The artificial intelligence (AI) era isn’t off in the distance or even nigh—it’s here.
In fact, it’s everywhere.
Want to search for something on the internet? You might be asking an AI-powered chatbot, and even if you’re not, the search engine will probably populate an AI overview. Watching TV? You’ll probably see at least a few ads where brands hawk the ways in which AI makes them better. Want to get away from everything AI? Too bad! The site you use to book a flight to the farthest reaches of the earth has probably figured out how to jam AI into its process, too.
And considering that all signs point to companies making even bigger bets on artificial intelligence (at least for now), that points to continued opportunities in AI stocks. But if you don’t feel like trying to pick winners in what is already a crowded field, you can skip the individual AI equities and dive into AI exchange-traded funds (ETFs) instead.
Read on as I review five of the most essential AI ETFs worth knowing. While they all broadly share a focus on artificial intelligence, they go about investing in the space in a few different ways.
Disclaimer: This article does not constitute individualized investment advice. These securities appear for your consideration and not as personalized investment recommendations. Act at your own discretion.
The AI Opportunity
While it’s impossible to predict the future, I can certainly point out what others seem to think—and broadly speaking, other businesses seem to think AI is only going to get bigger and more entwined with numerous facets of the human experience.
But rather than flood the zone, I’ll just provide three pieces of information that I think sums up the most prevailing thoughts on where AI is going:
- $15.7 trillion: “AI could contribute up to $15.7 trillion to the global economy in 2030, more than the current output of China and India combined. Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion is likely to come from consumption-side effects.” (PWC analysis)
- $480 billion: “We have expanded the scope of our AI estimates beyond the Big 4 [Microsoft, Amazon, Alphabet, and Meta] to now introduce global AI spend estimates. We expect global AI spend to increase by 60% year-over-year in 2025 to reach $360 billion and 33% in 2026 to reach $480 billion, with the Big 4’s share of AI spend to decline from 58% in 2025 to 52% in 2026.” (UBS analysis)
- 241: “Through Document Search, FactSet searched for the term ‘AI’ in the conference call transcripts of all the S&P 500 companies that conducted earnings conference calls from December 15 through March 14. Of these companies, 241 cited the term ‘AI’ during their earnings call for the fourth quarter. This number is well above the five-year average of 105 and the 10-year average of 67. In fact, this is the highest number of S&P 500 companies citing ‘AI’ on earnings calls over the past 10 years (using current index constituents going back in time).” (FactSet analysis)
And this is just a small sampling of the pro-AI estimates, forecasts, and analyses that have been sent my way in just the past few months.
A Fair Warning About AI Hype
A lot of money is betting that AI will be one of the greatest technological mega-trends of our time, but ultimately, those bets boil down to belief—they’re not a guarantee.
In fact, there are two ways in which betting on AI from here could go horribly wrong:
1. Timing
Once upon a time, the internet was treated exactly how AI is treated today—as “the next big thing” in technology. And those who predicted the internet would become a massive part of the global economy were indeed right.
But from an investment standpoint, the payoff sure didn’t come in a straight line.
The dot-com bubble became the dot-com bubble bust. Some seemingly promising internet companies collapsed. Some were never the same. Some eventually lived up to their potential and then some, but it took years for their shares to eclipse those 2000-01 levels.
“The internet will change everything. That was the story of the dot-com bubble: The “information superhighway” would revolutionize how we work, communicate, socialize, and inform ourselves. It was all true,” Research Affiliates’ Rob Arnott, Trent Commins, and Xi Liu write in a recent research paper. “Yet the 10 most valuable tech stocks in 2000 underperformed the S&P 500 for the next 15 years. Why? Because the narrative missed two important nuances: It assumed that early dominance meant enduring dominance, and it overestimated the pace of change.
“As with all technological innovation, the pace of human adoption may be slower than the visionaries predict. Some early leaders may lose their edge amid the fierce competition or even disappear altogether.”
2. Failure to Stick
Of course, there’s another possibility. AI might end up being less than it’s hyped up to be.
“Like the internet highflyers of 2000, today’s AI darlings must exceed already lofty expectations to beat the market in the years ahead,” Arnott, Commins, and Liu write. “If cracks form in the narrative—if the fundamentals fail to keep pace with investors’ fanciful projections—the broader story may begin to crumble and even collapse completely. This can cause sharp market downturns, outsized investor losses, and a cascading effect that turns bull markets into bears.”
It’s difficult to find business coverage of AI that’s even modestly critical. In many cases, reports about the latest developments from the likes of OpenAI and Anthropic read like glorified press releases. But at least a few journalists are looking for cracks in the AI narrative. For instance, Ed Zitron, writer of the Where’s Your Ed At newsletter and host of the Better Offline podcast, routinely punches holes in the rose-colored coverage of the AI industry.
