I’ve just looked at a couple of the notebooks, and I think that pyro looks very close neural-network style code; in that it helps to build up a loss function, and then you define your optimizer and go through the training loop. This will be familiar anyone who has taken a course in neural networks, and suddenly has to do inference. It is also probably faster to fit VI, since you have more direct control over the optimizer.
On my side, I haven’t seen any hype or surge in probabilistic programming.
I think there will always be room for a python framework that stylistically emphasizes the model first, and abstracts away the sampling and optimizer as much as possible.