text: ***7. Conclusion***
  
  This paper is a snapshot of an idea that is as underexplored as it is rooted in decades of 
  existing work. The concept of mass digitization of books, including to support text and data 
  mining, of which AI is a subset, is not new. But AI training is newly of the zeitgeist, and its 
  transformative use makes questions about how we digitize, preserve, and make accessible 
  knowledge and cultural heritage salient in a distinct way. 
  
  As such, efforts to build a books data commons need not start from scratch; there is much 
  to glean from studying and engaging existing and previous efforts. Those learnings might 
  inform substantive decisions about how to build a books data commons for AI training. For 
  instance, looking at the design decisions of HathiTrust may inform how the technical 
  infrastructure and data management practices for AI training might be designed, as well as 
  how to address challenges to building a comprehensive, diverse, and useful corpus. In 
  addition, learnings might inform the process by which we get to a books data commons — 
  for example, illustrating ways to attend to the interests of those likely to be impacted by the 
  dataset’s development.   41 
  
  While this paper does not prescribe a particular path forward, we do think ﬁnding a path (or 
  paths) to extend access to books for AI training is critical. In the status quo, large swaths of 
  knowledge contained in books are effectively locked up and inaccessible to most everyone. 
  Google is an exception — it can reap the beneﬁts of their 40 million books dataset for 
  research, development, and deployment of AI models. Large, well-resourced entities could 
  theoretically try to replicate Google’s digitization efforts, although it would be incredibly 
  expensive, impractical, and largely duplicative for each entity to individually pursue their own 
  efforts. Even then, it isn’t clear how everyone else — independent researchers, entrepreneurs, 
  and smaller entities — will have access. The controversy around the Books3 dataset 
  discussed at the outset should not, then, be an argument in favor of preserving the status 
  quo. Instead, it should highlight the urgency of building a books data commons to support an 
  AI ecosystem that provides broad beneﬁts beyond the privileged few. 