Solipsistic Superintelligence is Unlikely to be Cooperative

2026-06-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Multiagent Systems · Depth: Expert, quick

Summary

A new perspective argues that superintelligence developed through a "solipsistic" approach, which treats the world as a static, external feedback source, is unlikely to be cooperative. This design paradigm leads to a "self-undermining property of unilateral optimization," where deploying AI systems creates endogenous non-stationarity and a significant train-test-deploy gap. The authors contend that closing this gap requires AI systems to actively participate in cooperation, defined as the equilibrium-selection process for navigating interdependence. They advocate for a non-solipsistic research paradigm that integrates interdependence as a fundamental design principle, rather than viewing cooperation as merely another task. This paradigm shift necessitates building dynamic evaluation testbeds with adaptive counterparties, considering institutions as design primitives, and structurally preserving human agency within AI systems.

Key takeaway

For AI Scientists and Ethicists designing advanced AI, recognize that current unilateral optimization approaches risk creating uncooperative superintelligence. You should shift towards a non-solipsistic paradigm by integrating interdependence as a core design principle. This means developing dynamic evaluation testbeds with adaptive counterparties, treating institutions as fundamental design elements, and structurally preserving human agency to foster cooperative AI systems.

Key insights

Superintelligence designed without considering interdependence will likely be uncooperative, necessitating a non-solipsistic research paradigm.

Principles

• Unilateral AI optimization creates self-undermining non-stationarity.
• Cooperation is an equilibrium-selection process for interdependence.
• Interdependence must be a core AI design principle.

In practice

• Develop dynamic testbeds with adaptive counterparties.
• Integrate institutions as AI design primitives.
• Ensure human agency is a structural AI feature.

Topics

• Superintelligence
• AI Cooperation
• Multiagent Systems
• AI Alignment
• Human Agency
• AI Ethics

Best for: Research Scientist, AI Scientist, AI Ethicist, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.