Anthropic’s Historic $1.5B Copyright Settlement Gets Judge’s OK
Anthropic is set to pay a landmark $1.5 billion copyright violation settlement, Reuters reported Monday (June 20).
U.S. District Judge Araceli Martinez-Olguin approved the settlement, believed to be the largest such award in a copyright case in the United States, the report said.
A class action lawsuit had been brought by a group of authors who accused Anthropic of improperly using their works to train its Claude artificial intelligence chatbot, according to the report.
The case is one of several in which copyright owners such as authors and news organizations have sued tech companies over the use of their materials to train AI models. It is the first major U.S. case to settle, the report said.
“We reached this settlement in 2025, after the court’s landmark ruling that training AI on books is fair use under copyright law, which remains the law today,” Anthropic Deputy General Counsel Aparna Sridhar said in a statement, per the report. “We are pleased that more than 91% of authors and publishers covered by the settlement have claimed their share of the payment, and we’re looking forward to bringing this matter to a close.”
Justin Nelson, lead attorney for the plaintiffs, called it a “historic settlement,” according to the report.
“It is the largest known copyright recovery in history,” Nelson said, per the report. “We look forward to making distributions to the class as promptly as possible”
The judicial record on AI copyright cases like these is divided. For example, U.S. District Judge William Alsup has said copyright law “seeks to advance original works of authorship, not to protect authors against competition.”
But U.S. District Judge Vince Chhabria arrived at a different conclusion, warning that widespread AI training could undermine the economic incentives that fuel human creative work.
Daryl Lim, H. Laddie Montague Jr. Chair in Law at Penn State Dickinson Law, told PYMNTS in December that only a handful of companies can train frontier AI models at scale because these firms control compute, data, cloud infrastructure and distribution at the same time.
“When you train frontier models, you need to ingest vast repositories of works that may include copyrighted works,” Lim said.
