Want to Invest in AI? These 6 ETFs Might Be Your Best Bet

Without a doubt, artificial intelligence (AI) has been the investment trend of the past few years.

Thing is, AI once was a fairly limited technology that only a few companies were either directly providing or benefiting from. Today, all you need to do is rewatch Super Bowl ads to know that AI has become completely mainstream; providers number in the hundreds, while most companies are at least trying to find some way to juice their business models by plugging into the technology.

Consider Goldman Sachs Asset Management’s (GSAM) 2026 outlook. While it acknowledges that “AI is transforming the technology sector,” it adds that “while early GenAI enthusiasm was concentrated in a narrow group of stocks, we see compelling reasons for the investment landscape to broaden, unlocking new opportunities for emerging innovators.”

That makes picking winners and losers a lot harder than it once was … and makes a better case for investing via AI ETFs instead.

Today, I want to highlight some of the best artificial intelligence ETFs on the market. Importantly, while they all broadly invest in AI, they do so in ways that are meaningful enough to provide differentiated exposure to the technology.

Editor’s Note: Tabular data presented in this article are up-to-date as of Feb. 19, 2026.

Disclaimer: This article does not constitute individualized investment advice. Individual securities, funds, and/or other investments appear for your consideration and not as personalized investment recommendations. Act at your own discretion.

The AI Opportunity

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I can’t predict the future, but I can 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.

I won’t flood the zone with opinions. Instead, I’ll provide three numbers I believe sum 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)

— $2.5 trillion: “Worldwide spending on AI is forecast to total $2.52 trillion in 2026, a 44% increase year-over-year. … Building AI foundations alone will drive a 49% increase in spending on AI-optimized servers for 2026, representing 17% of total AI spending. AI infrastructure will also add $401 billion in spending in 2026 as a result of technology providers building out AI foundations.” (Gartner)

— 287: “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 June 15 through September 5. Overall, the term “AI” was cited on 287 earnings calls conducted by S&P 500 companies during this period. This number is well above the 5-year average of 124 and the 10-year average of 79. In fact, this is the highest number of S&P 500 earnings calls on which “AI” has been cited over the past 10 years (using current index constituents going back in time).” (FactSet)

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

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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,” Rob Arnott, Trent Commins, and Xi Liu write in a Research Affiliates 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

Over the past few months, you’ve probably heard a lot more than ever before about the possibility of an AI bubble.

It certainly wouldn’t be unprecedented.

“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.”

While business coverage of AI has become at least a little more critical and even-handed, I still frequently see reports from OpenAI and Anthropic developments that read like glorified press releases. But a few journalists continue to point out 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.

Lastly, AI could stick … but not in the way we expect it will. Artificial intelligence could prove extremely useful and lucrative in certain industries but come 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.

Our 6 Favorite AI ETFs

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If you want to get into AI without making potentially risky bets on just one or two stocks, exchange-traded funds 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.

I’m going to home in on five of the best AI ETFs. In no particular order …

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1. Global X Artificial Intelligence & Technology ETF

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— Inception: May 11, 2018

— Assets under management: $7.6 billion

— Expense ratio: 0.68%, or $6.80 per year on every $1,000 invested

First up is the juggernaut of AI ETFs: the Global X Artificial Intelligence & Technology ETF (AIQ). At well more than $7 billion in assets under management (AUM), this fund is easily the largest pure-play AI ETF on the market.

AIQ has an “unconstrained approach” that it explains like this: “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.”

Related: The 16 Best ETFs to Buy for a Prosperous 2026

Perhaps more simply put: Global X’s ETF believes AI profits will be made not just by companies that produce the technology, but also companies that adopt it. That’s reflected in AIQ’s tracking index, which breaks companies down into two categories, which themselves are broken down into a total of four easier-to-understand subcategories:

1. AI applied to products and services: Companies with developed internal AI capabilities and that are directly applying AI tech into their products and services. This can include image and/or language processing, threat detection, recommendation generation, and more.
2. 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.
3. AI hardware providers: Companies that produce semiconductors, memory storage and other hardware needed for AI applications.
4. 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 resulting 84-stock portfolio is unsurprisingly thick in tech stocks, but it’s not technology-exclusive. Right now, the tech sector accounts for 70% of assets, followed by communication services (10%), consumer discretionary (10%), and industrials (6%). The remainder is sprinkled across financials, health care, and materials.

Related: 5 Best Tech Dividend Stocks [According to the Pros]

Names such as Nvidia (NVDA), Samsung, and SK Hynix are the hardware providers you’re used to seeing in AI conversations. Microsoft (MSFT) and Alphabet (GOOGL) enable organizations to utilize AI through their software. 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 Samsung and Wolters Kluwer might suggest, AIQ isn’t a strictly U.S.-based fund. It’s global (read: U.S. plus international), with a roughly 65/35 split of American and foreign stocks. That international exposure includes China, South Korea, and Germany, 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.

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2. Robo Global Artificial Intelligence ETF

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— Inception: May 11, 2020

— Assets under management: $291.5 million

— Expense ratio: 0.68%*, or $6.80 per year on every $1,000 invested

The smaller Robo Global Artificial Intelligence ETF (THNQ)—launched two years to the day after AIQ—is similar to Global X’s fund. In fact, they even 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 divides THNQ’s holdings into “infrastructure” (currently 71%) and “applications & services” (29%).

Related: How to Rebalance Your Portfolio: A Quick Guide

Robo Global Artificial Intelligence ETF is global in nature, too, albeit with a much higher allocation to U.S. stocks than AIQ. Robo Global’s prospectus says it “expects” at least 25% of the portfolio to be international, and right now, it is. THNQ uses a scoring system as well. 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 only no stock has a weighting of more than 3%.

