What happened
NYU Stern School of Business professor Aswath Damodaran, known as the 'Dean of Valuation,' has analyzed the impact of AI-related capital expenditure (capex) on U.S. public companies. According to his analysis, capital spending reached $507.4 billion in the second quarter of 2026, a 35.66% increase year-over-year, with technology and communication services sectors leading the surge. Damodaran notes that while hyperscalers like Alphabet, Meta, Microsoft, and Amazon are capitalizing these outlays, the massive upfront cash requirements are creating a divergence between 'builders'—who are becoming increasingly asset-heavy—and 'suppliers' like NVIDIA, TSMC, and Broadcom, whose margins are bolstered by this spending.
Aswath Damodaran reported that U.S. public company capital spending hit $507.4 billion in Q2 2026, marking a 35.66% increase from the previous year. The technology sector saw a 79.88% rise in capex, while communication services increased by 62.01%.
The analysis identifies an 'asymmetric accounting loop' where hyperscalers (Alphabet, Meta, Microsoft, Amazon) capitalize their AI infrastructure spending, which reduces the immediate impact on earnings but creates a significant drain on free cash flow.
Conversely, this spending acts as revenue for hardware suppliers such as NVIDIA, TSMC, and Broadcom, whose profit margins are currently benefiting from the massive demand for AI-capable infrastructure.
Source details: benzinga.com ↗
Why it matters
The shift toward heavy AI infrastructure spending is fundamentally altering the financial profiles of major technology companies. By transitioning from capital-light, high-growth businesses to asset-heavy entities, these firms face a higher cost-of-capital hurdle to justify their valuations. Damodaran highlights that aggregate cash burn among these builders currently dwarfs net cash generation by suppliers, leading to payout ratios at 20-year lows. This trend forces investors to reconsider their exposure to AI, as the sustainability of these investments depends on whether they can eventually generate returns that exceed the cost of capital.
The transition of major tech firms into asset-heavy companies means they must now clear a much higher cost-of-capital hurdle to avoid market repricing, a departure from their previous status as capital-light, high-growth entities.
S&P 500 companies are returning approximately 63% of earnings to shareholders, the lowest level since 2004, as cash is diverted toward AI infrastructure rather than dividends or buybacks.
The sustainability of the current AI market rally is being questioned, as the aggregate cash burn from infrastructure builders is significantly outpacing the net cash generation of the broader market.
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What to watch next
Investors are advised to monitor how these companies manage their capital allocation and whether the promised returns from AI infrastructure materialize. Damodaran outlines four potential strategies for navigating this environment: maintaining an 'all-in' stance on AI builders and suppliers, relying on passive indexing, pivoting to defensive sectors like food processing or leisure, or adopting a contrarian approach by betting against overextended AI stocks. The central unknown remains whether the current surge in AI-related capex will lead to long-term productivity gains or a period of market repricing as companies struggle to maintain historical return levels.
Investors should watch for shifts in corporate capital allocation strategies as companies face pressure to prove that AI investments will generate sufficient returns.
The four strategies identified by Damodaran—ranging from 'Maximum Bull' to 'Direct Contrarian'—provide a framework for how market participants might if the current AI-driven capital cycle faces a correction.
A key unknown is the long-term impact of these capital-intensive investments on the balance sheets of major tech firms and whether they can maintain their current growth trajectories under higher debt and equity burdens.