I think he had something else in mind, somewhat related to overfitting -- you're maximizing (too much) a metric over an unrealistic solution space, and that solution when applied to a real solution space won't behave as intended.

So those are "overfitting" for the exact quirks of the engine. I agree, it's a stretch, but not unlike in ML, it shows that optimizing too much will show quirks if your 'objective function/simulation' pair is not exactly equal to the 'desired objective function/reality', or if there is an innumerable ensemble of situations you might expect the solution to behave well.