Remote Labor Index: Measuring AI Automation of Remote Work
Abstrak
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.
Penulis (47)
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
Xiaoke Li
Oliver Ingebretsen
Aditya Mehta
Jean Mottola
John Teichmann
Kevin Yu
Zaina Shaik
Adam Khoja
Richard Ren
Jason Hausenloy
Long Phan
Ye Htet
Ankit Aich
Tahseen Rabbani
Vivswan Shah
Andriy Novykov
Felix Binder
Kirill Chugunov
Luis Ramirez
Matias Geralnik
Hernán Mesura
Dean Lee
Ed-Yeremai Hernandez Cardona
Annette Diamond
Summer Yue
Alexandr Wang
Bing Liu
Ernesto Hernandez
Dan Hendrycks
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