Designing Europe’s Search Data Sharing Rules for Competition in the AI Era

2026-06-18 · Source: Tech Policy Press · Field: Legal & Regulatory — Regulatory Affairs & Government Relations, Compliance & Risk Management, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

The European Commission initiated specification proceedings against Google earlier this year regarding its noncompliance with Article 6(11) of the Digital Markets Act (DMA), which mandates sharing ranking, query, click, and view data with rivals. A public consultation on preliminary measures for implementing this obligation recently concluded, with the Knight-Georgetown Institute noting the proposals are a comprehensive, AI-responsive first step. To strengthen the framework, the Commission needs to clarify data scope, especially for AI-enabled features like AI Overviews, specify privacy and utility goals for anonymization, ensure ongoing evaluation by independent experts, and establish a dispute-resolution process. This initiative mirrors historical antitrust interventions, such as the 1956 AT&T consent decree that spurred a 12% innovation increase through patent licensing. The EU's approach, particularly its broad data scope and daily sharing parity principle, contrasts with the narrower *US v. Google* antitrust case remedies, offering a potential model for global competition policy in AI-powered search.

Key takeaway

For policy makers designing digital market regulations, the European Commission's approach to search data sharing offers a robust model. You should explicitly define data scope to include AI-enabled features, clarify data sharing frequency for fresh queries, and establish clear privacy and utility goals for anonymization. Implement independent expert evaluation and a strong dispute-resolution mechanism to ensure effective, fair competition and user privacy in the evolving AI search landscape.

Key insights

Mandatory search data sharing, guided by clear scope, frequency, privacy, and process, can catalyze AI-driven competition.

Principles

• Data sharing parity with dominant players is crucial.
• Explicit privacy and utility goals must guide anonymization.
• Independent expert evaluation ensures ongoing efficacy.

In practice

• Define "visual content" to include AI features like AI Overviews.
• Clarify daily data sharing with parity principle.
• Establish robust dispute-resolution for data access.

Topics

• Digital Markets Act
• Search Data Sharing
• AI Competition
• Antitrust Enforcement
• User Privacy
• Google

Best for: CTO, Executive, Investor, Policy Maker, Legal Professional, Consultant

Related on AIssential

• Tim Berners-Lee calls for AI to preserve 'original values' of web — AI development must prioritize individual data control and privacy, mirroring the web's foundational values.
• What the EU's First Digital Markets Act Review Actually Changes — The EU's first DMA review prioritizes enforcement and evidence gathering over immediate legislative expansion, despite calls for broader scope.
• Nobody needs AI to search the Internet, court says in ruling against Google — AI-generated search summaries are "independent, new, and substantive statements," making firms liable for inaccuracies.
• The missing layer in Europe’s AI strategy: data ownership — Data ownership, not just models, is the critical competitive differentiator in the AI-driven economy.
• Meta’s Worker Surveillance Tests EU Rules on AI and Labor — Meta's employee surveillance for AI training tests regulatory limits and highlights the value of human behavioral data for agentic models.
• Apple backs Google against EU AI access proposals — Mandating third-party AI access to mobile OS raises significant privacy, security, and operational concerns for platform holders.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Tech Policy Press.