12]

*   GDP growth may be constrained by zero-sum dynamics [34]

*   Consumer surplus effects may not fully register in traditional metrics [33]

Early adopters already show measurable economic benefits [53], but sustained growth requires strategic policy interventions [54].
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## V. AI AND LABOR MARKETS: CHALLENGES AND OPPORTUNITIES

The impact of AI on labor markets is a subject of intense debate and varies significantly across different segments of the economy [8], [43]. AI is projected to affect almost 40 percent of jobs globally, both replacing some roles and complementing others [37]. While AI can boost productivity and create new job categories requiring different skill sets, it also raises concerns about workforce displacement, rising inequality, and structural unemployment [13], [56].

The "AI efficiency trap" suggests that increased productivity from AI does not automatically translate into better work-ing conditions for humans; instead, it can create perpetual pressure [57]. The critical question is whether AI will lead to widespread prosperity. The answer hinges on deliberate policy choices that address the distribution of benefits and the need for workforce adaptation [58]. Initiatives like Michigan's AI and workforce plan aim to create good-paying jobs and enhance economic growth through investment in the workforce [59].

Moreover, the power dynamics in the labor market are crucial. Some argue that unbalanced labor market power, rather than technology itself, is what makes AI threatening to workers, emphasizing that boosting workers' bargaining position is the best" AI policy" to protect them [14]. This underscores the need for comprehensive strategies that include reskilling programs and social safety nets to ensure inclusive prosperity [60]. The Congressional Budget Office (CBO) has also examined how AI's widespread adoption could impact economic growth and employment, highlighting the need for careful consideration in federal budget planning [61], [62].

AI's labor market impact presents both disruption and opportunity:

### A. Employment Projections

<table><caption>TABLE V: GLOBAL LABOR MARKET PROJECTIONS</caption><thead><tr><td>Impact Area</td><td>Projection</td></tr></thead><tbody><tr><td>Job transformation</td><td>40% of global jobs affected [37]</td></tr><tr><td>Wage polarization</td><td>Increased inequality risk [13]</td></tr><tr><td>Skill displacement</td><td>Structural unemployment concerns [54]</td></tr></tbody></table>

### B. Policy Responses

Effective adaptation requires:

* Reskilling initiatives: Michigan's AI workforce plan [59]
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* Bargaining power rebalancing: Protecting workers from displacement [14]

* Social safety nets: CBO recommendations for federal planning [61]

The "AI efficiency trap" creates productivity gains without proportional worker benefits [57], necessitating deliberate pol- icy choices for equitable distribution [58].

## VI. GLOBAL COMPETITION, NATIONAL COMPETITIVENESS AND AI LEADERSHIP

AI has become a central battleground in the intensifying geopolitical competition, particularly between the United States and China [20], [22], [63], [64]. Both nations are vying for technological supremacy, recognizing AI as a critical component of future economic prosperity and national security [7], [18]. The U.S. aims to strengthen its global AI leadership through public investment and workforce development [65]–[67].

Concerns about China potentially surpassing the U.S. in AI application are widespread [21], [68]. China's AI strategy, characterized by state engineering and significant domestic challenges, has global competitive implications [23]. The competition extends to various domains, including the military market, where AI investments in surveillance, intelligence, cybersecurity, and autonomous systems are projected to see significant growth in both the U.S. and China [38]. The U.S. administration is pushing for advancements in AI infrastructure through executive orders, focusing on collaboration with industry and building next-generation data centers [69], [70]. Maintaining America's technological primacy requires a multi-faceted approach, including fostering a diversified, competitive market for AI systems to prevent incumbents from stifling innovation [71], [72]. Debates exist on whether AI can provide sustainable competitive advantage, with some arguing that its pervasive nature may diminish its edge, emphasizing creativity as a differentiator [73], [74]. However, many believe that winning the data game through AI offers a significant competitive advantage [75]. The U.S. Copyright Law is also seen as crucial for protecting global competitiveness in AI, particularly regarding fair use and broad training data access [76].

### A. US-China Rivalry

The AI competition between the US and China has become a focal point of economic strategy [21]. While the US main- tains leadership in foundational research [64], China's state- driven approach presents unique challenges [23].

Recent analyses suggest that export controls and investment restrictions may shape the trajectory of this competition. The Special Competitive Studies Project [77] emphasizes the need for comprehensive US strategy to maintain technological leadership.

### B. Regional Perspectives

Europe's approach to AI sovereignty [78] contrasts with the US market-driven model. The World
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Economic Forum [79] advocates for regional collaboration to enhance AI competi- tiveness across maturity levels.

The U.S.-China AI competition represents a critical geopo- litical battleground:

### C. Competitive Dynamics

* Military AI investments projected to grow 12.8% CAGR (2023-2030) [38]

* U.S. aims to maintain leadership through infrastructure investments [69]

* China's state-led approach raises competitive concerns [21], [23]

### D. Leadership Strategies

1. Market diversification: Preventing incumbent domi- nance [71]

2. Copyright frameworks: Protecting U.S. competitive- ness [76]

3. Data advantage: Leveraging information ecosystems [75]

AI's pervasive nature may diminish competitive advantages over time [73], but current leadership requires sustained inno- vation [67].

