Billions Of AI Prompts, Billions In Energy Costs: The Planet Pays As Data Centres Heat Up

 

 

Billions Of AI Prompts, Billions In Energy Costs: The Planet Pays As Data Centres Heat Up The title for the world's most powerful artificial intelligence model keeps changing hands as tech giants continue to roll out new and advanced models capable of understanding user needs better and performing tasks more efficiently—such is the nature of the AI race. The benefits of the technology can't be overlooked, as it has impacted almost every imaginable sector from healthcare to agriculture, education, governance, finance, and more, helping not only with increased productivity across industries but also playing an irreplaceable role in advancing scientific research. This has also led to a better-than-ever generative AI, allowing users to produce high-quality images and realistic videos with just a text prompt. While debates around the ethical use and implementation of AI continue, an equally urgent concern is its environmental impact—particularly the rising electricity consumption required to power these systems and the high amount of water to keep them from overheating. Organisations like the United Nations and the International Energy Agency have documented the growing carbon footprint of tech giants, largely driven by the energy demands of data centres supporting AI development. However, pinpointing the environmental cost of consumer-level generative AI applications remained difficult, leading to only speculations—until recently. We finally have an official estimate of energy and water consumption for an average AI query, thanks to numbers thrown by OpenAI and a detailed account shared by Google.

 

Environmental cost of ChatGPT and Gemini user queries

 

Back in June 2025, OpenAI CEO Sam Altman penned a blog revealing the energy consumption of a single ChatGPT query: 0.34 watt-hours of power (about what an oven would use in a little over one second, or a high-efficiency lightbulb would use in a couple of minutes) and 0.32176 millilitres of water (roughly one-fifteenth of a teaspoon). He did not specify which model the figures referred to, nor did he provide documentation explaining how the estimates were calculated. Google, however, recently published a paper detailing how it measures the carbon emissions, energy usage, and water consumption of its AI models. It says that the per-prompt energy impact of Gemini is equivalent to watching TV for less than nine seconds. According to the methodology, the median Gemini Apps text prompt uses 0.24 watt-hours (Wh) of energy, emits 0.03 grams of carbon dioxide equivalent (gCO2e), and consumes 0.26 millilitres (or about five drops) of water. Google calls this a comprehensive estimate that accounts for all critical elements of serving AI globally, and is considerably higher than simplified estimates that measure just 0.10 Wh of energy, 0.02 gCO2e, and 0.12 mL of water. It says that most current estimates only account for active machine usage, leading to overly optimistic figures, but its methodology reveals that the true energy, carbon, and water footprint of AI is significantly higher than simplified models suggest. Google’s methodology considers not just active computation but the full system dynamics across hardware, data centres, and model operations, which include:

 

Full system power usage, including actual chip utilisation

Idle machine energy, required for reliability and scalability

CPU and RAM consumption, beyond just TPUs and GPUs

 Data centre overhead, like cooling and power distribution (measured via PUE)

Water usage, especially for cooling systems

 

While the simplified estimates for the median Gemini App text prompt are 0.10 Wh, 0.02 gCO2e, and 0.12 mL water, Google’s comprehensive estimates put them at 0.24 Wh, 0.03 gCO2e, and 0.26 mL water, accounting for all critical elements of serving AI globally. In addition to providing Gemini’s energy usage, Google advocates for industry-wide consistency in measuring AI’s resource consumption to better reflect real-world operational efficiency. “By sharing our findings and methodology, we aim to drive industry-wide progress toward more efficient AI. This is essential for responsible AI development,” Google said.

 

The cause for concern remains

 

While industry experts and stakeholders praise Google’s move, calling it a step towards transparency, they also highlight that AI demands more resources than traditional computing, and the load caused by billions of user queries would be worlds apart when compared to a single prompt. “While today’s usage might seem under control, the real challenge is what happens when billions of such queries are made daily. That’s when the impact becomes huge,” Manoj Dhanda, Founder and CEO, Utho Cloud, tells ETV Bharat. Notably, the data obtained by Axios suggests that OpenAI’s ChatGPT alone sees more than 2.5 billion prompts every day, which is over 912.5 billion requests every year. If we bring in tools like Gemini, Grok, Copilot, Character AI, and others into the mix, the number of daily AI prompts worldwide will climb even higher. The environmental cost of training these models is another story altogether.OpenAI in 2018 revealed that the amount of “compute” required to train its largest AI models was doubling every 3-4 months. Researchers at the time estimated that training GPT-3 (with 175 billion parameters) consumed 1,287 megawatt hours (MWh) of electricity and generated 552 tons of carbon dioxide, equivalent to the annual emissions of 123 gasoline-powered passenger vehicles.

 

The emission is believed to be escalating since then, according to an Epoch report, as the training requirements for the more recent frontier AI models have increased by 4-5 times annually during the past four years. According to the National Institute of Communication Finance (NICF), data centres are on track to account for 14 per cent of all global emissions by 2040. While Google showcased a lower emission level for a single text prompt, its overall carbon footprint has increased exponentially, primarily due to its AI efforts. Driven by the energy demands of power-intensive data centres, indirect carbon emissions from the operations of four leading AI-focused tech companies—Google, Microsoft, Meta, and Amazon—have increased by an average of 150 per cent between 2020 and 2023, according to Greening Digital Companies 2025 report, published by the International Telecommunication Union (the UN agency for digital technologies) and the World Benchmark Alliance.

 

Environmental impact of AI data centres Data centres are used to train and run the large language models, multimodal models, and deep learning models that power scientific research, enterprise needs, and consumer-centric generative AI tools, allowing users to generate text, images, and videos. While they have been around for years to support the internet, cloud computing services, and other obligations, the AI race has prompted a sudden rise in their construction and operation. For instance, Meta plans to spend hundreds of billions of dollars to build several massive AI data centres for superintelligence, with its first multi-gigawatt data centre, dubbed Prometheus, expected to come online in 2026. Zigment AI Founder and CEO Dikshan Dave says, “Artificial intelligence has the power to transform industries, but its environmental footprint is also significant and should not be ignored.

 

Training and running AI models do consume significant electricity and water, since data centres need power to run servers and keep systems cool.” According to IEA, while traditional data centres use between 10 and 25 megawatts (MW) of power, demand by hyperscale AI centres can exceed 100 MW–equivalent to the annual electricity consumption of 100,000 households. The largest data centre announced is set to consume as much electricity as 5 million households.