My bitter experience, particularly with "big data" people, is that they just won't be told by those with long relevant research computing experience in HPC and similar. It's hardly that HPC people don't know what systems can do, particularly if they live and breathe things like distributed linear algebra. I despaired, and largely gave up, when someone strode into a chair From Industry and assured us that the university didn't do Big Data, notwithstanding LHC, astronomy, sequencing, synchrotron work, etc., and was then going to build a Big Data Cluster from a few knackered PCs (and in a basement!) to better the HPC systems. Then I had MPI explained to me.
Which beats using a physical model and highspeed film projected onto a wall covered in graph paper and manually digitising data.
There just aren't that many codes that can take full advantage of these monsters, and there are other, cheaper, faster ways to obtain roughly the same results. For example, in my ex-field of molecular dynamics, it seems like you can get away with minimal interconnect by running many simulations and pooling statistics across runs, which needs a bunch of IO bandwidth but doesn't need supercomputer-style interconnect.
What's interesting is that supercomputer hardware is really great for high performance ML training, but the supercomputer folks came to that realization later, and only after companies like Uber ported the tensorflow distributed implementation to MPI (Horovod). And most of the ML people were poking around with slow interconnects. Now it's converging- the largest use of supercomputer-style systems in 10 years will be ML training, not physical simulations.
Supercomputer spend has only limited impact on forward technological process.