A third wave of colonialism is underway. Unlike the first wave, which seized land and labor, or the second, which extracted mineral wealth through structural economic dependence, this third wave is invisible, consensual, and potentially irreversible. It colonizes not territories but data—the behavioral patterns, attention flows, emotional responses, and economic transactions of more than four billion people in the Global South.
The extractors are no longer European empires. On one side stands Meta, whose family of applications—Facebook, Instagram, WhatsApp—reaches an average of 3.58 billion daily active users, the overwhelming majority of them in the Global South. On the other stands ByteDance's TikTok ecosystem, which has grown to approximately 1.67 billion monthly active users globally, with Latin America now driving its fastest growth. Both operate with identical extractive logic. Both drain value from the Global South while returning little. And both, left unchecked, will lock in a digital dependency more complete than anything the colonial era produced.
This is not a conspiracy theory. It is the business model.
The Mechanics of Extraction
To appreciate data colonialism's full scale, consider a single country: Indonesia. Its 112 million WhatsApp users make it one of Meta's largest markets globally. The platform has become essential infrastructure—families use it to maintain social bonds across the vast archipelago; small businesses coordinate supply chains; political parties organize campaigns. Meta does not charge users for this service. Instead, it monetizes the behavioral data generated by these interactions. Click-to-WhatsApp advertisements alone are projected to generate approximately $10 billion in revenue in 2026, making them Meta's fastest-growing advertising format.
The colonial pattern is unmistakable. Data is generated in Indonesia, India, Brazil, Mexico, and Thailand. It is processed on servers in the United States. Advertising revenue flows to Menlo Park, California, enriching predominantly American shareholders. Meta's average revenue per user in the Asia-Pacific region is approximately $0.21—compared to $0.72 in Australia and Oceania. Global South users generate less direct revenue per capita but collectively produce the mass behavioral data that trains Meta's AI models, which in turn generate premium revenues in wealthy markets.
TikTok's extraction is equally aggressive, differing in form but not in substance. Indonesia is TikTok's second-largest market globally. Indonesian users spend more time per month on the platform than users in any Western country. TikTok's algorithm shapes taste, aspiration, political opinion, and self-image for a population in which more than half are under thirty. The platform's updated 2026 privacy policy expanded its data collection to include sensitive personal information—racial or ethnic origin, religious beliefs, health diagnoses, sexual orientation, and citizenship status. For hundreds of millions of users in nations where data protection laws are weak and enforcement minimal, TikTok's extractive apparatus operates essentially without constraint.
The symmetry is the point. An Indonesian teenager spending four hours daily on TikTok generates attention data that trains algorithms in Silicon Valley. A Thai small business owner using WhatsApp Business generates transaction data that enriches Meta's AI models. A Mexican street food vendor posting daily on Instagram generates content that drives engagement metrics. In every case, the value flows outward. The Global South produces the raw material—data—and receives in return a 'free' service that deepens its dependence on foreign-controlled infrastructure. This is colonialism without flags, without armies, without treaties. It is colonialism by consent, operating at the speed of light.
The Middle-Income Trap and the AI Lock-In
The stakes extend far beyond advertising revenue. The World Bank's 2024 World Development Report identified a crisis affecting more than 100 countries and roughly six billion people. Since 1970, the median per capita income of middle-income countries has never risen above 10 percent of the U.S. level. These nations have escaped extreme poverty but remain unable to generate the innovation and institutional capacity required to compete with advanced economies.
The AI era short-circuits the traditional development ladder. Countries that do not develop sovereign AI capacity within the next three years will find that the infrastructure for data processing, algorithmic decision-making, and digital commerce has been permanently captured by foreign platforms. By 2028—or sooner—the foundational architecture of the AI economy will be set. Data centers, cloud computing systems, and AI training pipelines will have been built and made impossible to replace. Nations that have not established sovereign capacity by then will find themselves permanently dependent on foreign-controlled infrastructure, just as nations that failed to develop domestic manufacturing capacity during the industrial revolution found themselves relegated to raw material exporters.
Indonesia's experience is instructive. Its 2022 Personal Data Protection Law forced Amazon Web Services to open a Jakarta data center. But data residency requirements alone do not address the fundamental asymmetry. The data may reside in Jakarta, but the algorithms that process it, the business models that monetize it, and the shareholders who profit from it remain in Menlo Park. Data residency without data sovereignty is the digital equivalent of requiring a colonial mining company to process ore locally while shipping the refined product back to the metropole.
Fragmentation as Leverage
The conventional analysis treats the fragmentation of AI governance across the Global South as a weakness. It is, in fact, a strategic asset.
In dozens of developing nations, AI governance is genuinely contested between American and Chinese influence. Thailand's framework reflects the country's traditional 'bamboo diplomacy,' bending toward whichever great power offers the most advantageous terms. Indonesia's approach is shaped by Chinese infrastructure investment through the Belt and Road Initiative alongside American tech dominance in consumer platforms. Mexico's proximity to the United States pulls it toward Silicon Valley norms, but Huawei and Xiaomi have deep market penetration. No single superpower has captured any of these nations entirely.
This means these nations can play the United States against China: threatening to adopt Chinese AI infrastructure if Washington insists on unfavorable terms, and threatening to embrace American platforms if Beijing pushes too aggressively. This is precisely the leverage that a coordinated coalition is designed to exploit. When a group of nations representing 400 million people—Thailand, Indonesia, Mexico in an initial phase—negotiates collectively, neither Washington nor Beijing can afford to ignore them. When India, Brazil, and South Africa are added in a second phase, the Global South commands market power comparable to the European Union.
