Speciesism, like other forms of prejudice, is thought to be underpinned by biased patterns of language use. Thus far, however, psychological science has primarily focused on how speciesism is reflected in individuals' thoughts as opposed to wider collective systems of meaning such as language. We present a large-scale quantitative test of speciesism by applying machine-learning methods (word embeddings) to billions of English words derived from conversation, film, books, and the Internet. We found evidence of anthropocentric speciesism: words denoting concern (vs. indifference) and value (vs. valueless) were more closely associated with words denoting humans compared to many other animals. We also found evidence of companion animal speciesism: the same words were more closely associated with words denoting companion animals compared to most other animals. The work describes speciesism as a pervasive collective phenomenon that is evident in a naturally occurring expression of human psychology – everyday language.