In this reflection from the Data for Public Goals event at Politecnico di Milano, we call for a radical reframing of how we understand and govern data for AI. Moving beyond technical metrics and ethical tweaks, the piece introduces a planetary justice lens that traces AI's impacts across its full supply chain–from resource extraction to model deployment. Highlighting the ecological, labor, and epistemic costs embedded in AI systems, the post challenges the narrative of data neutrality and questions the assumption that automation is inherently beneficial. What if, instead of optimizing AI, we asked whether it is needed at all–and who gets to decide?

AI is often seen as the answer to our planetary crises. But what if it’s also part of the problem? In this reflection from the ISEE/Degrowth 2025 Conference, Sara Marcucci argues that AI is not just a neutral tool—it’s a product of growthism and dualism, built on extractive infrastructures and driven by an ideology of more. Drawing on real-world supply chain impacts and grounded community practices, the piece offers a framework for reimagining AI through a degrowth lens: starting with collective purpose, guided by relational values, and enacted through democratic, low-impact practices.

Tracking and analyzing real-world impacts of the AI supply chain is a key part of our work, and the Observatory of Planetary Justice Impacts of AI is the tool we are using to collect and analyse such examples.

It aims to catalog the different stages of the AI supply chain and the impact that each stage has on various communities from a planetary justice perspective.

It is a dynamic and collaborative collection tool, where categories will evolve to attempt to map out the data as it grows. We aim for the Observatory to be a collaborative and bottom-up effort, therefore we have opened this module for anyone to be able to submit their case-studies for planetary justice impacts of AI.

The AI + Planetary Justice Alliance joined researchers, organizers, and practitioners in Bengaluru for the Mapping Data Work workshop — a gathering that foregrounded the invisible labor behind AI systems. In our blog, we report key takeaways, reflect on what it means to build “ethical datasets,” question the assumptions behind datafication, and share insights from our own research on epistemic justice in agricultural AI in India. What if the push to represent is just another form of surveillance? And why are we building AI in the first place? Read the full piece to explore the planetary justice implications of some of AI’s quietest supply chains: data work.

AI is not inevitable, nor is it a given. As Kate Crawford reveals in Atlas of AI, behind the sleek interface of artificial intelligence lies a vast network of extraction—of minerals, of labor, of data, of meaning. From lithium mines in Nevada to e-waste dumps in the Global South, AI systems are materially grounded in deeply unequal infrastructures. They are not just powered by code, but by centuries-old ideologies of control, colonialism, and capitalism. This review explores the human and ecological toll of AI’s supply chain and urges a shift from the language of ethics to a critical interrogation of AI’s power.