ICML 2026 invited keynote · Seoul · July 9, 2026 · personal archival copy (recording © ICML/SlidesLive — please don't redistribute this link)
Given rapid advances in AI, how should researchers and developers shift how we allocate our time? What new skills should we build so that we're not obsolete in the future? I argue that there will be plenty for us to work on, grounded in the “AI as normal technology” thesis, which holds that there are many bottlenecks between AI capability improvements and automation of tasks or jobs. The evidence suggests that AI is better seen as an augmentation than an automation technology. The balance of human effort will shift towards tasks that are less verifiable — from developing models to scaffolds, and from building towards evaluation and monitoring.