How could AI change your job?
Enter your role to see AI exposure and compare career paths by skills, pay and training time.
Free · No sign-up · Start with just your job title.
Interpreted queryRoleCustomer Service RepresentativesYour location noteUnited StatesPlanning horizonNext 3 years
Example only: Customer Service Representatives. Enter your role and select “Map my options” to make it yours.
Pay and occupation evidence use US national data. Location and planning horizon are saved as notes; they do not change scores or predict future job losses.
At a glance
Example: Customer Service Representatives
Make it yours
Set the constraints your next move must respect.
Paths meeting your pay and training limits appear first. Other paths are labeled with their shortfalls. Your plan stays in this browser unless you share its link; anyone with the link can read the included preferences.
Preferences save automatically in this browser.
Decision support, not a forecast
Example career transition map
Ranked by transferable skills, training runway, wage fit, and modeled AI resilience.
No paths in this set meet both your minimum pay and maximum training time. The alternatives below show what would need to change.
How the map thinks
One answer. Four transparent signals.
No black-box certainty. Every recommendation exposes the trade-offs that shaped it.
- 01
Interpret the role
Match plain-language job queries to a reviewed occupation record and task bundle.
- 02
Measure task pressure
Separate exposure, automation potential, and augmentation instead of collapsing them into fear.
- 03
Rank reachable moves
Compare skill overlap, training time, wage preservation, and target-role resilience.
- 04
Show the evidence
Link every planning signal to methodology, public labor data, and explicit limits.
Start with your real question
Research the risk. Then make a move.
Built for people and agents
The evidence layer is queryable.
Search engines, research agents, and developers can inspect the same occupation records, score definitions, and citations shown in the product.
Method before certainty
Questions worth asking.
How does displacement.ai estimate AI job displacement risk?
The model combines occupation tasks, AI exposure, automation and augmentation potential, wage context, transition feasibility, and source confidence. Scores are planning signals, not layoff forecasts.
Can displacement.ai recommend a safer career move?
Yes. The transition map ranks nearby roles using skill overlap, training runway, wage fit, and modeled AI resilience, then explains the evidence and trade-offs behind the leading path.
Where does the occupation data come from?
The public evidence layer uses O*NET 31.0 occupation and task data, May 2025 US Bureau of Labor Statistics wage estimates, and published research including 2026 evidence from OpenAI Economic Research and The Anthropic Institute.
Is the career transition map a prediction?
No. It is a decision-support tool. Users should confirm local hiring demand, pay, credentials, licensing, and employer-specific workflows before making a career decision.