Artificial intelligence (AI) is becoming part of more products, services and everyday tasks, making its environmental impact an increasingly important consideration. Green AI focuses on making AI systems more efficient and reducing the resources required to develop and operate them.
The good news is that you do not have to choose between advanced AI and sustainability. Researchers and engineers are developing improvements across software, hardware, data centers and energy systems that can help AI deliver useful computing with fewer resources.
Green AI is an approach to developing and using AI that places greater emphasis on energy efficiency and environmental impact. That can mean designing models that require less computation, using more efficient processors, improving data center operations or running workloads when cleaner electricity is available.
The need for these improvements is becoming more apparent as AI adoption grows. The International Energy Agency (IEA) reported that electricity consumption from AI-focused data centers rose 50% in 2025, compared with a 17% increase across data centers overall. At the same time, the IEA found that energy use per AI task has been falling rapidly because of improvements in hardware and software.
This creates an important opportunity for green AI, enabling greater AI capabilities through more efficient use of computing resources.
One of the most direct paths toward green AI is making models more efficient.
Larger models can require substantial computing resources during both training and deployment. Stanford’s 2025 AI Index reported that the power required to train frontier AI models has been increasing rapidly, even as AI hardware becomes more energy-efficient. Model size, data center efficiency and the carbon intensity of electricity all influence AI’s environmental footprint.
Researchers are exploring techniques that allow AI systems to accomplish useful tasks with less computation. Smaller models, model compression, improved algorithms and more efficient inference can all contribute to this goal.
This matters particularly after deployment. A model may be trained once, but it can serve millions or billions of requests over its lifetime. Improving the efficiency of each request can therefore produce meaningful cumulative savings.
The IEA found that energy use per AI task has declined by at least an order of magnitude annually in recent years. However, newer applications, including video generation, reasoning and AI agents, can require substantially more energy than simple text generation.
Green AI also depends on what happens underneath the software. Specialized processors can perform AI workloads more efficiently by being designed around the calculations that machine-learning systems need. Research into new computing architectures is also opening possibilities for improving performance while reducing energy consumption.
The National Institute of Standards and Technology’s (NIST) 2025 Energy Efficiency Scaling roadmap outlines a goal of achieving a 1,000-fold improvement in computing energy efficiency over two decades or less through continued research across semiconductors and microelectronics.
NIST is also studying brain-inspired circuits and architectures designed to achieve computing speeds and energy efficiencies beyond conventional approaches. These developments could make future AI systems more capable without requiring energy increases to keep pace with performance improvements.
Computing is only part of the environmental equation. AI hardware generates heat, so data centers need cooling systems to keep equipment operating safely. Cooling can also represent a significant share of a data center’s energy demand, making thermal management an important part of improving overall computing efficiency.
This has made cooling another important area for green AI innovation. The U.S. Department of Energy’s (DOE) COOLERCHIPS program supports technologies designed to make data center cooling more efficient. Its 2025 annual review said the program targets cooling energy expenditures below 5% of a typical data center’s IT load for high-density computing systems.
The DOE also recommends improving IT, electrical and cooling efficiency, reusing waste heat where possible and increasing the use of renewable energy in data center design. For green AI, this highlights the importance of improving both the algorithms and the physical infrastructure that supports them.
Another promising development is the ability to decide when and where AI workloads run. Electricity does not have the same carbon intensity at every moment. If an AI workload does not need to run immediately, operators can shift it to a time when cleaner electricity is available.
MIT researchers have investigated this approach, using real-time carbon data to switch between AI models based on grid conditions. In testing, their system reduced carbon emissions by nearly 80% over a one- to two-day period.
As data centers gain more flexibility in scheduling workloads, operators can consider the environmental impact of electricity alongside computing performance and cost.
Green AI also extends to applications that improve energy-consuming systems and accelerate the transition to cleaner energy.
Researchers are applying AI to renewable power forecasting, grid operation, energy storage and power-system control. A 2024 review in Nature Reviews Electrical Engineering found that deep learning, machine learning and reinforcement learning can support renewable-energy forecasting, power dispatch, grid control and electricity-market decisions.
AI can also improve energy efficiency in buildings. A study modeled AI adoption in U.S. medium-sized office buildings and found that combining AI with energy-efficiency policies and low-carbon power generation could produce substantial reductions in modeled energy use and carbon emissions by 2050.
These applications show why the sustainability conversation around AI needs to extend beyond data centers.
Measuring AI’s environmental impact helps developers evaluate efficiency alongside accuracy, speed and cost. Factors such as hardware, model architecture, workload, data center efficiency, location and electricity source all influence that impact. Stanford’s 2025 AI Index noted that many AI developers do not disclose complete carbon information for their models, making comparisons more difficult.
Stanford researchers have proposed evaluating intelligence per watt, which measures useful AI performance in relation to the energy consumed. This approach gives developers another way to assess efficiency as AI systems continue to evolve.
You can expect the future of green AI to bring together advances in efficient models, specialized processors, cooling systems, data center operations and clean-energy infrastructure. You can also see AI playing a growing role in improving power grids, renewable energy, buildings and industrial systems.
Together, these advances give you new ways to approach AI efficiency across its life cycle, from development and deployment to infrastructure and energy use. As green AI technologies mature, you can embrace increasingly capable systems that support innovation while making thoughtful use of energy and computing resources.
