AI job displacement
A change in labor demand caused by AI systems taking over, compressing, or redesigning parts of a job. It does not always mean a full job disappears; often the task mix changes first.
AI job displacement glossary
Use these definitions to interpret occupation pages, calculator results, task exposure scores, and transition planning recommendations on displacement.ai. Each term links to a detail page with examples from the occupation data.
Task exposure
The degree to which current AI systems can materially affect a task, such as drafting, summarizing, classifying, searching, coding, routing, or analyzing information.
Automation pressure
The likelihood that an exposed task can move from a worker to software after workflow integration, quality controls, and employer adoption.
Augmentation potential
The opportunity for AI to help workers complete a task faster or better while keeping humans accountable for judgment, context, and outcomes.
Displacement pressure
A blended planning score that combines exposure, automation, augmentation, wage vulnerability, transition feasibility, and confidence signals.
Wage vulnerability
The risk that workers in a role have limited wage buffer or weak bargaining power if parts of the job are automated, outsourced, or redesigned.
Transition feasibility
The estimated practicality of moving from one role into adjacent roles while preserving existing skills, salary, and career identity.
Adjacent career path
A role that reuses meaningful parts of a worker's current knowledge while adding a smaller number of new skills, credentials, tools, or work samples.
AI-resilient career
Work where AI can help, but where human judgment, physical context, licensing, trust, live coordination, or accountability remains central.
Source confidence
A signal for how much weight to put on a score based on available public evidence, task clarity, labor-market data, and uncertainty around adoption.
Routine work share
The share of an occupation's tasks that are rules-based and repeatable enough to be specified as a procedure, making them the first candidates for software assignment.
Near move
A transition tier for target roles that preserve the most existing skills and can usually be tested with a small proof project or an internal move.
Stretch move
A transition tier for target roles with real upside that require stronger evidence of skills before becoming the main plan.
Reset move
A transition tier for target roles that amount to a larger career change, attractive on paper but inappropriate as a default recommendation.
Wage preservation
The estimated share of a worker's current wage level retained in a transition target, where 100 means the target role's median wage roughly matches the current one.
Skill overlap
The estimated share of a worker's existing skills that transfer directly into a transition target role without retraining.
Training runway
The number of months a worker can realistically invest in retraining before needing the new role's income, compared against each pathway's estimated training time.
Task vs job automation
The distinction between automating individual tasks inside a job and automating the job itself; most AI impact arrives as task-level change long before whole occupations disappear.
O*NET-SOC code
The federal occupational classification code (for example 43-4051) used to match each displacement.ai occupation page to official O*NET task statements and labor statistics.
BLS OEWS
The US Bureau of Labor Statistics Occupational Employment and Wage Statistics program, the source of the median wage and employment figures shown on occupation pages.
Score model version
The versioned identifier for the scoring rules behind published occupation ratings — currently seed-v0.4-2026-05 — so every score can be traced to the model that produced it.
Regulatory or safety constraint
Licensing, certification, safety, or legal-accountability requirements that slow or prevent task reassignment to software, reducing effective displacement pressure for a role.
Adoption lag
The gap between what AI can technically do and what employers actually deploy, driven by integration cost, workflow redesign, quality control, and organizational change speed.
Proof project
A small, concrete work sample that demonstrates a target-role skill before a transition — evidence a hiring manager can evaluate instead of a claim on a resume.
90-day plan
The staged upskilling outline on each occupation page: a quarter-sized sequence of skill building, evidence creation, and career conversations rather than an open-ended resolution to retrain.
Risk band
The categorical label — low, moderate, high, or very high — assigned from an occupation's displacement pressure score to make the 0-100 planning signal readable at a glance.
How these terms work together
A useful AI career plan starts by separating exposure from displacement. Many jobs are exposed to AI because they include language, analysis, lookup, or reporting tasks. Fewer jobs are immediately automatable end to end. The difference matters because an exposed task may become a productivity tool, a quality-control workflow, a redesigned role, or a fully automated process.
displacement.ai uses these terms to avoid fear-based conclusions. The question is not simply whether AI can do part of a job. The better question is which responsibilities remain valuable, which adjacent roles preserve wage and skill overlap, and what proof a worker can build in the next 90 days.