We are pleased to announce an increase to Franklin Global Real Assets Fund (FGRAF)’s existing investment in DataBank, a leading provider of data center services in the US. This investment aligns with FGRAF's strategy to capitalize on the growing demand for digital infrastructure that is being driven by an increasing need for data storage and processing capabilities. This report will provide an overview of DataBank, the rationale behind FGRAF's investment, and how this investment ties into the broader data center investment thesis, particularly in the context of advancements in artificial intelligence (AI).
A "Picks and Shovels" Investment in AI
In a recent article in Barron's1, Jenny Johnson, Franklin Templeton’s President and CEO discussed a potential wave of pessimism surrounding AI investments. She highlighted the importance of a "picks and shovels" approach focused on investing in the infrastructure that supports AI development rather than the AI technologies themselves. This approach is akin to investing in the suppliers of tools during the gold rush rather than the miners. Johnson's insights are particularly relevant to FGRAF's investment in DataBank, as data centers are a critical component of the infrastructure needed to support AI workloads.
Why DataBank?
DataBank is a leading provider of data center services, offering colocation, cloud, and managed services to a diverse range of customers. The company operates 65 data centers across 27 cities in North America, with plans to expand further. DataBank's facilities are designed to support high-density AI workloads, making them an attractive partner for businesses looking to leverage AI technologies.
FGRAF's investment in DataBank is driven by several key factors:
- Critical Infrastructure: Data centers are essential for storing and processing large amounts of data, which is crucial for businesses and technology services. DataBank's facilities provide the necessary environment for computing resources, ensuring they operate efficiently and securely.
- Strong Sector Tailwinds: Rising demand for cloud services, AI, and low-latency computing is driving the growth of data centers. DataBank is well-positioned to capitalize on these trends, with a strong presence in key markets and a robust interconnection ecosystem.
- Predictable Cash Flows: DataBank has a stable revenue stream, with long-term contracts and low churn rates. In our view, the company's strong and stable margins, along with its highly visible growth profile, make it an attractive investment.
- Sustainability and ESG: DataBank is committed to sustainability, with well-defined goals for energy efficiency and renewable energy. The company's flexible cooling options reduce water usage, and its facilities are designed to minimize environmental impact.
Source: McKinsey & Company.
Our Data Center Investment Thesis
The investment in DataBank aligns with FGRAF's broader data center investment thesis, which incorporates several themes: Digitization: DataBank supports the digital transformation by providing critical infrastructure for data storage, processing, and management. Their extensive reach and interconnection ecosystems help businesses enhance their digital presence.
Artificial Intelligence (AI): DataBank's facilities are designed for high-density AI workloads. Strategic locations and advanced cooling solutions ensure optimal performance for AI applications.
Edge Presence: The shift from the training of AI models to inference is a significant trend that we expect will drive demand for edge data centers. DataBank has a strong presence in 27 U.S. markets, including 17 of the top 20 metropolitan statistical areas (MSAs).
Growth Strategy: DataBank has a large land bank with secured power and financing in place for more than three times capacity growth through 2027. The company's universal data hall design adapts to various workloads to support evolving customer needs.
Carrier Hotels: Critical Infrastructure for Digitization
Bustling cities house many different communication networks including phone lines, internet connections, and data services. Carrier hotels function as a central hub where different communication networks come together to exchange data, ensuring that information can travel smoothly and quickly from one place to another. They are strategically located in major cities and are often housed in older, sturdy buildings that have been retrofitted with the latest technology. These buildings are chosen because they are at the intersection of many fiber optic cables, making them ideal for connecting multiple networks.
Inside are special "meet-me rooms" where telecommunication companies and internet service providers can physically connect their cables and equipment to each other. This allows data to travel quickly and efficiently from one network to another. For example, if you're sending an email from your home in Calgary to a friend in New York, the data might pass through a carrier hotel in Toronto, where it gets routed to the appropriate network that will deliver it to your friend's inbox.
DataBank operates 18 carrier hotels across 17 markets, servicing more than 37,000 existing interconnections. This extensive network of carrier hotels ensures efficient and reliable data connectivity, enhancing DataBank’s ability to support diverse customer demands and contributing significantly to the company's strategic value.
From Training to Inference: Driving Demand for Edge Data Centers
The shift from training AI models to inference is a significant trend creating demand for edge data centers. Training AI models involves processing vast amounts of data to develop algorithms that can make predictions or decisions. This process requires substantial computational power and is typically performed in centralized data centers. However, once these models are trained, they need to be deployed in real-world applications. This is where inference comes into play.
Inference refers to the process of using trained AI models to make predictions or decisions based on new data. It often requires lower latency and faster response times, which can be achieved by processing data closer to the source. Edge data centers are located closer to end-users and can provide the necessary computational power with reduced latency.
We expect the shift from training to inference to drive significant growth in the demand for edge data centers, and DataBank's extensive network of edge data centers positions it well to capitalize on this trend. By providing the infrastructure needed for AI inference, DataBank can support the growing demand for real-time AI applications in autonomous vehicles, smart cities, and IoT (Internet of Things) devices.
Strategic Investment in Digital Infrastructure and AI Development
FGRAF's investment in DataBank is an important component of its strategy to capitalize on the growing demand for digital infrastructure, not only supporting the digital transformation but also positioning the portfolios to benefit from the increasing adoption of AI technologies. This focus on the critical infrastructure that underpins AI development epitomizes the "picks and shovels" approach described earlier. We believe it is a strategy that will more than prove itself in the days ahead.
Endnotes
- “A Wave of AI Pessimism Is Coming. How to Outlast It.” – Barron’s, November 27, 2024.
Important Legal Information
This material is intended to be of general interest only and should not be construed as individual investment advice or a recommendation or solicitation to buy, sell or hold any security or to adopt any investment strategy. It does not constitute legal or tax advice.
The views expressed are those of the investment manager and the comments, opinions and analyses are rendered as at publication date and may change without notice. The information provided in this material is not intended as a complete analysis of every material fact regarding any country, region or market.
Commissions, trailing commissions, management fees and expenses all may be associated with mutual fund investments. Please read the prospectus before investing. Mutual funds are not guaranteed, their values change frequently, and past performance may not be repeated.