In this edition of "Literature of Statistics," we examine two competing (straw man) positions about models. The first holds that we should design our models carefully, striving to incorporate domain knowledge and prioritizing interpretability.The second holds that anything goes: prediction performance matters above all, and model choice should be governed by computational constraints. We'll use Leo Breiman's "Two Cultures" paper as a jumping off point, and entertain arguments for and against both perspectives.