Global IT sector grapples with AI’s extractive labor and energy demands, revealing systemic dependency on unsustainable growth models

Indigenous knowledge systems warn against the extractive logic of AI, drawing parallels to colonial resource exploitation (e.g., rubber plantations, oil extraction) that disrupted ecosystems and displaced communities. Traditional ecological knowledge emphasizes the finite nature of energy and material resources, challenging the tech industry’s assumption of infinite growth. However, indigenous perspectives are systematically excluded from AI governance, with corporate 'consultations' often reduced to performative gestures rather than meaningful co-design.

The IT sector’s AI dilemma mirrors 19th-century industrial capitalism’s energy crises, where steam engines and later electricity enabled unprecedented growth but at the cost of labor exploitation and environmental degradation. Historical precedents like the Luddite rebellions and the 1970s oil shocks reveal how technological transitions often exacerbate inequality unless countered by strong labor and environmental policies. The current AI boom repeats these patterns, with data centers consuming more energy than some countries, yet lacking the regulatory frameworks that eventually curbed industrial excesses.

In Africa, AI’s energy demands are framed as a 'new resource curse,' where foreign tech firms exploit cheap labor and unreliable energy grids to power their operations, exacerbating local energy poverty. South Asian perspectives highlight the paradox of AI 'democratization' in regions where basic digital infrastructure is still lacking, revealing a digital divide that mirrors colonial-era resource extraction. Meanwhile, Indigenous Australian and Māori communities have successfully blocked or renegotiated data center projects by invoking cultural heritage protections and sovereignty over traditional lands.

Scientific consensus confirms that AI’s energy demands are unsustainable under current trajectories, with training a single large language model emitting as much CO2 as five cars in their lifetimes, and inference costs rising exponentially. Research also shows that the IT sector’s reliance on fossil-fueled grids (e.g., 70% of U.S. data centers run on coal or gas) undermines net-zero pledges, yet these findings are sidelined in corporate sustainability reports. Methodological gaps persist in quantifying AI’s full lifecycle impacts, particularly in e-waste and water consumption, due to industry-controlled data.

Artists and spiritual traditions critique AI as a manifestation of 'technological hubris,' where human creativity is reduced to algorithmic optimization and spiritual well-being is commodified as 'wellness tech.' Indigenous and Buddhist frameworks emphasize the interconnectedness of energy, labor, and consciousness, framing AI’s extractive model as a violation of cosmic balance. Creative resistance, from speculative fiction to protest art, exposes the dehumanizing logic of AI-driven efficiency, while Indigenous futurism offers alternative visions of technology rooted in reciprocity.

Future scenarios suggest that without radical intervention, AI’s energy and labor demands will trigger cascading crises: grid collapses in energy-poor regions, mass displacement from data center expansions, and algorithmic governance that deepens inequality. Modeling by the IEA and academic institutions indicates that AI could consume 10% of global electricity by 2026, yet most projections assume continued fossil fuel dependence. Alternative models, such as decentralized renewable-powered AI or 'slow tech' approaches, remain under-explored due to corporate and state incentives favoring centralized control.

Marginalized communities—particularly Black, Indigenous, and Global South workers—bear the brunt of AI’s labor and environmental harms, from e-waste dumping in Ghana to content moderation trauma in India. Their expertise in surviving extractive systems is systematically excluded from AI policy debates, which are dominated by elite technocrats and corporate lobbyists. Grassroots movements, such as the 'Tech Workers Coalition' and Indigenous data sovereignty advocates, are challenging this exclusion by demanding democratic control over AI development and deployment.