Modern neural network-based methods, known as “deep learning”, have transformed natural language processing (NLP) over the past 10 years with unprecedented progress on tasks such machine translation, question answering, dialogue systems and text generation. This isn't the first time that statistical learning has taken the field of NLP hostage, leaving apparently little room for linguistics, but somehow this time it feels different. In this talk, I will summarize how the field of NLP has changed over the past 10 years under the influence of deep learning, how this is similar to previous waves of empiricism and how it differs, which problems have been solved, which remain illusive and why deep learning, while ostensibly pushing linguistics out of picture, may in fact be opening up new research directions for linguists.