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 ↗Nuclear engineering is the most regulated engineering discipline in existence. Safety case requirements, novel reactor design, and decommissioning create growing demand for nuclear engineers.
Nuclear engineers design, build, operate, and decommission nuclear reactors and radiation-handling facilities. The safety case requirements for nuclear engineering are the most stringent in any engineering discipline — the consequences of failure are measured in decades of contamination and thousands of lives.
AI simulation tools model reactor neutronics, thermal hydraulics, and materials behaviour with increasing sophistication. AI fault detection identifies reactor anomalies earlier. These tools make nuclear engineers more effective.
But the nuclear safety case — the comprehensive demonstration that a reactor is safe to operate — must be produced and signed off by qualified nuclear engineers who bear personal legal responsibility for its adequacy. The Office for Nuclear Regulation (ONR) and Nuclear Regulatory Commission (NRC) require human engineers to certify nuclear facilities.
Nuclear renaissance: new reactor designs (SMR, fusion) and decommissioning of existing plants are creating significant new demand for nuclear engineers globally.
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 Nuclear Engineer 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.
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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 ↗