Is AI really draining our water supply? Alan Turing Institute on the brink of collapse; Meta's struggles to build its AI team; how top chatbots change your mind; Amazon ventures into neurosymbolic AI

Meta AI allowed "sensual chats" between its chatbots and children; Mistral sets its sights on the Middle East; companies are still struggling to see the ROI of AI; China's lead in open source AI

Aug 15, 2025

∙ Paid

A few weeks ago, I posted on Threads that Synthesia is looking to hire more engineers and researchers to build out its AI video platform. I hoped to get some of the usual reactions: What’s the work culture like? How can you compete with the Silicon Valley giants? How is building for AI video different to large language models?

Instead, I got into a brief exchange with someone who is not working in the technology industry but had a negative worldview on the environmental impact of AI shaped by some of the more alarmist press coverage.

“AI companies are boiling the planet and killing our waters” is the type of reaction you get when you’ve been fed a steady sugar rush of villains through clickbait headlines. That’s become increasingly clear with so much climate-and-tech discourse right now: we’ve traded inquiry for vibes, nuance for outrage, and analysis for whatever word pairs best with “crisis.”

If we wanted answers instead of social media adrenaline, the AI and energy + water conversation would look very different.

Let’s start with the basic physics of data centers: the buildings that keep your photos, emails and AI models alive. There’s no doubt: they run hot. Servers turn electricity into computation and then heat; you must dump that heat somewhere. Many facilities use evaporative cooling: spray water, evaporate it, carry the heat away. The industry often talks about a metric called water-use effectiveness (WUE), which is liters of water per kilowatt hour (kWh) of compute-related energy. Typical WUE across data centers hovers around 1.9 liters/kWh. That can sound alarming, until you notice the spread. Best-in-class sites hit far lower numbers, and air- or refrigerant-cooled designs can use nearly none on-site. Recently, some operators have reported WUEs of about a tenth of the typical figure, and Microsoft has rolled out a design which involves zero water use for cooling.

Then there’s off-site water. Even if a data center sips little or no water on its own property, the power plant feeding it often doesn’t. In the US, the average water intensity of electricity is roughly 2.18 L/kWh, and the mix swings wildly by technology and geography. Hydropower, the clean hero in so many narratives (including Canada, where I’m currently writing this newsletter from), can be very water-intensive if you count reservoir evaporation. So you can run a “dry” data center and still be indirectly “wet” through your grid connection, or vice versa.

Scale deserves honesty, too. North American data centers are estimated to use 1.5–1.7 billion liters of water per day directly. Globally, data centers may consume about half a trillion liters per year, and demand could double by 2030 as AI adoption grows. These numbers are definitely not rounding errors but here comes the part that you rarely get to read about in the media.

First of all, not all workloads are equal. Storing your photos and email (call it “cold-ish” storage) is less power-dense than training a frontier AI model or running high-intensity inference. In cooler climates, storage can often be cooled with minimal or zero water. But storage runs 24/7 and it’s typically replicated (often three copies for durability) so the total footprint scales with how much stuff we keep forever. The long tail of “just in case” data is a quiet resource hog. Also, the engineering and procurement are changing. While some operators such as Microsoft now avoid evaporating water entirely, others are switching to reclaimed or industrial water where possible.

Secondly, if you want another (better?) villain, I can help you. In 2023-2024, water companies in England and Wales alone lost one trillion liters from leaks (and dumped huge amounts of sewage into our fresh waters, but that’s a topic for another day). The worst performer in England leaked more than 200 billion liters in a year, equivalent to almost half of the global water consumption of data centers in that same timeframe. So if we were to plug all the water waste in just one country in Europe today, we’d have enough liters to satisfy the projected demand for the entire data center sector five years from now.

If you want a better internet, don’t engage in performative or illiterate activities like not using AI or deleting your old emails. Here are four suggestions that might actually have systemic impact:

Stop treating “hyperscalers” as a single cartoon villain. A warehouse cooled with reclaimed water in a cool, windy region using wind and solar is not the same as a desert facility flashing evaporative coolers while drawing from a thirsty, fossil-heavy grid. Lumping them together is how we reward greenwashing and punish genuine improvements.
Ask for the boring numbers. If you’re a large enterprise doing significant business with a cloud, SaaS, or AI service, look for the WUE numbers (and PUE, the energy companion metric). Prefer providers that publish these figures, site in cooler climates when possible, run on low-water energy (wind/solar), and use reclaimed water for cooling. Net zero slogans are great; actual intensity metrics are better.
Aim critiques at the system, not the selfie. Your marginal gigabytes won’t retire a server. Deleting a few emails is fine household hygiene, not climate policy. The big levers live with providers: cooling choice, siting, and grid mix. That’s where regulators, customers with buying power, and investors should push, through procurement standards, disclosure rules, and siting incentives that reward low-water, low-carbon designs.
Still clean up the junk in a smart and sensible way. Because storage does run constantly and is replicated, deleting truly unnecessary data has a real (if modest) cumulative effect. Think of it as flossing for infrastructure: won’t change your life today, will make the whole system healthier over time. If you’re a media organization (a heavy user of storage in data centers), start with your own vast archives before you ask the public to delete their personal data.

So is generative AI “worth” the environmental footprint? Reasonable people can disagree. I believe it does but of course I have my own biases. Perhaps the right way to decide isn’t by reaching for the nearest pitchfork. It’s by asking the right questions and carefully analyzing the answers rather than looking for simple explanations that sound too good to be true. And if we don’t like the answers, we should fix the inputs (procurement standards, plant design, siting) and not pretend that our performative uninstalls are climate strategy.

When we flatten complex tradeoffs like tech-related energy or water usage into “AI bad, hyperscaler worse,” we start having large blindspots and misfire our outrage. We also let the real culprits skate by while we yell at AI video companies.

And now, here are the week’s news:

❤️Computer loves

Our top news picks for the week - your essential reading from the world of AI

Keep reading with a 7-day free trial

Subscribe to Computerspeak by Alexandru Voica to keep reading this post and get 7 days of free access to the full post archives.