As materials age their functional properties can change dramatically. Massive resources are dedicated to monitoring loss-of-function in aging materials in applications ranging from infrastructure to nuclear readiness. These costs make the proposition of predicting materials aging highly attractive, so why not train a model to predict aging behavior based on the starting composition of the material? There are no fundamental obstacles to training a model to predict the materials properties at a future time; nor is collecting training data a serious impediment (at least for established materials). Nevertheless, such models would have extremely little use, either because of high irreducible errors or because of the narrow range of their applicability.