I may be wrong, but it seems to me that 20th century (theoretical) statistics research overemphasized efficiency at the expense of robustness. My guess is that this has to do with the (over-)mathematization of statistics in the past century, as opposed to a more empirical/engineering viewpoint.
Efficiency typically only holds under extremely narrow (and often impossible to check) assumptions, which is great for mathematicians proving theorems and creating theories of efficiency.
On the other hand, robustness is ideally about unknown unknowns and weak assumptions, which is hard to deal with mathematically.
It seems already the 21st century is seeing a more balanced emphasis on theory vs. real world applications though.
It seems already the 21st century is seeing a more balanced emphasis on theory vs. real world applications though.