Name: AI 竟然在用我們的飲用水？ BBC World Service (How AI uses our drinking water - BBC World Service)
Uploaded: 2026-03-30T11:17:23.000Z
Duration: 6 min 37 s

That's how much water the average single interaction with ChatGPT uses, according to Sam Altman, the boss of OpenAI.

根據 OpenAI 的老闆 Sam Altman 說，ChatGPT 每次互動平均只會用到這麼一點點水。

So if you type, "Can you help me solve this maths problem?" that's a drop.

所以，如果你打「你能幫我解這道數學題嗎？」這就是一滴水。

Or, "Can I put lime instead of lemon in this recipe?" that's a drop.

或是「這個食譜裡我可以用萊姆代替檸檬嗎？」這也是一滴水。

又或是「為什麼天空是藍色的？

Altman claims there are one billion messages sent to ChatGPT every day.

Altman 聲稱，每天有十億則訊息傳送給 ChatGPT。

而 ChatGPT 也只是一個 AI 機器人。

Chuck in Gemini, DeepSeek, Claude, and others.

再加上 Gemini、DeepSeek、Claude 等等。

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.

不過令人驚訝的是，有些專家對 Sam Altman 估計的用水量抱持懷疑態度。

At this point, there was just not enough information for me to agree with or trust the number.

當時，我沒有足夠的資訊可以同意或相信這個數字。

Their number was perhaps referring to some tiny models.

他們的數字或許指的是一些小型模型。

We're considering a medium-sized, larger language model.

我們正在考慮一個中大型的語言模型。

Basically, if you write an email or ask some questions, if you have 10 to 50 queries, you're going to be consuming roughly 500 millilitres of water.

基本上，如果你寫一封電子郵件或問一些問題，有 10 到 50 個查詢，你大概會消耗 500 毫升的水。

This calculation includes water used in cooling and electricity generation.

這項計算包含了冷卻和發電所使用的水。

The BBC asked OpenAI for more details about Sam Altman's estimate, but the company declined.

BBC 要求 OpenAI 提供更多關於 Sam Altman 估計的細節，但該公司拒絕了。

Either way, it's clear AI uses a lot of water.

不管怎樣，很明顯 AI 確實用了不少水。

Every time you send a prompt to an AI, it has to run complex calculations to understand and respond.

每次你給 AI 一個提示，它都需要進行複雜的計算來理解和回應。

This work is done by the most powerful and specialised computer chips in the world, housed inside enormous data centres.

這項工作是由世界上最頂尖、最專業的電腦晶片完成的，這些晶片都裝在巨大的資料中心裡。

Even before users can send prompts, the training process for the models uses the chips to carry out intense work.

在使用者能夠發送提示之前，語言模型的訓練過程就已經利用這些晶片進行高強度的運作了。

And all that extra power means the hardware can overheat and become damaged if not cooled properly.

而所有額外的電力消耗，意味著硬體如果沒有妥善冷卻，就可能過熱損壞。

Most data centres use air-cooling systems, which was fine until AI came along.

大部分的資料中心都使用空氣冷卻系統，這在 AI 出現之前都沒問題。

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.

但現在，由於這些資料中心以及正在建置的基礎設施耗能非常高，液體冷卻方法也開始被採用了。

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.

對於液體冷卻，水必須是乾淨的，以防止細菌滋生或造成系統堵塞和腐蝕，這意味著要使用大部分的飲用水。

Here's how the most common liquid-cooling process works.

最常見的液體冷卻過程是這樣運作的。

It begins by piping coolant over the processing chips within the servers.

它首先將冷卻劑透過管道輸送到伺服器內部的處理晶片上。

This cooling liquid absorbs the heat and takes it away from the electrics to a heat exchange unit.

這個冷卻液會吸收熱能，並將其從電子設備帶到熱交換單元。

Water is used to reduce the temperature of the coolant.

The coolant then recirculates back to cool the servers.

然後冷卻劑會再次循環，用來冷卻伺服器。

Meanwhile, the now hot water is piped to cooling towers, where a combination of fans and water vapour dissipate the heat, cooling the water.

同時，變熱的水會被輸送到冷卻塔，那裡有風扇和水蒸氣結合，將熱能散發掉，冷卻水。

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.

