AI Computing Demand: An Additional 55 GW of Power Capacity Required Globally by 2030

The rapid expansion of artificial intelligence (AI) is reshaping global energy landscapes, with data centers at the epicenter of this transformation. According to recent estimates from Citi, the surge in AI computing demand will necessitate an additional 55 gigawatts (GW) of power capacity worldwide by 2030.

This projection aligns with broader industry analyses, such as those from Goldman Sachs, which forecast a 165% increase in data center power demand by the end of the decade, building on current global consumption of around 55 GW.

As AI workloads intensify—driven by advancements in machine learning, cloud computing, and generative technologies—the strain on power infrastructure is becoming a critical bottleneck. This article explores the implications of this demand, including potential expenditures, investment opportunities in energy sectors, and viable power generation options amid supply chain hurdles.

Linking Power Demand to Massive AI Infrastructure Investments

The question arises: Will this 55 GW addition fuel a $1.5 trillion wave of AI computing expenditure in the United States alone? The short answer is that while the U.S. will see substantial investments, the $1.5 trillion figure more closely reflects global AI spending trends rather than U.S.-specific computing capex tied directly to this power increment. Citi’s analysis ties the 55 GW global power need to approximately $1.4 trillion in worldwide AI computing capital expenditures (capex) by 2030, encompassing data center builds, hardware, and supporting infrastructure.

However, broader forecasts paint an even larger picture. Gartner’s projections indicate that global AI spending could reach nearly $1.5 trillion as early as 2025, driven by cloud infrastructure, software, and data center expansions.

This includes not just computing hardware but also the energy systems required to support it. McKinsey estimates that worldwide data center investments to meet compute demands could total $6.7 trillion by 2030, with a significant portion allocated to AI-ready facilities.

In the U.S., where data centers are projected to consume up to 9% of electricity by 2030 (up from 3-4% today), AI-driven capex is expected to contribute heavily to this total.

Another report suggests global AI data center spending alone could exceed $1.4 trillion by 2027, with the U.S. capturing a lion’s share due to its dominance in tech giants like Google, Meta, and OpenAI.

While not exclusively pegged to the 55 GW figure, this power demand is a key driver of the investment surge. The U.S., as a hub for AI innovation, could see expenditures approaching or exceeding $1.5 trillion in cumulative AI-related infrastructure by 2030 when factoring in domestic data center builds, grid upgrades, and energy procurement. This represents a compounding effect: more power capacity enables more AI compute, which in turn demands further investment. However, exact U.S.-only figures for “AI computing expenditure” remain estimates, with global totals often serving as proxies.

Companies Poised for Investment in the AI Power Wave

The intersection of AI demand and energy infrastructure presents lucrative opportunities for investors. Companies involved in grid modernization, power generation, and transmission are particularly well-positioned, as utilities and tech firms race to scale capacity. Here are some standout players, based on their exposure to data center electrification and related trends:Quanta Services (PWR): As a leading provider of infrastructure solutions, Quanta specializes in power grid upgrades and transmission line installations. With AI data centers requiring robust, high-voltage connections, the company is deeply embedded in projects addressing surging demand. Analysts highlight its role in modernizing aging grids to handle intermittent loads.

GE Vernova (GEV): Spun off from General Electric, GE Vernova focuses on power generation equipment, including gas turbines and renewable technologies. Despite supply chain bottlenecks in turbines, its diversified portfolio—including wind and grid solutions—positions it to benefit from both fossil fuel and clean energy transitions fueled by AI needs.

Southern Company (SO): This utility giant operates in states like Georgia, a hotspot for data center growth. Southern is investing heavily in nuclear and natural gas expansions to meet rising electricity demands, with dividends appealing to income-focused investors.

Williams Companies (WMB): A major natural gas pipeline operator, Williams stands to gain from increased gas transport to power plants serving data centers. As AI drives gas-fired generation, its infrastructure network supports reliable fuel delivery.

Fluence Energy (FLNC): Specializing in energy storage systems, Fluence addresses the intermittency of renewables, crucial for AI’s 24/7 power requirements. Its battery solutions enable grid stability amid fluctuating data center loads.

Eaton Corporation (ETN): Eaton provides electrical components for power management, including transformers and switchgear essential for transmission upgrades. Its focus on efficient energy distribution aligns with the need for resilient grids.

Other notables include Entergy (ETR) for its Southern U.S. utility operations and EQT Corporation (EQT) as a natural gas producer supplying fuel for generation.

Investors should consider diversified ETFs like those tracking data center REITs (e.g., Equinix, Digital Realty) or infrastructure funds for broader exposure.