{"id":3256,"date":"2025-04-20T19:05:27","date_gmt":"2025-04-20T19:05:27","guid":{"rendered":"https:\/\/www.genesisarg.com\/?p=3256"},"modified":"2025-07-31T19:11:46","modified_gmt":"2025-07-31T19:11:46","slug":"the-carbon-footprint-of-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.genesisarg.com\/en\/novedades\/the-carbon-footprint-of-artificial-intelligence\/","title":{"rendered":"The Carbon Footprint of Artificial Intelligence"},"content":{"rendered":"<p>Recent studies show that the #CarbonFootprint ? of using #ArtificialIntelligence ? varies depending on the task performed.<br \/>\n? The more accurate the model, the less energy-efficient it tends to be. \u26a1<br \/>\nWith the rapid growth in AI usage, ensuring the #Mitigation of its #EnvironmentalImpact is essential.<\/p>\n<p>A study published in Frontiers and cited by TIME (Infobae, June 23, 2025) reveals that the environmental footprint of artificial intelligence varies greatly depending on the model and the task: certain prompts can generate up to 50 times more CO\u2082 emissions than others. For instance, a single ChatGPT query consumes about ten times more electricity than a Google search. Data centers, which accounted for 4.4% of electricity consumption in the U.S. in 2023, could rise to 6.7\u201312% by 2028. The analysis examined 14 models (ranging from 7 to 72 billion parameters) and found that more accurate models (such as Cogito and DeepSeek R1) emit significantly more than simpler ones, highlighting a trade-off between precision and sustainability. The authors call for greater transparency regarding the environmental cost per interaction to encourage more responsible AI usage.<\/p>","protected":false},"excerpt":{"rendered":"<p>Recent studies show that the #CarbonFootprint ? of using #ArtificialIntelligence ? varies depending on the task performed. ? The more accurate the model, the less energy-efficient it tends to be. \u26a1 With the rapid growth in AI usage, ensuring the #Mitigation of its #EnvironmentalImpact is essential. A study published in Frontiers and cited by TIME [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":3211,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[161,43,392,12,394,393,22,73,20],"_links":{"self":[{"href":"https:\/\/www.genesisarg.com\/en\/wp-json\/wp\/v2\/posts\/3256"}],"collection":[{"href":"https:\/\/www.genesisarg.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.genesisarg.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.genesisarg.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.genesisarg.com\/en\/wp-json\/wp\/v2\/comments?post=3256"}],"version-history":[{"count":1,"href":"https:\/\/www.genesisarg.com\/en\/wp-json\/wp\/v2\/posts\/3256\/revisions"}],"predecessor-version":[{"id":3257,"href":"https:\/\/www.genesisarg.com\/en\/wp-json\/wp\/v2\/posts\/3256\/revisions\/3257"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.genesisarg.com\/en\/wp-json\/wp\/v2\/media\/3211"}],"wp:attachment":[{"href":"https:\/\/www.genesisarg.com\/en\/wp-json\/wp\/v2\/media?parent=3256"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.genesisarg.com\/en\/wp-json\/wp\/v2\/categories?post=3256"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.genesisarg.com\/en\/wp-json\/wp\/v2\/tags?post=3256"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}