CONTRIBUTORS
Shane Hurst, CFA
Portfolio Manager
ClearBridge Investments
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.
Exhibit 1: AI Joins EVs as Driver of Power Demand
Data as of March 21, 2024. Source: Wells Fargo Securities, LLC estimates, “AI Power Surge – Quantifying Upside for Renewables & Natural Gas Demand.” Reprinted by permission. Copyright © 2024 Wells Fargo Securities (“Wells Fargo”). The use of the above in no way implies that Wells Fargo or any of its affiliates endorses the views or interpretation or the use of such information or acts as any endorsement of the use of such information. The information is provided "as is" and none of Wells Fargo or any of its affiliates warrants the accuracy or completeness of the information.
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).
Exhibit 2: US Solar Installations Propelled by AI
Source: Internal Research, WFG, EIA, BNEF, 2024. There is no assurance that any estimate, forecast or projection will be realized.
Exhibit 3: US Wind Installations Propelled by AI
Source: Internal Research, WFG, EIA, BNEF, 2024. There is no assurance that any estimate, forecast or projection will be realized.
Regulated utilities will benefit from such a structural AI tailwind as it intersects with that of the broader energy transition, where we believe they will stand out from other parts of the electricity value chain. While funding for the energy transition, likely exacerbating the cost-of-living crisis, threatens to reduce returns for subsidy-reliant parts of the electricity value chain, our contention is that regulators will continue to provide attractive returns for regulated utilities.
This is in part due to the large grid investment currently needed. Under the International Energy Agency’s status quo scenario, over US$600 billion of annual investment will be undertaken by 2050 (Exhibit 4). Annual investment in power networks peaks at over US$950 billion under a net-zero outcome scenario, however (Exhibit 5), requiring US$21 trillion of total spend versus US$13.5 trillion of total spend in the status quo. Maintenance investments, such as reinforcement and replacement, account for about 80% of all spend. For utilities, such investments should translate into attractive returns off their regulated assets.
Exhibit 4: Investment in Power Grid in Energy Transition Scenario
Source: BNEF, 2024. There is no assurance that any estimate, forecast or projection will be realized. In the energy transition scenario, policymakers pursue an energy transition relying only on historical efficiency trends and economically competitive, commercially at-scale clean energy technologies; the increase in global temperatures above pre-industrial levels reaches 2.6C by 2100.
Exhibit 5: Investment in Power Grid in Net-Zero Scenario
Source: BNEF, 2024. There is no assurance that any estimate, forecast or projection will be realized. The net-zero scenario reveals the sheer scale and scope of the challenge of remaining within 1.75C of global warming and achieving the goals of the Paris Agreement.
Conclusion
AI adds another captivating chapter to the growth story for listed infrastructure. AI complements infrastructure’s role as an inflation hedge while providing investors stable cash flows and dividends derived from regulation and long-term contracts. Its low correlation with most other major asset classes also makes it a viable portfolio diversifier, in our view. AI tailwinds boosting most of infrastructure’s subsectors offer one more compelling reason for investors building diversified portfolios to consider global listed infrastructure.
Endnotes
- Source: Internal Research, McKinsey, IEA. There is no assurance that any estimate, forecast or projection will be realized.
- Ibid.
WHAT ARE THE RISKS?
All investments involve risks, including possible loss of principal. Please note that an investor cannot invest directly in an index. Unmanaged index returns do not reflect any fees, expenses or sales charges. Past performance is no guarantee of future results.
Equity securities are subject to price fluctuation and possible loss of principal.
International investments are subject to special risks including currency fluctuations, social, economic and political uncertainties, which could increase volatility. These risks are magnified in emerging markets.
Commodities and currencies contain heightened risk that include market, political, regulatory, and natural conditions and may not be suitable for all investors.
Diversification does not guarantee a profit or protect against a loss. Dividends may fluctuate and are not guaranteed, and a company may reduce or eliminate its dividend at any time.
Investment strategies which incorporate the identification of thematic investment opportunities, and their performance, may be negatively impacted if the investment manager does not correctly identify such opportunities or if the theme develops in an unexpected manner. Focusing investments in information technology (IT) and technology-related industries carries much greater risks of adverse developments and price movements in such industries than a strategy that invests in a wider variety of industries.
