This site is not a crystal ball. It is a structured forecasting interface that combines published research with occupation-level argumentation.
We start from published work showing that AI exposure is uneven. Clerical and routine information-processing work is typically more exposed. Manual, embodied, and trust-heavy work is often less exposed.
Where the literature supports augmentation rather than total replacement, the site should lean contested instead of dying. This matters especially for teachers, analysts, managers, lawyers, creatives, and clinicians.
Even if AI can technically perform a task, rollout depends on regulation, cost, accountability, infrastructure, labour politics, and the tolerance for error in the real world.
Advanced economies often adopt earlier because they have more digital infrastructure and more cognitive-intensive work. Lower-income or rural contexts may face slower rollout because connectivity, capital, and institutions are weaker.
Exact job-loss numbers, exact dates, and exact regional thresholds are usually not directly stated in the source literature. The second pass of this site now treats such figures as interpretive estimates, not sourced certainties.
Each job page combines the occupation profile, displacement score, resistance factors, regional friction, and evidence notes into a single argument. The evidence tab shows which broad source family supports that argument.