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A new European Parliament-commissioned report recommends a statutory licensing regime for AI training, cautioning against litigation approaches reminiscent of early 2000s piracy battles, to balance innovation and creator rights.<\/p>\n<\/div>\n
When lawmakers and rights holders consider how to regulate generative artificial intelligence, they would do well to recall the online\u2011piracy battles of the early 2000s, a new European Parliament\u2011commissioned report argues. According to the original report, heavy reliance on litigation and ad\u2011hoc enforcement proved a blunt instrument against file\u2011sharing services such as Napster; the paper\u2019s author, Professor Christian Peukert, contends that repeating that approach for AI would be economically damaging and socially counterproductive. [1]<\/a><\/sup>[2]<\/a><\/sup><\/p>\n The report, titled “The Economics of Copyright and AI”, sets out a central premise: restricting access to vast amounts of published material for model training will slow innovation and reduce public welfare. Industry data and historical comparisons show that litigation against distribution platforms produced only temporary shifts in behaviour until lawful, convenient services met consumer demand , a pattern the report suggests could recur if policymakers attempt to block AI access to copyrighted content. [1]<\/a><\/sup><\/p>\n Peukert recommends a statutory, compulsory licensing regime as the most practicable solution. Under this model, AI developers would gain a guaranteed right to use published works for training while an independent authority would determine fair royalty rates to compensate creators. The report frames this as a pragmatic way to balance widespread model\u2011building needs with creators\u2019 economic interests. [1]<\/a><\/sup><\/p>\n The analysis emphasises scale as a decisive factor: unlike music\u2011streaming deals negotiated with a finite set of labels, AI training typically requires ingesting billions of texts, images and videos from the open web. The report notes that individually negotiating licences with millions of rightsholders is effectively impossible and would disproportionately disadvantage smaller AI startups. [1]<\/a><\/sup>[2]<\/a><\/sup><\/p>\n Perhaps most controversially, the report rejects “opt\u2011out” systems that let rightsholders exclude works from training datasets. It argues that opt\u2011outs create gaps that bias models and reduce overall societal value, and from an economic\u2011welfare perspective ranks opt\u2011out as the least desirable policy outcome , worse even than the status quo. The paper therefore recommends against permitting opt\u2011outs for training use. [1]<\/a><\/sup><\/p>\n The report also reinterprets the Napster precedent. Whereas early courts rejected statutory licences as a way for infringers to “pay to keep breaking the law”, Peukert argues the modern context differs: generative AI delivers large net benefits to society, estimated in some studies at tens of billions annually, and a regime permitting continued operation subject to fees could preserve that value while funding rights holders. According to the report, such a reversal of logic is justified by the net social gains from AI deployment. [1]<\/a><\/sup><\/p>\n Other recent scholarship and policy analysis map complementary approaches. One academic paper proposes multilayered pre\u2011training filtering pipelines to shift protection from post\u2011training detection to pre\u2011training prevention, aiming to safeguard creator rights while enabling AI innovation; legal commentators note striking differences between the EU\u2019s closed list of exceptions and the US\u2019s flexible fair\u2011use approach, each with distinct implications for enforceability and market certainty. These perspectives underline that any European statutory scheme would need technical, administrative and cross\u2011jurisdictional design work to be effective. [3]<\/a><\/sup>[5]<\/a><\/sup>[6]<\/a><\/sup><\/p>\n Whether rights holders and legislators will accept compulsory licensing remains uncertain. The report provides a policy road\u2011map rooted in economic welfare modelling and historical lessons, but implementation would require political agreement on scope, remuneration and governance , and careful calibration to avoid unintended market distortions. As the author warns, repeating the litigation\u2011first playbook from the Napster era risks the same cycle of costly enforcement followed by piecemeal remedies; the alternative he offers is statutory licensing coupled with independent rate\u2011setting and mechanisms to ensure broad access for training. [1]<\/a><\/sup>[2]<\/a><\/sup>[3]<\/a><\/sup><\/p>\n ##Reference Map:<\/p>\n\n