The AI Power Problem: Why Data Centers Are Building Their Own Power Plants

Senior Vice President
Director of Market Strategy

Vice President

Data centers running intense AI workloads need enormous amounts of electricity, but connecting to the U.S. electrical grid is often a slow and cumbersome process. Rather than wait, the industry is taking power into its own hands.

The growth in demand for artificial intelligence (AI) computing has sparked the largest infrastructure buildout in a generation. Major technology companies have committed well over $1 trillion in capital expenditures collectively since the launch of ChatGPT in late 2022.¹ Despite this unprecedented level of investment, the buildout faces several critical bottlenecks including GPUs, memory and storage. Power procurement for data centers may be the most pressing constraint of all.

Data centers running intense AI workloads need enormous amounts of electricity, but connecting to the U.S. electrical grid is often a slow and cumbersome process. Rather than wait, the industry is taking power into its own hands. Building power plants onsite at data centers is rapidly transforming a once-niche segment of the electrical power industry into a linchpin of the AI infrastructure buildout. As a result, we believe some of the companies enabling this shift could be well positioned to benefit from this multi-year capital expenditure cycle.

How Data Centers Source Power

Given the explosion in power needs, it helps to know how data centers get their power. There are two methods: (1) drawing from the public grid, known as front-of-the-meter (FTM), or (2) building power generation capability onsite at the data center itself, known as behind-the-meter (BTM) generation. In practice, data centers often blend both sources simultaneously by drawing from the grid while running onsite gas turbines and engines, which allows operators to reach full capacity faster than either method could deliver alone. Although both pathways are expanding to meet AI-driven demand, BTM solutions appear to be the fastest-growing means to power new data centers (see Figure 1).

Figure 1 shows How Data Centers

The FTM Problem: The Grid Can't Keep Up

From 1950 to 2005, U.S. electricity generation capacity expanded significantly, driven by industrialization, suburban development, and widespread adoption of electric appliances. Over the past two decades, however, demand plateaued as the economy shifted toward greater energy efficiency and outsourced heavy industrial production overseas. This period of demand stagnation appears to be ending. Electricity consumption in the U.S. is projected to rise materially due to a combination of reshoring, widespread electrification, and accelerating AI adoption (see Figure 2). Of these drivers, AI-related electricity demand from data centers may be the most concentrated and immediate. Data centers currently consume roughly 5% of total U.S. electricity, but given intensifying demands from AI workloads, that share could exceed 10% by 2030 (see Figure 3).

Figure 2 shows U.S. Net Electricity Generation (in TWh)

Figure 2 shows U.S. Net Electricity Generation (in TWh)

Figure 3 shows Data Center Power Demand as a % of Total U.S. Electricity Consumption

These projections become more concrete at the individual data center level. A single large AI data center campus, the kind that hyperscalers like Microsoft, Google, Amazon, Meta, and Oracle are routinely commissioning, can consume one gigawatt (GW) or more of continuous power, roughly equivalent to the electricity needs of a mid-sized American city like Nashville, Tennessee (1 GW is 1,000 megawatts, enough to power about one million homes).

The challenge with FTM power generation is that connecting a data center of this scale to the public grid can take five to seven years in most regions.² Each new connection requires sequential engineering studies to assess the impact on the local network, regulatory approvals from state and federal authorities, and physical infrastructure upgrades including new transmission lines and substations. Because each step must be completed before the next begins, interconnection queues have grown dramatically: nationally, the average wait time for a new large load connection has more than doubled over the past decade.³

As a result, the opportunity cost of delay becomes significant. Once operational, a 1 GW data center generates approximately $16 billion in annual revenue. This translates into an opportunity cost of roughly $1.3 billion in potential revenue per month.⁴ In our view, given the comparatively small component of a data center’s lifetime cost that power generation represents (~10-20%), it becomes economically rational for operators to pay a premium for faster generation capacity. This is precisely why data center operators are building their own onsite generation, delivered reliably around the clock, with zero tolerance for outages.

