Does the Rise of Artificial Intelligence Widen Economic Inequality?

The rapid spread of advanced AI, especially large language models, has renewed the debate over who benefits from technological change. This paper examines whether the current wave of AI adoption is widening economic inequality. To answer it, I pull together recent empirical work from labour economics, macroeconomics, industrial organization, and development economics, focusing specifically on frontier AI systems used across labour markets, firms, financial markets, and national economies. The evidence base here draws on the National Bureau of Economic Research, the IMF, Stanford's Digital Economy Lab, MIT Sloan, PwC's Global AI Jobs Barometer, UNCTAD, Oxfam International, the International AI Safety Report 2026, and a handful of other academic and policy sources. Rather than treating "inequality" as single number, I break it down into several moving parts: wage polarization, income inequality, labour's share of national income, capital accumulation, market concentration, wealth concentration, and the growing gap in AI capability between rich and poor countries. I also conducted a cross-country study using the most recent data from the World Bank World Development Indicators for 131 countries for which data were available for both indicators (aggregates and regional groupings excluded), covering research and development expenditure (percent of GDP) and the Gini index. What emerges is a mixed but fairly consistent picture. The evidence is a mixed bag at different levels of the economy. At the firm level, generative artificial intelligence can increase worker productivity and, in some settings, narrow the performance gap between novices and experts. At the same time, skills, capital, profits and computing capacity related to AI are becoming concentrated in certain workers, firms and nations. At the worker level, however, the evidence is mixed with some studies finding productivity gains for less-experienced workers and others finding growing demand for AI-related skills. Overall, the distributional implications of AI seem to depend not only on adoption, but also on access, ownership, skills and national technological capacity.