United States Artificial Intelligence (AI) Optimised Data Center Market Size & Share Analysis - Growth Trends & Forecasts (2025 - 2030)

United States Artificial Intelligence (AI) Optimised Data Center Market Analysis by Mordor Intelligence

The United States Artificial Intelligence Data Center Market size is estimated at USD 8.95 billion in 2025, and is expected to reach USD 32.95 billion by 2030, at a CAGR of 29.77% during the forecast period (2025-2030).

The United States data center landscape is experiencing significant transformation due to the rise of artificial intelligence, which is driving unprecedented demands on infrastructure, power, and cooling systems. Although specific market size figures are unavailable, the scale of investment highlights the magnitude of this shift. Major tech companies collectively spent over USD 200 billion in 2024 on AI and cloud computing infrastructure, with projections suggesting this figure will reach USD 250-300 billion in 2025. AI workloads, requiring 10-20 times the computing power of traditional applications, are fundamentally altering facility design, power needs, and cooling strategies across the industry.

The U.S. government has introduced several strategic initiatives to accelerate AI infrastructure development while addressing critical challenges. In January 2025, President Biden signed an executive order to expedite AI data center construction by making federal land available and directing agencies to identify at least six sites for new AI data centers by February 28, 2025. The CHIPS Act, which provides USD 52.7 billion in federal subsidies for semiconductor manufacturing, is bolstering a more resilient supply chain for AI acceleration hardware, with over USD 450 billion in private investments announced as of 2024. Additionally, the proposed 'America Forward' tax credit aims to incentivize domestic investment in AI data centers, though its specifics are still under development.

The White House has also established a Task Force on AI Datacenter Infrastructure to align development with U.S. economic and national security interests. A national security memorandum emphasizes streamlined permitting processes for clean energy and data infrastructure. Investment in the AI data center ecosystem has reached historic levels, with private equity firms investing nearly USD 200 billion in the sector since January 2022, acquiring over 450 companies. In 2024 alone, private equity accounted for 80-90% of mergers and acquisitions in the sector, with USD 115 billion spent across 95 transactions.

Major deals in the sector include Blackstone's USD 15.04 billion acquisition of AirTrunk and KKR's USD 15 billion buyout of CyrusOne. The hyperscale segment is witnessing particularly aggressive investment, with OpenAI, SoftBank, and Oracle's Stargate joint venture planning to invest USD 100 billion in AI data center infrastructure in Texas. Similarly, Amazon has announced plans to invest USD 100 billion in 2025 to expand its data centers amid concerns over capacity constraints.

This surge in investment is reshaping data center economics, with AI-driven demands fundamentally altering the industry's landscape. The focus on infrastructure, power, and cooling systems underscores the critical role of AI in driving innovation and growth in the data center market. As the sector evolves, both public and private stakeholders are working to address challenges and capitalize on opportunities presented by this transformative shift.

Surging Generative-AI GPU Cluster Build-outs by U.S. Hyperscalers

The US AI data center market is experiencing significant growth due to the increasing demand for advanced data center capacity, fueled by large-scale investments in AI infrastructure by major hyperscalers. These investments are reshaping the industry by requiring higher power densities, liquid cooling systems, and advanced power delivery setups to support massive GPU deployments. Facilities designed for AI workloads are commanding premium pricing and necessitate specialized designs to meet these requirements. Additionally, competition is intensifying as companies work to secure GPU allocations and develop AI-optimized facilities to strengthen their market position. The 2.3% impact value reflects the analysis of capital expenditure trends and the targeted focus on AI infrastructure by these hyperscalers.

In May 2025, OpenAI, SoftBank, and Oracle announced their Stargate joint venture, selecting sites in Texas for a massive AI data center project with a planned investment of USD 100 billion. The facilities will integrate Nvidia's latest AI chips to create one of the world's largest AI computing infrastructures, with initial capacity expected to come online by Q4 2025.

U.S. CHIPS Act Incentives Accelerating Domestic AI-Chip Supply Chain

The CHIPS Act is reshaping the semiconductor market by altering the economics and geography of AI chip production, which is significantly impacting the data center industry. This initiative operates through various measures, including direct subsidies to lower capital costs for domestic manufacturing, R&D funding to advance innovation in specialized AI processors, and workforce development programs to address talent shortages. With USD 52.7 billion in federal funding driving over USD 450 billion in private investments, the program is fostering a more resilient, innovative, and geographically concentrated supply chain for critical AI infrastructure components.

The AI data center market is experiencing significant changes due to the CHIPS Act, which addresses supply chain vulnerabilities while accelerating technological advancements. The development of domestic manufacturing capacity is reducing lead times for advanced AI chips from 12-18 months to 6-9 months, enabling quicker deployment of new data center capacity. The geographic concentration of semiconductor production in the United States is creating new "silicon clusters," where chip manufacturers, packaging specialists, and data center operators collaborate within integrated ecosystems, achieving notable efficiency gains. Additionally, advancements in specialized AI processors, supported by CHIPS Act funding, are delivering 2-3x performance improvements over general-purpose GPUs for specific workloads. These innovations are enabling new architectural designs for AI data centers, incorporating heterogeneous computing systems that optimize both performance and energy efficiency.

