At this point in time, most of us would agree that artificial intelligence feels almost weightless. The way we understand it is very similar to that of the internet...You basically open your laptop, type a question and within seconds, without noise or any visible effort, you get a detailed answer. It feels almost like if information simply appears out of nowhere.
But behind that simplicity is something very physical, very real and increasingly impossible to ignore, and this "something" is the fact that AI runs on infrastructure. An actually impressive kind of infrastructure that is massive in size, power hungry and incredibly heat generating.
And the point that most people actually miss is the fact that this infrastructure is quietly reshaping the way we consume energy and water across the world.
The crazy rise of AI, specially over the last 2 or 3 years, has triggered one of the fastest increases in computing demand in modern history. Companies like Google, Microsoft or Amazon, to name just three, are building enormous data centers filled with specialized hardware designed to train and run AI models, and these machines don’t just process information but they generate heat at a scale that requires constant cooling, and there is where the environmental story begins.
Cooling, understood from the AI perspective, is not a minor detail. It is actually one of the biggest operational challenges of modern data centers, because in order to keep servers from overheating, facilities rely on sophisticated systems that often use incredibly large amounts of water. In most cases, that water evaporates during the cooling process, meaning it cannot always be fully reused and actually a very small portion of it can get back again into the flow.
Some estimates suggest that a single large data center can consume millions of liters of water per day, depending on its size and cooling method. We are talking about quantities of water that would solve drinking issues in entire population groups out there, and actually in regions already facing water stress, this is becoming more than just a technical issue but a mainly societal one.
But water is not the only problem, it's actually only half of the story, because the energy required to power AI systems and "move" this water is also staggering. To understand that, let's say that just training a large language model can consume as much electricity as thousands of households use in a year, and even after training, running these models at scale requires a continuous and huge computational power. Every query, every response, every interaction adds to the total demand.
To put this into perspective and in any of our daily use cases, a single AI powered query can consume several times more energy than a traditional web search, and even though that difference may seem small at the individual level, when multiplied by millions or even billions of daily interactions, the impact grows to previously unthinkable levels.
If we consider the two above issues, water and energy, then it becomes inevitable to look at the future, because the global demand for AI is expected to surge dramatically over the next decade. Data centers, already responsible for roughly 1 to 2 percent of global electricity consumption, could see that number rise significantly as AI adoption accelerates all around a world of nearly 8B people. Some forecasts suggest that AI related workloads could double or even triple data center energy usage within a few years if efficiency improvements do not keep pace.
And because energy production is still closely tied to water in many parts of the world, the two issues are deeply connected. Power plants often rely on water for cooling, which means that increased electricity demand can indirectly increase water consumption as well.
So yes, AI has a very real environmental footprint, but the story doesn’t end there and we might also see some positive points on the horizon, because AI is not just a consumer of resources but is also a tool. And that distinction matters.
Artificial intelligence has also the potential to significantly improve efficiency across multiple industries. It can optimize energy grids, reducing waste and balancing supply and demand more effectively. It can also improve logistics, cutting down fuel consumption by finding more efficient routes. It can support climate research by analyzing vast datasets to identify patterns and predict environmental changes.
In some cases, AI is actually already helping reduce emissions by making systems smarter and more responsive. The same technology that consumes energy can also help save it, sometimes at scale, creating the paradox that comes when we see that AI is both part of the problem and part of the solution. The challenge probably lies in how we can manage that balance.
One of the most pressing concerns for many is the speed at which AI is growing, in a moment when demand for more powerful models is pushing companies to build larger and more complex systems. Bigger models require more training, more computation and more infrastructure, and without significant improvements in efficiency, this trend could lead to rapidly increasing environmental costs.
At the same time, competition is driving companies to scale quickly. The race to build better AI systems is extremely intense and that urgency can sometimes overshadow long term sustainability considerations when companies just want to win the race.
However, efforts are already underway to address these challenges, and luckily some tech companies are starting to invest heavily in renewable energy to power their data centers. Many facilities are being built in locations with access to cleaner energy sources, such as wind and solar. Cooling technologies are also evolving, with some data centers experimenting with air cooling, liquid cooling and even submersion techniques to reduce water usage. There is also growing interest in designing more efficient AI models, and we see many researchers exploring ways to achieve similar performance with less computation, reducing both energy consumption and cost, showing a shift toward efficiency that could become one of the most important trends in the future of AI.
Another promising approach is the reuse of heat generated by data centers. Instead of treating heat as waste, some systems capture it and use it to warm buildings or support industrial processes. While still not widespread, this idea reflects a broader shift toward more sustainable infrastructure design.
Ultimately, the future of AI and its environmental impact will depend on choices being made right now. The technology itself is of course not inherently harmful but something that is bringing many amazing things to the world, but it is the scale, the speed and the way it is deployed that determine its footprint.
What is obvious and clear is that we are at a point where AI is becoming deeply embedded in everyday life. It is shaping how we work, how we communicate and how we make decisions, and that makes it even more important to understand the hidden costs behind the convenience, because the intelligence may feel invisible, but the impact is not.
The question is whether that growth can be aligned with a more sustainable path.
If it can, AI could become one of the most powerful tools we have to improve efficiency and tackle global challenges, but if it cannot, it risks becoming another layer of demand on systems that are already under pressure.
And that is the tension defining this moment.
A technology that promises to make the world smarter is also forcing us to think more carefully about the resources that make that intelligence possible.
[How much water does AI use]https://www.aitooldiscovery.com/ai-infra/how-much-water-does-ai-use
[AI's thirst for water]https://sustainableict.blog.gov.uk/2025/09/17/ais-thirst-for-water/
[The water footprint of AI]https://www.sciencedirect.com/science/article/pii/S0043135426005488
Author: Translock IT, Luis Carlos Yanguas Gómez de la Serna
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