As a Georgia Tech computer science major, I spent a great deal of time at the Rich Computer Center. From 1978 to 1981, I vividly remember the CDC Cyber mainframe behind a large glass window, another glassed room with PR1ME and PDP minicomputers, and the raised floors, cooling, and steady hum. I eventually bought an ADDS Consul 580 terminal and an AJ-242 acoustic coupler for my Harrison dorm room, which my friends jokingly called “Rich Center East.” I could access computing power without standing beside it, but I knew where it lived.
As a programmer at the University of South Carolina Computer Services Division, where I started in 1981, I gave visitors tours of the computer center, which supported the statewide public university education system. I remember the delivery and installation of a refrigerator-sized 1+GB IBM 3380 disk drive among the mainframe equipment; you can buy 1GB thumb drives at a grocery store these days. Years later, after starting my own business, I ran hosting infrastructure in colocation facilities; the machines had changed; the raised floors, cooling, racks, noise, electrical service, and the need for someone to maintain everything had not.
I watched data communications move from ten characters per second teletypes to 300- and 1,200-baud modems, T1 lines, and broadband. Storage and processing improved just as dramatically. What changed was not only what these systems could do, but what it cost to do it. Better price/performance made services economical that would once have been impractical.
As Internet use and cloud computing expanded, Google, Amazon, and others built the facilities to support them. Bitcoin mining brought another wave of demand for computing, electricity, and specialized chips. Miners pushed from ordinary processors toward purpose-built chips that did more work per watt. By 2019, estimates of Bitcoin’s annual electricity use were being compared with that of entire countries. Now AI is driving another expansion of the infrastructure.[26]
Better technology has regularly eased one bottleneck while enabling new uses that expose the next.
Computing infrastructure has never been weightless. Today’s AI capacity is another stage in its development, not a wholly new infrastructure. Some projects are larger and more concentrated than what came before. But the national debate about their electricity and water use too often ignores history, comparable uses, and local choices. I want to cut through that rhetoric and ask what bargain each host community can actually make.
What changed inside the building
By 2023, colocation and hyperscale facilities housed about 74 percent of U.S. servers, up from roughly 20 percent in 2014.[9] That shift was well underway before generative AI became a mass service. AI is changing the work performed in those facilities and, at some sites, the demands placed on their power and cooling systems.
A former bitcoin-mining site may already have land and substantial electrical connections, but its mining chips cannot simply run AI models. Training builds or updates an AI model; inference is what happens when people use that model to answer a question, generate an image, or carry out a task. Each use requires computation; a longer task may call on the model many times as it plans, acts, and checks its work. Training draws attention because it is intensive, but inference creates continuing demand as a service gains users and uses. I also expect agents that work through longer tasks and run for more hours to add to that demand. OpenAI reported in September 2026 that ChatGPT had 1.2 billion weekly users. That helps explain why operators are building capacity, and why a host community needs to understand the electricity and water required to serve it.[20]
Why electricity matters
Data centers need electricity to run computers and remove the heat they produce. U.S. data-center electricity use rose nearly 90 percent from 2000 to 2005 as Internet services, business computing, and server installations expanded. It rose 24 percent over the next five years and about 4 percent from 2010 to 2014. Digital activity did not stop growing. Virtualization and more efficient facilities delivered more service with less additional electricity. That history matters because innovation is now addressing AI bottlenecks in chips, models, software, cooling, and how we use existing capacity.[9][27]
How much electricity is that?
What is the difference between power and energy? Power is the rate at which electricity is used at a given moment, measured in megawatts or gigawatts. Energy is the total used over time, measured in megawatt-hours or terawatt-hours. A project’s requested power describes the grid connection it seeks; its annual energy usage depends on what is built and how steadily it runs.
