Anthropic has been sued by Sony Music Publishing, Warner Chappell Music and other major music publishers, which accuse the artificial intelligence company of illegally obtaining and using thousands of copyrighted musical works to train its Claude AI models.

The publishers filed the lawsuit Friday in the U.S. District Court for the Northern District of California, naming Anthropic and co-founders Dario Amodei and Benjamin Mann as defendants. They accused the AI company and its founders of conducting what they described as a “brazen campaign of illegally torrenting, scraping, and downloading copyrighted works” on a massive scale.

The case adds the music industry to a growing legal battle over how AI companies obtain the enormous quantities of copyrighted material needed to train generative AI systems.

“Defendants Anthropic and its founders Dario Amodei and Benjamin Mann have conducted a brazen campaign of illegally torrenting, scraping, and downloading copyrighted works on a massive scale in order to develop, operate, and reap enormous profits from Anthropic’s ‘Claude’ series of artificial intelligence (‘AI’) models,” the publishers said in the complaint.

Sony Music Publishing and Warner Chappell said Anthropic obtained “thousands upon thousands” of copyrighted songs, including “Eye of the Tiger,” Marvin Gaye’s “Ain’t No Mountain High Enough,” Mariah Carey’s “All I Want for Christmas is You” and Taylor Swift’s “Paper Rings.”

The publishers allege that Anthropic acquired copyrighted material through several sources, including Library Genesis and Pirate Library Mirror, two digital archives that have been associated with pirated books and other content.

The lawsuit builds on an earlier copyright case against Anthropic involving books. In June 2025, a federal judge ruled that Anthropic had downloaded more than 7 million pirated books to train Claude.

The music publishers argue that those books also contained lyrics and sheet music belonging to their catalogues. They identified works including Bon Jovi’s “Livin’ On a Prayer,” Earth, Wind & Fire’s “September,” Jerry Lee Lewis’ “Great Balls of Fire,” the Allman Brothers Band’s “Ramblin’ Man,” and Leonard Cohen’s “Hallelujah.”

According to the complaint, the alleged infringement goes beyond the use of copyrighted works during model training. The publishers claim Claude can generate identical or nearly identical versions of copyrighted material in response to users’ prompts.

That allegation could become crucial because the case raises two separate questions for the AI industry: whether copyrighted works can lawfully be used to train AI models and whether AI systems can reproduce protected works closely enough to constitute infringement when responding to users.

The publishers argue that training Claude on copyrighted compositions enables Anthropic to generate lyrics that could compete directly with music created by human songwriters.

“Even the most revolutionary of technologies must develop within the bounds of the law, and Anthropic’s Claude models are no different,” the publishers said in the complaint.

They are seeking statutory damages and have requested a jury trial. Under U.S. copyright law, statutory damages can reach $150,000 per infringed work when the infringement is found to be willful. The potential exposure could therefore become substantial if the publishers succeed in establishing infringement across a large catalogue.

The lawsuit marks another escalation in the confrontation between copyright owners and AI companies over training data.

Generative AI systems require enormous datasets to develop their capabilities, creating a direct conflict with industries whose books, articles, photographs, music and other creative works form part of the material that AI companies seek to acquire.

For the music industry, the dispute has implications beyond compensation. Publishers and songwriters are becoming more concerned that generative AI could become a competitor to the very creators whose work helped train the systems.

The legal strategy also comes at a time when Anthropic is already facing significant financial exposure from copyright litigation. In September, the company agreed to pay more than $1.5 billion to settle a class-action lawsuit brought by authors over the use of pirated books.

Other major AI companies have faced similar lawsuits. OpenAI has been sued by publishers and content owners, including The New York Times and Encyclopedia Britannica, over allegations concerning the use of copyrighted material to train and operate its AI systems.

The cases could ultimately help determine the economics of the AI industry. If courts require companies to obtain licenses for large quantities of copyrighted training material, the cost of developing and operating AI models could rise substantially. If courts allow broader use of copyrighted material under fair use or other legal doctrines, publishers, authors, musicians and other rights holders could face greater pressure to adapt their business models.

