Licensing of Text for Generative AI: Learnings from Non-AI Licensing Practices

Models (“LLMs”) are moving fast, fueled by developer demand for access to, and the ability to use, increasing amounts of high-quality textual data. With this constant demand for quality literary works come questions around how licensing practices can enable technological developments while preserving the contours of copyright law and sufficiently incentivizing human authorship of books, journalism, and other literary content for human readers and AI uses alike. While some venture that licensing for generative AI purposes is “impossible,” many companies have negotiated partnerships with media publishers or publishing houses for generative AI uses. Meanwhile, others query whether there is a need to build or adjust licensing systems to better facilitate licensing of textual content, whether through regulatory updates, increased use of collecting societies, or augmenting data management infrastructure. Before declaring the status quo of marketplace licensing insufficient, it makes sense to take stock of where we have been, where we are, and where we might be going. This short piece hypothesizes that some current bumps in generative AI licensing stem from uncertainty in an emerging market, not inherent difficulties in licensing at scale for professionally published content. Given that generative AI is still in its nascency, content licensing is not close to a one-size-fits-all standard. The time is ripe for marketplace developments, and experimentation in private arrangements between rightsholders and users. The Article also provides a brief primer in copyright principles of licensing regulation and overviews guideposts for collective management of content, based on experiences outside of AI. While voluntary collective licensing can play a valuable role in the AI licensing market, these guideposts may assist authors and other licensees as they consider whether, with whom, and on what terms to affiliate with a licensing intermediary.