this stuff existed for decades and it hit a wall.
sampling is slow.
general AI needs structured prediction. yes, graphical models can do that (HMM, CRF etc.) but inference starts to get slow and special case implementations are required for different domains. [1]
I see no way for someone getting automatic inference and training for [1] with a probabilistic programming language.
given the new deepmind paper on discovering shortest path algorithms, it's quite clear that structured predicition assisted by deep networks works quite well (this was demonstrated by a vast array of work) and graphical models represented by probabilistic programming languages are far away from being that successful.