AI's Double Edge: How the Global South Can Seize Economic Opportunity Before It Vanishes

Nearly two-thirds of workers in India, Indonesia, and Nigeria embrace AI as an opportunity for economic advancement. Yet simultaneously, business process outsourcing jobs that have lifted millions from poverty face automation threats within the next decade.

This contradiction defines the AI moment for the Global South. While developing economies see artificial intelligence as a path to leapfrog legacy systems and accelerate growth, the traditional playbook of economic development through low-cost digital services is being rewritten in real time.

The question is no longer whether AI will transform the Global South, but whether countries can navigate this transition before the ladder they've been climbing disappears beneath them.

The Promise: Why AI Feels Like Leapfrogging Gold

For decades, the Global South has built prosperity on economic arbitrage. Why spend decades building manufacturing infrastructure when your young, educated population can perform digital services for global companies at a fraction of Western costs?

India's software industry exemplifies this strategy, creating an estimated 5 million jobs and generating over $200 billion in annual revenue. Countries like the Philippines, Vietnam, and Bangladesh followed similar paths, establishing themselves as global hubs for call centers, data entry, customer support, and backend services.

Generative AI offers something that seems even more powerful: the ability to skip entire technological eras. Rather than following the industrial revolution playbook that took developed nations two centuries, countries can adopt AI directly to transform healthcare, agriculture, education, and governance.

Governments across Africa and Asia describe AI as an opportunity to achieve inclusive growth and propel new solutions to developmental challenges through narratives around the Fourth Industrial Revolution.

The statistics seem to support this optimism. More than 40% of ChatGPT's global traffic in mid-2025 originated from middle-income countries led by Brazil, India, Indonesia, and Vietnam, while generative AI job vacancies surged 9-fold from 2021 to 2024, with one in five of these jobs located in middle-income countries.

As of 2025, 15 African nations have published concrete AI strategies, and an alliance of countries recently established a $60 billion fund to build domestic AI capabilities.

Real progress is happening. AI-powered computer vision identifies banana and cassava disease before widespread crop failure. Telemedicine platforms bridge healthcare gaps in rural regions. Educational applications provide personalized learning to students without access to premium tutoring.

The World Bank's latest economic outlook finds that growth in South Asia could be bolstered by advances in artificial intelligence, along with carefully sequenced trade reforms that create jobs and labor market policies that ease worker reallocation.

The Threat: The Digital Services Automation Crisis

But here's the harsh reality that economists and policymakers increasingly acknowledge: the very comparative advantage that built prosperity for hundreds of millions of workers is evaporating faster than anyone predicted.

Business process outsourcing jobs that include data entry, invoice handling, and claims processing are expected to see the loss of perhaps half a million jobs in India alone over the next three years, as AI systems can perform these tasks at near-zero marginal costs beyond API calls.

The challenge extends beyond specific sectors. A study by the International Monetary Fund found that 60% of jobs in advanced economies are exposed to AI impact due to cognitive-task-oriented work, but this exposure in emerging markets is 40% and in low-income countries is 26%.

What seems like lower exposure is actually a structural trap. Lower-skilled work in developing economies is precisely what AI can automate most easily and cheaply. Call centers powered by advanced chatbots, content moderation through automated systems, and data annotation through computer vision represent the jobs that have historically anchored the digital economy in the Global South.

Workers in India and the Philippines, where millions support global clients through business process outsourcing and call centers, are already questioning whether the current migration pathway will carry them forward or leave them behind as AI chatbots and automated platforms advance.

Unlike the Industrial Revolution, where displacement gradually created new sectors, this transition is accelerating exponentially. New jobs are emerging, but they require skills entirely different from the ones that built today's emerging economies.

Infrastructure and Access: The Hidden Chasm

Optimism about AI leapfrogging collides with brutal infrastructure realities. The World Bank found that just 35% of people in developing countries have access to the internet, compared with 80% for developed economies. Internet access reached 93% of the population in high-income countries in 2024 compared with just 27% in low-income economies.

Advanced AI requires distributed computing power, reliable electricity, and high-speed broadband. Most developing countries lack all three in adequate measure. Data, another critical AI input, reflects another disparity.

In 2024, U.S. private sector investment in AI reached $109.1 billion, and 100 firms, mainly located in the United States and China, account for roughly 40 percent of corporate research and development spending on AI globally. High-income countries account for 87% of notable AI models, 86% of AI startups, and 91% of venture capital funding despite representing just 17% of the global population.

