A paper published by Google DeepMind researchers, including Co-Founder Shane Legg, outlines four potential pathways from artificial general intelligence (AGI) to artificial superintelligence (ASI). The paper, *From AGI to ASI*, defines AGI as a system capable of achieving at least median human performance across a broad range of cognitive tasks, while ASI is described as a system capable of outperforming organisations comprising thousands of human experts. The four possible routes are: continued scaling of compute, models and data; algorithmic paradigm shifts; recursive self-improvement through automated AI research; and large-scale collectives of AI agents. The paper cites estimates suggesting hardware capabilities improve by roughly 1.5x per year, AI infrastructure investment by around 2.5x per year, and algorithmic efficiency by as much as 6x annually, implying effective compute growth of roughly 10x per year. The authors note that the four pathways are not mutually exclusive and could lead to compounding increases. The report argues that coordinating millions of AGI agents could produce superhuman collective intelligence, and discusses an “abstraction barrier” as a potential limitation on individual AI systems.