US tech giants’ AI spending exceeds operating cash flow — CNBC
In the United States, the combined capital expenditures of technology hyperscalers in the second quarter of 2026 exceeded their operating cash flow, causing the companies’ free cash flow to turn negative. This is stated in a Raymond James analysis, as reported by CNBC. Analysts noted that the problem is not profitability: the companies remain extremely profitable, but the scale of investment in artificial intelligence is no longer fully covered by their substantial cash flows.
Investors assess debt and cash flows
Rising spending on AI infrastructure is prompting some investors to assess companies’ financial resilience more carefully. Key indicators include free cash flow, return on equity, net debt leverage, and earnings stability.
Potomac Fund Management economic strategist Sean Snyder said that with yields on 10-year US Treasury bonds near 5%, investors are seeking companies with higher-quality balance sheets, strong cash flow, and less dependence on expensive debt.
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According to the US Census Bureau and the Federal Reserve Bank of St. Louis, spending on construction of AI data centers has increased by $51 billion since December 2023. At the same time, private construction spending in all other segments has declined by $120 billion.
Demand for quality funds
Against this backdrop, investors are showing greater interest in exchange-traded funds that select companies based on free cash flow and business quality metrics. VictoryShares Free Cash Flow ETF, which has $11 billion in assets, recorded net inflows of more than $900 million over the past month. By comparison, Pacer US Cash Cows 100 ETF, with $18 billion in assets, received $24 million in net inflows over the period.
At the same time, quality funds do not necessarily avoid large technology companies. The largest holdings of the iShares MSCI USA Quality Factor ETF are Microsoft, Apple, Nvidia, and Meta. Morningstar analyst Zachary Evens said investors want to ensure that companies have a viable business model and a user base capable of supporting large-scale AI deployment.
