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<\/div>\n AI is everywhere. Writing job applications, diagnosing illnesses, helping marketers write blog posts just like this one (but not this one). However, it comes with a dark cloud overhead: a growing concern about its environmental impact.<\/p>\n
From water-hungry data centres to carbon-intensive GPUs, AI has been branded as one of the digital world\u2019s dirtiest secrets. But is that fair? Or is it just another round of tech panic?<\/p>\n
Let\u2019s break it down; comparing AI to the wider internet, unpacking the biggest environmental burdens, exploring how politeness affects emissions, and identifying practical ways to reduce AI\u2019s footprint through smarter prompt design.<\/p>\n
There\u2019s no question: training large AI models uses a staggering amount of energy. GPT-3 consumed around 1,287 MWh<\/a>, which is roughly the same as 120 UK homes use in a year. It also emitted an estimated 500 metric tonnes of CO\u2082, and that\u2019s before a single user typed anything into a chat box.<\/p>\n But the bigger, long-term impact doesn\u2019t come from training. It comes from inference – the act of using the model over and over, millions of times a day. A single ChatGPT prompt<\/a> is estimated to consume 10 to 100 times the energy of a standard Google search. At over 2.5 billion prompts daily, that adds up quickly.<\/p>\n To understand how AI\u2019s energy use compares to the broader internet, it helps to look at query volumes. Google currently processes around 14 billion searches per day<\/a>. That\u2019s over 5 trillion searches per year. ChatGPT, by comparison, handles 2.5 billion prompts daily, more than double<\/a> what it was handling just eight months ago.<\/p>\n So yes, Google search still dwarfs AI usage by a factor of roughly six to one, but AI is growing at breakneck speed. That growth, combined with its significantly higher per-query energy cost, explains why it is getting so much attention around its environmental impact.<\/p>\n AI\u2019s energy needs are just one side of the story. The water required to cool the data centers powering large models like GPT-4 is another. These facilities rely heavily on water-based cooling systems.<\/p>\n Training GPT-3 alone is estimated to have used over 700,000 litres of fresh water<\/a>. At current growth rates, AI-related data centres could withdraw 6 billion cubic metres of water annually by 2027<\/a> \u2013 more than the total annual water consumption of the UK.<\/p>\n Given that access to water is fairly important to the survival of the species, we should probably start to worry when a digital system starts to compare to whole countries in terms of usage.<\/p>\n Here\u2019s a curveball you probably weren\u2019t expecting.<\/p>\n Sam Altman, CEO of OpenAI, recently revealed<\/a> that including polite phrases like \u201cplease\u201d and \u201cthank you\u201d in ChatGPT prompts contributes to tens of millions of dollars a year in added energy costs. Why? Because every word you type becomes a token that the AI model must interpret, analyze, and respond to.<\/p>\n Politeness might seem small in isolation, but when multiplied across billions of daily queries, the impact adds up. Some estimates suggest<\/a> that these extra tokens result in thousands of tonnes of CO\u2082 emissions annually.<\/p>\n Of course, there are good reasons to be polite. Many users find they get better responses with more courteous phrasing. But if you\u2019re looking to trim your digital carbon footprint, trimming your prompts might be a good place to start.<\/p>\n The concept of \u201ctokens\u201d is central to how AI models work. A token is typically around four characters, and every prompt and response is made up of them. More tokens mean more processing, which means more energy.<\/p>\n With some large models handling hundreds of trillions of tokens per month<\/a>, cutting token use at scale could significantly reduce energy consumption. One proposed strategy could lower usage by 60%, potentially saving millions in infrastructure and electricity costs.<\/p>\n Here are a few ways to cut token use without compromising output quality:<\/p>\nAI vs. Google: Scale matters<\/h2>\n
The hidden water cost<\/h2>\n
And then there\u2019s this: Politeness costs power<\/h2>\n
Token efficiency: How to write smarter prompts<\/h2>\n
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What about the internet more broadly?<\/h2>\n