{"id":12980,"date":"2026-03-19T12:37:00","date_gmt":"2026-03-19T12:37:00","guid":{"rendered":"https:\/\/betterbusiness.be\/?p=12980"},"modified":"2026-03-19T12:37:00","modified_gmt":"2026-03-19T12:37:00","slug":"le-cout-cache-chatgpt","status":"publish","type":"post","link":"https:\/\/betterbusiness.be\/en\/le-cout-cache-chatgpt\/","title":{"rendered":"The hidden cost of daily ChatGPT use: from our office to all of Belgium."},"content":{"rendered":"

Towards understanding the invisible impacts of AI<\/h2>\n

Have you ever wondered how much electricity and water your team consumes when using AI tools\u2014particularly large language models (LLMs) like ChatGPT\u2014on a daily basis?<\/p>\n

We have. And we also wondered what the overall consumption might look like across the Belgian business landscape if everyone used these tools the way we do. Because even small changes in how we use AI\u2014more queries, heavier files, more frequent usage\u2014can quickly scale and generate real impacts, both positive and negative, on the environment at a national level and beyond.<\/p>\n

Measuring internal usage: query volume and associated consumption<\/h3>\n

We measured the number of queries we sent to ChatGPT over the course of about one month. On average, a BetterBusiness employee makes 600 queries per month, or 30 queries per working day, for tasks such as email follow-ups, translations, regulatory summaries, and of course text generation. At our scale, this represents a consumption of 0.18 kWh per month\u00b9\u2014enough energy to power a standard 9W LED bulb for 20 hours per month, or about 1 hour per working day.<\/p>\n

We were also curious about the direct water consumption associated with these queries, which amounts to approximately 1.34 L\u00b9 per month. Translated into beers, that\u2019s enough for a typically Belgian Friday evening.<\/p>\n

Scaling up: extrapolation to Belgian companies<\/h3>\n

These figures may seem low, but we are not the only company using LLMs. Data shows that 12.7%\u00b2 of Belgian companies currently use LLMs in the workplace. We therefore wanted to estimate total electricity and water consumption if our usage of ChatGPT were replicated across these companies\u2014and the numbers quickly become much more significant. In 2025, Belgium had 1.2 million companies\u00b3, with around 4.2 million employees\u2074\u2014an average of 3.5 employees per company. Based on this, we estimate that electricity consumption related to LLMs in the business sector would reach 96.2 MWh, equivalent to powering 330 households per month (based on an average home outside Brussels)\u2075, or more than 10 million hours of lighting with a LED bulb.<\/p>\n

As for water consumption, the previous figures only account for water used directly to cool data centers. This represents approximately 718,000 liters, equivalent to the monthly consumption of 113 average Belgian households\u2076\u2077.<\/p>\n

Comparative analysis: LLM queries vs traditional web searches<\/h3>\n

What if we used a traditional search engine instead? An LLM query consumes 10 times more than a standard web search\u2078. Instead of powering 330 households per month, it would only power 33.<\/p>\n

When it comes to water, things become more complex. There is no clear public data on direct water consumption per query for a standard web search. In other words, we have not found a precise figure indicating how much water is used to cool servers for a single Google search.To ensure a fair comparison, we therefore used indirect water consumption linked to electricity production, referred to as \u201cwater in energy.\u201d This makes it possible to compare both technologies on a common basis: the amount of water associated with the electricity required to process a query.<\/p>\n

Some estimates still provide an order of magnitude. A United Nations study\u2079 estimates that a Google search consumes around 0.5 mL of \u201cwater in energy,\u201d while ChatGPT would consume around 500 mL for 5 to 50 queries.For our calculations, we use an assumption of 18 mL per LLM query. By extrapolating BetterBusiness usage to all Belgian companies, indirect water consumption could reach 5.7 million liters for LLMs, compared to around 160,000 liters for the same number of queries via a search engine.<\/p>\n

In other words, in this scenario, the indirect water footprint is approximately 36 times higher for LLM queries than for web searches. The reason is simple: an LLM query requires much more computing power. A search engine simply retrieves indexed information, while an LLM generates text through a complex neural network. More computation means more electricity\u2014and therefore more water associated with its production.<\/p>\n

Recommendations: towards more intentional and efficient use of LLMs<\/h3>\n

One approach could be to wait for LLMs to become more efficient and less resource-intensive. Another is to deliberately choose lighter, less energy-intensive models when possible. Or, in some cases, to avoid using AI altogether if that remains realistic in a productivity-driven world.At BetterBusiness, we chose not to wait. We implemented a simple internal usage framework. Employees are encouraged to use LLMs when there is a clear productivity gain or real time constraint\u2014not by default. We also created dedicated GPTs with structured system prompts to generate more relevant responses from the start. This reduces unnecessary back-and-forth and therefore the number of queries. Fewer iterations mean less computation.<\/p>\n

