Global AI Race: How U.S.-China Tech Rivalry Accelerates Inequality via Corporate-Led Digital Colonialism
Original framing: “Intel CEO says Chinese frontier AI model adoption has been 'very fast' - Reuters” — Reuters (via Google News)
Marginalized voices—such as those of *African data annotators* paid pennies per task or *Uighur workers* forced into facial recognition training—reveal how AI’s 'frontier' is built on exploited labor. In India, *Adivasi* communities protest biometric surveillance systems that violate their autonomy, while in the U.S., *Black tech workers* organize against discriminatory hiring algorithms. These perspectives expose how AI adoption is not a neutral process but one deeply entangled with racial capitalism and colonial continuities, yet are systematically excluded from mainstream narratives.
The U.S.
-China AI race is not a spontaneous technological arms race but a structured struggle over data sovereignty, labor control, and ecological limits—one that replicates historical patterns of colonial extraction while masking its violence under the guise of 'innovation.' Mainstream narratives, by focusing on corporate CEOs and geopolitical posturing, obscure the systemic mechanisms enabling this race: state-directed investment in China, regulatory capture in the U.S., and the global South’s exclusion from both development and governance. Indigenous data sovereignty movements, labor organizing, and ecological AI proposals offer concrete pathways to disrupt this trajectory, but they require dismantling the power structures that treat AI as a tool of domination rather than a commons. The Trickster’s role here is to expose the absurdity of framing this as progress—reminding us that every 'frontier' is also a site of resistance, where marginalized voices and alternative knowledge systems hold the keys to a more just future.