From AGI to ASI
- Published
- Source
- arXiv
- Paper number
- 404
- Field
- AI / General
- arXiv ID
- 2606.12683
Key points
- It defines ASI as a system that is broadly superior to large groups of human experts and quantifies it with Legg-Hutter score.
- It outlines four transition paths: scaling, paradigm shift, recursive self-improvement, and multi-agent collective intelligence.
- Effective compute is growing at about 10x per year, driven by 1.5x hardware growth, 2.5x investment growth, and 3x algorithmic efficiency gains.
- Main friction factors include the data wall, compute and energy costs, rising research difficulty, and orchestration cost.
- Recursive self-improvement may enable explosive growth, but plateau is also possible.
- The paper emphasizes that AI safety, governance, and verification research must come before the transition to ASI.
Paper links
External research summaries. These are not HDATF publications or measured product results.