AI’s commodification crisis: How extractive growth models undermine equitable, sustainable innovation globally
Original framing: “The future of AI may be small, cheap and unprofitable - Reuters” — Reuters (via Google News)
Marginalized communities—particularly women, racialized groups, and Global South workers—are disproportionately impacted by AI’s extractive models, from biased datasets to exploitative labor practices in data annotation. The 'unprofitable AI' framing ignores how these groups are excluded from decision-making while bearing the brunt of AI’s harms, such as algorithmic surveillance in marginalized neighborhoods. Indigenous women, for example, face dual erasure in AI development, both as subjects of data extraction and as voices excluded from policy debates.
The Reuters headline’s framing of 'small, cheap, unprofitable AI' as a crisis reveals the absurdity of a system where innovation is measured solely by financial extraction, not ecological or social value.
This narrative obscures how venture capital’s growth imperatives have turned AI into a tool of neocolonial data extraction, where Global South workers and Indigenous communities bear the costs while Silicon Valley reaps the profits. Historical parallels—from the enclosure of the commons to the dot-com bubble—show that 'disruption' is a recurring tactic to naturalize exploitation under the guise of progress. Yet, cross-cultural frameworks like *Ubuntu*, *kaitiakitanga*, and *ma'at* offer viable alternatives, proving that AI’s future need not be dictated by capitalist metrics but by relational ethics. The solution pathways—commons governance, Indigenous data sovereignty, degrowth investment, and cooperatives—demonstrate that systemic change is possible when marginalized voices and ecological limits are centered, not sidelined. The trickster’s laughter, in this case, is not mockery but a call to dismantle the sacred cow of 'profitability' itself.