7 Best Artificial Intelligence (AI) ETFs to Buy Now in December 2024

As of December 2024, there is no single best AI ETF for every investor. The most defensible choice depends on whether you want a low-cost technology proxy, a dedicated AI-and-robotics strategy, global AI exposure, software-development companies, broad innovation, or a speculative quantum-computing satellite.

For most investors comparing this group, FTEC and VGT are broad U.S. technology ETFs rather than pure-play AI funds. ROBT is the clearest explicit AI-and-robotics fund, ARTY is the correct December 2024 iShares ticker for the fund formerly known as IRBO, and QTUM is primarily a quantum-computing and machine-learning fund. None eliminates market risk or guarantees an AI-related return.

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The seven AI-related ETFs at a glance

These funds are better understood as a toolkit than as a ranked list of identical AI bets. The table identifies the role each fund can play and flags where the available figures come from.

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*The holdings counts come from the original coverage’s point-in-time table, which was associated with its May 22, 2024 publication rather than December 31, 2024. They are included for scale, not as December holdings data. The 109 figure is associated with the pre-August 2024 IRBO strategy and should not be treated as a current ARTY count. Holdings and weights change.

The supplied research does not provide reliable, same-date December 2024 top-five or top-10 weights for all seven funds. Printing apparently precise concentration figures from mixed dates would be misleading. Before buying, use each issuer’s latest holdings file to calculate the top-five and top-10 totals, largest position, semiconductor exposure, software exposure, and U.S.-versus-international allocation.

What counts as an AI ETF?

An AI ETF is an exchange-traded fund whose index or investment mandate is intended to provide exposure to companies that develop, supply, or use artificial-intelligence technology. That definition covers several very different businesses:

• AI enablers: semiconductor designers and manufacturers, memory suppliers, networking companies, data-center equipment makers, cloud infrastructure providers, cooling companies, and power-system suppliers.
• AI engagers: companies selling AI software, models, automation systems, enterprise tools, and robotics.
• AI enhancers: companies using AI to improve an existing business rather than selling AI as their main product.

ROBT’s prospectus uses the comparable categories of engagers, enablers, and enhancers in describing its index. That framework matters because a chipmaker, a cloud provider, a robotics manufacturer, and a business using machine learning may all benefit from AI adoption in different ways and with different risks.

There is also a difference between reported AI revenue and narrative or classification exposure. Some indexes select companies using business descriptions, regulatory-filing language, proprietary screens, or estimates of relevant activity. A company can therefore enter an AI-themed fund without generating a large, separately reported amount of AI revenue.

Is a technology ETF automatically an AI ETF?

No. VGT and FTEC track broad information-technology indexes. They can own semiconductor, software, hardware, cloud, and other technology companies that benefit from AI investment, but neither fund requires a company to pass an AI-specific screen.

Those funds are best described as:

• AI exposure through established technology leaders;
• an AI-infrastructure or technology-cycle proxy; or
• AI-adjacent technology ETFs.

That distinction is not merely academic. A broad technology fund may outperform a narrowly themed AI fund when non-AI technology companies lead the market. Conversely, a dedicated AI fund may provide more targeted exposure but also carry higher fees, more concentration, greater turnover, or a less transparent classification process.

How these funds were selected

The original seven-fund universe used by the target coverage applied three practical screens: an expense ratio no higher than 0.65%, net assets above $250 million, and at least 70 holdings. That screen explains why the list includes broad technology proxies alongside more narrowly themed products. It is not a guarantee that these are the only suitable AI-related ETFs. For example, BOTZ is another fund in the broader 2024 comparison universe, but it is not one of the seven discussed here; readers can review its issuer page separately.

For a more useful comparison than a simple recent-return ranking, evaluate each candidate on:

1. Theme purity, 20%: whether the index explicitly targets AI and whether exposure is based on revenue, business activity, or sector classification.
2. Diversification, 15%: holdings count, top-10 concentration, single-stock limits, and geographic mix.
3. Cost, 15%: expense ratio, turnover, and expected trading costs.
4. Liquidity and size, 15%: assets, trading volume, bid-ask spread, fund age, and closure risk.
5. Index transparency, 15%: eligibility rules, exposure thresholds, rebalancing schedule, and treatment of foreign listings.
6. Portfolio role, 10%: whether the fund works as a core technology holding or only as a satellite position.
7. Risk fit, 10%: volatility, foreign and emerging-market exposure, sector concentration, and dependence on immature technologies.

