From AGI to ASI
A 2026 orientation report on the post-AGI frontier, defining ASI as systems more intelligent and cognitively capable than large human organizations and mapping four paths from AGI to ASI.
Which measurable indicators would reveal that recursive improvement or multi-agent coordination is compounding capabilities faster than continued model scaling?
This is the site anchor paper. It is not a benchmark result or a narrow systems paper; it is a map of the territory immediately after AGI. The useful move is that it treats ASI as a continuum of machine intelligence rather than as a mythic discontinuity.
Why it matters
The report identifies four pathways worth tracking:
- Scaling AGI: continue increasing compute, data quality, context, memory, tool use, and post-training.
- Paradigm shifts: new model classes, learning algorithms, training objectives, memory systems, or search procedures.
- Recursive improvement: AI systems help improve the methods by which AI systems are built.
- Multi-agent collectives: large populations of specialized agents behave like cognitive organizations with machine-speed coordination.
For a pro-ASI technical agenda, the key conclusion is practical: progress can compound through many partially independent loops. A single “AGI arrives” date is less useful than monitoring which loops are already closing.
Research agenda
Track the bottlenecks explicitly: evaluation quality, compute allocation, long-horizon autonomy, automated experiment design, agent reliability, and the rate at which AI-generated improvements transfer into the next generation of systems.