Methodology

Career Atlas estimates how exposed each occupation is to today's AI. This explains exactly how that number is made, how we check it against independent research, and where it can be wrong. We would rather be transparent than confident.

What the score is

The AI-exposure score is a modeled estimate from 0 to 100, grounded in real AI usage. We start from each occupation's O*NET task list and weight every task by its measured share of actual Claude conversations (the Anthropic Economic Index), counting an occupation's core tasks more heavily than its occasional ones. That task-level signal is then nudged by two work-context cues, how digital and desk-based the role is and how little hands-on physical work it involves, and ranked across all occupations. Roles with no usage data for any of their tasks fall back to a work-context and sector blend. It is always labeled as modeled, never presented as fact.

Exposure is not elimination

A high score means more of the day-to-day work could change, not that the job goes away. In Anthropic's analysis of real AI usage, about 57% of use augmented people (working alongside AI) versus 43% that automated a task. Most exposure shows up as changed work, not lost work.

How we keep it honest

On every occupation we show our modeled score next to independent, published benchmarks, compared by percentile within each method's own distribution (the raw scores use different scales, so relative position is the fair comparison):

Microsoft Working with AI: AI applicability (usage) (CC BY 4.0)
Anthropic Economic Index: Claude usage intensity (relative) (CC-BY)

When our estimate and the published research disagree sharply, treat the number with extra caution.

Known limits

Circularity. Models that use AI to judge what AI can do tend to over-credit AI. We lean on O*NET work context and usage-grounded benchmarks rather than asking a model to grade itself.
Occupation averaging. A single number hides task-by-task variation within a role.
No market dynamics. Exposure says nothing about demand, regulation, or how fast change actually arrives.
Snapshot, not forecast. It reflects today's tools, which move quickly.

Sources

Pay and outlook: U.S. Bureau of Labor Statistics (OEWS, Employment Projections), public domain. Skills and work activities: O*NET, U.S. Department of Labor / ETA, CC BY 4.0. AI-exposure benchmarks as listed above. Career Atlas is independent and not affiliated with or endorsed by these organizations.

Career Atlas is an independent project. Pay and outlook data: U.S. Bureau of Labor Statistics (public domain). Skills and work activities: O*NET, U.S. Department of Labor / ETA, used under CC BY 4.0. AI-exposure and future-proof scores are modeled estimates, not professional or financial advice.