After hearing this I first thought about similar experiences I have had when I see computer scientists talk about ideas in physics, biology and economics that they seem to have really learned about the other field and tied the areas together well.
My next thoughts went in a different direction. When I have seen economists talk about computational models and complexity, my bread and butter, I just want to tear my hair out (or more accurately tear their hair out). How they get so excited about some stuff we consider completely obvious or known several decades ago, or worse using the completely wrong computational model for the problem they look at, for example examining the precise number of states of a finite automata when they shouldn't be using automata at all.
On the other hand I have heard many economists and physicists and biologists complain that (with some exceptions) they don't understand the relevance of much of the interdisciplinary research that comes from our community.
Part of this attitude comes from the general arrogance that you find among all academics. We computer scientists feel we know the right way to think about problems in all academic fields and researchers in other fields feel the same.
We give strong weight to interdisciplinary research, a good thing. unforunately we typically do this research in the context of impressing those in our own field with a system that encourages this behavior. It is our peers, other computer scientists, that will hire us, write us recommendation and tenure letters and review our grant proposals. So we find it more valuable to have STOC and FOCS paper than papers in good economics or biology journals that most CS people have barely heard about. And we give these talks mostly to our peers designed to impress them.
True interdiscplinary research is difficult enough because you have to deal with not just another field's language but also their culture about what kinds of problems one finds important. But we need a system that truly rewards those who can truly bring computer science ideas into other fields above those who just try to impress our own.