The Anthropic case could be especially consequential because music is subject to multiple layers of copyright protection, including rights in compositions and, in many cases, separate rights in sound recordings. A ruling against Anthropic could therefore have implications for how AI companies collect, process, and reproduce musical content at scale.

OpenAI said Friday it plans to stop providing its artificial intelligence models to Cursor, the coding platform recently acquired by Elon Musk’s SpaceX, escalating a long-running dispute between Musk and OpenAI CEO Sam Altman.

OpenAI said it proposed November 12, 2026, as the cutoff date for its agreement with Cursor, arguing that it could no longer be confident SpaceX would use its technology in accordance with the company’s terms of service.

“We are making this choice because we cannot be confident that SpaceX will use our technology within our terms of service, based on our experience with Elon Musk’s companies violating contracts,” OpenAI said in a blog post.

The decision adds a new corporate dimension to the bitter rivalry between Musk and Altman. Their dispute dates back years and intensified after Musk sued OpenAI and Altman, alleging that the company had abandoned its original nonprofit mission in pursuit of commercial interests.

Musk dismissed OpenAI’s latest move in a post on X early Saturday, writing, “I couldn’t care less.”

He also accused Altman and OpenAI President Greg Brockman of being “untrustworthy” and repeated his claim that they had “stole[n] an open source nonprofit.”

Musk’s lawsuit, which sought to challenge OpenAI’s transition toward a for-profit structure, went to trial earlier this year. A federal jury ultimately ruled against Musk, finding that he had filed the case too late.

The dispute now threatens to affect Cursor, one of the most prominent AI-powered coding tools. Cursor is developed by Anysphere, which SpaceX agreed to acquire in June in a $60 billion all-stock transaction. SpaceX completed the acquisition earlier this month.

Michael Truell, a Cursor co-founder who is now a SpaceX executive, said on X that Cursor was already in discussions with OpenAI to resolve the dispute.

OpenAI said its agreement with Cursor contained a limited period during which it could terminate the contract following a “change of control,” giving the company grounds to seek an end to the arrangement after SpaceX took ownership.

The company also pointed to its previous experience with Musk-owned businesses. OpenAI said X, formerly Twitter, breached the terms of its contract with OpenAI following Musk’s acquisition of the social media platform.

“To work with a large partner like SpaceX, we typically rely on custom contracts to ensure compliance with our terms of service and that the integration provides for safety at scale,” OpenAI said.

The timing is notable because Cursor has become an important distribution channel for leading AI coding models. Cutting off OpenAI models could force the platform to rely more heavily on competing providers, potentially changing the competitive balance in AI-assisted software development.

Anthropic moved quickly to capitalize on the dispute. Hours after OpenAI announced its decision, Anthropic co-founder Tom Brown said the company planned to increase computing capacity to support Claude models in Cursor. Anthropic already has an ongoing partnership with SpaceX.

That response highlights the broader stakes for AI model providers. Coding platforms such as Cursor sit between frontier-model developers and millions of software developers, making access to those platforms strategically valuable as companies compete to establish their models as the preferred tools for programming and agentic software development.

The dispute also illustrates how ownership changes are becoming a fault line in the AI industry. OpenAI’s decision was not based on Cursor’s technology or its use of AI models itself, but on the identity of its new owner and concerns over contractual compliance.

However, the immediate issue for Cursor is whether it can maintain access to OpenAI models while continuing to operate under SpaceX ownership. For OpenAI, the decision signals a willingness to sacrifice an important distribution relationship rather than accept what it considers unacceptable contractual and safety risks.

The move is another escalation in the Musk-Altman confrontation, which has evolved from a dispute over OpenAI’s corporate structure into a broader contest involving AI models, software developers and powerful technology companies.

AI startup Valon has introduced a policy requiring most new employees to learn their jobs without artificial intelligence before they are allowed to use the technology, as concerns grow that widespread reliance on AI could weaken workers’ judgment and leave companies paying heavily for tools that employees may be using unnecessarily.