## VII. POLICY AND GOVERNANCE IN THE AI ERA

Effective governance and proactive policy interventions are crucial for realizing AI's benefits and mitigating its risks [56], [80]. Policymakers face the delicate balance of foster- ing innovation while addressing concerns related to market competition, data privacy, copyright, national security, ethics, and financial stability [17], [81].

A strategic vision for U.S. AI leadership emphasizes sup- porting security, innovation, democracy, and global prosperity [25]. This involves maintaining American energy dominance in the age of AI [82], strengthening U.S. global AI leadership [83], and securing the fundamentals of U.S. competitiveness [84]. There's also a call for a modern industrial strategy for AI, interrogating the current U.S. approach [85].

International collaboration and regional approaches are also important. The World Economic Forum's" Blueprint for Intelligent Economies" provides guidance for nations to achieve a successful AI revolution through regional collaboration [79]. Europe, for instance, is considering its options to achieve digital sovereignty and enhance national economic competitiveness and security through AI [78], [86].

Policies must also adapt to changing economic conditions driven by digitalization and AI. For example, some research suggests that governmental efficiency, digitalization, and renewable energy
<figure><img src="image_17.png" /></figure>

use have a significant positive influence on economic performance, even in the face of resource
curse phenomena [87]. Investment in AI research is also seen as a power for driving economic
competitiveness [88], [89].

Effective governance must balance innovation with risk mitigation:

A. Strategic Frameworks

* U.S. national security integration [17]

• Energy infrastructure fortification [82]

• Digital sovereignty considerations [78]

B. Implementation Challenges

Policy must address:

* Market concentration risks [81]

• Workforce transitions [80]

• International regulatory alignment [86]

Regional collaboration models like the” Blueprint for Intelligent Economies” offer pathways for
coordinated adoption [79].

VIII. DIVERSE ECONOMIC IMPACTS AND FUTURE OUTLOOK

The economic impacts of AI are diverse and extend to various sectors and scales [44], [90]. From
boosting the productivity of small businesses [28], [29] to transforming cross-border fulfillment into
a competitive edge [40], AI's reach is extensive. It is set to profoundly shape the global economy,
necessitating careful consideration of how it will benefit humanity.

While some predict minor gains in the U.S. economy currently, future benefits are anticipated [91]. The debate continues on whether AI will save or sink the U.S. economy, with perspectives ranging from optimism to realism [92], [93]. Experts discuss how the U.S. economy should adapt to the AI boom, acknowledging both transformative potential and unprecedented risks [94], [95]. Troy University, for instance, is advancing AI outreach with a new business-focused research center to prepare for these changes [96].

The future of AI in the U.S. economy, according to Gold- man Sachs, will be characterized by
sustained productivity growth [97]. Organizations like the Special Competitive Stud- ies Project
(SCSP) are dedicated to ensuring America wins the techno-economic competition by 2030,
recognizing AI’s central role [77]. The consensus is that AI is not just another technological
advancement, but a strategic economic necessity that will shape future market dynamics [52].
<figure><img src="image_18.png" /></figure>

However, caution is advised to ensure that AI does not create greater inequality, and that policies are in place to address market competition concerns [9], [80], [81], [98], [99]. Discussions around visa policies under the AI Executive Order are also critical for attracting global talent and maintaining leadership [100].

AI's economic effects manifest differently across sectors:

A. Sectoral Analysis

* Small businesses: Productivity enhancements [28], [29]

* Manufacturing: Efficiency gains [31]

* Global trade: Cross-border fulfillment optimization [40]

B. Emerging Considerations

* Resource management synergies [87]

* Economic diversification effects [44]

* Long-term GDP projections [91]

While near-term economic gains appear modest [32], trans- formative potential remains contingent on policy choices [94].

IX. POLICY AND REGULATORY CONSIDERATIONS

A. National Strategies

The US has implemented various AI initiatives [27], in- cluding executive orders focusing on infrastructure [69] and workforce development [65]. Similar efforts are underway globally [25].

B. Economic Policy Challenges

Key policy challenges include:

* Workforce transitions [14]

* Market concentration risks [81]

* Measurement of AI's economic contribution [101]

The Congressional Budget Office [61] highlights the com- plex budgetary implications of widespread AI adoption.

