The Buildings Behind AI — Semiotic

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The buildings behind AI

Data centers did not begin with generative AI. This explainer separates the installed cloud base from facilities opened after ChatGPT, active construction, and announcements—and refuses to turn an announced gigawatt into an operating one. Every number names its denominator; every missing disclosure stays missing.

AN INFRASTRUCTURE ACCOUNT · AS OF JULY 3, 2026

The buildings behind the models

Electricity goes in. Computation happens. Nearly all of that electricity leaves as heat. Water use depends on cooling design and on the power plants behind the meter.

201458 TWh

All U.S. data centers—well before ChatGPT.

2023176 TWh

All U.S. data centers; accelerated servers used more than 40 TWh of it.

2028325–580 TWh

DOE/LBNL scenario range—hatched signs are possibilities, not promises.

EACH BOLT SIGN = 25 TWH OF ELECTRICITY IN A YEAR · HATCHED SIGNS SPAN THE PROJECTED RANGE

ALTITUDE AND DATA CENTERS, UNITED STATES

Operating is not the same as announced

Five west-to-east relief sections carry the sites at their longitudes, after the 1943 ISOTYPE altitude spread. This is an auditable sample, not a census: city signs mark markets that predate ChatGPT; repeated server signs appear only where an operator or government source discloses capacity.

Don't be deceived by this map of the United States—the five dark strips are schematic relief sections, drawn only to give an idea of the lie of the land and where the buildings stand on it. The signs mark selected, sourced sites; the terrain is generalized; nothing here is a navigational map. But you've heard of the Virginia internet corridor and the Texas build-out. Well, here they are.

OPERATIONAL HYPERSCALE CAPACITY · END 2024

More than half is in the United States

Capacity means critical IT load in megawatts—not floor area, facility count, or electricity consumed in a year.

Synergy reports the U.S. share directly and describes Europe and China as each roughly one-third of the remaining balance. The underlying worldwide MW denominator is proprietary, so this graphic does not invent it.

ENERGY, HEAT, WATER

One system, three linked concerns

Nearly all electricity leaves as low-grade heat—of which the IEA puts 70–80% within reach of heat pumps. The cooling design then decides whether that heat is shed with water or with more electricity. Every arrow is 25 TWh or 25 billion gallons; both water bundles rise from one baseline so the gap cannot hide.

THE ON-SITE WATER, IN SCALE

A familiar amount, on a steep curve

The 17 billion gallons consumed on site in 2023 is small against how the country already moves water—and it is projected to roughly double to quadruple by the end of the decade. Each drop below is 5 billion gallons; hatched drops span the projected range.

17 BILLION GALLONS IS…

≈ 1% of the water Americans pour on their lawns each year—one filled drop in a hundred.

≈ a city of 155,000 homes, supplied for a full year at the U.S. average of 300 gallons a household each day. Each sign is 10,000 homes.

…and about 3% of the 531 billion gallons U.S. golf courses use annually.

DIRECT COOLING WATER, PER YEAR

2023 · 17B GALLONS

PROJECTED · 33–73B GALLONS

EACH DROP = 5 BILLION GALLONS · SOLID DROPS REACH THE LOW PROJECTION, HATCHED DROPS THE HIGH ONE

CONSTRUCTION JOBS ARE NOT PERMANENT JOBS

A large build; a smaller operating staff

Company disclosures use different definitions and time windows. These two rows preserve those definitions instead of summing them.

META HYPERION

Peak construction workforce projected for June 2026 vs. completed-site operational jobs.

CONSTRUCTION · 5,000

OPERATIONS · MORE THAN 500

MICROSOFT FAIRWATER

People who contributed during two years of construction vs. current full-time onsite employees—not peak concurrent construction.

CONSTRUCTION · 10,000

OPERATIONS · NEARLY 550

Each worker sign represents 500 people. Partial signs preserve the reported amount. Virginia’s JLARC separately found a typical 250,000-square-foot data center employs about 50 full-time workers, roughly half contractors.

WHY THE NEW BUILDINGS?

Training compute rose faster than benchmark scores

One chip sign equals the entire compute used to train GPT-3—count them. Yellow bolts encode five MMLU points each, with partial bolts preserving the reported score. This juxtaposes scale and one benchmark; it does not claim compute alone caused the score.

Do not read this as a clean production function.

MMLU evaluation settings differ, the benchmark contains known errors and possible contamination, and training is only one demand source. Inference serves users continuously; research runs and failed experiments are not represented by a final model’s training FLOP.

Disclosure ledger

“Not disclosed” is a result. It prevents planned capacity, utility service, accelerator count, cooling-loop volume, and permanent employment from collapsing into one unsupported story.

