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AI data-center costs are not just the price of servers or the electricity on a monthly bill. They span land and buildings, computing equipment, cooling, ongoing power use, and the generation and grid infrastructure needed to serve large new loads. Technology companies and their tenants finance much of the buildout, but utilities, lenders, property developers and, depending on local rules, other electricity customers can also be involved. Whether households ultimately pay more depends on how a utility or regulator allocates the cost—not simply on who owns the data center.
What goes into the cost of an AI data center?
A data center is both a real-estate project and an electricity-intensive computing facility. Its costs fall into connected categories, and the distinction matters: the company that pays for equipment is not necessarily the party that pays for every power-system upgrade, while a building’s construction estimate does not include the cost of the computing equipment installed inside it.
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Land, site development and the building
The real-estate side includes land, site preparation, the building shell, core electrical systems and cooling infrastructure. Scope and location affect the cost, and a price per megawatt of building capacity should not be read as the price of a fully equipped AI facility.
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JLL Research’s 2026 global outlook estimates average shell-and-core construction costs at $7.7 million per megawatt in 2020 and $10.7 million per megawatt in 2025. It forecasts $11.3 million per megawatt for 2026. These are market estimates, not guaranteed prices for a particular project. JLL excludes tenant technology fit-out from those figures.
Computing and network equipment
The tenant fit-out adds the equipment that performs and supports the computing: accelerators, servers, networking and related technology. For AI infrastructure, JLL estimates that this fit-out can reach $25 million per megawatt. That is a separate estimate from shell-and-core construction, not an alternative building price or a universal bill for each facility.
The distinction helps explain why a real-estate developer and an AI company may report very different investments in the same site. A developer may fund or own the building and its core systems; a tenant may buy or finance the computing equipment. Lease arrangements can divide responsibilities differently.
Operating power and cooling
Once operating, a facility requires electricity for computing and for supporting systems such as cooling. Electricity is a recurring operating need, unlike the one-time construction of a building or purchase of equipment. The cost at a particular facility depends on its load, location, electricity arrangements and other project details. The available estimates do not establish a universal operating cost per AI query or per data center, so a global electricity-demand projection cannot be converted into a typical facility’s power bill.
Generation and grid connections
Serving a large, concentrated load may require more than connecting a building to an existing local supply. Generation, transmission, substations and interconnection work may all be relevant. Some operators are pursuing on-site power or batteries, which can be part of a project’s response to connection constraints. These additions can affect both capital needs and the schedule for getting a site online.
JLL’s 2026 outlook says the average wait for a grid connection in primary markets exceeds four years and describes speed to power as the main factor driving site selection. This is JLL’s market assessment; it is not a promised connection timeline for every market or project. Delays can matter financially because a completed or planned facility cannot deliver its intended capacity until the power arrangement is available.
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How large are the buildout estimates?
Different estimates describe different scopes and should not be added together as if they were line items in one project budget. JLL’s 2026 global outlook discusses real-estate asset value, debt financing and tenant fit-out spending as related but distinct parts of a possible expansion. Separately, an NBER working paper describes a large U.S. buildout scenario. Neither figure is a record of money already spent.
| Estimate | Scope and qualification |
|---|---|
| $7.7 million per MW in 2020; $10.7 million per MW in 2025; $11.3 million per MW forecast for 2026 | JLL Research, 2026 global estimates of average shell-and-core construction cost. The 2026 figure is a forecast; tenant technology fit-out is excluded. |
| Up to $25 million per MW | JLL Research, 2026 estimate for tenant technology fit-out for AI infrastructure; separate from shell-and-core construction. |
| Up to $3 trillion by 2030 | JLL Research’s global expansion outlook. Its related estimates include about $1.2 trillion in real-estate asset value, roughly $870 billion in new debt financing, and $1 trillion to $2 trillion in tenant fit-out spending. These are forecasts with distinct scopes, not a measured final spend or a set of amounts to sum indiscriminately. |
| Nearly $9 trillion for a 188 GW buildout by 2032 | A U.S. scenario estimate described in Stijn Van Nieuwerburgh’s 2026 NBER Working Paper 35865. It is conditional on that buildout scenario, not a certain or enacted investment plan. |
The numbers illustrate why one headline “cost of AI data centers” can mislead. Shell-and-core is not the same scope as fit-out; asset value is not the same measure as new debt; and a scenario estimate is not realized expenditure. A useful comparison always asks what is included, where the estimate applies and whether it is historical, forecast or conditional.
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Several parties can provide capital at different stages. Technology companies may use their own capital expenditure or pay for equipment and services as tenants. Developers may finance land and construction, while lenders and investors provide debt or other capital. Leases, joint ventures, project debt, private credit, securitization and special-purpose vehicles can spread financing across parties and over time.
JLL’s 2026 outlook identifies real-estate asset value, debt financing and tenant fit-out as components of the global expansion it expects. The NBER working-paper listing describes a U.S. scenario in which investment outgrows companies’ internal cash and financing shifts toward leases, joint ventures, project debt, private credit, securitization and special-purpose vehicles. Financing identifies who supplies capital and when; it does not, on its own, prove who ultimately bears the economic cost. The cost may be reflected in a tenant’s payments, a property owner’s returns, a lender’s exposure, or the terms negotiated among them.
