AI-themed ETFs have proliferated in recent years, and many of them loaded with so-called “magnificent seven” tech giants.
However, not all are designed to endure the next innovation cycle. As the discussion moves from “what’s hot” to “what stands the test of time,” a new generation of ETFs is on the rise—those that adapt.
One such fund is the Wedbush ETFMG Global Cloud Technology ETF IVES. The ETF, launched on June 4, managed to pull in $100 million in only five trading days.
IVES “is built to be disciplined and transparent,” Wedbush Fund Advisers CIO Cullen Rogers tells Benzinga. “We don’t react to short-term performance or headlines.”
IVES rebalances quarterly and aligns with revisions to the AI30 model developed by Wedbush’s Dan Ives, who created the methodology. This frequency steers clear of performance-chasing whiplash from more active managers, while retaining thematic relevance.
The fund “follows a clear set of rules and reflects the ongoing evolution of Dan’s AI30 framework. That combination of research-driven insight alongside a passive index structure gives us the ability to stay focused on the long term,” Rogers added.
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The Case Against Static AI ETFs
Global X Robotics & Artificial Intelligence ETF BOTZ and ROBO Global Robotics and Automation ETF (ROBO) both have portfolios that lean towards robotics, semiconductors, and infrastructure-heavy hardware.
BOTZ, for example, has Nvidia NVDA, Intuitive Surgical ISRG, and Keyence KYCCF as its leading holdings.
ROBO has a strong tilt into industrials and health tech.
But these ETFs tend to be afflicted by static index approaches that fail to adjust rapidly to new AI subsectors like inference engines, generative software, or quantum compute.
Inside IVES: A Living Portfolio For A Moving Target
IVES employs a modified market-cap weighting strategy to keep its biggest bets in check. Yes, it includes the likes of Microsoft MSFT, Nvidia, and Amazon AMZN. However, it also leaves room for lesser-known high-conviction picks.
“We intentionally moved away from traditional straight-line market-cap weighting to avoid exactly that issue (overconcentration and valuation risk tied to the magnificent seven stocks). The modified market-cap weighting approach we use helps large-cap leaders hold influence, but it also prevents them from overwhelming the rest of the portfolio. At the same time, it gives room for smaller, high-conviction names to matter,” said Rogers.
IVES takes a tight, research-driven approach, Rogers explains.
“This isn’t about market timing — it’s about giving investors access to the companies actually building and monetizing the AI revolution, in a format that’s disciplined and transparent,” he adds.
What Happens In A Downturn?
With valuations across the tech sector under renewed scrutiny, some wonder if AI ETFs are a bubble waiting to pop. Rogers isn’t fazed. “Short-term market swings are part of investing, but IVES is built for the long run. What sets the fund apart is its ability to evolve with the AI ecosystem. Not just where it is today, but where it’s going. That’s the advantage of anchoring the portfolio to Dan’s ongoing research. As AI matures and new players, platforms, or verticals emerge, the index can adapt,” he said.
“If the center of gravity in AI shifts from semis to inference, or from traditional compute to quantum, Dan’s research is positioned to catch that. And our structure is designed to follow it. So even through a downturn, we’re confident the fund will stay aligned with the most important parts of the AI value chain as they evolve,” Rogers emphasized.
Contrast that with holdings such as BOTZ, where old-school industrials and automation stocks constitute a huge percentage of the assets. They’re exposed to AI, perhaps, but sometimes indirectly and without the agility to change direction quickly.
Investor Takeaway: Adaptive > Aggressive
For individual investors, the message is straightforward: not to conflate AI exposure with AI relevance. BOTZ funds might have expansive baskets, but that expansiveness tends to be at the expense of thematic integrity and forward-looking design. ROBO still stands for its robotics theme and first-mover appeal, but perhaps it will not be the final say on AI innovation as it extends into software.
IVES, on the other hand, presents a more streamlined style that balances conviction with agility. It’s not attempting to be everywhere, it’s trying to be where the AI narrative is really headed.
If the previous cycle of AIs revolved around who could train the largest model, perhaps the next will be a question of who can best monetize it. In that game, flexibility, not merely exposure, may be the ultimate advantage.
As Rogers summarizes: “IVES isn’t a static snapshot, it’s a living framework.”
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