Also, it’s possible that AI sticks, just not in the way we think. For instance, it could be that AI proves extremely useful and lucrative in certain industries, but comes up short in others. Which, if you’re buying into AI, makes it all the more important to understand exactly how you’re buying into AI—say, how one AI ETF’s approach differs from the next.
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5 Best AI ETFs
If you want to get into AI without making potentially risky bets on just one or two stocks, ETFs are the way to go.
Why not mutual funds? AI mutual funds simply don’t exist. Thanks to less stringent requirements and regulations for ETFs, ETF providers can quickly pump out products to serve just about any emerging trend—mutual funds, not so much. The “funkiest” mutual funds you’re likely to see are sector-level funds, such as technology or health care funds. But you can find an ETF or two for the smallest of niches. And the number of artificial intelligence ETFs already numbers in the double digits.
And I’m going to home in on five of the best AI ETFs. In no particular order …
1. Global X Artificial Intelligence & Technology ETF
- Inception: May 11, 2018
- Assets under management: $3.2 billion
- Expense ratio: 0.68%, or 68¢ per year on every $1,000 invested
I’ll start with the Global X Artificial Intelligence & Technology ETF (AIQ), which at more than $3 billion in assets is the largest pure-play AI ETF on the market.
AIQ is best explained through its “unconstrained approach”: “AI spans multiple segments, and its most innovative companies include both household names and newcomers from around the world. AIQ invests accordingly, without regard for sector or geography.”
In other words, Global X’s ETF believes any profits to be made in AI aren’t solely in the companies that produce the technology—it’s in the companies willing to adopt it, too. To reap these profits, AIQ invests in an index that breaks companies down into two categories, which themselves are broken down into a total of four easier-to-understand subcategories:
- AI applied to products and services: These companies have developed internal AI capabilities and are directly applying AI tech into their products and services. This can include image and/or language processing, threat detection, recommendation generation, and more.
- AI-as-a-service for Big Data applications: Companies that provide AI capabilities to their customers as a service. They usually offer cloud-based platforms that let their customers apply AI techniques to big data without having to build their own capabilities.
- AI hardware providers: Companies that produce semiconductors, memory storage and other hardware needed for AI applications.
- Quantum computing: Companies developing quantum computing technology. This isn’t highly commercialized yet, but it’s expected to be a hotbed of potential in the AI space.
AIQ’s index takes 60 companies from Nos. 1-2, and 25 companies from Nos. 3-4. Stocks are given an “exposure score” (effectively, the more business exposure to AI, the greater the score). All stocks with a score greater than 20% are capped at 3% of assets at each rebalancing, while all stocks with a score less than 20% are capped at 1%. (Stocks can exceed these levels if they rise in value between rebalancings.)
The result is an 85-stock portfolio that’s technology-heavy but not technology-exclusive. Right now, the tech sector accounts for 70% of assets, consumer discretionary and communication services account for 10% apiece, industrials take up another 8%, and the remainder is sprinkled across health care, materials, and financials.
Names like Nvidia (NVDA) and Taiwan Semiconductor (TSM) are the hardware providers you’re used to seeing in AI conversations. Names like Netflix (NFLX), Shopify (SHOP), and Dutch info-services company Wolters Kluwer are examples of companies on the AI-application side of the equation.
And as the inclusion of TSM and Wolters Kluwer might suggest, AIQ isn’t a strictly U.S.-based fund. It’s global (read: U.S. plus international) fund, with a roughly 2-to-1 ratio of American stocks and foreign stocks. That international exposure includes China, South Korea, and Ireland, among other nations.
This broad coverage of the industry has made AIQ an immensely popular fund, and deserving of a spot on any list of the best AI ETFs.
Want to learn more about AIQ? Check out the Global X provider site.
Related: 15 Best Investing Research & Stock Analysis Websites
2. Robo Global Artificial Intelligence ETF
- Inception: May 11, 2020
- Assets under management: $182.8 million
- Expense ratio: 0.68%*, or $6.80 per year on every $1,000 invested
Conceptually, the smaller Robo Global Artificial Intelligence ETF (THNQ)—launched two years to the day after AIQ—is similar to Global X’s fund. And they spell it out up front (emphasis mine):
“Included in THNQ are companies developing the technology and infrastructure enabling AI, such as computing, data and cloud-services, as well as companies that apply AI in various verticals, from business processes to e-commerce and healthcare, among others.”
Indeed, whereas most fund providers break down their holdings by sector, Robo Global’s fact sheet for THNQ divides its holdings into “infrastructure” (currently 68%) and “applications & services” (32%).
