Investors have been watching the AI spending boom with a mix of excitement and unease. On the one hand, the largest U.S. tech companies are pouring unprecedented sums into chips, servers, data centers, and networking to build out AI capacity. On the other hand, that spending is increasingly being financed with fresh debt issuance, raising a natural concern: Could the tech giants eventually stretch their balance sheets and become meaningfully more leveraged over time?
That question has become even more important as capex continues to climb and operating cash flow coverage tightens. In recent quarters, the largest technology companies, often referred to as hyperscalers, have stepped up borrowing activity, and some market participants have assumed leverage must be rising in tandem. After all, debt-funded investment cycles often leave companies with higher net debt loads and weaker financial flexibility.
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However, a new report from Daily Chartbook challenges that narrative, indicating that AI hyperscalers are still carrying exceptionally low net debt relative to equity — significantly lower than the broader S&P 500. So what’s really going on beneath the surface of “debt-funded AI spending,” and what does it tell us about financial risk in tech titans versus the broader market? Let’s take a closer look.
AI Capex Boom Puts Hyperscaler Spending Under the Microscope
The so-called hyperscalers — Alphabet (GOOGL) (GOOG), Meta Platforms (META), Microsoft (MSFT), and Amazon (AMZN) — plan to spend up to $725 billion this year on capital expenditures (capex), largely directed toward AI data center infrastructure. Alphabet and Meta Platforms both boosted their full-year capex guidance in Q1, while Microsoft provided its first spending estimate through December, matching the Google parent at $190 billion. Amazon was the only one among the hyperscalers to leave its capex unchanged at $200 billion, although it reported a sharp increase in first-quarter spending that significantly reduced its free cash flow.
PIMCO reported last week that capex is projected to absorb 94% of hyperscalers’ operating cash flow in 2026. That compares with less than 50% just two years ago. Against this backdrop, hyperscalers have increasingly turned to the debt markets. PIMCO noted that index-eligible new debt issuance from hyperscalers has totaled approximately $136 billion year-to-date, already surpassing the 2025 figure. Barclays analysts said debt issuance by the hyperscalers is likely to exceed $200 billion this year and could climb even higher in 2027.
As AI-related spending accelerates at an unprecedented rate, investors are scrutinizing hyperscalers’ results and balance sheets far more closely than they did a year ago, seeking to identify which companies are generating the strongest returns on their massive AI investments. And the clearest validation of that thesis emerged during the recently concluded Q1 earnings season. Although all of the multi-trillion-dollar companies delivered strong results, only Amazon and Alphabet truly impressed investors.
AI Hyperscalers Remain Financially Strong Despite Rising Debt Issuance
Some investors have been questioning whether the wave of capital expenditures could eventually strain hyperscalers’ balance sheets. In particular, concerns have emerged around rising debt issuance and its potential impact on leverage ratios across the largest U.S. AI infrastructure companies. Still, recent data paints a far more reassuring picture.
Daily Chartbook recently assessed the leverage of a group of AI hyperscalers using the lease-adjusted net debt-to-equity ratio. This ratio measures all debt-like obligations, including leases, minus cash, relative to shareholder equity. The metric is especially well-suited for hyperscalers because it adjusts for long-term operating lease liabilities and cash balances. With that, a chart published by the market commentary account on X showed that the lease-adjusted net debt-to-equity ratio for a group of AI hyperscalers has remained below 0.1 so far this year. It means that for every dollar of shareholder equity, AI hyperscalers have less than $0.10 in net financial debt and long-term lease obligations.
Keep in mind that in calculating this leverage ratio, Daily Chartbook includes the following AI hyperscalers: Amazon, Alphabet, Meta Platforms, Microsoft, and Oracle (ORCL). I view the inclusion of Oracle as a very smart move, as it gives us a slightly higher figure, considering that Oracle has significantly more debt relative to its profits than the four main hyperscalers. And even with that inclusion, the group’s lease-adjusted net debt-to-equity ratio still points to very low financial risk.
The picture becomes far more interesting when we compare the AI hyperscalers’ ratio with that of the broader market. According to Daily Chartbook’s calculations, the lease-adjusted net debt-to-equity ratio for S&P 500 companies — excluding the AI hyperscalers — is nearing 0.6. This actually tells us that the AI hyperscalers carry significantly lighter net debt burdens than the broader S&P 500, even as they actively tap the debt markets to finance their AI initiatives.
Meanwhile, PIMCO used slightly different inputs and a different leverage ratio, yet arrived at the same conclusion. The firm calculated the net debt-to-EBITDA ratio for a group of A-rated (and higher) hyperscalers, which include Microsoft, Alphabet, Amazon, and Meta. As a result, the group’s ratio came in at 0.04, the lowest of any sector in the U.S.
Conclusion
The key takeaway is that the AI buildout does not appear to be creating the type of balance sheet stress some investors feared. Despite the rapid increase in AI-related capital expenditures and a growing wave of debt issuance, the largest hyperscalers still carry exceptionally low leverage in absolute terms and compared with the broader market. In other words, the companies leading the AI infrastructure race look financially strong enough to keep spending aggressively without meaningfully weakening their balance sheets.
On the date of publication, Oleksandr Pylypenko had a position in: GOOGL, META, MSFT, AMZN. All information and data in this article is solely for informational purposes. This article was originally published on Barchart.com
