At a Glance
| Metric | Figure |
| Global data center electricity use, 2024 | ~415 TWh (~1.5% of world electricity) — IEA |
| Projected global data center electricity use, 2030 | ~945 TWh, more than double 2024 — IEA |
| Typical AI-focused data center load | Roughly the electricity use of 100,000 households — IEA |
| Legacy enterprise rack power | ~5–10 kW per rack |
| NVIDIA GB200 NVL72 rack power | Approximately 120 kW (vendor documentation ranges ~100–132 kW by configuration) |
| Practical air-cooling ceiling | Roughly 40–50 kW per rack |
| Combined Q3 2025 capex — Microsoft, Amazon, Alphabet, Meta | Over $114 billion for the single quarter |
Short Verdict
AI isn’t just filling data centers with more servers — it’s forcing a redesign of what a data center is. Training and inference workloads concentrate far more electrical load into far less floor space than the web and cloud-computing era ever did, and that shift is rippling through every layer of the facility: how it’s powered, how it’s cooled, how dense its racks are, and increasingly, how much strain it puts on the local electrical grid. The numbers below are drawn from the IEA’s 2025 energy-and-AI analysis, hyperscaler earnings disclosures, and data-center industry research — and a few widely-quoted figures (exact next-generation rack power draw, specific 2026 capex totals) are flagged as approximate or unconfirmed rather than stated as fact.
Why AI Workloads Don’t Behave Like Ordinary Computing
A traditional enterprise or web-hosting server rack draws somewhere in the neighborhood of 5 to 10 kilowatts (kW) — low enough that a building’s existing electrical and cooling systems can usually absorb it without much fuss. An AI training or inference cluster is a different animal. A single NVIDIA GB200 NVL72 rack — 36 GB200 Superchips pairing 72 B200 GPUs with 36 Grace CPUs — draws approximately 120 kW, roughly 12 to 24 times a legacy rack’s draw packed into the same floor footprint. (Vendor documentation isn’t perfectly consistent here; depending on configuration, cited figures for this exact rack range from about 100 kW to 132 kW, so treat 120 kW as a representative midpoint rather than a fixed spec.) The successor GB300 NVL72 pushes that figure up to roughly 142 kW per rack, according to NVIDIA’s own reference architecture.
That density jump is the root cause of almost everything else in this article. Training large models means running thousands of GPUs continuously, at near-full utilization, for weeks or months at a stretch. Inference — actually serving a trained model’s responses to users — draws less per-query power but happens constantly, at scale, across a growing user base, so its aggregate electricity footprint is catching up with training’s. Either way, the facility has to supply, distribute, and remove far more power and heat per square foot than it was originally built for.
The Power Problem, in Real Numbers
The International Energy Agency’s Energy and AI report puts global data center electricity consumption at around 415 terawatt-hours (TWh) in 2024 — about 1.5% of total world electricity use. That demand has been growing roughly 12% a year since 2017, and the IEA names AI specifically as “the most important driver of this growth” going forward, projecting consumption more than doubling to around 945 TWh by 2030. The United States currently accounts for about 45% of global data center electricity use, China 25%, and Europe 15%. The IEA’s shorthand for scale: a typical AI-focused data center uses as much electricity as 100,000 households.
That demand has to come from somewhere, and increasingly it’s showing up as a line item on grid operators’ forecasts. PJM Interconnection — the grid operator covering a large swath of the mid-Atlantic and Midwest — projected in its 2025 forecast that peak electricity demand in its territory will climb from 160 GW to 210 GW by 2035, a 31% increase the forecast attributes “almost entirely” to data center buildout; large-load interconnection requests in PJM’s queue already total 66 GW through 2035. That’s pushed some hyperscalers toward unconventional power deals: Microsoft has contracted for output from the restarted Three Mile Island reactor (now renamed the Crane Clean Energy Center) under a 20-year agreement, and Google has a power-purchase arrangement tied to Kairos Power’s planned small modular reactors delivering power through the Tennessee Valley Authority. Exact capacity figures in some of these deals vary slightly across public reporting, but the direction is unambiguous: AI data centers are now significant enough electricity customers that hyperscalers are directly underwriting new power generation rather than simply buying off the existing grid.
