How Artificial Intelligence Is Impacting Our Drinking Water

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Explore how AI technology is transforming the management and safety of drinking water around the world. From detecting contaminants to optimizing distribution, learn how innovations are shaping the future of water use and sanitation through the lens of BBC World Service.  #AI #DrinkingWater #WaterTechnology #BBCWorld #WaterSafety #Innovation #SmartWater #EnvironmentalTech #GlobalWaterCrisis

Transcript

00:00One-fifteenth of a teaspoon. That's how much water the average single interaction with

00:06ChatGPT uses, according to Sam Altman, the boss of OpenAI.

00:11So if you type, can you help me solve this maths problem? That's a drop. Or, can I put

00:17lime instead of lemon in this recipe? That's a drop. Or, why is the sky blue? Help me write

00:23this email. Help me improve my website code.

00:27Mr Altman claims there are one billion messages sent to ChatGPT every day. And ChatGPT is

00:34just one AI bot. Chuck in Gemini, DeepSeek, Claude and others, it's clear that the AI revolution

00:41is a thirsty one. Striking though it is, some experts are more than a little sceptical of

00:48Sam Altman's estimate on water usage.

00:50Sam Altman, At this point, there was just not enough information for me to agree with

00:57or trust the number. Their number was perhaps referring to some tiny models. We are considering

01:05a medium-sized larger language model. That's the size of GPT-3. Basically, if you write an

01:12email or ask someone questions, if you have 10 to 50 queries, you're going to be consuming

01:17roughly 500 millilitres water. This calculation includes water used in cooling and electricity

01:23generation. The BBC asked OpenAI for more details about Sam Altman's estimate, but the

01:28company declined. Either way, it's clear, AI uses a lot of water. But why? Every time you

01:36send a prompt to an AI, it has to run complex calculations to understand and respond. This work

01:44is done by the most powerful and specialised computer chips in the world, housed inside

01:49enormous data centres. Even before users can send prompts, the training process for the

01:55models uses the chips to carry out intense work. And all that extra power means the hardware

02:02can overheat and become damaged if not cooled properly. Most data centres use air cooling systems,

02:09which was fine until AI came along. But now, because these data centres and the infrastructure

02:16that's going in is so much more energy intensive, there are liquid cooling approaches that are

02:23now being implemented. For liquid cooling, the water must be clean to prevent bacteria growing

02:29or clogs and corrosion in the system, which means using mostly drinking water. Here's how the

02:34most common liquid cooling process works. It begins by piping coolant over the processing

02:40chips within the servers. This cooling liquid absorbs the heat and takes it away from the

02:45electrics to a heat exchange unit. Water is used to reduce the temperature of the coolant.

02:51The coolant then recirculates back to cool the servers. Meanwhile, the now hot water is piped

02:56to cooling towers, where a combination of fans and water vapour dissipate the heat, cooling

03:01the water. Some of the water evaporates in that process, while the rest is recirculated

03:07through the cooling process several times before being discharged back into the nearby

03:12water source. Overall, up to 80% of the water evaporates.

03:16What it means is that this type of water is gone and that we are extracting water from a water

03:23circuit that is necessary for irrigation, for human consumption and hygiene.

03:31Communities around the world concerned about data centres putting stress on water sources

03:35and electricity grids are pushing back. Protests have been held in Spain, India, Chile, Uruguay

03:41and parts of the US. And it's not just the operations within the data centre that need water. Generating

03:48the electricity to run them requires a lot of water too, because power plants like coal, gas

03:53and nuclear heat water to create steam, which drives a turbine.

03:58The International Energy Agency has said electricity demand for AI optimised data centres is expected

04:04to increase by 400% by 2030 to 300 terawatt hours. That's roughly the electricity consumption

04:13of the whole of the UK for a year. And aside from electricity, water is also needed when manufacturing

04:20the semiconductor chips used to run AI.

04:24So water is both used directly and indirectly in the whole supply and creation chain of AI

04:31technologies. It is used for the refination of the critical raw materials that are needed

04:37to create the hardware of AI.

04:39Getting accurate figures on how much water it takes to build AI systems and run them is difficult.

04:45Google, Meta and Microsoft release annual figures showing that their data centres use billions of litres

04:52of water every year from local sources, but none of them indicate how much of it is due to AI.

04:58Most tech giants recognise the impact it's having. Many, including Google, Microsoft and Meta, have pledged to be water neutral by 2030.

05:11We hope that can happen, that there is a long way to go to get to those kind of numbers.

05:17Part of what we hope to see is, across the industry, a range of innovations that allow us to maybe minimise the use of water.

05:25Companies are trialling, for example, ways to cool data centres without evaporating any water at all,

05:31and to use the heat that's generated to warm homes.

05:34There are also experiments to move data centres away from communities entirely under the sea, to the Arctic, or even off the planet.

05:43Can we actually put capacity out in space?

05:47It's very, very early stage, so what we at NTT are looking at is,

05:52can we launch satellites that can at least do some more backup-oriented or other-oriented tasks?

05:59Though sceptics point to the many hurdles that need to be overcome,

06:03there is optimism, too, about a more sustainable future.

06:07Let's remember that that Gen.AI capability is still very, very young.

06:11It's moved exponentially fast, but as an industry and as a youth, it is still young.

06:17Ideally, we can learn together, as a society and as a world society,

06:22how do we minimise, again, the use of water and energy?

06:25Because this is all, you know, a world resource when we talk about water.

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