New research indicates that artificial intelligence (AI) is fundamentally reshaping labor markets, particularly revealing a significant divide in AI adoption between high-income countries and their lower-income counterparts. The findings, driven by anonymized usage data from Anthropic’s AI model Claude, show that workers in wealthier nations are harnessing AI tools far more extensively than those in developing regions.
Historically, scholars studying the implications of AI on employment had to work with a conceptual framework reliant on “exposure” estimates to gauge potential disruptions across various occupations. With the absence of actual usage data, the accuracy of these predictions remained largely theoretical. However, the recent release of Claude’s usage data from a one-week sampling period in November 2025 provides a clearer picture of AI’s foothold in various professions and countries.
The core finding from this research is that exposure indices are highly effective at predicting real-world AI adoption. Occupations identified as having low predicted exposure generally exhibited low usage rates, while those with high exposure saw substantial utilization of AI tools. Remarkably few occupations fell into mismatched categories of high exposure with low usage, or vice versa. The analysis spotlighted information and communications technology (ICT) professionals as the leading users of AI, which aligns with expectations given their access to technology and the immediate productivity benefits they derive from AI implementations.
However, an unexpected gap emerged among management-level roles, where high AI exposure did not translate into proportional usage. This disconnect may stem from privacy concerns regarding sensitive business decisions, time constraints that restrict experimentation, or organizational cultures still in the early stages of AI adoption. Understanding the reluctance of managers—who often dictate AI implementation strategies—to integrate these tools into their workflows is critical for forecasting broader organizational adoption trends.
Global Disparities in AI Adoption
Further analysis from the Anthropic AI Usage Index reveals stark disparities in AI utilization across countries, with only high-income nations reporting values above 1, signaling higher than expected usage relative to their working-age populations. These high-income countries average an index value of 2.02, while all other categories, including middle-income countries (MICs) and low-income countries, fall below this threshold. On average, high-income countries exhibit AI usage rates approximately four times greater than those in MICs, with only these wealthier nations surpassing global per-capita benchmarks.
The composition of AI usage varies significantly between these groups. In high-income countries, AI usage is distributed across various professions, whereas in middle-income countries, it is heavily skewed toward ICT professionals, who account for 48 percent of usage, and teaching professionals, who represent 24 percent. Collectively, these two sectors comprise nearly three-quarters of AI usage in MICs, contrasting with a more diverse distribution in HICs. This concentration likely reflects both heightened demand and greater accessibility to AI technology among these workers.
The study did not incorporate low-income countries due to insufficient data, highlighting another layer of the technological divide. The implications of these findings are critical for policymakers aiming to address the widening gap in AI adoption.
The research yields three key insights for decision-makers. First, the utility of exposure indices, such as the AI Occupational Exposure measure, is confirmed, enabling policymakers to better assess which industries and workers are most vulnerable to AI-related job impacts. Second, the adoption pattern follows a predictable trajectory, with early uptake among technologically adept ICT professionals before gradually extending to other fields. While high-income nations are experiencing accelerated AI diffusion, this process is still in its infancy in low-income contexts.
Lastly, the pronounced adoption gap necessitates proactive measures. The concentration of AI usage in wealthier nations raises concerns about the emergence of a new technological exclusion, potentially leaving low- and middle-income countries further marginalized in an increasingly AI-driven global economy. Without targeted investments in digital infrastructure, skills development, and conducive policy frameworks, these nations risk losing their historical advantages in labor-intensive industries to automation and reshoring trends.
As over a billion young people in developing countries approach working age in the next decade, the impact of AI on labor markets is not merely an academic discussion; it is a pressing development challenge. The significant disparities in AI adoption revealed by this study underscore the urgent need for strategic interventions to ensure that all countries can participate in and benefit from the emerging digital economy.
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