Andrew Wang, Valon’s CEO and cofounder, introduced the policy last month after giving employees broad access to AI tools and reviewing how they were being used. He said employees were frequently turning to the most expensive models for relatively simple tasks, raising concerns about both soaring computing costs and the effect on employees’ ability to understand their work.

“By doing the basic work, rather than relying on AI, you start to form an understanding,” Wang, a former Goldman Sachs analyst, told Business Insider.

The policy is an unusual position for Valon, a New York-based company that builds mortgage-servicing software powered by AI agents. The company employs about 320 people and has made artificial intelligence central to its products and operations.

“It’s a weird decision for a company that is at the frontier of AI usage,” Wang wrote in a blog post explaining the policy.

But Wang said the decision followed a pattern he observed after employees were given unrestricted access to AI. When he asked why they were using the most powerful models for basic assignments, employees told him the systems were almost always correct.

For Wang, that answer was itself a warning sign.

He said employees could become less inclined to question AI-generated answers or develop the expertise needed to recognize when a system is wrong. That creates a potential problem for companies deploying increasingly capable AI systems: workers may become more productive in the short term while losing the underlying knowledge required to supervise those systems effectively.

Under Valon’s policy, new hires in almost every part of the business, including senior employees, must initially work without AI. They can begin using AI only after their managers determine that they understand their responsibilities well enough to identify incorrect AI output.

Engineers are exempt because Valon requires all code to undergo peer review before it is released. Wang said functions such as finance and human resources do not have equivalent safeguards, making unrestricted AI use more difficult to justify.

The policy also highlights a less obvious cost of the AI boom. While companies have focused heavily on how much AI can save by automating work, the technology can create additional costs when employees use powerful models for tasks that do not require them.

Wang said Valon’s annualized spending on AI tokens is now expected to fall to roughly $4 million to $5 million this year, from an estimated $15 million to $20 million previously.

The savings come alongside what Wang considers a more important benefit: new employees are now asking experienced colleagues for help rather than asking an AI system to solve problems for them. That interaction recreates an older form of workplace training in which junior employees acquire institutional knowledge by observing experienced colleagues, performing basic tasks and gradually taking on more complicated responsibilities.

The issue is becoming bolder as companies deploy AI across entry-level functions that traditionally served as training grounds for young workers. If AI takes over those tasks immediately, employees may reach more senior positions without having developed the practical judgment that those assignments were intended to teach.

The concern extends beyond Valon.

A September 2025 survey by BetterUp and Stanford’s Social Media Lab found that 40% of 1,150 full-time U.S. desk workers had received AI-generated work from a colleague during the previous month. Respondents said dealing with each instance took nearly two hours on average. That suggests companies can incur a hidden productivity cost when employees submit AI-generated material that colleagues must check, correct, or rewrite.

The emergence of the term “meat proxies” reflects the growing frustration with this behavior. The phrase, popularized by German software developer Niklas Gruhn in an August 3 blog post, describes people who effectively act as intermediaries for unchecked AI output, passing machine-generated work to others without adequately reviewing it.

For companies, the central issue is not necessarily whether employees should use AI, but whether they have sufficient expertise to supervise it.

That could get into play more as AI agents move from generating text and code to carrying out multi-step tasks with limited human intervention. The more responsibility companies delegate to AI, the greater the need for employees who understand the underlying processes well enough to detect errors and intervene when systems go off course.

Valon’s approach points to a reversal of the assumption that faster AI adoption is always better. Instead of measuring success simply by how much work AI can perform, Wang is placing greater emphasis on whether employees understand the work being automated.

The policy has not faced significant internal opposition, according to Wang. He said experienced employees have generally welcomed it because they had been spending time correcting poor-quality AI-generated work produced by newer hires.

Still, the approach has drawn criticism from some AI enthusiasts, including people who responded to a LinkedIn post in which Wang described the policy.

Wang’s response is straightforward: “If you have a much better idea here of how to make sure people learn, please tell me.”

Valon’s experiment could offer an important lesson for companies racing to integrate AI into their operations. The technology may reduce the need for humans to perform routine tasks, but eliminating those tasks entirely could also eliminate some of the mechanisms through which workers acquire expertise.