![](\"https:\/\/fortune.com\/img-assets\/wp-content\/uploads\/2025\/12\/GettyImages-2245485836-e1764971682378.jpg?w=2048\")
<\/p>\n<\/p>\n
Nearly all leading artificial intelligence developers are focused on building AI models that mimic the way humans reason, but new research shows these cutting-edge systems can be far more energy intensive, adding to concerns about AI\u2019s strain on power grids.<\/p>\n
AI reasoning models used 30 times more power on average to respond to 1,000 written prompts than alternatives without this reasoning capability or which had it disabled, according to a study released Thursday.\u00a0The work was carried out by the AI Energy Score project, led by Hugging Face research scientist Sasha Luccioni and Salesforce Inc. head of AI sustainability Boris Gamazaychikov.<\/p>\n
The researchers evaluated 40\u00a0open, freely available AI models, including software from OpenAI, Alphabet Inc.\u2019s Google and\u00a0Microsoft Corp. Some models were found to have a much wider disparity in energy consumption, including one from Chinese upstart DeepSeek. A slimmed-down version of DeepSeek\u2019s R1 model used just 50 watt\u00a0hours to respond to the prompts when reasoning was turned off, or about as much power as is needed to run a 50 watt lightbulb for an hour. With the reasoning feature enabled, the same model required 7,626\u00a0watt hours to complete the tasks.<\/p>\n
The soaring energy needs of AI have increasingly come under scrutiny. As tech companies race to build more and bigger data centers to support AI, industry watchers have raised concerns about\u00a0straining power grids\u00a0and raising energy costs for consumers. A Bloomberg\u00a0investigation\u00a0in September found that wholesale electricity prices rose as much as 267% over the past five years in areas near data centers. There are also environmental drawbacks, as\u00a0Microsoft, Google and\u00a0Amazon.com\u00a0Inc. have previously acknowledged the data center buildout could\u00a0complicate their long-term climate objectives.\u00a0<\/p>\n
More than a year ago, OpenAI released its\u00a0first reasoning model, called o1. Where its prior software replied almost instantly to queries, o1 spent more time computing an answer before responding. Many other AI companies have since released similar systems, with the goal of solving more complex multistep problems for fields like science, math and coding.<\/p>\n
Though reasoning systems have quickly become the industry norm for carrying out more complicated tasks, there has been little research into their energy demands. Much of the increase in power consumption is due to reasoning models generating much more text when responding, the researchers said.\u00a0<\/p>\n
The new report aims to better understand how AI energy needs are evolving,\u00a0Luccioni said. She also hopes it helps people better understand that there are different types of AI models suited to different actions. Not every query requires tapping the most computationally intensive AI reasoning systems.<\/p>\n
\u201cWe should be smarter about the way that we use AI,\u201d Luccioni said. \u201cChoosing the right model for the right task is important.\u201d<\/p>\n
To test the difference in power use, the researchers ran all the models on the same computer hardware. They used the same prompts for each, ranging from simple questions \u2014 such as asking\u00a0which team won the Super Bowl in a particular year \u2014\u00a0to\u00a0more complex math problems. They also used a software tool called\u00a0CodeCarbon\u00a0to track how much energy was being consumed in real time.<\/p>\n
The results varied considerably. The researchers found one of Microsoft\u2019s Phi 4 reasoning models used 9,462 watt hours with reasoning turned on, compared with about 18 watt hours with it off. OpenAI\u2019s largest gpt-oss model, meanwhile, had a less stark difference. It used 8,504 watt hours with reasoning on the most computationally intensive \u201chigh\u201d setting and 5,313 watt hours with the setting\u00a0turned down to \u201clow.\u201d\u00a0<\/p>\n
OpenAI, Microsoft, Google and DeepSeek did not immediately respond to a request for comment.<\/p>\n
Google\u00a0released internal research\u00a0in August that estimated the median text prompt for its Gemini AI service used 0.24 watt-hours of energy, roughly equal to watching TV for less than nine seconds. Google said that figure was \u201csubstantially lower than many public estimates.\u201d\u00a0<\/p>\n
Much of the discussion about AI power consumption has focused on large-scale facilities set up to train artificial intelligence systems. Increasingly, however, tech firms are\u00a0shifting more resources to inference, or the process of running AI systems after they\u2019ve been trained. The push toward reasoning models is a big piece of that as these systems are more reliant on inference.<\/p>\n
Recently, some tech leaders have acknowledged that AI\u2019s power draw needs to be reckoned with.\u00a0Microsoft CEO Satya Nadella said the industry must earn the \u201csocial permission to consume energy\u201d for AI data centers in a November\u00a0interview. To do that, he argued tech must use AI to do good and foster broad economic growth.<\/p>\n<\/div>\n
#rise #ofAI #reasoning #models #big #energy #tradeoff<\/p>\n","protected":false},"excerpt":{"rendered":"
Nearly all leading artificial … <\/p>\n","protected":false},"author":1,"featured_media":974,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[2],"tags":[237,811,815,814,812,813,70,816],"_links":{"self":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts\/973"}],"collection":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=973"}],"version-history":[{"count":0,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts\/973\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/media\/974"}],"wp:attachment":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=973"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=973"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=973"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}