AI datacenter

An AI datacenter (or artificial intelligence datacenter) is a specialized data center facility designed explicitly to support the high-performance computing (HPC) workloads required for the training and inference of artificial intelligence models. Unlike traditional datacenters that host a variety of general-purpose computing tasks (like web services and databases), AI datacenters are optimized for the unique computational demands of machine learning, particularly deep learning. These facilities are characterized by extreme power density per rack (often exceeding 50–100 kW), advanced liquid cooling systems, and low-latency, high-bandwidth networking fabrics to facilitate parallel processing.

The rise of generative AI since 2022 has triggered a global boom in the construction of AI datacenters, making them a critical and strategically important piece of national infrastructure. Companies like Microsoft, Google, Meta, and Amazon are investing tens of billions of dollars to build facilities containing over 100,000 AI accelerators each. This massive build out is causing a resurgence in nuclear power plants. In 2025 Google spent $95 billion on capex, with much of that for AI datacenters. In 2025 U.S. tech companies spent $370 billion on capex with much of that spending for AI datacenters. OpenAI wants to create a process for new datacenter expansion every week.

By 2026, AI data centers are projected to consume over 90 TWh of electricity annually. In the U.S. energy use is growing by 33% per year attributed to AI datacenter growth. A startup company was created to harness nuclear energy for the AI datacenter boom. The largest AI datacenter in 2025 cost $7 billion , and uses 300 MW of power—as much as 250,000 households. Cornell University study estimates that the AI datacenter build out between 2024 and 2025 will contribute between 24 and 44 metric tons of addition CO2.

The doubling of RAM and NAND prices has been attributed to the AI datacenter boom.

The Trump administration is promoting the build out of AI datacenters. U.S. president Trump hints at deregulation to promote AI datacenters. There is local opposition.

History

Early AI workloads in the 2010s were often run on general‑purpose high‑performance computing (HPC) clusters or small GPU servers in conventional data centers. As deep learning models and datasets grew, cloud providers began to build dedicated infrastructures for AI training, including GPU clusters exposed through services such as Google Cloud TPU, Amazon EC2 P-series instances, and Microsoft Azure’s ND‑series virtual machines.

Around 2022–2024, the rapid adoption of large language models (LLMs) and generative AI led to a surge in demand for specialized AI datacenters with thousands or tens of thousands of accelerators connected through high‑speed fabrics. Several technology companies announced multibillion‑dollar investments in new or expanded AI‑focused campuses, often near abundant power supply or renewable energy sources.

Architecture

Data centers for building and running large machine learning models contained specialized computer chips, GPUs, that used 2-4 times as much energy as their regular CPU counterparts (250-500 watts). Companies such as Google and Nvidia constructed GPUs specifically for machine learning, which could process thousands of calculations per second. Thousands of these GPUs were stored closely together in data centers, alongside specialized hardware and cables to quickly migrate data between these chips. To cool these systems, AI data centers have developed new techniques for doing so. Google pumps large amounts of water through its data centers, using pipes that run next to the computer chips, which can strain nearby water supplies. Cirrascale uses large chillers to cool the water, which is largely recycled, but uses more electricity.

American Big Tech companies believe that these facilities are essential to building artificial general intelligence.

Technical architecture

• Compute Density: Traditional server racks typically consume 5–15 kW of power. AI racks, utilizing hardware like Nvidia H100s or Google TPUs, consume 40 kW to over 100 kW per rack.
• Cooling Systems: Due to the heat generated by high-density chips, AI data centers often abandon traditional air cooling (CRAC units) in favor of liquid cooling technologies, such as direct-to-chip cooling or immersion cooling.
• Networking: AI training requires thousands of chips to communicate simultaneously. This necessitates specialized non-blocking network architectures rather than standard Ethernet used in web servers.

Difference with data center

An AI datacenter is a type of data center.

	Typical Datacenter	AI datacenter
Purpose	Generic data processing	AI training and AI results serving
Compute	Focus on traditional compute such as Intel and AMD CPU. Billions of operations per second per CPU core.	Linear algebra / matrix processing, GPU and TPU. Trillions of operations per second.
Density	Not as focused on density	Very high density because of enormous compute demands
Power		2 to 5 times more power consumed
Growth	Linear growth	Exponential Growth
RAM impact	Not notable for impacting RAM	Blamed for price increases and or shortages for NVRAM, DRAM, and HBM memory
AI Bubble		Frequently mentioned in discussions of possible over investment

OpenAI

OpenAI's datacenter project is called Stargate. The partnership includes Softbank, MGX and Oracle. In September 2025, it was announced the building of 5 new AI datacenters. The new sites are in Texas, New Mexico, Wisconsin, and Ohio. Capacity is estimated to be 6.5 gigawatts and $400 billion investment over 3 years. OpenAI's revenue for 2025 was estimated to be less than $12 billion.

