There will always be people who need them and use them but that proportion is going to keep decreasing (I'm somewhat sad about this, but the math is hard to argue with).
In the meantime (unless you are dealing with video) most text and image datasets out there that avg Joe needs can easily be stored/processed entirely locally thanks to cheap terabyte drives/multicore chips these days. People just haven't realized there isn't that much useable textual data OR that local computing doesn't require all the overhead of handling millions of requests a second. This is Google problem not an avg Joe problem that is being solved with cloud compute.
There is no dispute with the size or amount of compute available on desktops.
Because almost everyone already uses the model of co-locating the search index and query code on a single computer (both Wikipedia and Stackoverflow use Elastic Search which does this).
They use multiple physical servers because of the number of simultaneous requests they serve.
This has never been the use-case for Hadoop.
I've built Hadoop based infrastructure for redundantly storing multiple PB of unstructured data with Spark on top for analysis. This is completely different to search.
That's very different to the Wall St analyst running desktop analysis in Matlab, or the oil/gas exploration team doing the same thing.