Remote Labor Index: Measuring AI Automation of Remote Work
Authors: name: Mantas Mazeika; name: Alice Gatti; name: Cristina Menghini; name: Udari Madhushani Sehwag; name: Shivam Singhal; name: Yury Orlovskiy; name: Steven Basart; name: Manasi Sharma; name: Denis Peskoff; name: Elaine Lau; name: Jaehyuk Lim; name: Lachlan Carroll; name: Alice Blair; name: Vinaya Sivakumar; name: Sumana Basu; name: Brad Kenstler; name: Yuntao Ma; name: Julian Michael; name: Xiaoke Li; name: Oliver Ingebretsen; name: Aditya Mehta; name: Jean Mottola; name: John Teichmann; name: Kevin Yu; name: Zaina Shaik; name: Adam Khoja; name: Richard Ren; name: Jason Hausenloy; name: Long Phan; name: Ye Htet; name: Ankit Aich; name: Tahseen Rabbani; name: Vivswan Shah; name: Andriy Novykov; name: Felix Binder; name: Kirill Chugunov; name: Luis Ramirez; name: Matias Geralnik; name: Hernán Mesura; name: Dean Lee; name: Ed-Yeremai Hernandez Cardona; name: Annette Diamond; name: Summer Yue; name: Alexandr Wang; name: Bing Liu; name: Ernesto Hernandez; name: Dan Hendrycks
Published: 2025-10-30 17:58:04

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.