With this research diary, Esha introduces a new series to AIPJ, “The AI Wave”, which explores planetary justice implications of AI across the supply chain through one lens: water.

In its first part, the series focuses on documenting the extractive nature of mining and materials manufacturing to understand how much water is consumed, how it is governed, and who it affects, while also highlighting gaps in data availability and consistency across minerals and locations. 

In this research diary, Bella introduces the new series “Mineral Extraction for AI“, which explores the extractive foundations of AI by examining how the mining of critical minerals underpins AI hardware and infrastructure. Through a set of case studies grounded in a planetary justice framework, the series investigates the environmental, social, and political impacts of mineral extraction, while also highlighting gaps in data, accountability, and supply chain transparency.

This research diary traces the ongoing development of the Talavera de la Reina case study, reflecting on both early findings and the challenges of investigating a data centre that has yet to be built. In the diary, Nicolás highlights how information is fragmented across technical documents, political narratives, and media coverage–making it difficult to piece together a clear picture of impacts on land, water, and energy.

Behind every battery, every circuit, and every device are minerals like lithium, cobalt, nickel, and rare earth elements. This is the first Research Diary for our newly launched project, Below the Algorithm. From the AIPJ Team, Ambika and Beatrice explore the importance of minerals for AI and what we’re doing to make this dependence more visible and actionable.

The first Research Diary for our Rooted Clouds project, a mapping project that traces where data centers are located and what their presence means for local ecosystems and communities. From the AIPJ Team, Nicolás and Lakshmee explore why we need to root the digital cloud in its material realities and what we plan to do about it.

Behind every AI system lies a complex chain–from mining and manufacturing to model training, deployment, and disposal–that affects our environment and communities. This framework is our effort to map out those hidden impacts and ask the right questions about what we know—and what we still don’t know.

Our approach is aspirational. It outlines our ideal vision of understanding an AI system’s true costs by starting with clear, local impacts–such as the effects of mining or assembly on nearby communities. Although tracing every detail in a multi-tiered supply chain is challenging (even giants like Intel have struggled), we believe that beginning with local effects will gradually build a more complete picture.