Copyright Is Not Enough

Copyright Is Not Enough

Reporting

Why writers need new strategies to stand up to AI.

JULY 7, 2026

In 2022, U.K. tech company Stability AI released Stable Diffusion, a text-to-image generative artificial intelligence (GenAI) model. Its intention: “democratizing image generation” by “empowering billions of people to create stunning art within seconds.” Among the billions of images in the dataset on which Stable Diffusion was trained were millions of photographs and other visual works scraped from the stock photography website Getty Images. Getty acts as an intermediary, licensing works created by photographers around the world to third parties for use in publications, social media and advertising. Having been trained on many of these works, Stable Diffusion could generate images that looked very similar to Getty’s works and even bore a Getty-style watermark. Stability had not asked Getty for permission to use its images for training Stable Diffusion.

Outraged, Getty wanted legal redress. In January 2023, it sued Stability for copyright and trademark infringement in the English Court — a lawsuit that would lead to the United Kingdom’s first legal judgement on the troubled question of how copyright law applies to GenAI.

Copyright infringement occurs when the whole or a substantial part of a work protected by copyright — such as a photograph, illustration or novel — is reproduced without the author’s permission. This includes making, storing or distributing copies of a work, and dealing with infringing copies, such as importing pirated books or DVDs. In the lawsuit, Getty made three copyright claims. First, that Stability made and stored copies of Getty’s works and used these to train Stable Diffusion. Second, that Stability reproduced a substantial part of Getty’s works in Stable Diffusion’s outputs. Third, in a novel legal argument, Getty claimed that Stable Diffusion, i.e. the model itself, was an infringing copy of Getty’s works.

Had Getty’s case succeeded, Stability would have had to pay damages and perhaps even remove Stable Diffusion from the U.K. market. This would have set a useful precedent for all copyright-holders whose works have been used for AI training without their consent. But at trial, Getty’s copyright case hit two roadblocks. There was no evidence that the training process had taken place in the U.K. and therefore within the English Court’s jurisdiction. Stability also showed that it had subsequently blocked the prompts used to generate outputs in the style of Getty’s works. Getty was forced to abandon its first and second claims.

The remaining copyright issue before the court was whether Stable Diffusion itself was an infringing copy of Getty’s works, which Stability had imported into the U.K. after training the model elsewhere. After considering expert evidence on how the model works, the judge decided that Stable Diffusion was not an infringing copy because it did not store any of Getty’s works. She did, however, observe that Getty may be able to maintain a claim in the jurisdiction where the model was trained (in the United States, or elsewhere). Getty’s litigation against Stability in the U.S. is ongoing.

Models may be developed in one country, trained on servers in another using data scraped from websites hosted in multiple countries, before being deployed in digital systems and products worldwide. This makes it difficult for creators and rights-holders like Getty to determine exactly where the copying has taken place and who is responsible.

Writers, artists and other creators have long viewed copyright as their first line of defense when threatened with the theft of their work. As a copyright lawyer working in England, I’ve been advising the creative industries on the copyright implications of GenAI since the launch of Stable Diffusion, ChatGPT and other models. The theft is twofold. First, AI companies have used works protected by copyright to train their models without the creators’ permission, which also deprives creators of income in the form of licensing fees. Second, GenAI output can reproduce part of a creator’s work or imitating their style, free riding on the years spent developing their craft. This feels unfair, especially when much AI development is being undertaken by large corporations with deep pockets.

But the judgment in Getty v. Stability indicates the challenges of using copyright law to protect creators when GenAI models are involved. Developing AI models requires resources and labor procured from a complex global supply chain. Models may be developed in one country, trained on servers in another using data scraped from websites hosted in multiple countries, before being deployed in digital systems and products worldwide. This makes it difficult for creators and rights-holders like Getty to determine exactly where the copying has taken place and who is responsible. It may require them to bring proceedings (presuming they can afford it) in multiple countries to enforce their rights. This is further complicated by the fact that copyright laws differ between territories. Although the digital world can feel like it exists beyond borders, it is subject to a patchwork of national laws. What is permitted in one country may not be in another. And in most countries, it is not yet clear whether using copyright works for AI training without the creator’s consent is permissible.

As a result, some creators and legal experts are starting to suggest that copyright law is no longer fit for purpose, and needs to be reformed or supplemented by a different legal framework, one that takes into account the global nature of AI development and the novel threat that GenAI poses to creators. But copyright reform can harm as well as protect creators, and there is as yet no consensus on what a new framework might look like.

