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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.26787 |
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| _version_ | 1866917050768687104 |
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| author | Mazeika, Mantas Gatti, Alice Menghini, Cristina Sehwag, Udari Madhushani Singhal, Shivam Orlovskiy, Yury Basart, Steven Sharma, Manasi Peskoff, Denis Lau, Elaine Lim, Jaehyuk Carroll, Lachlan Blair, Alice Sivakumar, Vinaya Basu, Sumana Kenstler, Brad Ma, Yuntao Michael, Julian Li, Xiaoke Ingebretsen, Oliver Mehta, Aditya Mottola, Jean Teichmann, John Yu, Kevin Shaik, Zaina Khoja, Adam Ren, Richard Hausenloy, Jason Phan, Long Htet, Ye Aich, Ankit Rabbani, Tahseen Shah, Vivswan Novykov, Andriy Binder, Felix Chugunov, Kirill Ramirez, Luis Geralnik, Matias Mesura, Hernán Lee, Dean Cardona, Ed-Yeremai Hernandez Diamond, Annette Yue, Summer Wang, Alexandr Liu, Bing Hernandez, Ernesto Hendrycks, Dan |
| author_facet | Mazeika, Mantas Gatti, Alice Menghini, Cristina Sehwag, Udari Madhushani Singhal, Shivam Orlovskiy, Yury Basart, Steven Sharma, Manasi Peskoff, Denis Lau, Elaine Lim, Jaehyuk Carroll, Lachlan Blair, Alice Sivakumar, Vinaya Basu, Sumana Kenstler, Brad Ma, Yuntao Michael, Julian Li, Xiaoke Ingebretsen, Oliver Mehta, Aditya Mottola, Jean Teichmann, John Yu, Kevin Shaik, Zaina Khoja, Adam Ren, Richard Hausenloy, Jason Phan, Long Htet, Ye Aich, Ankit Rabbani, Tahseen Shah, Vivswan Novykov, Andriy Binder, Felix Chugunov, Kirill Ramirez, Luis Geralnik, Matias Mesura, Hernán Lee, Dean Cardona, Ed-Yeremai Hernandez Diamond, Annette Yue, Summer Wang, Alexandr Liu, Bing Hernandez, Ernesto Hendrycks, Dan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26787 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Remote Labor Index: Measuring AI Automation of Remote Work Mazeika, Mantas Gatti, Alice Menghini, Cristina Sehwag, Udari Madhushani Singhal, Shivam Orlovskiy, Yury Basart, Steven Sharma, Manasi Peskoff, Denis Lau, Elaine Lim, Jaehyuk Carroll, Lachlan Blair, Alice Sivakumar, Vinaya Basu, Sumana Kenstler, Brad Ma, Yuntao Michael, Julian Li, Xiaoke Ingebretsen, Oliver Mehta, Aditya Mottola, Jean Teichmann, John Yu, Kevin Shaik, Zaina Khoja, Adam Ren, Richard Hausenloy, Jason Phan, Long Htet, Ye Aich, Ankit Rabbani, Tahseen Shah, Vivswan Novykov, Andriy Binder, Felix Chugunov, Kirill Ramirez, Luis Geralnik, Matias Mesura, Hernán Lee, Dean Cardona, Ed-Yeremai Hernandez Diamond, Annette Yue, Summer Wang, Alexandr Liu, Bing Hernandez, Ernesto Hendrycks, Dan Machine Learning Artificial Intelligence Computation and Language 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. |
| title | Remote Labor Index: Measuring AI Automation of Remote Work |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2510.26787 |