The EU’s rapidly evolving digital agenda, particularly its commitment to unlocking and reusing large datasets for AI development, puts publishers at a critical crossroads.

Based on the four European Commission documents—Political Guidelines 2024–2029, the AI Continent Action Plan, the European Data Union Strategy, and the Consultation on the Use of Data in AI—this essay explains why scholarly publishers should pay close attention to these materials. The EU's strategic pivot toward an AI-driven, data-centric economy has serious implications for content ownership, research integrity, and the long-term stewardship of knowledge.

Why Scholarly Publishers Must Engage

by ChatGPT-4o

Scholarly publishers are stewards of validated knowledge and the gatekeepers of peer-reviewed research integrity. The EU’s rapidly evolving digital agenda, particularly its commitment to unlocking and reusing large datasets for AI development, puts publishers at a critical crossroads. These documents outline sweeping initiatives to simplify data-sharing rules, build AI super-infrastructure, and launch a Data Union Strategy that supports cross-border and cross-sectoral data access. All of this will directly affect how scholarly content is used, licensed, and monetized.

If publishers do not proactively engage, they risk having their content mined, commoditized, or distorted by AI developers without adequate safeguards for attribution, integrity, or financial return.

Key Issues of Relevance to Scholarly Publishers

1. The Push for Cross-Sectoral Data Reuse

The Data Union Strategy and the AI Continent Action Plan emphasize seamless, interoperable data sharing for AI development—including from public institutions and the private sector. Scholarly articles, journals, and research datasets could be swept into this “common data space” under expanded reuse policies, unless specifically protected.

2. Reframing of Legal and Governance Frameworks

The EU proposes simplifying data laws to encourage reuse, eliminate administrative burden, and create legal coherence. While this benefits innovation, it may dilute existing protections for proprietary scholarly content, especially in the context of AI training and generative models.

3. Creation of AI “Factories” and “Gigafactories”

With budgets exceeding €30 billion, these facilities aim to amass and process vast data volumes to train foundation models. Scholarly content is precisely the kind of high-quality, structured, and vetted material such models seek. The documents reference “Data Labs” collecting diverse datasets without specifying provenance safeguards.

4. Political and Economic Pressure for European AI Sovereignty

The Political Guidelines 2024–2029 and Action Plan argue that Europe must reduce its dependence on U.S.-based AI platforms. This may result in policies that favor domestic AI development even at the cost of existing IP relationships with international rights holders like publishers.

Arguments Publishers Should Make to Protect Their Interests

1. Protecting the Version of Record and Research Integrity

Publishers should assert that scholarly articles are not simply “data” but intellectual works where integrity, peer review, and version control matter. AI scraping or reuse without such context undermines scientific reliability and risks misinformation.

2. Reinforcing Licensing as an Enabler of Trustworthy AI

Licensing regimes (e.g., STM, Crossref, CCC) can be presented not as obstacles but as infrastructure for ethical AI. Publishers should position themselves as providers of structured, trusted, and legally sound datasets that AI developers can rely on.

3. Safeguarding Attribution and Value Transfer

Without attribution, AI-generated knowledge derived from scholarly works breaks the academic chain of credit. Publishers should argue for mandatory attribution and traceability mechanisms, especially when content influences model behavior.

4. Stressing the Importance of Scientific Integrity in AI Outputs

Publishers can align themselves with EU goals on democracy, truth, and societal trust by arguing that scraping academic content without context leads to AI hallucinations and misinformation.

5. Demanding Participation in EU Consultations and Frameworks

Given the active calls for evidence and open feedback (e.g., until 18 July 2025), publishers must engage directly with the European Commission to shape how data strategies unfold.

Conclusion: Strategic Imperatives for Scholarly Publishers

The EU’s digital and AI ambitions are on an unstoppable trajectory—positioning data as the fuel of European prosperity, sovereignty, and competitiveness. Scholarly publishers must avoid being passive bystanders in this process. Instead, they should assert themselves as partners in responsible innovation by:

• Framing themselves as providers of verified knowledge essential for trustworthy AI;

• Advocating for content usage frameworks that uphold integrity, licensing rights, and attribution;

• Insisting on purpose-bound data use with clearly defined legal protections;

• Participating in shaping the Data Union Strategy before it becomes a fait accompli.

Europe’s data revolution must not come at the expense of scholarly values or the sustainability of the knowledge ecosystem. It must be built on partnerships that honor intellectual labor, not on silent expropriation.