A change in the underlying fundamentals can make a formerly predictive model inaccurate. In some fields this problem is referred to as the problem of distribution shift. Regardless of the nomenclature, the point is that circumstances can change so severely that the probabilistic mapping that formerly mapped x to y is no longer valid. A popular example would be the difference between performance in practice and a real match. An analogous engineering example would be the difference between performance in a digital twin and the real system. Obviously, tremendous effort is being put towards closing the gap between these two, but the point is that in both cases the same type of f(x) might apply, but changes in context may render them non-transferable.