From AGI to ASI
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.