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’s strain on power grids.
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.
The new report aims to better understand how AI energy needs are evolving, Luccioni 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.
“We should be smarter about the way that we use AI,” Luccioni said. “Choosing the right model for the right task is important.”
The results varied considerably. The researchers found one of Microsoft’s Phi 4 reasoning models used 9,462 watt hours with reasoning turned on, compared with about 18 watt hours with it off. OpenAI’s largest gpt-oss model, meanwhile, had a less stark difference. It used 8,504 watt hours with reasoning on the most computationally intensive “high” setting and 5,313 watt hours with the setting turned down to “low.”
OpenAI, Microsoft, Google and DeepSeek did not immediately respond to a request for comment.