X. INDUSTRY-LEVEL IMPACTS

A. Productivity Enhancements

Case studies demonstrate AI's potential to:

* Optimize supply chains [40]

* Enhance data-driven decision making [75]
<figure><img src="image_19.png" /></figure>

* Create new business models [42]

## B. Competitive Dynamics

AI is reshaping industry competition [102], with implications for:

* Market entry barriers [71]

* Sustainable advantage

* Antitrust considerations [98]

# XI. Quantitative Findings on AI's Economic Impact

This section synthesizes key numerical projections and empirical findings from the literature, highlighting the scale and scope of AI's economic impact across macroeconomic, labor, sectoral, and geopolitical dimensions. The reviewed literature provides several quantitative projections regarding AI's impact on economic growth and productivity:

* Acemoglu (2024) projects modest macroeconomic effects, estimating AI could contribute no more than a 0.71% increase in total factor productivity (TFP) over 10 years, with more conservative estimates below 0.55% [12].

* Aghion and Bunel estimate AI could increase annual productivity growth by 0.68 percentage points using a task- based framework, while alternative approaches suggest 0.8-1.3 percentage points annually [33].

* Cognizant and Oxford Economics predict generative AI could inject $1 trillion into the U.S. economy over 10 years, affecting 90% of jobs [36].

* PwC's global study estimates AI could contribute $15.7 trillion to the global economy by 2030, increasing global GDP by 14% [55].

* UNCTAD (2025) projects AI could create $4.8 trillion in economic value while warning of potential inequality [6].

* Kuznetsova et al. (2025) suggest AI implementation in industrial parks could boost Russia's GDP by 1% by 2025 [51].

* Naisho (2025) forecasts AI could contribute an additional 1.2%-2% to annual U.S. GDP growth by 2043, cumulatively increasing GDP by 25%-45% [60].

* Goldman Sachs estimates AI may begin boosting U.S. GDP starting in 2027 [103].

These projections vary significantly based on methodology and assumptions, but consistently suggest nontrivial economic impacts from AI adoption, particularly in developed economies like the United States.

## A. Macroeconomic Impact

* **Global GDP Contribution:** AI is projected to add $4.8 trillion to global GDP by 2030 [6].
<figure><img src="image_20.png" /></figure>

* **U.S. GDP Impact:** Generative AI could contribute $1 trillion to U.S. GDP over the next 10 years [36].

* **Global Productivity Growth:** AI is estimated to drive 3.5% of global GDP growth by 2030 [35].

* **Annual GDP Growth from AI:** Estimates range from 0.5% to 1.3% annually due to AI adoption [12].

* **Industrial AI:** Integration of AI in industry could yield 1% GDP growth by 2025 [51].

B. Labor Market Projections

C. Sector-Specific Projections

* **Manufacturing:** Early AI adopters report efficiency gains of 15–30% [30].

* **Small Business:** AI tools can boost productivity by up to 25% for small enterprises [29].

<table><caption>TABLE VI GLOBAL LABOR MARKET IMPACTS</caption><thead><tr><td>Metric</td><td>Projection</td><td>Source</td></tr></thead><tbody><tr><td>Job transformation</td><td>40% of jobs affected globally</td><td>[37]</td></tr><tr><td>Wage polarization</td><td>Significant inequality risk</td><td>[13]</td></tr><tr><td>Military AI investment</td><td>12.8% CAGR (2023–2030)</td><td>[38]</td></tr></tbody></table>

* **Financial Analytics:** AI improves forecasting accuracy by 40% [50].

D. Geopolitical Investment

* **U.S.-China Military AI Market:** Projected to reach $38.5 billion by 2030 [38].

* **Federal AI Infrastructure:** U.S. federal AI investments are growing at 18.4% annually [69].

E. Limitations and Contested Projections

* **TFP Gains:** Some studies estimate total factor productivity gains from AI at only 0.55–0.71% over 10 years [12].

* **GDP Metrics:** Consumer surplus from AI may not be fully captured in GDP statistics [34].

* **AI Efficiency Trap:** Productivity gains do not automatically translate into worker benefits [57].

These quantitative findings illustrate both the significant promise and the nuanced challenges of AI's economic integration, emphasizing the importance of policy frameworks, workforce adaptation, and strategic investment for realizing AI's full economic potential.

F. Quantitative Projections and Economic Forecasts
<figure><img src="image_21.png" /></figure>

The economic literature and industry reports provide various quantitative estimates and projections regarding AI's impact on global and national economies. These numbers underscore the potentially monumental, though sometimes debated, scale of AI's influence.

Several studies offer concrete figures on AI's projected contribution to Gross Domestic Product (GDP). For instance, a joint Oxford Economics/Cognizant study predicts that generative AI alone could inject a substantial $1 trillion into the U.S. economy over the next decade [36], [41]. On a broader scale, IDC forecasts that Artificial Intelligence will contribute a staggering $19.9 trillion to the global economy by 2030, equating to a 3.5% share of global GDP in that year [35]. The UN Trade and Development also highlights AI's future value, estimating it to be around $4.8 trillion, while cautioning about potential economic divides [6].

