Series

The European Union’s draft Merger Guidelines assign multiple meanings to several key terms, making competition enforcement less predictable. Anouk van der Veer, Max van Iersel, and Giorgio Monti explore the Guideline’s inconsistent use of three of these terms: competitiveness, dynamic, and capabilities.

The European Union’s draft Merger Guidelines consider import competition from foreign rivals to be a powerful competitive constraint on domestic producers, thus easing clearance for mergers between those domestic competitors. This stipulation ignores how a merger between domestic rivals can lead to subsequent trade barriers, writes Felix Montag.

Change is the core theme of the European Union’s draft Merger Guidelines, which seek to embed flexibility into merger assessment to account for the...

Although the European Union’s draft Merger Guidelines acknowledge that consolidation in digital platforms can harm the democratic process, they need to up their efforts to protect media competition and a quality public sphere. Vicky Robertson provides three steps to rectify this oversight.

The European Commission draft merger guidelines reorient merger analysis to focus on “capabilities”: how firms compete in current markets and pivot to future ones. However, the guidelines combine “capabilities” with what strategic management scholars call “resources.” This erroneous amalgamation oversimplifies merger assessment and risks inaccurate analysis, writes Selcukhan Unekbas.

The European Commission’s draft merger Guidelines adopt the advice of the Draghi report on European Union competitiveness to tailor competition law to promote goals that have traditionally fallen under industrial, trade, and national security policy. Conceptual ambiguity and the convergence of these policy areas risk undermining consumer welfare, entrenching incumbents, and opening regulation to business capture, write Annika Stöhr and Oliver Budzinski.

The largest artificial intelligence firms are able to afford access to quality data from content producers like the New York Times, while smaller startups are being left out. This dynamic risks concentrating markets and creating unassailable barriers to entry. Compulsory licenses offer one solution to lower barriers to entry for nascent AI firms without harming content producers and consumers, writes Kristelia García.

Christian Peukert argues that the market for licensing content from copyright owners like newspapers or online forums requires a standardized regime if access to this data, used to train artificial intelligence models, is to remain available for more than just the largest AI firms. A failure to maintain non-discriminatory access will result in the consolidation of both the AI and content production markets.

Is there a world where AI developers could get the training data they need through content licensing deals? Matthew Sag argues that content licensing deals between developers of artificial intelligence and content owners are only possible for large content owners and cannot feasibly apply to the bulk of producers and owners of content on the internet.

Large AI firms like OpenAI and Amazon are licensing content to train their models that they might otherwise have been able to access for free under the fair use doctrine. Mark A. Lemley and Jacob Noti-Victor write that this behavior may constitute anticompetitive acquiescence—where large firms agree to license content they don’t have to in order to raise rivals’ costs.