Artificial intelligence can do some pretty amazing things, including for the climate. But, as with most technology, there are significant trade offs. The energy used by AI is massive and growing. 

Tech giants like Microsoft, Google and Amazon are building enormous data centers to make AI possible. Karen Hao, a contributing writer for the Atlantic who also has an engineering degree from MIT, visited one of these data centers in Arizona. It was a 97 degree day, and the data center stretched on into the desert. Hao decided she would walk around it to get a visceral idea of how big it is. She says, “Within two legs of the rectangle. I just started feeling very, very heat exhausted and I'd run out of water. It had already taken me around 20, 25 minutes and I was like, I gotta get out of here.” Companies are making huge investments in giant data centers. Hao says Microsoft alone is spending around 10 billion a quarter now on data centers. 

Most of the hype right now is around generative AI. Think: ChatGPT. As a matter of fact, the G in ChatGPT stands for generative. The basic idea is that AI is being fed our data to train models that generate more data like that. Karen Hao says, “It's taking our writing to generate more writing. It's taking our images to generate more images.” 

But not all flavors of AI use the same amount of energy. Much of theAI that might benefit the  reasons is referred to as predictiveAI. Predictive AI tends to use existing data to help it make predictions, rather than generating new sentences or images the way generative AI does. For example: it might use our images to make a prediction about what's in another image. Hao says, “Like cancer detection systems or facial recognition systems.” And predictive AI uses far less energy. This is because predictive AI is trained on a specific task, and once it achieves the desired accuracy, its energy use falls dramatically. 

Predictive AI is also being used to track emissions. Climate TRACE, an independent greenhouse gas emissions tracker backed by former Vice President Al Gore, is one such organization. Gavin McCormick, Co-Founder of Climate TRACE, says, “we can see that some steel facilities pollute about 10 times more emissions than others to produce the same product.” That data helped companies like GM and Tesla switch to steel factories that produced less emissions. McCormick says, “Our hope is that this is a way that data can make it kind of painless to reduce emissions.” 

“AI is being used in all sorts of ways to facilitate climate action from things like helping us better forecast solar power on electric power grids in order to help us balance grids with large amounts of renewables,” says Priya Donti, Assistant Professor at MIT and Co-founder and Chair of Climate Change AI. Efficiency is one of the best ways to reduce carbon pollution. If we didn't need so much power, we wouldn't need to burn so much fossil fuel. With more efficiency we could switch to renewable energy more quickly. AI can help do that, even with simple tasks like optimizing heating and cooling systems in homes and buildings to save energy.  

Nowcasting is a weather forecasting model that combines a description of the current state of the atmosphere and a short-term forecast. Amy McGovern, Professor of Computer Science at the University of Oklahoma, says, “our current average [nowcasting] warning is about 15 minutes. Can you imagine if you could bring that up to 30 minutes or 60 minutes?”  McGovern also says, “As our climate is changing, a lot of these extreme weather events are changing. And I think AI can be used to help us improve our prediction and understanding of these events and, and be able to weather them better.”