The potential productivity impact of Large Language Models in Hungary
Abstract
This paper examines the potential impact of large language models (LLMs) on productivity and economic growth in Hungary. The analysis builds on the frameworks of Acemoglu (2025) and Aghion and Bunel (2024), using occupation-level estimates of LLM exposure derived from job posting data. The results indicate that the share of economic activities affected by LLMs is moderate, implying a 0.25–0.81 percentage point increase in total factor productivity (TFP) over the next decade. This corresponds to an annual GDP growth effect of 0.05–0.15 percentage points. The estimated effects are lower than those for the United States, primarily due to lower labour market exposure and a smaller labor income share. The findings highlight the importance of sectoral structure, labour market composition, and income distribution in shaping the growth potential of AI.
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