This is not hypothetical leverage. It mirrors exactly the dynamic that produced the Bandung Conference in 1955, when newly decolonized nations of Asia and Africa declared independence from both Cold War blocs. The AI Middle Way updates this logic for the twenty-first century—with one critical difference: unlike the Non-Aligned Movement, which remained aspirational, a data sovereignty coalition creates binding institutional structures, controls data and market access that both superpowers need, and operates under a hard 2028 deadline before lock-in becomes permanent.
The Uncaptured Market
There is also a positive economic case, not merely a defensive one. The most consequential economic opportunity in the world today is the 2.1 billion lower-middle-class people in the Global South who earn between $3,000 and $12,000 per year in purchasing power parity terms. These are the taxi drivers in Bangkok, the taco vendors in Mexico City, the batik sellers in Yogyakarta, the market women in Lima. They operate in the informal economy—which in Global South nations accounts for 30 to 60 percent of GDP and employs the majority of the working population.
The global microfinance market was valued at approximately $235 billion in 2024 and is projected to reach $523 billion by 2032. The microcredit and small-medium enterprise financing gap in developing economies is estimated at $5.2 trillion. Yet this entire sector remains essentially untouched by AI. Neither American nor Chinese AI platforms have captured it—and neither is particularly well-positioned to do so, given the deep local knowledge required and the structural unsuitability of surveillance-based business models for populations that have every reason to be suspicious of foreign data extraction.
AI-enabled cooperative formalization offers something fundamentally different from what either superpower provides. Instead of requiring informal businesses to navigate labyrinthine bureaucracies—a process that in most Global South countries involves dozens of steps, months of waiting, and significant bribery—AI-powered platforms would allow informal workers to formalize gradually and organically. An AI application could help a taco vendor track her sales, manage her inventory, calculate her profits, file simplified tax returns, and access formal credit—all on her existing smartphone. As she formalizes, she enters the tax base, gains access to formal financial services at lower interest rates, and builds a credit history that enables growth. She moves from the informal to the formal economy not through coercion or surveillance but through tools that make formalization advantageous.
The Chinese model of AI-enabled social credit—which monitors behavior, scores citizens, and punishes dissent—is anathema to this vision. So is the American model of algorithmic advertising, which uses behavioral profiling to manipulate attention. A data sovereignty coalition offers a third option: cooperative formalization that expands the tax base, enables formal economic participation, and protects individual dignity—without surveillance.
The Geopolitical Stakes
The relationship between data colonialism and global security is underappreciated in mainstream policy discourse. The current architecture of U.S.-China competition in the Indo-Pacific is, at its foundation, a competition over who controls the physical and digital infrastructure of the data economy. The confrontation over 5G, which saw the United States pressure allies to exclude Huawei, was fundamentally a contest over who controls the data layer.
When middle-income nations have sovereign AI capacity, they are no longer prizes to be won in a superpower competition. They are independent actors capable of maintaining relationships with both the United States and China without being captured by either. Nations with sovereign digital infrastructure do not need to be 'protected' by either superpower's military. This reduces the pressure toward zero-sum thinking in both Washington and Beijing—and reduces the structural drivers of military escalation that, left unchecked, risk a conflict that would dwarf the harm of any data extraction.
A coalition of this kind has a natural institutional home: the G20's Digital Economy Working Group, which uniquely convenes both the United States and China alongside the major middle-income nations. No other multilateral body offers the same combination of reach and mandate. The UK's 2025–2026 G20 presidency, with its stated emphasis on global economic resilience, provides an opening that will not recur.
What Must Happen Before 2028
Dario Amodei, CEO of Anthropic and one of the most thoughtful voices in AI governance, recently argued that the central challenge of the AI era is ensuring that democratic values rather than authoritarian ones shape the technology's development. He is right—but his framework, like most Western AI governance thinking, treats the Global South primarily as a passive recipient of decisions made elsewhere. The four billion people of the developing world are not a secondary consideration in AI governance. They are its central subject.
Building a data sovereignty coalition will require three things. First, philanthropy must lead. The communities the coalition seeks to serve are precisely those that venture capital has deemed insufficiently profitable. The Ford Foundation, Omidyar Network, and McGovern Foundation each have programmatic missions—democratic governance, digital rights, economic inclusion—that a data sovereignty coalition directly advances. The philanthropic investment required is modest relative to the potential return, measured not in financial terms but in civilizational ones.
Second, the coalition must be grounded in the philosophical traditions of the communities it serves. The EU AI Act, for all its sophistication, is grounded in a European liberal tradition that privileges individual autonomy and market efficiency. Buddhist philosophy in Thailand and Myanmar, Pancasila in Indonesia, and the rich indigenous traditions of Mexico and Peru offer substantive intellectual resources for AI governance that Western frameworks lack—resources that take seriously the relationship between consciousness and intelligence, and that resist the reduction of human beings to data points.
Third, the coalition must move with urgency. By 2028, the foundational infrastructure of the AI economy will be set. The window is not years away. It is now.
The transformation of two billion people from invisible informality to dignified, formal economic participation would be the most significant development achievement since decolonization. The question is whether the political will to build the coalition can be assembled before the window closes.
Till now, the AI Middle Way stands alone as that alternative. The question is whether we will build it in time.
Craig Warren Smith is Founder and Chairman of the Digital Divide Institute and co-leads the AI Middle Way Coalition. He has held appointments at Harvard Kennedy School, MIT Media Lab, Chulalongkorn University, and Peking University. Soraj Hongladarom is a former Professor of Philosophy at Chulalongkorn University and author of The Ethics of AI and Robotics: A Buddhist Viewpoint (Lexington Books, 2020). Both are based at www.middleway.ai.