Behind the result of large language models like Chat GPT lies a journey with complex environmental and social impacts, from mineral extraction by children in the Democratic Republic of the Congo, to training systems that expose people to violent and degrading images in countries like Nigeria, and huge, resource-intensive data centers in regions where energy, water and access to transmission infrastructure are cheap. This means that the rise of AI has the potential to create new economies of resource production and consumption, likely in communities that are already marginalized or have been subject to previous resource booms and busts.
However, these costs are rarely recognized and raise profound questions about sustainability, not only from the point of view mineral resources point of viewbut also in the broader moral sense: do we want to build a society that benefits from the suffering of the world’s most marginalized? Will this end up fracturing societies and lead to the politics of resentment?
Akhil Bhardwaj
Akhil Bhardwaj is Associate Professor of Strategy and Organization at the University of Bath, UK. It studies extreme events, ranging from organizational disasters to radical innovations.
Grete Gansauer
Dr. Grete Gansauer is an assistant professor at the Haub School of Environment and Natural Resources at the University of Wyoming. She is an economic geographer and interdisciplinary public policy researcher focused on regional policy and the effects of sustainability transitions in rural and natural resource production contexts.
The journey that powers AI-produced text and images begins with rare earth minerals used in computer chips. Rare earth minerals are “rare” because they are found in small, isolated pockets in the Earth’s crust and are difficult to extract using physical and chemical processes.
Currently, China dominates global rare earth production in mining and processing; The United States ranks second in mining, but lacks the infrastructure to process rare earths once they are out of the ground.
Many critical minerals, such as lithium and cobalt, are also important for AI processing and storage. Unlike rare earths that are designated due to their chemical properties, the designation of critical minerals is political and is assigned to minerals of key strategic, geopolitical, or national security importance.
Many of these minerals are found in currently war-torn regions (e.g. Ukraine It has some of the largest lithium reserves in Europe and Russia is the world’s largest producer of uranium. Others, such as cobalt, are found in regions such as the Congowhere many of the mines are controlled by Chinese interests.
Geopolitical concerns aside (although they are certainly very important), concerns about labor practices also arise. Many of these mines use artisanal mining, which is often a euphemism for child labor: artisanal mining can involve children. digging for minerals with your hands. These minerals are then mixed with those extracted from industrial mining, making traceability impossible. Working conditions can be horrificwith high mortality rates, often due to exposure to air and water pollutants that cause terminal illnesses.
Consequently, the intensification of resource production driven by the demands of AI and a highly digitalized economy could produce a new “resource curse” in the peripheries of the Global North and South. The wealth produced by local labor is extracted and used to prop up some of the most lucrative digital services industries in the world. The trap, then, is that communities whose material contributions are embedded in the global AI value chain will once again be vulnerable to the same boom-and-bust dynamics that affect economies based on the production or extraction of other resources, such as oil or diamonds.
Beyond the extraction of mineral wealth, many AI models require considerable training, and humans must perform that training. LLMs are trained on a growing corpus of “labeled” data containing violent and pornographic content. Leaving aside the precariousness of commissioned work, the content itself can be very disturbing and can result in traumatize workers. Much of this work takes place in countries such as Nigeria and India, where the cost of labor is low and workers have little protection.
Once these models are trained, running them involves the use of massive data centers to cool the servers that process them. These server farms/data centers consume enormous resources, both energy and water. These centers are an emerging business frontier with important implications for land use change and resource impacts.
Private land-owning companies are rapidly seeking resource frontiers with the most affordable combination of cheap land, cheap water, cheap energy and access to transmission infrastructure, proximity to dense population centers, and cheap but skilled labor. However, such a geographical unicorn is difficult to find.
Many data centers are located or are being prospected in water-scarce regions like Nevada and Arizona, where labor and land are cheap. This trend seems It’s true globally.. In addition to cheap land, deserts have low humidity, which reduces the likelihood of metal corrosion. These centers also challenge the capacity of local power gridsand because energy is often purchased in bulk or “ahead of market,” it can increase rates for the average consumer. Researchers have estimated that using AI to write an email consumes half a liter water.
While there is a massive push to adopt the use of LLM around the world, especially with the lure of economic and labor efficiency gains and other potential benefits, including their employment for searching for information and writing as well as automate repetitive tasksWe must be fully aware of the material and social costs it imposes. Do you need ChatGPT to write that email? You In fact Do you need to generate an image of a cat riding a banana?
Regardless of how we might answer these questions, it seems we need to fundamentally reevaluate what it means to be sustainable: claiming to be sustainable while adopting and promoting LLMs is suspect, to say the least.
And do we really want the progress that LLMs can bring if they are based on the suffering of others? This is a question that we, as a society, must urgently answer.
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