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The Open Source Economic Index of AI Adoption and Capability

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Computer Science > Computers and Society

arXiv:2606.26118 (cs)
[Submitted on 23 May 2026]

Title:The Open Source Economic Index of AI Adoption and Capability

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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).
Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2606.26118 [cs.CY]
  (or arXiv:2606.26118v1 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2606.26118
arXiv-issued DOI via DataCite

Submission history

From: Seamus Somerstep [view email]
[v1] Sat, 23 May 2026 18:45:42 UTC (3,063 KB)
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