Method & Data
How the durability model is built
Transparent assumptions, evidence links, and update notes.
Data last reviewed: 2026-02-25
Model assumptions
- Embodied work increases durability when tasks require on-site judgment and physical execution.
- Legal accountability raises replacement friction because a licensed human remains responsible for outcomes.
- Supply scarcity supports wage durability when qualified labor pipelines are constrained.
- AI risk is directional, not deterministic; scores are planning signals, not certainty.
Primary evidence sources
- IMF Staff Discussion Note: Gen-AI and the Future of Work (2024)
- OECD Employment Outlook 2023 (AI and labour market chapters)
- Oxford Martin: The Future of Employment (2013)
Macro context references
- IMF (Jan 2024): almost 40% of global employment is exposed to AI
- McKinsey (Jun 2023): generative AI could add up to $4.4T annually
- OECD Employment Outlook 2023: 27% of jobs in high-risk occupations
Data policy
Salary benchmark year: 2024. Role-level salary citations appear directly on each job page. Training links prioritize licensing boards, apprenticeship directories, and public workforce resources.
Changelog
- 2026-02-27 — Editorial decision board updated with scenario presets, baseline deltas, and role recommendations.
- 2026-02-26 — Category visual system and citation links normalized across directory and job cards.
- 2026-02-25 — 40+ role dataset completed; salary citation mapping and training-link coverage expanded.