The impact of these new AI datacenters is sparking concern.

Operators

As of August 2025, The Information tracked 18 planned or existing AI data centers in the United States, operated by Amazon Web Services, CoreWeave, Crusoe, Meta, Microsoft/OpenAI, Oracle, Tesla, and xAI. Other AI data center operators include Digital Realty and Alibaba. Data centers are also being built in China, India, Europe, Saudi Arabia, and Canada. The New Yorker described CoreWeave as the most prominent AI data center operator in the United States.

In response to the Stargate project, Amazon launched in October 2025 an AI data center on 1,200 acres of farmland in Indiana. This data center, known as Project Rainier, is one of the largest AI data centers in the world, with Amazon spending $11 billion on the project. Rainier is specifically intended for training and running machine learning models from Anthropic. As of that time, this facility contains seven data centers (out of an estimated 30 planned) and will use 2.2 gigawatts of electricity (equivalent to 1 million households) and millions of gallons of water per year. Computer chips from Annapurna Labs and Anthropic, Trainium 2, were designed for use in such facilities. Amazon pumped millions of gallons of water out of the ground to construct the data center, and as of June 2025, Indiana state officials are investigating whether this dewatering process led to dry wells for local residents.

In November 2025, Anthropic announced a plan in partnership with Fluidstack to develop artificial intelligence infrastructure in the United States, including data centers in New York and Texas, worth $50 billion.

Other AI data center projects include the Colossus supercomputer from xAI, a Lousiana-based project from Meta, Hyperion, expected to use 5 GW of power, and a second Ohio-based Meta project, Prometheus, with a capacity of 1 GW. A 3,200-acre AI data center, capable of 4.4-4.5 GW of power and located on the decommissioned Homer City Generating Station, is under construction as of 2025, and will use seven 30-acre gas generating stations supplied by EQT.

As of December 2025, CRH is working on over 100 data centers in the United States.

In 2025, ExxonMobil and NextEra announced plans to build a data center powered by natural gas and using carbon capture technology, with 1.2 GW of power capacity. They previously purchased 2,500 acres of land in the Southeastern United States and plan to market the data center to an artificial intelligence company.

The increased interest in AI data centers has led to several executives from companies in that space becoming billionaires, including CoreWeave, QTS, Nebius, Astera Labs, Groq, Fermi (which is connected to former United States Secretary of Energy Rick Perry), Snowflake and Cipher Mining.

Finances

Between January and August 2024, Microsoft, Meta, Google and Amazon collectively spent $125 billion on AI data centers. Citigroup forecasted that $2.8 trillion would be spent on AI data centers by 2030, while McKinsey and Company estimated that almost $7 trillion would be spent globally by that time.

Energy sourcing

As of 2024, data centers in the United States are primarily powered by natural gas, which supplies 40% of their electricity (with renewable energy at 24%, nuclear at about 20% and coal at about 15%). The Associated Press reported that electricity for AI data centers in the United States would likely come from natural gas or oil, as companies prefer using currently available power plants, which primarily use fossil fuels. Non-renewable energy is also often cheaper in locations where data centers are developed, and experts believe that energy demands from generative AI and data centers would be difficult to fulfill with renewable energy alone. Some companies such as Google, Amazon and Meta have expressed interest in nuclear power for their data centers. Other data centers, such as Wonder Valley in Canada, plan to use their own natural gas and geothermal plants that are off-grid. Similarly, xAI is using onsite gas turbines for Colossus, while OpenAI and Meta have planned to use natural gas generators at their Stargate and Prometheus projects, respectively. Electric vehicle batteries have also been used for powering data centers, including for Colossus. Power utility companies make upgrades to their infrastructure to handle demands of new data centers, and the price for these changes typically falls on consumers: smaller businesses or individual households.

