AI agents: How corporate orchestration of autonomous systems deepens inequality and accelerates extractive innovation

Indigenous epistemologies view AI agents as a continuation of colonial extraction, where knowledge is commodified and alienated from its cultural context. Traditional systems of innovation, such as those in Māori or Quechua communities, emphasize reciprocity and collective benefit, starkly contrasting with the profit-driven, individualistic ethos of Silicon Valley. The unchecked deployment of AI agents risks erasing these alternative models of progress, replacing them with a singular, extractive vision of innovation.

The rise of AI agents mirrors past waves of automation, from the Luddites' resistance to mechanized looms to the offshoring of manufacturing jobs in the late 20th century. Each wave has been accompanied by promises of liberation and efficiency, yet has ultimately deepened inequality and concentrated power in the hands of capital. The current moment echoes the enclosure movements of the 18th century, where common lands were privatized for industrial use—a parallel to how AI agents privatize and monetize collective knowledge.

In East Asia, AI agents are often framed as tools for social harmony and collective welfare, contrasting with Western individualism. Japanese and Korean approaches to AI emphasize human-machine collaboration in service of societal goals, such as aging populations or disaster response. However, even these models risk replicating extractive logics when deployed under corporate or state control, as seen in China's social credit systems.

AI agents are not neutral tools but are shaped by the data they are trained on, which often reflects historical biases and power imbalances. Studies show that autonomous systems can exacerbate disparities in drug development, where treatments are prioritized for profitable markets over global health needs. The scientific literature also highlights the environmental costs of training large models, which are rarely accounted for in mainstream narratives about efficiency.

Artists and spiritual traditions have long warned about the dehumanizing effects of mechanization and automation. The Dadaists and Surrealists critiqued the reduction of life to logic, while Indigenous cosmologies often frame technology as a tool for relational balance rather than domination. The unchecked rise of AI agents risks severing the creative and spiritual dimensions of innovation, reducing it to a transactional, profit-driven endeavor.

If current trends continue, AI agents will likely deepen the concentration of power in a handful of corporations, accelerating the precarization of labor and the commodification of knowledge. Scenario modeling suggests that without robust regulation, these agents could exacerbate global inequalities, as wealthier nations and corporations capture the benefits while offloading costs to marginalized communities. Alternative futures, such as decentralized AI cooperatives, remain under-explored in mainstream discourse.

Marginalized communities, including Black and Indigenous workers, are disproportionately affected by automation yet are rarely consulted in the design of AI agents. The voices of gig workers, who are already being replaced by AI-driven platforms, are systematically excluded from narratives about AI's benefits. Feminist economists highlight how AI agents often replicate gendered divisions of labor, further marginalizing care work and reproductive labor.