The Open Source Economic Index of AI Adoption and Capability
We work towards measuring both AI adoption and the capability of AI to perform discrete labor tasks across various occupations. To measure adoption, we develop an open-source economic index that uses publicly available user-LLM chat data and O*NET tasks to replicate studies produced by frontier AI labs, finding that occupations in the finance, computer science, and arts sectors are those with the highest adoption rates. To measure capabilities, we build a system that generates benchmark scenarios grounded in O*NET occupations, tasks, and model-context-protocol (MCP) servers. We test Kimi-k2.5 with an OpenAI agents SDK harness on scenarios across 9 occupations that appear frequently in our...
| Authors | Seamus Somerstep, Aritra Guha, Divesh Srivastava et al. |
|---|---|
| Primary category | cs.CY |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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