There are many learning tasks — detecting bank fraud, predicting treatment effectiveness — that are done by separate entities, but where sharing the data would produce better results. However, there are both privacy and incentive problems with sharing data. We propose the use of learning brokers that receive data from various entities and perform two valuable functions. First, they run the training algorithms over the data, and only give the inference engines to others, not the raw data. Second, they share the data in such a way that all entities have an incentive to share everything with the broker. This work is in the very early stages.