Earlier quoted context omitted.
I don't know much about H.266, but some of the advances in H.265 depended on players having enough RAM to hold a bunch of previous decoded frames, so they could be referred to by later compressed data. Newer codecs tend to have a lot more options for the encoder to tune, so they need a combination of faster CPUs and smarter heuristics to explore the space of possible encodings quickly.
I wonder if instead of heuristics, machine learning could be used to figure out the best parameters.
Once you get to an AI that has full comprehension of what humans perceive to be reality, you can just give them a rough outline of a story, add some information on casting, writers, and Spielberg's mood during production, and they'll fill in the (rather large) blanks.
That's a bit exaggerated, but I remember reading about one such algorithm a few days ago (by Netflix, maybe?). It was image compression that had internal representations such as "there is an oak tree on the left".
It would then run the "decompression", find the differences to the original, and add further hints where neccessary.