Every figure includes its primary source, publication date, check date, supported conclusion and attribution boundary. Copy the citation or open the full evidence page.
🔬Every number has a primary source and an attribution grade. The Supports / Does not support lines define its scope. Grades follow the published rubric; items marked Context describe market conditions rather than an AI attribution.
Electricity
11.8%of U.S. electricity projected for data centers in 2030, LBNL reference case
⚡AI-driven
Lawrence Berkeley National Laboratory’s reference case projects data centers will use 649 TWh in 2030 — 11.8% of U.S. electricity — with a scenario range of 9.5% to 15.3%.
✓Supports
National data-center demand growth, with AI servers as a major documented driver.
⚠️Does not support
That AI raised any specific household’s electric bill. That effect depends on the utility, its rate agreements and who funds dedicated upgrades.
80–90% / 36%modeled generation / transmission capacity growth needed in Virginia by 2040
＋AI-contributing
Virginia’s legislative auditor estimated that serving even half of unconstrained demand would require generation capacity to grow 80% to 90% and transmission capacity to grow 36% by 2040.
✓Supports
The extraordinary infrastructure scale implied by Virginia’s data-center-driven demand forecast.
⚠️Does not support
That every requested load will materialize, that this exact resource mix will be built or that the forecast is a bill impact.
1.6 → 1.4estimated improvement in average U.S. data-center PUE, 2014 to 2023
·Context
LBNL estimates average U.S. data-center power usage effectiveness improved from about 1.6 in 2014 to 1.4 in 2023, reducing infrastructure overhead from roughly 40% to 30% of facility electricity.
✓Supports
Meaningful efficiency gains in cooling and power delivery during a period of growing computation.
⚠️Does not support
A decline in total data-center electricity use, useful computation per kilowatt-hour or the footprint of any individual facility.
+2.1%estimated residential-price effect of data-center entry, 2010 to 2024
?Contested
An MIT CEEPR working paper estimates that data-center entry increased residential electricity prices by 2.1% between 2010 and 2024, with larger effects at investor-owned utilities.
✓Supports
A measurable historical relationship between data-center entry, infrastructure investment and residential price increases in one causal study.
⚠️Does not support
That every data center raises rates, that the estimate applies to future supply constraints or that the effect came from AI rather than data centers generally.
≈−4%estimated residential-price effect of doubling data-center capacity, 2015 to 2024
?Contested
A competing 2026 study estimates that doubling data-center capacity reduced residential electricity prices by roughly 4% from 2015 to 2024 by spreading fixed system costs across more sales.
✓Supports
Evidence that durable large loads can reduce average rates when spare capacity and economies of scale dominate.
⚠️Does not support
That new data centers will lower future bills where power is scarce or infrastructure is overbuilt; the authors explicitly warn that supply constraints could reverse the result.
$14–$37/momodeled Dominion residential generation and transmission increase by 2040
＋AI-contributing
Virginia’s legislative auditor estimated that a typical Dominion residential customer could pay $14 to $37 more per month in constant dollars by 2040 for generation and transmission under the modeled demand buildout.
✓Supports
A quantified forward ratepayer risk in a state where data centers are the main forecast demand driver.
⚠️Does not support
A current bill increase or an inevitable outcome; the estimate predates later large-load rate protections and depends on demand, construction and cost allocation.
85% / 60%minimum contracted T&D / generation demand charges for qualifying Virginia large loads
·Context
Beginning in 2027, qualifying Dominion large-load customers must pay for at least 85% of contracted transmission and distribution demand and 60% of contracted generation demand.
✓Supports
A regulator using minimum charges and a separate customer class to put underuse and stranded-asset risk on large loads.
⚠️Does not support
That every residual system cost has been isolated from other customers or that comparable protections exist outside Dominion’s Virginia territory.
3:1HBM-to-DDR5 production capacity trade ratio reported by Micron
⚡AI-driven
Micron reports that producing high-bandwidth memory for AI consumes roughly three times the manufacturing capacity needed for the same amount of standard DDR5.
✓Supports
A documented production trade-off between AI memory and conventional DRAM at a leading producer.
⚠️Does not support
A specific dollar or percentage effect on retail RAM prices. That pass-through has not been measured.
−6.3%change in the aggregate BLS semiconductor PPI, Jan 2024 to Jun 2026
·Context
The broad BLS producer price index for semiconductor manufacturing fell 6.3% from January 2024 to June 2026, even as memory and storage supply tightened.
✓Supports
A caution: “AI made all chips more expensive” is not visible in the aggregate index.
⚠️Does not support
Price movements in specific segments such as HBM, which this aggregate series does not isolate.
+41%growth in U.S. distribution-transformer demand since 2019, per DOE
＋AI-contributing
The Department of Energy reports distribution-transformer demand up 41% since 2019, driven by post-pandemic demand, aging infrastructure and new loads including data centers.
✓Supports
Data centers adding pressure to an already strained transformer market.
⚠️Does not support
That AI started the transformer shortage. DOE traces it to causes that predate the AI buildout.
1–2+ yrtypical distribution-transformer lead time in 2024, per DOE
＋AI-contributing
DOE-reported lead times for distribution transformers reached one to two years or longer in 2024, with large power transformers reaching three to four years.
✓Supports
Long waits for grid equipment affecting utilities, housing and industrial projects alike.