Why do local megawatts matter? A facility drawing 100 megawatts continuously is a large single-site load, roughly equivalent to one-tenth of a large nuclear reactor running at full output. In a year, it would use roughly as much electricity as 84,000 average U.S. residential customers did in 2024. Concentrating that load at one connection can require new substations and lines. For the host community, the central questions are who builds them, who pays, and what value the project delivers in return.[11]
How large is U.S. data-center use today? Berkeley Lab estimated 192 terawatt-hours in 2024, about 4.7 percent of U.S. electricity. For scale, that is about one-eighth of residential electricity sales and roughly eleven times estimated electric-vehicle electricity use that year.[2][11][15]
How fast might it grow? Berkeley Lab estimates that U.S. data centers used 192 terawatt-hours in 2024; its 2030 reference forecast is 649, with scenarios from 521 to 843. The reference case implies about 22.5 percent annual growth from 2024 to 2030, compared with about 13.7 percent annually during the 90 percent rise from 2000 to 2005. The International Energy Agency reports that energy use per AI task fell by at least tenfold each year in recent years as hardware and software improved, even as more demanding uses such as reasoning, video generation, and agents became more common. Cheaper inference uses less electricity per task, but it also makes new uses economical. Efficiency, price, capability, and demand are all changing at once. I expect efficiency to bend the electricity-growth curve again, as it did after the early Internet expansion, even if total use continues rising for some time.[2][10]
Demand can also be flexible. Some computing work can move to hours when the grid is less heavily used, easing costly peaks. Brattle modeled more than $100 billion in U.S. consumer savings over a decade from better use of the existing grid; flexible demand is one way to get there. A community can ask whether a proposed facility will actually shift its load, and on what terms.[17]
Could today’s shortage become tomorrow’s surplus?
Companies build data centers, generation, and grid connections based on forecasts made years in advance. Meanwhile, inference may become much cheaper per task, and cheaper AI may create uses no one forecast. Capacity could briefly outrun demand, or new demand could absorb it. We have seen both forces in earlier technology buildouts. The question is how they will interact this time.
U.S. utilities built for peak demand in the 1970s, which turned out to be lower than forecast. A natural-gas plant construction boom in the early 2000s again left excess generating capacity in many places.[5] Internet backbone providers also laid enough fiber that a 2001 federal review found ample backbone capacity, even as connections closer to customers remained constrained.[22]
ERCOT’s planning case includes 25 gigawatts of additional data-center load by 2035, roughly the power of twenty-five large nuclear reactors operating at full output.[6][21] There is a practical dilemma: build too far ahead and some generation or grid capacity may sit idle; build too slowly and infrastructure may hold back useful services. The answer will vary by region, timing, and the terms under which utilities and customers commit to investment.
What about supply? I see the growth of renewables as an advantage: it diversifies electricity supply, and solar and wind can be paired with on-site generation and batteries where the site and economics permit. The electric-vehicle supply chain is helping scale batteries for stationary storage, which can ease short peaks and make variable generation more useful. The International Energy Agency projects that renewables will supply nearly half of the additional electricity data centers use globally between 2024 and 2030, while natural gas and coal together supply more than 40 percent. This buildout is likely to use several energy sources, not a single national recipe.[12]
Galaxy’s Helios campus in Texas shows what this evolution looks like on the ground. Originally developed for bitcoin mining, the site now has its first AI phase for CoreWeave. Galaxy reports roughly 200 megawatts of gross electrical capacity for that phase, including 133 megawatts for computing equipment; the rest covers cooling, power delivery, and other supporting systems. Its land, electrical access, and experience running a power-intensive site combine to meet AI demand.[1]
Where does the heat, and the water, go?
Almost all the electricity a computer uses ends up as heat. Cooling moves that heat from the chips to the outdoors.
How is the heat removed? Older rooms often used chilled air. Dense servers may use rear-door exchangers, liquid cold plates on chips, or immersion in a nonconductive fluid. In one immersion design, the fluid stays liquid; in another, it vaporizes and condenses. The final stage may use dry air, evaporative cooling, or a hybrid. A liquid or “closed-loop” system can therefore have low on-site water use, or it can ultimately rely on an evaporative tower.[3]
What should a community ask about water? How much would the whole campus withdraw and consume, particularly in a dry season? How much wastewater would it return, and what would it contain? Where does the source water come from, and what happens during drought? A dry-cooling choice may reduce site water consumption while raising electricity demand. Water used to generate that electricity may be consumed in a different watershed.
