The global AI divide: How unequal access cripples developing economies

Artificial intelligence stands as one of the most transformative technologies of our era, promising unprecedented advances in productivity, innovation, and economic growth. Yet a troubling paradox defines the AI revolution: while wealthy nations and multinational corporations reap exponential benefits, developing economies find themselves increasingly marginalized in both AI development and deployment.

The International Monetary Fund warns that artificial intelligence could exacerbate cross-country income inequality, with growth impacts in advanced economies potentially more than double those in developing nations. This disparity often termed the “AI divide” represents not merely a technological gap but a fundamental threat to global equity and sustainable development. Understanding how automation and robotics in the physical world deepen this divide is essential for policymakers, economists, and development practitioners seeking to navigate the AI-driven future.

The AI divide is fundamentally rooted in structural limitations that separate the Global North from the Global South. Unlike previous technological revolutions, AI adoption requires a demanding, multifaceted infrastructure that most developing countries simply cannot afford. This infrastructure encompasses technical systems, computational models, high-quality datasets, and most critically, skilled human capital. The World Economic Forum’s assessment of 181 countries revealed that the lowest-scoring regions for AI readiness include much of sub-Saharan Africa, significant portions of Central and South Asia, and several Latin American nations.

Without an enabling operating environment characterized by a robust technology sector, adequate data infrastructure, and strategic governance frameworks disparities in AI readiness inevitably feed into deepening global inequality. The financial barrier alone proves insurmountable for resource-constrained countries. Training advanced AI algorithms costs millions of dollars, with training GPT-3 requiring approximately 1,300 megawatt-hours of electricity, equivalent to the annual power consumption of 130 American households. The more sophisticated GPT-4 demands fifty times more electrical energy.

Setting up and maintaining AI infrastructure remains unaffordable for most developing nations, let alone the ongoing operational costs. Beyond capital requirements, the lack of local data specifically tailored to regional contexts presents another critical obstacle.

Developing AI systems useful and responsible for local communities requires large amounts of high-quality, contextually relevant data for training and testing. In resource-constrained regions, inadequate infrastructure maturity and limited practitioner capacity combine to create severe data availability challenges. These structural limitations systematically exclude developing nations from both the creation and meaningful customization of AI technologies.

The proliferation of industrial robots and automation technologies presents perhaps the most visible manifestation of the AI divide’s economic consequences. Industrial robots have already transformed manufacturing in developed nations, handling complex tasks in automotive, electrical, and electronics industries. However, the implications for developing countries diverge sharply depending on regional economic structures and policy responses.

Research examining automation’s impact reveals a paradox: while robots displaced jobs in developed economies, some developing countries initially experienced positive employment effects. An analysis of Indonesian manufacturing plants found that robotization boosted operational efficiency and allowed firms to upgrade their position in global value chains.

Between 2018 and 2022, industrial robot adoption in five ASEAN countries created approximately 2 million jobs for skilled, formal workers. However, this employment gain came at the cost of displacing 1.4 million low-skilled workers engaged in routine and manual tasks. The benefits concentrated overwhelmingly among younger workers equipped with engineering and technical skills capable of operating and maintaining robotic systems.

The more troubling dynamic emerges through reshoring effects. US robots have demonstrably reduced employment in Mexico by facilitating the return of manufacturing production to the United States, reducing the need for low-wage offshore workers.

Research on Mexican labor markets found that areas with average exposure to US robots experienced 3.5 percentage points lower employment-to-population ratio growth compared with unexposed areas, translating to approximately 2 million lost jobs nationally.

This reshoring mechanism operates through a deceptively simple economic logic: as automation reduces production costs in advanced economies, the traditional labor-cost advantage of developing countries erodes. Manufacturing becomes capital- and technology-intensive rather than labor-intensive, fundamentally disrupting development models that have driven growth in emerging economies for decades.

The UNCTAD policy brief “Robots and Industrialization in Developing Countries” warns that developing nations face a severe dilemma. They require robotic technology to enhance competitiveness and upgrade manufacturing capabilities, yet face enormous social pressure as automation threatens employment. Export-oriented manufacturing historically the engine for absorbing surplus agricultural labor and driving productivity gains now encounters structural headwinds. The share of manufacturing in both GDP and employment in many developing countries steadily declines, undermining the traditional pathway through which countries escape poverty.

