Methodology

Measure AI displacement at the task level

displacement.ai treats job displacement as a transition problem, not a headline number. The model starts with tasks, then separates exposure, automation, augmentation, labor-market pressure, and transition fit.

Task exposure

How much of the work can be assisted or performed by current AI systems, regardless of whether a job disappears.

Automation potential

How much exposed work is repeatable, low-context, low-stakes, and likely to be reassigned to software.

Augmentation potential

How much AI increases worker productivity while humans stay accountable for goals, quality, or relationships.

Labor-market pressure

Wage context, hiring demand, role growth, offshoring pressure, and employer adoption speed.

Transition adjacency

Nearby roles that preserve skills, reduce wage loss, and can be reached with realistic training time.

Source confidence

Evidence strength, source recency, agreement across sources, and whether a score is reviewed or draft.

Draft rollup formula

The public score is a transparent index, not a layoff forecast. The current platform keeps scores directional while the dataset is expanded and calibrated.

displacement pressure =
  0.35 * automation potential
  + 0.20 * near-term adoption
  + 0.15 * employment scale
  + 0.15 * wage vulnerability
  + 0.10 * routine work share
  - 0.10 * regulatory or safety constraint
  - 0.05 * transition feasibility

Important caveat

Exposure is not displacement. A role can be highly exposed and still grow if AI removes bottlenecks, expands demand, or shifts workers into higher-value tasks. A role can also be moderately exposed and still experience layoffs if employers use AI mainly as a cost-reduction tool.

Scores are planning signals, not employment predictions.
Occupation-level scores should be localized with employer task inventories and regional wage data.
Policy and workforce decisions should use confidence intervals, not single deterministic scores.