Water usage effectiveness, or WUE, measures on-site water use per kilowatt-hour used by computing equipment. Berkeley Lab modeled a U.S. average of about a tenth of a gallon per equipment kilowatt-hour in 2023. That average hides a wide range: heat rejected through dry air uses very little on-site water, while evaporative systems use water to reduce electricity needs. A hybrid can switch between the two as conditions change.[3]
Utah’s proposed Stratos campus combines computing, on-site power generation, and a water system in one large project. It shows why a community should negotiate the whole system, not just ask whether a server room uses water: the power plant, cooling equipment, treatment, and discharge can all shape the local bargain. Box Elder County and Utah have tools to decide whether the proposed terms work for them. Local jurisdictions' ability to set terms is a strength of the American approach, not an obstacle to remove.[4]
Could cheaper electricity make water cheaper? Yes, by lowering pumping and desalination costs. Then treatment, pipelines, and concentrated brine become the next bottlenecks. Once one constraint eases, solving the next often becomes more attractive. Stratos is a useful test of whether that process can work inland, where water quality and concentrate disposal matter.[7]
What are we choosing to scrutinize?
How much freshwater do familiar activities consume? Counting the electricity that runs them, Berkeley Lab estimates U.S. data centers used about 230 billion gallons in 2023. A rough estimate for U.S. golf courses in 2024 is about 400 billion gallons. A beef-production model estimates about 6.1 trillion gallons per year, mostly for irrigating feed crops. That puts golf in the same broad range as data centers on this rough estimate, while beef production is far larger.[13]
Why do we scrutinize one use so intensely while treating other familiar uses as ordinary? Why do some loud voices claim that water use from computing infrastructure growth is uniquely objectionable? We need a consistent standard: what water is being used, what value that use produces, and what alternatives the community has. When we single out a new use while overlooking familiar ones, our prior beliefs may do more of the work than the numbers.[16]
The next constraint
In The AI Industrial System, I described how pressure can shift from one part of the AI system to another. For a data-center project, the grid connection may be the immediate constraint; elsewhere, water, heat removal, equipment, labor, or community consent may come next. Those pressures are already driving responses: on-site power generation, immersion and on-chip liquid cooling, and new ways to manufacture gas-turbine blades. I expect some attempts to fail and others to work. But pressure to solve these engineering constraints comes from more than today’s AI services. Workforce changes may strengthen demand for the computing they support.[3][24]
Many societies are also facing a shrinking pool of working-age people. The United Nations says population has already peaked in 63 countries and areas, including China, Germany, and Japan, even though the world as a whole is still growing. Across the OECD, the population aged 20 to 64 is projected to fall 8 percent between 2023 and 2060; in a quarter of member countries, the projected decline exceeds 30 percent. China’s own statistics recorded 3.39 million more deaths than births in 2025, continuing its population decline. Those trends do not mean every occupation faces a shortage or that software can simply replace people. They do mean that aging societies have reason to make their workers more productive. I expect that pressure to increase investment in robotics and AI, making computing demand a response to a real economic constraint, not just a technology fad.[23][25]
The measure that matters is useful work delivered for the energy, water, and capital invested, with humans still accountable for how the systems are used.[14][23]
The local choice
Data centers cluster where power, fiber, land, customers, and favorable terms come together, a pattern evident in Northern Virginia, the world’s largest concentration of data centers. Existing networks and expertise attract additional facilities, and that concentration can increase pressure on shared utilities. Texas, Utah, and Georgia offer different combinations of these ingredients. Jurisdictions cannot be forced to accept data center construction they don’t want, and that’s as it should be.[18]
The jurisdictional and regulatory framework for data center construction is not new: land-use rules, water rights, utilities, environmental protections, and contracts already shape what can be built. Municipalities can accept, condition, or reject projects where they hold that authority; counties and state bodies decide some projects elsewhere. Variation among jurisdictions creates a marketplace, giving developers choices and communities leverage. This is the American way!
Atlanta shows the local bargain in action. The city restricted new data centers in certain transit-oriented areas, and Digital Realty is seeking an exception for a West End site. Whatever happens to that request, the case shows a city making its own judgment about where a data center belongs and what terms it should meet.[8]
Some projects will be good bargains and some will not. A project that shifts grid-upgrade costs onto households or strains scarce dry-season water could be a bad bargain. In investment banking, I learned to examine the terms before accepting the headline. Communities can ask what infrastructure must be built, who pays, what water is needed, what benefits are plausible, and which obligations are enforceable. Tax incentives may help win a project, but they are a price the jurisdiction chooses to pay, not a substitute for permission to build.[19]
One community may welcome a data center for its tax base and infrastructure; another may reject a particular parcel or water source. Existing state and federal protections still apply where required. If residents dislike their rules, they can change them through the responsible jurisdiction’s own process. That is how self-government should work.