Historical performance should not be the primary score. A strong 2024 result could simply reflect a small group of already-expensive AI beneficiaries, and performance comparisons are meaningless unless they identify the exact dates, whether they use NAV or market price, whether distributions are included, and which benchmark is used.

1. FTEC: the lowest-cost broad technology proxy

Best for: an investor who wants broad U.S. technology exposure at an exceptionally low stated expense ratio and accepts that the fund is not specifically selecting AI companies.

What it owns and how the index works

FTEC tracks the MSCI USA IMI Information Technology 25/50 Index. Its November 29, 2024 prospectus says the fund uses representative sampling and normally invests at least 80% of its assets in securities included in the underlying index. The 25/50 designation refers to diversification requirements used in the index construction; it does not turn FTEC into an AI-specific fund.

Fidelity lists an inception date of October 21, 2013. The November 29, 2024 prospectus reported total annual operating expenses of 0.084%. That is approximately $8.40 per year for every $10,000 invested, before trading costs and changes in account value.

Why it may fit

• It provides exposure to the semiconductor, software, hardware, and technology businesses that can supply or benefit from AI investment.
• Its stated fee is the lowest among the seven funds in this comparison.
• Its broad sector mandate may be less dependent on one narrow AI definition than a thematic fund.

Risks and overlap

FTEC remains a sector fund. Its returns can be driven by a relatively small group of large technology and semiconductor companies even when the fund has hundreds of holdings. It may substantially overlap with VGT, an S&P 500 fund, a total-market fund, or a Nasdaq-100 fund. Owning it alongside VGT usually represents duplication more than diversification.

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Choose FTEC over VGT after comparing the fee, assets, bid-ask spread, trading volume, tax situation, and account availability. Do not assume that owning both creates two independent AI allocations.

2. VGT: the longest-established broad technology proxy

Best for: an investor who wants a long operating history and broad U.S. information-technology exposure, not a pure-play AI strategy.

What it owns and how the index works

VGT’s objective is to track an information-technology stock benchmark. Its prospectus does not describe an AI-revenue screen. The fund began operations on January 26, 2004, giving it the longest operating history among the two broad technology choices discussed here.

The historical expense ratio shown in the target coverage was 0.10%, or roughly $10 per year per $10,000 invested. Because expense ratios can change, an investor reading this later should confirm the applicable prospectus rather than rely on a stale fund comparison.

Why it may fit

VGT offers a relatively simple way to obtain exposure to the established technology sector, including businesses involved in AI infrastructure and related software. Its long history can make it easier to evaluate across multiple market cycles than a newer thematic fund.

Risks and overlap

VGT can be highly sensitive to technology valuations, semiconductor cycles, corporate capital spending, export restrictions, and the fortunes of a few very large holdings. It is also likely to overlap heavily with FTEC and with broad-market holdings. A large existing allocation to an S&P 500 or total-stock-market fund may already provide meaningful exposure to several companies that investors associate with AI.

VGT is therefore best viewed as a broad technology allocation with AI beneficiaries, rather than as a fund whose results will track the success of generative AI specifically.

VGT versus FTEC: choose one before considering both

The funds’ stated objectives and broad sector mandates make them close substitutes. This is an inference from their index descriptions, not a claim that every daily holding or weight is identical. Holdings, weighting methods, and fees should be checked before trading.

3. ROBT: the clearest explicit AI-and-robotics strategy

Best for: an investor who wants an index expressly built around AI and robotics rather than a general technology-sector fund.

What it owns and how the index works

ROBT tracks the Nasdaq CTA Artificial Intelligence and Robotics Index and normally invests at least 90% of net assets in index securities. Its methodology divides companies into:

• Engagers, which develop or use AI and robotics applications;
• Enablers, which provide the hardware, software, and infrastructure needed by those applications; and
• Enhancers, which use the technologies to improve an existing business.

The prospectus describes eligibility screens including at least $250 million in worldwide market capitalization, a three-month average daily dollar volume of at least $3 million, and a minimum free float of 20%. The index selects the top 30 companies in each category, including ties, and is rebalanced quarterly and reconstituted semiannually.

ROBT’s total annual fund operating expenses were 0.65%, the highest fee in this seven-fund group. That equals approximately $65 per year per $10,000 invested, before trading costs and changes in value.

Why it may fit

The category structure gives investors a clearer explanation of how the fund intends to capture the AI-and-robotics value chain. It can include more than the largest U.S. technology companies and may reach industrial automation and robotics businesses that a conventional technology-sector ETF does not emphasize.

