I think it really just suggests the models are being trained improperly. We just don't train alogorithms on data as complex as the real world. So, it's not surprising that simpler (less correct) models win out over more accurate nuanced ones.
Also, now that I think of it, although we often talk about how much more data-efficient humans are compared to ML models, we could say that we're actually much more data-inefficient than most (or all) other animals. Our gestation period is long and then we're totally useless as a baby. We require a lot of learning and development early on in order to reap the benefits later (which consist of generality and flexibility like you mentioned and also, for example, being able to generate our own synthetic data via imagination and counterfactual reasoning).