It comes down to earnings durability and breadth.
With equities hovering near all-time highs and the S&P 500 closing out its strongest monthly performance since late 2020, the current phase of the cycle is being defined less by multiple expansion and more by the consistency and scalability of earnings growth. For wealth advisors and RIAs, the central question is no longer whether earnings are strong—it is whether they are sufficiently broad, persistent, and investable across sectors to justify continued exposure at these levels.
Consensus expectations for first-quarter earnings growth are running at approximately 15%, a level that materially exceeds long-term averages. Historically, periods of double-digit earnings expansion have provided a durable underpinning for equity markets, even in the presence of macro uncertainty. The current environment reflects that dynamic. When earnings growth is both visible and accelerating, downside risk tends to be muted unless disrupted by exogenous shocks.
The earnings season data reinforces this narrative. According to FactSet, roughly 84% of reporting companies delivered positive earnings surprises through late April. That level of outperformance is not only statistically significant but also indicative of conservative guidance practices and resilient operating leverage. For advisors constructing portfolios, this suggests that earnings momentum remains intact across a wide swath of the market, not just concentrated in a handful of names.
However, the composition of that growth matters. A substantial portion of incremental earnings strength is being driven by capital investment tied to artificial intelligence infrastructure. The scale of spending from hyperscalers—Microsoft, Amazon, Meta Platforms, and Alphabet—is reshaping not only the technology sector but also adjacent industries. This is not a narrow thematic trade; it is an ecosystem-level expansion that touches semiconductors, energy, industrials, and real assets.
The implications for portfolio construction are significant. AI-related capital expenditures are cascading through supply chains, benefiting companies that historically would not have been considered part of the “technology trade.” For example, Caterpillar has seen renewed demand driven by data center infrastructure requirements, particularly in power generation and energy systems. This reflects a broader trend: the monetization of AI is not limited to software or cloud platforms—it is increasingly embedded in physical infrastructure.
At the macro level, this investment cycle is now a measurable contributor to economic growth. Data from the US Bureau of Economic Analysis shows that business investment was the primary driver of first-quarter GDP growth, surpassing consumer spending. For decades, the U.S. economy has been consumption-led. The current shift toward investment-led growth, even if temporary, has important implications for asset allocation. It suggests that cyclical and capital goods sectors may play a more prominent role in portfolios than they have in recent years.
Moreover, this investment boom may be offsetting other macro headwinds. Elevated energy prices—particularly with crude oil trading above $100—and geopolitical risks, including disruptions in critical shipping lanes, would typically exert downward pressure on growth. Yet the scale of AI-driven investment appears to be counterbalancing these factors. As noted by economists at Moody’s Analytics, absent this surge in capital expenditure, recession risks would likely be materially higher.
For advisors, this creates a nuanced backdrop. On one hand, the earnings and investment cycle provides a compelling case for maintaining equity exposure. On the other, it introduces concentration risk, particularly within mega-cap technology. While these companies are the primary drivers of earnings growth, they are also deploying significant capital, which raises questions about margin sustainability and return on invested capital over time.
This tension is already visible in market reactions. Despite strong earnings reports, some large-cap technology companies have experienced muted or negative price responses, as investors weigh revenue growth against rising cost structures. Input costs—especially for memory and advanced semiconductors—are increasing. These components are essential for AI infrastructure, from data centers to edge devices, and their pricing dynamics could compress margins if not offset by pricing power or efficiency gains.
The key issue for RIAs is how long these companies can absorb or pass through rising costs without eroding profitability. While management teams have demonstrated discipline thus far, the sustainability of that balance is uncertain. This is particularly relevant in an environment where expectations are already elevated. When valuations are predicated on continued earnings acceleration, even modest disappointments can lead to outsized volatility.
From a strategic perspective, this argues for a more balanced approach to AI exposure. Rather than concentrating portfolios in a small number of mega-cap names, advisors may want to consider a broader set of beneficiaries across the AI value chain. This includes semiconductor manufacturers, equipment suppliers, infrastructure providers, and energy companies that support data center expansion.
The concept of layering exposure across “enablers,” “intelligence,” and “applications” is particularly useful. Enablers include companies involved in chip design and fabrication. Intelligence refers to platforms and models that process and analyze data. Applications encompass end-user software and services that integrate AI into business processes. Each layer has distinct risk-return characteristics and different sensitivities to economic and technological variables.
Diversification across these layers can help mitigate concentration risk while preserving exposure to the underlying growth theme. It also allows advisors to capture second-order effects that may not be immediately apparent. For instance, increased demand for data center capacity drives not only semiconductor sales but also energy consumption, cooling systems, and real estate development.
Another area worth attention is the software sector. While it has underperformed relative to other parts of the technology ecosystem, there are signs of stabilization. The iShares Expanded Tech-Software Sector ETF has shown signs of recovery, reflecting improved sentiment and the potential for AI-driven transformation within software business models.
Importantly, AI is both a disruptor and an enabler for software companies. Legacy models may face pressure, particularly those reliant on traditional licensing or labor-intensive services. However, companies that successfully integrate AI into their offerings can enhance productivity, reduce costs, and create new revenue streams. This creates a bifurcation within the sector, where winners and losers are likely to diverge more sharply than in previous cycles.
For RIAs, this reinforces the importance of active selection within sectors undergoing structural change. Passive exposure may not adequately capture the dispersion of outcomes. Instead, a more selective approach—focused on companies with strong balance sheets, scalable platforms, and clear AI integration strategies—may be warranted.
Looking ahead, forward estimates remain constructive. Some institutional forecasts project continued upside for equities through 2026, albeit with adjustments for macro variables such as energy prices. While these targets should not be taken as deterministic, they reflect a broader consensus that the earnings cycle has further room to run.
That said, advisors should remain mindful of the potential for volatility. Markets rarely move in a straight line, particularly when driven by a dominant theme like AI. Periods of consolidation or rotation are likely, especially as investors reassess valuations and reallocate capital.
One practical approach is to use periods of strength—particularly in highly concentrated segments of the market—to rebalance portfolios. This does not imply reducing exposure to AI altogether but rather reallocating toward underrepresented areas that may offer more attractive risk-adjusted returns. This could include mid-cap companies, international equities, or sectors that are indirect beneficiaries of AI investment.
Risk management remains critical. While the current environment is supportive, it is also contingent on continued execution by corporate management teams and sustained investment levels. Any slowdown in capital expenditure, whether due to economic conditions or diminishing returns, could alter the trajectory of earnings growth.
In summary, the current market environment is fundamentally earnings-driven, with AI-related investment serving as a key catalyst. The breadth and durability of this growth provide a strong foundation for equities, but they also introduce new complexities in terms of valuation, cost pressures, and concentration risk. For wealth advisors and RIAs, the opportunity lies in navigating these dynamics with a disciplined, diversified approach that captures the full spectrum of AI-driven growth while managing downside risks.
