On the (im)possibility of sustainable artificial intelligence. Why it does not make sense to move faster when heading the wrong way

Abstract: AI is currently considered a sustainability "game-changer" within and outside of academia. In order to discuss sustainable AI this article draws from insights by critical data and algorithm studies, STS, transformative sustainability science, critical computer science, and public interest theory. I argue that while there are indeed many sustainability-related use cases for AI, they are likely to have more overall drawbacks than benefits. To substantiate this claim, I differentiate three 'AI materialities' of the AI supply chain: first the literal materiality (e.g. water, cobalt, lithium, energy consumption etc.), second, the informational materiality (e.g. lots of data and centralised control necessary), and third, the social materiality (e.g. exploitative data work, communities harm by waste and pollution). In all materialities, effects are especially devastating for the global south while benefiting the global north. A second strong claim regarding sustainable AI circles around so called apolitical optimisation (e.g. regarding city traffic), however the optimisation criteria (e.g. cars, bikes, emissions, commute time, health) are purely political and have to be collectively negotiated before applying AI optimisation. Hence, sustainable AI, in principle, cannot break the glass ceiling of transformation and might even distract from necessary societal change. To address that I propose to stop 'unformation gathering' and to apply the 'small is beautiful' principle. This aims to contribute to an informed academic and collective negotiation on how to (not) integrate AI into the sustainability project while avoiding to reproduce the status quo by serving hegemonic interests between useful AI use cases, techno-utopian salvation narratives, technology-centred efficiency paradigms, the exploitative and extractivist character of AI and concepts of digital degrowth.