The New Corporate Colonialism: AI and the Digital Divide in Emerging Markets

By Amara Singh Sep. 13, 2025

Artificial intelligence is often marketed as humanity’s great equalizer—a universal tool for innovation, inclusion, and economic growth. Yet beneath this rhetoric lies a sobering truth: the global AI revolution is widening the technological divide between those who own data and those who merely generate it. Across Africa, Latin America, and South Asia, emerging economies find themselves supplying the raw material—human labor and data—for algorithms built and monetized elsewhere. What was once colonial extraction of minerals and crops has evolved into the extraction of information, computation, and human cognition. The result is a new hierarchy of digital dependency—one where sovereignty is measured not by territory or trade, but by control over data and AI infrastructure.

The Digital Divide Reimagined

The digital divide is no longer just about internet access; it’s about who controls the engines of intelligence. As of 2024, 80 percent of global AI patents are held by firms based in the United States and China, while Africa and Latin America combined account for less than 1 percent, according to the World Intellectual Property Organization (WIPO). Similarly, 70 percent of global data center capacity resides in just six countries—chiefly the United States, China, and EU members—leaving much of the Global South dependent on foreign servers and cloud services.

This concentration of technological capital ensures that the value generated from AI flows almost exclusively to the developed world. Multinational tech giants—Amazon, Google, Microsoft, Alibaba—dominate the computational backbone of the digital economy, charging developing nations for access to platforms built from data those same nations produce. The UN Conference on Trade and Development (UNCTAD) warns that this imbalance risks creating “digital colonialism,” where low-income countries provide raw data in exchange for finished AI systems that they neither own nor control.

Data Extraction: The New Resource Curse

In the 19th century, colonial empires extracted natural resources; in the 21st, corporations extract behavioral data. The mechanism is familiar: cheap labor, unequal contracts, and externalized benefits. AI systems rely on massive datasets—often scraped from users in developing countries whose online activity is governed by weaker privacy laws. The Electronic Frontier Foundation estimates that over 60 percent of publicly available AI training data originates from users in the Global South.

This data fuels the creation of language models, facial-recognition tools, and recommendation algorithms that primarily benefit Western firms. For instance, OpenAI’s GPT-4 and Google’s Gemini models were partially trained using global internet data, much of it scraped from non-Western users without consent or compensation.

The imbalance extends to human labor. Hidden behind “AI automation” are tens of thousands of underpaid workers in Kenya, the Philippines, and Venezuela, who spend hours labeling toxic or violent data to make Western models “safe.” A 2023 Time investigation revealed that Kenyan workers earning less than US$2 per hour helped filter training data for ChatGPT—performing psychological triage on disturbing content that wealthy nations would not tolerate. It is the new sweatshop: invisible, digital, and global.

Infrastructure Dependence and the Cloud Monopoly

Emerging economies face another structural dependency: the lack of AI infrastructure. Building and maintaining data centers requires immense capital, technical expertise, and stable electricity—conditions scarce in many low- and middle-income countries. As a result, foreign firms dominate cloud infrastructure. Statista estimates that as of 2025, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud collectively control 66 percent of the global cloud market, with nearly all major African and South Asian firms relying on one of the three.

This reliance carries both economic and political costs. When domestic industries depend on foreign servers, they effectively surrender data sovereignty. In 2023, Nigeria’s central bank suspended a domestic fintech initiative after discovering that its transaction data was stored in U.S.-based servers—raising national security concerns. Similar scenarios have played out in Indonesia and Brazil, where dependency on external cloud infrastructure has forced governments to balance privacy, cost, and geopolitical alignment.

The parallels with 20th-century extractive industries are striking. Just as oil-dependent nations ceded control to Western energy firms, data-dependent economies now face digital rent-seeking: paying perpetual licensing and cloud fees for access to tools built from their own information.

Algorithmic Bias and the Global South

The inequities of AI are not only economic but epistemic. Models trained primarily on Western data replicate Western assumptions. As MIT Technology Review reports, large language models systematically underperform in African and South Asian dialects, misinterpret non-Western cultural idioms, and reinforce stereotypes about “developing” nations.

