{"id":718640,"date":"2026-06-06T08:39:20","date_gmt":"2026-06-06T08:39:20","guid":{"rendered":"https:\/\/www.newsbeep.com\/ca\/718640\/"},"modified":"2026-06-06T08:39:20","modified_gmt":"2026-06-06T08:39:20","slug":"ai-could-soon-use-more-water-than-humanity-drinks-un-report-warns-sciencealert","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ca\/718640\/","title":{"rendered":"AI Could Soon Use More Water Than Humanity Drinks, UN Report Warns : ScienceAlert"},"content":{"rendered":"

One argument often used to quell concerns about the rising energy and resource demand of data centers is that artificial intelligence<\/a> (AI) models will need less in the future as they improve and become more efficient.<\/p>\n

But this seemingly logical thinking is a trap, according to a new United Nations report<\/a> that quantifies the environmental costs of AI.<\/p>\n

The report estimates that by 2030, AI’s energy use could double to consume 3% of the world’s electricity, produce emissions to equal the UK and deplete more water for cooling than the annual drinking water need of the global population.<\/p>\n

It also anticipates the use of AI will follow an economic principle known as the “Jevons paradox”, which predicts that when technological improvements increase the efficiency of a resource, it leads to a rise, rather than a fall, in the total consumption of that resource.<\/p>\n

The paradox is named after economist William Stanley Jevons<\/a>, who observed this effect with the use of coal in 19th-century England. Efficiency gains did not reduce overall consumption.<\/p>\n

Instead, the lower costs resulted in expanded use and higher overall demand.<\/p>\n

\"YouTube frameborder=”0\u2033 allow=”accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share” referrerpolicy=”strict-origin-when-cross-origin” allowfullscreen><\/p>\n

As AI models become cheaper and more attractive, the report expects this to encourage new uses and higher volumes of use, eroding and possibly erasing any savings from efficiency advances.<\/p>\n

To avoid falling into this trap, it lays out a roadmap for responsible AI use based on guiding principles of transparency, efficiency by design, equity and justice, lifecycle responsibility, global cooperation and sustainable use.<\/p>\n

The scale of the problem<\/p>\n

Last year, data centers already consumed as much electricity as Saudi Arabia, which ranks as the world’s 11th largest electricity consumer<\/a>.<\/p>\n

If electricity use doubles as projected by 2030, the associated carbon footprint would require 6.7 billion trees grown over ten years to offset this demand.<\/p>\n

Data centers would also require 9.3 trillion liters of water and land nearly ten times the size of Mexico City.<\/p>\n

\"AIData centers consume not only electricity, but water to cool them down. (4X-image\/Getty Images Signature\/Canva)<\/p>\n

Beyond resource use, the report also underscores the structural inequity at the heart of the AI boom, with only 32 nations hosting AI-specific cloud infrastructure and 90% of that capacity located in the US and China.<\/p>\n

It warns of a widening digital divide between nations that build and control AI systems and those that consume them, with the latter often bearing a disproportionate environmental burden caused by mineral extraction and e-waste.<\/p>\n

Responsible AI use<\/p>\n

Two main forces shape AI’s operational footprint: how much we use it and how we use it.<\/p>\n

This involves all tasks AI models perform, from text and code generation to image and video. Each of these tasks requires different levels of computational effort.<\/p>\n

The model choice also matters, as each AI system performs these tasks with distinct energy and environmental costs.<\/p>\n

The report argues responsible AI requires full value-chain governance, from mineral sourcing to recycling and safe disposal.<\/p>\n

It calls for a twinning of capability and environmental stewardship \u2013 thinking about both what AI can do for us and the protection of the natural environment.<\/p>\n

This would mean making environmental disclosures a routine part of AI development, at both the model and task level, and incorporating projected AI demand in climate and energy planning.<\/p>\n

Responsible AI is crucial as countries are promoting and adopting AI across government and the public sector.<\/p>\n

In Aotearoa New Zealand, the government has launched a national AI strategy<\/a> and a public service AI framework<\/a>.<\/p>\n

While the framework was informed by the OECD’s values-based AI principles<\/a>, including inclusive and sustainable development, there is no requirement for environmental disclosures and no regulator compiling energy use or emissions.<\/p>\n

Likewise in Australia, improving public services is part of the national AI plan<\/a>. For example, the National Film and Sound Archive of Australia has created Bowerbird<\/a>, a machine learning-enabled mass audio and video transcription engine, to document material.<\/p>\n

\"Subscribe<\/a><\/p>\n

The Department of Veterans Affairs has developed a proof-of-concept tool<\/a> to see whether AI can help speed up the processing of claims.<\/p>\n

Both countries take a deliberate “light touch” and principles-based regulatory approach to AI. But this approach risks overlooking the growing environmental cost of AI that can’t be solved by improving it.<\/p>\n

Related: AI Is Heading For an Energy Crisis That Has Tech Giants Scrambling<\/a><\/p>\n

The natural environment is foundational to the economy, culture and wellbeing. It should be at the center of our thinking.<\/p>\n

It’s time to rethink the AI innovation playbook and shift focus toward a sustainable tech future.\"The\n<\/p>\n

Amanda Turnbull-McRae<\/a>, Senior Lecturer in Law, University of Waikato<\/a><\/p>\n

This article is republished from The Conversation<\/a> under a Creative Commons license. Read the original article<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"One argument often used to quell concerns about the rising energy and resource demand of data centers is…\n","protected":false},"author":2,"featured_media":718641,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[62,276,277,49,48,61],"class_list":["post-718640","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-artificial-intelligence","tag-artificialintelligence","tag-ca","tag-canada","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/718640","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/comments?post=718640"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/718640\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media\/718641"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media?parent=718640"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/categories?post=718640"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/tags?post=718640"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}