Large language models are better than theoretical linguists at theoretical linguistics, at least in the domain of verb argument structure; explaining why (for example), we can say both The ball rolled and Someone rolled the ball, but not both The man laughed and *Someone laughed the man. Verbal accounts of this phenomenon either do not make precise quantitative predictions at all, or do so only with the help of ancillary assumptions and by-hand data processing. Large language models, on the other hand (taking text-davinci-002 as an example), predict human acceptability ratings for these types of sentences with correlations of around r = 0.9, and themselves constitute theories of language acquisition and representation; theories that instantiate exemplar-, input- and construction-based approaches, though only very loosely. Indeed, large language models succeed where these verbal (i.e., non-computational) linguistic theories fail, precisely because the latter insist – in the service of intuitive interpretability – on simple yet empirically inadequate (over)generalizations.