The climate case for AI has not been made
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.
Key points
- The host has already called on countries to commit to ensuring that AI is designed, powered, and deployed in ways that support climate action.
- Some academic researchers have examined these AI-related climate claims.
- 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.
Additional details
- 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.
- In one of the clearest efforts to achieve this, the AI and food tech company NotCo recently formed a joint venture with Kraft Heinz using its platform Giuseppe, which analyzes plant molecular data to pinpoint combinations replicating the taste, texture, and smell of animal products.
- 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.
- The transportation sector offers some hopeful examples of the type of AI integration necessary to achieve the study’s projections, particularly among battery manufacturers and ride-sharing platforms.
- 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.
How this summary was made: extractive summary — built from the sources' own reporting. It is drawn only from the reporting of the sources listed below — read the originals for full detail. How we summarize →
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