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 ↗AI writes. Great writers create meaning. The craft end of writing is contested; the artistic end is protected by the authenticity of human experience.
Literature is not primarily about linguistic competence. It is about insight into human experience, expressed with linguistic artistry and structural intelligence. The insight comes first.
AI can write technically competent fiction, generate plot structures, and produce readable prose at scale. The commercial genre end of fiction — category romance, airport thriller, formulaic series — is genuinely threatened. AI outputs are competitive with the average mid-list genre novel on technical grounds.
But literature that matters — that changes how readers understand themselves and the world — comes from a human being who has lived, suffered, loved, and wondered, and who has something to say about what that means. Cormac McCarthy, Toni Morrison, Karl Ove Knausgård: their work is an is moving quickly but still depends on deployment, regulation, and economics expression of their specific human consciousness. No language model has a human consciousness to express.
The commercial writing market is contested. The literary writing market is protected — but small.
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 Writer / Novelist 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 6 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 ↗Argues advanced economies are better positioned to benefit from AI due to infrastructure, skills, and institutions.
OPEN SOURCE ↗