THNQ is global in nature, too, albeit with a much higher allocation (78%) to U.S. stocks, though Robo Global’s prospectus says it “expects” at least 25% of the portfolio to be international.
And the fund uses a scoring system, too. Its “THNQ score” is based on revenues derived from AI, investments in AI, and leadership in the AI industry. Companies need a score of at least 50 to be included, and weights are determined by score, though currently no stock has a weighting of more than 3%.
The two major differences between the Robo Global and Global X offerings worth noting are:
- Company size: THNQ specifically calls out its focus on providing more exposure to mid- and small-cap stocks, which it does. Also, its large-cap holdings are, on average, smaller than AIQ’s. This leads to substantially different average market caps: $215 billion for AIQ, and just $50 billion for THNQ.
- Portfolio breadth. Right now, THNQ is just 55 stocks wide, so it holds 30 stocks fewer than AIQ.
Practically speaking, both funds are considered “large growth” plays on AI, and they largely move in tandem with one another. But THNQ tends to exhibit more volatility (what you’d expect from having more exposure to mid- and small-caps), and in theory, those smaller holdings could have more growth potential over time.
Just note that neither difference is guaranteed to continue in perpetuity. The level of exposure to various market caps isn’t mandated in the methodology, and THNQ’s index allows the fund to hold up to 100 constituents.
* 0.75% gross expense ratio is reduced with a 7-basis-point fee waiver until at least Aug. 31, 2025.
Want to learn more about THNQ? Check out the Robo Global provider site.
Related: The 10 Best Vanguard ETFs for 2025 [Build a Low-Cost Portfolio]
3. Invesco AI and Next Gen Software ETF
- Inception: June 23, 2005
- Assets under management: $426.8 million
- Expense ratio: 0.58%, or $5.80 per year on every $1,000 invested
You’ll notice that the Invesco AI and Next Gen Software ETF (IGPT) boasts an inception of 2005, which makes it roughly 20 years old. But before you clap Invesco on the back for being wildly ahead of its time, understand that this AI ETF has only existed in its current form since Aug. 28, 2023, when it changed its name and ticker from the Invesco Dynamic Software ETF (PSJ).
I guarantee you the marketing department let out a big sigh of relief when that decision was green-lit.
Anyways, IGPT is another broad-AI-industry fund that works similarly to the aforementioned AIQ and THNQ, but without any requirements that are explicitly tied to artificial intelligence. Instead, IGPT’s tracking index, the STOXX World AC NexGen Software Development Index, requires a baseline amount of exposure to (specifically, at least 50% of revenues from one or more) subsectors “associated with future software development.” This includes areas like AI and robotics, but it’s not limited to them.
IGPT is also a global fund (albeit the least global of the three right now, at 85% U.S. exposure), and it’s similar to THNQ in that it has roughly 20%-25% exposure to mid-caps and small-caps.
The big difference-maker here is that IGPT is prone to higher single-stock concentrations than the other two funds. For instance, at the moment, Nvidia (NVDA), Facebook parent Meta Platforms (META), and Google parent Alphabet (GOOGL) account for roughly 8% of assets each, and another three stocks command weightings of about 6%. Compare that to AIQ and THNQ, whose top holdings sit at less than 4% and 3%, respectively.
This is because IGPT factors both revenue exposure and market capitalization when weighing stocks. It caps constituents at 8% between rebalancings, but that’s still an enormous difference that means IGPT’s returns are far more beholden to the AI industry’s mega-caps than similar funds. Good news: That could provide more stability in flat and down markets. Bad news: That could mean a little less upside in up markets.
However, IGPT has one clear leg up that has landed it on my list of the best AI ETFs: costs. The fund charges 10 basis points less in annual fees than the two aforementioned products. (A basis point is one one-hundredth of a percentage point.)
Want to learn more about IGPT? Check out the Invesco provider site.
Related: 17 Best Investment Apps and Platforms [Free + Paid]
4. ARK Autonomous Technology & Robotics ETF
- Inception: Sept. 30, 2014
- Assets under management: $1.0 billion
- Expense ratio: 0.75%, or $7.50 per year on every $1,000 invested
Predating both of those funds is the ARK Autonomous Technology & Robotics ETF (ARKQ)—one of the first two funds to launch under Cathie Wood’s innovation-minded ARK Invest firm.
The first three funds that I covered are examples of broad AI ETFs whose goal is to invest widely in the potential of artificial intelligence. But the actively managed ARKQ is an example of AI ETFs that aim to capture specific corners of the AI industry.
ARKQ holds autonomous technology and robotics companies “relevant to the Fund’s investment theme of disruptive innovation.” These are companies that develop, produce, or enable autonomous mobility, intelligent devices, advanced battery technologies, adaptive robotics, neural networks, reusable rockets, next-gen cloud technology, and 3D printing. In selecting companies, Wood is looking for three types of companies: “automation transformation,” “energy transformation,” and “artificial intelligence.”
