Who Uses AI? Platform Selection and the Measurement of Occupational AI Exposure
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910266123354112 |
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| author | Yin, Michelle Ogut, Burhan |
| author_facet | Yin, Michelle Ogut, Burhan |
| contents | Conversation logs from AI platforms are increasingly used to measure occupational exposure to artificial intelligence, but the users observed in these logs are not the workforce. We show that platform-derived exposure scores combine task-level AI applicability with the occupational composition of the platform's user base. Holding the empirical design fixed, changing only the platform input changes the post-ChatGPT employment coefficient by a factor of 1.9, and consumer and enterprise channels within the same vendor disagree in sign. We formalize the resulting non-classical measurement error, decompose it into between- and within-occupation selection, and construct workforce-reweighted partial-identification bounds. Reweighting to Bureau of Labor Statistics employment shares attenuates estimates by 42 to 93 percent. The bias captures augmentation among observed users more directly than substitution in the workforce. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_21743 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Who Uses AI? Platform Selection and the Measurement of Occupational AI Exposure Yin, Michelle Ogut, Burhan Artificial Intelligence General Economics Economics Conversation logs from AI platforms are increasingly used to measure occupational exposure to artificial intelligence, but the users observed in these logs are not the workforce. We show that platform-derived exposure scores combine task-level AI applicability with the occupational composition of the platform's user base. Holding the empirical design fixed, changing only the platform input changes the post-ChatGPT employment coefficient by a factor of 1.9, and consumer and enterprise channels within the same vendor disagree in sign. We formalize the resulting non-classical measurement error, decompose it into between- and within-occupation selection, and construct workforce-reweighted partial-identification bounds. Reweighting to Bureau of Labor Statistics employment shares attenuates estimates by 42 to 93 percent. The bias captures augmentation among observed users more directly than substitution in the workforce. |
| title | Who Uses AI? Platform Selection and the Measurement of Occupational AI Exposure |
| topic | Artificial Intelligence General Economics Economics |
| url | https://arxiv.org/abs/2605.21743 |