Environmental footprint

Average AI data centers have an electricity footprint equivalent to 100,000 households, and use billions of gallons of water for cooling their hardware. In 2025, the International Energy Agency estimated that the larger AI data centers currently under construction could consume as much electricity as 2 million households. A 2024 report from the United States Department of Energy stated that data centers overall used 17 billion gallons of water per year in the United States, primarily due to "rapid proliferation of AI servers", and that this usage was forecasted to grow to nearly 80 billion gallons by 2028. Researchers estimated that AI data centers in the United States would emit 24-44 metric tons of carbon dioxide and use 731-1,125 million cubic meters of water per year between 2024 and 2030.

AI data centers in space

Several tech companies, including Google, Nvidia, Blue Origin and SpaceX, have announced projects for or otherwise expressed interest in building data centers in outer space. Google announced Project Suncatcher, which plans to test whether its Tensor Processing Unit (TPU) chips (which are specifically designed for machine learning) can function in the environment of space. The company aims to deploy satellites with these chips in a data center by 2027. In November 2025, Starcloud, a startup supported by Nvidia, launched a satellite with an Nvidia H100 GPU that the company used to deploy and develop a large language model. CNBC reported that this model, NanoGPT, was the first model trained in space. Starcloud deployed a second model based on Google's Gemma.

AI data centers in the United States

In the United States, both the Biden administration and second Trump administration supported the construction of AI data centers. In January 2025, then-president Joe Biden signed an executive order for federal government agencies to support AI data centers on federal sites built by private companies, study their effect on energy prices, and encourage their use of renewable energy. In April 2025, the United States Department of Energy suggested 16 possible sites, including Los Alamos National Laboratory, Sandia National Laboratories and Oak Ridge National Laboratory. In its July 2025 AI Action Plan, the second Trump administration supported increased production of AI data centers. Several states in the country have incentivized local data center construction. For example, in 2024, lawmakers in Michigan approved tax breaks for data center equipment and construction material. In December 2025, Democratic senators Elizabeth Warren, Chris Van Hollen, and Richard Blumenthal wrote to seven technology companies (Google, Microsoft, Amazon, Meta, CoreWeave, Digital Realty, and Equinix) that they will investigate the effects those companies' operations on consumer energy bills, highlighting AI data centers in particular.

Concerns and opposition

Some analysts have expressed concerns about overbuilding of AI data centers, warning that their infrastructure risks obsolescence due to changes in demand and technology. Estimates for the lifespan of the GPUs used to power data centers range from one to eight years.

As of 2025, Data Center Watch reported that multiple data center projects collectively worth $64 billion had been stopped or delayed. Local communities in Texas, Oregon, Tennessee, Pennsylvania, Florida, Michigan, Minnesota, Wisconsin, Missouri, Kentucky, Ohio, Arizona, Indiana, Virginia, and Maryland have resisted these projects. Critics have pointed out that jobs created by data centers tend to be temporary or few in number. Residents have been concerned about air, water and noise pollution, as well as property devaluation and traffic. Other environmental concerns involving AI data centers include e-waste and construction materials that emit greenhouse gases such as concrete and cement. Advocates have also linked data center construction to the AI bubble.

In November 2025, the North American Electric Reliability Corporation warned that building new data centers could negatively affect the electrical grid and cause power outages during extreme weather. The independent monitor of PJM Interconnection warned that its power grid cannot support new data centers and supported a federal moratorium on data centers.

Machine learning developers, when training large language models, often use data centers at their full capacity, conflicting with other users (including households) during peak usage, which may lead to blackouts.

Large technology companies who are building data centers have asked public officials and land owners to sign non-disclosure agreements and have appeared to use shell companies. In one case, when a joint OpenAI-Oracle data center was rejected by Saline Township in Michigan, landowners and developers responded by suing the town.

In 2025, over 230 groups have signed a letter supporting a moratorium on constructing AI data centers in the United States, concerned by impact on the environment and energy bills. Signatories include Food & Water Watch, Greenpeace, Friends of the Earth and Physicians for Social Responsibility. Senator Bernie Sanders has also supported a moratorium on AI data centers.

Ashley LaMont, of Honor the Earth, argued that data centers on tribal lands would not help Native Americans obtain data sovereignty.

The NAACP has also expressed opposition to AI data centers.

See also

• AI boom
• AI bubble
• AI Factory
• Tensor Processing Unit

External links

• Video claims two new AI datacenters are built every week
• Video explores AI datacenter boom, with focus on Amazon $11 billion dollar AI datacenter
• Video gives an overview of AI datacenters

Further reading

• You’re Thinking About AI and Water All Wrong (December 12, 2025) by Wired
• How Tech’s Biggest Companies Are Offloading the Risks of the A.I. Boom (December 15, 2025) by The New York Times