No Data, No Party: Why Competition for High‑Quality Journalism Matters in the GenAI Race

In this thought piece for The Platform Law Blog, author Konstantina Bania (Geradin Partners) shares the view that high-quality, human-generated data—particularly professional journalism—has become a critical competitive input for generative AI, alongside compute and talent. She argues that discussions around AI have focused too heavily on infrastructure and expertise while overlooking the role of high-quality data in determining model performance and shaping competition.

The author explains that large technology firms enjoy a significant competitive advantage through their extensive first-party data, while smaller AI developers must rely on licensing third-party content such as news and media. This imbalance, she argues, creates a self-reinforcing cycle in which dominant firms continue to improve their AI models while smaller competitors struggle to access the high-quality data needed to compete effectively.

Bania contends that copyright law alone cannot address these challenges. She argues that the practices of dominant AI developers—such as imposing unfair licensing terms, leveraging market power to secure access to publishers’ content, or using that content to strengthen their own AI services—also raise important competition law concerns. She points to ongoing scrutiny of Google’s AI Overviews as an example of how these issues extend beyond intellectual property into market fairness.

The article continues, advocating for an integrated legal approach that combines copyright law, Article 102 TFEU, the Digital Markets Act, and the AI Act to protect competition for third-party content. The author calls for stronger transparency around AI training data, fair licensing markets for publishers, and remedies that prevent dominant firms from consolidating control over the key inputs—compute, talent, and data—that will shape the future of generative AI…

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