The U.S. Can't Afford AI Copyright Lawsuits

It's time for President Trump to invoke the Defense Production Act and resolve the crisis.

I have a new post at Lawfare making this argument. Here's a summary:

Anthropic just paid $1.5 billion to settle a copyright case that it largely won in district court. Future litigants are likely to hold out for much more. A uniquely punitive provision of copyright law will allow plaintiffs who may not have suffered any damage to seek awards in the trillions. (Indeed, observers estimated that Anthropic dodged $1 trillion in liability by settling.)  The avalanche of litigation, already forty lawsuits and counting, doesn't just put the artificial intelligence (AI) industry at risk of spending their investors' money on settlements instead of advances in AI. It raises the prospect that the full bill won't be known for a decade, as different juries and different courts reach varying conclusions.

A decade of massive awards and deep uncertainty poses a major threat to the U.S. industry. The Trump administration saw the risk even before the  Anthropic settlement, but its AI action plan offered no solution. That's a mistake; the litigation could easily keep the U.S. from winning its race with China to truly transformational AI.

The litigation stems from AI's insatiable hunger for training data. To meet that need, AI companies ingested digital copies of practically every published work on the planet, without getting the permission of the copyright holders. That was probably the only practical option they had. There was no way to track down and negotiate licenses with millions of publishers and authors. And the AI companies had a reasonable but untested argument that making copies for AI training was a "fair use" of the works. Publishers and authors disagreed; they began filing lawsuits, many of them class actions, against AI companies.

The American public will likely have little sympathy for a well endowed AI industry facing the prospect of hiring more lawyers, or even paying something for the works it copied. The problem is that peculiarities of U.S. law—a combination of statutory damages and class action rules—allow the plaintiffs to demand trillions of dollars in damages, a sum that far exceeds the value of the copied works (and indeed the market value of the companies). That's a liability no company, no matter how rich and no matter how confident in its legal theory, can ignore. The plaintiffs' lawyers pursuing these cases will use their leverage to extract enormous settlements, with a decade-long effect on AI progress. At least in the United States. China isn't likely to tolerate such claims in its courts.

This is a major national security concern. The US military is already building AI into its planning, and the emerging agentic capabilities of advanced AI holds out the prospect that future wars will become contests between armies deploying coordinated masses of autonomous weapons. Even more startling improvements in AI could come in the next five years, with transformative consequences for militaries that capitalize on them as well as those that don't. Not surprisingly, China is also pursuing military applications of AI. Given the US stake in making sure its companies do not fall behind China's, anything that reduces productive investment in AI development has national security implications. As Tim Hwang and Joshua Levine laid out in an earlier Lawfare article, this means the U.S. can't afford to let the threat of enormous copyright liability hang over the AI industry for the decade or more it could take the courts to reach a final ruling.

The Trump administration should cut this Gordian knot by invoking the Defense Production Act (DPA) and essentially ordering copyright holders to grant training licenses to AI companies on reasonable terms to be determined by a single Article I forum. This is the only expeditious way out of the current mess. It is consistent with the purpose and with past uses of the DPA. And it creates a practical solution that copyright holders have long used in similar contexts.