Listed infrastructure powers AI—and vice versa

Key takeaways

• The potential of AI and data growth to drive significant infrastructure investment represents an emerging long-term thematic driver for the listed infrastructure asset class.
• In addition to expected benefits from increased power demand, AI may also reduce costs for utilities, potentially improving efficiency by optimizing operations of the grid and reducing energy waste.
• Beyond utilities, we find that AI and data demand may impact almost every major infrastructure subsector, while the role of AI in powering decarbonization is also underappreciated.

Listed infrastructure has a number of major long-term thematic drivers: decarbonization, significant electricity network investments to replace aging infrastructure and improve resiliency, and its role as a unique inflation hedge. Meanwhile, the valuation divide between listed and unlisted infrastructure showcases the attractiveness of the former.

Add to these another emerging theme: the potential of artificial intelligence (AI) and data growth to drive significant infrastructure investment. AI and data growth leads to increased power demand and a greater focus on smart grids (Exhibit 1). We also believe AI’s usage within infrastructure will have several benefits, streamlining processes, enhancing reliability and boosting efficiency within the sector. AI and data growth may also increase the role of infrastructure such as regulated utilities in decarbonization.

Utilities are stepping up to meet the challenge of AI’s growing power demand, and this is being recognized. Over the next five years, consumers and businesses are expected to generate twice as much data than over the past 10 years, with major tech companies expected to invest US$1 trillion in data centers. Globally, power demand is expected to increase at a compound annual growth rate of 14% over the next three years.¹

Global investment in data centers is expected to grow at 5% compounded annually, rising to US$41 billion by 2026. AI data center racks could require 7x more power than traditional data center racks, leading to a high case of power demand growth of almost 20% annually by 2026.²

An increased power load growth profile across the entire network should lower residential customer bills as total network costs (a large portion of which is fixed cost) are socialized across a larger megawatt-hour usage base. Also, in terms of generation, transmission and distribution, increased capital spending is likely to lead to accelerating asset base growth, revenues and earnings.

AI improves infrastructure processes

Utilities should also benefit from AI in a variety of ways. For one, AI should reduce costs for utilities, potentially improving efficiency by optimizing operations of the grid and reducing energy waste. AI can also be used to predict and prevent outages, improve grid security and manage demand response programs, increasing overall grid reliability and facilitating the growth of smart grids. There is also the possibility that AI will improve customer service. As large language models improve, AI should provide the ability to automate tasks such as answering questions and resolving complaints, freeing up customer service representatives for more complex issues.

AI and data demand supports major infrastructure subsectors

Beyond utilities, we find that AI and data demand are likely to impact almost every major infrastructure subsector:

• Toll roads will use smart traffic management to allow for optimized traffic flow and benefit from predictive maintenance. Longer term, the integration of autonomous vehicles on toll roads can increase efficiency, safety and reliability, creating new revenue opportunities and lowering operational costs.
• Freight rail will benefit from load planning optimization, which has historically been a manual process, and improved arrival time estimation to optimize dwell times — how long cargo is expected to wait at a given point in its journey.
• Water utilities can use AI to improve leak detection, water quality monitoring and drought prediction.
• Midstream energy may see similar benefits from real-time monitoring, which can help midstream and energy infrastructure assets detect leaks and optimize hydrocarbon system flows.
• Communication towers will likely see increased data demand, which leads to greater tenanting on their towers, higher organic leasing and amendment revenues. They may even be able to site cloud on-ramps on tower sites, offering customers lower latency in their cloud connectivity.
   

AI and decarbonization

We believe the role of AI in powering decarbonization is also underappreciated. Utilities will need to add generation capacity to meet the increased power load from AI. Most of this will likely come from renewable sources. According to estimates, AI’s demand for power could propel investment in solar capacity at a compound annual growth rate of from 8% to 16% through 2030 (Exhibit 2) and investment in wind capacity from 18% to 31% over the same time frame (Exhibit 3).