Without carefully designed policy interventions, automation threatens to lock poorer nations into primary commodity production while denying them the middle-income manufacturing opportunities that richer nations leveraged during their development transitions.

The energy crisis and infrastructure deficit

Beyond employment effects, the energy intensity of AI infrastructure creates a profound disadvantage for developing economies lacking reliable electricity systems. A single generative AI query requires approximately ten times more energy than a typical Google search.

As AI adoption accelerates globally, data centers the computational backbone of all AI systems consume ever-increasing power. The International Energy Agency projects that data center electricity consumption will more than double by 2030, surpassing Japan’s total current consumption. With AI representing the most important driver of this increase, the strain on already-fragile electrical grids in developing nations becomes acute. The disparity in data center capacity starkly illustrates this infrastructure gap. India, hosting approximately 20 percent of the world’s data, possesses merely 3 percent of global data center capacity. The United States maintains 19 times more leading cloud and colocation data centers than India, the emerging market economy with the most data centers.

This computational deficit directly translates to limited AI capabilities and dependence on foreign cloud services controlled by American and Chinese technology giants, deepening the global AI divide. Training AI models, running inference operations, and storing the massive datasets required for AI applications demand both stable electricity and substantial computing infrastructure luxuries most developing nations cannot yet afford. Paradoxically, some developing countries like India and Southeast Asian nations are becoming attractive destinations for data center investment precisely because of environmental pressures on wealthy nations. Stringent environmental and regulatory laws in the Global North increasingly direct AI infrastructure investment southward, where energy is cheaper and regulations less stringent, further reshaping the contours of the AI divide.

However, this “solutions” carries hidden costs. Locating power-hungry data centers in regions with underdeveloped energy infrastructure and climate vulnerabilities exacerbates environmental degradation while undermining decarbonization goals. The Global South risks becoming a computational dumping ground, hosting the energy-intensive infrastructure necessary for Global North AI dominance while bearing disproportionate environmental and development costs.

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At the human level, the AI divide manifests most acutely through a severe shortage of skilled professionals capable of developing, deploying, and maintaining AI systems. The Global South suffers a talent drought in data science, machine learning, and AI engineering precisely the expertise required to build local AI ecosystems.

This scarcity stems partly from inadequate university training programs and technical education infrastructure. Yet the deeper problem involves brain drain: talented individuals from developing countries migrate to Silicon Valley, European research centers, and other wealthy regions offering superior salaries, research opportunities, and career advancement prospects.

India exemplifies this pattern within the AI divide. Despite producing world-class computer scientists and engineers, many migrate to developed nations for better opportunities. Similarly, though Nigeria and other African nations possess promising AI talent, migration drains their human capital. The result is a vicious cycle: without local talent and expertise, developing countries cannot build indigenous AI capabilities, making them perpetually dependent on technology transfer from richer nations. This dependency extends beyond mere technology adoption; it prevents the customization of AI systems for local contexts and constraints, limiting their effectiveness in addressing regional problems and reinforcing the AI divide.

Kyndryl’s People Readiness Report reveals that 71 percent of global business leaders assess their workforces as unprepared to use AI successfully, with only 14 percent of companies actively upskilling employees during AI deployment.

In developing regions, this gap appears far more severe. While only 5.5 percent of employment in developing countries faces potential AI automation exposure, the figure rises to 26.6 percent in developed economies a counterintuitive finding that actually reflects the Global South’s limited AI integration. However, this apparent insulation masks a deeper vulnerability: when AI automation does arrive in developing nations, their workforces lack the training, educational foundations, and social safety nets to adapt. The transition threatens to be far more disruptive for societies with fragile labor markets, high informal employment, and limited fiscal space for reskilling programs.

Africa: The last frontier of automation

Africa presents a compelling case study of emerging opportunities and persistent challenges within the global AI divide. The African industrial process automation market, valued at $687.2 million in 2024, is projected to reach $1.57 billion by 2030, representing a compound annual growth rate of 14.8 percent. South African warehouse automation alone is forecast to grow from $134.6 million in 2024 to $657 million by 2033. Yet Africa accounts for less than 1 percent of global industrial robot adoption, underscoring both the scale of the opportunity and the depth of the deployment gap that defines the AI divide.