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The origins of modern copyright lie in 15th-century Europe, when a technological breakthrough — the advent of the printing press — enabled the mass production of literary works for the first time. The reduced time and cost of printing soon created a thriving market for unauthorized copies of published works. To address this, Britain’s first Copyright Act of 1710 granted authors the exclusive right to authorize copies of their works for up to 28 years. Ensuring that authors were compensated for their intellectual and creative efforts was intended to incentivize production of further works, which in turn would benefit wider society.

Early copyright laws like the 1710 Act were wholly territorial, meaning they applied only in the country in which they originated. This created a roaring trade in the U.S. for unauthorized copies of foreign authors’ works: American publishers would get hold of texts by British authors, for example, and then produce and sell their own copies, without having to pay those authors any money at all. Charles Dickens, who was enormously popular in the U.S., lobbied for international copyright protection during his 1842 American book tour. Recognizing that pirated copies were now a global problem, countries developed international agreements to protect their citizens’ works.

One hundred and forty years on from the Berne Convention, the basic principles of copyright remain the same, but GenAI poses a threat to authors different from anything that has existed before. Its novel technology is not only destabilizing what it means to reproduce works, but what it means to produce them.

While copyright is still territorial, each country’s rules must comply with any international agreement that it has signed. The 1886 Berne Convention for the Protection of Literary and Artistic Works is the most influential, with more than 180 signatories today including the U.S., all European Union countries, the U.K., India, Australia and China. At a minimum, each signatory must grant authors from any signatory country the exclusive right to reproduce their literary and artistic works during their lifetime and for 50 years after their death.

The author’s rights, however, are not absolute. Signatories are permitted to enact national exceptions that allow the public to copy works for certain purposes without the author’s permission, provided these purposes do not conflict with the author’s normal use of their work or unreasonably prejudice their legitimate interests. These exceptions can differ significantly from country to country. In the U.K., for example, a person can copy works for the purpose of “text and data mining” for non-commercial research. Text and data mining, or TDM, is the automated analysis of text and data to identify patterns, trends and other information, such as analyzing medical papers to find hidden links and develop new treatments. In the EU, in contrast, a person can undertake TDM for any purpose, including commercial purposes if the rights-holder has not opted out.

One hundred and forty years on from the Berne Convention, the basic principles of copyright remain the same, but GenAI poses a threat to authors different from anything that has existed before. Its novel technology is not only destabilizing what it means to reproduce works, but what it means to produce them.

AI companies often argue that the way the technology works means that the author’s consent is not required to use their works for AI training. Put simply, during the training process copies of these works are downloaded and stored before being broken down into discrete units called tokens. Text is broken into words, sub-words and characters; images into grid-like patches. The model learns the statistical relationships between these tokens so that, when prompted, the model can predict the tokens associated with the prompt and generate new content.

This means that AI advocates can claim that the model is only copying facts, ideas and concepts, which are not protected by copyright law, and does not “memorize” — i.e. store or reproduce — the author’s original expression. They also often claim that AI copying is permitted by specific national exceptions to copyright, such as the European TDM exception. As in the Getty case, this creates a loophole where a model can be trained on servers in a country where AI training is permitted under a national exception and then deployed in a country where there is no such exception.

These arguments overlook the fact that sometimes the content generated by the model does reproduce original parts of the author’s work. The model must have, in some way, taken or learned the original parts to be able to reproduce them in AI outputs.

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How existing copyright laws apply to GenAI is an unsettled question. Creators and corporate rights-holders are bringing test cases worldwide, most of them in the U.S. (113 of an estimated 143 global cases as of late May), but also in China, Europe, Canada, South Korea, Japan and Brazil. Many of these cases are still pending.

Among those cases that have been decided, courts in different jurisdictions have taken different views. The U.K. Getty v. Stability case held that Stable Diffusion did not memorize copies of works, but a German case, GEMA v. OpenAI, held that the lyrics of nine popular German songs were memorized by OpenAI’s models and reproduced almost verbatim in ChatGPT’s outputs, constituting copyright infringement in Germany. Yet the position may change again. Getty was given permission to appeal, and OpenAI’s appeal is pending. In China’s first decision on these issues, in February 2024, the Guangzhou Internet Court ruled that an AI platform had committed copyright infringement by allowing a user to generate images that featured the Japanese cartoon character “Ultraman.” It will likely take years, or even decades, for national courts to set clear precedents.

Even within the same jurisdiction, courts are taking divergent approaches. Bartz v. Anthropic and Kadrey v. Meta were both filed in the Northern District Court of Califor

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