Notes Toward Sensible ASI Governance
Governance for ASI should make fast development more legible, reliable, and deployable: measurement, evaluations, incident learning, and standards that scale with capability rather than freezing progress.
The premise of this track is pro-progress: ambitious technology scales fastest when it earns trust. Aviation, vaccines, cars, semiconductors, and cloud infrastructure all moved from invention to civilization-scale deployment through measurement, standards, audits, and fast feedback from incidents.
ASI governance should be judged by the same standard. It should increase the rate at which capable systems can be shipped responsibly, not turn uncertainty into paralysis.
Working mechanisms
- Capability evaluations tied to concrete deployment thresholds.
- Incident reporting that improves engineering practice without becoming a theater of blame.
- Secure sandboxes for self-improving systems that execute generated code.
- Third-party measurement of AI R&D automation, including researcher time allocation, AI-authored code share, and oversight capacity.
- Standards for model-generated scientific claims: executable artifacts, review logs, benchmark traces, and reproduction packages.
- Agent-control layers: sandboxing, permissioning, monitoring, intervention, and incident response for models that can use tools over long horizons.
Current anchor
Measuring AI R&D Automation is the most useful current governance paper in this library because it treats acceleration as something to measure. If AI begins performing a large share of AI research, institutions need live telemetry on both capability progress and oversight capacity.
New anchors
LifeSciBench and GeneBench-Pro point toward domain-grounded scientific evaluations. The Google DeepMind AI Control Roadmap and OpenAI third-party evaluation playbook point toward the deployment side: harness design, supervision, controls, and evidence trails for agentic systems.