AI is guzzling energy. Scientists estimate North American data centers’ power requirements increased nearly 100% from 2022 to 2023, largely driven by generative AI (GenAI). By 2026, they anticipate data centers to become the fifth largest electricity consumers in the world, exceeding the usage of most countries.
However, speculation about AI’s detrimental effects on the environment might be overblown. For many corporations, particularly those that produce or sell physical goods, AI technology makes up only a small portion of their overall emissions. Oversimplifying AI as “carbon-intensive” diverts attention from its impactful sustainability opportunities. When used wisely, AI has the potential to offset its own footprint and actively contribute to a greener future.
What is AI’s emissions trajectory?
AI’s carbon output is primarily measured through data center energy consumption. These algorithms, especially GenAI, require significant computational power for training and operation. As usage grows, so does the electricity drain.
These impacts are significant. However, the belief that AI will remain an exponential data hog ignores the rapid pace of innovation in model design, hardware, deployment and the transition to renewable energy.
Today’s algorithms are likely the most inefficient they will ever be. Techniques like model distillation are becoming more prevalent, creating smaller, more energy-conscious models, and manufacturers are designing more energy-efficient AI chips.
Additionally, the energy grid is getting greener, translating to fewer emissions from data centers. Consider these factors: According to the World Resources Institute, renewables outpaced other energy generation sources, accounting for 90% of the United States’ new installed capacity in 2024.
The International Renewable Energy Agency states that more than 80% of renewable capacity additions produce cheaper electricity than fossil fuel alternatives.
BloombergNEF reported that more than 40% of the world’s electricity came from zero-carbon sources in 2023. Major companies, including Google, Microsoft and Amazon, are investing in clean energy to power their growing data centers.
Experts predict that economics alone could drive renewables to account for 50% of electricity by the end of the decade. Meaningful government policies could accelerate that transition. This momentum makes me optimistic that we can mitigate the environmental impacts of AI use.
AI emissions also attract significant attention because they are easy to track. Unlike the complex, fragmented emissions from manufacturing and global supply chains, AI’s carbon footprint stems primarily from data centers, which are fixed physical locations with measurable electricity consumption. This creates clear accountability, as we can directly attribute these emissions to specific technology providers and data center operators.
AI’s traceability can skew public and corporate attention toward it over other potentially more significant sources of emissions that are harder to quantify. For many companies, addressing only AI emissions is a drop in the bucket. To make meaningful progress on climate goals, organizations must work to reduce carbon emissions across all business operations, including their value chain.
AI as a sustainability enabler
Focusing solely on AI’s carbon footprint misses the opportunity to unlock new reduction and efficiency opportunities.
Efficiency improvements, often the first step in corporate decarbonization, can be amplified through AI. For example, predictive maintenance prevents energy-wasting malfunctions and extends equipment’s life span. Optimizing logistics and supply chains reduces transportation distances and fuel consumption. Intelligently adjusting energy consumption, distribution and storage can maximize efficiency and resource utilization — all while minimizing costs as well.
AI is also a powerful enabler for sustainability professionals. AI can support routine tasks like data collection, reporting and drafting communications so teams with limited resources can focus on impactful strategic efforts.
These benefits extend to more complex sustainability initiatives, like supply chain decarbonization. AI-powered solutions can inform business planning by aggregating and analyzing supplier data at scale. Teams can quickly detect trends, highlight emissions hotspots and track progress to prioritize action on the most urgent and impactful reduction opportunities. For example, rather than focusing on broad procurement policies, organizations can directly engage suppliers responsible for a disproportionate amount of emissions, resulting in more impactful reductions.
Predictive modeling enables companies to forecast emissions trends, identify future risks and calculate the impacts of different decarbonization strategies for proactive, long-term business planning and supply chain resilience.
As sustainability becomes more integrated across different business functions, AI will help organizations efficiently incorporate these initiatives into their everyday work.
A word of caution about AI
AI will not solve climate issues on its own; it’s a tool to amplify human efforts. Algorithms are only as good as the data they use. Emissions data — especially from value chains — can be sparse, inconsistent or incomplete. AI won’t meaningfully fill the gaps, but it will guide teams in their decarbonization strategy.
In addition, many AI models are black boxes. This lack of transparency poses a serious problem for emissions reporting, where audibility and traceability are essential. Auditors, investors and regulators need to see the underlying methodology. AI’s conclusion may be accurate, but it can’t be the foundation of reporting if companies can’t explain it.
However, we can’t let perfection be the enemy of good. If AI helps you do your job more effectively, and your job is helping decarbonize the planet, then use it.
We can’t discount AI entirely based on its carbon emissions. Every technology has tradeoffs; anyone in sustainability knows this fact all too well. Sustainability professionals should leverage AI’s decarbonization potential while understanding the adverse effects. In the broader context of climate action, AI’s energy demands are a challenge — but not the biggest one we face.
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