While the world’s languages differ in many respects, they share certain commonalities: these can provide crucial insight on our shared cognition and how it impacts language structure. In this project, we explore the learnability sources and consequences of one of the most striking commonalities across languages: the way word frequencies are distributed. Across languages, words follows a Zipfian distribution, showing a power law relation between a words’ frequency and its’ rank. Intuitively, this reflects the fact that languages have relatively few high frequency words and many low frequency ones, and that frequency does not decrease in a linear way. The source of this distribution has been heavily-debated, with ongoing controversy about what it can tell us about language. Here, we propose that such distributions confer a learnability advantage, leading to enhanced language acquisition in children, and to the creation of a cognitive pressure to maintain similarly skewed distributions over time. In the first part, we examine the learnability consequences of Zipfian distributions. We characterize the greater predictability of words in such distributions using the information-theoretic notion of efficiency, and ask three questions: (1) Are different languages similarly predictable? (2) If so, is learning uniquely facilitated in language-like predictability in both children and adults? and (3) Is this facilitation limited to the linguistic domain? In the second part, we explore the learnability sources of Zipfian distributions to ask whether learning biases can help explain why such distributions are so common in language.