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

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.