In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2-year-olds are so skilled at learning noun categories that they seem to intuit the whole range of things in the category from hearing a single instance named they are biased learners. This is not the case for children below the 20th percentile on productive vocabulary (late talkers). This paper looks at the vocabulary composition of age-matched 18-30-month-old late- and early-talking children. The results of Experiment 1 show that late talkers vocabularies are more variable than early talkers vocabularies. Crucially, Experiment 2 shows that neural networks trained on the vocabularies of individual late talkers learn qualitatively different biases than those trained on early talker vocabularies. These simulations make testable predictions for world learning biases of late- vs. early-talking children. The implications for diagnosis and intervention are discussed.