He leaves that to an artificially intelligent system built by several thousand data scientists whose names he doesn't know.

Under the banner of a startup called Numerai, Craib and his team have built technology that masks the fund's trading data before sharing it with a vast community of anonymous data scientists. Using a method similar to homomorphic encryption, this tech works to ensure that the scientists can't see the details of the company's proprietary trades, but also organizes the data so that these scientists can build machine learning models that analyze it and, in theory, learn better ways of trading financial securities.

"We give away all our data," says Craib, who studied mathematics at Cornell University in New York before going to work for an asset management firm in South Africa. "But we convert it into this abstract form where people can build machine learning models for the data without really knowing what they're doing."