Artificial intelligence is now a critical component of business operations, from pricing to marketing. This transition sees AI moving from mere experimental tools to becoming essential to business infrastructure. The scrutiny of regulators over this shift is intensifying, as they explore the implications of AI in perceived competitive structures. Often, AI’s autonomy in decision-making bypasses traditional indicators of collusion, leading to market outcomes that may mimic anticompetitive behavior.
How Have Regulatory Actions Addressed AI Concerns?
Recent enforcement actions highlight regulators’ approaches to AI in business contexts. In the United States, the Department of Justice scrutinized RealPage’s use of AI in rent recommendations, which allegedly used private competitor data. Meanwhile, the European Commission suggested that using a common pricing algorithm might violate competition laws, even absent direct communication between firms. These examples show a broader regulatory focus on AI’s role across different markets and industries.
What Are Emerging Antitrust Risks with AI?
The reach of AI in business strategies creates potential antitrust scenarios. An analysis by Steptoe underlines that AI could simulate market or customer allocation without necessary human involvement. Algorithms that personalize pricing might draw regulatory attention under various competition laws if they exploit consumers’ willingness to pay. Furthermore, AI-driven predatory pricing, underpricing certain competitors, adds another layer of complexity and potential scrutiny in legal and economic evaluations.
Steptoe also emphasizes the growing influence of AI on network effects and market dominance. Large datasets, crucial for AI operations, can limit competition by reinforcing existing power dynamics and erecting barriers to new market entrants. In this context, control over data becomes instrumental in reinforcing a company’s position before consumer detriment becomes evident.
Research dating back several years already identified potential issues with shared algorithmic tools possibly inciting unlawful coordination. Regulatory bodies have consistently monitored such occurrences.
Enforcement agencies are anticipated not to create new AI-specific rules but rather to adjust existing doctrines. Companies heavily foraying into AI should anticipate increased scrutiny regarding algorithm structure and data dependency. This approach requires businesses to evaluate their AI systems’ competitive impacts proactively, safeguarding against regulatory confrontations.
“While the price-fixing issues in RealPage and Gibson have dominated the landscape, the rapid integration of AI into business strategies across industries is creating new and evolving antitrust risks,” articulate the authors at Steptoe. Their analysis further mentions, “AI systems that learn to avoid competing head-to-head for customers or geographies effectively replicate market or customer allocation without any human agreement.”
