ILO Working Paper 140 (2025): Generative AI and Jobs: A Refined Global Index of Occupational Exposure
Task-level occupational exposure framework for generative AI, built from expert input and model predictions.
OPEN SOURCE ↗Hairdressing is skilled physical work on a living human with strong personal preferences. No robot can cut hair. The social dimension makes it is moving quickly but still depends on deployment, regulation, and economics.
Hairdressers and barbers cut, colour, style, and treat hair on living, moving clients who have detailed and deeply personal preferences about their appearance. This requires: technical cutting skill, colour chemistry knowledge, the ability to interpret client wishes (often expressed vaguely), and the social relationship that makes the experience enjoyable.
No robotic hairdressing system exists. The variability of human hair types, textures, and densities — combined with client movement and the need to read and interpret ambiguous preferences — has made robotics impossible.
Furthermore, hairdressing salons are social spaces. Clients have often visited the same hairdresser for years. The relationship, the conversation, and the trusted reading of client wishes are central to the service value. This is a recession-resilient business — people cut their hair regardless of economic conditions.
These are the genuine threats to this profession. They are real, but they are not sufficient to overturn the fundamental analysis. Here is why.
Put the case that Hairdresser / Barber will not survive AI displacement. The system responds with counterarguments from the research base. Strong arguments shift the score — up to a maximum of ±15 points. The system is not an AI. It is a structured argument engine.
This question layer is generated from the job verdict, the resistance case, the regional rollout logic, and the evidence status of this page. Use the filters to focus the discussion, or trigger a random question and work through the role from multiple angles.
Keep the framework, but add at least one sector-specific source and remove any remaining implied precision.
TIER 3 review queue with 6 core sources and 1 framework signals.
This page is grounded in task exposure research and labour-market trend reports, then translated into a reasoned occupation-level argument.
This site now treats exact timelines, total job-loss counts, and regional speed as interpretive estimates unless a cited source states them directly. The argument on this page should be read as a structured forecast, not a guaranteed future.
These impact figures are site estimates for comparison and should not be read as official labour-market counts.
Task-level occupational exposure framework for generative AI, built from expert input and model predictions.
OPEN SOURCE ↗Finds clerical work is the most highly exposed occupational group and that augmentation is often more likely than full occupation automation.
OPEN SOURCE ↗Shows AI exposure is highest in many white-collar cognitive occupations, while manual occupations tend to have lower exposure.
OPEN SOURCE ↗Advanced economies are more exposed to AI because they have more cognitive-intensive jobs; infrastructure and skills limit adoption elsewhere.
OPEN SOURCE ↗Large-employer survey showing clerical roles among the fastest-declining and care, education, software and green-transition jobs among growth areas.
OPEN SOURCE ↗Argues advanced economies are better positioned to benefit from AI due to infrastructure, skills, and institutions.
OPEN SOURCE ↗