在這個過程中，一部分水會蒸發，其餘的水會在循環冷卻過程中重複使用幾次後，再排放回附近的水源。

Overall, up to 80 percent of the water evaporates.

總體來說，高達 80% 的水會蒸發。

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

這意味著這種水就消失了，而且我們正在從一個對灌溉、人類飲用和

衛生至關重要的水循環中抽取水。

Communities around the world concerned about data centres putting stress on water sources and electricity grids are pushing back.

世界各地對資料中心給水資源和電網帶來壓力感到擔憂的社區正在反擊。

Protests have been held in Spain, India, Chile, Uruguay, and parts of the US.

在西班牙、印度、智利、烏拉圭和美國部分地區都舉行了抗議活動。

And it's not just the operations within the data centre that need water.

而且不只資料中心內部的運作需要水。

Generating the electricity to run them requires a lot of water, too, because power plants like coal, gas, and

為它們供電的電力產生過程也需要大量的水，因為像煤炭、天然氣和

nuclear heat water to create steam, which drives a turbine.

核能發電廠會加熱水來產生蒸氣，進而驅動渦輪機。

The International Energy Agency has said electricity demand for AI-optimised data centres is expected to increase by 400 percent by 2030, to 300 terawatt-hours.

國際能源總署表示，到 2030 年，AI 優化資料中心的電力需求預計將增加 400%，達到 300 太瓦時。

That's roughly the electricity consumption of the whole of the UK for a year.

這大約是整個英國一年的電力消耗量。

And aside from electricity, water is also needed when manufacturing the semiconductor chips used to run AI.

除了電力之外，製造用於運行 AI 的電腦晶片時也需要水。

So water is both used directly and indirectly in the whole supply and creation chain of AI technologies.

所以，水在 AI 技術的整個供應和創建鏈中，都是直接和間接被使用的。

It is used for the refination of the critical raw materials that are needed to create the hardware of AI.

它被用於提煉創造 AI 硬體所需的關鍵原物料。

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

要獲得關於建造和運行 AI 系統所需水量準確的數字是很困難的。

Microsoft release annual figures showing that their data centres use billions of litres of water every year from local sources.

Microsoft 每年發布的數據顯示，他們的資料中心每年從當地水源使用數十億公升的水。

But none of them indicate how much of it is due to AI.

但沒有一家公司說明其中有多少比例是用於 AI。

Most tech giants recognise the impact it's having.

大多數科技巨頭都意識到這項影響。

Many, including Google, Microsoft, and Meta, have pledged to be water-neutral by 2030.

包括 Google、Microsoft 和 Meta 在內的許多公司都承諾要在 2030 年前實現水資源中和。

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

我們希望這能夠實現，要達到那樣的數字還有很長的路要走。

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.

我們希望看到的是，整個產業能有一系列創新，讓我們能夠盡量減少用水量。

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

例如，公司正在試驗各種方法來冷卻資料中心，而完全不蒸發任何水分，

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

並利用產生的熱能來為住家供暖。

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

也有實驗要把資料中心完全移離社區，放到海底、北極，甚至外太空。

Can we actually put capacity out in space?

我們真的有可能把運算能力放到外太空嗎？

So what we at Entity are looking at is, can we launch satellites that can at least do some more backup-oriented or other-oriented tasks?

所以我們 Entity 在看的是，我們能不能發射衛星，至少能做一些備份導向或其他導向的任務？

Those sceptics point to the many hurdles that need to be overcome.

那些懷疑論者指出了許多需要克服的障礙。

There is optimism, too, about a more sustainable future.

對於一個更永續的未來，也有樂觀的看法。

Let's remember that GenAI capability is still very, very young.

讓我們記住，GenAI 的能力仍然非常非常年輕。

It's moved exponentially fast, but as an industry and as a use, it is still young.

它們的發展速度呈指數級增長，但作為一個產業和一種用途，它仍然很年輕。

Ideally, we can learn together as a society and as a world society how do we minimise against the use of water and energy.

理想情況下，我們社會和世界社會可以一起學習，如何盡量減少水和能源的使用。

Because this is all a world resource when we talk about water.

因為當我們談論水資源時，這一切都是世界資源。