Amazon raised its capital spending forecast to $220 billion, the highest among the four hyperscalers, while the company reported negative free cash flow of $7.6 billion over the trailing 12 months. That’s the machine at work: giant firms pouring money into AI infrastructure while investors keep cheering from the sidelines, as if the bill won’t land somewhere.
Who Pays for the Buildout
AI spending among the megacaps is projected to reach $765 billion this year, before rising to nearly $1.2 trillion in 2027, according to Goldman Sachs. Those numbers don’t describe innovation in the abstract. They describe a race to sink more capital into data centers, chips, and systems, with the costs pushed down through balance sheets and, eventually, onto everyone else who buys the products or depends on the services.
A day earlier, Meta disclosed a 91% drop in cash generation from a year earlier. Last week, Alphabet said cash flow turned negative for the first time on record. Alphabet finance chief Anat Ashkenazi told analysts on the earnings call that free cash flow will remain under pressure as the company seizes on the "AI opportunity." The language is polished. The pressure is real.
The earnings season has made it plain that AI investments are distorting balance sheets, even as industry leaders keep talking up the future benefits of their large bets. The companies at the top are treating the rest of the economy like a funding source for their next round of hardware, debt, and speculation.
The Memory Bottleneck
One major reason costs are rising is the memory crunch, driven by strong demand for AI processors that rely on memory supplied by a small set of vendors. Tesla CEO Elon Musk described memory pricing as "insane" on the automaker's earnings call last week, and Amazon CEO Andy Jassy said the "inflated price" of memory chips pushed his company's capex guidance higher.
Apple, which is spending far less than its Big Tech peers, is especially exposed to the memory crisis because the technology is a key part of every consumer device. Apple has already raised prices on Macs and iPads, and many analysts expect iPhone price hikes later this year. On Thursday, the company issued a weaker-than-expected forecast because of what CEO Tim Cook called "supply constraints." He said, "If you look beyond September, we see the market pricing for memory continuing to increase, which could drive an increasing impact on our business. And we're continuing to evaluate this."
Cook, who's stepping down as CEO on Sept. 1, said the problem isn't expected to ease up this year. For Apple, memory is a revenue problem as the company prepares for weaker consumer demand because of higher prices. For the hyperscalers, it's becoming a major cost hurdle as prices rise for the memory-hungry AI systems they buy from Nvidia. The whole setup is a squeeze from both ends: workers and consumers get higher prices, while the firms at the top scramble to protect margins.
Wall Street Picks Winners, Then Moves On
Investor reactions varied sharply. Tesla and Alphabet both sank last week as they turned cash-flow negative and pointed to faster spending. Meta fell after its report on Wednesday because of a weak forecast and continued uncertainty around its AI monetization strategy. Microsoft had its best day on the market since 2008 after better-than-expected results and higher capex guidance.
Wells Fargo analysts, who recommend buying Microsoft shares, wrote, "MSFT has room to meaningfully re-rate." The rally cut Microsoft's stock drop for the year to about 7%. Apple shares slid after its Q3 print as the memory shortage weighed on its outlook, while Amazon's surging cloud growth helped drive its stock higher.
Mark Mahaney, an analyst at Evercore ISI, told CNBC's "Closing Bell: Overtime" after Amazon's report, "Not only is the revenue growth dramatic, but the profitability is rising." He said Amazon Web Services had been lagging Microsoft Azure and Google's cloud business, and that "this is just the breakout that the stock needed." Wedbush analysts said in a Friday note that Amazon's report was the "cleanest beat" among the hyperscalers it covers, while management offered the clearest explanation of how it will achieve returns on its capex spend. "This clean beat and walk through are the factors in our view on the different share reaction between GOOGL and AMZN on what we view as similarly strong fundamental prints with raises in capex," the analysts wrote.
Across the megacap landscape, none of the stocks — unless you include Micron — are having breakout years, despite healthy revenue growth. The muted market moves reflect growing skepticism over whether the massive AI buildout, fueled increasingly by debt, will ultimately pay off. That skepticism sits right beside the hype, and Wall Street keeps trying to separate the winners from the losers while the spending spree rolls on.
Chinese AI labs have released new and updated models that are narrowing the performance lead held by OpenAI and Anthropic at much lower prices, as corporate America becomes more frugal about spending on AI services. The so-called open-weight models can be downloaded, tweaked and hosted on whatever infrastructure the user chooses. That matters because it chips away at the pricing power of the dominant firms, even as the market keeps treating the whole sector like one giant trade.
With so much of the AI market built around OpenAI and Anthropic, which are both valued at close to $1 trillion on the private market, any potential threat to their business presents risks to the AI trade as a whole. In a report last week, Dana Harlap, investment strategist at JPMorgan Chase, asked, "Is it all one big AI trade?" Harlap said the reaction to Google's report shows that Wall Street is scrutinizing spending.
That's true even when companies beat revenue estimates, which Google did while reporting 82% cloud growth. Harlap wrote, "We're seeing the market become more critical — and more discriminating — across hyperscalers as investors try to separate AI winners from losers. Long-term, the success (or failure) of the hyperscalers to generate an acceptable return on investment on their heavy capex investments will likely be correlated with the returns of the AI ecosystem." The people making the bets get to call it strategy. Everyone else gets the risk.