Political Pressure Is Accelerating the Shift

While data center economics alone would be enough to drive the shift to onsite power generation (significant monthly revenue foregone waiting for interconnection), political dynamics are reinforcing it. Consumers in regions where data centers are most concentrated, particularly the mid-Atlantic (PJM), are bearing a growing share of capacity costs and transmission upgrades through higher utility bills, and data centers have become a convenient target. For context, rising power demand does not necessarily mean the grid is heading toward widespread blackouts. Data centers are typically limited in how much power they can draw from the grid, with physical protections and penalties enforcing those caps. Moreover, those using a hybrid approach (combining grid and onsite power) can also curtail grid usage during peak demand events (e.g., hot summer days), actively reducing strain on the local network. But that nuance is sometimes lost in the political debate.

The ratepayer burden helped flip utility commission seats in Georgia and became a central issue in Virginia’s 2025 gubernatorial race. At the federal level, the Trump administration brought major hyperscalers to the White House in March 2026 to sign the Ratepayer Protection Pledge, a voluntary commitment to build, bring, or buy their own power generation and cover the full cost of grid infrastructure upgrades for their data centers. The Federal Energy Regulatory Commission (FERC) is separately proposing to require data centers to pay these costs rather than allocating them across all ratepayers. With 2026 midterm elections approaching, regulatory pressure could further constrain where and how quickly grid-connected data centers are built — accelerating the shift toward onsite generation.

The Solution: Bring Your Own Generation

Grid constraints and political pressure have moved Bring Your Own Generation (BYOG) from a niche solution into a central strategy. BYOG — the practice of building and operating power generation onsite at the data center —carries a cost premium over grid power, but for operators prioritizing speed to power, we believe this is a worthwhile tradeoff. For hyperscalers and neoclouds (AI-native companies that rent out compute capacity), BYOG sidesteps two challenges at once: (1) the interconnection queue and (2) the backlash over rising electricity costs. Data center operators typically use onsite power to begin operations quickly, then retain it as supplemental capacity if and when they obtain grid power, though some operators may choose to remain entirely off-grid in “islanded” mode. While onsite BTM power generation is employed in roughly 6% of U.S. data centers today, this share could increase to over 30% by 2030.⁵

Below we discuss the primary onsite generation technologies—gas turbines, reciprocating internal combustion engines, and solid-oxide fuel cells—each with distinct advantages and tradeoffs (see Figure 4).*

Figure 4 shows BYOG Technology Comparison

Figure 4 shows BYOG Technology Comparison

Gas Turbines come in two configurations that serve complementary roles. Aeroderivative gas turbines, adapted from proven aircraft engine technology, are fast-starting, modular units that can be shipped and brought online relatively quickly (xAI initially powered its Colossus 1 data center at launch using this approach).⁶Industrial gas turbines in combined-cycle configuration — the same baseload technology used by utility companies to power the grid around the clock — require more time and engineering relative to aeroderivative turbines but deliver the highest efficiency at scale and the lowest lifetime fuel cost, saving operators potentially hundreds of millions of dollars over a 20-year operating life compared to simpler turbine configurations.⁷ GE Vernova, Siemens Energy, and Mitsubishi Power dominate the market across both configurations and lock in decades of aftermarket revenue through Long-Term Service Agreements, where recurring service revenue often exceeds the margin on the initial turbine sale. The key constraint to deploying gas turbines is supply: lead times to source critical components (e.g., turbine blades, combustion hardware) are long and rising, giving manufacturers pricing power that hyperscalers are willing to absorb because time-to-power matters more to them than upfront cost. New manufacturing entrants are racing to break the bottleneck — Boom Supersonic, for example, is repurposing jet engine technology to build turbines for data center operator Crusoe. In our view, the combination of aeroderivative and industrial gas turbines represents the most attractive onsite power solution: operators can deploy aeroderivatives quickly, then layer in industrial units for long-term efficiency, retaining onsite capacity as supplemental power if and when grid access arrives.

