I think the notion that “3D printing will change how we put stuff together” hasn’t really manifested in meaningful ways.
I mean, you can 3D print cakes and buildings. But you could also just, you know, dumb-stack stuff and get the same result.
In the same vein there are many examples of machine learning solutions searching high and low for problems to solve, when it could really be solved by much simpler means.
Anecdotally I once heard a talk on how a local government thought they needed AI to solve housing allocation. They later found was that, for historical reasons, some applications had to go through an unnecessary number of hoops before being accepted. By policy changes alone they eliminated this bottleneck. I wish I could find the case, if it’s published anywhere.
3D Printing is a very apt comparison to ml, but for the exact opposite reason.
It didn't replace the entirety of all production like some people predicted it to, but it has become an extremely valuable tool for quick, low-cost prototyping and small production runs that otherwise wouldn't be economical because of the huge setup cost of traditional manufacturing (like injection molding).
I'm fairly certain that ml will be the same way. Transforming from the magical bullet that we currently want it to be, into just another tool in the kit.
As with 3d printing this process will take some time where some people apply it way too much, while other people won't even consider it at all.
Eventually we will settle on a sweet spot where most people have an understanding of when to use it and when not to.