I'll talk about a result with Kaarel Hanni where we prove some results about generalization properties of certain infinite width learning results, and a related question of its computational complexity. The main result is a complexity theoretic statement, that at fixed finite depth, a model has good bayesian generalisation properties at any width if and only if it has good generalisation properties at infinite width in the mean field regime.