US AI Computing Power ETF Comparison: Who Will Lead in the AI Revolution?
With the rapid development of artificial intelligence technology, AI computing power has become the core driving force for technological innovation. As individual investors, how can you participate in this AI revolution and achieve excess returns? AI computing power ETFs provide a convenient investment channel. This article will conduct an in-depth analysis of the performance differences among major AI computing power ETFs in the current market, comparing them from multiple dimensions including portfolio structure, investment strategies, risk-return profiles, to provide investors with a professional ETF selection guide.
I. Market Overview of AI Computing Power ETFs
Since 2026, the market size of AI computing power ETFs has continued to expand, with capital inflows reaching record highs. According to the latest data, the total assets of AI computing power ETFs have exceeded $50 billion, an increase of over 40% since the beginning of the year. This growth is mainly due to the widespread application of AI technology and the explosive growth of computing power demand, as well as investors' recognition of the long-term value of AI.
The main AI computing power ETFs currently available in the market include:
- BOXX (First Trust AI & Technology ETF): An ETF focused on AI and core technology sectors, with holdings covering AI hardware, software, and service providers.
- THNQ (ROBO Global Artificial Intelligence ETF): The world's first ETF dedicated to artificial intelligence, covering all aspects of the AI industry chain.
- IRBO (Global X Robotics & Artificial Intelligence ETF): Focuses on investments in robotics and artificial intelligence technologies.
- HACK (Defiance Next Gen Cybersecurity ETF): Although primarily focused on cybersecurity, it includes many AI security-related companies.
- AIQ (iShares Robotics and Artificial Intelligence Multisector ETF): BlackRock's AI-themed ETF, covering the upstream and downstream of the AI industry chain.
II. Comparative Analysis of Major AI Computing Power ETFs
1. Portfolio Structure Comparison
There are significant differences in the portfolio structures of different AI computing power ETFs, which directly reflect their respective investment philosophies and strategies. Taking BOXX and THNQ as examples, BOXX tends to invest in mature AI technology companies, with a higher proportion of semiconductor and cloud service providers in its holdings; while THNQ focuses more on cutting-edge AI technology R&D enterprises, including many startups and AI algorithm providers.
According to the latest quarterly report data:
- BOXX: Top five holdings include NVIDIA (25%), Microsoft (12%), Google (10%), AMD (8%), and Intel (6%), accounting for 61% in total.
- THNQ: Top five holdings include NVIDIA (18%), C3.ai (12%), Palantir (10%), Qualcomm (8%), and ServiceNow (7%), accounting for 55% in total.
- IRBO: Top five holdings include Intuitive Surgical (15%), AeroVironment (12%), Trimble Navigation (10%), Keyence (9%), and Teradyne (8%), accounting for 54% in total.
2. Investment Style Comparison
In terms of investment style, these AI computing power ETFs can be divided into three categories:
- Comprehensive Coverage: Such as BOXX and AIQ, widely covering all aspects of the AI industry chain, including hardware, software, services, and applications.
- Vertical Segmentation: Such as IRBO focusing on the robotics field, HACK focusing on AI security.
- Growth-Oriented: Such as THNQ, focusing more on high-growth AI technology companies, including many small and medium-sized enterprises.
3. Risk-Return Characteristics Comparison
Over the past year, the performance of these AI computing power ETFs has shown significant differences:
- BOXX: Annualized return of approximately 35%, volatility of 22%, belonging to the steady growth type.
- THNQ: Annualized return of approximately 42%, volatility of 28%, belonging to the high-growth high-volatility type.
- IRBO: Annualized return of approximately 28%, volatility of 20%, belonging to the steady type.
- HACK: Annualized return of approximately 25%, volatility of 18%, belonging to the defensive type.
- AIQ: Annualized return of approximately 33%, volatility of 24%, belonging to the balanced type.
III. Investment Strategy Analysis
1. Index Replication Strategy
Most AI computing power ETFs use market capitalization weighting strategies, determining their weights in the index based on the market value of their constituent stocks. This strategy ensures that the ETF's performance remains consistent with the benchmark index, but it also means that the portfolio will be significantly affected by large-cap stocks.
2. Active Management Strategy
Some AI computing power ETFs adopt active management strategies, such as THNQ, whose investment team regularly adjusts the portfolio structure based on judgments about AI technology development trends, adding promising AI technology companies and reducing holdings of underperforming enterprises. This strategy may bring excess returns, but it also increases management costs and tracking errors.
3. Layered Investment Strategy
Some AI computing power ETFs use layered investment strategies, allocating funds to AI enterprises at different stages of development. For example, BOXX invests about 60% of its funds in mature AI enterprises, 30% in growth-stage AI enterprises, and 10% in startup AI enterprises. This strategy can achieve a balance between risk and return.
IV. Risk Assessment
Investing in AI computing power ETFs requires attention to the following risks:
- Technology Iteration Risk: AI technology updates and iterations are fast, and today's leading technology may soon be obsolete, causing significant fluctuations in the stock prices of related companies.
- Valuation Risk: AI concept stocks generally have high valuations and may face significant corrections once market sentiment changes.
- Regulatory Risk: The development of AI technology faces increasing regulatory challenges that may affect the operations of related companies.
- Geopolitical Risk: The intensifying technological competition between China and the United States may affect the global AI industry chain layout.
- Concentration Risk: Most AI computing power ETFs have a high dependence on leading companies like NVIDIA, and if these companies' performance does not meet expectations, it will affect the overall performance of the ETF.
V. Investment Recommendations
1. Selection Based on Risk Preference
For investors with strong risk tolerance, high-growth AI computing power ETFs like THNQ can be chosen; for conservative investors, BOXX and AIQ may be better choices; for defensive investors, ETFs focused on AI security like HACK are worth attention.
2. Diversified Investment
Due to differences in investment strategies and portfolio structures among different AI computing power ETFs, investors can consider allocating 2-3 different AI computing power ETFs simultaneously to diversify risks and obtain more comprehensive AI industry chain investment opportunities.
3. Regular Adjustment
AI technology is developing rapidly, and investors should regularly review their ETF allocations and adjust their investment portfolios in a timely manner according to technology development trends and market changes.
4. Long-term Holding
AI technology is an important direction for future technological development, and investors should have a long-term holding mentality, avoiding making irrational decisions due to short-term market fluctuations.
VI. Market Outlook
With the continuous advancement of AI technology and the expansion of application scenarios, AI computing power demand will continue to grow. According to IDC predictions, by 2030, the global AI market size will reach $1.5 trillion, with a compound annual growth rate of about 30%. This will drive the continuous expansion of the AI computing power ETF market.
In the future, the following development trends may emerge for AI computing power ETFs:
- Product segmentation will become more specialized, with more ETFs focusing on specific AI application areas.
- ESG factors will increase in weight in ETF investment decisions, and more AI computing power ETFs will focus on AI ethics and sustainable development.
- Actively managed AI computing power ETFs will increase to obtain excess returns.
- Cross-border AI computing power ETFs will emerge, helping investors participate in global AI industry chain investments.
In conclusion, AI computing power ETFs provide individual investors with an effective way to participate in the AI revolution. By deeply understanding the characteristics and differences of various AI computing power ETFs, investors can choose appropriate AI computing power ETFs based on their own risk preferences and investment goals, and share the investment opportunities brought by AI technology development.
