LEVATUS Investments | A More Discerning Market
Why AI's Biggest Spenders May Not Be Its Biggest Winners
Photo: Tim Mossholder
October's earnings season and the market divergence that accompanied it expose a growing tension: technology giants are spending billions on AI that could disrupt some of their established revenue streams, while other companies may already be benefiting from the capabilities those investments create.
October Market Assessment
In October, the S&P 500 fell 0.9%. The same companies, when weighted equally, gained nearly 5%. What appeared to be broad market weakness was, to a considerable extent, concentrated among the largest companies.
In our earlier article, The Quiet Transformation of Passive Investing, we identified growing index concentration as a risk that could increasingly distort perceptions of broader market performance. October's divergence illustrates that concern: the performance of a handful of exceptionally large companies can obscure what is happening across the rest of the market.
The divergence may also be an early indication of how market leadership will evolve as the AI productivity cycle matures.
The enormous spending commitments of the largest technology companies are beginning to change their financial profiles. In the third quarter, Alphabet's capital expenditures reached $51.4 billion, exceeding its $42.3 billion in operating cash flow. Amazon reported negative free cash flow of $42.4 billion over the trailing twelve months, despite strong growth in operating cash flow. In both cases, infrastructure investment is now exceeding operating cash generation, although the figures cover different reporting periods.
Both companies continue to deliver impressive operating results. The question is how much capital they will ultimately need to sustain that growth, and whether that spending will translate into higher revenue, wider margins, and free cash flow that justify the investment.
Our expectation is that AI will create substantial economic value. We are less convinced that every company financing its infrastructure will capture a proportionate share of that value. We see particular risk among businesses dependent on selling computing capacity to third parties, especially where substantial debt financing adds to the pressure to generate adequate returns.
The pace of capacity expansion, rapid technological advances, and growing competition make some degree of overinvestment likely. History offers a precedent. Railroads in the 1800s and fiber-optic networks in the late 1990s created lasting value for the businesses and consumers that used them, even though returns to those who financed the construction often proved disappointing.
This is particularly relevant to AI, where rapid advances in computing efficiency and competition among providers could make increasingly sophisticated capabilities more affordable. Such developments would accelerate adoption and potentially improve productivity across the broader economy, while putting pressure on returns from some of the underlying infrastructure investments.
At the same time, many of today's technology leaders possess exceptional competitive advantages, financial resources, and opportunities to benefit from AI that do not depend on their infrastructure investments. They can build AI into products already used by billions of people, use their proprietary data to improve advertising and recommendations, and lower costs across their own operations. Following the recent correction, valuations for some have become considerably more reasonable relative to their growth prospects. The investment outlook is not uniformly negative, even among the companies making the largest spending commitments.
In our earlier research, Escape Velocity | The Productivity Cycle (2026–2031), we examined how investment opportunities could progressively broaden beyond the companies building AI infrastructure, toward businesses capable of using these technologies to improve productivity, expand margins, and generate sustainable cash flow.
Recent developments reinforce that view. They also suggest the transition is unlikely to proceed smoothly.
Investment Implications
After an initial phase led by the companies building AI infrastructure, the next phase will depend increasingly on adoption. The economic benefits of AI should extend well beyond technology companies as businesses incorporate new capabilities into their operations. Enterprise software, semiconductor design, industrial automation, electrification, and energy technology are among the areas where innovation is creating new opportunities to improve efficiency, productivity, and profitability.
However, the distinction between an attractive industry and an attractive investment remains critical. Some of the most obvious infrastructure beneficiaries have already experienced substantial share price appreciation, reflecting high expectations for future growth. Even where demand is likely to remain strong, investment returns will depend on how much of that growth translates into sustainable earnings and cash flow, and how much investors have already paid for it.
October's market divergence does not establish a lasting change in leadership, but it is consistent with growing investor scrutiny of capital requirements, returns on invested capital, and free cash flow. We expect these considerations to become increasingly important as the productivity cycle matures.
We are also monitoring the financial resilience of companies relying on leverage and circular financing arrangements premised on returns that may not materialize; thus far, the strains that have emerged have been absorbed without broader market disruption.
Bottom Line
Our base case remains a successful AI-driven productivity cycle, accompanied by uneven investment returns. We expect some of today's technology leaders to continue delivering attractive results, while overinvestment, excess capacity, and growing competition produce disappointing returns elsewhere.
We believe the next phase of the cycle will increasingly reward companies that translate technological advances into measurable improvements in their businesses. As evidence of those gains emerges, we expect market leadership to broaden, creating opportunities beyond the companies that dominated the initial infrastructure buildout.
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ABOUT THE AUTHOR
Susan Dahl is a seasoned executive, industry leader, and dedicated client advisor, with over thirty years of international and domestic investment experience working for both large and small organizations. Susan is known for her ability to unravel complex questions, and her steadfast commitment to well-designed process. This background has translated directly into her work on investment process design for private wealth clients, as well as the LEVATUS Integrated Wealth Service model. Susan's investment background spans asset classes and geographies — a combination that is increasingly rare and directly relevant to the complexity her clients navigate.
October's earnings season and the market divergence that accompanied it expose a growing tension: technology giants are spending billions on AI that could disrupt some of their established revenue streams, while other companies may already be benefiting from the capabilities those investments create.