But a lot of these claims, it turns out, have very little—if any—actual proof behind them.
Joshi is the author of a new report, released Monday with support from several environmental organizations, that attempts to quantify some of the most high-profile claims made about how AI will save the planet. The report looks at more than 150 claims made by tech companies, energy associations, and others about how “AI will serve as a net climate benefit.” Joshi’s analysis finds that just a quarter of those claims were backed up by academic research, while more than a third did not publicly cite any evidence at all.
“People make assertions about the kind of societal impacts of AI and the effects on the energy system—those assertions often lack rigor,” says Jon Koomey, an energy and technology researcher who was not involved in Joshi’s report. “It’s important not to take self-interested claims at face value. Some of those claims may be true, but you have to be very careful. I think there’s a lot of people who make these statements without much support.”
Another important topic the report explores is what kind of AI, exactly, tech companies are talking about when they talk about AI saving the planet. Many types of AI are less energy-intensive than the generative, consumer-focused models that have dominated headlines in recent years, which require massive amounts of compute—and power—to train and operate. Machine learning has been a staple of many scientific disciplines for decades. But it’s large-scale generative AI—especially tools like ChatGPT, Claude, and Google Gemini—that are the public focus of much of tech companies’ infrastructure build-out. Joshi’s analysis found that nearly all of the claims he examined conflated more traditional, less energy-intensive forms of AI with the consumer-focused generative AI that is driving much of the buildout of data centers.
David Rolnick is an assistant professor of computer science at McGill University and the chair of Climate Change AI, a nonprofit that advocates for machine learning to tackle climate problems. He’s less concerned than Joshi with the provenance of where Big Tech companies get their numbers on AI’s impact on the climate, given how difficult, he says, it is to quantitatively prove impact in this field. But for Rolnick, the distinction between what types of AI tech companies are touting as essential is a key part of this conversation.
“My problem with claims being made by big tech companies around AI and climate change is not that they’re not fully quantified, but that they’re relying on hypothetical AI that does not exist now, in some cases,” he says. “I think the amount of speculation on what might happen in the future with generative AI is grotesque.”
Rolnick points out that from techniques to increase efficiency on the grid, to models that can help discover new species, deep learning is already in use in a myriad of sectors around the world, helping to cut emissions and fight climate change right now. “That’s different, however, from ‘At some point in the future, this might be useful,” he says. What’s more, “there is a mismatch between the technology that is being worked on by big tech companies and the technologies that are actually powering the benefits that they claim to espouse.” Some companies may tout examples of algorithms that, for instance, help better detect floods, using them as examples of AI for good to advertise for their large language models—despite the fact that the algorithms helping with flood prediction are not the same type of AI as a consumer-facing chatbot.