Low-income countries’ inability to match rapid AI progress leads not only to stagnation but also propels them backwards, leaving them increasingly marginalized as advanced economies surge ahead. This article highlights the dangers of the marginalization of some parts of the world that are unable to catch up with the AI boom.
Artificial intelligence is advancing at a breathtaking pace. Across the globe, governments and companies are rushing to regulate, manufacture, and compete in this rapidly evolving sector. Yet, while advanced economies lead the charge, with their corporations ranking among the world’s most innovative, much of the world is left watching on the sidelines.
For high-income nations, AI offers the promise of immense social and economic benefits, provided it is regulated responsibly. These countries are actively seeking to strike a balance between innovation and control. But for low-income countries, the story is very different. They struggle not only to adapt to AI advancements but also to develop the basic infrastructure needed to join the race. For low-income countries, failure to keep pace with the rapid global advancement of AI does not merely result in stagnation. It propels them backwards, leaving them increasingly marginalized as the rest of the world advances.
Therefore, it is clear that this AI race has a ripple effect that directly and indirectly impacts others outside the circle.
Environment and AI
The first ripple effect to consider is environmental. AI depends on data centers, which require vast amounts of electricity to operate. Researchers have warned that the rapid expansion of data centers could slow down or even reverse the global shift towards net-zero. Currently, the global electricity consumption of expanding data centers has grown by around 12% each year since 2017 [1]. Additionally, according to forecasts, the global electricity consumption of AI data centres will increase eleven-fold from the reference year of 2023 to 2030. This is associated with an increase in greenhouse gas emissions from data centres from 212 million tonnes in 2023 to 355 million tonnes in 2030, despite the assumed expansion of the use of renewable energies for electricity production [2]. This exacerbates climate change impacts, which disproportionately affect low-income countries already facing severe environmental marginalization. In addition, water-intensive AI infrastructure may push advanced economies to expand into resource-rich but poorer nations, creating a new political and sovereignty crisis.
Manufacturing Reshoring and Job Displacement
Second, is manufacturing reshoring and Job displacement resulting in low demand for low-cost labor in countries like Bangladesh, India, or Indonesia, following AI-powered automation and efficiency. Textile factories, once reliant on human workers, could relocate back to developed economies where robots replace cheap labor [3]. This threatens to displace millions of workers and dry up foreign direct investment that once flowed into low-income economies because of their labor cost advantages.
Brain Drain
Brain drain acceleration is yet another element impacted, where ambitious talent from low-income countries often seeks opportunities abroad to join the AI race, depriving home countries of the very minds needed to foster local innovation, creating a cycle of dependency and stagnation.
Eroding Export Competitiveness
Advanced economies will be able to produce goods more efficiently, cheaply, and at a higher quality with AI, eroding export competitiveness and undermining the latter from countries reliant on traditional, labor-intensive manufacturing. Furthermore, AI will help companies optimize their use of natural resources, contrary to lower-income ones.
Technology Dependency and Digital Colonialism
Another challenge is Technology dependency and digital colonialism, where AI leaders shape global narratives, platforms, and politics. Control over data and technology creates new forms of dependency, limiting sovereignty in low-income countries [4]. This technological imbalance is not just theoretical; during the recent Israeli genocide in Gaza, advanced technologies were weaponized in ways that amplified asymmetries of power.
Premature Deindustrialization
Economist Dani Rodrik coined the term “premature deindustrialization” to describe a troubling phenomenon: developing countries are losing their manufacturing sectors before achieving high income levels or building alternative economic bases [5]. This represents a fundamental departure from the historical development pathway followed by today’s advanced economies, which industrialized fully before transitioning to service-based economies, putting low-income countries further at risk.
Health, Education, and Social Protection Gaps
Lastly, the AI divide deepens inequalities in health, education, and social protection. Low-income countries risk falling further behind, widening the developmental chasm with the absence of safety nets, active labor policies, and advanced healthcare access, which in turn impacts the nation’s social development.
In conclusion, AI advancement should not be viewed as a purely negative force. On the contrary, it brings with it numerous potential benefits and opportunities. Yet, for low-income countries, the challenge lies in recognizing the urgency of catching up. Without deliberate efforts to participate in this technological race, they risk sinking deeper into marginalization and vulnerability.
References
[1] Josh Gabbatiss, ‘AI: Five charts that put data-centre energy use – and emissions – into context’, 15 September 2025 https://www.carbonbrief.org/ai-five-charts-that-put-data-centre-energy-use-and-emissions-into-context/
[2] Informationdienst Wissenschaft, ‘AI at the expense of climate protection: energy demand of data centres will double by 2030’, 14 May 2025 https://nachrichten.idw-online.de/2025/05/14/ai-at-the-expense-of-climate-protection-energy-demand-of-data-centres-will-double-by-2030#:~:text=This%20is%20associated%20with%20an,renewable%20energies%20for%20electricity%20production.
[3] Hubert Nii-Aponsah, et. al, ‘Automation-induced reshoring and potential implications for developing economies’, 22 May 2023, https://cris.maastrichtuniversity.nl/en/publications/automation-induced-reshoring-and-potential-implications-for-devel
[4] Iraqi Youth Model United Nations, ‘The AI Power Divide: How Can Developing Countries Keep Up?’, n.d, https://iymun.net/the-ai-power-divide-how-can-developing-countries-keep-up/
[5] Dani Rodrik, ‘Premature Deindustrialization’, January 2015 https://www.ias.edu/sites/default/files/sss/pdfs/Rodrik/Research/premature-deindustrialization.pdf