The user principle has long served as a compensatory mechanism in situations where a copyright claimant cannot establish traditional, causation-based financial loss. As generative AI systems increasingly train on vast corpora of copyrighted texts, this doctrinal tool has become central to contemporary litigation. This article traces the roots of the user principle, analyses leading authorities, and provides an expanded examination of its application to AI training practices. It argues that the courts are likely to adapt the doctrine into a structured, standardised, and potentially industry-wide valuation framework capable of addressing mass, data-driven infringements.
The assessment of copyright damages in English law has historically required a nuanced balance between traditional compensatory principles and the evidential realities of infringement. Where the claimant cannot demonstrate quantifiable economic loss, but the defendant has nevertheless appropriated protected works, the courts have relied on the compensatory user principle (negotiating damages), derived from the reasoning in Wrotham Park Estate Co Ltd v Parkside Homes Ltd [1974] 1 WLR 798.
Under this principle, damages are assessed by determining what a reasonable licensee would have paid a reasonable licensor immediately before the infringement occurred. It enables courts to price the value of the lost opportunity to licence the rights. Crucially, it is not restitutionary; it does not seek to strip profits but to compensate for the inherent economic value of controlled exploitation.
The exponential rise of large-language models (LLMs) and generative AI has brought the user principle to the forefront of copyright litigation. AI developers gain commercial value from large-scale ingestion and tokenisation of copyrighted works, yet authors struggle to prove lost sales or quantifiable harm. In this environment, the user principle becomes both practical and principled.
This article examines the doctrinal foundations of the user principle, its development in jurisprudence, and most significantly, its contemporary application to AI training in a data-driven technological ecosystem.
The user principle compensates the rightsholder by awarding damages equivalent to the fee that would have been agreed in a hypothetical negotiation. It focuses on the objective economic value of the use, not on any actual loss suffered. It is therefore applicable where the defendant utilises a work in a way that has economic value but leaves no direct evidence of displacement of sales.
Unlike damages based on actual loss (as in General Tire & Rubber Co v Firestone Tyre & Rubber Co Ltd [1976] RPC 197), and unlike accounts of profits under CDPA s 96(2), the user principle preserves its strictly compensatory nature. It also differs from additional damages under CDPA s 97(2), which address flagrant or reckless infringement.
While not expressly stated in statute, the user principle falls comfortably within the discretionary framework of CDPA s 97(1), enabling courts to award damages “as the court considers just.” Judicial development has therefore shaped the doctrine, refining it into a sophisticated valuation tool.
3. Development of the User Principle Through Case Law:
The Court of Appeal confirmed the cross-applicability of user-principle valuation across the spectrum of intellectual property rights, emphasising the economic logic behind hypothetical-licence analysis.
Here, the court established detailed valuation criteria including market comparators, royalty benchmarks, and exploitation patterns, now widely relied upon in entertainment and media disputes.
The court emphasised real-world market evidence, such as stock-image pricing and available licensing options, reinforcing the importance of objectively measurable benchmarks. Conduct-based adjustments and the potential for additional damages under s 97(2) were also highlighted.
Nugee J offered one of the clearest expositions of hypothetical-licence construction, considering territorial reach, duration, and exploitation rights. This structured approach is particularly relevant for AI cases involving varied types of copying (e.g., reproduction, storage, tokenisation, transformation).
Courts typically consider:
This flexible, evidence-based approach provides the scaffolding for application in AI training disputes.
Modern generative AI requires large-scale ingestion and tokenisation of copyrighted works. These acts typically involve:
Each of these processes may constitute reproduction under UK copyright law.
Most authors cannot show:
AI training is usually non-consumptive: the defendant does not commercially distribute the text. Yet the economic benefit to the developer is substantial, training data contributes to model performance, product competitiveness, and ultimately revenue.
The traditional causation model, therefore, collapses.
The user principle is precisely tailored for situations where:
Courts can therefore:
This transforms an otherwise intangible harm into a clearly quantifiable one.
Drawing on authorities such as Henderson and Reformation Publishing, courts may assess:
Given the scale of datasets, often hundreds of thousands of works, courts may adopt:
This could culminate in a standardised “AI Training Tariff,” particularly if endorsed by the Copyright Tribunal or future legislation.
AI developers face increasing exposure to:
The user principle therefore becomes not only a remedial mechanism but a regulatory force shaping industry practice.
Where thousands of works are ingested, the courts may employ:
These mechanisms reflect existing approaches in collective licensing and digital-rights management.
The user principle has matured into a central pillar of copyright damages in the United Kingdom. In the age of AI training, its compensatory logic is more relevant than ever. It offers a principled method for valuing mass, low visibility copying where traditional proof of loss breaks down. As AI litigation expands, English courts are likely to further refine the doctrine, potentially developing structured or tariff-based licensing models that could redefine the economics of AI development.
The result may be a hybrid system where judicial reasoning, collective licensing, and emerging AI regulation converge, anchored fundamentally by the compensatory rationale of the user principle.