​​A’s growing energy demand, particularly through data centers, has significant environmental consequences. These data centers require substantial energy to store and process the vast amounts of data AI systems generate. 

 

To meet the demand and become pioneers in the industry, organizations and governments worldwide are investing in massive digital infrastructures to sustain AI growth. This project challenges the short-sightedness of such decisions, offering a cautionary example of the need to design for alternative models of AI distribution. The aim is to foster long-term thinking and reflection on how today’s choices will affect future generations.

 

Data centers are often built discreetly, rendering them invisible to the public eye. This invisibility contributes to a lack of awareness and accountability regarding their environmental impact. While e-waste is often discussed in terms of physical degradation, the hidden costs of AI—such as energy consumption, water usage, and labor exploitation—remain largely unexamined in mainstream discourse. Research on digital waste, AI energy consumption, and e-waste provides valuable context for understanding AI’s environmental footprint.

 

The data centers that house these systems are energy-intensive, requiring constant cooling and maintenance, leading to high carbon emissions and resource depletion. Yet, these consequences remain invisible to users, creating a “shadow” of hidden waste that is externalized, ignored, or forgotten. 

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This project questions whether our reliance on massive AI infrastructure is inevitable, or if alternative futures remain possible. The project builds on research into AI’s physical dependencies, including energy grids, water systems, and rare earth minerals; case studies of data center failures, such as facilities shutting down due to extreme heat waves; and speculative design projects that examine the material consequences of digital technology.