Measuring AI R&D Automation

TL;DR

This paper proposes metrics to measure the extent and effects of AI R&D automation, aiming to inform safety, oversight, and policy decisions amid uncertain impacts.

Contribution

It introduces new metrics for tracking AI R&D automation and its consequences, addressing gaps in existing capability benchmarks.

Findings

Metrics include capital share of AI R&D spending

Researcher time allocation and AI subversion incidents

Guidelines for organizations and governments to monitor AI R&D automation

Abstract

The automation of AI R&D (AIRDA) could have significant implications, but its extent and ultimate effects remain uncertain. We need empirical data to resolve these uncertainties, but existing data (primarily capability benchmarks) may not reflect real-world automation or capture its broader consequences, such as whether AIRDA accelerates capabilities more than safety progress or whether our ability to oversee AI R&D can keep pace with its acceleration. To address these gaps, this work proposes metrics to track the extent of AIRDA and its effects on AI progress and oversight. The metrics span dimensions such as capital share of AI R&D spending, researcher time allocation, and AI subversion incidents, and could help decision makers understand the potential consequences of AIRDA, implement appropriate safety measures, and maintain awareness of the pace of AI development. We recommend that companies and third parties (e.g. non-profit research organisations) start to track these metrics, and that governments support these efforts.