AI’s generational opportunity comes with generational uncertainty

By David Wismer

Over the past several months, Americans have closely followed several momentous events:

•  The Artemis II mission, which set a record for the greatest distance humans have traveled from Earth.

•  The national celebration of the Semiquincentennial, marking the 250th anniversary of the signing of the Declaration of Independence.

•  The unprecedented staging of the FIFA World Cup across three North American host countries: Canada, Mexico, and the United States.

Yet the story that continues to captivate retail investors, institutional managers and analysts, and financial advisers is the rapid growth, adoption, and evolution of artificial intelligence (AI).

The rise of AI represents a generational, transformative technological shift that rivals, if not surpasses, the emergence of the internet or the spread of personal and cloud computing. Its effects will extend far beyond the technology industry, reshaping how companies operate, how people work, and how decisions are made. Over time, AI is likely to influence nearly every sector of the economy and become increasingly embedded in daily life. Its full consequences will unfold over many years, creating significant opportunities as well as difficult challenges.

The scale of AI-related capital expenditures

As AI continues to change the nature of cybersecurity and modern warfare, and to drive advances in medicine, manufacturing and logistics, communications, software development, and many other fields, it will require significant ongoing investment.

In May 2026, Goldman Sachs published a lengthy, high-level look at the projected growth of AI-related capital expenditures—and the assumptions and drivers behind them, noting the following:

“The scale of these expenditures is enormous. Estimates of $4 trillion to $8 trillion of total capital investment over the next five years have featured prominently in recent market commentary. That capital is used to buy new chips, build new data centers, and construct new power, all in an effort [to] assemble sufficient computing infrastructure to meet the moment. Debates about whether this figure is ‘too high’ are usually framed around a demand-side question: Will AI adoption and monetization justify the spend?”

Although the answer may be unknowable today, the sheer magnitude of the projected spending is impressive.

The changing dynamics of AI infrastructure and AI hyperscalers

The artificial-intelligence trade is starting to split into two distinct groups: the companies supplying the infrastructure needed to power AI, and the large technology companies racing to build AI into their products, platforms, and business models.

The first group includes the “picks and shovels” of the AI build-out: chips, data centers, power, cooling, networking, memory, and other critical components. The second group includes many of the best-known platform companies, which are spending heavily to develop, deploy, and monetize AI capabilities.

That distinction matters because the economics of the AI boom may not be evenly distributed. Infrastructure companies may benefit more directly from the current capital-spending cycle as demand for computing power, storage, energy, and specialized hardware continues to rise. Meanwhile, the companies building AI applications still have to prove that their investments can translate into durable revenue growth, margin expansion, and shareholder returns.

MarketWatch recently reported on analysis from Holt, UBS’s research unit, which said that artificial-intelligence infrastructure stocks are set to vastly outperform most of the “Magnificent Seven” technology companies:

“Three years ago, Holt’s framework ranked Apple, Microsoft, Alphabet, Meta and Amazon as the top five economic profit generators in the industry. For 2027, Nvidia, Samsung, SK Hynix, Micron and Alphabet are in the lead.”

The following chart illustrates how one segment of the infrastructure build-out—semiconductors—outperformed the Magnificent Seven during the first half of 2026.

The vast, unpredictable opportunity of AI

The following chart shows the gap between AI’s theoretical capabilities and its observed use across occupational categories. Note that Anthropic, the company that conducted the analysis, develops large language models (LLMs) and agentic workflows.

One commentator described the implications of that gap this way:

“In most knowledge work roles, computer science, finance, management, AI could theoretically handle 80-95% of tasks. Real usage is closer to 15-40%.

“That’s not just a workflow problem. The technology is still immature. … Most businesses can’t bet core operations on a tool that’s still figuring itself out.

“Adoption is going to take years. That’s not pessimism, that’s just how it goes with any major technology shift. …”

Balancing AI investment exposure with risk management

Even the largest technology companies could come under pressure if their extraordinary spending on AI infrastructure fails to produce adequate returns. As a result, the eventual winners and losers may be more numerous—and less predictable—than the initial boom in several well-known stocks might suggest.

Whether the AI trade is overvalued is a subject of intense debate among financial analysts. Pure-play AI analytics and software companies can command elevated price-to-earnings ratios as investors price in expectations for rapid future growth. Some economists warn that market concentration and stretched multiples resemble the dot-com bubble. Others point to tangible AI-related revenue and suggest that using low-cost index funds may help reduce the risk associated with owning individual stocks.

Yet financial advisers say that fear of missing out, or FOMO, is a very real concern among clients—one that broader index funds may not fully address.

What if advisers could help clients gain more direct exposure to some of the biggest, most visible names in the S&P 500 and NASDAQ while also incorporating risk-management tools?

In late 2025, Flexible Plan Investments (FPI) launched FlexDirex, the first U.S. suite of actively managed strategies using leveraged and inverse single-stock ETFs.

FlexDirex includes two offerings—Tech Plus and Focused Core—designed to give financial advisers tactical options for both growth-oriented and more diversified portfolios. Both use Direxion single-stock ETFs, which seek daily leveraged or inverse exposure to individual stocks. Direxion is a leading provider of tactical ETFs.

Most relevant to this discussion is the Tech Plus strategy, which is designed for investors and advisers managing portfolios with QQQ exposure. It offers a focused, tactically managed way to participate in the performance of many major technology names while seeking to manage volatility and concentration risk. The strategy is intended for aggressive growth investors who want to enhance or hedge NASDAQ-heavy exposure. It is also designed to pursue opportunities in both rising and falling high-volatility stocks.

While it might be immodest to call FlexDirex a generational product, it offers advisers and their clients what FPI President and CIO Jerry Wagner calls “a significant step forward in ETF innovation.”

You can learn more about FlexDirex here or watch a replay of a recent webinar about the strategies here (the webinar is for financial professionals only).