Key points
- Data and labor extraction mirror colonialism
- Infrastructure, governance gaps widen inequality
- Local innovation and resistance are emerging
ISLAMABAD: In the glitzy world of artificial intelligence, the headlines often feature Silicon Valley, Shenzhen, and London. What rarely makes news is how much of the Global South is becoming the mining ground — of data, cheap labor, and untapped markets — while reaping little of the value created. The question now is: Is AI colonialism the next frontier of inequality?
Over the last decade, AI development and investment have been overwhelmingly concentrated in rich countries. The US and China lead by a large margin. But for many developing nations, the path is steep and uncertain. Less than one-third of developing countries had a national AI strategy in 2023, compared to two-thirds of developed economies. Meanwhile, the global AI market is projected to reach around US$4.8 trillion by 2033 — a prize largely controlled by a few.
Throughout the Global South, signals suggest both hope and warning. Many countries are leapfrogging adoption — using AI tools for health, agriculture, and governance — but core infrastructure, talent, governance, and fairness lag far behind. The risk: AI may magnify existing inequalities, rather than close them.
Hidden mines: data, labour, and environmental costs
One of the most insidious aspects of this new digital colonialism is the extraction of data from communities in the Global South. Tech giants often train AI models using massive datasets drawn from user behavior, language, images, voice, and more — frequently without transparency or fair compensation to those who produced the data.
Because major generative AI systems are built on data drawn heavily from Western sources, they tend to embed Western norms and biases, excluding the cultural, social, and material realities of many non-Western populations.
Next, there is the unglamorous but critical work of data labeling, moderation, annotation — the digital “underclass” of AI development. In countries like Kenya, India, and the Philippines, workers are contracted to sift through huge volumes of content, sometimes including graphic or disturbing material, often for as little as US$1.50 an hour. These laborers are essential to training AI systems, but their work is largely invisible in the narrative of innovation.
Finally, consider the environmental and infrastructure toll. Running data centers and training large models demands vast electricity and water. Some of these facilities are sited in regions with fragile energy grids or compromised ecosystems, straining local resources or exacerbating climate stress. Because of these hidden costs, the “price” of AI is often borne disproportionately by communities already vulnerable.
Lopsided playing field
The dominance of AI happens not just through resource extraction, but via how power is structured. Countries in the Global North not only develop the core models and platforms, but they also dominate standard-setting, regulation, and governance bodies.
When rules about privacy, data flows, liability, or surveillance are drawn up, voices from the Global South are often marginalized or absent.
In the investment ecosystem, AI startups in developing countries struggle to access capital. A study of middle-income nations found that domestic policies, limited venture capital networks, and skewed regulatory regimes hamper their growth.
Even when they attract foreign investment, these partnerships can reinforce dependency — local startups become peripheral nodes rather than equals.
Furthermore, governance capacity is weak in many nations. Issues like electricity, internet infrastructure, education, and institutional capacity remain major barriers. Without robust policies on algorithmic accountability, data protection, or equitable access, many countries are vulnerable to misuse, bias, or digital exploitation.
Stories of resistance
Yet the story is not all bleak. Across the Global South, communities, governments, and researchers are pushing back and forging new paths.
In Latin America, Brazil has taken a bold step: its government recently proposed a US$4 billion AI investment plan to boost homegrown innovation and reduce reliance on imports. Rather than playing catch-up, it is trying to shape its own AI trajectory.
In the field of language and local knowledge, grassroots movements in Africa, South Asia, and Latin America are building datasets, translation tools, and AI models attuned to local languages and contexts. Organisations like Masakhane (for African languages) and Ghana NLP are expanding participation in AI research from the bottom up.
Another hopeful trend is the idea of AI co-creation — systems designed with communities, not for them. Advocates argue that true inclusion means giving control over data, design, and policy to those traditionally marginalized. A growing number of researchers and activists are calling for “global co-creation,” with fairer participation in rule-making, labour standards, and resource allocation.
Passive resource zone
Some countries are also making regulatory moves. For instance, Uzbekistan has drafted a law to regulate AI, define accountability, and require labeling of AI-generated content. Such steps may help anchor local agency rather than leaving everything to global firms.
The stakes are high. AI is not just another technology; it has the potential to reshape how power, wealth, and knowledge flow across the globe. If the Global South remains a passive resource zone, old patterns of inequality may simply be redrawn in digital form.
The alternative — a diverse, inclusive AI future — is still possible. But it requires intention, activism, and structural shifts. The Global South must not just be a backdrop to the AI revolution. It must be one of its authors.
