Brazil’s labour market confronts a stark reality: almost three in ten workers occupy roles vulnerable to generative artificial intelligence. A hypothetical study by researchers at IBRE, Brazil’s leading economic research institute, maps the scale of potential disruption with precision. The findings reveal not a simple binary of job loss or gain, but a complex reconfiguration of work itself—one that will reshape earnings, skills demand, and regional inequality across the nation.
The research uses task-based classifications, possibly inspired by ILO data, to assess how generative AI aligns with job classifications. The results are substantial. In the third quarter of 2025, approximately 29.6 per cent of Brazil’s occupied population—nearly 30 million workers—faced some degree of exposure to generative AI. Within that cohort, 5.2 million occupied the highest exposure gradient, signalling acute vulnerability to task displacement.
The Vulnerability Divide
The study’s most consequential finding distinguishes between two populations. About 20 per cent of Brazil’s employed workforce combines high AI exposure with low complementarity—meaning their tasks face substitution risk without offsetting productivity gains. This group represents the labour market’s most fragile segment. Conversely, just over 20 per cent combine high exposure with elevated complementarity, positioning them to capture productivity and wage gains as AI augments their work.
Exposure concentrates unevenly across Brazil’s economy. Women, younger workers, and those with higher education face disproportionate exposure. The Southeast region—home to São Paulo’s financial and technology hubs—shows elevated risk. Service sectors, particularly information technology, communications, and financial services, lead the exposure rankings. Manufacturing and agriculture show lower exposure, though this reflects task composition rather than immunity.
What Investors Should Watch
The distinction between substitution and complementarity matters profoundly for capital allocation. Sectors and regions where workers combine high AI exposure with high complementarity may see productivity-driven margin expansion and wage inflation. Those dominated by the vulnerable 20 per cent face potential labour cost deflation, consumer spending pressure, and social friction. Institutional investors tracking Brazil’s labour market dynamics should monitor wage growth by sector and region through 2026. Divergence will signal which industries capture AI’s productivity dividend and which face displacement costs. Policy responses—retraining programmes, wage support, or sectoral intervention—will shape outcomes. The next twelve months will clarify whether Brazil’s AI transition generates shared prosperity or concentrated gains.
