text: Today, the Center’s tools are not suitable for AI training, in that they don’t allow the speciﬁc 
  types of technical manipulation of underlying text necessary to train an AI. Nevertheless, the 
  Center demonstrates that building a books data commons for computational analysis is 
  possible, and in turn points to the possibility of creating such a resource for AI training.   36 
  
  **Implications of Overall Approach**
  
  By relying on existing limitations and exceptions in copyright law, the number of books one 
  could include in the corpus of a books data commons is far greater and more diverse. Of 
  course, a bigger dataset doesn’t necessarily mean a higher quality dataset for all uses of AI 
  models; as HathiTrust shows, even a multimillion book corpus can skew in various 
  directions. Still, dataset size generally remains signiﬁcant to an LLM’s performance – the 
  more text one can train on, or rather the more tokens for training the model, the better, at 
   37 
  least along a number of performance metrics. 
  
  While holding the potential for a broader and more diverse dataset, a key limitation in 
  pursuing this approach is that it is only feasible where relevant copyright limitations and 
  exceptions exist. Even then, legal uncertainty means that going down this path is likely to 
  litigation and regulatory 
  generate, at a minimum, expensive and time-consuming 