To rephrase... because the models predicting model failure (often) use the same assumptions/knowledge as the original models, so they both fail at the same time

To rephrase... because the models predicting model failure (often) use the same assumptions/knowledge as the original models, so they both fail at the same time once assumptions become invalidated. "intertemporal heterogeneity in the relationship between variables that are frequently used to predict [...] imply that model instability is a significant source of risk for [folks] that rely heavily on predictive statistical models and machine learning algorithms [...]" No shit, Sherlock! aka "overfitting" I don't mean to deride the actual research that was done, but I would hope that people understand this general property better before jumping on the bandwagon to use statistical models -- without needing the benefit of hindsight.