I reflect on the past, present, and future of nonparametric Bayesian statistics. Current nonparametric Bayes research tends to be split between theoretical studies, seeking to understand relatively simple models, and machine learning, defining new models and computational algorithms motivated by practical performance. I comment on the current landscape, open problems and promising future directions in modern big data applications.