Agile's Framework for Artificial Intelligence

We believe we are in the early stages of the Artificial Intelligence boom, one of the great investment themes of our lifetimes. This is a foundational technological shift that will fundamentally reshape the global economy for the next twenty years and beyond. The transformation is already underway, and it is happening whether we like it or not—creating a new class of leaders and leaving others behind. We expect the path forward will be a persistent uptrend, punctuated by pauses and periods of consolidation, which we view as buying opportunities.

AI is under-hyped - Video primer about AI and how it’s under-hyped. Eric Schmidt’s most recent message particularly resonates with our view: the market is not overhyping AI. If anything, as he points out, we are collectively and wildly underestimating the magnitude of the power, change, and overall effect this technology will have.

A critical aspect of this initial phase is the immediate value created by AI's internal adoption, resulting in savings and efficiencies. Before AI revolutionizes products for many companies, it first revolutionizes their operations. By automating routine tasks, optimizing complex supply chains, accelerating software development, and improving customer service, companies can unlock significant productivity gains. This ability to drive internal efficiency is a powerful tool for providing earnings resilience, making these companies more robust in challenging economic environments.

A thoughtful allocation strategy focused on the best risk-reward opportunities is critical for sticking with the investment and allowing for future growth. Our strategy has been carefully crafted over time and is designed to capitalize on this shift by investing across the entire ecosystem. Based on our updated portfolio, including bonds, holdings with direct and indirect exposure to AI represent 45% of the total portfolio.

Our investment strategy is broken down into four layers of the AI stack, ranked by weighting:

Total Exposure: 19% of portfolio

Why it's important: This category represents the Toll Road of the AI revolution. These companies are building the massive, centralized cloud infrastructure—the digital foundation—where the vast majority of AI models are trained and deployed. Their immense capital expenditures are the primary demand driver for the entire hardware and infrastructure ecosystem. An investment in this layer is a direct investment in the core platforms that will own and operate AI for the masses.

• Individual Stocks in this Category: AMZN, GOOGL, META, MSFT, ORCL

• ETFs in this Category: FDN, JEPQ, PNQI

Total Exposure: 12% of portfolio

Why it's important: This layer represents the tangible, physical backbone of the digital AI revolution. Data centers are massive, power-hungry industrial facilities. Their explosive growth creates a massive secondary boom for the companies that build the physical world around them, including utilities, engineering firms, energy suppliers, and industrial equipment providers. This is a critical way to invest in the AI theme that is grounded in real assets.

• Individual Stocks in Category: CRH, CVX, DE, KMI, LNG, MYRG, NEE, PWR, SNA, URI

• ETFs in this Category: EXI, PKB, RSHO

Total Exposure: 9% of portfolio

Why it's important: This layer is the essential "engine room" of the AI boom. The complex calculations required by AI models demand a new class of specialized semiconductors and networking gear. The companies in this category own the critical intellectual property and design blueprints to produce this hardware. Progress in AI is fundamentally constrained by the power and availability of these chips.

• Individual Stocks in Category: AVGO, ESLOY, NVDA, TER, TSM

• ETFs in this Category: IGM

Total Exposure: 4.5% of portfolio

Why it's important: These companies are the "face" of AI for most consumers. They are expert integrators, weaving AI into their platforms to enhance user experiences, create new services, and build powerful competitive moats in areas like entertainment, travel, and communication. They prove the real-world utility and adoption of AI.

• Individual Stocks in Category: MA, BKNG, DIS, MELI, NFLX, SPOT, TMUS, UBER

While our current five-layered approach forms the core of our strategy, we are actively monitoring other critical areas to enhance our positioning as the AI boom matures. We believe in remaining agile and are looking for opportunities to increase our exposure in the following areas as valuations become more attractive.

• Increased Energy Exposure (Nuclear & Natural Gas): The power demand from AI data centers is unprecedented. To meet this, we are looking to increase our exposure to nuclear power for long-term, carbon-free baseload energy and natural gas pure plays as a critical and reliable bridge fuel.

• Added Cybersecurity Exposure: As the digital footprint of AI expands, the "attack surface" for malicious actors grows exponentially. We view cybersecurity as a non-negotiable, essential component of the AI build-out and are patiently watching for opportune moments to establish a more significant position.

• Robotics and Automation: One of the final frontiers of AI is its application in the physical world. Several of our current holdings, like Amazon, Deere & Co., and NVIDIA, are key players. As this field matures, we will seek to add exposure to pure-play leaders in areas such as surgical robotics and advanced factory automation.

• AMZN (Amazon): A dominant cloud provider with AWS, which is a primary platform for training and deploying AI models. Amazon is also a leader in using AI for its own e-commerce logistics and retail recommendations.

