JOURNAL ARTICLE

Diverging Approaches to Intellectual Property and a Reform Proposal Prompted by AI.

  • Published In: GRUR International: Journal of European & International IP Law, 2025, v. 74, n. 5. P. 416 1 of 3

  • Database: Legal Source 2 of 3

  • Authored By: Ghidini, Gustavo 3 of 3

Abstract

This article examines the historic and ongoing divergence between two juris-political approaches to intellectual property (IP) law: a "protectionist" trend that expands exclusive rights to maximize private interests, and a "reductionist" trend that seeks to balance IP protection with competition and social access to culture. It highlights how European Union legislation exemplifies protectionist tendencies rooted in traditional industrial models emphasizing fixed investments and monopolistic exploitation, while also tracing the rise of reductionist approaches favoring innovation-sharing, open standards, and cooperative business models, especially in the digital and network economy. The article discusses the Essential Facilities doctrine as a legal mechanism shifting IP from absolute property rights toward liability-based access, and argues for a new IP paradigm adapted to the digital age and artificial intelligence (AI), proposing a model of open, fair-compensated access to data and AI-generated outputs inspired by historic Italian copyright law. It critically addresses challenges posed by current IP regimes to AI development, particularly regarding data use and copyright exceptions, and calls for systemic reform to reconcile creators’ rights with technological and societal needs.

Additional Information

  • Source:GRUR International: Journal of European & International IP Law. 2025/05, Vol. 74, Issue 5, p416
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2025
  • ISSN:26328550
  • DOI:10.1093/grurint/ikaf026
  • Accession Number:185320934
  • Copyright Statement:Copyright of GRUR International: Journal of European & International IP Law is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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