The oil and gas sector, particularly in Nigeria and Angola, drives much of Africa’s automation adoption, where advanced technologies ensure operational efficiency and safety in challenging extraction environments. However, this sectoral concentration creates vulnerability.

The pharmaceutical and energy industries benefit from capital-intensive automation, but broader manufacturing remains largely untouched, reflecting the realities of the AI divide. Among South African manufacturers, 61 percent expect AI to drive growth by 2029, yet approximately 50 percent of large firms still operate primarily manually, with only 10 to 16 percent achieving full digital system integration. This lag reflects both capital constraints and the absence of local technical expertise to implement and maintain sophisticated systems key factors reinforcing the AI divide.

Brazil and Mexico: Reshoring and deindustrialization

Brazil’s experience illuminates the reshoring mechanism’s real-world consequences within the AI divide. Research analyzing local labor markets across Brazil reveals that heightened robot usage in advanced economies correlates directly with reduced employment in high-value manufacturing sectors and increased employment in raw material extraction. Foreign automation in trade-partner countries diminishes manufacturing employment while expanding mining sector employment, reflecting a shift from value-added production to primary commodity extraction. This pattern mirrors broader deindustrialization trends in resource-rich emerging economies, where automation in wealthy nations undermines comparative advantages in manufacturing labor, reinforcing the AI divide.

Mexico’s case proves more acute. US robot adoption triggered significant employment losses in Mexican manufacturing, particularly in automotive sectors. Local labor markets with average exposure to US robots experienced 3.5 percentage points lower employment-to-population ratio growth compared with unexposed areas. At the national level, this represents approximately 2 million lost jobs.

The reshoring mechanism operates through reduced US demand for Mexican exports rather than direct factory automation in Mexico itself a crucial distinction demonstrating that developing countries face automation-induced displacement even when they lack domestic robot adoption.

Asia-Pacific: Divergent development paths

Asia-Pacific nations demonstrate more differentiated experiences. China, leveraging substantial government investment approximately $912 billion over the past decade with $209 billion (23 percent) directed specifically to AI firms has positioned itself as both an AI creator and large-scale implementer. India, by contrast, emerges primarily as an AI adopter rather than innovator. Despite this, India demonstrates surprisingly robust AI adoption among IT professionals, with 59 percent reporting organizational AI integration, placing it among global leaders in deployment. The AI Readiness Index scored India at 49.8 out of 100, comparable to some developed economies despite India’s developing nation status.

Southeast Asia presents perhaps the most promising trajectory for regional cooperation. AI could add $1 trillion to the ASEAN region’s GDP by 2030 if properly implemented. Currently, 14 percent of Southeast Asian firms have adopted AI to some extent, with 37 percent planning adoption within the next five years. Daily AI usage rates exceed adoption in developed economies: 32 percent in India and 19 percent in Southeast Asia compared to 8 percent in Australia and 4 percent in Japan, suggesting rapid adoption momentum among populations with fewer legacy systems constraining integration.

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The AI divide threatens to widen both within and between countries. The productivity gains from AI accrue primarily to wealthy nations and large multinational corporations, creating what the World Bank describes as a concentration of benefits among “a few global superstar companies.” These gains bypass most developing economies, limiting their ability to participate in AI-driven productivity improvements that advanced economies leverage for competitive advantage.

Within countries, AI adoption exhibits disturbing patterns of inequality. Research examining AI’s impact on income distribution in China reveals that rapid AI development significantly exacerbates urban-rural income gaps.

The impact intensifies in areas with superior digital infrastructure, greater government backing, and sophisticated industrial technologies precisely the characteristics concentrating in wealthy regions and urban centers. Vocational skills and employment opportunities emerge as crucial pathways through which AI affects income inequality, highlighting how unequal access to training and education translates into economic disparities.

Gender dimensions add another troubling layer to the AI divide. Women face more than double the risk of job displacement from AI automation compared to men, reflecting occupational segregation into routine, automation-vulnerable roles. In developing countries with already limited social protection systems and higher levels of informality, such gender-differentiated job losses threaten to deepen existing inequalities and increase female poverty and economic vulnerability within the AI divide.

