post-colonial inequality
Cheap clothing and trendy footwear often hide a harsh truth: many items are produced by workers in low-wage regions such as Bangladesh, Myanmar, Indonesia, and parts of China. The demand for inexpensive fashion can fuel labor practices that fall short of fair treatment. Technology mirrors this pattern, showing up in the supply chains behind the devices people use every day.
Though unseen by most, an invisible thread links these products to a broader global network. The smartphones people carry, the apps they rely on, and the algorithms that power social networks all depend on a complex South-North supply chain. It stretches beyond raw materials like lithium, coltan, or cobalt to the people who assemble, test, and maintain the systems that run digital life.
ChatGPT, a program capable of simulating human conversation and delivering information in real time, exemplifies this shift. It can summarize lengthy texts, assist with homework, compose music, or imitate literary styles. The impressive performances come from learning on vast data drawn from the internet, a system used by hundreds of millions each month. The technology relies on continual data intake and refinement to improve its responses.
post-colonial inequality
In January, a TIME investigation shed light on how AI models are tested and maintained. It reported that workers in Kenya were employed to verify the system’s operations, flagging incorrect answers and helping reduce bias. The compensation for these tasks, while stable for some, raised concerns about fair wages in a global context where salaries can differ dramatically from one country to another, even when the work is similar in nature.
The choice to assign such tasks to Kenyan workers underscored ongoing post-colonial tensions in the tech industry. Wages in Kenya can be higher than the national average but still far below what is typical in high-income economies for the same tasks. In contrast, executives at the same company often earn six-figure annual salaries. After significant investment, the value of the AI companies has soared, illustrating a stark disparity between earnings for frontline labor and senior leadership.
“More workers are competing for lower wages,” a technopolitical researcher noted, referencing a system that spans multiple continents and economic contexts. The model, with reach across the world, continues to evolve and expand its footprint.
North-South asymmetry
This pattern is not isolated. OpenAI contracts parts of its moderation and testing work through Sama, a California-based firm that operates centers in Africa. Giants like Google, Walmart, Microsoft, and others have similar arrangements. When users scroll through Facebook or Instagram, they may see a curated stream that avoids explicit violence or graphic content, a result of moderation work that some workers have performed. The psychological impact of this invisible labor is real.
Sama presents itself as an ethical intermediary, aiming to create digital economy opportunities for vulnerable people. Yet legal actions have accused it of facilitating forced labor, human trafficking, and unions suppression. One notable case involved a complaint from a worker connected to a major platform, highlighting how precarious labor rights can be in global tech ecosystems. International pressure occasionally leads to shifts in practice, including pauses in specific moderation services.
economic benefit
Across industries like textiles and agriculture, northern technology firms have benefited from a supply chain anchored in the global south. Reducing labor costs and tapping subsidies in some regions can boost profits but also deepens cycles of unequal power. Research indicates that multinational leaders have leveraged loopholes in privacy laws and tax regimes to maximize returns, a dynamic that has drawn scrutiny from nonprofit watchdogs and advocacy groups.
Major producers concentrate much of their manufacturing in places such as China, where production costs have historically been lower. Geopolitical tensions can spur shifts in strategy, with some companies moving portions of production to other countries to diversify risk and reduce dependency. Nonetheless, wage differences remain a central driver of where and how work gets done, reinforcing a global pattern of cost-driven labor allocation.
The result is a loop: lower development levels in some regions enable higher corporate profitability while challenging the workers who sustain those operations. Analysts caution that this model relies on globally uneven protections for workers and opportunities for profit, making the system vulnerable to shifts in policy and market conditions.
This dynamic highlights how the global economy can reward efficiency at the expense of broad-based fair treatment. The conversation about who profits, who bears risk, and how rights are protected continues to shape debates about corporate responsibility, governance, and the real cost of the digital age.