BBC 全球服务 人工智能如何使用我们的饮用水
2026-09-26
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1/15 of a teaspoon. That's how much water the average single
interaction with ChatGPT uses, according to Sam Altman, the
boss of OpenAI. So if you type, can you help me solve this
maths problem? That's a drop. Or can I put lime instead of
lemon in this recipe? That's a drop. Or why is the sky blue?
Help me write this email. Help me improve my website code.
Mr. Altman claims there are 1 billion messages sent to
ChatGPT every day, and ChatGPT is just one AI bot. Chuck in
Gemini, DeepSeek, Claude and others. It's clear that the AI
revolution is a thirsty one. Striking though it is, some
experts are more than a little sceptical of Sam Altman's
estimate on water usage.
At this point, there was just not enough information for me
to agree with or trust the number. Their number was perhaps
referring to some tiny models. We're considering a medium
sized large language model that's the size of GPT-3.
Basically, if you write an email or ask some questions, if
you have 10 to 50 queries, you're going to be consuming
roughly 500ml of water.
This calculation includes water used in cooling and
electricity generation. The BBC asked OpenAI for more
details about Sam Altman's estimate, but the company
declined. Either way, it's clear AI uses a lot of water. But
why? Every time you send a prompt to an AI, it has to run
complex calculations to understand and respond. This work is
done by the most powerful and specialised computer chips in
the world, housed inside enormous data centres. Even before
users can send prompts, the training process for the models
uses the chips to carry out intense work. And all that extra
power means the hardware can overheat and become damaged if
not cooled properly. Most data centres use air cooling
systems, which was fine until I came along.
But now, because these data centres and the infrastructure
that's going in is so much more energy intensive, there are
liquid cooling approaches that are now being implemented.
For liquid cooling the water must be clean to prevent
bacteria growing or clogs and corrosion in the system, which
means using mostly drinking water. Here's how the most
common liquid cooling process works. It begins by piping
coolant over the processing chips within the servers. This
cooling liquid absorbs the heat and takes it away from the
electrics to a heat exchange unit. Water is used to reduce
the temperature of the coolant. The coolant then recirculates back to cool the servers.
Meanwhile, the now hot water is piped
to cooling towers, where a combination of fans and water vapour dissipate the heat,
cooling the water. Some of the water
evaporates in that process, while the rest is recirculated through the cooling process
several times before being
discharged back into the nearby water source. Overall, up to 80% of the water evaporates.
What it means is that this type of water is gone, and that
we are extracting water from a water circuit that is
necessary for irrigation, for human consumption and hygiene.
Communities around the world concerned about data centres
putting stress on water sources and electricity grids are
pushing back. Protests have been held in Spain, India,
Chile, Uruguay and parts of the US. And it's not just the
operations within the data centre that need water. Generating the electricity to run
them requires a lot of water too, because power
plants like coal, gas and nuclear heat water to create steam,
which drives a turbine. The International Energy Agency
has said electricity demand for AI optimised data centres is
expected to increase by 400% by 2030 to 300 terawatt hours.
That's roughly the electricity consumption of the whole of the
UK for a year. And aside from electricity,
water is also needed when manufacturing the semiconductor chips used to run AI.
So water is both used directly and indirectly in the whole
supply and creation chain of AI technologies. It is used for
the refination of the critical raw materials that are needed
to create the hardware of AI.
Getting accurate figures on how much water it takes to build
AI systems and run them is difficult. Google, Meta and
Microsoft release annual figures showing that their data
centres use billions of litres of water every year from
local sources, but none of them indicate how much of it is
due to AI. Most tech giants recognise the impact it's
having. Many, including Google, Microsoft and Meta, have
pledged to be water neutral by 2030.
S5 We hope that can happen,
there is a long way to go to get to those kind of numbers. Part of what
we hope to see is, across the industry,
a range of innovations that allow us to maybe minimise the use of water.
Companies are trialing, for example, ways to cool data
centres without evaporating any water at all. And to use the
heat that's generated to warm homes. There are also
experiments to move data centres away from communities
entirely under the sea, to the Arctic, or even off the
planet.
Can we actually put capacity out in space? It's very, very
early stage. So what, you know, we at NTT are looking at is,
can we launch satellites that can at least do some more
backup-oriented or other oriented tasks?
Though skeptics point to the many hurdles that need to be
overcome, there is optimism, too, about a more sustainable
future.
Let's remember that that GenAI capability is still very,
very young. It's moved exponentially fast, but as an
industry and as a use, it is still young. Ideally, we can
learn together as as a society and as a world society, how
do we minimise against the use of water and energy? Because
this is all, you know, a world resource when we
talk about water.
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