Texaco case proves that fair use won’t scale up.

Timothy Lee and James Grimmelmann, “Why The New York Times might win its copyright lawsuit against OpenAI,” 02-20-2024, https://arstechnica.com/tech-policy/2024/02/why-the-new-york-times-might-win-its-copyright-lawsuit-against-openai/

Fair use doesn’t always scale

One lesson of the MP3.com case is that a use that’s fair in a personal or academic context may not be fair if it is practiced on a commercial scale.

It was and is legal to buy a CD and rip it to an MP3 player for personal use. But that didn’t mean it was legal to buy tens of thousands of CDs and rip them to a server for a commercial streaming service. And this was true even though MP3.com ensured that only verified owners could stream music from any given CD.

The oil company Texaco learned a similar lesson in the early 1990s. Texaco had an internal library that purchased subscriptions to various scientific and technical journals and made them available to staff scientists. The scientists routinely photocopied individual articles from these journals (or had Texaco's librarians make copies for them) and kept them in their offices for future reference.

Academic publishers sued Texaco for copyright infringement. Texaco claimed fair use, noting that copyright law allows copying for purposes of scholarship and research. But the federal Second Circuit Court of Appeals rejected that reasoning, distinguishing between “copying by an individual, for personal use in research or otherwise,” and “photocopying by 400 or 500 scientists” doing research on behalf of a for-profit company.

People in the AI community may be making a mistake similar to Texaco's.

A lot of early AI research was done in an academic setting; the law specifically mentions teaching, scholarship, and research as examples of fair use. As a result, the machine-learning community has traditionally taken a relaxed attitude toward copyright. Early training sets frequently included copyrighted material.

As academic researchers took jobs in the burgeoning commercial AI sector, many assumed they would continue to enjoy wide latitude to train on copyrighted material. Some feel blindsided by copyright holders’ demands for cash.

“We all learn for free,” Jeffries wrote in his tweet, summing up the view of many in the AI community. “We learn from the world around us and so do machines.”

The argument seems to be that if it’s legal for a human being to learn from one copyrighted book, it must also be legal for a large language model to learn from a million copyrighted books—even if the training process requires making copies of the books.

As MP3.com and Texaco learned, this isn't always true. A use that’s fair at a small scale can be unfair when it’s scaled up and commercialized.