AI Exposure by Profession: Keep the Meaning of the Number Clear
By Shawn Geeman · 2 min read · Published · Updated
Exposure is the beginning of an analysis. It asks where a technology could affect work. Automation, adoption, and employment outcomes come later, and each requires additional evidence.
Four questions that should stay separate
Exposure: could a technology affect a task? Automation: can a workflow perform the task to an acceptable standard? Adoption: will an organization deploy that workflow? Employment: how will that change jobs, hours, pay, or demand?
A positive answer to the first question does not settle the other three. Reliability, implementation cost, regulation, customer preference, and the amount of new work created can all affect what follows.
The score is an interpretation
The Index uses a relative 0 to 100 scale. Public occupation values are stored estimates. Personal results can be generated from your description or fall back to a matched occupation baseline.
There is no verified conversion from each point on this scale to a percentage of workers displaced, tasks removed, or wages lost. For implementation details and the separate Career Health formula, see the methodology.
Compare the work behind two titles
When comparing professions, inspect what a typical worker is responsible for. Routine document production and a regulated decision may both involve writing, yet have very different review and accountability requirements.
A broad occupation average can conceal specialization. Use the occupation pages to orient yourself, then examine real job descriptions and talk to people working in the relevant setting.
Use research for its actual claim
Eloundou et al. (2023) explores potential occupational exposure to language models. The OECD publication from 2025 provides a capability framework. BLS data provides wages, employment, and projections. These are different evidence types.
Combining them can generate useful questions, but it does not create an official consensus forecast for a particular profession. Cite the original work and make your additional assumptions visible.
Improve the work, not just the score
A practical goal is to gain useful capability and responsibility. Test an appropriate task, improve its quality, and understand what judgment remains necessary. Keep evidence of the result.
Changing the language in an assessment can change its output. That alone is not proof that your work is more resilient. Prefer observed changes in what you can do and the value others receive.
Frequently asked questions
Is exposure the same as automation?
No. Exposure concerns potential effects; reliable automation and actual adoption require additional evidence.
Can a growing profession still have exposed tasks?
Yes. Employment demand and task exposure are different dimensions. Technology can change tasks while demand for a service grows.
Sources and methodology
The score is a relative index of task exposure from 0 to 100. It is not a percentage chance of losing a job or a forecast of layoffs. Individual tasks, employer choices, adoption, and regulation all matter.
The Index uses stored BLS-based occupation data. O*NET, OECD capability indicators, and task-exposure research are contextual references. The derivation of the stored occupation scores is not reproducibly documented in the published dataset.
The source snapshot does not record the wage-survey year or employment-projection window. Check the original BLS release before treating these figures as current. Editorial changes and export dates do not establish data freshness.
This essay reflects the evidence available when written; future outcomes remain uncertain.
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