These kinds of questions have an essential unknowability to them, in that every phenomenon you try to model through computing is an inexact approximation - whether it's something like the precision of a mathematical computation or "what is this human being's real name".

What we have done to date is the low-hanging fruit: take known categories of application and juice them up by applying CPU power. And for that, the amount of power you need is "enough to have an effect". A lion's share of the benefits of putting computers in the loop were realized in the 70's or 80's, even though the measured amount of power that could be applied then was relatively miniscule. There were plenty of alternatives at various times that were "better" or "worse" in some technical sense, but succeeded or failed based on other market factors. The real story going forward from then has been changes in I/O interfaces, connectivity and portability - computing has merely "kept up with" the demands imposed by having higher definition display, networking, etc.

So then we have to ask, what are the remaining apps? If we can engineer a new piece of hardware that addresses those, it'll see some adoption. And that's the thrust of going parallel: It'll potentially hit more categories at once than trying to do custom fast silicon for single tasks.

But the long-term trend is likely to be one of consolidating the bottom end of the market with better domain solutions. These solutions can be software-first, hardware-later, because the software now has the breathing room to define the solution space and work with the problem domain abstractly, instead of being totally beholden to "worse-is-better" market forces.