In April 2025, Intel received USD 8.5 billion in direct funding under the CHIPS Act for its new fabrication facilities in Ohio and Arizona, specifically focused on producing advanced AI accelerator chips. The company announced that these facilities will begin production in 2026, with capacity to manufacture over 100,000 wafers per month dedicated to AI processors

Impact of Corporate Net-Zero Mandates on AI Data Centers
Corporate sustainability commitments are reshaping how AI data centers source and manage energy, addressing the significant power demands of AI workloads. Companies are setting science-based targets for emissions reduction, typically aiming for carbon neutrality by 2030 and net-zero emissions by 2040 across all operations. These commitments are implemented through mechanisms such as power purchase agreements (PPAs) to secure renewable energy at scale, investments in on-site generation, and advanced energy management systems to optimize consumption. Collectively, major tech companies have contracted over 25 GW of new renewable capacity specifically to power AI infrastructure

In the AI data center market, energy sourcing has become a strategic factor rather than just an operational necessity. Companies with advanced renewable energy strategies benefit from long-term price stability, enhanced brand value among environmentally conscious stakeholders, and improved regulatory positioning as carbon pricing mechanisms expand. Sustainability requirements are also driving innovations in energy-efficient computing architectures, advanced cooling systems, and AI-powered infrastructure management, which can reduce energy consumption by 15-30%. These advancements create a cycle where AI increases energy demand while enabling more sophisticated energy optimization.

In May 2025, Amazon Web Services signed a 15-year power purchase agreement (PPA) with NextEra Energy for 2 GW of new solar and wind capacity in Texas, specifically to power its expanding AI data center operations. This represents one of the largest corporate renewable energy deals in history and will enable AWS to maintain its commitment to 100% renewable energy despite the increased power demands of AI workloads

AI-Optimised Edge Deployments Supporting 5G & Autonomous-Vehicle Roll-outs

The integration of edge computing, 5G networks, and AI is enabling computational capabilities to extend beyond centralized data centers to the network edge. Specialized micro data centers are being deployed at cell towers, network aggregation points, and other edge locations, forming a network of AI-capable infrastructure that processes data with minimal latency. These facilities are smaller in size, typically 100-500 square feet, and are optimized for inference workloads rather than training. Partnerships between telecom providers, cloud platforms, and application developers are driving these deployments, creating new business models and revenue opportunities.

This shift is transforming the AI data center market by incorporating distributed edge locations, significantly influencing the architecture of AI infrastructure. Latency-sensitive applications such as autonomous vehicles, augmented reality, and industrial automation benefit from this development, as they require processing times in milliseconds. Operators are focusing on securing strategic edge locations, scaling standardized deployment models, and managing distributed AI workloads efficiently. Hybrid architectures are being developed to dynamically allocate workloads between edge, regional, and central facilities based on latency, computational needs, and energy efficiency.

In May 202,5, Verizon and AWS expanded their Wavelength Zones to 15 additional U.S. cities, bringing the total to 40 locations. These edge computing facilities are designed to support AI inference for autonomous vehicles and augmented reality applications, achieving latency reductions of up to 80% compared to traditional cloud processing.

Grid Congestion & Power-Allocation Moratoria

Power infrastructure limitations have become a significant factor affecting AI data center growth in the United States, creating bottlenecks in key markets and altering expansion strategies. These limitations arise from physical grid capacity constraints that prevent new connections, utility-imposed moratoria on power allocations for data centers, and extended lead times for infrastructure upgrades that can delay projects by 24-36 months. Power requirements for AI facilities often exceed 100 MW per site, equivalent to the electricity needs of 80,000 homes, placing demands that existing grid infrastructure cannot support. These constraints are driving up costs through premium pricing in capacity-constrained markets, expensive alternatives like on-site generation, and delays in deployments.

In May 2025, Dominion Energy Halts New Data Center Connections in Northern Virginia Amid Grid Strain. The Washington Post reports that Dominion Energy has implemented an indefinite moratorium on new data center connections in Loudoun County, citing unprecedented demand from AI facilities that has overwhelmed existing infrastructure. The utility estimates that USD 3.5 billion in grid upgrades are needed before additional capacity can be accommodated, with completion not expected until 2028.

Shortage of Skilled Workforce for High-Density AI Operations

Restraint Overview: The lack of specialized talent has become a significant limitation on AI data center expansion, affecting various stages such as design, construction, and operations. This issue is evident in roles requiring expertise in both traditional data center operations and AI-specific demands, such as cooling engineers for GPU clusters, electrical engineers for high-power systems, and operations specialists managing AI software and infrastructure. The construction industry reports a workforce deficit of approximately 500,000 workers needed for data center projects, WTW. These labor constraints are extending project timelines by 15-25% and increasing costs by 10-15% due to higher compensation requirements, slowing the deployment of new AI capacity.

In May 2025 The Wall Street Journal reports that major AI infrastructure projects across the U.S. are experiencing average delays of 4-6 months due to workforce constraints, with some projects in secondary markets seeing delays of up to 9 months. The article cites industry executives who report paying 30-40% premiums for specialized roles, including liquid cooling technicians and high-voltage electrical engineers