Why Data Centre Energy Stocks Are Beating Big Tech in 2026

What’s happening
By 2026, more of the market’s excitement about AI has shifted away from software and platforms toward the physical side: electricity, grid access, interconnection, and data centre capacity.

Why it matters
Chips are no longer the only bottleneck in AI. Power delivery is becoming just as important. This shift is changing where pricing power, backlog visibility, and investor focus are found.

What the market is missing
This isn’t just about energy or technology. It’s really a story about how capital is allocated. Infrastructure owners are already making money from scarcity, while many platforms are still working to prove the value of their AI investments.

Key risk to watch
This idea could lose strength if AI becomes much more power-efficient sooner than expected, or if a lot of new power generation and capacity come online before demand catches up.

Investor lens
Pay attention. The key question isn’t who has the best model, but who controls the bottleneck.

During most of the AI boom, the market expected the main winners to be companies building models, selling chips, and offering cloud services. That made sense and explains much of what happened from 2023 to 2025. But by 2026, it’s clear that AI isn’t just about software. It’s also about physical infrastructure, and those who control scarce resources tend to benefit most. Today, those scarce resources include power, substations, interconnection rights, land ready for development, cooling systems, and long-term contracted capacity.

This is the simplest way to bring the three drafts together into one clear idea. Data centre energy and infrastructure stocks are doing better because the market is now valuing what’s hardest to provide, not just what’s easiest to sell. There are plenty of algorithms, but not enough permitted megawatts.

The International Energy Agency now projects that global electricity consumption by data centres will more than double by 2030 to roughly 945 TWh, with usage rising about 15% a year from 2024 to 2030. In the United States, the IEA says data centres account for nearly half of electricity demand growth through 2030. Reuters, citing the EIA, reported this week that U.S. electricity consumption is expected to hit record highs again in both 2026 and 2027, with AI and crypto data centres among the main drivers.

This matters because power grids weren’t built to handle thousands of GPU-intensive facilities running nonstop, such as factories. Uptime Institute’s 2025 survey shows the industry is facing higher costs, tighter power limits, supply chain delays, and the challenge of meeting AI’s power needs. Earnings calls can’t ignore this part of the AI story. Even if a model can scale in the cloud, a data centre still needs to be built, connected, cooled, permitted, financed, and powered reliably.

In markets, bottlenecks work like toll booths. When everyone has to go through the same narrow spot, whoever owns that spot usually benefits.

This doesn’t mean Big Tech is struggling to operate. It just means expectations are higher. Investors no longer reward big AI spending just because it’s large. They want proof that big infrastructure investments will turn into steady, high returns. BlackRock’s 2026 survey found that, among 732 EMEA-based institutional clients, only about one in five saw the biggest U.S. tech companies as the best AI opportunity. More than half preferred companies supplying power to data centres, and 37% chose infrastructure as their top AI investment.

That is a subtle but important shift. When the market starts favouring firms supplying energy for the AI buildout over those buying it, the narrative has changed. Power is revenue for the utility, the generator, or the infrastructure owner. Power is a cost for the hyperscaler. Capex is optional in theory, but competitively mandatory in practice. That is not a pleasant combination for valuation multiples.

Primary-source grounding for the table (selected):

The first group to benefit is the data centre infrastructure. Equinix reported $9.217 billion in revenue and $1.848 billion in operating income for 2025, along with record annualised gross bookings of $1.6 billion and steady monthly recurring revenue growth. Digital Realty had about $1.6 billion in fourth-quarter 2025 revenue and ended the year with a backlog of $817 million in annualised GAAP base rent. These aren’t wild numbers; they’re based on contracts and backlogs, which is exactly what the market values when there’s little visibility elsewhere.

The second group includes utilities and generation platforms capable of handling large new power needs. NextEra’s 2025 results showed continued investment growth at Florida Power & Light and increased backlog in its renewables division. Reuters reported in late 2025 that NextEra was seeing strong power demand from the AI boom and added about 3 GW of renewables and storage to its backlog that quarter, bringing the total backlog close to 30 GW.

The third group is companies providing on-site and power equipment. This area is riskier, but the idea is simple: if connecting to the grid takes years, faster on-site or nearby power solutions quickly become valuable. The market is basically paying for how fast you can deliver power.

What these businesses have in common isn’t just their link to AI. It’s that they benefit from the scarcity they can turn into revenue.

A platform company can tell a compelling story about future AI revenues. However, those revenues still depend on product adoption, pricing, competition, regulation, and whether users will eventually pay enough to justify the spend. By contrast, a data centre REIT with signed leases, or a utility with a regulated asset base and growing demand, is monetising something more immediate: capacity that customers need today.

That’s why this stage of the cycle feels different from earlier AI trends. At first, the market paid for computing power and stories. Now, it’s paying for power and the ability to deliver.

People often think businesses that rely on heavy infrastructure struggle when capital is expensive. But some are thriving because scarcity is so severe that having funded capacity is now more valuable. Digital Realty has talked openly about its private-capital strategy and managed vehicles. Equinix continues to focus on bookings, recurring revenue, and cash flow. These companies aren’t just selling hope; they’re selling access, interconnection, and contracted capacity.

For many Big Tech companies, AI spending shows up first as capital expenses and only later, sometimes much later, as revenue. Investors don’t dislike this; they value it differently.

There are real risks. The biggest is efficiency. If model designs improve quickly, chips use less power, and cooling and utilisation improve significantly, today’s high-power-demand forecasts might turn out to be too high. The IEA’s projections are just scenarios, not promises, and Uptime points out that technology changes are hard to predict.

There’s also the risk of execution. Signing a lease doesn’t mean you have revenue right away. Backlogs can be delayed by factors such as transformers, switchgear, interconnection work, permits, and utility schedules. Public policy can help or hurt. Environmental reviews can change which types of power generation succeed. And after a big price jump, some parts of this trend aren’t cheap anymore. A good idea bought at a bad price is still a bad trade. That’s not being negative; it’s just math.

In 2026, data centre energy stocks are outperforming Big Tech because the market has shifted from merely admiring AI to funding its bottlenecks. The most valuable assets are now physical, local, slow to permit, and hard to copy. Electricity demand is going up, and grid limits are real. Institutional investors are moving toward businesses that are closest to these constraints. Companies like Equinix, Digital Realty, and certain utilities are benefiting because their profits come from scarce capacity, steady contracts, and clear visibility, not just the future promise of demand for AI software.

In other words, the AI trade has grown up. It still loves chips and models. But in 2026, it has also learned to love substations.