Cooling: Why Air Is Running Out of Room
Conventional air cooling — chilled air pushed through a hot-aisle/cold-aisle layout, sometimes assisted by rear-door heat exchangers — works fine up to roughly 40 to 50 kW per rack, according to data-center engineering sources and the Uptime Institute’s own research. Past that point, the sheer volume of air (and fan power) needed to carry the heat away becomes impractical. A 120 kW GB200 rack is roughly two to three times past that ceiling, which is why liquid cooling has gone from a niche option to effectively mandatory for AI clusters at this scale.
Two liquid approaches dominate. Direct-to-chip (also called cold-plate) cooling runs a liquid coolant loop directly to plates mounted on the GPU and CPU packages, and comfortably handles the 100–150 kW per rack that current AI hardware generates — this is the approach built into NVIDIA’s GB200/GB300 reference designs. Immersion cooling, which submerges hardware entirely in a dielectric fluid, can handle similar or higher densities but has seen more limited deployment so far, mostly in specialized or experimental installations rather than as the default for new hyperscale builds. For context on just how much the industry baseline has shifted: the Uptime Institute’s surveys show the typical (not AI-specific) data center rack density was still under 8 kW as recently as 2024, rising to a reported 11 kW in 2026 — meaning even “normal” racks are climbing, while AI racks have leapt an order of magnitude past that baseline.
The Buildout: Capex and Gigawatt-Scale Campuses
The dollar figures behind this shift are large enough to show up directly in Big Tech earnings calls. In the third quarter of 2025 alone, the four largest hyperscalers reported combined capital expenditure — most of it data-center and AI-infrastructure related — of roughly $114 billion: Amazon around $36 billion in total corporate capex (AWS accounting for about $28 billion of that), Microsoft about $35 billion, Alphabet/Google about $24 billion, and Meta about $19 billion, with year-over-year growth rates ranging from roughly 44% to over 110% depending on the company. For the full 2025 fiscal year, Amazon guided to approximately $125 billion in cash capex, Alphabet raised its guidance to $91–93 billion, and Meta guided to $70–72 billion — all figures the companies disclosed on their own Q3 2025 earnings calls. (Various analyst projections for 2026 full-year industry capex circulate in trade press, but because those are forward estimates rather than confirmed actuals, we’ve left them out here.)
That spending is increasingly measured in gigawatts rather than square footage. Meta’s October 2025 Hyperion campus in Louisiana — a $27 billion joint venture with Blue Owl Capital — was announced with a first phase around 2 GW of capacity, with Mark Zuckerberg stating it could scale up to 5 GW over several years. When a data center’s binding constraint is described in power-grid units rather than real estate, that’s a reasonably clean signal of how central electricity supply has become to where and how these facilities get built.
Bottom Line
AI hasn’t just increased demand for data centers — it has changed what counts as a well-designed one. Rack power density that would have been unthinkable a decade ago is now standard for frontier AI hardware, air cooling has hit a physical ceiling that liquid cooling is stepping in to solve, and the resulting electricity demand is large enough to show up in grid operators’ decade-ahead planning and to push hyperscalers into financing new power generation directly. None of this is slowing down: combined hyperscaler capex is still growing by double-digit to triple-digit percentages year over year, and the IEA expects data-center electricity use to more than double again by 2030. Anyone evaluating where to site, build, or invest around AI infrastructure needs power and cooling capacity, not just chip availability, at the center of that decision.
Sources
- IEA — Energy and AI, executive summary (global data center electricity use, 2024 baseline and 2030 projection)
- Platformonomics — Follow the CAPEX: Q3 2025 Scoreboard (hyperscaler Q3 2025 actual capex and FY2025 guidance)
- Data Center Dynamics — Meta’s $27B Hyperion joint venture with Blue Owl
- Modo Energy — PJM’s 2025 demand forecast and data centers
- Data Center Knowledge — AI rack density’s real limits: power, cooling, failure risk
- Data Center Knowledge — Uptime Institute global data center survey (PUE and rack density history)
- Upsite Technologies — Data center rack density trends
- Introl — Liquid cooling vs. air: the 50kW GPU rack guide
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