The climate case for AI has not been made

This week, as countries meet in Antalya, Turkey for the United Nations climate conference, one topic likely to dominate the conversation is artificial intelligence. The host has already called on countries to commit to ensuring that AI is designed, powered, and deployed in ways that support climate action. But so far, industries around the world aren’t doing nearly enough to harness the technology to actually drive down the greenhouse-gas emissions heating up the planet, despite some very optimistic claims from AI companies. In 2024, OpenAI CEO Sam Altman declared that AI would unleash “astounding triumphs,” including “fixing the climate.” The year before, in a 2023 op-ed , the head of sustainability at Google and her coauthor wrote that AI could reduce global emissions 5% to 10% by 2030. Some academic researchers have examined these AI-related climate claims. A peer-reviewed study published last year, led by researchers at the London School of Economics’ Grantham Research Institute and the firm Systemiq, estimated that AI could help cut global emissions by the equivalent of 3. 2 to 5. 4 billion tons of carbon dioxide annually by 2035—roughly on par with the US’s annual energy-related emissions on the high end, even after subtracting AI’s own energy footprint. Those potential gains are mostly concentrated in three sectors: power, meat and dairy, and vehicles. But projections like these are only as good as our willingness to test them against present-day reality. So is there evidence that the largest companies in these three sectors are beginning to integrate AI in line with the study’s projections, and if so, do the results match expectations? Or is the 2035 number still mostly aspirational? Companies across these sectors have announced plenty of initial efforts to incorporate AI into various business practices. But an AI-assisted review of public information from significant market actors in each sector, conducted and reviewed by the second author of this piece, found that few have announced they’re pursuing the specific emissions-reducing routes that informed the projections in the study—and fewer still claim they’ve used them to achieve meaningful climate progress. The power sector is where the earlier study’s underlying math is most concrete: AI-driven forecasting of supply and demand could increase the amount of renewable electricity that reaches the grid from already-built solar and wind facilities by up to 20%, which could prevent an estimated 1. 8 billion tons of emissions a year. Yet of the grid operators reviewed in China, the United States, and Europe, only one deployed a known meaningful, working AI program of this sort, according to the analysis. A partnership between the UK’s National Grid ESO and Open Climate Fix halved the grid operator’s solar-forecast error (the difference between the amount of incoming sunlight predicted and the amount that actually reaches solar panels), avoiding hundreds of thousands of tons of carbon dioxide a year. Beyond this example in the UK, though, many other grid operators in the EU, the US, and China had little to show in the public record in terms of the AI integration envisioned by the study. The meat and dairy sector offers the starkest contrast between technology readiness and market reality. To achieve the 2035 emission reductions anticipated in the earlier study, AI would need to close the taste and cost gap between traditional proteins and alternative ones, such as lab-grown or plant-based burgers and milk substitutes…