Risks and who should avoid it

ROBT can be more volatile than a broad-market fund, and robotics exposure does not always move in step with cloud AI or generative-AI software. Foreign and emerging-market exposure, liquidity screens, index rebalancing, and the fund’s relatively high fee also matter. An investor who already owns substantial technology and semiconductor exposure should review the latest holdings before adding ROBT.

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ROBT is a reasonable candidate for targeted AI-and-robotics research, but its explicit theme does not make it a lower-risk investment.

4. ARTY: the corrected iShares AI entry for December 2024

Best for: an investor seeking global AI-themed exposure and willing to accept a short live history under the fund’s new strategy.

IRBO became ARTY

The fund formerly called the iShares Robotics and Artificial Intelligence Multisector ETF traded under the ticker IRBO. Its name, ticker, objective, and benchmark changed effective August 12, 2024. The current December 2024 reference is therefore iShares Future AI & Tech ETF, ticker ARTY.

The benchmark changed from the NYSE FactSet Global Robotics and Artificial Intelligence Index to the Morningstar Global Artificial Intelligence Select Index. The change is documented in the relevant SEC filing and exchange corporate-action notice. Readers searching for IRBO may find older articles and performance charts, but those results should not automatically be presented as ARTY’s post-change performance.

What it owns, cost, and limitations

ARTY’s post-change mandate is more directly AI-oriented and global in scope. The historical expense ratio was approximately 0.47%, or about $47 per year per $10,000 invested. However, by December 2024 the fund had only a short post-change live history. Combining pre-August IRBO returns with post-August ARTY results would create a broken performance series because the benchmark and strategy changed.

ARTY can suit investors who want an international AI value-chain fund rather than another U.S.-only technology allocation. It can also introduce foreign-company, currency, regulatory, and market-liquidity risks. Check the latest country weights, top holdings, semiconductor exposure, and top-10 concentration before buying.

5. KOMP: broad innovation diversification

Best for: an investor who wants exposure to a wide range of emerging-economy and innovation themes, with AI as one component rather than the entire thesis.

What it owns and how the index works

KOMP tracks the S&P Kensho New Economies Composite Index. The index covers areas including robotics, automation, artificial intelligence, connectedness, processing power, digital payments, drones, cybersecurity, space, and other new-economy themes.

Its methodology scans company regulatory filings for relevant terms and classifies companies as core or non-core holdings. That approach can identify businesses whose public disclosures connect them to a theme, but it is not the same as requiring a minimum percentage of reported AI revenue.

The historical expense ratio was approximately 0.20%, or about $20 per year per $10,000 invested. The source snapshot listed approximately 435 holdings, the largest count in this group, but a high holding count does not guarantee low economic concentration or a large amount of AI exposure.

Why it may fit

KOMP may spread thematic exposure across more subsectors than a narrowly focused AI fund. That can reduce dependence on one part of the technology cycle, although it also means the fund may behave partly like a digital-payments, clean-technology, drone, space, or cybersecurity fund.

Risks and who should avoid it

KOMP is a poor fit for someone who wants a precise bet on AI software, AI chips, or generative-AI leaders. Its classification process can also create exposure based on filing language or business descriptions rather than a clean measure of AI revenue. Review the latest subsector weights and geographic allocation to understand what is actually being purchased.

6. IGPT: software-development and digital-innovation exposure

Best for: an investor who prefers software development, digital tools, and related technologies over industrial robotics or quantum hardware.

What it owns and how the index works

IGPT is based on the STOXX World AC NexGen Software Development Index. The index focuses on companies with direct revenue exposure to technologies and products contributing to future software development. Invesco says the fund normally invests at least 90% of total assets in securities in the index.

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The index rebalances after the close on the second Friday of March, June, September, and December. The historical expense ratio reported in the target coverage was approximately 0.61%, or about $61 per year per $10,000 invested. Verify the applicable December 2024 prospectus before publication or purchase because fees can change.

Why it may fit

IGPT offers a more specific software and digital-development angle than VGT or FTEC. It may appeal to an investor who expects AI coding tools, development platforms, enterprise software, and digital infrastructure to be important beneficiaries of future software creation.

Risks and overlap

IGPT is not exclusively an AI fund. It can overlap with broad technology ETFs and may be sensitive to high-growth software valuations, interest rates, competition, and rapid product obsolescence. Its direct-revenue approach may be more focused than a broad innovation screen, but readers still need to inspect the index rules and current holdings rather than rely on the name alone.

