Artificial Intelligence (AI) is not a monolith—it is a layered, rapidly evolving megatrend that permeates multiple sectors and value chains. Investors seeking exposure to AI need more than just a broad "tech" exchange-traded fund (ETF). WisdomTree offers a range of different ETFs that touch the AI narrative from six distinct vectors: quality growth, cybersecurity, cloud computing, biotechnology, the broad-based AI ecosystem and technology-focused real estate.

Each Fund is a building block representing a unique slice of the AI-driven future. Below, we explore the conceptual logic behind each ETF's AI linkage, helping investors think beyond performance and toward portfolio construction based on differentiated AI exposures.

WisdomTree U.S. Quality Growth Fund (QGRW)

QGRW is built to track the total return performance, before fees and expenses, of the WisdomTree U.S. Quality Growth Index. The specific methodology focuses on large-cap U.S. companies with strong return on equity, return on assets and earnings growth metrics. The group of so-called Magnificent 7 companies has tended to qualify for inclusion, and the market capitalization-weighting schematic has led to these stocks having high exposure.

At the cutting edge of AI, we're witnessing a high-stakes race among the world's most resource-rich companies to train the largest and most sophisticated models in history. These are the trillion-dollar firms building trillion-parameter systems—engines of productivity, automation and knowledge generation. While many groundbreaking models are still incubating within private firms, QGRW continues to lean into public market giants that are deploying capital at unprecedented scale to stay ahead in AI's arms race. It's not just exposure to AI—it's exposure to the platforms driving AI's exponential frontier.

WisdomTree Cybersecurity Fund (WCBR)

WCBR is built to track the total return performance, before fees and expenses, of the WisdomTree Team8 Cybersecurity Index. The specific methodology focuses on companies that are engaged in the provision of specific cybersecurity services that fit within the eight distinct themes that Team8 has delineated as representing the future of cybersecurity. These companies are also required to meet certain revenue growth requirements.

AI being adopted at scale is effectively a new paradigm within technology that requires all of us to think about cybersecurity in new ways. This strategy is seeking the publicly traded companies that are focusing on the future of cybersecurity, many of which will use different types of AI to help secure our systems.

WisdomTree Cloud Computing Fund (WCLD)

WCLD is built to track the total return performance, before fees and expenses, of the BVP Nasdaq Emerging Cloud Index. The specific focus is on software-as-a-service (SaaS) cloud companies that are generating more than 50% of their revenues from cloud computing services sold to enterprise customers. There are also revenue growth criteria.

It is interesting to remember that software is, at the core, what AI really is. However, large language models (LLMs) have changed so much about what we think software can do that we have to re-assess how we think about the value of different kinds of software. Robotic process automation is a great example. Before LLMs, this was seen as an exciting way to move toward automating certain types of complex tasks. After LLMs, particularly with the capabilities that we see in 2025, the market is re-thinking what robotic process automation looks like.

WisdomTree BioRevolution Fund (WDNA)

WDNA is built to track the total return performance, before fees and expenses, of the WisdomTree BioRevolution Index. The specific focus is on companies that are delivering innovations in such areas as human health, agriculture and food, materials, chemicals and energy, and biological machines and interfaces.

However, looking beyond that high-level focus, it is important to recognize that a lot of biological innovation comes from being able to process information at greater and greater scale. Consider DNA and mRNA, two types of encodings of biological information. Processing more and more of this information more and more quickly could lead to a treasure trove of future innovation. It was telling that a Nobel prize was awarded to the effort behind the AlphaFold protein shape prediction system, for example.

WisdomTree Artificial Intelligence and Innovation Fund (WTAI)

WTAI is built to track the total return performance, before fees and expenses, of the WisdomTree Artificial Intelligence and Innovation Index.

As can be gleaned from the name of the strategy, the full approach is focused on AI. We often think of it as "full ecosystem" exposure. AI, for example, could not be calculated if not for semiconductors, one of the major areas of exposure. Many of the larger players tend to be included, but smaller, innovative companies can also make their way into the strategy.

WisdomTree New Economy Real Estate Fund (WTRE)

WTRE is built to track the total return performance, before fees and expenses, of the WisdomTree New Economy Real Estate Index. The proliferation of our global technological footprint indicates that technology-focused real estate is essential.

Of late, a lot of focus has been placed on data centers, as this is where the physical training and inference compute for the large AI models tends to be located. This is an exposure in this strategy, along with communications-related technology, technology-oriented warehouse real estate, life sciences real estate and even real estate that can be used to facilitate the development of the cryptocurrency ecosystem.

