Harvard CS 2881, Fall 2026, Lecture 4: recursive self-improvement and AI trajectories

Class notes for Lecture 4 of the Harvard CS 2881R "AI Alignment and Safety" course focus on recursive self-improvement and AI trajectories, with guest speakers Dwarkesh Patel and Daniel Kokotajlo. Drawing on the pre-reading material "AI 2027," the notes state that once AI can help improve successor systems it will form a feedback loop that accelerates progress, and that this scenario projects that automated AI R&D combined with recursive self-improvement could achieve ASI in 2027. The notes also point out that METR's time horizon benchmark has large uncertainty, and that benchmark scores may fail to reflect true capability due to luck, exploits or test-taking strategies.