7. QTUM: a quantum-computing and machine-learning satellite

Best for: a high-risk investor who wants a small, differentiated position in quantum computing and machine learning.

What it owns and how the index works

QTUM tracks the BlueStar Quantum Computing and Machine Learning Index. It uses a modified equal-weight approach and is tied to companies involved in quantum computing and machine learning. The prospectus describes a requirement that companies derive at least 50% of annual revenue or operating activity from quantum-computing or machine-learning technology under the index methodology.

The fund’s expense ratio was 0.40%, approximately $40 per year per $10,000 invested. The index can include international and emerging-market companies. Exposure may extend to quantum hardware, machine-learning technology, advanced semiconductor packaging, data management, and related businesses.

Why it may fit

QTUM is more differentiated from a standard U.S. technology fund than FTEC or VGT. Its modified equal-weight construction can reduce dependence on the largest companies, although equal weighting does not eliminate volatility or business risk.

Why it is not a core generative-AI fund

Quantum computing remains an immature technology with uncertain commercial timelines. QTUM should not be treated as a direct proxy for the profitable generative-AI businesses driving current data-center spending. It is better framed as a speculative satellite allocation for an investor who can tolerate the possibility of prolonged underperformance or technology disappointment.

Pure-play theme versus technology proxy

How concentrated are these ETFs?

Holding 300 companies does not necessarily mean a fund is economically diversified. If the largest 10 positions account for more than half of the portfolio, the fund remains heavily dependent on a small group of companies. The same issue can occur in a 70-stock thematic fund if the largest positions dominate its industry exposure.

For each fund, check the latest issuer holdings file for:

• the largest individual position;
• the combined weight of the top five and top 10 holdings;
• semiconductor, software, hardware, robotics, and cloud exposure;
• U.S., developed-market, and emerging-market weights;
• the percentage already held through an S&P 500, total-market, or Nasdaq-100 fund; and
• the date on which the holdings were measured.

A simple overlap review can be more useful than comparing fund names. Match the tickers in your existing funds against the proposed ETF, then add the weights of repeated companies. Pay particular attention to overlap among FTEC, VGT, IGPT, and broad-market technology holdings. ARTY, ROBT, KOMP, and QTUM may add more differentiated exposure, but they can still share semiconductor, software, or large multinational positions.

What the expense ratios mean in dollars

These are approximate fund expenses, not a bill charged separately to the investor. The amount changes with the account balance and is reflected in the fund’s net asset value. Expense ratios do not include brokerage commissions, bid-ask spreads, taxes, or the cost of buying at a premium to net asset value.

A low fee is valuable, but it does not automatically make FTEC or VGT the better choice. A broad technology fund can be the wrong tool for an investor seeking global AI or robotics exposure, while a higher-cost thematic fund can be inappropriate if the investor already owns the same companies elsewhere.

Why use an ETF instead of individual AI stocks?

ETFs can make AI exposure easier to implement because they:

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• spread single-company risk across multiple holdings;
• provide access to a defined industry or global theme;
• rebalance according to an index rather than requiring the investor to trade each holding;
• can include smaller suppliers and infrastructure companies that are difficult to identify individually; and
• allow an investor to size a thematic allocation without choosing one company such as a semiconductor designer, cloud provider, or software company.

But diversification is not protection from loss. An ETF can hold dozens or hundreds of companies and still be concentrated in one sector, one technology cycle, one country, or a few mega-cap positions. The fund can also dilute the winners by owning companies with weak or indirect AI exposure.

Risks every AI ETF buyer should understand

Valuation risk

AI expectations may already be reflected in stock prices. Even if adoption grows, shareholders can lose money if earnings do not justify the valuations investors have paid or if expectations become less optimistic.

Mega-cap and sector concentration

Broad funds and thematic funds alike can depend heavily on a few large technology companies. A large holding count does not prevent the portfolio from behaving like a concentrated technology position.

Semiconductor cyclicality

AI infrastructure depends on chips, memory, networking, data centers, power, and cooling. These businesses can experience inventory cycles, abrupt changes in capital spending, supply constraints, export restrictions, and customer concentration.

Technology obsolescence and competition

Hardware and software can be displaced quickly. Open-source models, cheaper chips, new architectures, rival cloud platforms, and changing developer tools can reduce the expected profits of current leaders.

Regulation and legal risk

Export controls, privacy requirements, copyright disputes, antitrust proceedings, data rules, and AI-specific regulation can affect the companies held by these funds. The impact can differ significantly between U.S. and foreign companies.

