"I'm afraid to drink the water."
Tainted Water
A retiree in rural Georgia has accused Meta's new AI data center, which is situated around 1,200 feet from her home, of polluting her water.
As the BBC reports, the resident, Beverly Morris, believes the construction of the tech giant's data center disrupted her private water well, causing a buildup of sediment.
"I'm afraid to drink the water, but I still cook with it, and brush my teeth with it," Morris told the broadcaster. "Am I worried about it? Yes."
Meta has since denied these allegations, telling the BBC in a statement that "being a good neighbour is a priority." The company commissioned a groundwater study, finding that its data center did "not adversely affect groundwater conditions in the area."
Regardless how it ultimately shakes out, the incident highlights how a massive push to build out infrastructure to support incredibly power-hungry AI models is disrupting lives across the country. We're only beginning to understand the enormous environmental toll of AI tech, from staggering water usage to an enormous carbon footprint due to soaring emissions.
Down the Drain
And it's only getting worse as companies like OpenAI, Google, and Meta, continue to pour tens of billions of dollars into building out thousands of data centers across the world. In November, researchers estimated that global AI demand could account for up to 1.7 trillion gallons annually of water by 2027, more than four times the total water withdrawal of the country of Denmark.
Estimates vary wildly, adding to the uncertainty, ranging from a ChatGPT query using up as much water as a 16-ounce bottle or as little as "roughly one-fifteenth of a teaspoon," per OpenAI CEO Sam Altman's recent estimates.
Activists have since pointed to potentially dangerous sediment runoff from construction that could land in water systems, as may be happening with Morris' well.
It remains to be seen how committed the AI industry will be to sustainability. After making a big deal about their efforts to lower emissions at the beginning of the decade, the surge in interest in AI has majorly shifted the conversation.
And as AI models become more sophisticated, researchers have found that they are using exponentially more energy — so what we're hearing now may turn out to be merely prelude.
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