Training Compute Thresholds: Features and Functions in AI Governance

Compute thresholds offer several advantages that are difficult to achieve with other metrics, making them a useful complement (Section 3): Risk-tracking: Higher training compute is associated with greater model capabilities and potential risks. Quantifiability and ease of measurement: Training compute is a quantifiable metric that is relatively straightforward and cost-effective to calculate. Difficulty of circumvention: Reducing training compute to evade regulation is likely to simultaneously reduce a model's capabilities and risks. Knowable before development and deployment: Training compute can be estimated prior to a model's development and deployment, facilitating proactive measures. External verifiability: Compute usage can potentially be verified by external parties without compromising sensitive information. Targeted regulatory scope: The metric is proportionately higher for models that cost more to develop, minimizing the burden on smaller actors while focusing on the most well-resourced ones. Regulation of frontier models based on compute thresholds is primarily concerned with ensuring government visibility and the capacity to act if these models are found to present serious societal-scale risks. It is not intended to address all possible downstream impacts of AI on society, many of which should be regulated at the use level.