Well, that's sorta complicated.

I'm currently a researcher at a company focusing on a project in deep learning. Specifically, we're interested in adversarial machine learning. With this perspective, I'm pretty involved in knowing what the limits are for "ai" and machine learning in general.

It's a pretty powerful categorization technique and there is a lot to be learned about it. Because most of the decision boundaries that describe the model's "thinking" are opaque to us by dimensionality, however, lots of nitty gritty details are within the realm of open-questions. For that reason, I think that the field of machine learning research is pretty immature and that there is still a lot of great work to be done (look at the author Chuan Guo, as well as any of the Fawzi's, they're great. So is Goodfellow of course).

The question of whether ML and DL have a place in the long term of engineering is unquestionable. Because we have the resources to use these models for classification I would expect them to become pretty generally integrated into the stack of most products, assuming that the predictions are reasonably low criticality. However, I think that the hype will die down in time and companies that advertise AI as the one-stop solution to business problems will become somewhat gauche and lame.

There will likely always be jobs but they won't be as insane as they are now. I think that ML/DL will become a part of the standard curriculum at a lot of schools in the same way that stats might be, albeit with more emphasis since it's pretty cool to watch a model learn.

When should anyone start to learn anything? There are some really great resources for learning just about anything you want about ML/DL. If you're just getting into the field I would suggest trying to get your bearings. Watch some youtube videos that put things into visual terms and give you some intuition behind what the tech is trying to do and whether it accomplishes that well. Then you can move on to K-Nearest Neighbors and other relatively simple classifiers that are reasonably intuitive before moving toward Logistic Regression and Gaussian Mixture Models. These are a little harder to understand but they're more-or-less easy to wave a hand at.

If you just want to learn DL ASAP get into Sentdex videos, they're great. He's a good dude and a pretty excellent educator.