When examining productivity gains, the estimates show some variation. Initial research indicates that AI's macroeconomic effects, while significant, might be modest. Daron Acemoglu's work suggests total factor productivity (TFP) gains could be no more than a 0.71% increase over 10 years, and potentially even less (0.55%), especially for more complex tasks [12]. Conversely, other analyses, like Aghion and Bunel's, estimate that the AI revolution could increase aggregate productivity growth by between 0.8 and 1.3 per- centage points per year over the next decade, with a median estimate of 0.68 percentage points in additional annual TFP growth [33].

Further projections indicate AI's long-term economic potential. By 2043, AI could contribute an additional 1.2% to 2% to annual GDP growth, cumulatively increasing GDP by approximately 25% to 45% [60]. Even at a more immediate scale, some forecasts suggest AI could ensure a country's GDP growth by 1% by 2025, particularly through its integration into industrial processes [51]. The overall AI market size itself was estimated to be around $196.6 billion in 2023, reflecting a rapidly expanding sector [13]. These quantitative insights underscore the diverse and substantial economic implications that AI is set to bring forth.

## XII. POSSIBLE SCENARIOS, PROJECTIONS, AND COMPARATIVE OUTCOMES

The literature on AI's economic impact presents a range of scenarios and projections, reflecting both optimism and caution. This section reviews and compares key outcomes predicted by different authors, highlighting areas of consensus and divergence. The literature presents diverse scenarios regarding AI's economic impact, with varying assumptions leading to different projected outcomes. The literature presents a spectrum of possible scenarios and projected outcomes concerning AI's economic impact, reflecting varying methodologies, assumptions, and focal points of different authors and institutions. This section compares these perspectives, highlighting areas of convergence and divergence.

One key area of difference lies in the magnitude of projected productivity and GDP growth. Daron Acemoglu, in [12], offers a more conservative estimate, suggesting that AI's macroeconomic effects on total factor productivity (TFP) gains might be modest, ranging from no more than a 0.71% increase over 10 years, potentially even less at 0.55%. This outcome is derived from a task-based
<figure><img src="image_22.png" /></figure>

model focusing on cost savings and productivity improvements at the task level, emphasizing that early gains might come from easier-to- automate tasks. In contrast, Aghion and Bunel provide a more optimistic outlook in [33]. Their analysis, which leverages parallels with past technological revolutions and a revisited task-based framework, projects higher aggregate productivity growth, estimating between 0.8 and 1.3 percentage points per year over the next decade, with a median TFP growth of 0.68 percentage points. This divergence indicates differing views on the breadth and depth of AI's microeconomic impact translating into macroeconomic gains.

Beyond productivity, the overall contribution to GDP also varies across projections. IDC forecasts a substantial contribution of $19.9 trillion to the global economy by 2030, accounting for 3.5% of global GDP [35]. This large-scale projection underscores a vision where AI profoundly reshapes the global economic landscape. Similarly, the UN Trade and Development highlights AI's" $4.8 trillion future," while also emphasizing the critical need for action to prevent widening economic divides, suggesting a scenario where benefits are not automatically equitably distributed [6]. Focusing on the U.S. economy, a joint Oxford Economics/Cognizant study foresees generative AI injecting a significant $1 trillion over 10 years [36], [41]. Naisho presents a long-term optimistic scenario, projecting that AI could contribute an additional 1.2% to 2% to annual GDP growth by 2043, leading to a cumulative GDP increase of approximately 25% to 45% [60]. Kuznetsova et al. also offer a specific short-term projection for industrial integration, suggesting AI could ensure a country's GDP growth by 1% by 2025 [51].

However, not all authors envision universally positive or substantial aggregate economic uplift. Adair Turner, for ex- ample, posits a more skeptical view in [34], arguing that AI may not" supercharge GDP growth" in the way some expect, potentially leading to more zero-sum games or consumer surplus that is not fully captured in traditional GDP statistics. This suggests a scenario where the benefits are real but manifest differently or are absorbed without a dramatic boost to measured economic output. Similarly, Monica de Bolle raises the fundamental question of whether AI will" save or sink the US economy," highlighting the ongoing debate and the dependence on AI's ability to improve labor productivity [92]. Marquis Green's perspective, reflecting on the U.S. economy, suggests" minor gains now, but future benefits," indicating that the net effect on productivity, employment, and GDP has been" negligible at best" so far, with significant impacts visible only in certain economic segments [91]. Tyler Cowen reinforces this nuanced view, suggesting that AI's effect on the U.S. economy will be" wildly uneven," with businesses vulnerable to competition facing pressure to adopt rapidly, while sectors like government and education might see slower adoption [43].

Regarding labor market outcomes, the IMF projects that AI will affect" almost 40 percent of jobs around the world," replacing some roles while complementing others [37]. This highlights a scenario of significant job transformation, necessitating policy responses. The concept of the" AI efficiency
<figure><img src="image_23.png" /></figure>

trap” introduced by Knowledge at Wharton further complicates the picture, suggesting that while AI boosts productivity, it doesn’t automatically guarantee better working conditions for humans, potentially creating” perpetual pressure” [57]. This points to a potential outcome where productivity gains do not universally translate into improved worker welfare without specific interventions.

