Remote Labor Index: Measuring AI Automation of Remote Work
This provides empirical evidence to ground discussions of AI-driven labor automation for stakeholders, though it is incremental as it focuses on measurement rather than solving automation itself.
The authors tackled the problem of measuring AI's economic impact by introducing the Remote Labor Index (RLI), a benchmark for evaluating AI agents on real-world remote work tasks, and found that AI agents performed poorly, with the highest automation rate at only 2.5%.
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.