This bias is not accidental—it reflects the geography of AI’s creation. Only 3 percent of published AI research in top-tier journals comes from African institutions. Meanwhile, datasets such as ImageNet and Common Crawl continue to overrepresent Western contexts, embedding systemic bias into global systems of decision-making. When AI tools are used for credit scoring, hiring, or content moderation in developing countries, they often penalize local users due to cultural and linguistic misalignment.

For example, a 2024 Carnegie Endowment for International Peace study found that automated loan-assessment systems deployed in East Africa had error rates 40 percent higher than in Western markets, leading to credit denial for qualified borrowers. The same technology that promises efficiency in the West risks deepening exclusion elsewhere.

Resistance and Reclamation: Emerging Models of Digital Sovereignty

Not all developing nations are passive participants in this new hierarchy. A handful are beginning to assert digital sovereignty through policy and innovation.

In Kenya, the government’s National Artificial Intelligence Strategy (2024–2029) mandates that data generated within national borders must be stored in domestic servers and used primarily for local research before export. Similarly, India’s Data Protection Act of 2023 requires multinational firms to obtain explicit consent before transferring personal data abroad—an attempt to retain value within its own digital ecosystem.

Brazil is piloting an alternative approach through its AI for the Amazon Initiative, which uses open-source AI to monitor deforestation while maintaining national control over environmental data. These policies mark a growing recognition that digital infrastructure is not merely a technical asset but a pillar of sovereignty.

Yet resistance remains limited. Only 22 percent of developing nations have enacted comprehensive AI or data-protection frameworks, according to the OECD Digital Policy Observatory. Without coordination, isolated national strategies cannot counterbalance the dominance of global tech monopolies.

A Call for Digital Equity

The solution to digital colonialism is not isolationism but equitable globalization. Three pillars are essential:

First, data-sharing agreements must be reciprocal. If corporations extract behavioral data from developing countries, they must provide proportional access to resulting AI technologies, training resources, and profits.

Second, the global community must invest in AI infrastructure for the Global South. A World Bank proposal suggests a US$10 billion Global Digital Equity Fund to help low-income nations build regional data centers and open-source AI labs—akin to a digital version of the Green Climate Fund.

Third, global governance must evolve. A UN Framework Convention on Digital Sovereignty—as proposed by the UN AI Advisory Body—could standardize fair data-trade practices, prevent monopolistic concentration, and enforce transparency in AI model development.

Without such reforms, the AI revolution will replicate the colonial patterns of the past—extractive, unequal, and self-justifying.

Conclusion

Artificial intelligence holds the potential to democratize knowledge, but its current trajectory risks recreating the inequities of empire—coded not in race or geography, but in data and algorithms. Emerging markets stand at a crossroads: either remain data colonies feeding the intelligence of others, or build sovereign ecosystems that reflect their own values, languages, and priorities.

Progress without parity is not development—it is domination. If the Global South is to escape the algorithmic shadow of the North, it must reclaim the most valuable resource of the 21st century: its own data.

Works Cited

“World Intellectual Property Indicators 2024.” World Intellectual Property Organization (WIPO), 2024, https://www.wipo.int/global_ip_report.

“Digital Economy Report 2023: Data and Development.” United Nations Conference on Trade and Development (UNCTAD), 2023, https://unctad.org/publication/digital-economy-report-2023.

“AI Data Labor in the Global South.” Time Magazine Investigation, 2023, https://time.com/ai-data-labor-kenya.

“Global AI Infrastructure and Market Share 2025.” Statista Research Department, 2025, https://www.statista.com/topics/ai-cloud-market.

“Algorithmic Inequality in Emerging Markets.” Carnegie Endowment for International Peace, 2024, https://carnegieendowment.org/ai-bias.

“OECD Digital Policy Observatory: Global AI Legislation Tracker.” Organisation for Economic Co-operation and Development (OECD), 2025, https://oecd.ai/en/policy-tracker.

“AI and Data Sovereignty in Developing Nations.” World Bank Digital Development Report, 2024,

 https://worldbank.org/digitalsovereignty.