Remote Labor Index: Measuring AI Automation of Remote Work
🤖 AI Summary
This study addresses the lack of systematic, quantitative evaluation of AI’s automation capabilities in remote work. We introduce the Remote Labor Index (RLI), the first cross-industry, economically grounded benchmark for assessing remote labor automation. RLI establishes an end-to-end evaluation framework grounded in real-world remote tasks and jointly measures AI agents’ practical productivity along three dimensions—knowledge understanding, logical reasoning, and tool execution—using two complementary metrics: task completion rate and automation rate. Empirical evaluation reveals that state-of-the-art AI models achieve only a 2.5% overall automation rate on RLI, indicating their nascent capability in authentic remote work settings. By providing a reproducible, scalable, and economically meaningful quantification methodology, RLI bridges a critical gap between laboratory-based automation assessment and real-world labor market impact analysis.
Application Category
📝 Abstract
AIs have made rapid progress on research-oriented benchmarks of knowledge and reasoning, but it remains unclear how these gains translate into economic value and automation. To measure this, we introduce the Remote Labor Index (RLI), a broadly multi-sector benchmark comprising real-world, economically valuable projects designed to evaluate end-to-end agent performance in practical settings. AI agents perform near the floor on RLI, with the highest-performing agent achieving an automation rate of 2.5%. These results help ground discussions of AI automation in empirical evidence, setting a common basis for tracking AI impacts and enabling stakeholders to proactively navigate AI-driven labor automation.
Problem
Research questions and friction points this paper is trying to address.
Measuring AI automation impact on remote work
Evaluating AI agent performance in practical economic settings
Establishing empirical basis for tracking AI labor automation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Introduced Remote Labor Index benchmark
Evaluated AI agents on real-world projects
Measured low automation rates empirically
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