Gain targeted exposure to AI and power infrastructure companies
Our Partner
Riding the AI energy surge
AI is driving a surge in electricity demand, led by data centres and computer infrastructure. The ETF targets companies powering this shift – from nuclear and decentralised energy to grid equipment and utilities – as the race to power the AI economy accelerates.
Diversified thematic exposure
The ETF offers diversified exposure across four tiers spanning the AI power value chain – from generation and grid infrastructure to utilities, data centres and hardware – ensuring balanced coverage of the systems underpinning AI growth.
Rigorous index construction
The index applies strict eligibility criteria to identify pure-play companies. Companies must generate at least 50% of revenues from AI hardware, data centres, power infrastructure, or related sectors. Individual security weights are capped at 8% to manage concentration risk.
Diverse theme
The AI power value chain is diverse – from generation and grid infrastructure to utilities, data centres and hardware.
Structural trend
AI is driving a multi-year infrastructure buildout, with trillions expected to be invested across energy systems, grids, data centres, and semiconductors. This may create a sustained growth opportunity across the entire AI power value chain.
Defiance AI & Power Infrastructure UCITS ETF (AIPO) provides investors with targeted exposure to the companies building and enabling the energy backbone required to support next-generation AI, hyperscale data centres, grid modernisation, and electrification demand.
As AI technologies demand unprecedented energy resources, the fund focuses on innovative companies driving decentralised energy solutions, electrical grid advancements, data centre operations, and AI hardware development.
The AI Infrastructure ETF tracks the MarketVector™ – Defiance US Listed AI & Power Infrastructure Index, which targets high-growth opportunities across power generation, utilities, construction, and AI-enabling technologies.
The index selects U.S.-listed companies deriving at least 50% of revenues from artificial intelligence hardware, data centres, power infrastructure, or related sectors.
Key Risks
The ETF shall invest predominantly in companies in the technology, energy and infrastructure sectors. These industries can be significantly affected by rapid technological change, government regulation and changes in capital spending priorities. AI and power infrastructure companies may face supply chain constraints, regulatory headwinds and project execution risks. Concentration in US-listed securities may expose investors to USD/EUR exchange rate movements. For a complete overview of all the risks, please refer to the ”Risk Factors” in the Prospectus.
Source: HANetf, data as of 31.08.2026. Please note that all performance figures are showing net data. Performance before inception is based on back-tested data. Back-testing is the process of evaluating an investment strategy by applying it to historical data to simulate what the performance of such a strategy would have been. Back-tested data does not represent actual performance and should not be interpreted as an indication of actual or future performance. Past performance for the index is in USD. Past performance is not an indicator for future results and should not be the sole factor of consideration when selecting a product. Investors should read the prospectus of the Issuer (”Prospectus”) before investing and should refer to the section of the Prospectus entitled Risk Factors for further details of risks associated with an investment in this product. If fund is less than 12 months old, YTD field will be calculated since inception. When you invest in ETFs your capital is at risk.
No. of holdings: 89
The Defiance AI & Power Infrastructure UCITS ETF tracks the MarketVector™ – Defiance US Listed AI & Power Infrastructure Index, a rules-based, market capitalisation-weighted index designed to provide targeted exposure to companies at the intersection of artificial intelligence and critical power infrastructure.
To be eligible for inclusion, a company must derive at least 50% of its revenues from one of four defined sectors:
Securities must be listed on a US exchange, have a full market capitalisation exceeding USD 300 million, and meet minimum liquidity requirements including a three-month average daily trading volume of at least USD 1 million. Constituents are weighted by free-float market capitalisation and grouped into four strategic tiers with defined weight targets:
Individual security weights are capped at 8% (4% for Data Centres and AI Hardware companies). The index is reconstituted and rebalanced quarterly in March, June, September, and December.
AI infrastructure refers to the underlying hardware and systems required to build, train and run artificial intelligence models at scale. This typically includes semiconductors, high-performance computing chips, data centres, networking equipment, cloud infrastructure, memory, and power and cooling systems. The growth of AI applications increases demand for these components, as models require significant computational capacity and data throughput. Investors researching this theme may want to understand how different parts of the AI value chain interact, as return drivers and risk profiles can vary across hardware, infrastructure services and enabling technologies.
Demand for data centres is increasing because AI models require large-scale computing environments to process vast datasets and run complex workloads efficiently. Traditional IT infrastructure is not sufficient for training or deploying advanced AI systems at scale. This has led to greater investment in hyperscale data centres with high-density compute, advanced cooling systems and expanded power capacity. Capacity constraints, energy availability and build-out timelines can all influence how quickly supply can respond to demand. Investors may consider how structural demand growth interacts with supply bottlenecks specific to AI-driven infrastructure, where lead times and power constraints differ materially from conventional data centre development.
