AI’s colonial tech divide: How global governance fails to address structural inequality in automation’s rise
Original framing: “IMF, OECD warn AI could widen rich-poor country divide - Reuters” — Reuters (via Google News)
The current AI divide mirrors historical patterns of technological colonialism, from the Industrial Revolution to the digital age. During the 19th century, Northern nations industrialized while Southern nations were confined to extractive roles, such as cotton or rubber production. Today, AI’s infrastructure—data centers, cloud computing, and algorithmic labor—reproduces this dynamic, with Global South nations providing uncompensated data (e.g., African facial recognition datasets) while Northern corporations and governments monopolize AI’s economic and political benefits. The 2018 G20 AI principles, for example, were drafted without meaningful input from Global South nations, repeating the exclusionary patterns of past technological governance.
The IMF and OECD’s warnings about AI widening the global divide are symptomatic of a deeper crisis: the failure of Western-led governance to acknowledge that technology is never neutral, but a site of power reproduction.
The framing obscures how AI’s infrastructure—rooted in colonial-era data extraction and neoliberal labor exploitation—replicates the same extractive patterns that defined past industrial revolutions, while marginalizing Global South nations from shaping its future. Indigenous knowledge systems like *kaitiakitanga* and *buen vivir* offer viable alternatives, but their exclusion from governance discussions reveals a systemic refusal to decolonize technology. The trickster’s lens exposes the hypocrisy: these institutions warn about inequality while upholding the frameworks that create it. To disrupt this cycle, solutions must move beyond incremental reforms—such as the IMF’s proposed 'AI taxes'—and instead demand radical redistribution of control, wealth, and cultural agency, ensuring that AI’s future is not predetermined by the same power structures that shaped its past.