I expect those decisions to shape the data center rollout, not stop it. Demand for computing has driven successive waves of infrastructure, and AI is just the next wave. Places that plan well, including economically struggling communities seeking investment, can compete for projects and ask for favorable terms. The long-term value will show up in tax receipts, utility costs, water use, jobs, and complementary activity, not in the scale of a press release.
The cloud has always lived somewhere. I saw one of its earlier homes through glass at Georgia Tech. Today’s campuses are another stage of that evolution. Communities can decide what to welcome, what to require, and what to refuse. The stronger the demand, the more important it is to be clear about the bargain.
You can reach me directly at dave@aworkingmodel.com.
Sources
[1] Galaxy, Helios phase-I release, July 6, 2026: https://www.galaxy.com/newsroom/galaxy-completes-phase-i-of-its-helios-data-center-campus ; Galaxy, mining-site repurposing, May 14, 2025: https://www.galaxy.com/newsroom/building-the-infrastructure-for-an-ai-powered-future.
[2] Lawrence Berkeley National Laboratory, United States Data Center Energy Usage Report: 2025 Update, June 2026: https://www.energy.gov/documents/united-states-data-center-energy-usage-report-2025-update . Model and scenario, not metered AI-only energy.
[3] U.S. Department of Energy, Best Practices Guide for Energy-Efficient Data Center Design, 2024: https://www.energy.gov/sites/default/files/2024-07/best-practice-guide-data-center-design_0.pdf ; DOE, Cooling Water Efficiency Opportunities, 2019: https://www.energy.gov/cmei/femp/cooling-water-efficiency-opportunities-federal-data-centers ; Lawrence Berkeley National Laboratory, 2024 U.S. data-center report: https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf . Site WUE is annual on-site water per IT kWh; Berkeley Lab’s modeled 2023 average is just over 0.36 L/IT-kWh. Its 4.52 L/kWh indirect estimate uses total facility electricity and regional grid-mix assumptions; it is not site withdrawal, and the two denominators must not be added as written. Cooling designs trade site water against electricity and differ by climate and operation.
[4] Box Elder County, Stratos FAQ, accessed September 25, 2026: https://www.boxeldercountyut.gov/661/Stratos-Page-FAQ-Page ; State-hosted FAQ: https://governor.utah.gov/wp-content/uploads/FAQ-on-Stratos-Project.pdf ; MIDA Resolution 2026-06, April 24, 2026: https://www.midaut.org/s/20260424-Stratos-Project-Area-Plan-MIDA-BOARD-Resolution-06-dggg.pdf ; county–MIDA agreement, May 4, 2026, section 5(j): https://www.boxeldercountyut.gov/DocumentCenter/View/2189/Box-Elder-County-MIDA-Stratos-Agreement-26-15-.
[5] U.S. Energy Information Administration historical capacity assessment: https://www.eia.gov/electricity/pdfpages/perfiss/index.php ; early-2000s gas buildout: https://www.eia.gov/todayinEnergy/detail.php?id=1690 ; California curtailment: https://www.eia.gov/todayinEnergy/detail.php?id=65364 . Distinct kinds of surplus.
[6] North American Electric Reliability Corporation, 2025 Long-Term Reliability Assessment, Texas RE-ERCOT section, published 2026: https://www.nerc.com/globalassets/our-work/assessments/nerc_ltra_2025.pdf . Planning forecast and its adjustments; no claim of a realized 2035 outcome.
[7] Bureau of Reclamation, Estimating the Cost of Brackish Groundwater Desalination in Texas, July 2014: https://www.usbr.gov/gp/otao/estimating_cost_brackish_groundwater_desalination_texas.pdf ; Texas Water Development Board desalination FAQ: https://www.twdb.texas.gov/innovativewater/desal/faq.asp . Planning-level cost drivers from Texas, not a Stratos estimate.
