XGBoost did not change much from the "Greedy function approximation: A gradient boosting machine." paper, but uses a few tricks to be much much faster, allowing for better tuning.
XGBoost is popular for structured data competitions on Kaggle. Even there: The winner is often an ensemble of XGBoost and Keras. And some structured data competitions are won by neural nets alone: http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it... and https://www.kaggle.com/c/higgs-boson/discussion/10425 (Neural nets won the Higgs Boson Detection Challenge, where XGBoost was introduced)
I'd say Tensorflow/Keras can handle a wider variety of problems, with the same, or improved accuracy, than tree-based methods can. NN's do well on structured problems (the domain of tree-based methods), but also own computer vision and, increasingly, NLP. I agree on the pitfalls of applying neural nets vs. forests.
It is true that tree-based methods are academically a bit out of vogue: The exciting stuff is happening in the neural network space. You have more chance of getting published with deep learning (this used to be the other way around).