⚠️Does not support
Attribution of any single delayed project to AI without project-specific evidence.
+5.9%rise in the BLS power and distribution transformer PPI, Oct 2024 to Jun 2026
＋AI-contributing
The BLS producer price index for power and distribution transformers rose 5.9% between October 2024, the first observation of the current series, and June 2026.
✓Supports
Continued price pressure in grid equipment markets.
⚠️Does not support
Separating AI-driven orders from utility replacement and electrification demand.
5 yearsmedian request-to-operation time for generation projects completed in 2023
×Scapegoated
Generation projects completed in 2023 spent a median of five years in interconnection queues, according to LBNL — a backlog that predates the current AI buildout.
✓Supports
Slow grid processes as a long-standing structural problem.
⚠️Does not support
The claim that AI caused the five-year queue. The delay predates the AI buildout.
≈2,600 GWof proposed generation capacity waiting in U.S. queues, over 95% zero-carbon
·Context
Nearly 2,600 gigawatts of proposed generation was waiting in U.S. interconnection queues as of LBNL’s 2024 study, more than 95% of it zero-carbon resources.
✓Supports
The scale of supply waiting to connect while new demand grows.
⚠️Does not support
That data centers displace those projects. Generation queues and large-load processes are different procedures.
14%of queued generation capacity in the studied cohorts ultimately built
·Context
Across the queues for which LBNL had completion data, 19% of projects and 14% of capacity requesting connection from 2000 to 2018 had been built by the end of 2023.
✓Supports
Treating the 2,600 GW generation-queue headline as developer interest rather than supply certain to reach operation.
⚠️Does not support
That the remaining queue has no value or that generation already under construction cannot help serve new demand.
>10,000×variation in measured workload-level data-center water use, per LBNL
?Contested
LBNL’s review of data-center water use found more than 10,000-fold variation across workloads, depending on cooling design, climate, utilization and grid mix.
✓Supports
Why no single per-prompt water figure is reliable.
⚠️Does not support
The claim that every AI prompt uses a bottle of water. No universal per-prompt estimate fits the measured range.
38%of Loudoun County’s FY2026 General Fund revenue generated by data centers
·Context
Loudoun County reports that data centers generate 38% of its General Fund revenue, and it maintains a stabilization reserve for volatility in those receipts.
✓Supports
Large local fiscal benefits—and meaningful revenue dependence—in the country’s most concentrated data-center market.
⚠️Does not support
A net-benefit calculation after incentives and public costs or a result that less mature markets can expect to reproduce.
$0.4M–$10.8Mfive-year local tax range for the same $150M equipment example
·Context
JLARC found that Virginia localities could collect between $0.4 million and $10.8 million over five years from the same $150 million of data-center equipment because tax rates and depreciation schedules differ.
✓Supports
Local policy changing the public return from otherwise identical equipment by more than twenty-five-fold.
⚠️Does not support
The complete fiscal impact of a real project; the comparison excludes real-property taxes, incentives, infrastructure and service costs.
$928M / 90%Virginia FY2023 sales-tax savings / industry capacity using the exemption
·Context
Virginia’s data-center sales-and-use-tax exemption provided $928 million in tax savings in FY2023 and covered about 90% of the industry measured by megawatts.
✓Supports
The large fiscal scale and broad industry reach of one state’s data-center incentive.
⚠️Does not support
The exemption’s net cost after induced investment and local tax collections, or how much of the exempt capacity serves AI.
84% / 68%share of announced Virginia development investment / share spent on IT and mechanical equipment
·Context
Data centers represented 84% of capital investment across Virginia economic-development projects announced in FY2022–24, while 68% of data-center investment was IT and mechanical equipment largely sourced outside the state.
✓Supports
Both the dominance of data-center capital spending and the boundary between headline investment and locally retained activity.
⚠️Does not support
That 84% of the investment became Virginia income, or that out-of-state equipment spending creates no local construction or tax benefit.
29% / 10%Virginia sites within 200 feet of residential zoning / sites with problematic-noise reports
·Context
JLARC found 29% of operational Virginia data-center properties within 200 feet of residentially zoned land and identified problematic-noise reports at about 10% of operational sites.
✓Supports
Problematic-noise reports at a minority of sites, concentrated by location and facility design.
⚠️Does not support
That every nearby site caused a nuisance, reduced property values or harmed health; the distance is measured property-line to property-line.
<4% / ≤0.1%regional NOx / carbon-monoxide and particulate emissions from data-center generators
·Context
JLARC estimated Northern Virginia data-center diesel generators contributed less than 4% of regional nitrogen-oxide emissions and 0.1% or less of carbon-monoxide and particulate emissions.
✓Supports
Backup generators being a comparatively small share of measured regional air pollution under ordinary operating conditions.
⚠️Does not support
The absence of exposure near an individual facility or the impact of an unusual prolonged outage when many generators run together.
Cite the primary source first — that is what the number rests on. If you also credit this site, use “Who Pays for AI (whopaysforai.org), checked 2026-07-17” and link the statistic’s permalink so readers can see its attribution scope. Corrections are logged publicly on the Changes page; report an error via Corrections.
The underlying observations are downloadable: ↓baseline CSV. The Crowding Index page documents the basket, comparisons and calculation rules.