Methodology

The AI Task Exposure Score is deterministic, versioned, explainable, and reproducible. This page is the complete definition — there is no hidden model behind the number.

What the score is

Each occupation is decomposed into its constituent tasks. Each task carries a structured exposure value from 0 (AI contributes little) to 100 (current AI systems can perform most of the task), and a classification: automatable, augmentable, or durable. The occupation score is the share-weighted aggregate:

occupation_exposure = Σ(task_share × task_exposure) / Σ(task_share)

In the current release (2026.07.11-r1), task shares are equal within each occupation — the source corpus does not yet include per-task time weights. That is a documented limitation, not a hidden assumption; share calibration is on the roadmap, and a share change will create a new score version.

What the score is not

This score estimates how exposed the tasks in a role are to current and near-term AI capabilities. It does not predict whether a specific person will lose a job. It is not a probability of firing, an “AI replaces you by 20XX” date, or an actuarial forecast. We never publish those claims, and the paid report is prohibited — by validation code, not just policy — from inventing them.

Percentile, rank, and bands

• Percentile = share of the 968-occupation corpus with a strictly lower score. Tied scores share the same percentile — equal scores are never displayed as different.
• Rank uses competition ranking (1 = most exposed); tied scores share the rank.
• Bands are corpus quartiles, not emotional thresholds: Low ≤ 38, Moderate ≤ 48, High ≤ 59, Very High above. Labels stay stable relative to the analyzed labor market.

The personalized score (paid report)

The $9 report recalculates the same formula over your task mix: an AI model extracts and normalizes tasks from the text you paste and maps them to our task taxonomy; the application then resolves IDs, applies the numeric formula, and computes percentile, band, and confidence deterministically. The model never invents the score. Tasks that can’t be mapped to the taxonomy are listed but excluded from the number — they reduce the stated confidence instead of silently distorting the result.

Confidence

Reported as Low / Medium / High with the reason, computed from observable coverage: how much of your described work matched the taxonomy, input length and quality, and whether task shares were supplied or inferred. We do not fabricate decimal certainty.

Lower-exposure adjacent roles

A role is labeled lower-exposure only when all of the following hold under the same score version: it scores at least 10 points lower, skill overlap is at least 50%, and both roles have current score data. In the current release only 7.7% of the adjacency graph qualifies — the rest are shown as “related roles” with no safety claim. We consider that honesty a feature.

Data provenance and versioning

• Occupation, alias, task, and skill data originate from a versioned corpus derived from public occupational data (O*NET-linked task statements with structured exposure values), imported once as data release 2026.07.11-r1 with a recorded SHA-256 checksum.
• Scores are recomputed from raw tasks on our side — release jr-v1, published 2026-07-12.
• Every stored result records its data version, score version, and (for paid reports) prompt and model versions. A methodology change creates a new score version; historical reports keep their numbers.
• Labor-market context (median wage, employment, openings, projected growth) is display context only — it is never an input to the exposure score.

Known limitations

• Equal task weights within an occupation (until share calibration ships).
• Task exposure values are structured estimates of current and near-term AI capability against typical task content; they are periodically revised via new data releases, not live-updated.
• Occupation-level results average over real variation between employers, seniority levels, and regions — that is exactly what the personalized report exists to correct.
• The adjacency graph measures skill overlap, not hiring demand; a lower-exposure adjacent role is not a guaranteed or recommended career outcome.

Questions about the method? Contact us — corrections are published on this page.