CFP AAG 2026: Political-Industrial Ecologies of AI
jeb525
Organizers: Jenn Baka (Pennsylvania State University), Josh Cousins (SUNY ESF), John Kendall (Pennsylvania State University), and Mark Ortiz (Pennsylvania State University)
For better or for worse, the AI boom is dramatically reshaping energy and resource geographies (Cellard, Parker, and Haines, 2025). Power-hungry data centers are resurrecting old energy infrastructures, such as decommissioned nuclear facilities, and delaying the phaseout of coal-fired power plants. Immense amounts of water needed for evaporating cooling are forcing new conflicts over water access as AI infrastructure spreads across arid regions. More broadly, emerging industrial partnerships between AI chip manufacturers and AI software developers have been dramatically rearranging global tech trade networks. And finally, on the application side, AI promises to help the planet solve the climate crisis while also threatening to replace millions of jobs. Either way, its ever-expanding use will undoubtedly impact how and where energy and materials are extracted, processed, transported, and consumed.
In this session, we seek to explore how political-industrial ecology (PIE) (Newell and Cousins, 2015, Baka, 2025) might advance geographers' understandings of the ongoing social, material, and ecological transformations catalyzed by the AI boom. In brief, we aim to stimulate conversation around the multiple, interlinked sites that constitute AI infrastructure — from mineral extraction at mines to data centers and beyond — emphasizing their roles as differentiated nodes within planetary-scale flows of materials and energy, rather than as homogeneous or standalone entities. Moreover, we want to understand better how these flows are managed, navigated, and contested by thick, multiscalar networks of a wide variety of actors, from tech giants, tech workers, and extractive/energy sector developers to public officials, environmentalists, and impacted local communities. All of these actors are differently situated with respect to the benefits and harms of AI development and thus they tend to mobilize conflicting interests in and ideologies for the future of AI.
What, in brief, are the PIEs of AI? How is the blistering rollout of AI infrastructure variably shifting flows of materials, energy, and labor, and what kinds of conflicts have become engendered in such shifts (Edwards, Cooper, and Hogan, 2025)? And, conversely, how is the application of AI itself reorganizing material and energy flows across industrial and urban systems (Cugurullo et al., 2025), and how are these changing political-industrial ecologies impacting the ‘future of work’ (Benanav, 2020; Smith, 2020)?
We invite paper contributions from a wide range of topics related to the PIEs of AI, including but not limited to:
-Local resource competition in AI data center development
-The impact of the AI boom on global trade networks and international industrial ecologies
-Enrollment of new or otherwise shifted extraction zones into AI infrastructure development
-AI infrastructure’s role in reshaping rural space and rural-urban interconnections
-AI-related promises for and challenges to energy transitions/“Earth for AI” (Nost and Colven, 2022)
-AI applications and their political-ecological consequences for industrial and urban systems
-Indigenous, environmentalist, and other grassroots movements mobilized by AI data center development
-PIE-informed, non-capitalist or otherwise alternative visions for the future of AI
Sponsorship:
Cultural and Political Ecology Specialty Group
Energy and Environment Specialty Group
Please email your abstract (250 words) to Jenn Baka (jeb...@psu.edu) by October 25. We will notify you of an acceptance decision by October 30.
References
Baka, J. (2025) Cracking Appalachia: A political-industrial ecology perspective. Annals of the American Association of Geographers, 115(4): 743-763.
Benanav, A. (2020) Automation and the Future of Work. Verso.
Cellard, L., Parker, P., and Haines, F. (2025) Beyond AI as an environmental pharmakon: Principles for reopening the problem-space of machine learning’s carbon footprint. Environment and Planning E: Nature and Space, 8(3): 1020-1045.
Cousins, J.P., and Newell, J.J. (2015) The boundaries of urban metabolism: Towards a political–industrial ecology. Progress in Human Geography, 39(6): 702-728.
Cugurullo, F. et al. (2025) The rise of AI urbanism in post-smart cities: A critical commentary on urban artificial intelligence. Urban Studies, 61(6): 1168-1182.
Edwards, D., Cooper, Z.G.T., and Hogan, M. (2025) The making of critical data center studies. Convergence, 31(2): 429-446.
Nost, E. and Colven, E. (2020) Earth for AI: A political ecology of data-driven climate initiatives. Geoforum, 130: 23-34.
Smith, J. (2020) Smart Machines and Service Work: Automation in an Age of Stagnation. U Chicago Press.