The rise of generative artificial intelligence (GenAI) – think tools like ChatGPT and image generators – is poised to dramatically reshape the global labor market, but the impact won’t be felt equally. A new study by the International Labour Organization (ILO) and the World Bank reveals a stark divide: while advanced economies are likely to spot significant exposure to GenAI-driven changes, developing nations face a unique set of challenges that could accelerate job displacement without a corresponding boost in productivity. Understanding the nuances of this uneven impact of generative AI on jobs is crucial for policymakers and workers alike.
The research, a background study for the forthcoming World Development Report 2026, examined labor market exposure to GenAI across 135 countries, representing roughly two-thirds of global employment. It found that the key to understanding the differing outcomes lies in a country’s digital infrastructure and the specific tasks that make up its jobs. While the potential for AI to augment human work exists everywhere, the ability to realize those gains is heavily dependent on access to technology and the skills needed to utilize it effectively. This isn’t simply a question of technological adoption. it’s about who benefits from that adoption, and how quickly.
A “Small Buffer, Big Bottlenecks” in Developing Economies
The study identifies a concerning dynamic in developing economies: a “small buffer, big bottlenecks” scenario. Workers in jobs susceptible to automation are often *already* connected to the internet, meaning they could experience job losses relatively quickly. Still, those in positions with the potential for AI-driven productivity gains frequently lack reliable internet access, hindering their ability to leverage the technology. This creates a situation where the risks of automation are realized faster than the opportunities for augmentation. According to the World Bank, approximately 30 to 32 percent of employment in high-income countries is exposed to GenAI, while in low-income countries, that figure is closer to 1 to 15 percent.
This disparity is particularly worrying for women and young workers in lower- and lower-middle-income countries. The study highlights the risk of a “white-collar bypass,” where the types of office-based jobs that historically provided pathways to upward mobility and greater female labor force participation in advanced economies may not materialize in the same way in developing nations. These jobs, often in the service sector, represent a crucial step on the ladder to decent work, and their potential erosion could have significant social and economic consequences.
The Tasks Matter: How Work Differs Across Borders
It’s not just about access to technology; it’s about *how* work is performed. The ILO-World Bank research emphasizes that even when job titles appear similar across countries, the actual tasks involved can differ significantly. Workers in lower-income economies tend to perform fewer non-routine analytical tasks and rely less on computers, focusing instead on more routine or manual work. This limits the scope for productivity gains from GenAI implementation. Essentially, the kinds of jobs that are easily augmented by AI are less common in places where the technology is least accessible.
This difference in task composition is a critical factor. GenAI excels at automating tasks that are repetitive and rule-based. If a job primarily consists of these types of tasks, it’s more vulnerable to automation. Conversely, jobs that require creativity, critical thinking, and complex problem-solving are less susceptible and more likely to be augmented by AI, allowing workers to become more productive. The study suggests that a lack of opportunities for workers in developing countries to engage in these higher-level tasks is a key constraint.
Policy Responses and the Need for Investment
The report isn’t simply a warning; it’s a call to action. The authors stress that the impact of GenAI on labor markets will depend not only on technological capability but also on digital infrastructure, task organization, and skills development. Expanding digital connectivity is paramount, but it’s not enough. Investments in education and training are crucial to equip workers with the skills they need to adapt to the changing demands of the labor market. Strengthening labor market institutions and social protection systems is also essential to ensure that the benefits of GenAI are more widely shared and that those who are displaced by automation have a safety net.
Specifically, the study points to the need for policies that promote digital literacy, support lifelong learning, and provide access to affordable internet. It also emphasizes the importance of strengthening social safety nets, such as unemployment insurance and retraining programs, to help workers navigate the transition to a new economy. Fostering an environment that encourages innovation and entrepreneurship can help create new jobs, and opportunities.
The challenges are significant, but not insurmountable. Addressing the digital divide and investing in human capital are essential steps towards ensuring that the benefits of generative AI are shared by all, not just a select few. The next major checkpoint in this evolving landscape will be the full release of the World Development Report 2026, expected later this year, which will provide a more comprehensive analysis of the long-term implications of GenAI for global development.
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