This synopsis is based on the full paper available here: paper.
AI’s substantial ecological costs are eliciting increasing concern. Large language models (LLMs) in particular have become a prominent fixture in the AI landscape and across the wider economy, while neural machine translation (NMT) is also frequently upheld as a premiere example of the triumphs of AI. Amid the worsening climate crisis, language professionals and translation scholars are grappling with the environmental dimensions of the field’s frequent engagement with these technologies.
Confronting AI’s environmental harms within and beyond professional translation requires contending with the powerful structural forces that shape and escalate these technologies’ use across society. To this end, the article takes up Piñeiro’s (2022) call for “counterhegemonic translation” (la traducción contrahegemónica) in support of collective efforts to challenge powerful actors perpetuating socioenvironmental harms. This approach gives central importance to translation as a vital process through which those “on the frontline of the fight against extractivism” (en la primera línea de la lucha contra el extractivismo) are able to “access the information they need, secure key materials, and build networks of international solidarity” (acceden a la información que necesitan, se apropian de materiales claves y construyen redes de solidaridad internacional) (Piñeiro, 2022, p. 159, my translation). Adopting this ethos of resistance, this article develops a framework for eco-translation practice that confronts the extractivist forces perpetuating AI’s environmental harms both within and beyond the translation field.
While awareness of the problem of AI’s ecological costs is growing, the nature and scale of these harms are still poorly understood among translation professionals, scholars, and activists. This paper therefore outlines the extent and complex nature of AI’s environmental harms in terms of three main areas: carbon emissions, water consumption, and pollution from mining rare minerals. The complexity of these harms makes evident that significant social change is necessary to contend with AI’s ecological impacts.
For translation to effectuate positive social change, it is necessary to consider how narrative framings of societal problems give rise to assumptions about how to solve them (Baker, 2019). The dominant narrative framings regarding the problem of and proposed solutions to AI’s environmental harms have centred voluntary corporate sustainability initiatives (e.g., carbon offsetting and water positivity pledges), technological efficiency, and improved disclosure practices. As briefly discussed in this paper, all of these approaches have proven deficient, particularly as the explosion in the aggregate use of AI eclipses improvements in model efficiency (Varoquaux et al., 2024; Bhardwaj et al., 2025; Luccioni et al., 2025). A more critical framing of AI’s planetary harms draws upon the planetary boundaries framework (Rockström et al., 2009), evaluating the cumulative impacts of these technologies on Earth’s delicate systems and resource limitations (see Falk et al., 2024; Bhardwaj et al., 2025). As illustrated throughout the article, the tech industry’s concentrated economic power is a massive factor in shaping and accelerating resource consumption associated with AI (Hogan & Blue, 2024; Kwet, 2024). The concentration of power among tech giants presents an unambiguous hegemony to confront in formulating a counterhegemonic translation agenda centred on AI’s environmental costs.
Severe power imbalances frequently lead to a high degree of distancing with respect to interlinked forms of resource consumption. Distancing denotes “the increasingly isolated character of consumption choices as decision makers at individual nodes are cut off from a contextualized understanding of the ramifications of their choices, both upstream and downstream” (Princen et al., 2002, p. 16). Princen (2002, p. 129) argues that lowering distance will reduce the dispersion of accountability among production chains’ webs of decision-makers, thereby encouraging ecological feedback loops that inhibit the overconsumption of resources and prevent environmental degradation. Translation may play a key role in reducing distancing in its capacity to strengthen information access for frontline communities and international solidarities in support of resistance to extractive practices (Piñeiro, 2022).
As detailed in this article, there is a high degree of distancing at each of the nodes in AI’s global production chain, such as the construction of data centres. Confronting AI’s environmental harms thus requires reaching beyond the limited contexts in which end users (e.g., translation professionals) encounter these technologies, instead confronting the structural forces driving AI’s increasing use across society—namely, Big Tech’s unilateral control of AI’s global production chain. As such, resistance strategies may be categorized according to two levels or scales:
Vocational resistance denotes strategies that minimize the ecological impacts of AI language technologies deployed within the translation industry. This perspective emphasizes translation as a profession—one that is already closely and increasingly intertwined with AI technologies.
Structural resistance denotes strategies that directly challenge the broader structural forces behind intensifying and expanding the extractive processes associated with AI’s use across all of society. This perspective emphasizes translation as a practice—one with great potential to support vital access to information and transnational solidarities.
The article offers recommended actions for both vocational resistance and structural resistance. While the resistance strategies suggested in this article necessarily constitute a tentative and incomplete list, they may offer a starting point for translators to consider tangible actions for resisting AI’s ecological harms by decreasing distancing. These recommendations also amplify and draw inspiration from relevant efforts by existing activist groups. The distancing concept is useful in identifying and opposing the power structures underwriting the extractivism of AI’s diffuse supply chain. However, the actions proposed here also take inspiration from a rich tradition of activist translation emerging primarily from the Global South (see Gould & Tahmasebian, 2020), likewise understanding activism to involve confronting power (Bandia, 2020). These recommended actions also follow Piñeiro’s (2026) emphasis on the need to connect translation and translation research with concrete acts of resistance, joining the historical struggles against extractivism that originate in the Global South and long predate AI technologies.
A radical shift in perspective is needed in order to confront the ecological harms of AI technologies used both within and beyond the translation profession. This article asserts that Big Tech’s grow-at-all-costs mandate is the primary force behind AI’s increasing environmental harms, with power consolidation among these cloud providers contributing to severe distancing along AI’s global production chain. As argued here, effective resistance to these environmental harms involves reducing this distancing via strategic translation practices at both the vocational and structural levels.
