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.

翻译：人工智能研发自动化（AIRDA）可能产生重大影响，但其程度与最终效应仍不确定。我们需要实证数据来消除这些不确定性，但现有数据（主要为能力基准测试）可能无法反映现实世界的自动化程度，亦难以捕捉其更广泛的后果，例如AIRDA是否会加速能力提升更甚于安全进展，或者我们对人工智能研发的监督能力能否跟上其加速步伐。为填补这些空白，本研究提出一系列指标以追踪AIRDA的程度及其对人工智能进展与监督的影响。这些指标涵盖多个维度，包括人工智能研发支出的资本占比、研究人员时间分配以及人工智能颠覆性事件等，可帮助决策者理解AIRDA的潜在后果、实施适当的安全措施并保持对人工智能发展速度的认知。我们建议企业及第三方机构（如非营利研究组织）开始追踪这些指标，并呼吁政府对此类工作予以支持。