[8] Atlanta City Council, 2024 data-center zoning action: https://citycouncil.atlantaga.gov/Home/Components/News/News/3928/175?arch=1 ; Digital Realty, Atlanta West End project page, accessed September 26, 2026: https://www.digitalrealty.com/about/projects/atlanta-west-end ; NAACP Legal Defense Fund, August 21, 2026 opposition letter: https://www.naacpldf.org/wp-content/uploads/Atlanta-City-Council-data-center-letter-8.17-FINAL-2.pdf.
[9] Lawrence Berkeley National Laboratory, United States Data Center Energy Usage Report, 2024: https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf ; Lawrence Berkeley National Laboratory, United States Data Center Energy Usage Report: 2025 Update, June 2026: https://www.energy.gov/documents/united-states-data-center-energy-usage-report-2025-update ; Lawrence Berkeley National Laboratory, 2016 assessment: https://bies.lbl.gov/publications/united-states-data-center-energy . Modeled electricity and server-location shares, not a consistent historical series of facility floor area; report vintages revise prior estimates.
[10] International Energy Agency, Key Questions on Energy and AI, April 2026: https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary . Global growth rates are IEA estimates, not U.S. project-meter readings; AI-focused and all-data-center categories differ.
[11] U.S. Energy Information Administration, estimated light-duty EV electricity use, 2024, table D1: https://www.eia.gov/electricity/monthly/current_month/april2026.pdf ; U.S. Energy Information Administration, average U.S. residential electricity purchases in 2024: https://www.eia.gov/todayinenergy/detail.php?id=65244 . EV and household figures are national annual energy comparisons; neither gives local coincident peak demand. The 100 MW example assumes continuous full load for 8,760 hours.
[12] International Energy Agency, Energy and AI, April 2025: https://www.iea.org/reports/energy-and-ai/energy-supply-for-ai ; IEA, Electricity 2026, flexibility section: https://www.iea.org/reports/electricity-2026/flexibility ; IEA, Global EV Outlook 2026, EV batteries section, May 2026: https://www.iea.org/reports/global-ev-outlook-2026/electric-vehicle-batteries . Supply shares and battery deployment are projections or market observations, not proof of any P007 site’s generation mix or storage duration. EV battery terawatt-hours measure storage capacity, not annual electricity consumption.
[13] Lawrence Berkeley National Laboratory, 2024 data-center report: https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf ; Golf Course Superintendents Association of America, 2025 water survey: https://www.gcsaa.org/docs/default-source/what-we-do/gcep-phase-4-water-report.pdf?sfvrsn=c829dd3e_0 ; Rotz et al., U.S. beef farm-gate water-footprint model, 2019: https://orcattle.com/wp-content/uploads/2021/02/Footprint-of-US-Beef-Production.pdf . For a consumption-based comparison, Berkeley Lab estimates that in 2023, data centers used 66 billion liters directly and nearly 800 billion liters indirectly for electricity, or about 230 billion gallons combined. The GCSAA estimates 1.63 million acre-feet of golf irrigation in 2024; applying a historical 75% golf-course consumptive-use coefficient cited by the U.S. Geological Survey (https://pubs.usgs.gov/sir/2007/5197/) gives roughly 400 billion gallons. That coefficient comes from a Great Lakes study, so the national golf figure is approximate. Rotz et al. model 23.2 trillion liters of annual blue-water consumption for U.S. beef production, or about 6.1 trillion gallons, mostly from feed-crop irrigation. The estimates span different years and activity boundaries.
[14] IEA, Key Questions on Energy and AI, April 2026: https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary ; U.S. Bureau of Labor Statistics, labor-force projection 2024–34, September 2025: https://www.bls.gov/careeroutlook/2025/data-on-display/economy-and-labor-force.htm . Energy per task varies enormously; the labor projection is context for possible demand, not proof of AI adoption or facility utilization.
[15] U.S. Energy Information Administration, Electric Power Annual, table 2.8, final 2024 retail sales: https://www.eia.gov/electricity/annual/table.php?t=epa_02_08.html . Residential: 1,482.874 TWh; industrial: 1,034.584 TWh. Data-center electricity is not an extra sector to add to those sales; EIA notes classification and reporting changes for data centers and mining in commercial and industrial sales.
