In complete sincerity, I think that speeding up Turing-complete probabilistic programming to the kinds of inference speed we can get in the gradient-descent training of deep neural networks would be a "change the world"-level advance for ML/AI.
Variational inference also only works for continuous probability models, so it can't be used for most interesting use-cases of probabilistic programming.
