Methodology
Your AI Exposure Score is transparent and explainable by design — here's exactly how it's built.
What the score means
The AI Exposure Score is a 0–100 estimate of how much your role's tasks overlap with what AI can do today and in the near future. A higher number means more of your current tasks are exposed — it is not a prediction that your job will disappear, and every score is paired with a constructive next step.
The bands
Recalibrated July 2026: the earlier thresholds assumed scores would spread across close to the full 0–100 range. In practice, real scores across all 968 occupations cluster more tightly (roughly 23–84), so we adjusted the boundaries so each of the five tiers reflects a meaningful share of real occupations. No occupation's underlying 0–100 score changed — only which band label it falls into.
How the composite is weighted
The score blends seven components. The weights are published so you can see what drives a number:
| Component | Weight |
|---|---|
| Task automation potential | 35% |
| AI augmentation / productivity pressure | 20% |
| Digital & routine task intensity | 15% |
| Labor-market resilience (BLS) | 10% |
| Human judgment / relationship requirement | 10% |
| Transferability to safer roles | 5% |
| User-specific context | 5% |
Task-level exposure
Beyond the single number, your role is split into its real tasks, each tagged:
- Automatable — likely to be largely done by AI.
- Augmentable — AI speeds it up; you stay in the loop.
- Durable — resists automation (judgment, relationships, accountability).
This is the actionable part: shift your time toward the durable and augmentable core.
How each task gets its own score: rather than guessing from the words in a task description, we look at how automatable that same real-world activity tends to be across every occupation that performs it (via O*NET's own activity taxonomy) — not a guess specific to your role. One honest limitation this creates: a task is scored by what kind of work it is, not who's doing it — so a relationship- or judgment-heavy task can still read as more exposed than expected if the same underlying activity is shared with more automatable work elsewhere. We disclose that rather than force artificial variety into a role's breakdown that the data doesn't actually support.
Where the data comes from
Each occupation's score starts from O*NET 29.1 descriptors (work activities, work context, skills, job zone) and BLS projections (labor-market resilience, wages, demand). The automation and augmentation components are then empirically grounded by three public research datasets. Two independent measures set the exposure magnitude: the OpenAI/Penn "GPTs are GPTs" task-exposure study (~88% of roles) and the Felten, Raj & Seamans AI Occupational Exposure (Language-Modeling variant; ~78% of roles) — one task-based, one ability-based, so they corroborate rather than echo each other. The Anthropic Economic Index of observed Claude usage then nudges whether real-world use of a role leans toward automation or augmentation (~69% of roles). Roles a dataset doesn't cover keep the transparent O*NET-derived estimate — we never fabricate a value. These are exposure measures (what AI could affect), not predictions of job loss; the Anthropic signal reflects Claude.ai usage specifically, so it is a proxy, not a census. See the full data sources & attribution.
Release tag
penn-gpts-are-gpts+aei-2026-06-26+aioe-lm-2023 · refreshed on the monthly recompute.Exposure is not destiny
An exposure score measures what AI could affect about a role — a theoretical ceiling — not a prediction that the job disappears. The early real-world evidence fits that distinction. Stanford's "Canaries in the Coal Mine" study finds a roughly 13% relative employment decline for early-career workers (ages 22–25) in the most AI-exposed occupations — while broader analyses (the Yale Budget Lab and BLS) find overall employment has so far held up; PwC's 2026 AI Jobs Barometer even finds the industries most exposed to AI have seen faster wage growth and productivity, and that workers with AI skills now command a roughly 62% wage premium — exposure is reshaping work, not simply erasing it. The strain is showing up first in entry-level cognitive work, not across the whole economy.
The constructive read: the roles that hold up pair durable human judgment, relationships, and domain depth with AI-augmented work — so the move is to build those earlier, whatever your exposure band. And because the published exposure measures disagree in magnitude (which is exactly why we blend several rather than trust one), we never present a score as a certainty. See the corroborating research behind this.