The data-center customer and its investors
A company can pay directly for some costs through equipment purchases, construction commitments, rent or a utility bill. If it leases a facility, the owner and tenant divide capital responsibilities according to their agreement. Debt or joint-venture financing changes who provides funds upfront and how repayment or returns are structured. It does not make the underlying equipment, power or construction requirement disappear.
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Utilities, regulators and other electricity customers
Power-system costs have a separate allocation question. An operator may fund generation or connection work directly, or a utility may make an investment and seek to recover eligible costs through rates. Contracts and regulatory decisions determine whether costs are assigned to the data-center customer, spread across a broader customer base, or handled through another arrangement. Utility ownership and rate design matter as well.
That is why “the company pays its electric bill” does not settle whether the wider electricity system or other customers are affected. The bill covers the customer’s electricity under its tariff or contract; the treatment of new generation, transmission or other system costs is a distinct decision. Nor does the presence of a data center alone prove that a particular household’s bill went up because of that facility.
What does the evidence say about electricity prices?
A 2026 MIT Center for Energy and Environmental Policy Research (CEEPR) study by Robyn Meeks, Jacquelyn Pless, Zhiyuan Qi and Zhenxuan Wang examines U.S. data-center entry from 2010 through 2024. The authors report an average retail electricity-price increase of 2.7% associated with the data-center entry they study, with reported effects of 2.1% for residential customers, 2.8% for commercial customers and 4.2% for industrial customers.
The study also finds substantial differences across utility types. It reports an average retail-price increase of 5.6% among investor-owned utilities, much smaller effects among publicly owned utilities, and no effect reported for cooperatives. The authors relate the differences to regulatory and market structures. These are empirical results for the study’s U.S. setting and period, not a forecast for every utility, a finding that every customer class in every location saw the same change, or proof that one named facility caused an individual bill increase.
The evidence supports a conditional conclusion: electricity-price effects can vary with how infrastructure is financed and how costs are recovered. To understand a local case, a reader would need to know which utility serves the area, what new investment is required, how the project’s power is contracted, and what regulators permit the utility to include in rates. A national average cannot answer those local questions by itself.
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Why does the global electricity mix matter?
More data-center electricity demand means more generation is needed somewhere in the system, but projections of demand do not by themselves specify how each facility is powered or who pays for each plant or network upgrade. The International Energy Agency’s 2025 base case projects global electricity generation for data centers rising from 460 terawatt-hours in 2024 to more than 1,000 TWh in 2030 and 1,300 TWh in 2035.
For the additional demand through 2030, the IEA expects renewables to meet nearly half, while natural gas and coal together supply more than 40%. These are global projections under the IEA’s base case, not a description of the mix at every location. Actual physical electricity consumption is tied to the local power system; an operator’s contractual claims about its electricity mix are a separate matter. The distinction is important when interpreting renewable-energy commitments: a contract or accounting claim should not automatically be mistaken for the physical generation serving a particular facility at every moment.
The IEA figures establish the scale and changing composition of projected global demand, but they do not supply a universal cost per unit of AI computation. Local prices, facility efficiency, load, contract terms and system conditions still matter. They also do not determine whether a given generation or grid investment is paid directly by an operator or recovered through broader utility rates.
How to evaluate who pays in a specific project
There is no single answer without the project’s power and financing arrangements. These questions help separate direct payments from wider system effects:
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- Identify the project boundary. Separate the building owner’s shell-and-core investment from the tenant’s equipment and fit-out. Check whether a reported cost is per megawatt of capacity and what that estimate includes.
- Find out who funds power upgrades upfront. Determine whether the operator, a utility, a generator or another party is financing generation, transmission, substations and interconnection work. On-site generation and batteries may change the arrangement, but do not by themselves establish who bears all related costs.
- Check the cost-recovery terms. Ask whether the data-center customer is responsible under a contract or tariff, whether utility investment may be recovered through general rates, or whether regulators have approved a different allocation.
- Account for the utility and regulatory setting. Utility ownership and regulation can affect cost recovery. The U.S. study’s different findings for investor-owned, publicly owned and cooperative utilities are evidence against assuming one outcome everywhere.
- Separate a physical supply from a contractual mix. When assessing an electricity or renewable claim, distinguish the electricity delivered by the local system from the operator’s contracts or accounting claims about its supply.
- Keep the timeline in view. A grid connection delay can affect when a facility can operate, while financing terms determine when capital is supplied and repaid. A cost assigned to one party upfront may be allocated differently over a project’s life.
These checks do not predict a bill change without local data. They clarify the mechanism to investigate: who committed capital, who is contractually responsible, and whether any system costs can be recovered from customers beyond the facility.
Quick Recap
What the headline numbers do—and do not—tell you
- Construction estimates are not total facility prices. JLL’s shell-and-core figures exclude tenant technology fit-out, which it estimates separately for AI infrastructure.
- Global demand is not local demand. The IEA’s generation projections cover data centers globally; they do not state a specific region’s load, tariff or power mix.
- Historical U.S. price results are not universal forecasts. The CEEPR estimates cover data-center entry from 2010 to 2024 and show variation by utility type.
- Financing totals are not final spending records. JLL’s 2030 outlook and the NBER paper’s 2032 U.S. scenario are estimates tied to their own assumptions and scopes.
- A customer’s direct payment is not the whole incidence question. Utility investment and rate decisions can affect how system costs are distributed, but the outcome depends on contracts and institutions.
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