What Powers Your AI Query? More Than You Think

by YOUxTalks | Jul 3, 2025 | Technology & AI

As generative AI tools like chatbots become part of everyday life, questions about their environmental impact are more important than ever. Every prompt you type into an AI model uses energy — and over time, those small interactions can add up to a significant carbon footprint.

The Energy Behind Your Prompt

Recent estimates suggest that a single AI query might consume about as much energy as an oven running for just over one second. While this comparison offers a rough benchmark, it’s important to understand that energy use varies widely depending on the type of query, the model used, and where and how the servers are powered.

Currently, major AI companies do not publicly share detailed energy usage data, which makes it difficult to pin down exact numbers. Instead, researchers rely on measurements from open-source models and simulations to get a clearer picture.

Why Large Language Models Use So Much Energy

AI models are often described by their number of “parameters” — internal settings fine-tuned during training. The larger the model, the more computing power it needs. Today’s most advanced models contain hundreds of billions to over a trillion parameters, requiring large-scale data centers filled with high-performance GPUs to operate efficiently.

These GPUs consume significant energy not only to run the models but also to keep data centers cool. In the U.S. alone, data centers currently account for around 4.4% of all energy use, and that number is expected to reach as high as 12% by 2028.

The Hidden Carbon Costs of AI

Before a model can be used, it must be trained — a process that takes weeks and consumes massive amounts of energy. Unfortunately, most companies do not disclose details such as how long training takes or what types of energy sources are used, making it difficult to assess the full environmental cost.

After training, models enter the inference stage, where they generate answers for users. Over time, inference may become the largest contributor to a model’s total emissions because it happens billions of times across the globe.

The environmental impact of each prompt can differ based on factors like the data center’s location, the energy grid it draws from, and even the time of day.

Estimating AI’s Energy Use

Though training emissions remain largely opaque, researchers can make reasonable estimates for inference by running open-source models locally and tracking GPU usage.

One study tested 14 such models and found major differences in energy use. Reasoning models, which explain their thinking step-by-step, used far more energy than standard models because they processed more text (or “tokens”). On average, a reasoning model used over 500 tokens per question — compared to fewer than 40 for standard ones.

For example, running 600,000 prompts on a large reasoning model could emit as much CO₂ as a round-trip flight from London to New York.

To reflect total energy use more accurately, researchers also factor in energy needed for cooling and supporting infrastructure — roughly doubling the GPU energy estimates. AI systems get hot while working, so extra energy is needed to run cooling fans and air conditioners.

What’s still not accounted for are emissions from manufacturing the hardware and building the data centers, also known as embodied carbon.

How to Use AI More Sustainably

There are several ways individuals and developers can reduce the environmental impact of AI:

Use smaller models for simple tasks. Not every query requires the most powerful model available.
Avoid unnecessary computation. For instance, minimizing polite phrases like “please” and “thank you” can reduce processing time.
Schedule prompts smartly. Using AI outside peak energy times, such as at night or during cooler months, reduces stress on power grids.
Choose efficient models. Tools like the AI Energy Score and ML.Energy rank models by energy use and can help users make better choices.

Ultimately, however, real change may require regulation. An energy rating system for AI models — similar to the labels on household appliances — could help keep growing demand in check and ensure future AI systems are built with sustainability in mind.

As AI continues to expand into every part of life, its environmental footprint can no longer be ignored. Transparent reporting and conscious usage choices will be critical to ensuring a greener future for AI.