This concentration of innovation creates dependency. When outside funding and international partnerships end, local AI efforts often stall. Leapfrogging risks being built on borrowed foundations rather than self-sustaining ecosystems.

The Skills Gap: Knowledge Without Infrastructure

Even when talent is available, the mismatch is stark. One in five emerging market jobs are in generative AI, yet middle- and low-income countries face severe shortages of trained professionals to develop and oversee AI systems. Training programs exist, but they're inconsistent, often unaffordable, and disconnected from local labor market needs.

Countries like Singapore and Korea are addressing this through comprehensive national initiatives. Singapore's SkillsFuture for Digital Workplace 2.0 initiative targets adults in jobs likely to be affected by automation with subsidized two-day courses covering automation, cybersecurity, data analytics and digital tools. But few developing nations have comparable resources or governance structures to implement such programs at scale.

The irony is painful. Countries have the motivation and young populations with digital inclinations, yet lack the institutional capacity to build AI literacy programs that prevent wholesale workforce displacement. The gap between need and capability widens daily.

The Policy Imperative: Building Ecosystems, Not Just Adopting Tools

Successful economic leapfrogging requires more than access to AI technology. It demands strategic focus on five critical areas. First, developing countries must invest in foundational infrastructure.

The Global Digital Compact, adopted in September 2024, recognizes digital connectivity as foundational to development, and the World Bank and IMF should move AI infrastructure from the margins to the core of what they finance, treating distributed AI infrastructure with the same priority that ports and roads received a century ago.

Second, governance frameworks must be inclusive. Currently, AI governance frameworks are being written in Washington, Brussels, and Beijing, risking that priorities get set without participation from those who will implement and use these tools. The Global South must demand a seat at international policy tables.

Third, developing nations should prioritize open-source AI. Rather than building proprietary systems, leveraging open models allows countries to customize solutions to local contexts without reinventing foundational technologies. Bangladesh using AI for agricultural productivity gains or Ghana reducing antimicrobial resistance through AI systems demonstrates this approach's potential.

Fourth, workforce transition planning must begin immediately. Training programs aimed at enhancing digital literacy and AI competencies can help reduce the skills gap, with programs targeting different levels of the workforce from basic digital skills to advanced AI training for specialists. Proactive reskilling beats reactive crisis management.

Finally, countries should identify economic niches where AI creates comparative advantage. Rather than competing globally in commodity AI services, focus on sectors where AI enhances existing strengths: agricultural AI adapted to local crops, healthcare AI for tropical diseases, education AI in local languages, or climate adaptation AI specific to regional vulnerabilities.

The Realistic Path Forward

The future of work in the Global South will be neither uniformly positive nor catastrophic. Some regions will build successful AI ecosystems and create high-skill, high-wage opportunities. Others will see traditional employment pathways collapse without adequate replacements. The difference will be determined by decisions made in the next two to three years.

Countries that treat AI as inevitable technology to adopt passively will experience disruption without benefit. Those that invest in infrastructure, skills, governance participation, and strategic focus on comparative advantages can genuinely leapfrog. The window for this transition is closing.

As AI capabilities accelerate and automation extends into service sectors, the ladder that developing economies have been climbing is being incrementally dismantled. Seizing economic opportunity requires recognizing that leapfrogging isn't about adopting the newest technology. It's about building the resilience, skills, and ecosystems that allow a nation to thrive regardless of which technologies emerge next.

Fast Facts: AI and Economic Leapfrogging in the Global South Explained

What does economic leapfrogging mean in the context of AI development?

Economic leapfrogging means developing countries skip earlier technological stages and adopt advanced AI directly to transform sectors like healthcare, agriculture, and education. Rather than following the centuries-long path developed nations took, the Global South aims to compress technological progress by applying AI to solve local development challenges.

How does AI create job opportunities but also threaten employment in developing economies?

AI creates new high-skill roles in AI development and deployment while simultaneously automating routine cognitive tasks like data entry, customer service, and content moderation that employ millions in the Global South. The paradox is that new jobs require different skills and training than those being displaced, creating a dangerous mismatch.

What are the biggest barriers preventing the Global South from building successful AI ecosystems?

Infrastructure gaps, limited internet access (just 35% in developing countries versus 80% in developed economies), severe shortages of AI professionals, insufficient computing power, and unequal global distribution of AI investment and models all hinder the Global South's ability to develop independent, sustainable AI capabilities.