Conventional wisdom in Data Science/Statistical Learning tells us that when we try to fit a model that is able to learn from our data and generalize what it learned to unseen data, we must keep in mind the bias/variance trade-off. This means that as we increase the complexity of our models (let us say, the number of learnable parameters), it is more likely that they will just memorize the data and will not be able to generalize well to unseen data. On the other hand, if we keep the complexity low, our models will not be able to learn too much from our data and will not do well either. We are told to find the sweet spot in the middle. But is this paradigm about to change? In this article we review new developments that suggest that this might be the case.