7 AI infrastructure stocks positioned for the boom beyond NVIDIA

The artificial intelligence boom has created a complex investment landscape where traditional tech giants compete alongside specialized infrastructure providers and emerging players across the entire AI value chain. Understanding where to allocate capital requires recognizing that AI’s exponential growth demands massive investments in computing power, energy infrastructure, and specialized components that extend far beyond software companies.

Smart investors are positioning themselves across multiple layers of the AI ecosystem, from the semiconductors that power machine learning to the data centers that house them. This approach recognizes that AI’s infrastructure requirements create opportunities across industries that might not immediately appear connected to artificial intelligence but are essential to its continued expansion.

The current market presents both compelling opportunities and significant risks, with AI-focused investments likely to experience continued volatility as the technology matures and market expectations adjust to reality.

Memory and semiconductor infrastructure

Micron Technology (MU) represents one of the most direct plays on AI’s voracious appetite for memory. Artificial intelligence applications, particularly large language models and computer vision systems, require massive amounts of high-speed memory to process data efficiently. Unlike traditional computing applications, AI workloads must access enormous datasets simultaneously, creating unprecedented demand for advanced memory solutions.

Micron, one of the world’s largest memory manufacturers, produces both DRAM (dynamic random-access memory) and NAND flash storage that serve as the foundation for AI computing systems. The company has invested heavily in next-generation memory technologies specifically designed for AI applications, including high-bandwidth memory (HBM) that can transfer data at speeds necessary for advanced machine learning operations.

ASML Holding (ASML) provides the specialized equipment necessary to manufacture the semiconductors that power AI systems. ASML, a Dutch company, maintains a near-monopoly on extreme ultraviolet (EUV) lithography machines—the sophisticated equipment required to create the most advanced computer chips. These machines, costing over $200 million each, are essential for producing the cutting-edge processors used in AI applications.

The semiconductor equipment sector benefits from AI’s requirement for increasingly powerful and efficient chips. As AI models become more complex, they demand processors with smaller transistors and higher performance, driving continuous investment in manufacturing equipment.

Power and energy solutions

Canadian Solar (CSIQ) addresses AI’s substantial energy requirements through solar power infrastructure. Data centers running AI workloads consume significantly more electricity than traditional computing facilities, with some estimates suggesting that training a single large language model requires as much energy as several hundred homes use in a year.

The connection between solar power and AI infrastructure reflects a broader trend toward sustainable computing. Major technology companies are increasingly committed to powering their AI operations with renewable energy, both for environmental reasons and to manage long-term energy costs. Solar installations provide predictable, long-term energy pricing that helps data center operators manage the substantial electricity costs associated with AI computing.

Amprius Technologies (AMPX) develops advanced battery technologies that could prove crucial for mobile AI applications, particularly autonomous drones and robotics. As AI systems become more sophisticated, they’re increasingly deployed in mobile applications that require high-energy-density batteries capable of supporting intensive computational workloads while maintaining portability.

Computing and cloud infrastructure

Alphabet (GOOGL) offers multiple AI-related growth drivers through its diverse technology portfolio. The company’s Tensor Processing Units (TPUs) provide specialized hardware optimized for machine learning workloads, competing directly with NVIDIA’s offerings in the AI chip market. DeepMind, Alphabet’s AI research division, continues developing breakthrough technologies that could create new market opportunities. Additionally, Waymo, the company’s autonomous vehicle division, represents a potentially massive application of AI technology in transportation.

Super Micro Computer (SMCI) specializes in data center infrastructure specifically designed for AI workloads. The company designs and manufactures the servers, cooling systems, and networking equipment that house AI computing systems. AI applications place unique demands on data center infrastructure, requiring specialized cooling solutions and high-speed interconnects between processors.

Celestica (CLS) derives nearly half its revenue from data center-related business, positioning the company to benefit from continued expansion in AI computing infrastructure. The company provides electronics manufacturing services and solutions for data center equipment, benefiting from the ongoing buildout of AI-capable computing facilities.

Market positioning and risk management

NVIDIA (NVDA) remains the dominant force in AI computing hardware, though its premium valuation reflects high market expectations. The company’s graphics processing units (GPUs) have become the standard for AI training and inference, creating a powerful competitive moat. However, the stock’s significant appreciation has created valuation concerns that make it a more complex investment decision.

Sophisticated AI investors are implementing hedging strategies to manage the sector’s inherent volatility. Short positions in S&P 500 futures provide protection against broader market declines that could affect AI stocks despite strong fundamental performance. This approach recognizes that AI investments, despite their growth potential, remain susceptible to macroeconomic factors including trade policies and interest rate changes.

SOFR (Secured Overnight Financing Rate) futures positions reflect expectations that AI-driven productivity growth could lead to higher interest rates as economic expansion accelerates. This positioning acknowledges that successful AI adoption could create inflationary pressures as demand for skilled labor and specialized equipment increases.

Investment timing and market dynamics

The AI investment landscape continues evolving rapidly, with periodic market corrections creating both risks and opportunities. Recent volatility, including sharp declines triggered by competitive developments like DeepSeek’s emergence, demonstrates how quickly market sentiment can shift in response to technological developments.

Current market conditions suggest that while AI stocks may form speculative bubbles, the underlying technology trends remain robust. The infrastructure investments required to support AI adoption create multi-year growth opportunities for well-positioned companies, though investors should expect continued volatility as markets adjust to new developments.

Practical considerations

• For growth investors: Focus on companies with direct exposure to AI infrastructure bottlenecks, particularly memory, specialized semiconductors, and data center equipment.
• For value investors: Look for established technology companies trading at reasonable valuations despite significant AI-related growth opportunities.
• For risk management: Consider hedging strategies that protect against broader market declines while maintaining exposure to AI-specific growth trends.

Closing thoughts

AI investing requires understanding the technology’s infrastructure demands and identifying companies positioned to benefit from this unprecedented buildout. Success depends on recognizing that AI’s impact extends far beyond software companies to include energy providers, equipment manufacturers, and specialized component suppliers across the entire technology stack.