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Meta’s AI Spending Crisis: Inside the $760B Big Tech Bet

Big Tech

Big Tech’s $760B AI Bet: Who’s Cashing In, Who Isn’t

Meta’s free cash flow just fell to $784 million. SpaceX’s AI capex grew sixfold in a single quarter. Amazon’s cloud arm is finally showing the receipts. Q2 2026 earnings season didn’t answer whether AI spending is a bubble. It answered something more useful: which companies can prove it, and which ones are still asking investors to trust them.

The Number That Broke the Spell

For three years, “trust us” was a perfectly good answer to the question of why Big Tech kept raising AI spending guidance. That stopped working for at least one company this earnings season.

Amazon, Microsoft, Alphabet and Meta now plan to spend roughly $725 billion to $760 billion combined on AI infrastructure in 2026, up 77% to 84% from about $410 billion in 2025, according to guidance aggregated across each company’s Q2 earnings release. Alphabet alone raised its ceiling to $185 billion to $205 billion, a jump that overshadowed an otherwise strong quarter and helped drag the “Magnificent Seven” down 5.7% during the week of July 20 to 26.

Then Meta reported. Revenue beat consensus at $60.80 billion, up 28% year over year. But capital expenditures hit $31.08 billion for the quarter, and free cash flow, the number that tells you what’s actually left over after the bills get paid, came in at just $784 million. That’s not a typo. Meta generated $31.86 billion in operating cash flow and spent nearly all of it building AI infrastructure.

Why this matters: Free cash flow near zero is the closest any major hyperscaler has come to running out of room during the AI buildout. It’s a specific, checkable red flag for one company, not evidence the whole sector is collapsing.
Then SpaceX reported, for the first time as a public company. Its capex soared more than sixfold to $18.4 billion, more than double total quarterly sales, with over 80% of that going toward AI. CEO Elon Musk told investors the company’s targeted $100 billion AI-related annual run rate by December 2026 “is not a question mark.” Shares fell anyway, despite a 92% revenue jump that beat estimates.

2026 AI Capex Guidance, by Company

Company 2026 Capex Guidance Direction vs. Prior Guidance
Amazon~$220 billionRaised, citing memory chip costs
Alphabet$185 billion to $205 billionRaised
Microsoft~$190 billionRaised year over year
Meta$130 billion to $145 billionNarrowed upward
Source: Company Q2 2026 earnings releases, aggregated by Statista and ValueAdd VC.

The Accounting Fight Over Depreciation

If you want the sharpest version of the bear case, it doesn’t come from a hedge fund manager calling the whole thing a bubble. It comes from Michael Burry, the investor who predicted the 2008 housing collapse, making a narrow, specific, falsifiable claim about how hyperscalers do their math.

Burry’s argument: hyperscalers are stretching the assumed useful life of AI chips and servers well beyond the real 2 to 3 year replacement cycle, which artificially lowers depreciation expense and inflates reported earnings. He first made the case on X in November 2025 and escalated it through early 2026.

“Understating depreciation by extending useful life of assets artificially boosts earnings, one of the more common frauds of the modern era.”
Michael Burry, Founder, Scion Asset Management. Source: CNBC
By his estimate, this could understate industry depreciation by $176 billion to $226.6 billion between 2026 and 2028, with average earnings overstated by roughly 24% across named hyperscalers, and as much as 48% to 62% at Oracle specifically. Enron short seller Jim Chanos has voiced similar concerns.

Here’s the part that gets left out of most coverage of Burry’s thesis: it’s his model, not an audited finding, and he holds disclosed put options against Nvidia and Palantir, a material conflict of interest worth weighing. Bulls also point out that older GPUs don’t necessarily get scrapped when they’re replaced for training work. They often get repurposed for lower intensity inference workloads, extending their effective economic life even if the top tier training life is shorter than hyperscalers assume.

Goldman Sachs’ own research team is split on the broader question. Jim Covello, the bank’s head of global equity research and author of the influential 2024 “too much spend, too little benefit” report, has hardened his skepticism.

“At some point, you’ve got to make money… we’ve gotten further away from that over the last couple of years instead of closer to it.”
Colleagues Kash Rangan and Eric Sheridan, working at the same firm, take the opposite view: current spending, adjusted for revenue scale, isn’t dramatically out of line with prior tech investment cycles.

The Circular Financing Problem

OpenAI’s total disclosed infrastructure and compute commitments now run somewhere between $1.15 trillion and $1.4 trillion through the mid 2030s, spread across seven-plus vendors: Broadcom (~$350 billion), Oracle (~$300 billion), Microsoft (~$250 billion), Nvidia (up to $100 billion in equity plus chip commitments), AMD (~$90 billion), AWS (~$38 billion) and CoreWeave (~$22 billion).

