AI’s next phase – from capital spending to capital returns

More mixed messaging for markets to digest. Resilient U.S. economic data and the ongoing Middle East conflict remained key market drivers last week. On the data front, Friday's release of the Labor Department's nonfarm payrolls report showed the economy added +172,000 jobs in May, far exceeding consensus expectations, while monthly totals for March and April were revised upward by a combined +93,000. The unemployment rate held steady at 4.3%. This upbeat report capped a week of labor market signals that, despite inflation and geopolitical headwinds, continued to convey strength: ADP private payrolls rose by +122,000 jobs in May, an improvement over April's +105,000, and the JOLTS (Job Openings and Labor Turnover Survey) for April reported a better-than-expected 7.6 million job openings. On a less favorable note, weekly first-time unemployment claims ticked up to a 17-week high of 225,000, and layoffs as measured by the Challenger, Gray & Christmas report surged 16% in May, to 97,006 — the most job cuts in a single month since the depths of Covid.

Last week's reported jump in job openings may be an inflationary signal with respect to its impact on average hourly earnings (Figure 1). And though this wage growth metric moderated to 3.4% year over year in nominal terms (not factoring in inflation), it remains sufficiently elevated to reinforce the Federal Reserve's hawkish policy posture. Real (i.e., after inflation) wage growth, however, remained negative in May for the second month in a row as the spike in energy prices continued to erode purchasing power.

Even with Trump appointee Kevin Warsh now at the helm, market odds currently see a higher probability of a Fed rate hike by year-end, rather than the cuts previously hoped for. A further pickup in consumer prices, especially combined with robust job growth, could cement expectations for the federal funds rate to reach 4%.

Uncertainty around inflation and interest rates has investors focusing sharply and more selectively on artificial intelligence (AI) as a catalyst. Markets are rewarding capital-intensive companies that are successfully monetizing AI demand, while backing away from heavily indebted firms or those deemed vulnerable to AI disruption.

Portfolio considerations

The AI investment cycle is evolving. What began as a race to build foundational models, fund data centers, acquire specialized chips and expand scale is now entering a trickier phase: determining the winners in a highly competitive field. Which companies can actually convert their capital spending on AI into durable earnings growth and shareholder value? The answer is far from obvious.

As shown in Figure 2, AI capital expenditures by hyperscalers (massive cloud computing and data management providers including Amazon Web Services, Google Cloud and Microsoft Azure) have surged dramatically relative to the broader universe of S&P 500 Index companies. And management commentary from hyperscalers gives no indication that such spending will slow.

But capital deployment alone isn't a guarantee of success — it's a means to an end, and not every company will get there. The real and potential disruption caused by AI is already creating a divide between industry sectors and subsectors that stand to benefit from the transformative technology and those that face daunting or even existential threats from it. We think cybersecurity and infrastructure-related investments are generally more insulated from AI disruption risks, while certain traditional software and IT services companies appear more vulnerable. Overall, the businesses best-positioned to win the next leg of the AI marathon will likely share some defining characteristics:

• Proprietary data advantages. Whether sourced from health care records, financial transactions, enterprise workflows or demonstrated customer behavior, unique data sets can create protective barriers (often called data “moats”) that competitors cannot breach — or easily replicate.

• Scale and dynamic “ecosystems.” Leading cloud platforms and other large tech companies with interconnected hardware, software, services and platforms can spread research and development costs across expansive customer bases while reinforcing the efficiency, convenience and cost benefits of brand loyalty versus switching to multiple providers.

In contrast, risks are mounting for companies that cannot demonstrate a credible path to monetizing AI. Increased spending on AI infrastructure, power generation and related needs puts pressure on margins and returns on invested capital if that spending doesn't translate into meaningful revenue growth.

Financial markets have, by and large, rewarded the AI narrative generously in the past few years, with the Magnificent Seven and other large cap growth stocks often driving the bulk of equity benchmark index gains. Sustaining that kind of investment performance going forward will require companies to show they can do more than raise their AI budgets. Investor focus is shifting increasingly toward measurable AI productivity gains, margin expansion and earnings quality. In our view, success will be led not by businesses that have laid the structural groundwork for AI, but by those who can profit most from its continued advances.

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