Audio Edition: How Can AI Researchers Save Energy? By Going Backward.

Reversible computing offers a potential solution to the mounting energy demands of artificial intelligence by running programs backward as easily as forward. Traditional computing faces physical limitations because deleting data inevitably generates heat—a principle proven by physicist Rolf Landauer in 1961. By utilizing "uncomputation," a method proposed by Charles Bennett where calculations are run forward and then reversed to reclaim resources without deleting information, computers can theoretically operate with near-zero energy loss. While early prototypes in the 1990s struggled with practicality, researchers like Michael Frank and Hannah Early are now optimizing the trade-off between heat and speed. By running multiple reversible chips in parallel at slower speeds, AI systems can achieve massive energy savings and higher density without traditional cooling constraints. This decades-old theoretical framework is now transitioning into commercial development to sustain computational progress as conventional silicon chips reach their physical scaling limits.