The two major differences between the Robo Global and Global X offerings worth noting are: 

1. 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: $342 billion for AIQ, and just $84 billion for THNQ.

2. Portfolio breadth: Right now, THNQ’s holdings list includes just 50 companies, so it holds 34 fewer stocks 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, 2026.

** There are different ways to define “cap” levels. We’re going by Morningstar’s definition, which says the largest 70% of companies by market capitalization within a fund’s “style” are large-caps, the next 20% by market cap are mid-caps, and the smallest 10% by market cap are small caps.

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3. Invesco AI and Next Gen Software ETF

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— Inception: June 23, 2005

— Assets under management: $702.2 million

— Expense ratio: 0.56%, or $5.60 per year on every $1,000 invested

The Invesco AI and Next Gen Software ETF (IGPT) boasts an inception of 2005, which makes it a little more than 20 years old … but don’t congratulate Invesco for unparalleled prescience. This AI ETF has only existed in its current form since Aug. 28, 2023, when the fund provider changed its name and ticker from “Invesco Dynamic Software ETF (PSJ).”

Do, however, congratulate the marketing department for a smart pivot.

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.

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IGPT is also a global fund (albeit the least global of the three right now, at 81% U.S. exposure), and it’s similar to THNQ in that it typically has 20%-25% exposure to mid-caps and small caps.

The primary difference-maker here is that IGPT is prone to higher single-stock concentrations than the other two funds. Micron Technology (MU) currently accounts for more than 11% of assets. Alphabet, Meta Platforms (META), Nvidia, and SK Hynix all account for between 7% and 8% apiece. Compare that to AIQ and THNQ, whose top holdings sit around 4%.

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 12 basis points less in annual fees than the two aforementioned products. (A basis point is one one-hundredth of a percentage point.)

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4. ARK Autonomous Technology & Robotics ETF

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— Inception: Sept. 30, 2014

— Assets under management: $2.0 billion

— Expense ratio: 0.75%, or $7.50 per year on every $1,000 invested

The ARK Autonomous Technology & Robotics ETF (ARKQ) differs from the first three AI ETFs in a couple of meaningful ways. For one, it’s actively managed (it was one of the first two funds to launch under Cathie Wood’s innovation-minded ARK Invest firm). And while the previous three funds are invest broadly in the potential of artificial intelligence, ARKQ aims to capture a specific corner of the AI theme.

ARK Autonomous Technology & Robotics 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.

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While technology accounts for a third 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 (14%) and a decent amount of communication services (7%), with sprinklings across energy, health care, and utilities.

ARKQ has less exposure to large caps than any of the aforementioned funds, with about 35% of assets invested in mid-caps and 15% dedicated to small- and micro-cap stocks. But Wood, who runs a tight ship of fewer than 40 holdings, is also more than happy to concentrate weights in her favorite bets; Teradyne (TER) and Tesla (TSLA) currently account for more than 20% of assets combined.

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.

Related: 9 Best Fidelity ETFs for 2026 [Invest Tactically]

5. Roundhill Generative AI & Technology ETF

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— Inception: May 18, 2023

— Assets under management: $1.1 billion

— Expense ratio: 0.75%, or $7.50 per year on every $1,000 invested

The Roundhill Generative AI & Technology ETF (CHAT) is a somewhat more focused play on “generative” AI, which is AI used to generate content (so, text, images, even video) from a prompt.

“AI-driven tools and platforms are significantly boosting enterprise productivity, efficiency, and decision-making,” Roundhill says of the opportunity. It points to OpenAI’s ChatGPT, which “has become one of the fastest applications of all time to surpass 100 million users.”

However, I said it’s a “somewhat” more focused play for a reason.

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The actively managed CHAT’s 43-holding portfolio does invest in first-to-mind gen-AI names such as Alphabet, with its Google Gemini platform, and Microsoft (MSFT), which is home to Copilot. But those are multitrillion-dollar behemoths with many business lines outside generative AI. Similarly, chipmaker holdings such as Nvidia, AMD, and Broadcom (AVGO) produce semiconductors that power generative AI, sure, but also many other applications of artificial intelligence.

But that’s not necessarily a bad thing. If you believe not just in the upside potential of generative AI, but also the ability for that field to widen, CHAT should over time become more specialized.

I can say the same about the final fund on this list, too.

Related: 10 Dividend-Growth Stocks That Wall Street Loves Now

6. Defiance Quantum ETF

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— Inception: Sept. 4, 2018

— Assets under management: $3.7 billion

— Expense ratio: 0.40%, or $4.00 per year on every $1,000 invested

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.

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“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 85 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.

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But quantum computing is downright nascent. Quantum computers technically exist, but they’re nowhere near commercial development. For instance, Nvidia CEO Jensen Huang received flak last year after saying quantum computing wouldn’t be useful for at least 15 years “on the early side.” Not long after, he walked back some of his skepticism and generally said he underestimated the pace of progress. (But I’ll note that 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 Rigetti Computing (RGTI) and D-wave Quantum (QBTS). However, most of its holdings, such as Lockheed Martin (LMT), ABB (ABB), and Mitsubishi Electric are plays on many other things that aren’t quantum. QTUM also uses a modified equal-weighting system, so those few pure-play quantum companies are structurally limited as to how much influence they can wield.

But I also feel obligated to point out that Defiance Quantum’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.

Related: 9 Best Robo-Advisors for Investing Money Automatically

Does the Fund Say It’s “AI”? Be Careful!

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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.