The study explores Generative AI’s impact on occupational exposure, highlighting greater task vulnerability in wealthier countries. Clerical, administrative, and financial roles, particularly among mid-educated women, are at heightened risk of significant transformation.
Task-Based Framework and Cross-Country Analysis
This study expands a task-based framework using Large Language Models (LLM) to evaluate occupational exposure to Generative Artificial Intelligence (GenAI). There is notable variation in exposure across countries based on income levels, with higher-income economies facing increased task-level exposure. Currently, the potential of GenAI to augment human work is more significant than its automation risks; however, this balance may change as AI systems advance and integrate with other emerging technologies.
Assessing Future Impacts of Advanced AI
The paper explores occupational exposure under a hypothetical scenario where artificial general intelligence (AGI) becomes a reality. By combining LLM-based task evaluations with detailed employment data, the study provides insights into potential shifts in labor dynamics. This framework helps illustrate the nuanced impact GenAI could have across various sectors, emphasizing the need to prepare for evolving AI capabilities.
Focus on Specific Occupations and Demographics
A country-specific case study in Brunei reveals moderate overall exposure to GenAI. However, occupations in clerical, administrative, and financial services are particularly susceptible to GenAI transformations. The study highlights that exposure is higher among individuals with mid-level education and among women, underscoring critical areas where targeted strategies might be necessary to mitigate impacts and support affected workers effectively.
Source: Labor Market Exposure to AI: From GenAI to Future AGI – ASEAN+3 Macroeconomic Research Office
