At a glance
- Source
- Sedai (2026). Exposure Is Not Adoption: Measuring Generative AI and Labor Demand. SSRN working paper. doi:10.2139/ssrn.7528420 · Not peer-reviewed: other researchers have not yet checked it for a journal.
- How it was done
- The author compared two kinds of measure against demand for workers: scores of what AI could do in each occupation, and a measure of what people actually use AI for. Data analysis. It builds a measure from existing data, such as job and task records.
- Who took part
- Occupations and demand for workers in them.
- Where
- United States.
“Occupational exposure indices measure what generative AI could do, not what it is used for.”
What it found
Exposure scores and real use are different things. In this analysis, a measure of what people actually use AI for predicted demand for workers better than exposure scores did.
When the result holds
The point applies to any score built from task descriptions. Such a score tells you where to look. It does not tell you what is happening.
Limits
- A working paper. It has not been through peer review.
Practices that use this study
This study supports:
Assessments linked to this study
- Which of our roles could AI touch most? AI Occupational Exposure measure (AIOE) Nobody is surveyed · You match your job titles to United States occupation codes Free to use, ask before changing
- In which roles could generative AI take over tasks, and in which could it assist? ILO index of occupational exposure to generative AI Nobody is surveyed · You match your job titles to a standard list of occupations Free to use and adapt
- Which of our own tasks could AI do or help with? TPAH task analysis Nobody is surveyed. Process owners check the results · An analysis from a description of the company and its roles Paid service