The path forward: Policy solutions and collaborative approaches

Addressing the AI divide requires comprehensive, multi-level policy interventions spanning infrastructure development, education, governance, and international cooperation. The Asian Development Bank identifies several crucial actions for developing countries: clearly defining national AI vision aligned with specific development needs, building broad stakeholder consultations involving government, industry, academia, and civil society, and establishing robust policy frameworks that prioritize infrastructure and capacity building, data governance, ethical AI development, and economic incentives for innovation.

Infrastructure investment presents the immediate priority. Developing nations must prioritize digital infrastructure, broadband connectivity, and energy system upgrades enabling reliable AI deployment. Rather than each nation building isolated data centers, regional cooperation offers substantial cost savings potentially reducing expenses by half compared to isolated national infrastructure builds.

ASEAN, African Union, and CARICOM entities could lead the way in establishing shared regional data centers, reducing financial barriers to entry and promoting digital sovereignty.

Education and workforce development require urgent attention. Governments must reform educational systems to foster AI literacy alongside critical thinking, creativity, and ethical reasoning. Targeted programs must support underrepresented groups in AI-related fields while providing comprehensive digital literacy training enabling workforce engagement with AI technologies. Public-private partnerships can leverage private sector expertise and resources to expand training capacity beyond government capabilities.

Policy frameworks must encourage responsible, locally-contextualized AI development rather than passive technology consumption. Developing nations should support public-private partnerships enabling local firms to build context-appropriate AI tools addressing regional priorities: food security, resilient health systems, inclusive education, and climate adaptation. South-South collaboration through regional research networks and shared pilots allows countries to generate AI applications serving local needs rather than relying exclusively on foreign solutions.

The international community must establish mechanisms supporting developing country AI ecosystems. A proposed Global South AI Development Fund, co-governed by representatives from developing regions, would ensure local priorities drive investment rather than external institutions setting agendas. Concessional financing favoring applications augmenting human productivity rather than replacing workers would direct AI adoption toward inclusive growth. Development finance should prioritize local AI ecosystems the startups, universities, and cross-border collaborations building relevant solutions.

Regarding industrial automation and robotics specifically, developing nations face a strategic choice rather than an either/or dilemma within the AI divide. Evidence from developing countries demonstrates that domestic robot adoption can enhance competitiveness and support upgrading in global value chains when accompanied by complementary policies.

However, passive technology adoption without local capability development and workforce transition support risks reproducing the negative patterns observed in developed economies: job displacement without compensating job creation.

Strategic automation adoption requires careful sequencing and complementary policies. Governments must implement robot taxation or surcharges funding transition programs for displaced workers. Educational investment must prepare workforces for human-machine collaboration rather than replacement.

Industrial policies should target automation in sectors offering genuine competitive advantages, rather than wholesale factory modernization regardless of comparative advantage. Regional integration and market expansion can provide sufficient scale to make domestic automation economically viable without triggering reshoring to wealthier nations.

The global AI divide represents both profound challenge and genuine opportunity. The challenge is clear: without deliberate policy interventions, AI will concentrate wealth and capability further in already-wealthy nations while marginalizing most of humanity. The opportunity lies equally clear: developing countries need not repeat the technological pathways of wealthy nations. They can leapfrog traditional barriers, leveraging AI for sustainable development if they invest strategically in local ecosystems, prioritize inclusive applications, and coordinate regionally to reduce costs.

The coming decade will determine whether AI becomes a tool for inclusive global development or another mechanism widening inequality. The choices developing nations make regarding AI policy, education investment, and infrastructure development will shape not only their own economic futures but global equity for generations.

The urgency cannot be overstated: compute power the ability to train and deploy AI systems is fast becoming the new frontier of inequality and a defining fault line of the AI divide. Nations that build local capabilities, invest in human capital, and coordinate regionally will participate meaningfully in the AI revolution. Those that remain passive risk permanent marginalization in an AI-driven global economy. The time for comprehensive, coordinated action is now, before the AI divide becomes irreversible.