Job Displacement Model
Estimate when AI may displace your role based on capability forecasting and friction modeling.
Step 1: Select Your Role Category
Step 2: Your Hierarchy Position
Select based on how many people above you in your specific workflow could absorb your responsibilities if your role were eliminated. A senior engineer who owns a system has no one above (Level 4-5), while a VP with C-suite above might be Level 2-3.
Job characteristics
Answer these questions to calibrate the model for your specific role.
1. Current AI Performance
How well can AI already perform core tasks in your field?
2. Data Availability
How much example work and documentation about your type of role is available (training materials, online guides, industry examples, case studies)?
3. Benchmark Clarity
How easily can success in your role be measured objectively?
4. Task Digitization
What percentage of your work inputs and outputs currently exist in digital/text format?
5. Task Decomposability
Can your work be broken into discrete, measurable tasks?
6. Task Standardization
How standardized are procedures/workflows in your role?
7. Context Dependency
How much does your work require understanding unique organizational context?
8. Feedback Loop Speed
How quickly do you get feedback on work quality?
9. Tacit Knowledge
How much of your expertise is documented vs. learned through experience?
10. Task Reallocation Risk
If your position were eliminated, how easily could your responsibilities be redistributed to existing team members?
11. Human Judgment & Relationships
How critical are human relationships, empathy, and trust in your role?
12. Physical Presence
How much does your work require physical presence?
13. Company AI Adoption Readiness
How prepared is your organization for AI integration?
14. Labor Cost Pressure
How cost-sensitive is your employer to labor expenses?
15. Labor Market Tightness
How difficult is it to hire people with your skills?
16. IT Infrastructure
How modern is your organization's technical infrastructure?
17. Skill Transferability
How transferable are your core skills to other roles/industries?
18. Adaptability/Learning
How quickly can you learn and adopt new tools/technologies?
19. Job Performance
How good are you at your job, relative to your peers?
Understanding your output
Your results are forecasts, not certainties. They depend on assumptions about how fast AI capabilities improve and how quickly your industry, organization, and role will adopt them, all of which remain deeply uncertain.
If you disagree with the underlying assumptions or the model output, visit the Methodology to understand the math and plug in your own assumptions in Advanced Model Tuning (below). To understand what drives your timeline, how to interpret your results, and how to act on them, visit the Guide.
Displacement Probability Over Time
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50% Job Loss Risk
90% Job Loss Risk
Risk by 2030
Risk by 2031
Examine Re-Employment Likelihood
Industry Friction
Industry-level friction multiplier. Note: Your questionnaire answers (Q4-Q12) already capture most domain differences.
AI Reliability
How reliable must AI be before it replaces tasks?
Adjust your job's baseline time split across the model's task duration buckets: <10 min, 10-45 min, 45-180 min, 3-8 hr, >12 hr.
See methodology for math
Fine-tune every coefficient, clamp, and question weight used in the hazard/compression model. Toggle questions off to remove them from calculations.
Note: Extreme inputs (i.e., 1-day doubling) are still gated by domain misalignment, task thresholds, hazard caps, and implementation delay. If you want fast-takeoff behavior, loosen these guards (lower thresholds/penalties, raise caps) and keep other sliders realistic.