AI is reshaping middle-class jobs — can it reduce poverty?

The rapid adoption of generative AI in workplaces is creating new opportunities but risks deepening existing economic divides, particularly in developing nations.

Current state of AI adoption: Middle-income countries now represent over half of all generative AI web traffic, with 39% of the U.S. working-age population already using this technology.

• 66% of leaders across 31 countries indicate they would not hire candidates lacking AI skills
• In Latin America, executives are prioritizing AI expertise over traditional work experience
• Experimental studies show significant productivity gains from GenAI use, particularly among workers with lower skill levels

Digital divide challenges: Access to GenAI benefits remains heavily skewed toward middle-class workers in urban areas with formal sector jobs.

• Only 7-14% of workers across Latin America and the Caribbean can effectively utilize GenAI in their roles
• In Brazil and Mexico, workers in the highest income bracket are twice as likely to have GenAI-compatible jobs compared to the poorest workers
• When factoring in digital technology access, this disparity grows even wider – in Mexico, the wealthiest workers are 5.6 times more likely to have both GenAI-suitable jobs and necessary computer access

Automation risks and vulnerable groups: Certain sectors face significant disruption potential from GenAI automation.

• 1-6% of jobs across Latin American countries are at high risk of GenAI automation
• Banking, finance, public sector, and customer support services face the greatest automation threat
• Women and youth workers are disproportionately represented in these at-risk positions

Potential opportunities: GenAI could have transformative effects in sectors crucial for poverty reduction.

• Education could benefit from personalized instruction and enhanced teacher effectiveness
• Healthcare delivery could improve through better clinical decision-making and expanded telemedicine
• These improvements could strengthen human capital development in underserved communities

Infrastructure barriers: Basic technological prerequisites remain a significant obstacle in many regions.

• Over one billion people in developing nations lack reliable electricity access
• 17 million jobs across Latin America could benefit from GenAI but lack basic digital infrastructure
• Learning gaps between wealthy and poor countries continue to persist

Future implications: The successful integration of GenAI technology will require coordinated policy action to prevent the widening of global inequality.

• Infrastructure development must address basic needs like electricity and internet access
• Education systems need strengthening to provide foundational skills necessary for AI adoption
• Without targeted interventions, GenAI risks becoming another factor that exacerbates rather than reduces global economic disparities