Language reflects social beliefs. Yet, until recently, quantifying such beliefs remained impossible. Advances in machine learning, specifically word embeddings, now make it possible to transform natural language features (i.e., word concordance) into quantitative indices of beliefs. Using word embeddings derived from 7 corpora (65+ million words), we provide the first large-scale comparative test of gender stereotypes that exist within children’s and adults’ natural linguistic corpora, including child-produced, child-directed (parent speech, TV/movies, and books), and adult-produced/-directed text (adult-to-adult speech, TV/movies, and books). We find strong, pervasive associations of male/female with work/home, science/arts, math/reading, and bad/good consistently across *all* corpora. Further analyses of 170 trait words and 82 profession labels reveal 23-24% of traits/professions are gendered and are also significantly correlated with real-world gender representation. This approach illustrates the gains from methodological advancements to provide new insights into surprisingly early and widespread prevalence of gendered beliefs in children’s and adult’s natural language.