A shift from a single company handling all software and hardware inhouse, to a sort of divergence of the two. Kind of cool to think about, given how that sort of divergence seems to have happened in a few other areas (a dell computer runs windows, an LG TV runs apple TV, your iphone runs airbnb, spotify, instagram)
When we can control the environment precisely, we should as it simplifies so much---we can clip the world to platonic geometries and apply common tricks and trigonometry.
But when we cannot control the environment, really really much harder. Self-driving cars are a good example how hard it is, and still mainly focusing on relatively structured environments like cities.
Arbitrary environment is really where many expect AI/ML/probabilistic robotics to bring some necessary flexibility. And still out of reach today, except some astonishing use cases like Roombas (well, more like a best effort approach, still).
Seems to me is that if you restrict the problem space, have well defined tasks, then marry the semantic understanding and reasoning of VLMs with the geometric tools of classical approaches, then you could have something practical and economical.