The climate case for AI has not been made

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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. 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. But few other alternative-protein companies had such a deeply integrated way of using AI in product development, the AI-assisted review suggests. Meanwhile, the sector seems to be facing more fundamental problems, including stubbornly slow mainstream consumer adoption and declining funding levels. Most notably, venture capital flowing into cultivated meat fell from a $989 million peak in 2021 to around $83 million last 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. CATL—which controls roughly 40% of the global market for electric-vehicle batteries—launched an AI system trained on more than 100,000 battery design cases and 600 terabytes of test data. It cut the time needed to screen candidate materials by two orders of magnitude, according to the company. Independent researchers also found that the ride-sharing platform Didi Chuxing was able cut per-trip emissions 16% on average by using AI-optimized routing to minimize detours. There’s another big problem in the climate case for AI, though: Oil and gas companies are also interested in these tools for many reasons, including the potential to increase production of fossil fuels. Technology firms that committed to sustainability goals in the past should answer questions about how their tools could be used to boost old, dirty energy as well as clean, renewable energy. Finally, it’s also worth noting that integrating AI is just the first step, and it remains unclear to what degree the technology can accelerate the sorts of improvements and innovations envisioned. AI also does little to shorten the standard paths to certifying, permitting, building, packaging, marketing, and selling any resulting products, facilities, or services. The findings of the analysis, carried out by Anthropic’s Claude Code, were reviewed against public documents and media accounts to ensure their accuracy. It is possible that some companies and utilities in these industries are pursuing AI-assisted applications that would fit into the initial study’s scenarios but haven’t yet been disclosed publicly. Greater and better-targeted AI integration and efficiency gains in these three sectors may well materialize, bringing the 2035 projections closer to reality. But it won’t happen without deliberate and sustained efforts to ensure that AI delivers more environmental benefits than harms. Notably, so far the rapid adoption of AI models and buildout of data centers has driven a huge increase in energy consumption and emissions, and this has led major tech companies like Microsoft, Google, and others to fall well off track in their corporate climate goals. At this point it would be surprising if the sector even manages to zero out its climate pollution: Emissions associated with global data centers (which may handle AI as well as non-AI computing tasks) could exceed 300 million tons annually in 2035, according to the IEA’s base forecast. Industry leaders must implement the technology in the ways that promise to do the most to drive down emissions. Corporate customers should select models with the lowest environmental impacts and opt to work with the AI developers doing the most to reduce their climate footprint. And states and regions need to do more to incentivize or require all these companies to operate in more responsible ways. The need has never been more apparent. Mary Nichols is distinguished counsel at the UCLA School of Law and the former chair of the California Air Resources Board. Jeremy Tamanini is the founder of Dual Citizen, a sustainability research and advisory firm.

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.

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.

But few other alternative-protein companies had such a deeply integrated way of using AI in product development, the AI-assisted review suggests. Meanwhile, the sector seems to be facing more fundamental problems, including stubbornly slow mainstream consumer adoption and declining funding levels. Most notably, venture capital flowing into cultivated meat fell from a $989 million peak in 2021 to around $83 million last 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. CATL—which controls roughly 40% of the global market for electric-vehicle batteries—launched an AI system trained on more than 100,000 battery design cases and 600 terabytes of test data. It cut the time needed to screen candidate materials by two orders of magnitude, according to the company. Independent researchers also found that the ride-sharing platform Didi Chuxing was able cut per-trip emissions 16% on average by using AI-optimized routing to minimize detours.

There’s another big problem in the climate case for AI, though: Oil and gas companies are also interested in these tools for many reasons, including the potential to increase production of fossil fuels. Technology firms that committed to sustainability goals in the past should answer questions about how their tools could be used to boost old, dirty energy as well as clean, renewable energy.

Finally, it’s also worth noting that integrating AI is just the first step, and it remains unclear to what degree the technology can accelerate the sorts of improvements and innovations envisioned. AI also does little to shorten the standard paths to certifying, permitting, building, packaging, marketing, and selling any resulting products, facilities, or services.

The findings of the analysis, carried out by Anthropic’s Claude Code, were reviewed against public documents and media accounts to ensure their accuracy. It is possible that some companies and utilities in these industries are pursuing AI-assisted applications that would fit into the initial study’s scenarios but haven’t yet been disclosed publicly.

Greater and better-targeted AI integration and efficiency gains in these three sectors may well materialize, bringing the 2035 projections closer to reality. But it won’t happen without deliberate and sustained efforts to ensure that AI delivers more environmental benefits than harms.

Notably, so far the rapid adoption of AI models and buildout of data centers has driven a huge increase in energy consumption and emissions, and this has led major tech companies like Microsoft, Google, and others to fall well off track in their corporate climate goals. At this point it would be surprising if the sector even manages to zero out its climate pollution: Emissions associated with global data centers (which may handle AI as well as non-AI computing tasks) could exceed 300 million tons annually in 2035, according to the IEA’s base forecast.

Industry leaders must implement the technology in the ways that promise to do the most to drive down emissions. Corporate customers should select models with the lowest environmental impacts and opt to work with the AI developers doing the most to reduce their climate footprint. And states and regions need to do more to incentivize or require all these companies to operate in more responsible ways.

The need has never been more apparent.

Mary Nichols is distinguished counsel at the UCLA School of Law and the former chair of the California Air Resources Board.

Jeremy Tamanini is the founder of Dual Citizen, a sustainability research and advisory firm.

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Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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