Published on: 2025-10-22
AI exchange-traded funds (AI ETFs) provide diversified exposure to companies that develop, enable, or apply artificial intelligence, offering investors an efficient way to participate in the AI megatrend.
However, careful selection is necessary due to concentration, valuation, and thematic-drift risks.
Below, the article will unpack what AI ETFs are, how they are constructed, leading funds and comparison metrics, the principal risks, and practical implementation strategies for investors.
Exponential growth in data creation and storage requirements.
Rising demand for high-performance compute, including GPUs and specialised accelerators, to train and deploy large AI models.
Enterprise adoption of AI tools across productivity, automation, healthcare, manufacturing, and finance.
Investors view AI as a long-duration technological platform, comparable to the internet or cloud computing waves.
Thematic ETFs aim to capture the broader ecosystem rather than individual companies.
Since 2023. AI-thematic ETFs have been among the largest recipients of thematic fund inflows in US and European markets, reflecting strong investor demand for AI exposure.
Target companies primarily engaged in AI research, products, and services.
Include AI-focused firms within a wider technology index.
Focus on robotics and automation while integrating AI as a core element.
Manager-run ETFs selecting AI leaders based on fundamental research.
| Ticker | Fund name | Focus / Index | Net assets (approx.) | Expense ratio |
|---|---|---|---|---|
| AIQ | Global X Artificial Intelligence & Technology ETF | Indxx Artificial Intelligence & Big Data Index, broad AI and data exposure | ~$6.6bn | 0.68% |
| BOTZ | Global X Robotics & Artificial Intelligence ETF | Indxx Global Robotics & AI Thematic Index, robotics and AI industrial focus | ~$3.1bn | 0.68% |
| ARTY | iShares Future AI & Tech ETF | Morningstar Global Artificial Intelligence Select Index, broad AI and future tech | ~$1.9bn | 0.47% |
| IVES | Dan Ives Wedbush AI Revolution ETF | Analyst-curated list focused on leading AI beneficiaries | Launch flows; expense ~0.75% | 0.75% |
Notes:
Net asset values fluctuate daily; figures are rounded and representative as of October 2025. Expense ratios and index descriptions are fund-reported. Refer to issuer pages for the latest fund sheets.
Many AI ETFs track specialist indices screening companies by AI-related revenue, product activity (chips, cloud, AI software), or R&D intensity.
Proprietary indices from providers like Indxx and Morningstar are common.
High allocation to Information Technology, significant exposure to large-cap software and semiconductor firms, smaller allocations to industrials and discretionary sectors.
Many ETFs labelled as AI have top holdings similar to large technology leaders, increasing correlation with broad tech indices and reducing incremental diversification.
Thematic ETFs often have higher turnover than broad passive funds due to rebalancing based on the evolving theme, which can affect tax efficiency and transaction costs.
Diversified exposure to the AI ecosystem with a single trade.
Access to niche mid-cap companies and non-US firms that are difficult for individual investors to assemble.
Professional index construction or active management that screens for AI relevance.
As a satellite allocation within a growth sleeve; advisors often recommend single-digit allocations (2–10%) rather than using AI ETFs as a core holding.
AI is a multi-year structural theme; a horizon of 5+ years is recommended to navigate volatility and product cycles.
Valuation risk:
Many AI companies trade at elevated multiples reflecting high growth expectations.
Concentration risk:
Funds may be heavily weighted in a few mega-caps.
Thematic drift:
Index rules may change, broadening fund exposure beyond AI.
Regulatory/geopolitical risk:
Export controls, privacy rules, and AI governance proposals can affect parts of the AI value chain.
Technology obsolescence:
Rapid innovation can displace current leaders.
Select funds with transparent methodology and broad diversification.
Limit portfolio allocation sizes and rebalance regularly.
Consider combining passive AI ETFs with active funds to capture unique opportunities while managing concentration.
Criteria to consider:
Expense ratio:
Lower is generally preferable, but weigh against index quality and active management.
AUM and liquidity:
Sufficient AUM and daily volume reduce tracking error and bid-ask costs.
Holdings and concentration:
Number of holdings, top-10 weight, sector breakdown.
Index methodology:
Does it capture true AI exposure? Are inclusion/exclusion rules transparent?
Performance metrics:
Multi-period returns, volatility, beta vs. benchmark.
Domicile/tax considerations:
US vs UCITS or local listings.
Manager reputation:
Experience with thematic ETFs and operational robustness.
Additional costs:
Bid-ask spread, tracking difference, tax inefficiencies.
| Criterion | Why it matters | Good threshold |
|---|---|---|
| Expense ratio | Reduces returns | <0.6% preferred for passive; <0.8% reasonable for niche themes |
| AUM | Liquidity and fund longevity | >$500m considered healthier; >$1bn preferred |
| Top-10 weight | Concentration risk | <50% preferable |
| Turnover | Trading costs, tax drag | Lower turnover is better |
| Index transparency | Understand holdings | Clear methodology publicly available |
Conservative: 0–3% in high-growth satellite allocation
Balanced growth: 3–7%
Aggressive growth: 7–15% for high volatility tolerance
Strategic: Buy and hold with periodic review
Tactical: Use dollar-cost averaging to avoid poor timing during high valuations
Rebalance when allocations drift beyond chosen thresholds (e.g., ±25%)
Use rebalancing to trim gains and manage risk
Non-US investors should consider domicile differences: US ETFs may have different tax reporting and withholding implications compared with UCITS or local ETFs.
Several AI-thematic ETFs outperformed many thematic funds in the past 18–36 months, driven by returns from chipmakers and cloud incumbents.
New launches, including analyst-branded ETFs, expand choice but require careful due diligence.
AI ETFs have experienced strong thematic inflows, highlighting sustained investor interest.
Increases in enterprise AI adoption and deployment of large language models
New chip architectures and supply-chain developments
AI governance frameworks, export controls, and data privacy rules may affect certain segments
Monitor whether returns concentrate among few leaders or the theme broadens to mid-cap beneficiaries, influencing which ETFs perform best
Define your objective: growth, diversification, or concentrated thematic exposure
Use the checklist in Section 7 to shortlist ETFs
Review issuer fact sheets and holdings
Decide allocation and approach: lump-sum or phased purchases
Implement risk controls: position size limits, rebalancing, and periodic reassessment
An exchange-traded fund providing exposure to companies involved in AI, from chips and infrastructure to AI software and applications.
Expense ratio, AUM/liquidity, index methodology, holdings concentration, and domicile/tax implications.
Yes, they carry valuation, concentration, and thematic risks. Treat them as satellite allocations.
Typically 1–3% for tactical positions or up to ~10% for aggressive investors.
No, many include global holdings. Check the regional breakdown in the fund factsheet.
Disclaimer: This material is for general information purposes only and is not intended as (and should not be considered to be) financial, investment or other advice on which reliance should be placed. No opinion given in the material constitutes a recommendation by EBC or the author that any particular investment, security, transaction or investment strategy is suitable for any specific person.