Reciprocating Internal Combustion Engines (RICEs) operate on the same principle as a standby generator at your home, but at industrial scale. Units are often packaged such that they can be loaded onto trucks and each unit can produce 5-15 MW of power. Key manufacturers include Caterpillar, Wärtsilä, INNIO (Jenbacher), Cummins, and Bergen Engines. RICE units can generate power within weeks of delivery, but given high demand, delivery times can exceed a year depending on order size. Data centers can deploy RICE units to begin operations quickly, then transition to more efficient gas turbines or grid power over time. However, the tradeoff is scale. Powering a 1,000 MW campus with RICEs would require roughly 200 units and over 1,000 annual maintenance events, a level of operational complexity that would become difficult to sustain long-term. Given these tradeoffs, RICEs often serve as a bridge technology at the largest campuses, buying operators time while more scalable power solutions come online. At smaller scale, however, RICEs can serve as the primary, long-term power solution. Nebius, for example, is deploying RICEs to generate power for its ~400 MW data center in New Jersey, prioritizing speed to power, serving Microsoft under a multi-billion dollar agreement. ⁸
Solid-Oxide Fuel Cells take a fundamentally different approach to onsite power generation by eliminating combustion entirely. In a solid-oxide fuel cell, natural gas and oxygen react across a ceramic membrane to produce electricity, resulting in minimal harmful air pollutants. By avoiding combustion, fuel cells largely sidestep the air quality permitting process that can delay other onsite generation projects by months or years, a particularly valuable attribute as political scrutiny of data center emissions intensifies. That said, fuel cell stacks degrade every five to six years, requiring costly replacement, and slow ramp-up times mean fuel cells cannot serve as backup power. Bloom Energy is currently the only manufacturer operating at scale, targeting two gigawatts per year of production capacity by end of 2026.⁹

The Broader Power Ecosystem

While this paper focuses on BTM solutions, FTM capacity is projected to grow meaningfully as well. Independent power producers (IPPs) like NRG Energy, Constellation Energy, and Talen Energy play a central role, owning large existing generation assets, including nuclear and natural gas plants, and are positioning themselves to supply dedicated power directly to data center campuses. Several hyperscalers have partnered with IPPs through long-term power purchase agreements (PPAs), offering a faster path to reliable baseload power than building new generation from scratch. In turn, this gives IPPs a contracted, creditworthy revenue stream that enhances the value of their existing fleet for 20 years or more, often priced at a significant premium to wholesale electricity. Some IPPs are also looking to add entirely new supply. NRG Energy, for example, has partnered with GE Vernova and Kiewit to build new natural gas plants dedicated to data center customers under long-term PPAs, with over 5 GW planned across Texas (ERCOT) and the mid-Atlantic (PJM).¹⁰

Regardless of the power source, none of these technologies operate in isolation. Every unit of power requires supporting infrastructure to safely deliver electricity from generator to chip. As examples, switchgears route and protect the flow of power across the facility, while transformers convert voltage to usable levels. On the switchgear and power management side, firms like Eaton, Schneider Electric, and Vertiv are seeing surging demand as every new data center, whether BTM or FTM, requires their equipment to safely distribute and manage power. At the same time, global demand for power transformers has surged as utilities race to expand and upgrade grid capacity, pushing lead times to 18–24 months at major manufacturers like Hitachi and HD Hyundai Electric and further lengthening the already extended timeline to connect to the public grid.

In our view, the companies providing these components are essential enablers of the broader buildout and may be well positioned to benefit from rising demand. Beyond equipment, constraints in skilled labor for engineering, procurement, and construction are beginning to cause delays in data center development timelines, adding another layer of complexity to both grid-connected and onsite generation projects, thereby reinforcing demand for scaled specialists like Sterling Infrastructure in site development and Comfort Systems USA in mechanical and electrical installation.

Powering the AI Opportunity

The AI infrastructure buildout has created a demand for power that the U.S. electrical grid cannot satisfy on the timeline the industry demands. With political pressure mounting over ratepayer costs, onsite generation has shifted from a niche industrial solution to a central pillar of the AI supply chain. Gas turbines, reciprocating engines, and fuel cells each play a distinct role as complementary layers of a broader power strategy that prioritizes speed today and efficiency over time. We believe the companies that deliver power fastest hold near-term pricing power, while those with durable service relationships and growing installed bases may be well positioned to generate recurring revenue for decades. While some may worry that today’s BTM capacity constraints could give way to an eventual supply glut, original equipment manufacturers remain judicious in capacity expansion by prioritizing pricing discipline and long-term service contracts over market share, which makes a disorderly oversupply scenario unlikely for the foreseeable future. In our view, BYOG represents one of the more differentiated investment themes to emerge from the AI infrastructure buildout, driven by demand dynamics that are likely to persist as AI workloads continue to scale.

