Almost four years into the AI boom, the biggest tech companies are burning through cash. Goldman Sachs projects megacap AI spending will reach $765 billion this year and nearly $1.2 trillion in 2027.
The latest earnings season turned that backdrop into a moment of reckoning. Investors are no longer giving credit simply for revenue growth; they are asking which companies can turn huge data-center investments into profits. The sharp divergence between how Alphabet and Amazon were received shows how much scrutiny capex plans now receive.
In the latest quarter, cash flow went negative at Amazon, Alphabet and Tesla, while Meta's cash generation fell 91%. Amazon lifted its capex guidance to $220 billion - more than the other three hyperscalers' plans. Alphabet posted its first-ever negative cash flow. Anat Ashkenazi, Alphabet's CFO, said the company will keep seeing tight cash flow as it goes after the "AI opportunity." Alphabet's cloud revenue jumped 82%.
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Microsoft raised capex guidance and got its best day since 2008. "MSFT has room to meaningfully re-rate," Wells Fargo analysts wrote. Thanks to that rally, Microsoft's year-to-date decline narrowed to around 7%.
Tesla and Alphabet both sank after turning cash flow negative. Meta plummeted on a weak forecast and AI monetization uncertainty. "Not only is the revenue growth dramatic, but the profitability is rising," Mark Mahaney of Evercore ISI said, adding that "this is just the breakout that the stock needed." Wedbush analysts described Amazon's quarterly report as the "cleanest beat" among the companies they track, adding that management was most explicit about how capital spending would pay off. They wrote: "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."
For hyperscalers, prices are soaring for the memory-hungry AI systems they buy from Nvidia. Tesla's Elon Musk called memory pricing "insane" and thanked Micron for "a very significant allocation on reasonable terms." Amazon's Jassy cited the "inflated price" of memory chips.
Apple, which spends far less than Big Tech peers, is especially exposed because every consumer gadget depends on memory. Apple has already boosted prices on Macs and iPads, and analysts widely expect iPhone price increases. Thursday brought a revenue forecast that missed expectations, and Tim Cook attributed the shortfall to what he called "supply constraints." Cook 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." Cook, who will hand off the CEO role on Sept. 1, added, "And we're continuing to evaluate this." Apple shares slid after the report. Richard Kramer at Arete argues that Apple is in "great relative shape" even as the broader market struggles.
It is increasingly clear that AI spending is skewing company financials, even while executives tout the eventual rewards from their huge data-center and chip investments.
Then there's the China conundrum. Chinese AI labs have recently released a stream of models that close much of the performance gap with OpenAI and Anthropic at a fraction of the price. Because these open-weight models are freely distributed, users can download, modify and run them on any setup they prefer.
OpenAI and Anthropic have valuations around $1 trillion each, so any threat to their business endangers the broader AI trade. Dana Harlap, a JPMorgan Chase investment strategist, posed the question: "Is it all one big AI trade?" She pointed to Google's earnings report, where revenue topped forecasts, as evidence that spending is being scrutinized even when results are strong. "We're seeing the market become more critical - and more discriminating - across hyperscalers as investors try to separate AI winners from losers," Harlap wrote.
Still, most megacap stocks have not had breakout years so far, Micron being a notable exception, even though their revenue is growing healthily. The muted market response shows increasing skepticism that the debt-funded AI buildout will eventually deliver returns.
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