Charlie O’Neill

AI researchers debate how close we are to recursive self-improvement

John Schulman, Charlie O’Neill, Beren Millidge

Experts predict that while AI will function as full-time remote workers within three years, exponential takeoff before 2036 remains unlikely due to persistent generalization bottlenecks and sample efficiency gaps that prevent narrow benchmarks from scaling to broad real-world domains. The prevailing consensus suggests current architectures will plateau under diminishing returns on scaling, forcing a reliance on distillation and human-defined objectives rather than recursive self-improvement to drive the projected tenfold researcher productivity gains. Consequently, a dominant "ASI" model capable of mastering every cognitive and physical field is forecast only within five to ten years, contingent on overcoming fundamental barriers in non-cumulative task learning and environment creation.