Jim founded Predata on the bold promise to help the world get better at predicting the future. It’s been almost two-and-a-half-thousand years since the oracle at Delphi, with Persian forces approaching, advised Themistocles and the Athenians to “await not in quiet the coming of the horses” and retreat instead to Salamis. Human predictions have struggled to maintain that early high standard. Forecasting is a notoriously tough business. Most of last year’s market predictions from financial analysts turned out to be wrong, and almost every political scientist of repute misjudged the outcomes in the 2016 Brexit referendum and the U.S. election. Many of the deadliest terrorist attacks of recent times—Paris, London Bridge, Brussels Airport—have been cast as intelligence “misses,” as failures of prediction.
Predata’s technology progressed from a simple observation: the Arab Spring showed that communities (communities of protesters, in this case) organize online before they organize on the ground. (Though this insight, too, is at least as much urban legend as demonstrated fact.) The idea of turning this observation into a business came not from Jim, he told me, but from Daniel Nadler, CEO of much-hyped (and much-hated) financial technology startup Kensho, writer of imagined ancient love poems, and semi-professional avoider of mirrors. Some time in 2014, Nadler, whose own startup Jim has invested in, came up with the idea of building a signal based on social media pages about the mining sector in Chile, the world’s largest copper producer. By observing fluctuations in the signal, Jim said, Nadler was able to anticipate mining strikes and buy copper ahead of expected supply disruptions, which would be likely to push the commodity’s price higher. This, apparently, earned Nadler a handsome profit. I was never able to confirm this story with Nadler, and Jim himself became more vague on the origin story once Predata got off the ground.
From misty origins a business emerged. The company’s main epistemological bet was that the history of social media could be studied to predict the future. Through the application of machine learning, a branch of artificial intelligence, social media signals could be used to predict events—all sorts of events, from protests and strikes to terrorist attacks, election outcomes, and financial market moves—before they materialized in real life. The system’s algorithm was trained to get better at recognizing the characteristic patterns of online activity that precede events, and to alert users whenever those patterns were beginning to recur. This “predictive intelligence” tool, as Jim called it, was then to be packaged and sold to government intelligence and defense agencies, hedge funds and investors, and a host of other deep-pocketed corporate worthies.
In July 2016, fresh from a flukishly correct prediction of the outcome in the Brexit referendum, the company raised more than $3 million in venture capital. That’s a small sum by Silicon Valley standards, but for a company that was little more than an idea and some people, it represented a major declaration of market faith. Predata’s funding was part of a surge in capital toward the machine learning and neural net technologies considered the core of modern artificial intelligence. In recent times, these technologies have given us self-driving cars, cancer-detection imaging, and instant translation earbuds; they are now the roost of Silicon Valley’s wildest imaginings.
