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Author:
Dr. Gaurav Solanki
Assistant Professor , Economics, SaraswatiVidhyaMandir Law College , Shikarpur Bulandshahr (U.P.)

DOI: doi.org/10.58924/rjhss.v5.iss4.p2

Published Date: 12-Sep, 2026

Keywords: Artificial Intelligence, Antitrust Policy, Market Tipping, Dynamic Efficiency, General-Purpose Technology, Regulating Innovation

Abstract:
This paper analyzes the economic trade-offs inherent in regulating Artificial Intelligence (AI) within the global technology market, focusing tightly on the tension between antitrust policy and technological innovation. As a foundational General-Purpose Technology (GPT), AI displays unique economic traits: high upfront fixed costs, near-zero marginal costs, and massive data-driven network effects. These characteristics trigger a structural tendency toward market tipping and monopolistic concentration. This study examines the diverging global regulatory approaches—specifically the European Union’s ex-ante risk-mitigation strategy (EU AI Act) versus the United States’ historically reactive, price-centric ex-post antitrust framework. Using microeconomic models of market structures and innovation incentives (Schumpeterian creative destruction versus Arrow’s replacement effect), we evaluate how aggressive antitrust remedies affect dynamic efficiency. We find that while heavy-handed structural interventions risk dampening developer incentives and driving capital to less restrictive jurisdictions, unmitigated consolidation blocks rivalrous innovation by entrenching incumbents through asymmetric infrastructure control. Finally, this paper proposes an optimized, macro-prudential, tiered regulatory framework designed to preserve competitive entry barriers without freezing technological progress.

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