How accelerating artificial intelligence adoption intersects with women’s earnings, occupations and employment patterns.
Private AI investment is skyrocketing, as is the rate at which notable AI models are being released. Together, these trends show that both capital investment and technological development in AI are growing rapidly.
Automation risk is unevenly distributed across occupations, while highly exposed jobs also show substantial differences in gender pay. In several occupations with high automation exposure, women earn below the overall female average and less than men working in the same occupation.
Automation exposure varies across occupation sectors and women’s earnings levels rather than following a uniform pattern. Some lower- and middle-paid occupation groups show relatively high exposure, meaning technological risk may compound existing earnings disadvantage.
Women’s representation in automation-exposed work differs across Australian states and territories because their occupational employment patterns vary. The chart shows where women are concentrated across broad occupation groups and how those groups compare using national automation exposure scores.
The gender difference in the highest GenAI exposure category widens as country income increases. In high-income countries, 9.6% of women’s employment falls within the highest-exposure category, compared with 3.5% of men’s employment.