Big Tech AI spending strains cash flow as memory prices rise

Big Tech AI spending is starting to show up in the numbers investors use to judge financial flexibility: cash flow, capital spending and guidance. Amazon, Alphabet and Tesla reported negative cash flow in the latest earnings cycle, while Meta said its cash generation fell 91% from a year earlier, according to CNBC.

Goldman Sachs projects AI spending among the largest technology companies will reach $765 billion this year and rise to nearly $1.2 trillion in 2027, CNBC reported. Amazon raised its 2026 capital spending forecast to $220 billion, the highest among the four major cloud “hyperscalers,” a term for companies that run vast data-center networks for customers.

Capital spending, or capex, means money used to buy long-lived assets such as servers, chips and buildings. Free cash flow is the cash left after a company pays operating costs and capital expenses, so it can fall even when revenue is growing if investment rises faster.

Why is Big Tech AI spending hurting cash flow?

The main pressure comes from the race to build data centers packed with AI chips and related systems. Amazon reported negative free cash flow of $7.6 billion over the trailing 12 months, CNBC reported. Alphabet said its cash flow turned negative for the first time on record, and finance chief Anat Ashkenazi told analysts that free cash flow would stay under pressure as the company pursues the “AI opportunity.”

These companies are still reporting healthy revenue in many core businesses, but investors are paying closer attention to how much cash is being absorbed by AI infrastructure. The group includes several large stock market value companies, often called megacaps, whose moves can influence broader indexes held by everyday investors.

Why are memory prices rising?

Memory chips are components that help store and move data inside computers and AI servers. CNBC reported that AI processors rely on memory supplied by a small group of vendors, and demand from the AI buildout has tightened supply.

Tesla CEO Elon Musk described memory pricing as “insane” on the company’s earnings call, according to CNBC. Amazon CEO Andy Jassy said higher memory-chip prices helped push up Amazon’s capital spending guidance.

Apple faces a different version of the same problem. The company spends less on AI infrastructure than its Big Tech peers, but memory is used across its consumer devices. Apple has already raised prices on Macs and iPads, CNBC reported, and many analysts expect iPhone price increases later this year.

Apple issued a weaker-than-expected revenue forecast for the current quarter after CEO Tim Cook pointed to “supply constraints.” Cook, who CNBC reported is stepping down as CEO on Sept. 1, told analysts that memory market pricing is expected to keep rising beyond September and “could drive an increasing impact” on Apple’s business.

How did investors react?

Market reactions were split. CNBC reported that Tesla and Alphabet fell after turning cash-flow negative and pointing to faster spending. Meta dropped after its report because of a weak forecast and continued questions about how it will make money from AI.

Microsoft moved the other way. CNBC reported that the stock had its best market day since 2008 after the company paired stronger-than-expected results with higher capex guidance. Wells Fargo analysts, who rate Microsoft shares as a buy, wrote that the stock has room to “meaningfully re-rate.”

Amazon shares rose after its cloud business showed faster growth. Evercore ISI analyst Mark Mahaney told CNBC’s “Closing Bell: Overtime” that Amazon Web Services’ growth had been trailing Microsoft Azure and Google Cloud, and called the report the breakout the stock needed. Wedbush analysts said Amazon delivered the “cleanest beat” among the hyperscalers it covers and gave the clearest explanation of potential returns on capex.

CNBC also reported that investors are watching cheaper Chinese open-weight AI models, which can be downloaded, modified and hosted on a user’s chosen infrastructure. JPMorgan investment strategist Dana Harlap wrote that the market is becoming more critical across hyperscalers as investors try to separate AI winners from losers, with long-term returns tied to whether heavy AI capex produces acceptable returns.

This story draws on original reporting from CNBC.