One-sentence Summary

This report investigates the transition from human-level artificial general intelligence to artificial general superintelligence by analyzing four potential pathways including scaling AGI, AI paradigm shifts, recursive improvement, and ASI emerging from large-scale multi-agent collectives, arguing that AI progress may involve a series of transformative societal changes rather than a single transformative step change, a prospect that necessitates a massively interdisciplinary endeavour of global scope to prepare for profound impacts.

Key Contributions

• The report characterizes the transition from human-level AGI to artificial superintelligence by defining Universal AI as the endpoint of a machine intelligence continuum. This framework provides formal grounding for analyzing development trajectories where systems exceed the cognitive capabilities of large human organizations.
• Four potential pathways to ASI are identified, including scaling AGI, AI paradigm shifts, recursive improvement, and multi-agent collectives, alongside specific frictions for each trajectory. Analysis details how economic limits, diminishing returns, and orchestration efforts may impede progress along these trajectories.
• Concrete research agendas are proposed to prepare for superintelligent systems, such as developing multi-agent scaling laws and monitoring mechanisms for AI-enabled research acceleration. These recommendations aim to advance foundational understanding of AI limits and track the degree of human-in-the-loop involvement in automated development processes.

Introduction

With human-level artificial general intelligence emerging as a near-term target, researchers must now investigate the trajectory toward artificial superintelligence to prepare for profound societal impacts. The authors note that existing forecasts often lack concrete technological pathways and struggle with uncertainties regarding compute scaling, algorithmic efficiency, and potential bottlenecks such as data exhaustion or economic limits. To navigate this uncertainty, the report characterizes superintelligence and outlines four potential technological pathways from AGI to ASI, including scaling, paradigm shifts, recursive improvement, and multi-agent collectives. Furthermore, the authors identify specific frictions along these routes and frame them as open research questions to guide future efforts in reducing uncertainty about AI capabilities.

Experiment

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