The Chicago Tribune has filed a copyright infringement lawsuit against AI search engine Perplexity, accusing the company of using its copyrighted content verbatim and bypassing paywalls. The suit, filed in a federal court in New York, highlights growing tensions between news publishers and generative AI platforms over intellectual property rights.

According to the complaint, the Tribune's lawyers contacted Perplexity in mid-October to inquire about the AI search engine's use of its content. Perplexity's legal team reportedly responded that it did not train its models with the Tribune's work but "may receive non-verbatim factual summaries." However, the Tribune contends that Perplexity is, in fact, delivering its content verbatim.

Allegations Against Perplexity's RAG System and Comet Browser

A key focus of the lawsuit is Perplexity's Retrieval Augmented Generation (RAG) system. RAG is a method designed to reduce AI "hallucinations" by ensuring models rely on accurate and verified data sources. The Tribune alleges that Perplexity is using the newspaper's content within its RAG systems, scraped without permission. Furthermore, the lawsuit claims that Perplexity's Comet browser circumvents the paper's paywall to provide detailed summaries of its articles, effectively offering paid content for free.

Broader Legal Landscape for AI and Publishers

This lawsuit is part of a larger trend of news organizations challenging AI companies over content usage. The Chicago Tribune is one of 17 news publications from MediaNews Group and Tribune Publishing that sued OpenAI and Microsoft over model training material in April. Another nine from these publishers sued the model maker and its cloud provider in November.

The legal challenges extend beyond the Tribune. Perplexity AI is facing multiple other lawsuits:

While numerous creators have filed many lawsuits against AI model makers for using their work in training, the Tribune's case against Perplexity specifically raises questions about the legal liabilities associated with Retrieval Augmented Generation (RAG) systems and their direct use of copyrighted material. The outcome of this and similar cases could significantly shape the future of AI's interaction with published content.