In summary, while there is broad agreement on AI's trans- formative potential, authors diverge on the scale, timing, and distribution of its economic impacts. Some envision a significant, rapid boost to GDP and productivity, while others predict more modest or uneven gains, coupled with substantial challenges related to labor market adjustments and inequality. The diverse projections underscore the uncertainty and the crucial role of policy in shaping AI's ultimate economic outcomes.

A. Optimistic Growth Scenarios

Several studies forecast substantial economic gains from AI adoption:

* **Productivity Boom:** Aghion and Jones (2024) model AI as a new factor of production that could drive significant productivity gains, particularly if it automates idea production [2].

* **Transformational Impact:** McKinsey projects general AI could add $2.6-$4.4 trillion annually to the global economy, with 75% of value concentrated in customer operations, marketing, and software engineering [42].

* **US Leadership:** Lander (2025) argues early AI adoption could maintain US economic dominance, particularly if infrastructure investments keep pace with demand.

* **Global GDP Boost:** Reports such as [6] and [35] estimate that AI could contribute between $4.8 trillion and $19.9 trillion to global GDP by 2030, with generative AI alone adding $1 trillion to the U.S. economy within a decade [36].

* **Productivity Acceleration:** Early adopters and industry- focused analyses predict annual GDP growth rates of 0.5% to 1.3% due to AI-driven productivity, especially in sectors like manufacturing and finance [30], [50].

* **Sectoral Transformation:** Small businesses could see up to a 25% productivity increase [29], while manufacturing efficiency gains are estimated at 15-30% [30].

B. Moderate Impact Scenarios and Cautious Projections

Other authors urge caution, suggesting that macroeconomic gains may be more modest:

* **Gradual Adoption:** Green (2025) finds current AI impacts negligible on aggregate metrics, suggesting benefits may take longer to materialize [91].

* **Sectoral Variation:** Cowen (2024) predicts uneven adoption, with competitive
<figure><img src="image_24.png" /></figure>

industries adopting quickly while government and education lag [43].

* **Task-Based Limits:** Acemoglu (2024) emphasizes diminishing returns as AI moves from "easy-to-learn" to complex tasks, capping productivity gains [12].

* **Limited TFP Gains:** [12] projects total factor productivity gains from AI at only 0.55-0.71% over ten years, citing challenges in automating complex tasks.

* **GDP Measurement Issues:** [34] argues that consumer surplus from AI may not be fully captured in GDP statistics, potentially overstating the realized economic benefit.

* **Efficiency Trap:** The "AI efficiency trap" described in [57] highlights that productivity gains do not automatically translate into improved worker welfare or broad-based prosperity.

### C. Labor Market Scenarios

* **Displacement and Polarization:** According to [37], up to 40% of global jobs could be transformed or replaced, with [13] warning of increased wage polarization and inequality.

* **Policy-Driven Outcomes:** [14] and [58] emphasize that the distribution of AI's benefits depends on deliberate policy choices, workforce reskilling, and social safety nets.

* **Potential for New Job Creation:** Some studies, such as [8], suggest that AI could also create new job categories, especially if complemented by targeted education and training programs.

### D. Geopolitical Outcomes and National Scenarios

* **US-China Race:** Singer (2024) analyzes competing approaches, with China's state-led model potentially gaining in applied domains while US leads in innovation [22].

* **Regional Divergence:** Amundi (2025) projects developed economies capturing disproportionate benefits due to existing digital infrastructure [44].

These scenarios highlight how structural factors (adoption rates, task complexity, policy responses) and measurement challenges lead to divergent projections. The most likely outcome may combine elements across scenarios - significant but uneven productivity gains, accompanied by distributional challenges and geopolitical competition.

* **US-China Competition:** [20], [21], and [23] describe scenarios where the U.S. and China vie for AI supremacy, with outcomes dependent on investment, data access, and policy agility.

* **Military and Infrastructure Investment:** Military AI investment is projected to grow at 12.8% CAGR through 2030 [38], while U.S. federal AI infrastructure spending is increasing at 18.4% annually [69].

* **Competitive Advantage Debate:** Some authors, such as [73], argue that AI's widespread adoption could erode competitive advantage, while others see sustained leader-ship hinging on innovation and data ecosystems [75].
<figure><img src="image_25.png" /></figure>

E. Comparative Analysis

The differences in projections stem from varying assumptions about:

- The pace of AI adoption and diffusion across sectors.

- The effectiveness of policy interventions and workforce adaptation.

- The ability to measure and distribute AI-driven gains equitably.

- The geopolitical context and international collaboration or competition.

F. Pessimistic Scenarios

- **Inequality Risks:** Sood and Khanna (2024) warn of job polarization and wage stagnation if productivity gains aren't widely shared [13].

