Perhaps surprisingly, statistics has an answer, and that answer is no. If in your application the usefulness of a statistical model is equivalent to its predictive performance, then choose your model using cross-validation, which directly optimizes for predictive performance. When that gets too expensive, use the AIC, which is equivalent to cross-validation as the amount of data grows without bound. But if the true model is available, neither AIC nor cross-validation will pick it out of the set of models being considered as the amount of data grows without bound.
Perhaps surprisingly, statistics has an answer, and that answer is no. If in your application the usefulness of a statistical model is equivalent to its predictive performance, then choose your model using cross-validation, which directly optimizes for predictive performance. When that gets too expensive, use the AIC, which is equivalent to cross-validation as the amount of data grows without bound. But if the true model is available, neither AIC nor cross-validation will pick it out of the set of models being considered as the amount of data grows without bound.