ETF Picks for Every Part of the Artificial Intelligence (AI) Value Chain

Powering AI: Energy and Infrastructure ETFs

Artificial intelligence runs on electricity, and lots of it. The International Energy Agency estimates that global data center electricity consumption now sits around 415 terawatt-hours per year, equal to about 1.5% of global demand.

As AI workloads grow, that figure could more than double by the end of the decade. Training large language models and running inference around the clock requires an enormous amount of energy, cooling, and power stability.

What most people overlook is that the bottleneck is no longer generation but distribution. The challenge is getting power where it is needed, when it is needed. That means transformers, high-voltage direct current (HVDC) transmission systems, substation switchgear, cooling infrastructure, and advanced power electronics. In short, electrification.

For investors looking to capture this theme, the VistaShares Electrification Supercycle ETF

takes an active approach I find refreshing. It focuses not on utilities or oil producers but on the companies building, upgrading, and supplying the grid itself.

Names include electrical component manufacturers, grid service providers, and engineering firms operating across the United States, Europe, and Asia. This exposure sits outside the usual benchmarks and tilts toward industrial innovation rather than regulated utilities.

What stands out is POW’s “bill of materials” methodology, which maps the entire power grid supply chain and selects firms directly tied to its growth. This framework connects abstract ideas like “electrification” to tangible businesses making the physical components that enable AI to run.

 For investors who believe AI’s long-term growth depends on the grid catching up, I think POW offers a practical way to bet on that connection.

Hosting AI: Data Center and Cloud ETFs

AI models live and run inside massive data centers. These facilities house thousands of specialized servers that need constant power, cooling, and connectivity. The companies that own, operate, and lease these facilities—often called hyperscalers—form the backbone of the AI ecosystem.

Hyperscalers spend tens of billions each year building and expanding their data center capacity to meet rising demand from AI training and inference workloads. Their scale and integration allow them to manage everything from chip deployment to energy procurement, but that also makes them capital-intensive and sensitive to utility costs, land availability, and grid reliability.

Not every firm rebranding itself as “AI infrastructure” is worth owning. I’m especially cautious of former crypto miners pivoting to AI data centers. These operators often lack stable tenants, long-term contracts, or the engineering depth required for sustained high-performance computing workloads.

Their balance sheets tend to carry high leverage from the crypto era, and many are still adapting their facilities for AI’s much higher power density and cooling requirements.

For more established exposure, I prefer the Pacer Data & Infrastructure Real Estate ETF

, which tracks the Solactive GPR Data & Infrastructure Real Estate Index. Its holdings include leading data center and telecommunications REITs. These firms own the physical assets—server farms, fiber networks, and cell towers—that enable global cloud and AI connectivity.

SRVR also fills a unique niche for income-focused investors. While many AI-related ETFs focus solely on growth, SRVR’s REIT focus means its holdings are required to distribute most of their income to shareholders. That gives it a respectable 2.6% 30-day SEC yield.

In effect, SRVR is a landlord play on the AI revolution, letting investors participate in the digital infrastructure build-out while earning steady cash flow along the way.

Building AI: Semiconductor and Hardware ETFs

There’s no shortage of semiconductor ETFs, but not all of them are designed for AI. Many legacy chipmakers specialize in analog or embedded semiconductors used in cars, appliances, and industrial systems—important markets, but far removed from AI workloads. These chips handle sensors, controllers, and connectivity rather than high-performance computing.

In contrast, a smaller group of companies has made AI central to their strategy, focusing on the advanced processors and hardware needed to train and run large models. These firms sit at a critical juncture in the AI value chain, powering everything from cloud-based supercomputers to edge inference devices.

A strong way to target this theme is the Global X AI Semiconductor & Quantum ETF

, a new entrant that tracks a proprietary index of companies involved in advanced chips, memory, networking, and system enablement.

I’m less enthusiastic about the quantum component of the theme, since many early-stage players in that space lack earnings or even working products, but CHPX largely sidesteps that risk through its weighting.

The fund leans toward established leaders such as Broadcom, Nvidia, Taiwan Semiconductor, ASML, Micron, and AMD. Together, these firms represent the foundation of the AI semiconductor ecosystem, producing the GPUs, memory, and lithography systems that make machine intelligence possible.

Developing AI: Software and Model Innovation ETFs

Some ETFs hold private AI companies through limited allocations of up to 15%, but until more goes public, direct access to these model developers isn’t possible.

Fortunately, the firms backing them are already among the world’s largest public companies. Amazon has a major stake in Anthropic; Microsoft is OpenAI’s key partner, and both Alphabet and Meta have built their own proprietary models—Gemini and Llama, respectively.

These firms represent the software layer of the AI value chain. Their focus is on large language models (LLMs), which enable natural language understanding, code generation, and image recognition.

The broader race is toward artificial general intelligence (AGI), where systems could perform most tasks as well as or better than humans. For investors, exposure to these companies means participation in the software-driven phase of AI adoption.

One ETF that captures this well is the Roundhill Generative AI & Technology ETF

. Despite a higher 0.75% expense ratio, it has justified its cost so far with  a 64.35% one-year return, beating most AI ETF competitors and the Nasdaq-100 index.

CHAT uses a proprietary scoring methodology that ranks companies based on their revenue, profit, and R&D investment in AI, along with parameters for market capitalization and liquidity.

The result is a focused portfolio of around 45 holdings that includes major AI developers and software leaders, along with a meaningful allocation to semiconductor firms that support their infrastructure.

Please note this article is for information purposes only and does not in any way constitute investment advice. It is essential that you seek advice from a registered financial professional prior to making any investment decision.