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AI investment opportunities can sit outside companies that sell models or software. Data centres need land and power; the wider build-out also depends on grids, cooling, electrical equipment, industrial suppliers and materials. These are areas to investigate—not guaranteed winners. Whether a company benefits depends on completed projects, durable customer demand, execution, financing and the price investors pay.
Why AI infrastructure matters to investors
The scale of investment in computing infrastructure has increased alongside AI adoption. The International Energy Agency (IEA) says the largest technology companies’ capital expenditure exceeded USD 400 billion in 2025 and was expected to rise by another 75% in 2026. The 2026 figure is the IEA’s forecast, not a reported result. IEA, Key Questions on Energy and AI (2026).
The demand signal extends to electricity: global data-centre electricity demand grew 17% in 2025, while electricity consumption from AI-focused data centres grew 50%, according to the IEA. Those figures describe recent demand, not the future earnings of any particular utility, equipment maker or property owner.
Where to investigate beyond AI software and models
Institutional sources point to several parts of the physical value chain. Treat these as research categories, not a ranking of likely returns: exposure to a theme alone does not show that a company has secured business, can deliver projects profitably or is attractively valued.
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Power generation and utilities
Data centres require substantial, dependable electricity. Utilities and power producers may be relevant where they can supply new or expanded loads, but the opportunity is local: available generation, transmission access, permitting, customer commitments and project economics all matter. An announced data-centre plan does not by itself establish future electricity sales.
Grid infrastructure and electrical equipment
Connecting new facilities can require grid upgrades and equipment such as transformers and other electrical systems. Companies supplying this infrastructure may participate in build-out without selling AI products. Investors still need to distinguish a broad industry theme from a supplier’s actual orders, delivery capacity, customer mix and earnings conversion.
Cooling and data-centre systems
Data centres need systems to manage heat as computing capacity grows. Cooling providers and other facility-equipment suppliers are therefore potential areas to investigate. The scale and timing of demand can vary by site and design, so a general increase in computing does not establish how much business any particular supplier will win.
Data-centre real estate and construction
Property owners, developers and contractors may have exposure through land, facilities or construction work. A proposed project is not the same as a completed, occupied facility: examine construction progress, customer commitments, financing and operating utilization before treating planned capacity as an earnings driver.
Industrial suppliers and materials
AI infrastructure also relies on a network of industrial equipment and supporting materials. BlackRock and Columbia Threadneedle identify these kinds of categories in their thematic perspectives on the AI build-out. Their lists are research directions, not assurances that every company in a category will benefit. See BlackRock’s 2026 thematic investing update and Columbia Threadneedle’s July 2026 analysis.
How to assess an AI-infrastructure investment
Use the same questions whether a company is a utility, equipment supplier, data-centre operator or industrial business. A compelling narrative is not a substitute for evidence that demand becomes durable revenue and earnings.
- Demand: Separate operating or contracted demand from announced capacity, management forecasts and project pipelines. The IEA cautions that not all proposed data-centre projects will be completed.
- Bottlenecks: Check whether power access, grid connections, cooling, electrical equipment or materials are genuine constraints for the company’s customers and projects. These conditions vary by location and project.
- Execution and earnings: Follow the chain from planned facility or supply relationship to completed construction, customer commitments, operating utilization and reported earnings. A contract or plan is not automatically profitable growth.
- Financing and concentration: Assess capital needs, funding sources and reliance on a small number of customers or counterparties. The IMF identifies circular financing arrangements among firms in parts of the AI stack as a possible channel through which problems at one firm could spread to others; it presents a risk, not a prediction of crisis. IMF, AI: Deployment and Disruption (2026).
- Valuation and alternatives: Compare the earnings a company might generate with the price of its shares, and consider other ways to gain exposure. The cited sources do not establish company-level valuations or identify an attractive security.
Why demand forecasts remain uncertain
The IEA describes AI’s energy needs as the result of three fast-changing factors: efficiency improvements, adoption and changing model capabilities. Greater efficiency may reduce energy use per task, while broader uptake and more energy-intensive applications may raise total demand. The balance is uncertain, and the IEA cautions that not every proposed data-centre project will be built.
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AI adoption in the energy sector also faces practical barriers. The IEA identifies shortages of digital skills, fragmented data, privacy and cybersecurity concerns, and insufficient digitalisation of equipment. Established energy companies could use AI as a tool, but the presence of a possible use case does not show that adoption will be rapid or financially material.
What this means for an investor
Looking beyond AI software can reveal exposure to the infrastructure required to build and run data centres. The useful next step is not to buy a sector label, but to investigate companies with verifiable demand, credible execution and a financing profile that can withstand delays or customer concentration. The evidence here supports areas to research; it does not establish expected returns, security valuations or suitability for an individual investor.
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