- **GDP Illusion:** Turner (2025) argues AI may create consumer surplus that doesn't appear in GDP statistics, leading to "everywhere but invisible" impacts [34].

- **Infrastructure Constraints:** Multiple sources warn AI's power demands (e.g., 5GW datacenters) could strain US infrastructure [9].

**In summary,** while some authors envision transformative economic gains and productivity booms, others caution about modest aggregate effects, labor market disruption, and the risk of deepening inequality. The most optimistic scenarios rely on proactive policy, investment in human capital, and global cooperation, while the most pessimistic highlight measurement limitations and structural barriers to broad-based prosperity.

XIII. Comparative Impact of Different AI Types: Narrow AI, Agentic AI, and AGI

As the field of artificial intelligence evolves, it is critical to distinguish between different types of AI systems—namely, narrow AI, agentic AI, and Artificial General Intelligence (AGI)—as each presents unique capabilities and potential economic impacts.

A. *Narrow AI (Task-Specific)*

Narrow AI (also known as "weak AI") refers to systems designed to perform specific tasks, such as language translation, image recognition, or financial forecasting. These systems have already demonstrated measurable productivity gains and sector-specific efficiencies.

- **Impact:** Focused productivity gains in specific domains (e.g., manufacturing, customer service)

- **Projections:**
  - Acemoglu (2024) estimates 0.55-0.71% TFP growth from task automation [12]

- McKinsey projects $1-2 trillion annual value from current applications [42]

- **Characteristics:** Limited scope, predictable impacts, easier integration

- **Economic Impact:** Narrow AI is credited with up to 1.3% annual GDP growth in early-
<figure><img src="image_26.png" /></figure>

adopting economies and 15-30% efficiency gains in manufacturing and logistics sectors [30], [35].

* **Labor Market Effects:** Narrow AI tends to automate routine or repetitive tasks, leading to job transformation and displacement in certain sectors, but also creating demand for new digital and analytical roles [13], [37].

* **Limitations:** The benefits of narrow AI are often unevenly distributed, with gains concentrated in data-rich industries and advanced economies [12].

## B. Agentic AI (Autonomous Systems)

Agentic AI refers to systems that can autonomously pursue goals, make decisions, and adapt to changing environments with minimal human intervention. Examples include advanced personal assistants, autonomous vehicles, and AI-powered supply chain managers.

* **Impact:** KPMG (2025) predicts competitive transformation within 24 months as agents move beyond experimentation [39]

* **Characteristics:**

- Newton (2025) highlights supply chain optimization benefits [40]

- Singh (2025) demonstrates operational efficiency gains in manufacturing [75]

* **Risks:** Barney (2025) warns of competitive parity as adoption spreads

* **Potential Impact:** Agentic AI could unlock higher-order productivity by enabling end-to-end automation of complex workflows, dynamic resource allocation, and real-time strategic decision-making.

* **Economic Scenarios:** Some projections suggest agentic AI could double the economic impact of current narrow AI systems, potentially contributing several trillions of dollars to global GDP by 2035 if widely adopted [6], [41].

* **Risks and Challenges:** The deployment of agentic AI raises concerns about increased job displacement, ethical decision-making, and the need for robust governance frameworks to ensure alignment with human values [4].

* **Comparative Outcome:** While narrow AI optimizes individual tasks, agentic AI could reconfigure entire industries and business models, amplifying both opportunities and risks.

## C. Generative AI

* **Impact:**

- Cognizant projects $1 trillion US GDP impact over 10 years [36]

- 40% of working hours potentially affected (McKinsey) [42]

* **Sectoral Variation:** Strongest in content creation, design, and R&D

* **Risks:** FTC (2023) notes competition concerns from data advantages [81]
<figure><img src="image_27.png" /></figure>

D. *Comparative Analysis and Outlook*

* **Current Impact:** Narrow AI is already reshaping productivity and competitiveness in targeted sectors.

* **Near-Term Evolution:** Agentic AI promises broader automation and economic transformation, but also height- ened governance challenges.

* **Long-Term Scenario:** AGI could fundamentally alter the nature of work, value creation, and global economic order, with outcomes highly dependent on policy and societal choices.

E. *Agentic AI and Economic Transformation*

Agentic AI refers to systems designed to pursue specific goals autonomously, performing actions in an environment to achieve desired outcomes. Unlike conventional AI tools that assist human users or perform predefined tasks, agentic AI systems can plan, reason, and execute multi-step processes with minimal human intervention. This inherent autonomy has profound implications for economic transformation:

* **Enhanced Automation and Efficiency:** Agentic AI could automate entire workflows and decision-making processes that currently require human oversight. This goes beyond simple task automation (as discussed in [12]) to autonomous process management, potentially leading to significantly higher productivity gains and cost reductions across industries. For example, in manufacturing, agentic AI could manage supply chains, optimize production schedules, and even self-diagnose and repair machinery, augmenting the impacts noted in [30], [31].

