The introduction of A/B Testing represented a great leap forward in recommender systems research. Like the randomized control trial for evaluating drug efficacy; A/B Testing has equipped recommender systems practitioners with a protocol for measuring performance as defined by actual business metrics and with minimal assumptions. While A/B testing provided a way to measure the performance of two or more candidate systems, it provides no guide for determining what policy we should test. The focus of this industry talk is to better understand, why the development of A/B testing was the last great leap forward in the development of reward optimizing recommender systems despite more than a decade of efforts in both industry and academia. The talk will survey: industry best practice, standard theories and tools including: collaborative filtering (MovieLens RecSys), contextual bandits, attribution, off-policy estimation, causal inference, click through rate models and will explain why we have converged on a fundamentally heuristic solution or guess and check type method. The talk will offer opinions about which of these theories are useful, and which are not and make a concrete proposal to make progress based on a non-standard use of deep learning tools.