Artificial intelligence does not run on software alone. Every model, application, and digital service depends on physical infrastructure, and every data center depends first on reliable electricity. This article is the second post in our eight-part AI investment series. The series overview introduced AI as a connected economic value chain rather than a narrow group of technology stocks. The sequence begins with Raw Materials → Power → Physical Infrastructure → AI Semiconductor Supply Chain → AI Compute & Networking → AI Intelligence → AI Applications → Physical AI. Power is the second layer. It sits between the materials required to build the system and the physical infrastructure required to house it. If electricity generation, transmission, and grid interconnection cannot keep pace, the rest of the AI value chain will encounter a practical constraint. AI Data Centers Are Electricity-Intensive Facilities A large AI data center can consume as much electricity as a small city. Unlike many commercial facilities, it often operates continuously, with high power density and demanding reliability requirements. Training and inference workloads place substantial demands on servers, networking equipment, cooling systems, backup power, and electrical distribution. The International Energy Agency expects global data-center electricity consumption to approach roughly 1,000 terawatt-hours by 2030 in its base-case outlook, approximately double the level earlier in the decade. Exact forecasts vary because analysts must estimate future AI adoption, model efficiency, server utilization, and data-center construction. The direction of travel is clear: data-center load is growing rapidly, and AI is a primary driver. The challenge is not simply producing more electricity. A data center needs power: In the right location At the right voltage With sufficient reliability On a timeline that matches the facility’s construction schedule At a cost that supports the economics of the project That distinction matters. A region may have adequate generation capacity in aggregate but still lack the transmission, substations, or distribution equipment needed to serve a new hyperscale campus. The bottleneck can therefore exist at the grid-connection level even when total national generation appears sufficient. The IEA’s analysis of energy demand from AI provides useful context on the relationship between computing growth and electricity consumption. For investors, the important conclusion is that AI expansion creates demand across the entire power system, not only for power producers. Three Demand Drivers Are Reinforcing One Another 1. Data-center load growth AI data centers are increasing electricity demand in concentrated geographic markets. This concentration can magnify the impact on local utilities and regional transmission organizations. A single large campus can represent a material new customer for a utility, while several campuses in the same region can require new generation, substations, transmission lines, and gas infrastructure. Utilities and grid operators must also account for the timing and reliability of the load. A data center cannot easily reduce consumption during a heat wave, cold snap, or period of grid stress. That raises the value of firm capacity and well-planned system reserves. 2. Broader electrification AI is arriving alongside other sources of electricity demand growth, including electric vehicles, heat pumps, industrial reshoring, hydrogen-related projects, and manufacturing facilities. These trends compete for the same generation and transmission resources. As electrification expands, demand growth becomes less dependent on traditional residential and commercial consumption. The result is a more capital-intensive power system, with larger requirements for generation, grid modernization, and long-duration planning. 3. Corporate clean-power procurement Many hyperscalers have public carbon-reduction objectives and are pursuing long-term power purchase agreements, renewable-energy contracts, nuclear arrangements, and other forms of dedicated supply. These commitments can support new generation, but they do not eliminate the need for a functional grid. Wind and solar resources may be located far from data-center clusters. Transmission is required to move that power, and intermittent generation generally needs storage, firming resources, or broader market coordination. Nuclear power can provide carbon-free baseload generation, but new nuclear facilities require significant lead time. Meanwhile, grid interconnection queues remain lengthy in many markets. Projects can spend years waiting for studies, approvals, transmission upgrades, and cost-allocation decisions. This creates a meaningful difference between announced power capacity and power that can actually be delivered. Where Investors May Find Exposure The power layer contains several distinct investment themes. They should not be treated as interchangeable, and each has a different risk profile. Nuclear generation and uranium fuel Nuclear power offers high-capacity-factor generation with minimal direct carbon emissions during operation. Existing nuclear fleets may benefit from license extensions, uprates, improved operating economics, and renewed demand for firm power. The investment universe includes: Owners and operators of existing nuclear plants Uranium miners and fuel-cycle companies Engineering and equipment suppliers Developers of small modular reactors Companies pursuing long-term nuclear power agreements Representative publicly traded examples include Constellation Energy, Cameco, Centrus Energy, and NuScale Power. Nuclear-focused ETFs such as the VanEck Uranium and Nuclear Energy ETF (NLR) and the Global X Uranium ETF (URA) provide different forms of exposure. The distinction between existing nuclear assets and early-stage SMR developers is important. Existing facilities have operating histories and established regulatory frameworks. SMR developers may offer substantial long-term potential but face technology validation, licensing, construction, financing, and commercialization risk. Natural gas generation and pipelines Natural gas is positioned to provide much of the near-term flexibility required as electricity demand rises. Gas-fired plants can generally be developed faster than large nuclear projects and can operate as baseload, intermediate, or peaking resources depending on design and market conditions. The opportunity extends beyond power plants to: Natural gas pipelines Storage facilities Gathering and processing infrastructure Liquefied natural gas infrastructure Gas turbines and related equipment Representative examples include Williams, Kinder Morgan, Energy Transfer, Vistra, and GE Vernova. These companies have different business models and should not be viewed as equivalent investments. Natural gas also introduces direct commodity and policy exposure. Fuel prices, pipeline constraints, emissions regulation, permitting, and changing market rules can materially influence returns. Gas may be essential to near-term reliability while still facing long-term competition from nuclear, renewables, storage, and demand-response technologies. Regulated utilities Regulated electric utilities may benefit from rate-base growth