Large Language Models and the Labour Market: Spatial Evidence from Job Ads

Despite the rapid rise of large language models (LLMs) and their implications for productivity and employment, little is known about how exposure to LLMs varies within and across countries. Understanding these patterns matters because technology spillovers are often geographically localised, and regional disparities can affect domestic and international labour market flows, long-term growth, social cohesion, and political stability.

Using geolocated, task-level data from all job advertisements on Hungarys largest online job portal, we apply an LLM-based mapping approach to estimate job exposure. Extrapolating to the national labour market, we find Hungarys average LLM exposure to be 8%, substantially lower than estimates for the United States. This gap is partly explained by Hungarys higher share of physically intensive occupations and lower prevalence of office-based roles.