text: content repositories, like libraries, with that of AI developers. A “books data commons” needs 
  to be both responsibly managed, and useful for developers of AI models. 
  
  We use “commons” here in the sense of a resource that is broadly shared and accessible, 
  and thus obviates the need for each individual actor to acquire, digitize, and format their own 
  corpus of books for AI training. This resource could be collectively and intentionally 
  managed, though we do not mean to select a particular form of governance in this paper. 4 
  
  This paper is descriptive, rather than prescriptive, mapping possible paths to building a 
  books data commons as deﬁned above and key questions relevant to developers, 
  repositories, and other stakeholders, building on our workshop discussions. We ﬁrst explain 
  why books matter for AI training and how broader access could be beneﬁcial. We then 
  summarize two tracks that might be considered for developing such a resource, highlighting 
  existing projects that help foreground both the potential and challenges. Finally, we present 
  several key design choices, and next steps that could advance further development of this 
  approach. 5 