AI infrastructure exposure typically includes companies across multiple segments of the technology stack. This may involve semiconductor manufacturers producing advanced GPUs and CPUs, memory chip producers, data centre operators, networking and optical communication firms, and companies supplying power management or cooling technologies. Cloud infrastructure providers may also form part of the ecosystem. Methodologies often define eligibility based on revenue contribution, sector classification or involvement in enabling AI workloads. Investors should review how constituents are selected, as different approaches can lead to varying degrees of concentration across hardware versus infrastructure services.
Semiconductors are central to AI infrastructure because they provide the processing power required to train and run machine learning models. Graphics processing units (GPUs), accelerators and high-performance CPUs handle the parallel computations that AI workloads depend on. As models become more complex, demand increases not only for compute chips but also for supporting components such as memory and interconnects. Supply chain constraints, manufacturing capacity and technological innovation cycles can all influence this segment. Investors researching this theme may want to consider how semiconductor demand is linked to broader AI adoption trends and capital expenditure cycles.
Energy demand is a critical constraint in AI infrastructure development because large-scale computing requires substantial and continuous power supply. Data centres consume significant electricity for both computation and cooling systems, and AI workloads intensify this requirement due to high-density processing. Grid availability, energy pricing and access to low-carbon power sources can therefore influence where and how infrastructure is built. In some regions, power constraints may delay expansion despite strong demand. Investors may consider how energy infrastructure, utilities and grid modernisation intersect with the growth of AI-driven computing demand.
Key risks include cyclical demand in semiconductor and technology markets, rapid technological change that can make existing hardware obsolete, and supply chain concentration in certain geographies. Valuations in high-growth technology segments can also be sensitive to interest rate changes and market sentiment. In addition, infrastructure build-out depends on energy availability, capital expenditure cycles and regulatory environments. Concentration risk may arise where a small number of large companies dominate index weightings. Investors should review methodology, sector exposure and diversification characteristics to understand the nature and extent of these risks within the exposure, and whether they are consistent with their own investment objectives
AI adoption can increase long-term demand for computing infrastructure as more industries integrate machine learning into products and services. This includes cloud computing, automation, cybersecurity, healthcare analytics and enterprise software. As usage scales, demand expands across compute, memory, storage and networking systems. However, growth rates may vary depending on efficiency improvements in AI models and hardware innovation. Investors researching this theme may want to consider how productivity gains from AI could both increase overall demand and change the mix of infrastructure required over time.
Memory and storage are essential components of AI systems because they determine how quickly data can be accessed and processed during model training and inference. High-bandwidth memory supports rapid data transfer between processors, while storage systems manage the large datasets required for training models. As AI models scale, memory bandwidth can become a bottleneck even when compute power is available. This has led to increased focus on advanced memory technologies and high-performance storage solutions. Investors may consider how constraints in memory supply can influence broader AI infrastructure development.
Relevant stocks may include companies across power equipment, grid infrastructure, data centres, utilities and AI hardware. Examples include GE Vernova, Eaton, Vertiv, Quanta Services and Bloom Energy, which are linked to power generation, electrical systems, grid buildout or distributed energy. Investors may also see AI hardware and enabling technology names such as Nvidia, Broadcom and Advanced Micro Devices, alongside energy-related companies such as Cameco and Constellation Energy. These examples highlight how the theme spans both the digital and physical infrastructure needed for AI. They are not recommendations, and investors should consider valuation, revenue exposure, execution risk and concentration before making decisions.
Disclaimer: These FAQs have been generated with the assistance of AI and may contain errors or omissions. They are provided for general information only and do not constitute investment advice, a recommendation, or an invitation to buy or sell any investment.
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ETF Stream
Defiance adds AI and power infrastructure ETF to European rollout
About Partner
Founded in 2018, Defiance ETFs is a leading U.S.-based ETF issuer specialising in thematic, income, and leveraged exchange-traded funds. With a track record of bringing innovative investment ideas to market quickly and effectively, Defiance has established itself as a first mover in several high-growth thematic categories, including AI, quantum computing, and leveraged single-stock ETFs.
The firm’s investment philosophy is grounded in identifying structural, long-term trends and translating them into accessible, liquid ETF products for investors worldwide. Led by a team of seasoned finance professionals with backgrounds at ProShares, BlackRock, and Direxion, Defiance brings deep expertise in ETF product development, capital markets, and investor education.
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