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TRUVACE RECORD VERSION
record: TRV-2026-2248
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-25T14:21:01.701433Z
status: published
lens: p_space
sector: entertainment
headline: AI AND COPYRIGHT: FROM A DOCTRINAL CRISIS TO A HYBRID MODEL OF COLLECTIVE LICENSING
dek: The explosive development of generative artificial intelligence (hereinafter – AI) has triggered a fundamental crisis within intellectual property law. A central conflict has emerged between artificial intelligence developers, who require access to vast datasets for model training, and rights holders, who seek to protect their works and receive fair compensation for their use. The purpose of this article is to demonstrate the inadequacy of existing legal doctrines in addressing the use of copyrighted data for tr…
gain_title: (none)
problem_title: Existing US fair use litigation and the EU TDM opt-out are argued to leave AI training on copyrighted works legally unstable and unable to deliver meaningful compensation to creators.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Existing US fair use litigation and the EU TDM opt-out are argued to leave AI training on copyrighted works legally unstable and unable to deliver meaningful compensation to creators.
problem_evidence: the EU's TDM opt-out mechanism has been shown to be functionally incapable of providing meaningful compensation to creators | The case-by-case litigation engendered by the fair use doctrine proves to be an inefficient and unpredictable means of governance
quick_read: A peer-reviewed legal study examines the clash between generative AI developers, who need large datasets for training, and rights holders, who want protection and fair payment. It compares US and EU law and concludes that neither fair use nor the TDM exception resolves the conflict.

The study matters because creators' compensation and legal certainty for AI developers both depend on which governance model prevails. Its proposed hybrid compulsory collective licensing model is a scholarly proposal, and the text gives no evidence that it has been adopted or tested in practice.
limitation: The collective licensing model is the author's proposed hypothesis, and the abstract provides no evidence of implementation or measured outcomes.
tag: Evidence-backed problem
key_points: The article analyzes US and EU law using formal-legal, comparative-legal, systemic and synthesis methods. | It calls fair use flexible but unpredictable, and the TDM exception formalized but ineffective. | Systemic analysis looks at challenges AI poses to authorship, originality and economic incentives. | The author proposes a hybrid model of compulsory collective licensing as the most rational alternative.
rundown: The source frames generative AI's growth as triggering a fundamental crisis in intellectual property law. The central dispute pits developers needing vast training datasets against rights holders seeking to protect works and be compensated.

The methodology combines analysis of US and EU norms with a comparison of the two approaches' relative effectiveness. Copyright is treated as an integrated system whose core elements, authorship, originality and economic incentives, are challenged by AI.

The conclusion is that continued reliance on legacy doctrines produces perpetual instability. The proposed remedy is a hybrid architecture of compulsory collective licensing, presented as a hypothesis formulated by the author.
sources:
- peer_reviewed | Legal Horizons | https://doi.org/10.54477/lh.25192353.2025.3.pp.68-76 | 2025-08-29
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