International, currency, and emerging-market risk

ARTY, ROBT, KOMP, QTUM, and IGPT can provide foreign exposure or use global methodologies. Foreign securities introduce currency movements, political risk, different accounting standards, market holidays, liquidity issues, and, in some cases, emerging-market risk.

Index and classification risk

An index may classify a company as AI-related using regulatory filings, business descriptions, revenue estimates, or a proprietary screen. The label may not represent a separately reported AI business. Rebalancing can also add or remove companies at times that do not match an investor’s expectations.

Tracking and trading risk

Fund returns can differ from index returns because of fees, representative sampling, transaction costs, cash holdings, and rebalancing. ETF shares can trade above or below NAV, particularly during market stress. A wide bid-ask spread can matter more than a small difference in expense ratios, especially for a small or infrequently traded fund.

Tax and distribution risk

In a taxable account, dividends and capital-gains distributions can create tax consequences. Index turnover does not automatically mean an investor will receive a taxable distribution, but it can contribute to one. The tax result depends on the fund, account type, holding period, jurisdiction, and individual circumstances.

A practical December 2024 buying checklist

1. Define the exposure first. Decide whether you want broad technology, AI infrastructure, robotics, global AI, software, innovation, or quantum computing.
2. Read the applicable prospectus. Confirm the expense ratio, investment policy, index, risks, and whether the fund uses full replication or sampling.
3. Download dated holdings. Record the top five, top 10, largest position, sector weights, country weights, and holdings date.
4. Measure incremental exposure. Compare the ETF against your S&P 500, total-market, Nasdaq-100, VGT, FTEC, and other technology holdings.
5. Check execution quality. Review assets, average volume, bid-ask spread, and the ETF’s premium or discount to NAV. Use a limit order when appropriate rather than assuming the quoted price is frictionless.
6. Check the index methodology. Ask whether companies qualify through direct revenue, operating activity, filing language, a sector classification, or a proprietary screen.
7. Choose a portfolio role. Broad technology funds may serve as a larger technology allocation; QTUM and other narrow themes are more naturally satellites. The appropriate size depends on the rest of the portfolio and the investor’s risk tolerance.
8. Have a rebalancing rule. Decide in advance how much the theme can grow before you trim it and what would cause you to reassess the thesis.

Should you buy after a large AI rally?

The word buy in a headline should not be interpreted as a promise that an ETF is attractive at every price. An investor who wants exposure but is concerned about valuation can consider a staged purchase, dollar-cost averaging, or a smaller initial position. Those approaches do not remove loss risk and do not guarantee a better entry price, but they can reduce the pressure to make a one-day market-timing decision.

Before adding an AI ETF, consider whether your existing broad-market funds already give you substantial exposure to the same large technology companies. The relevant question is often not whether AI will be important, but how much additional AI and technology concentration your portfolio can absorb.

Which of the seven is the best fit?

This is a use-case framework, not a forecast of returns. An investor may reasonably choose none of these funds, particularly if a diversified portfolio already contains substantial technology exposure or if the investor cannot tolerate thematic volatility.

Frequently Asked Questions

Is ARTY the same ETF as IRBO?

ARTY is the December 2024 name and ticker for the fund formerly known as IRBO, but the fund also changed its benchmark and strategy effective August 12, 2024. Pre-change IRBO performance should not be combined with post-change ARTY performance without explaining the methodology break.

Are VGT and FTEC complementary AI ETFs?

Usually not. Both are broad U.S. information-technology funds with potentially substantial overlap. Compare their latest holdings, fees, liquidity, and account availability; buying both may add duplication rather than meaningful diversification.

Is QTUM a conventional artificial-intelligence ETF?

Not exactly. QTUM primarily targets quantum computing and machine learning. It may complement an AI or technology allocation, but its commercial thesis and risks differ from those of generative-AI software, cloud, and semiconductor funds.

The Bottom Line

Bottom line: FTEC and VGT are the lower-cost, broad technology choices; ROBT is the most explicit AI-and-robotics strategy; ARTY is the corrected global iShares AI ticker after the August 2024 change; KOMP is broader innovation; IGPT emphasizes software development; and QTUM is a speculative quantum-and-machine-learning satellite. Compare each fund’s current holdings, concentration, overlap, liquidity, methodology, and prospectus before buying—and do not treat any historical December 2024 ranking as a guarantee of future performance.

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