Politics

EU report warns that suing AI for copyright infringement risks repeating Napster’s mistakes

Monday, 8 December 2025 3:53PM UTC

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.

When lawmakers and rights holders consider how to regulate generative artificial intelligence, they would do well to recall the online‑piracy battles of the early 2000s, a new European Parliament‑commissioned report argues. According to the original report, heavy reliance on litigation and ad‑hoc enforcement proved a blunt instrument against file‑sharing services such as Napster; the paper’s author, Professor Christian Peukert, contends that repeating that approach for AI would be economically damaging and socially counterproductive. ^[1]^[2]

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]

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‑building needs with creators’ economic interests. ^[1]

The analysis emphasises scale as a decisive factor: unlike music‑streaming 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]^[2]

Perhaps most controversially, the report rejects "opt‑out" systems that let rightsholders exclude works from training datasets. It argues that opt‑outs create gaps that bias models and reduce overall societal value, and from an economic‑welfare perspective ranks opt‑out as the least desirable policy outcome , worse even than the status quo. The paper therefore recommends against permitting opt‑outs for training use. ^[1]

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]

Other recent scholarship and policy analysis map complementary approaches. One academic paper proposes multilayered pre‑training filtering pipelines to shift protection from post‑training detection to pre‑training prevention, aiming to safeguard creator rights while enabling AI innovation; legal commentators note striking differences between the EU’s closed list of exceptions and the US’s flexible fair‑use approach, each with distinct implications for enforceability and market certainty. These perspectives underline that any European statutory scheme would need technical, administrative and cross‑jurisdictional design work to be effective. ^[3]^[5]^[6]

Whether rights holders and legislators will accept compulsory licensing remains uncertain. The report provides a policy road‑map 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‑first 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‑setting and mechanisms to ensure broad access for training. ^[1]^[2]^[3]

##Reference Map:

^[1] (TorrentFreak / Peukert, European Parliament) - Paragraph 1, Paragraph 2, Paragraph 3, Paragraph 4, Paragraph 5, Paragraph 6, Paragraph 8
^[2] (TorrentFreak summary) - Paragraph 1, Paragraph 4, Paragraph 8
^[3] (arXiv paper) - Paragraph 7
^[5] (EU Directive on Copyright in the Digital Single Market) - Paragraph 7
^[6] (Hannes Snellman analysis) - Paragraph 7

Source: Noah Wire Services

More on this

https://torrentfreak.com/eu-report-distills-ai-training-lessons-from-napster-piracy-era-dont-sue-license/ - Please view link - unable to able to access data
https://torrentfreak.com/eu-report-distills-ai-training-lessons-from-napster-piracy-era-dont-sue-license/ - An in-depth analysis on AI training and copyright, commissioned by the European Parliament, cautions against repeating the mistakes of the early 2000s when online piracy boomed. Instead of suing AI companies into oblivion or relying on "opt-out" mechanisms, the report sees "statutory licenses" as the optimal solution. This effectively authorizes the use of copyrighted works for AI training and should ultimately benefit creators and AI companies.
https://arxiv.org/abs/2512.02046 - This paper examines the regulatory landscape of AI training data governance in major jurisdictions, including the EU, the United States, and the Asia-Pacific region. It identifies critical gaps in enforcement mechanisms that threaten both creator rights and the sustainability of AI development. The authors propose a multilayered filtering pipeline to shift copyright protection from post-training detection to pre-training prevention, aiming to protect creator rights while enabling continued AI innovation.
https://www.napster.ai/terms-of-service - Napster's Terms of Service outline provisions under the European Union Digital Services Act (DSA), which regulates digital intermediary services in the EU. The terms set rules on the role of providers, impose content moderation requirements, and transparency obligations. Users are prohibited from providing, publishing, or transmitting content that violates these terms or applicable EU laws. Napster may take action against unauthorized content and complies with the Digital Millennium Copyright Act (DMCA).
https://en.wikipedia.org/wiki/Directive_on_Copyright_in_the_Digital_Single_Market - The Directive on Copyright in the Digital Single Market is a European Union directive adopted in 2019, aiming to ensure a well-functioning marketplace for copyright. It introduces exceptions for text and data mining (TDM) for scientific research and any TDM, which are relevant to AI training. The directive has been implemented in EU member states, with some countries considering the applicability of these exceptions to generative AI training.
https://www.hannessnellman.com/news-and-views/blog/ai-and-copyright-part-ii-the-european-and-us-approach/ - This article compares the European and US approaches to AI and copyright. It discusses the EU's closed list of exceptions, which may require AI developers to license every piece of content used for training unless exceptions apply. In contrast, the US applies a flexible, case-by-case fair use doctrine, leading to unpredictability in outcomes. The article highlights the potential impact of these differing approaches on AI development and legal compliance.
https://www.euronews.com/next/2024/02/21/how-ai-tools-are-trained-is-alarming-creators-is-copyright-law-enough-of-a-roadblock-to-stop-them - This article discusses concerns among creators regarding AI tools trained on copyrighted material. Experts suggest that the issue of AI training using copyrighted works will likely be interpreted by courts on a case-by-case basis. The article highlights the complexity of determining copyright infringement in AI training and the need for clear legal frameworks to address these challenges.

Noah Fact Check Pro

The draft above was created using the information available at the time the story first emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed below. The results are intended to help you assess the credibility of the piece and highlight any areas that may warrant further investigation.

Freshness check

Score: 10

Notes: The narrative is based on a recent European Parliament-commissioned report titled "The Economics of Copyright and AI," published on 3 December 2025. This report is the primary source, and the article provides a timely summary of its findings. No evidence of recycled or outdated content was found. The report is accessible on the European Parliament's website. ([europarl.europa.eu](https://www.europarl.europa.eu/thinktank/es/document/IUST_STU%282025%29778859?utm_source=openai))

Quotes check

Score: 10

Notes: The article includes direct quotes from Professor Christian Peukert, the author of the report. These quotes are consistent with the content of the original report, with no discrepancies or variations found. No evidence of reused or misquoted material was identified.

Source reliability

Score: 10

Notes: The narrative originates from TorrentFreak, a reputable outlet known for its coverage of digital rights and technology issues. The primary source is the European Parliament's official publication, authored by Professor Christian Peukert, a recognised expert in digitisation and intellectual property. The report is accessible on the European Parliament's website. ([europarl.europa.eu](https://www.europarl.europa.eu/thinktank/es/document/IUST_STU%282025%29778859?utm_source=openai))

Plausability check

Score: 10

Notes: The claims made in the narrative align with the findings of the European Parliament-commissioned report. The report's recommendations, such as the adoption of compulsory, statutory licensing for AI training, are consistent with the article's content. The language and tone are appropriate for the subject matter, and the article provides specific details, including the report's title, author, and publication date, enhancing its credibility.

Overall assessment

Verdict (FAIL, OPEN, PASS): PASS

Confidence (LOW, MEDIUM, HIGH): HIGH

Summary: The narrative accurately summarises the findings of a recent European Parliament-commissioned report, with no evidence of recycled content, misquoted material, or reliability issues. The claims are plausible and supported by specific details, leading to a high confidence in the assessment.

AI regulation
Copyright law
European Parliament