An often missed point is that the NFL has a practical consequence and that is, learning doesn't happen if the model architecture is not well adapted to the stru

An often missed point is that the NFL has a practical consequence and that is, learning doesn't happen if the model architecture is not well adapted to the structure of the data. There being structure is not on its own strong enough to allow for any algorithm to do well. The model itself needs to be aligned with the structure in the data. This applies to animals, humans too, by inserting structural priors, evolution sacrificed performance on certain kinds of problems to do well on the kind we met in the natural world. You can read more in: Antipredictable Sequences: Harder to Predict Than Random Sequences by Huaiyu Zhu and Wolfgang Kinzel