Project	Status	Power	Compute	Jobs	Water
Colossus 1 · MemphisxAI	Post-2022 operating	150 MW utility service disclosed by MLGW	Over 220,000 H100/H200/GB200 GPUs reported in May 2026	xAI says hundreds of permanent jobs; exact count not disclosed	Facility consumption not disclosed; a shared recycled-water plant is proposed
Fairwater · WisconsinMicrosoft	Post-2022 operating	Site MW capacity not publicly disclosed	Microsoft says hundreds of thousands of NVIDIA GPUs	Nearly 550 full-time onsite; nearly 10,000 construction workers	Closed-loop liquid cooling; 90% of capacity has no evaporation loss
Stargate · AbileneOpenAI / Oracle / Crusoe	Post-2022 operating	206 MW initial building; campus planned for 1.2 GW	GB200 racks delivered; rack count not disclosed	Site-specific permanent headcount not disclosed	Site water consumption not publicly disclosed
Hyperion · Richland ParishMeta	Building now	Described by Meta as multi-gigawatt; exact site capacity not disclosed	Planned as Meta’s largest AI training cluster	5,000 peak construction; more than 500 operational jobs	Consumption not disclosed; Meta pledges watershed restoration equal to use
Stargate · Milam CountyOpenAI / SB Energy	Building now	1.2 GW lease disclosed January 2026	GPU count not disclosed	Thousands of construction jobs; permanent count not disclosed	Site water demand not publicly disclosed
Stargate · LordstownOpenAI / SoftBank	Building now	Only a combined 1.5 GW scale figure was disclosed for two SoftBank sites	GPU count not disclosed	Site-specific counts not disclosed	Site water demand not publicly disclosed
Stargate · Shackelford CountyOpenAI / Oracle	Planned	Part of a combined >5.5 GW group; no site allocation disclosed	GPU count not disclosed	Only a five-site combined jobs estimate was disclosed	Site water demand not publicly disclosed
Stargate · Doña Ana CountyOpenAI / Oracle	Planned	Part of a combined >5.5 GW group; no site allocation disclosed	GPU count not disclosed	Only a five-site combined jobs estimate was disclosed	Site water demand not publicly disclosed

Graphics can carry epistemic status

The shared ISOTYPE signs are deliberately simple; the data contract is not. Each map record carries status, date language, denominator, source, and caveat. Every repeated sign is a glyph scene node allocated by tokenLayer as aunitized-measure — canvas-painted pictograms whose partial fills preserve the reported amounts — so the rich poster stays observable, keyboard-navigable, and accessible without a parallel bookkeeping layer.

JSX

import { GeoCustomChart } from "semiotic/geo" import { geoHitTarget, hatchFill, tokenLayer } from "semiotic/recipes" // Each site's capacity becomes an explicit tokenized measure, and each token // becomes a feet-anchored glyph scene node standing on the relief — canvas- // painted, with the partial final sign riding the node's fraction + ghostColor. // One geoHitTarget per site keeps the stack a single keyboard/hover mark. function dataCenterMapLayout(ctx) { const placed = sites.map((site) => { const section = sectionFor(site) // one of five parallels const [west, east] = outlineExtentAtLatitude(US_OUTLINE, section.latitude) const t = (site.lon - west) / (east - west) // position along the section const x = interpolate(P(west, lat)[0], P(east, lat)[0], t) const y = baseline - profileElevationAt(section.profile, t) * 34 return { site, x, y } }) return { nodes: [ ...placed.map(({ site, x, y }) => geoHitTarget({ x, y, r: 14, datum: site, id: site.id })), ...placed.flatMap(({ site, x, y }) => tokenLayer({ input: site.powerMW, encoding: { tokenType: "glyph", tokenSemantics: "unitized-measure", countStrategy: "unitized", unitValue: 100, unitMeaning: "one server sign = 100 MW", }, options: { tokenSize: 11, glyph: SERVER_SIGN, // a multi-part GlyphDef color: STATUS_META[site.status].color, // one cut, many inks ghostColor: PAPER_DEEP, datum: null, positionToken: (unit) => ({ x: x + unit.index * 12, y, // standing on the terrain }), }, }).nodes, ), ], overlays: <ReliefSectionsAndLabels water={hatchFill({ id: "sea", angle: 90 })} />, } } <GeoCustomChart areas={[US_OUTLINE]} points={sites.filter((site) => visibleStatuses.has(site.status))} projection="equirectangular" layout={dataCenterMapLayout} enableHover accessibleTable onObservation={inspectSite} />