A) It's no longer practical to incorporate into real products given the hardware costs for training and inference. In NLP, large transformer based models can easily occupy a 5 thousand dollar GPU at a paltry 50-60 words per second processing pace.
B) The Research often demonstrates things that existing non-ML toolchains were already reasonable at doing with a better result, and a faster iteration time.
This demo looks like it's replicated the core mechanics of pac-man with worse graphics, 3-4 orders of magnitude higher resource consumption, and undoubtably many odd bugs hidden away from the pretty demo. It even needed the game to exist in the first place! While this is fascinating from the technical and academic perspective, it's hard to be convinced that any gains we see in the field won't be offset by increased hardware/latency costs from a product standpoint.