Remote Labor Index: Measuring AI Automation of Remote Work
Mantas Mazeika, Alice Gatti, Cristina Menghini, Udari Madhushani Sehwag, Shivam Singhal, Yury Orlovskiy, Steven Basart, Manasi Sharma, Denis Peskoff, Elaine Lau, Jaehyuk Lim, Lachlan Carroll, Alice Blair, Vinaya Sivakumar, Sumana Basu, Brad Kenstler, Yuntao Ma, Julian Michael

TL;DR
The paper introduces the Remote Labor Index (RLI), a benchmark to evaluate AI's practical automation capabilities across sectors, revealing current AI agents perform poorly, with only 2.5% automation rate, thus informing discussions on AI's economic impact.
Contribution
It presents the RLI as a new multi-sector benchmark for assessing AI automation in real-world remote work scenarios, bridging research progress with economic implications.
Findings
AI agents perform near the floor on RLI
Highest-performing agent achieves 2.5% automation rate
RLI provides empirical basis for AI automation discussions
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.
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