Artificial Intelligence and Antitrust Regulation: Lessons from the European Union, United States and China

This article explores the implications of artificial intelligence (AI) for antitrust regulation through a comparative analysis of the European Union, the United States, and China. The growing use of algorithms in pricing, recommendation, and platform management has created efficiency gains but also new risks of collusion, monopolization, and exclusionary conduct. Employing a normative legal research method with a comparative approach, the study examines statutes, regulatory frameworks, case law, and academic debates across the three jurisdictions. The findings reveal divergent approaches: the EU has developed a proactive and risk-based framework through the AI Act; the U.S. relies on case-driven enforcement rooted in the Sherman, Clayton, and FTC Acts; while China combines antitrust law with state-led governance of data and algorithms. These differences highlight the limitations of conventional doctrines in addressing algorithmic collusion and system design choices that may substitute for explicit agreements. This study emphasize that complementary measures such as algorithmic transparency, independent audits, and interoperability requirements are needed to mitigate risks without stifling innovation shall be applied within the Indonesian competition law regime.