With GPUs we have proven that parallelism can be just as good or even better than speed increases in enhancing computation. And there again have been speed increases trickling in.
I don't think it's realistic to say that more speed advances are unlikely. We have already been through many different paradigm shifts in computing, from mechanical to nanoscale. There are new paradigms coming up such as memristors and optical computing.
It seems like 1000x will make Stable Diffusion-style video generation feasible.
We will be able to use larger, currently slow AI models in realtime for things like streaming compression or games.
Real global illumination in graphics could become standard.
Much more realistic virtual reality. For example, imagine a realistic forest stream that your avatar is wading through, with realtime accurate simulation of the water, and complex models for animal cognition of the birds and squirrels around you.
I think with this type of speed increase we will see fairly general purpose AI, since it will allow average programmers to easily and inexpensively experiment with combining many, many different AI models together to handle broader sets of tasks and eventually find better paradigms.
It also could allow for emphasis on iteration in AI, and that could move the focus away from parallel-specific types of computation back to more programmer-friendly imperative styles, for example if combined with many smaller neural networks to enable program synthesis, testing and refinement in real time.
Here's a weird one: imagine something like emojis in VR, but in 3d, animated, and customized on the fly for the context of what you are discussing, automatically based on an AI you have given permission to.
Or, hook the AI directly into your neocortex. Hook it into several people's neocortices and then train an animated AI 3d scene generation system to respond to their collective thoughts and visualizations. You could make serialized communication almost obsolete.