Dan Ives just launched an ETF, and frankly, the timing couldn't be more fascinating. The Wedbush Securities managing director unveiled his "IVES AI Revolution ETF" this week, calling artificial intelligence the "fourth industrial revolution" and predicting it's "just the beginning." With $2 trillion in AI-related spending projected over the next three years, Ives isn't making small bets—he's positioning for what he believes will be a generational shift in how technology transforms business.
But strip away the marketing speak, and the question becomes: What do the actual investment patterns tell us about where AI is headed? The numbers are substantial, complex, and reveal both extraordinary opportunity and sobering realities that deserve our attention as growth practitioners who need to navigate this space intelligently.
The scale of AI investment is genuinely staggering. According to Goldman Sachs Research, global AI investment could approach $200 billion by 2025, with the U.S. accounting for roughly half at $100 billion. Meanwhile, tech giants are planning massive spending: Amazon, Google, Meta, and Microsoft combined their capital expenditure from $151 billion in 2023 to $246 billion in 2024, with projections potentially surpassing $320 billion in 2025.
That represents a 63% jump year-over-year, driven entirely by AI infrastructure, data centers, and related technologies. Just five AI hyperscalers are projected to spend more than $1 trillion in capex collectively from 2024 to 2027, according to S&P Global data.
But here's where it gets interesting: Ives' new ETF targets 30 companies across semiconductors, software, infrastructure, and autonomous systems—not just the usual suspects. The fund is built around his proprietary research framework, targeting what he calls "second, third, fourth derivatives" of the AI theme. Translation: We're moving beyond the obvious plays like Nvidia and Microsoft into companies that will benefit from the ripple effects of AI adoption.
This broader approach reflects a crucial market dynamic that's often missed in AI discussions: the real money might not be in the headline-grabbing foundation models, but in the unglamorous infrastructure, enterprise software, and specialized applications that make AI actually work at scale.
While the investment thesis sounds compelling, actual corporate results tell a more nuanced story. According to McKinsey survey data highlighted in Stanford's 2025 AI Index, most companies reporting cost reductions from AI implementations saw savings of less than 10 percent. For revenue increases, most gains were under 5 percent.
This isn't necessarily negative—it's realistic. The corporate world has poured $150 billion in private investment into AI in 2024 alone, with about $33 billion going specifically to generative AI ventures. Yet as the Stanford report notes, "corporations haven't yet seen a transformation that results in significant savings or substantial new profits."
Ives acknowledges this reality while maintaining his bullish stance: "In 25 years covering tech, I've never seen a bigger theme than the AI revolution," he told Fox Business. His Tesla analysis provides a perfect example of this long-term thinking. Despite Tesla sales being down 15% in China while BYD is up 14%, Ives believes "90% of the future value" lies in AI and autonomous robotics, not traditional automotive metrics.
That's either visionary investing or expensive wishful thinking, depending on your perspective. The bet is that current financial performance matters less than positioning for a fundamental shift in how these companies operate and create value.
Investment patterns reveal some fascinating geographic dynamics that complicate the "fourth industrial revolution" narrative. The U.S. has raised nearly half a trillion dollars in AI funding from 2013 to 2024—more than the rest of the world combined ($471 billion vs. $289 billion). China follows at $119 billion, with the UK at $28 billion.
But there's a quality convergence happening that's worth noting. According to Stanford's AI Index, while the U.S. still leads on quantity of notable AI models released, Chinese models are rapidly catching up on performance. In January 2024, the top U.S. model outperformed the best Chinese model by 9.26 percent; by February 2025, this gap had narrowed to just 1.70 percent.
This suggests that raw investment dollars don't automatically translate to sustained competitive advantage. The AI race might be more dynamic and internationally distributed than the current investment concentration suggests.
Ives' ETF attempts to capture this complexity by focusing on infrastructure plays—companies that provide the backbone for AI development regardless of which models or applications ultimately succeed. It's a picks-and-shovels approach that acknowledges the uncertainty while betting on the overall trend.
Here's where things get genuinely interesting from an investment perspective. The top 10 stocks in the S&P 500 now account for over 40% of research and development expense despite representing only 13% of revenues. The Magnificent Seven make up nearly 35% of the S&P 500 market cap and have driven over 70% of returns since the beginning of 2023.
According to J.P. Morgan Asset Management analysis, while the rest of the S&P 500 trades on a 12-month forward earnings multiple of 19x, the largest 10 stocks now trade on 29x. This valuation gap creates what they call an "unsustainable" situation that will likely resolve through either a "catch up" or "catch down" scenario.
Ives is clearly betting on the catch-up scenario—that AI adoption will be broad enough to justify current valuations and drive performance across his 30-stock portfolio. But unlike the dot-com bubble, much of the current run-up has been supported by actual earnings growth, not just speculation.
Take Amazon, whose P/E ratio has declined from 48x to 35x over the past 12 months while delivering a 46% return driven by earnings growth. Or Nvidia, where a 145% change in 12-month forward earnings contributed significantly to the stock's 207% return, despite multiple expansion.
So where does this leave growth practitioners trying to make sense of AI investment in 2025? The data suggests we're in a fascinating middle ground—significant capital deployment is happening, but the transformational returns haven't materialized yet.
Ives' focus on cybersecurity as a top second-derivative play makes particular sense: "That's where I think you're going to see more and more of the AI spent to protect the workloads," he noted. As AI systems become more central to business operations, the security infrastructure around them becomes increasingly valuable.
His Tesla thesis—that autonomous robotics could be worth $1 trillion alone to the company—exemplifies the kind of long-term thinking required to navigate this space. Whether you buy that specific valuation or not, the underlying logic reflects how AI investments require patience for technologies that may take years to fully monetize.
The convergence of energy and AI, highlighted by recent deals like Constellation Energy and Meta's nuclear partnership, represents another practical consideration. AI requires massive computational power, which requires massive energy infrastructure. The companies that solve the energy equation may be better positioned than those focused purely on AI algorithms.
Worldwide AI spending is forecast to reach $632 billion by 2028 according to IDC, with generative AI expected to represent 32% of that total. That's substantial, but it's also spread across thousands of companies and use cases, suggesting the opportunity is both significant and highly distributed.
Dan Ives may be right about AI representing a fourth industrial revolution, but the timeline and specific beneficiaries remain genuinely uncertain. His ETF launch represents a sophisticated attempt to capture the breadth of AI opportunity while acknowledging that predicting specific winners in a rapidly changing field is nearly impossible.
The investment patterns suggest we're past the pure speculation phase—too much real money from sophisticated investors is being deployed—but we're also not yet in the mature returns phase where AI's economic impact is clearly measurable across most implementations.
For marketers and growth leaders, this creates both opportunity and obligation. The opportunity lies in identifying the second and third-order effects of AI adoption in your specific industry or customer base. The obligation is to remain realistic about timelines and returns while positioning for long-term shifts that may take years to fully materialize.
The $2 trillion spending projection over three years isn't just a number—it represents thousands of companies betting their future on AI integration. Some of those bets will pay off spectacularly. Others will join the long list of expensive technology implementations that never quite delivered on their promises.
The smartest approach may be following Ives' lead: broad exposure to the theme, patience for long-term development, and focus on infrastructure plays that benefit regardless of which specific AI applications ultimately succeed. Whether that adds up to a fourth industrial revolution or just another technology cycle with better marketing remains to be seen.
Ready to develop an AI strategy that balances opportunity with realistic expectations? Contact Winsome Marketing's growth experts to build an approach that leverages AI's potential while avoiding the hype-driven pitfalls that sink so many technology investments.