Amazon has quietly crossed a technological Rubicon in its warehouses. With over one million robots now deployed—rivaling the number of human workers—Amazon has transformed fulfillment into a highly automated system where machines handle everything from shelf picking and order sorting to packaging and cart transport. At facilities like its three-million-square-foot site in Shreveport, robots perform synchronized tasks alongside humans, pushing products through 25% faster than legacy sites. This integration of robotics and AI hasn't just improved speed—it has fundamentally reshaped the economics. The number of packages handled per employee has skyrocketed from approximately 175 in 2015 to nearly 3,870 in 2025. That's a twentyfold leap in throughput per worker, driving Amazon's revenue-per-employee sharply higher while shrinking its labor footprint per facility to the lowest level in 16 years.¹

Notably, one of the pillars of WTRE's strategy is exposure to the next generation of logistics technology.

Figure 1 looks at the standardized performance of these strategies.

Figure 1: Standardized Returns

Even if many agree that AI has been a major force in 2025, the policy environment, particularly the evolving tariff policy from the Trump Administration, has played a major role in the returns that we have seen. Figure 2 shows how these strategies have performed so far in 2025.

• Real estate unexpectedly leads all themes: WTRE tops the chart at 13.5% year-to-date, reflecting renewed demand for data centers, logistics hubs and infrastructure critical to AI and cloud deployments—despite rate volatility.
• AI and cybersecurity sustain high-single- to low-double-digit momentum: WTAI and WCBR post 11.7% and 9.5%, respectively, showing continued investor confidence in essential digital enablers, even after a turbulent Q1 across growth equities.
• Cloud and biotech underperform amid capital rotation: WCLD drops 4.6%, suggesting margin scrutiny and platform fatigue in SaaS; WDNA ekes out just 0.5%, as investor patience wears thin on long-horizon biotech narratives.

Figure 2: A Wide Dispersion across Different Strategies Touching AI

Measuring Risk in These Different AI-Exposed Strategies

There is no single AI solution because the concept is proliferating so strongly across the global economy. In figure 3a, we consider the risk across these six strategies from the perspective of exposure to companies with negative earnings. More exposure to firms with negative earnings could represent greater risk.

• High-growth thematics still come with an earnings gap: Funds like WDNA (58.09%), WCLD (53.53%) and WCBR (47.92%) are heavily allocated to companies with negative earnings, signaling investor conviction in innovation—but also heightened sensitivity to interest rates and risk-off sentiment.
• QGRW stands apart on profitability discipline: With just 2.69% exposure to unprofitable firms, QGRW offers thematic exposure to AI and innovation through the lens of operational strength—making it a relative strategic ballast in high-volatility environments, at least compared to the other strategies in figure 3a.

Figure 3a: Considering Relative Risk between Strategies—Negative Earnings

Market capitalization size exposure is another way to conceptualize risk. More exposure to smaller companies could delineate greater risk.

• QGRW is focused on large companies, per its methodology, and this shows here with 100% exposure to large-cap companies.
• WCBR, WCLD, WTRE and WDNA were the strategies with the largest exposure to small-cap companies within this group of six. They were also strongly represented in the mid-cap size spectrum.

It's astonishing how the hierarchy of company sizes has shifted in the age of AI. Nvidia—once a niche GPU designer—is now the world's first $4 trillion company, leapfrogging Apple and Microsoft in market cap.² We often talk about the Magnificent 7 as trillion-dollar titans, but that threshold is increasingly becoming the new baseline in tech's upper echelons. Against this backdrop, the standard definition of "large cap"—companies above $10 billion—feels almost quaint. Many private firms riding the AI megatrend already exceed that mark, even before going public. On the other end, companies under $2 billion are considered small caps—yet any firm meaningfully involved in generative AI or foundational infrastructure can cross that threshold rapidly. The lines between size segments are being redrawn in real time by the velocity of innovation.

With market capitalization, we are seeing the consequence of there being no real upper limit as companies have been generally getting larger and larger over time.

Figure 3b: Considering Relative Risk between Strategies—Market Cap Size

¹ S. Herrera, "Amazon Is on the Cusp of Using More Robots than Humans in its Warehouses, The Wall Street Journal, 6/30/25.

² R. Whelan, et al., "How Nvidia Became the World's First $4 Trillion Company," The Wall Street Journal, 7/9/25.