Your Company Uses And Trains AI Tools. Here Is What the New Wave of Lawsuits Means for You

The Growing Copyright Risk in AI Model Training

The rapid growth of generative AI has created a set of unresolved legal questions, the most consequential being whether training large language models on copyrighted works constitutes infringement. Companies that develop and deploy AI tools face real exposure as courts, regulators, and rights holders test how existing copyright rules apply to these technologies. The core question is whether ingesting copyrighted material to train a model is permissible or whether it requires a license from the rights holder. That issue remains unsettled, and the litigation is accelerating. A new lawsuit filed on May 5, 2026, highlights the immediacy of this risk.

Case Summary

On May 5, 2026, five major publishers, Hachette, Macmillan, McGraw Hill, Elsevier, and Cengage, along with novelist Scott Turow, filed a proposed class-action copyright infringement lawsuit against Meta Platforms Inc. and Mark Zuckerberg personally in the United States District Court for the Southern District of New York (Elsevier Inc. et al. v. Meta Platforms Inc. et al., Case No. 1:26-cv-03689).

The core allegation is that Meta trained its Llama models on millions of copyrighted books and journal articles that its engineers downloaded from piracy sites, including Anna's Archive, LibGen, Z-Library, and Sci-Hub, rather than licensing the material. The complaints go further, alleging that Zuckerberg personally authorized the conduct. The suits collectively assert direct infringement, contributory infringement, and DMCA violations for stripping copyright management information. All three suits describe the alleged infringement as the product of an industry-wide "arms race," where Meta and Anthropic took legal risks to keep pace with rivals.

The plaintiffs seek an injunction requiring Meta to destroy all illegally acquired copies of copyrighted works and to stop using them in training, along with monetary damages. Meta has said it will fight the allegations and has argued that courts have found that training AI on copyrighted material can qualify as fair use.

What Makes This Case Different

These cases are more aggressive than prior AI copyright suits in several respects. First, they name Zuckerberg individually, signaling a theory of personal liability for directing the infringement. Second, they address the market-harm gap that sank a prior author suit against Meta in June 2025, where the court found insufficient evidence of market dilution. The new complaints allege that Llama can generate "sequels, prequels, spin-offs, and other adaptations" of copyrighted works and that AI-generated books are "already flooding" Amazon in volumes that "materially displace human-authored works." When prompted, Llama reportedly produced a nearly 5,000-word, ten-chapter sequel to Scott Turow's Innocent, using Turow's characters, settings, and style. Third, all three suits frame the conduct as an industry-wide "arms race," casting the decisions as deliberate, competition-driven risk taking rather than inadvertence.

Key Legal Issues

Fair Use. The central defense Meta is expected to raise, and has already previewed, is fair use. The fair use inquiry under 17 U.S.C. § 107 considers the purpose and character of the use, the nature of the copyrighted work, the amount used, and the effect on the market for the original. Fair use becomes a considerably harder argument, however, when the underlying copies were obtained from pirate sites, a fact that colors the entire analysis and makes it difficult to characterize the use as transformative or in good faith. The plaintiffs allege that Llama's outputs are "similar enough to copyrighted works — in subject matter, plot details, sequencing of events, character names and traits, or other creative choices — that they replace the original work for many readers or consumers".

Broader Litigation Landscape. This case does not stand alone. Writers and publishers have sued OpenAI, Anthropic, Google, and xAI for using copyrighted works in AI training without authorization. Last fall, Anthropic agreed to pay a $1.5 billion settlement to writers whose books were used to train its AI program. The volume of these suits reflects a pattern of liability exposure that AI companies have created by failing to scrutinize the provenance of their training data. Each new case reinforces the same basic point: companies that skip proper due diligence on data sourcing are inviting litigation on multiple fronts.

Business Implications

This lawsuit has significant implications for a broad range of stakeholders:

AI Model Developers face the most direct exposure. The allegations about sourcing training data from pirated repositories, coupled with the claim that Meta's CEO directed the conduct, show that plaintiffs are pressing theories that could reach individual officers and directors.

Companies Using Third-Party AI Services should account for downstream risk. If the models behind a company's AI-enabled products or services were trained on infringing material, the company could face contributory infringement claims, reputational harm, or a sudden loss of access to a tool that is enjoined. If granted, the request that Meta destroy all illegally acquired training materials could force model retraining or withdrawal, which would be a severe outcome for any business that has built products or services on top of Llama.

Content Creators and Publishers are asserting their rights, and the Anthropic settlement of $1.5 billion shows the financial scale of these claims. The fact that trade publishers, academic publishers, and a prominent author have all filed suit on similar theories reflects how broadly AI companies have exposed themselves by failing to obtain proper licenses. Any company that relies on third-party AI models, not just those that build them, should evaluate exposure now.

Practical Takeaways and Recommendations

In light of this evolving litigation landscape, we recommend that stakeholders consider the following steps:

1. Audit AI Training Data Provenance. Companies that develop or fine-tune AI models should review, in detail, the sources used to compile training datasets. The complaints' focus on pirated repositories as training data sources suggests that provenance will be a focal point for courts assessing infringement claims. For example, if your engineering team used Common Crawl or other large-scale web scrapes to build a training corpus, you should be able to document the chain of custody for that data and confirm that none of it was sourced from or routed through known piracy platforms like LibGen or Sci-Hub.

2. Review and Strengthen Licensing Agreements. Organizations that license copyrighted content should confirm whether existing agreements cover AI training and whether they need additional permissions. The publishing industry's posture, as articulated by the Association of American Publishers, favors a sustainable AI landscape built on transparency and fair participation by rights holders. As a practical matter, if your company licenses a database of academic journals for internal research purposes, you should verify whether the license grant extends to machine learning applications or whether a separate AI training license is required.

3. Assess Indemnification and Liability Provisions in AI Vendor Contracts. Companies that procure AI services from third-party vendors should scrutinize indemnification for IP infringement, data sourcing representations, and limitations of liability. Given the risk of injunctive relief that could interrupt services, business continuity provisions deserve careful attention. For instance, if your company integrates Llama or another open-source model into a customer-facing product, your vendor agreement should include broad IP indemnification, a representation that training data was lawfully obtained, and a transition plan in the event the model becomes unavailable due to litigation.

4. Monitor Output Risk. The complaints underscore the risk that AI-generated content may closely track protected works in subject matter, plot details, sequencing, character names and traits, or other creative choices. Companies using generative AI should implement review and guardrails for outputs before publication or commercial use. If your marketing team uses a generative AI tool to draft content, establish a workflow where someone checks outputs against known source material before anything goes to market, particularly for long-form text that could inadvertently reproduce copyrighted passages or creative elements.

5. Stay Current on the Evolving Legal Landscape. With major cases pending against most leading AI developers, the law is developing quickly. Companies should track judicial decisions, potential legislation, and industry licensing frameworks to keep their AI strategies defensible. As one example, a ruling on fair use in the pending Thomson Reuters v. Ross Intelligence case or in the New York Times v. OpenAI litigation could materially change the risk calculus for any company that relies on AI models trained on third-party content.

This alert is provided for informational purposes and does not constitute legal advice. If you have questions about how these developments may affect your business, please contact a member of our Intellectual Property practice.