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 ↗Artisan blacksmithing is one of the most ancient and most protected crafts. It is growing, not shrinking. AI and robots cannot hot-forge metal by eye.
Artisan blacksmiths forge, shape, and weld metal to create structural ironwork, architectural elements, artistic metalwork, and bespoke commissions. The judgment — reading the colour of hot metal to know its temperature and workability, sensing when to strike and how hard — is tactile, experiential human intelligence that no robotic system can replicate.
Artisan blacksmithing is experiencing a renaissance: conservation and heritage work on historic buildings requires traditional skills, artistic metalwork for interior design is a growing luxury market, and the makers movement is driving renewed appreciation for craft.
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 Blacksmith / Metalworker (Artisan) 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.
Safe to present as a framework-level forecast, provided the page remains labelled as interpretive and source-grounded rather than certain.
TIER 3 review queue with 7 core sources and 3 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 ↗Notes substantial automation risk remains, while observed labour-market effects remain mixed rather than universally destructive.
OPEN SOURCE ↗Argues advanced economies are better positioned to benefit from AI due to infrastructure, skills, and institutions.
OPEN SOURCE ↗