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*Although other onsite generation technologies exist, such as small modular reactors, we believe these are unlikely to scale meaningfully in the near term. ¹Company filings and earnings guidance of Amazon.com, Inc., Alphabet Inc., Microsoft Corporation, Meta Platforms, Inc., Oracle Corporation, Apple Inc., Nvidia Corporation, and Broadcom Inc. (2022–2025).
²Teplin, C., Swisher, J., & Strowbridge, D. (2025, November 4). PJM's speed to power problem and how to fix it. RMI; Rand, J., Wiser, R., Will, G., Seel, J., Darghouth, N., Kemp, J., Jeong, S., & Gorman, W. (2025). Queued up: 2025 edition. Lawrence Berkeley National Laboratory.
³Rand, J., Wiser, R., Will, G., Seel, J., Darghouth, N., Kemp, J., Jeong, S., & Gorman, W. (2025). Queued up: 2025 edition. Lawrence Berkeley National Laboratory.
⁴JLL and Alger. 2026 data center outlook. Assumes approximately $16 million in annual cloud services revenue per MW of operational data center capacity, a commonly used industry benchmark for hyperscale workloads. At 1 GW (1,000 MW), this implies roughly $16 billion in annual revenue, or approximately $1.3 billion per month in foregone revenue during an interconnection delay.
⁵Bank of America Global Research. (2025). On-site power for data centers: Grid resiliency implications, citing Wood Mackenzie data on global power transformer lead times. Bloom Energy. (January 2026). 2026 Data Center Power Report.
⁶Data Center Dynamics. (November 2024). Fury from campaigners as Elon Musk's xAI gets 150MW for Colossus supercomputer in Memphis. Data Center Dynamics.
⁷Alger estimate based on the efficiency differential between combined-cycle (~60%) and simple-cycle (~40%) gas turbines at $4/MMBtu natural gas (U.S. Energy Information Administration, 2025) and 90%+ capacity factor. A 100 MW+ data center campus could save approximately $6–10 million annually in fuel costs, compounding to hundreds of millions over a 20-year operating life.
⁸Nebius Group. (September 2025). Nebius announces multi-billion dollar agreement with Microsoft for AI infrastructure. Nebius.
⁹Bloom Energy Corporation. (February 2026). Fourth quarter 2025 earnings call transcript.
¹⁰NRG Energy. (2025, February 26). NRG Energy, GE Vernova and Kiewit accelerating new generation capacity to support demand growth [Press release]. The Electric Reliability Council of Texas (ERCOT) is the independent system operator (ISO) that manages the electric grid and wholesale power market for approximately 90% of Texas' electric load, serving over 27 million customers. PJM stands for Pennsylvania-New Jersey-Maryland Interconnection, the largest Regional Transmission Organization (RTO) and independent system operator (ISO) in the U.S.

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The following represents the noted percentages of firmwide assets under management as of January 31, 2026: OpenAI, 0.0%, Microsoft Corporation, 7.6%, Alphabet Inc. Class A, 0.3%, Alphabet Inc. Class C, 4.4%, Amazon.com, Inc., 6.3%, Meta Platforms Inc Class A, 4.6%, Oracle Corporation, 0%, x.AI Corp., 0.0%, GE Vernova Inc., 1.1%, Siemens Energy AG, 0%, Mitsubishi Heavy Industries, 0.0%, Caterpillar Inc., 0.2%, Boom Technology, Inc., 0.0%, Crusoe Energy Systems, 0.0%, Wärtsilä Oyj Abp, 0.0%, INNIO Group, 0.0%, Cummins Inc., 0.0%, Bergen Engines AS, 0.0%, Nebius Group N.V. Class A, 2.9%, Bloom Energy Corporation Class A, 0.1%, NRG Energy, Inc., 0%, Constellation Energy Corporation, 0.5%, Talen Energy Corp, 2%, Kiewit Corporation, 0.0%, Eaton Corp. Plc, 0.1%, Schneider Electric SE, 0.0%, Vertiv Holdings Co. Class A, 0.4%, Hitachi, Ltd., 0.1%, HD Hyundai Electric, 0%, Sterling Infrastructure, Inc., 0.1%, and Comfort Systems USA, Inc., 0.1%.

The AI Power Problem: Why Data Centers Are Building Their Own Power Plants

How Data Centers Source Power

The FTM Problem: The Grid Can't Keep Up

Political Pressure Is Accelerating the Shift

The Solution: Bring Your Own Generation

The Broader Power Ecosystem

Powering the AI Opportunity

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