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But many of these claims seem to have very little, if any, actual evidence behind them.
Joshi is the author of a new report, released Monday with support from several environmental organizations, that attempts to outline some of the most high-profile claims about how artificial intelligence will save the planet. the a report It looks beyond the claims of technology companies, energy associations and others about how “AI will be a net climate benefit”. Joshi’s analysis finds that only a quarter of these claims were supported 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 its impacts on the energy system, and those assertions often lack accuracy,” says John Comey, an energy and technology researcher who was not involved in the Joshi report. “It’s important not to take claims of self-interest too seriously. Some of these claims may be true, but you have to be very careful. I think there are a lot of people who make these statements without much support.”
Another important topic the report explores is what Kind AI, exactly, is what technology companies talk about when they talk about AI saving the planet. Many types of AI are less power-hungry than the consumer-focused generative models that have dominated headlines in recent years, and which require massive amounts of computing and energy to train and run. Machine learning has been a staple of many scientific disciplines for decades. But large-scale generative AI — especially tools like ChatGPT, Cloud, and Google Gemini — is the general focus of much of tech companies’ infrastructure buildouts. Joshi’s analysis found that almost all of the claims it examined merged more traditional, less power-hungry forms of AI with the consumer-focused generative AI that drives much data center construction.
David Rolnick is an assistant professor of computer science at McGill University and president of Artificial Intelligence for Climate Change, a non-profit organization that advocates for machine learning to address climate problems. He’s less concerned than Joshi about where big tech companies get their numbers on AI’s climate impact, given how difficult it is to prove impact quantitatively in this area, he says. But for Rolnick, distinguishing between the types of AI technology companies tout as essential is a key part of this conversation.
“My problem with the claims that big tech companies are making about AI and climate change is not that they are not fully quantified, but that they rely on hypothetical AI that does not exist now, in some cases,” he says. “I think the amount of speculation about what might happen in the future with generative AI is bizarre.”
Rolnick points out that from technologies to increase efficiency on the grid, to models that can help discover new species, deep learning is already in use in countless sectors around the world, helping to cut emissions and combat climate change right now. “But that’s different from ‘At some point in the future, this might be useful,’” he says. Moreover, “there’s a mismatch between the technology that big tech companies are working on and the technologies that actually enhance the benefits they claim to espouse.” Some companies may promote examples of algorithms that, for example, help better detect floods, and use them as examples of AI for Good to advertise their big language models — despite the fact that the algorithms that help predict floods are not the same type of AI that Used by consumer-facing chatbots.