METR outlines method for assessing AI-driven acceleration of AI R&D
METR published an article describing how an assessor could investigate whether AI capabilities progress is accelerating due to automation of AI R&D. The proposed assessment would analyze unreleased model capabilities, growth of key inputs (labor, compute, data, algorithms), and researcher input, while also examining bottlenecks and method limitations.
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This article discusses how an assessor could study whether capabilities progress could accelerate dramatically due to AI-driven acceleration of AI R&D. Such an assessment would answer: Capabilities Progress : How fast are internal capabilities improving, and how much has this progress accelerated recently? Acceleration from AI : If there is an acceleration, how much was it driven by AI automation versus other factors? Bottlenecks to Future Growth : Are there important bottlenecks that could slow growth or limit acceleration in the near term? Limitations : Would these methods reliably identify much more dramatic acceleration if it had occurred? Assessing this would involve analyzing capabilities of unreleased models, understanding the growth of key AI R&D inputs (labor, compute, data, and algorithms), and talking openly with relevant researchers about how AI is impacting their work. More details on access below . Scope Capabilities Progress To understand whether capabilities progress ha
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