Research Notes: The Aquifer and the Algorithm

I had an interesting call with a Future Tense subscriber last week. As a result of that call I started wondering how much water a single data centre actually uses. Not the industry total, not the national figure, just one building. The answer turned out to be the thread that revealed a measurement gap so wide you could lose an entire industry’s environmental footprint inside it.

The Research Trail

The first number that stopped me was from Brookings, in a November 2025 analysis: a large data centre can consume up to five million gallons of water per day for cooling. That’s roughly the daily water needs of a town of fifty thousand people. One facility. One town’s worth of water. Every day.

That figure checked out across multiple sources (the Environmental and Energy Study Institute had the same ballpark, plus a national total before the current AI buildout). But the number that really caught my attention came next. I went looking for how well the industry tracks its own water consumption and found the Uptime Institute’s 2022 Global Data Center Survey, which surveyed about 800 operators worldwide. Only 39% reported their water usage. And that was down from the previous year. The industry is using more water and reporting less of it. That combination felt like the real story.

Before going further, I spent a couple of hours chasing something that didn’t pan out. I tried to find aggregate water withdrawal figures for data centres pulling from the same aquifer or watershed, thinking someone must be tracking cumulative draw at the regional level. Nobody is. The EPA doesn’t compile it. State agencies issue permits facility by facility. The USGS tracks groundwater depletion at the aquifer level but doesn’t attribute it by industry sector in any granular way. That dead end turned out to be the point: the absence of aggregate tracking is itself the problem.

So then I needed context on the water itself. Not the demand side, the supply side. The New York Times published a major investigation in August 2023, mapping eighty thousand monitoring wells across the country. The picture was grim: 45% of wells showed significant decline over forty years, four in ten hit all-time lows in the preceding decade, and every year since 1940, more wells fell than rose. 2022 was the most damaging year on record. That investigation was published before the current infrastructure surge. The aquifer depletion problem predates AI; AI just accelerated it.

Which led me to Memphis. The xAI Colossus facility there became the concrete case study I kept returning to. Protect Our Aquifer, a local advocacy group, has documented the situation in detail: peak water demand of roughly five million gallons daily, drawn from the Memphis Sand Aquifer. Arsenic contamination in the shallow aquifer already threatens adjacent communities. The facility’s environmental justice implications run deep, with predominantly Black neighbourhoods bearing the brunt of gas turbine emissions from power generation. One facility, one aquifer, one set of neighbours who didn’t get a vote.

Source Evaluation

The source mix here splits into two clean categories: the water science is rock-solid, and the financial projections are credible, though the assumptions underneath could move.

On water: USGS data on long-term groundwater depletion is about as reliable as you’ll find anywhere. The NYT investigation builds on decades of USGS monitoring with original reporting across 80,000+ wells and over a hundred expert interviews. Brookings and EESI are nonpartisan and well-sourced. These numbers aren’t in dispute.

On the financial side, JLL’s year-end 2025 data centre report (covered by CNBC in February 2026) puts the scale in perspective: the top five hyperscalers have $710 billion in planned capital expenditure for 2026, with vacancy at a historic low of 1% and 92% of capacity under construction pre-committed. CBRE’s numbers are slightly different (1.4% vacancy), which is worth noting, but both land in the same place: building at unprecedented speed.

The source I found most analytically useful was an independent Substack by Gadallon, who published a bullwhip effect analysis of AI infrastructure in December 2025. It’s not Tier 1 (it’s one analyst’s framework), but the reasoning is sound and it cites Morgan Stanley and McKinsey data. The core insight: chips take 6 to 12 months to produce, data centres take 12 to 24 months to build, and power infrastructure takes 3 to 5+ years. Those mismatched timelines create classic supply-chain oscillation. You can see the bullwhip forming in real time.

I was careful with Protect Our Aquifer. They’re an advocacy organisation, so there’s a built-in lean toward conservation. But their data comes from municipal records and permit filings, which are verifiable. Used them for facts, not framing.

Extrapolation Mechanics

Here’s where the research got uncomfortable. The question I kept circling was: what happens when you multiply one “sustainable” water permit by a thousand?

Each individual facility passes its local environmental review. The engineering checks out. The recharge models hold. For a single site, the draw is sustainable. But nobody is modelling the aggregate. There’s no desk where all the permits converge. The IEEE Planet Positive 2030 compendium, published in late 2024 with contributions from 200+ engineers across 30 countries, flags exactly this gap. They call the water-energy-food nexus a critical risk and conclude that “the trajectory of computing appears unsustainable from both energy and materials perspectives.” Four hundred pages of careful institutional language that adds up to a warning.

The efficiency counter-argument is real. DeepSeek’s R1 model, released in January 2025, performed at the level of leading American AI systems while consuming 45 times less compute. Quantisation techniques shrink models to a quarter of their size. Edge computing pushes inference onto consumer devices. The models being trained inside these data centres are actively undermining the case for building them at this scale.

That’s the dot-com parallel, and Fortune’s October 2025 coverage of Ares Management Co-President Kipp deVeer puts it plainly: “Typically when this much capacity comes online, some of it at the end of the day has to be marginal.” During the dot-com boom, roughly $2 trillion went into 80 million miles of fibre-optic cable, and 85 to 95% of it went dark. The losses were staggering, but the damage was financial. Investors lost money, companies folded, cable sat unused underground. The important thing is that the cable just sat there. It didn’t degrade the landscape by existing. Stranded data centres are a different kind of wreckage. They leave behind depleted aquifers that don’t refill on a market cycle. One bust is recoverable. The other changes the geology.

Terminology and Craft Choices

The title pairing (”aquifer” and “algorithm”) was deliberate. “Water supply” would have been more accessible, but “aquifer” does work that “water supply” can’t. An aquifer is ancient, geological, slow to form and slow to recover. It carries the weight of deep time. “Algorithm” does similar heavy lifting on the other side. “AI” would imply autonomy, something that acts on its own. “Data centre” would imply a building, something static. “Algorithm” implies a process, a set of instructions, something designed and deliberately set in motion. Placing the two words next to each other creates a tension that mirrors the core problem: something that operates on millisecond timescales is drawing down something that operates on millennial timescales. The mismatch in the words is the mismatch in the system.

What Didn’t Fit Cleanly

Three complications worth flagging.

The efficiency gains that could strand these facilities are also the thing making AI more useful and widespread. More efficient models could mean less centralised compute, or they could mean more total usage at lower per-query cost. Jevons paradox is lurking here, and I haven’t resolved which direction it cuts. If efficiency drives down the cost per query, total demand might expand to consume all the savings and then some. That tension sits at the heart of every projection about future data centre demand.

Not all data centres use evaporative cooling. Some use closed-loop systems or air cooling. The water intensity varies enormously by climate, design, and operator. The five-million-gallon figure is a ceiling, not a floor. Plenty of facilities use far less.

The “92% pre-committed” statistic cuts both ways. It could mean genuine demand. It could also mean a closed loop where the same five companies are both building and leasing to themselves. Probably some of each.

Closing Reflection

The thing I keep returning to is the measurement gap. Only 39% of operators reporting water usage, and the number declining. We have better data on groundwater levels in monitoring wells drilled fifty years ago than we do on the water consumption of facilities built last quarter. That asymmetry, between the precision of the engineering and the absence of the accounting, feels like it contains something important about how large-scale environmental costs get distributed. Not through malice. Through nobody being able to see the whole picture at once. That’s probably worth following further.