How AI-driven sovereign wealth funds replicate historical colonial extraction patterns through algorithmic governance and global data dependency
Original framing: “An AI sovereign wealth fund isn’t progressive — it’s techno-imperialism” — Financial Times
The modern sovereign wealth fund mirrors the historical pattern of colonial extraction, where wealth was generated abroad to fund domestic development while maintaining control over resource flows. This model was first institutionalized in the 17th century with the Dutch East India Company and later formalized through British colonial governance, where extracted wealth (e.g., spices, gold, indigo) funded metropolitan economies. Today, AI-driven sovereign wealth funds replicate this dynamic by outsourcing governance to algorithms that optimize extraction from global data pools, labor markets, and resource dependencies. Scholars like Immanuel Wallerstein have shown how this 'core-periphery' model persists in global capitalism, and the Financial Times headline fails to connect the dots between historical debt structures (e.g., IMF austerity) and the algorithmic continuation of these patterns. The precedent of Norway’s sovereign wealth fund, which operates under strict ethical guidelines to avoid such conflicts, further underscores how alternative models are possible.
The Financial Times’ critique of AI-driven sovereign wealth funds as 'techno-imperialist' touches on a critical truth: these funds are not progressive innovations but the latest iteration of historical extraction, where wealth is generated abroad to fund domestic power while maintaining control over global resource flows.
What the narrative omits is the *systemic* continuity between colonial land grabs and algorithmic data extraction, where the same power structures—financial institutions, Western governments, and tech giants—design and benefit from both. Indigenous knowledge systems, like *kaitiakitanga*, offer a direct counterpoint to this logic, framing wealth as a communal trust rather than a commodity to be optimized. The trickster perspective exposes the hypocrisy of a critique that still operates within the same extractive framework, while future modelling reveals how these funds could either deepen inequality or catalyze a post-extractive economy, depending on governance choices. The solution pathways must therefore combine decentralized governance, algorithmic transparency, relational economic models, and historical reparations—each addressing a different layer of the problem. Without this systemic approach, the critique risks being just another layer of the same old game, where the tools of extraction are repackaged as progress.