We even worked on writing style. Shorter, clearer prompts generate shorter and more efficient responses. Our teams were sometimes a bit too polite and verbose with AI\u2014which is charming, but leads to more text and therefore more processing. Being precise, direct, and structured is more efficient\u2014for both humans and models.We also introduced practical guidelines, particularly around \u201ceffective prompting,\u201d prioritizing clarity over excessive experimentation. Finally, we monitor usage transparently to maintain awareness within our culture\u2014not to slow down innovation, but to encourage conscious and responsible use.<\/p>\n

None of these actions completely eliminate the impact. But they help shift from automatic usage to intentional usage\u2014and at scale, that difference matters.<\/p>\n

Conclusion: balancing usage and value creation<\/h3>\n

AI adoption will only continue to grow in the coming years. This tool\u2014accessible, versatile, and time-saving\u2014is simply too useful not to be used.This article does not even account for private, educational, or many other uses of AI already embedded in our daily lives\u2014meaning these figures are still far from the full picture.<\/p>\n

If consumption continues to grow at this pace, the efforts required to offset it will not be negligible. This will have implications for environmental and social balances and could exacerbate certain inequalities. The costs and \u201cbenefits\u201d of AI will continue to rise, without everyone necessarily being compensated for this shift. Some may end up paying\u2014directly or indirectly\u2014for a technology that primarily benefits companies and organizations.<\/p>\n

Of course, one could argue that AI enables better work, faster execution, and positive outcomes. But the more accessible a tool becomes, the more it is used\u2014even when unnecessary. Overuse may ultimately cancel out the expected benefits, especially in a context where many organizations are already striving to reduce inequalities.<\/p>\n

Yes, we are more productive than ever. But is it worth it?<\/p>\n

The real question may not be \u201chow much more can we do thanks to AI,\u201d but rather \u201chow much truly useful progress do we produce per unit of AI consumed?\u201d<\/p>\n

An equation worth exploring\u2014and likely one that will become increasingly difficult to ignore.<\/p>\n

 <\/p>\n

References:<\/span>\u00a0<\/span><\/p>\n

Jegham, N.,Abdelatti, M., Koh, C. Y.,\u00a0Elmoubarki, L., & Hendawi, A. (2025). How hungry is ai? benchmarking energy, water, and carbon footprint of\u00a0llm\u00a0inference.\u202f<\/span>arXiv\u00a0preprint arXiv:2505.09598<\/span><\/i>.<\/span>\u00a0<\/span><\/p>\n

https:\/\/www.acerta.be\/en\/inspiration\/using-artificial-intelligence-in-the-workplace-belgian-companies-in-top-3-in-europe<\/span>\u00a0<\/span><\/p>\n

https:\/\/www.belganewsagency.eu\/over-113000-new-companies-established-in-belgium-last-year<\/span>\u00a0<\/span><\/p>\n

https:\/\/www.onss.be\/actualites\/2025\/09\/01\/barometre-du-travail-salarie—juin-2025<\/span>\u00a0<\/span><\/p>\n

https:\/\/www.energuide.be\/en\/questions-answers\/what-is-the-average-electricity-and-gas-consumption-in-the-brussels-region\/273<\/span><\/p>\n

https:\/\/callmepower.be\/fr\/eau\/consommation#:~:text=Quelle%20est%20la%20consommation%20moyenne%20d’eau%20en%20Belgique%20?&text=Appelez%20CallMePower%20pour%20recevoir%20des,sur%20votre%20contrat%20d’eau<\/span><\/a>.<\/span>\u00a0<\/span><\/p>\n

https:\/\/statbel.fgov.be\/en\/news\/there-were-5163139-private-households-belgium-1-january-2024<\/span><\/p>\n

De Vries, A. (2023). <\/span>The growing energy footprint of artificial intelligence.\u202f<\/span>Joule<\/span><\/i>,\u202f<\/span>7<\/span><\/i>(10), 2191-2194.<\/span>\u00a0<\/span><\/p>\n

Gupta, J., Bosch, H., & van Vliet, L. (2024). AI\u2019s excessive water consumption threatens to drown out its environmental contributions.\u202f<\/span>The Conversation<\/span><\/i>.<\/span>\u00a0<\/span><\/p>\n

\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Towards understanding the invisible impacts of AI Have you ever wondered how much electricity and water your team consumes when using AI tools\u2014particularly large language models (LLMs) like ChatGPT\u2014on a daily basis? We have. And we also wondered what the overall consumption might look like across the Belgian business landscape if everyone used these tools […]<\/p>\n","protected":false},"author":3,"featured_media":12981,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[55],"tags":[],"class_list":["post-12980","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-carbone"],"acf":[],"yoast_head":"\nThe hidden cost of daily ChatGPT use: from our office to all of Belgium. - BetterBusiness \u2014 Sustainability at the heart of your strategy<\/title>\n<meta name=\"description\" content=\"What if each AI request had an invisible cost? 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