• GOOGL (Alphabet): A leader in foundational AI research (DeepMind) and a major cloud platform (GCP). AI is deeply integrated across all its core services, from Search and advertising to YouTube content algorithms.

• META (Meta Platforms): Investing heavily in developing and open-sourcing its own powerful AI models (Llama). This strategy drives massive data center and hardware investment and positions them as a key player in the future of AI development.

• MSFT (Microsoft): A key AI player through its deep partnership with OpenAI and the aggressive integration of "Copilot" AI assistants across its entire Windows, Office, and Azure cloud software suite.

• ORCL (Oracle): Leveraging its long-standing strength in enterprise databases to offer specialized cloud infrastructure (OCI) and AI services that are specifically tailored for corporate and large-scale business clients.

• CRH (CRH plc): A global leader in building materials, including cement, concrete, and aggregates. These materials are the essential physical foundation required for constructing new, large-scale data and power centers.

• CVX (Chevron): A major energy producer providing the natural gas required for the reliable, 24/7 power generation that AI data centers demand, which cannot be met by intermittent sources alone.

• DE (Deere & Co.): A leader in industrial automation and robotics through its autonomous tractors and precision agriculture technology, which leverage advanced AI, machine learning, and GPS to operate.

• KMI (Kinder Morgan): Owns and operates one of the largest networks of natural gas pipelines in North America, representing critical infrastructure for transporting fuel to the power plants that supply electricity to data centers.

• LNG (Cheniere Energy): A leading producer and exporter of Liquefied Natural Gas (LNG), providing a key and flexible fuel source to power grids globally that are seeing increased demand from AI.

• MYRG (MYR Group): A specialty electrical contractor focused on building and maintaining the high-voltage transmission lines, substations, and complex wiring required for large-scale industrial projects, including data centers.

• NEE (NextEra Energy): A major U.S. utility and the largest generator of renewable energy, making it critical for meeting the massive and growing electricity demand from the AI sector, often with a focus on clean energy sources.

• PWR (Quanta Services): A leading engineering and construction firm directly involved in building and upgrading energy and communications infrastructure, including the power grids and fiber optic networks that connect data centers.

• SNA (Snap-on): Provides high-end, specialized tools and diagnostic equipment essential for the skilled technicians who build, install, and maintain the complex machinery and electrical systems within data centers and other industrial sites.

• URI (United Rentals): As the world's largest equipment rental company, it provides the necessary heavy machinery, power generators, and tools required for the physical construction of data center buildings.

• AVGO (Broadcom): A key supplier of custom chips (ASICs) and advanced networking hardware, which are essential, high-performance components for building AI data centers and enabling rapid data transfer.

• ESLOY (Essilor Luxottica): Employs significant automation, robotics, and advanced data processing in its manufacturing and lens-crafting labs; it is also uniquely positioned to be a leader in future AI-enabled smart eyewear.

• NVDA (NVIDIA): The undisputed leader in GPUs, the essential processors that power nearly all AI model training and inference. The company's hardware and software ecosystem make it the primary engine of the AI boom.

• TER (Teradyne): Provides critical automated test equipment (ATE) for the semiconductor industry, ensuring the quality and reliability of the increasingly complex and powerful chips required for all AI applications.

• TSM (Taiwan Semiconductor): The world's leading semiconductor foundry, responsible for manufacturing the most advanced chips for nearly all major AI players, including NVIDIA, Broadcom, and others.

• BKNG (Booking Holdings): Leverages AI extensively for personalized travel recommendations, dynamic pricing optimization, and customer service automation to enhance user experience and drive bookings.

• DIS (The Walt Disney Company): Uses AI to power the recommendation engines on its Disney+ streaming service, optimize theme park operations and crowd management, and enhance its advertising platforms.

• MELI (MercadoLibre): Utilizes AI throughout its e-commerce and fintech ecosystem in Latin America for advanced fraud detection, logistics optimization, credit scoring, and personalized product recommendations.

• NFLX (Netflix): A pioneer in using AI for its world-class content recommendation algorithms, which are crucial for driving user engagement, reducing churn, and informing its multi-billion dollar content acquisition strategy.

• SPOT (Spotify): Built on a foundation of AI-powered music and podcast discovery. Its core value proposition lies in using machine learning to create personalized playlists and recommendations for users.

• TMUS (T-Mobile US): Employs AI to optimize its 5G network traffic for better performance, manage customer service inquiries with bots, and personalize marketing campaigns to increase customer retention.

• UBER (Uber Technologies): Depends entirely on AI for its core logistics, including the algorithms for dispatching drivers, optimizing routes, forecasting demand, and setting dynamic prices for both its ride-sharing and food delivery services.

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