I am looking for references that specifically show that Akaike's Information Criterion (AIC), or its corrected form (AICc), can in some practical applications -- that is, not in the asymptotic regime -- highly underestimate the penalty for model complexity, favoring overly complex model that would then perform worse on new data; and possibly ways to detect this "failure mode" of AIC (the obvious one I can think of is cross-validation).
More generally, I am also looking for some authoritative reference, besides basic common sense, that advises against "blind model selection" -- that is, merely deciding that an hypothesis is true after comparing models with some criterion, without "predictive checks" or other forms of independent validation. Ideally, I am looking for a strong statement (e.g., something like this, perhaps a bit less graphic), with examples for why it is such a bad idea.
Any suggestion, off the top of your head?
(As you would expect, there is a massive number of questions related to AIC and model selection on this website, but I could not find something that specifically addresses my issue.)
PS: To clarify, regarding the first question, I am interested in references that talk about AIC, but it's fine if the paper discusses information criteria in general (e.g., both AIC and BIC), as long as AIC is included.