In this work, for the first time, we demonstrate that computers can automatically learn from observing the heuristic efforts of the last four decades, stand on the shoulders of the existing Internet congestion control (CC) schemes, and discover a better-performing one. To that end, we address many different practical challenges, from how to generalize representation of various existing CC schemes to serious challenges regarding learning from a vast pool of policies in the complex CC domain and introduce Sage. Sage is the first purely data-driven Internet CC design that learns a better scheme by harnessing the existing solutions. We compare Sage's performance with the state-of-the-art CC schemes through extensive evaluations on the Internet and in controlled environments. The results suggests that Sage has learned a better-performing policy. While there are still many unanswered questions, we hope our data-driven framework can pave the way for a more sustainable design strategy.