Evidence first
Scores separate task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and confidence. The calculator should be treated as a planning signal, not a layoff forecast.
A practical planning tool for work that is changing
displacement.ai helps workers, employers, and researchers turn AI job displacement anxiety into a concrete transition plan. The product is built around a simple principle: exposure is not destiny, and career advice should explain its assumptions.
Career moves, not panic
The product prioritizes reachable paths, skill preservation, training runway, and concrete next actions. A bigger reset can be useful, but it should be labeled honestly.
Transparent limits
The current public model uses versioned seed data. Local hiring demand, state wage data, credentials, licensing, and employer-specific tasks still need verification before a major career decision.
Who this is for
- Workers who want a clearer next step before their role changes.
- Career coaches and educators helping people choose realistic upskilling paths.
- Employers comparing redesign, retraining, and transition options.
- Policy teams studying occupational exposure without treating exposure as automatic job loss.
Editorial policy
Public pages should distinguish facts, modeled estimates, and recommendations. Occupation pages cite source families, show caveats, and avoid presenting single scores as certainty. Recommendations should explain why a path fits and what would make it weaker.
The next trust milestone is a versioned scoring pipeline with regression tests, data vintage labels, and clearer per-occupation source notes.