Issue Brief

Assessing the impact of Artificial Intelligence on Advanced and Emerging Economies

Introduction

Artificial Intelligence (AI) has the potential to drastically transform the nature of productivity and efficiency within the global economic system. According to the International Monetary Fund (IMF), AI is projected to affect almost 40 percent of jobs worldwide. However, the impact of AI is projected to vary significantly between advanced and emerging markets. In advanced economies, AI is projected to impact approximately 60 percent of jobs; however, while half of those jobs are projected to be positively impacted by AI, the other half are projected to have their occupations automated significantly by AI. Therefore, AI is projected to lower labour demand, wages, and hiring. Moreover, especially within advanced economies, AI is projected to worsen earnings and wealth gaps as incomes become tied to the ability for workers to leverage and implement AI into their workflows.

However, in emerging and low-income markets, the impact of AI, while substantial, is much more muted. According to the same IMF study, AI exposure is expected to be 40 percent and 26 percent respectively. However, while the labour market in these economies may be more protected, many of these countries do not have the infrastructure to implement AI at scale, which may pose future challenges in development, which could later cause the economic gap between developed and developing nations to grow.

Therefore, this Issue Brief explores the present and potential impact of AI in advanced and emerging economies across the G20.

Advanced Economies

High-income, advanced countries are amongst the most prepared for the AI revolution with the average advanced nation having a preparedness index of approximately 70 percent. Amongst these economies, the United States, the current G20 Chair, has secured approximately USD 67.2 billion in AI-related private investments, which has allowed the United States to become the global leader in AI thus far with over 61 notable AI models developed. The global dominance of American AI models like Google's Gemini, OpenAI's ChatGPT, and Anthropic's Claude positions the global industry for AI firmly within the United States, and subsequently, the Global North as well as high-income nations.

Moreover, advanced economies have reliable access to inputs needed to effectively operate and train AI models with 93 percent of households in high-income countries having consistent access to the internet. Therefore, it is easier for high-income countries to leverage AI effectively, which may give these nations an advantage in sectors of the economy that are particularly sensitive to rising AI implementation such as advanced manufacturing, defence, and finance. Additionally, high-income countries have significantly lower fixed broadband costs. Specifically, for high-income countries, broadband costs account for approximately 1 percent of monthly gross national income (GNI). However, in low-income countries, fixed broadband costs account for 31 percent of monthly gross national income (GNI).

Finally, high-income nations have greater resistance to shifts, particularly in the labour market, caused by AI. Countries like Germany and France have well developed social safety nets that could help mitigate economic consequences for employees displaced by AI and help retrain these workers for new occupations created from AI.

Emerging Economies

Emerging economies are also playing a significant role in developing the global AI space. According to Stanford's global AI vibrancy rankings, China and India rank 2nd and 3rd globally, which highlights the fact that the global AI race is not completely dominated by countries from high-income nations. Moreover, countries like Brazil and Malaysia are represented amongst the top 30 countries globally on the index.

However, at the same time, emerging economies are susceptible to significant threats from increased AI adoption. Significant sectors of emerging economies face displacement from mass implementation of AI. For example, AI is increasingly used to automate customer service functions for corporations worldwide. Countries like India that employ significant numbers of dedicated customer service staff for global companies may face significant reductions in their workforce, which may cause extreme unemployment in these industries. Emerging economies typically do not have the same social safety nets or established training and reskilling facilities that high-income nations do, which may make it much more difficult for these countries to effectively reintegrate these displaced people into the economy.

However, at the same time, emerging economies have emerged as some of the biggest markets for AI businesses, which has subsequently developed AI skills amongst several demographies in these economies, particularly amongst the youth. In South Africa, an emerging, G20 economy, more than 45 percent of the population has adopted AI for daily use. Likewise, India, another G20 emerging economy, has become the largest adopter of AI globally with 93 percent of students and 83 percent of employees leveraging AI in their workflows. Moreover, India recently hosted one of the largest AI focused summits in the world in February 2026, where global leaders in the AI space attended and spoke about their planned expansion in India and other emerging markets. The summit played a pivotal role in incorporating perspectives from emerging markets into the global AI dialogue and improving accessibility for founders based in emerging markets to global AI investors and models.

Conclusion

Fundamentally, there is still a significant gap between developed and emerging markets, particularly with respect to access and availability of AI models. Therefore, platforms like the B20 and G20 should play a larger role in improving accessibility to AI models for citizens across the G20, especially those from vulnerable and disadvantaged backgrounds. Moreover, the nations within the B20 should improve cooperation between firms in the global north and south to increase familiarity with AI models to further stimulate the integration of AI into the global economy. Finally, there should be a dedicated funding mechanism for AI entrepreneurs to improve access to energy and training centres to develop models.