* **New Service and Business Models:** The ability of agentic AI to operate independently could enable entirely new service offerings and business models. These might include fully automated financial advisory services [50], self-managing logistics networks [40], or personalized educational platforms that adapt and deliver content autonomously. This could drive innovation and competitiveness in ways that current AI applications cannot [52].

* **Accelerated Innovation Cycles:** Agentic AI systems capable of autonomous research and development could dramatically shorten innovation cycles. By independently experimenting, analyzing data, and generating hypotheses, they could accelerate scientific discovery and technological advancement, further contributing to economic growth as anticipated by optimists like Aghion and Bunel [33].

However, the increased autonomy of agentic AI also presents heightened risks, particularly concerning job displacement and the need for robust ethical and regulatory frameworks to ensure control and accountability [56].

F. *Artificial General Intelligence (AGI) and Macroeconomic Singularity*

Artificial General Intelligence (AGI), defined as AI with human-level cognitive abilities across a
<figure><img src="image_28.png" /></figure>

wide range of tasks, represents a hypothetical yet highly significant future scenario. Should AGI be achieved, its economic impact would likely be far more radical and pervasive than any current or near-term AI application. The potential economic outcomes associated with AGI often border on a" singularity," a point beyond which economic and technological growth becomes uncontrollable and irreversible, leading to unforeseeable changes to human civilization [2].

AGI refers to hypothetical AI systems with human-level cognitive abilities, capable of understanding, learning, and applying knowledge across a broad range of tasks.

* **Transformative Potential:** AGI could revolutionize the global economy, potentially driving exponential growth, automating creative and strategic roles, and solving complex scientific and societal challenges [24], [33].

* **Divergent Projections:** Some authors forecast AGI could lead to unprecedented prosperity and abundance, while others warn of massive labor market upheaval, economic inequality, and existential risks if not properly governed [34], [57].

* **Policy Implications:** The realization of AGI's benefits depends on international cooperation, ethical safeguards, and proactive policy to manage transition risks [5], [25].

* **Projections:**

– Aghion et al. model potential” singularity” scenarios with exponential idea production [2]

– Turner (2025) questions measurable GDP impact despite transformative potential [34]

* **Economic Implications:**

– Complete redefinition of comparative advantage [104]

– Potential for both utopian abundance and labor dis- placement extremes

* **Uncertainty:** Most analyses emphasize speculative nature of projections

* **Unprecedented Productivity Surges:** AGI, by its very nature, would be capable of performing virtually any intellectual task that a human can, but at potentially vastly greater speeds and scales. This would imply an exponential increase in productivity across all sectors, far exceeding the modest gains predicted by Acemoglu for current AI [12]. This could lead to a rapid acceleration of economic growth, potentially validating the most optimistic projections of AI's contribution to GDP [35], [60].

* **Fundamental Labor Market Restructuring:** The emergence of AGI would fundamentally reshape labor markets. While current AI can complement human workers or automate specific tasks, AGI could automate entire professions and even intellectual creation. This would necessitate a complete re-evaluation of the role of human labor, income distribution models, and social welfare systems, far beyond the challenges posed by current AI- driven job transformations [13], [14], [37]. The concept of an" AI efficiency trap" [57] would take on an entirely new dimension.
<figure><img src="image_29.png" /></figure>

* **New Economic Paradigms:** AGI could lead to the creation of entirely new economic paradigms, where intellectual capital is generated primarily by AI, and human roles shift towards managing, governing, or consuming AI-produced goods and services. The current frameworks for understanding economic growth and competition [46] might become obsolete, demanding novel economic theories and policy approaches.

The comparison between agentic AI and AGI highlights a spectrum of potential economic futures. While agentic AI represents a significant advancement in automation and autonomy that builds on existing trends, AGI implies a more profound, potentially discontinuous, economic transformation that could challenge fundamental assumptions about labor, capital, and growth. Policymakers must therefore consider these distinct trajectories when formulating long-term strategies for AI governance and economic adaptation [25], [26].

## G. Differentiated Impacts of AI Architectures: Agentic AI and AGI

The economic implications of AI are not monolithic, but rather depend significantly on the nature and capabilities of the AI systems being deployed. While much of the current discussion revolves around narrow AI applications, the emergence of more advanced forms, such as agentic AI and Artificial General Intelligence (AGI), promises to introduce new layers of complexity and potentially distinct economic outcomes.

## H. Comparative Impact of AI Types

The economic implications vary significantly across different classes of artificial intelligence, each presenting distinct opportunities and challenges.

Table VII categorizes different AI types by their timeframe, potential GDP impact, and associated labor disruption risks, highlighting the increasing economic influence and risks from Narrow AI to AGI.

The economic impacts scale with AI capabilities, but so do implementation challenges and disruption risks. While narrow AI offers measurable but bounded benefits, advanced forms could trigger nonlinear economic shifts whose trajectories remain highly uncertain.

