The nature of AI: Metabolism, energy, water, labour and justice in the urban political ecology of artificial intelligence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The integration of vast volumes of Artificial Intelligence (AI) technology into the built environment is changing the metabolism of urban spaces. Due to the presence of various AIs in urban systems, there are now more agentic forces influencing the trajectory of urban development and entangling with pre-existing biological intelligences. Because of AI's substantial environmental costs, more resources are now needed to satisfy cities' technological appetite. Urban futures are also becoming more uncertain as private AI companies gain considerable power in urban governance through oligarchic schemes that leave citizens with no voice. In this paper, we bridge Urban Political Ecology (UPE) and urban AI literature, in order to critically examine the nature of AI as it intertwines with urban living and urban infrastructure. More specifically, we offer a threefold contribution to knowledge. First, we examine how the advent of urban AI is altering urban metabolism, zooming in on specific socio-environmental issues pertaining to energy, water and labour. Second, we discuss how the urban metabolisms altered by AI are reproducing uneven dynamics of development that are ultimately leading to different forms of injustice. Third and finally, we propose a potential course of action to politicize urban AI and intervene on its evolution.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it