If it produces interesting content as good as or better than traditional methods or has the same level of ease as traditional methods, then sure why not, otherw

If it produces interesting content as good as or better than traditional methods or has the same level of ease as traditional methods, then sure why not, otherwise why? In the end, the results are what's important. It's cool to have fancy, new ways of doing things, but if something works and the new way isn't significantly better or easier than traditional ways and ends up being out of reach or significantly different than traditional wqys for little to no gain then, yeah, it's not likely to be widely adopted. That all being said, it wouldn't surprise me if some day, some enterprising deep learning fan does come up with some new interesting or more efficient thing that sparks a new line of research and innovation. It's the way with a lot of things, the early adopters and enthusiasts pave the way for the others. I tend to regard a lot of machine learning, deep learning, ai kind of stuff as overblown, buzz wordy, euphemisms for fancy algorithms with internal memory and data analysis and manipulation capabilities. But, that doesn't mean there aren't still innovative things that can be done with it. Likely, someone will come along at some point with something easily accessible made with deep learning that revolutionizes procedural generation and helps in creating more realistic random environments. We're not there yet though, hence the general disregard for it.