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For this reason, Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences, or say you don’t need an explanation at all.

Most important, Dauner’s study highlights that not all AI models are created equally, said Sasha Luccioni, the climate lead at AI company Hugging Face, in an email. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task.

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Even within the same AI company, different model offerings can vary in their reasoning power, so research what capabilities best suit your needs, Dauner said.

When possible, Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks.

Why it’s hard to measure AI’s environmental impact
Putting a number on the environmental impact of AI has proved challenging.

The study noted that energy consumption can vary based on the user’s proximity to local energy grids and the hardware used to run AI models.
That’s partly why the researchers chose to represent carbon emissions within a range, Dauner said.

Furthermore, many AI companies don’t share information about their energy consumption — or details like server size or optimization techniques that could help researchers estimate energy consumption, said Shaolei Ren, an associate professor of electrical and computer engineering at the University of California, Riverside who studies AI’s water consumption.

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Additionally, as more companies push to add generative AI tools to their systems, people may not have much choice how or when they use the technology, Luccioni said.

“We don’t need generative AI in web search. Nobody asked for AI chatbots in (messaging apps) or on social media,� Luccioni said. “This race to stuff them into every single existing technology is truly infuriating, since it comes with real consequences to our planet.�
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With less available information about AI’s resource usage, consumers have less choice, Ren said, adding that regulatory pressures for more transparency are unlikely to the United States anytime soon. Instead, the best hope for more energy-efficient AI may lie in the cost efficacy of using less energy.

“Overall, I’m still positive about (the future). There are many software engineers working hard to improve resource efficiency,� Ren said. “Other industries consume a lot of energy too, but it’s not a reason to suggest AI’s environmental impact is not a problem. We should definitely pay attention.�

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