Metrics & Indicators
Overview
Section titled “Overview”This section documents metrics and indicators that can be tracked to monitor AI safety progress and risk dynamics. Quantitative measurement helps move beyond intuition toward evidence-based assessment.
Metric Categories
Section titled “Metric Categories”Measures of AI system capabilities:
- Benchmark performance trends
- Capability emergence timing
- Task completion rates
Infrastructure indicators:
- Training compute trends
- Chip production and access
- Energy consumption
Progress on alignment and safety:
- Publication rates and citations
- Researcher headcount
- Funding levels
Technical alignment indicators:
- Interpretability coverage
- Evaluation robustness
- Safety case development
How labs are operating:
- Pre-deployment testing time
- Safety team size ratios
- RSP adoption rates
Policy and institutional indicators:
- Regulatory progress
- International coordination
- Standards development
Perception and discourse:
- Public awareness surveys
- Expert probability estimates
- Media coverage trends
Socioeconomic impacts:
- Automation displacement rates
- AI investment levels
- Productivity changes
Why Metrics Matter
Section titled “Why Metrics Matter”Good metrics help:
- Detect early warning signs of increasing risk
- Evaluate intervention effectiveness over time
- Enable accountability for labs and governments
- Ground debates in data rather than speculation
See individual metric pages for current values, historical trends, and data sources.