[16] Ziva Kunda, “The Case for Motivated Reasoning,” Psychological Bulletin, 1990: https://pubmed.ncbi.nlm.nih.gov/2270237/ ; Raymond S. Nickerson, “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises,” Review of General Psychology, 1998: https://doi.org/10.1037/1089-2680.2.2.175 ; Valerie F. Reyna and C. J. Brainerd, “Numeracy, Ratio Bias, and Denominator Neglect in Judgments of Risk and Probability,” Learning and Individual Differences, 2008: https://www.sciencedirect.com/science/article/pii/S1041608007000428 . These sources describe general tendencies, not the motives of any named P007 opponent or developer. A national denominator may supply perspective but cannot settle a local water or power constraint. The symmetrical questions are an editorial test, not a psychological diagnosis.
[17] Brattle, The Untapped Grid (2026). Illustrative utility-system modeling, not observed savings or proof of flexibility at any named site.
[18] Virginia Joint Legislative Audit and Review Commission, Data Centers in Virginia. Identifies reasons for Northern Virginia’s concentration, not the cause of every project elsewhere.
[19] Texas governor’s tax-incentive proposal; Texas Comptroller’s qualifying-data-center exemption guidance; Pennsylvania Executive Order 2026-05 announcement. The Texas phase-out is proposed; Pennsylvania directed conditions on an existing exemption.
[20] OpenAI, DevDay 2026 Recap (September 29, 2026): https://openai.com/index/devday-2026-recap/ . Company-reported 1.2 billion weekly users.
[21] U.S. Department of Energy, “How Much Power Does a Nuclear Reactor Produce?” (March 31, 2021): https://www.energy.gov/ne/articles/infographic-how-much-power-does-nuclear-reactor-produce . A typical reactor is approximately one gigawatt of generating capacity.
[22] U.S. Government Accountability Office, Telecommunications: Characteristics and Competitiveness of the Internet Backbone Market (October 16, 2001): https://www.gao.gov/products/gao-02-16 . The review found ample backbone fiber but identified possible local access constraints.
[23] OECD, Employment Outlook 2025 (July 9, 2025): https://www.oecd.org/en/about/news/press-releases/2025/07/oecd-job-markets-remain-resilient-but-population-ageing-will-cause-significant-labour-shortages-and-fiscal-pressures.html ; United Nations, World Population Prospects 2024 summary (July 11, 2024): https://www.un.org/sustainabledevelopment/blog/2024/07/press-release-wpp2024/ ; International Federation of Robotics, World Robotics 2026 release (September 24, 2026): https://ifr.org/ifr-press-releases/five-million-robots-now-operate-in-factories-globally . Population and robot-installation trends; the effect on AI demand is Dave’s forecast.
[24] Siemens, 3D-printed gas-turbine blade tests (February 6, 2017): https://press.siemens.com/global/en/pressrelease/siemens-achieves-breakthrough-3d-printed-gas-turbine-blades ; Jennifer Hiller, The Wall Street Journal, Is 3-D Printing the Answer to AI’s Gas Turbine Crunch? (September 24, 2026): https://www.wsj.com/business/energy-oil/is-3-d-printing-the-answer-to-ais-gas-turbine-crunch-865ae5d0 . A component test and a reported attempt to accelerate turbine-blade production.
[25] National Bureau of Statistics of China, Statistical Communiqué on the 2025 National Economic and Social Development (February 28, 2026): https://www.stats.gov.cn/english/PressRelease/202602/t20260228_1962661.html. Official 2025 population, births, deaths, and age distribution; the forecast that demographic pressure increases AI demand is Dave’s interpretation.
[26] Cambridge Centre for Alternative Finance, Cambridge Bitcoin Electricity Consumption Index launch, July 2, 2019: https://www.jbs.cam.ac.uk/2019/cambridge-bitcoin-electricity-consumption-index-cbeci/ ; Cambridge Judge Business School, Bitcoin electricity consumption: an improved assessment, 2023: https://www.jbs.cam.ac.uk/2023/bitcoin-electricity-consumption/ . The 2019 annualized estimate and country comparisons give historical scale; the later review describes specialized mining hardware and revised estimates.
[27] U.S. Environmental Protection Agency, Report to Congress on Server and Data Center Energy Efficiency, 2007: https://datacenters.lbl.gov/sites/default/files/EPA_Datacenter_Report_Congress_Final1.pdf . Contemporary account of Internet services, business digitization, record retention, and disaster recovery as contributors to data-center growth; it does not apportion the 2000–2005 electricity increase.