In summary, the economic impact of AI will depend not only on technological advances but also on the type of AI deployed, the speed of adoption, and the effectiveness of governance. Policymakers and industry leaders must anticipate and prepare for the distinct opportunities and risks associated with each AI paradigm.

# XIV. EMERGING CHALLENGES, FUTURE DIRECTIONS AND RISKS
<figure><img src="image_30.png" /></figure>

A. Infrastructure Demands

AI's computational requirements pose significant infrastructure challenges [9], particularly regarding energy consumption [82].

B. Workforce Impacts

Studies project significant labor market disruptions [36], with 90% of US jobs potentially affected. Effective response strategies are critical [59].

C. Research Priorities

Key areas for future research include:

* Long-term productivity measurements [91]

* International governance frameworks [105]

* Ethical considerations [86]

D. Policy Recommendations

Based on our analysis, we recommend:

* Balanced regulation fostering innovation [70]

* Strategic investments in AI infrastructure [19]

* Comprehensive workforce transition programs [54]

<table><caption>TABLE VII AI TYPES AND ECONOMIC IMPACT PROFILES</caption><thead><tr><td>AI Type</td><td>Timeframe</td><td>GDP Impact Potential</td><td>Labor Disruption Risk</td></tr></thead><tbody><tr><td>Narrow AI</td><td>Near-term</td><td>Medium (0.5-2% growth)</td><td>Low-Medium</td></tr><tr><td>Agentic AI</td><td>Medium-term</td><td>High (sector-specific)</td><td>Medium-High</td></tr><tr><td>Generative AI</td><td>Medium-term</td><td>High (1-4% growth)</td><td>High</td></tr><tr><td>AGI</td><td>Long-term</td><td>Extreme (uncertain direction)</td><td>Extreme</td></tr></tbody></table>

XV. CONCLUSION

This comprehensive analysis demonstrates that Artificial Intelligence represents both a transformative opportunity and a complex challenge for U.S. economic competitiveness. Our examination of macroeconomic projections reveals significant potential, with AI likely to contribute between $4.8 and $19.9 trillion to global GDP by 2030 and drive 0.5-1.3% annual productivity growth, though these gains depend critically on adoption rates and complementary investments. The labor market analysis presents a dual reality: while 20-40% of jobs face transformation through AI, strategic workforce development and policy interventions can convert displacement risks into opportunities for job quality improvement and new career pathways.

The intensifying U.S.-China AI competition, evidenced by projected military AI markets of $38.5 billion and $32 billion respectively, underscores AI's geopolitical significance. Our sectoral analysis highlights manufacturing efficiency gains of 15-30% and small business productivity improvements
<figure><img src="image_31.png" /></figure>

up to 25%, demonstrating AI's uneven but widespread economic impacts. The comparison of AI paradigms—from narrow AI's task-specific automation to AGI's potential exponential effects—reveals an evolving technological landscape that demands differentiated policy responses.

Three critical policy imperatives emerge from this analysis. First, substantial investment in digital infrastructure and R&D is needed to maintain technological leadership while addressing AI's growing energy demands. Second, comprehensive workforce transition programs must prepare workers for AI- augmented jobs through reskilling initiatives and strengthened social safety nets. Third, international governance frameworks should balance innovation promotion with ethical considerations and market competition safeguards.

As AI capabilities evolve from narrow applications toward agentic systems and potentially AGI, policymakers must adopt adaptive approaches that foster innovation while mitigating risks of inequality and market concentration. Future research should focus on quantifying distributional impacts across economic sectors and demographic groups, evaluating policy effectiveness in real-world settings, and developing metrics to track AI's contribution to inclusive growth. Ultimately, the nations that will thrive in the AI era are those that can harness its productivity potential while ensuring broad-based participation in its economic benefits.

AI represents a dual-edged sword for global economic competitiveness. While projections indicate substantial GDP growth potential [6], realization requires addressing labor market disruptions [8], geopolitical tensions [22], and governance challenges [25]. The U.S.-China technological race underscores AI's strategic importance [20], but sustainable advantages will emerge from workforce investments [60], competitive markets [72], and international cooperation [24]. Future research should quantify distributional impacts and policy effectiveness across economic segments.

International collaboration will also be vital to address the global implications of AI and ensure its equitable development. The continued vigilance and adaptive policymaking, as discussed in numerous sources [27], [101], [106]–[112], will be crucial in navigating the AI-driven future successfully, ensuring that AI becomes a tool for widespread human benefit rather than a source of exacerbating disparities.

While significant opportunities exist for productivity growth and innovation [11], realizing these benefits requires careful policy design and international cooperation [80]. The coming decade will be crucial in determining whether AI becomes an engine of broad-based prosperity or a source of increased inequality and market concentration.
<figure><img src="image_32.png" /></figure>

## DECLARATION

The views are of the author and do not represent any affiliated institutions. Work is done as a part of independent research. This is a pure review paper and all results, proposals and findings are from the cited literature.

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