Language models succeed in part because they share information-processing constraints with humans. These information-processing constraints do not have to do with the specific neural-network architecture nor any hardwired formal structure, but with the shared core task of language models and the brain: predicting upcoming input. I show that universals of language can be explained in terms of generic information-theoretic constraints, and that the same constraints explain language model perfor...