The Open Source Economic Index of AI Adoption and Capability

Seamus Somerstep; Aritra Guha; Divesh Srivastava; Yuekai Sun

The Open Source Economic Index of AI Adoption and Capability

Computers and Society 2026-05-23 v1 Artificial Intelligence Machine Learning

Authors: Seamus Somerstep , Aritra Guha , Divesh Srivastava , Yuekai Sun

Abstract

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 index, finding that AI correctly executes high-level workflows but often errs in the granular details (such as specific tool calls used).

Keywords

AI adoption Economic index Labor tasks Open source Model evaluation

Cite

@article{arxiv.2606.26118,
  title  = {The Open Source Economic Index of AI Adoption and Capability},
  author = {Seamus Somerstep and Aritra Guha and Divesh Srivastava and Yuekai Sun},
  journal= {arXiv preprint arXiv:2606.26118},
  year   = {2026}
}