When machines start picking off all the easy work for themselves, many white-collar jobs are going to get a lot harder. About five years ago, I was the vice president of data for Kickstarter. People would come to the crowdfunding platform with wild ideas—before I got hired there, I used Kickstarter to raise funds for a translation of Moby-Dick into emoji—and company staff got to decide which projects could solicit money from the public. For those working on the Kickstarter projects team, being able to see an obviously worthy idea, and approve it five seconds later, was a total blast. In the early days, it felt like everyone at the company was competing to see what kind of fun and interesting projects we could recruit to our platform.
But as the number of inventors and artists seeking money on Kickstarter exploded, the projects team needed an automated solution to help process the influx of new ideas during surge periods. Holiday weekends were especially difficult, as the projects team’s queue would back up with hundreds of worthy projects. Those delays began frustrating creators who were waiting for the team to approve their proposals. So I oversaw the development of an automated system that would consider each product’s stated purpose, its creator’s past record of success, and other factors, and fast-track the most promising ones.
The idea behind the automation was straightforward: Projects that scored the highest—such as Santorini, a tabletop game that ended up raising more than eight times its goal and quickly became a best seller outside Kickstarter—would gain approval to launch without human intervention.
Our machine-learning system soon took 40 to 6 ..
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