Put differently: This fund absolutely provides AI exposure, but AI isn’t explicitly the point.
Indeed, while technology accounts for a significant 30% of assets, it’s not even the biggest sector by weight—the industrial sector is, at 40%. There’s also a healthy helping of consumer discretionary stocks (~20%) and communication services stocks (7%), with sprinklings across energy, health care, and utilities.
ARKQ has less exposure to large caps than any of the aforementioned funds, with fully a third of assets invested in mid-caps (and a sprinkling dedicated to small- and micro-cap stocks). But Wood is also more than happy to concentrate weights in her favorite bets. At the moment, Tesla (TSLA) and Kratos Defense & Security (KTOS) alone account for nearly a quarter of assets.
That aggression can cut both ways, performance-wise. But Wood’s ability to capture upside is a strong argument for putting ARKQ among the market’s best AI ETFs.
Want to learn more about ARKQ? Check out the ARK Invest provider site.
Related: 7 Best Fidelity ETFs for 2025 [Invest Tactically]
5. Defiance Quantum ETF
- Inception: Sept. 4, 2018
- Assets under management: $1.3 billion
- Expense ratio: 0.40%, or $4.00 per year on every $1,000 invested
Somewhere, in an alternate universe, Raphael is yelling at Casey Jones: “Quantum? Nobody understands quantum! You gotta know what a qubit is to understand quantum!”
My tortured brain’s asides aside, quantum computing is an eagerly awaited game-changer for artificial intelligence … but it takes a little background to understand. This video by Veritasium is helpful, but here’s an extremely shortened version of what you need to know:
All of today’s devices use “classical computing,” which is based on bits. Bits, short for “binary digit,” represent either a 0 or a 1, and they’re the smallest unit of digital information in computing. And the more of these bits you string together, the more complex functions you can tackle. However, in “quantum computing,” the smallest units are qubits, which can exist in multiple states simultaneously.
“A quantum computer solving a maze would not try each path one at a time to find the exit. Instead, it will try each route simultaneously, finding the exit in a fraction of the time,” says Ahmet Erdemir, PhD, Associate Staff at Cleveland Clinic’s Center for Computational Life Sciences.
Why does this matter for AI? “AI methods are currently limited by the abilities of classical computers to process complex data,” Erdemir says. “Quantum computing can potentially enhance AI’s capabilities by removing the limitations of data size, complexity, and the speed of problem solving.”
Put simply: Quantum computing can take AI to the next level.
The Defiance Quantum ETF (QTUM) tracks roughly 70 holdings in the global quantum computing and machine learning industries. This includes developing quantum computers or chips, making applications built on quantum computers, software specializing in big data, and more.
The thing is, quantum computing is downright nascent. Quantum computers technically exist, but they’re nowhere near commercial development. Nvidia CEO Jensen Huang received flak in January 2025 after saying quantum computing wouldn’t be useful for at least 15 years “on the early side.” A couple months later, he walked back some of his skepticism and generally said he underestimated the pace of progress … but he also didn’t give a quicker concrete timetable, either.
I say all that to say this: Over time, QTUM will become a more direct play on quantum computing, but it’s not right now because quantum computing simply doesn’t drive meaningful revenues right now. Yes, it holds dedicated quantum companies like D-wave Quantum (QBTS) and Rigetti Computing (RGTI). However, most of its holdings, such as Alibaba (BABA), Palantir (PLTR), and Fujitsu, are plays on many other things that aren’t quantum.
But I also feel obligated to point out that QTUM’s lack of purity hasn’t held the fund back at all. QTUM has beaten the pants off AI pure-play and adjacent ETFs alike since it hit the market in 2018.
Want to learn more about QTUM? Check out the Defiance ETFs provider site.
Related: 9 Winning Tech Stocks to Buy
Does the Fund Say It’s “AI”? Be Careful!
One final warning to would-be AI ETF buyers: Don’t judge these books by their covers.
Some funds with “AI” in their name are wholly dedicated to AI stocks, while others might have a specific focus on certain industries impacted by AI.
But most deceiving of all are a few funds that say “AI” but aren’t trying to invest in AI at all.
Two funds immediately come to mind: the Amplify AI Powered Equity ETF (AIEQ) and the WisdomTree U.S. AI Enhanced Value Fund (AIVL). Rather than explicitly investing in AI stocks, these two funds use AI to select equities—the former uses AI to select generally attractive stocks, while the latter uses an AI model to select value-priced stocks. But in neither case are these ETFs trying to invest your money in AI technology.
So, as I always say: Don’t buy until you’ve looked under the hood.
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