Nvidia’s up to $100 billion commitment to OpenAI, announced in September 2025, drew an immediate warning from Bernstein Research’s Stacy Rasgon.

“Clearly fuel ‘circular’ concerns… likely fuel these worries much hotter than what we have seen previously, and perhaps justifiably raise concerns over the rationale behind the action.”
The mechanics are simple enough to explain in one sentence: a chip or cloud vendor invests in an AI lab, and that lab turns around and spends the money buying the vendor’s own products, which makes demand look stronger than it might be on a standalone basis. UBS estimates the Nvidia-OpenAI arrangement alone could represent up to 13% of Nvidia’s projected 2026 revenue.

This is the risk that doesn’t show up if you look at any single stock in isolation. OpenAI is privately held and reportedly on track to lose around $14 billion in 2026, nearly triple its 2025 loss, while targeting $100 billion in annual revenue by 2029. If its growth or fundraising slows, the shock wave could hit Oracle, Nvidia, Microsoft, Broadcom, AMD and CoreWeave at the same time, a correlated exposure that ordinary sector diversification does nothing to protect against, since on paper these are chip, cloud and software companies in entirely different categories.

Receipts vs. No Receipts

The most important story of this earnings season isn’t “AI spending, yes or no.” It’s that the spending is starting to split into two very different categories, and Wall Street is treating them differently.

Amazon’s AWS segment posted around $42.2 billion in Q2 2026 revenue with $16.6 billion in operating income, including an AI specific run rate above $25 billion growing at triple digit rates. Segment operating margin expanded meaningfully year over year, above pre-AI-cycle AWS margins. That is the strongest single piece of evidence that the “ROI is bad” thesis doesn’t apply everywhere.

Compare that to Meta and SpaceX, where the AI spending is still, largely, a promise. Meta’s near zero free cash flow quarter and SpaceX’s sixfold capex jump both came with confident guidance about future payoff, not present day proof of it.

Companies also aren’t spending blindly. Reporting indicates all four major hyperscalers now commit early only to long lived assets, land, buildings, power infrastructure, while deferring GPU and chip purchases until closer to deployment based on visible demand. That reduces, though doesn’t eliminate, the risk of a stranded asset write-down if demand disappoints.

The Case Wall Street Isn’t Giving Up on AI

Here’s what complicates any clean “investors have had enough” narrative: JPMorgan just got more bullish, not less. In the same window that Meta’s cash flow spooked traders, JPMorgan raised its 2026 S&P 500 target to 8,000 and lifted its EPS forecasts to $365 for 2026 and $420 for 2027, arguing cloud growth and contract backlogs are starting to validate the capex cycle. At least seven major brokerages now share that 8,000 target for year end.

Dan Ives at Wedbush, one of Wall Street’s most consistently bullish tech analysts, frames the moment as an inflection point rather than a warning sign, and still names Microsoft among his top picks on Azure monetization confidence.

Our read: the “AI bubble” framing is too blunt for what’s actually happening. This isn’t a sector-wide verdict. It’s a company by company sorting exercise, and this quarter drew the lines more clearly than any before it.

The Enterprise Side Tells the Same Story

There’s a reason to take the hyperscaler skepticism seriously that has nothing to do with depreciation schedules. It’s what’s happening one layer up, inside the companies actually buying AI tools. A widely cited MIT study found that 95% of enterprise generative AI pilots fail to deliver measurable profit and loss impact, despite an estimated $30 billion to $40 billion in enterprise investment. Only around 5% of deployments generate significant, measurable value.

S&P Global separately found that 42% of companies abandoned most of their AI projects in 2025. Morgan Stanley found only 21% of S&P 500 companies could point to a measurable AI benefit at all. IBM put the share of AI initiatives delivering expected ROI at 25%.

Those numbers matter to hyperscaler earnings even though they’re about a different set of companies. If enterprise customers can’t extract value from AI tools, that puts a ceiling on how much they’ll eventually pay for the compute Amazon, Microsoft, Google and Meta are building. It’s the enterprise side answer to the same question Wall Street is asking about capex.

What to Watch Over the Next 18 Months

The infrastructure buildout is real, and cloud revenue already proves it in Amazon’s and Microsoft’s numbers. The unresolved question is whether the application layer, the consumer facing AI products that are supposed to justify trillion dollar valuations for OpenAI-adjacent companies, ever closes the gap. Right now, that’s where hype is furthest ahead of evidence.

  • Useful life assumptions. Watch 10-K footnotes for any hyperscaler quietly shortening the assumed lifespan of AI hardware. That would validate Burry’s thesis and the market would likely punish it hard.
  • Free cash flow trajectory. If Alphabet or Microsoft posts a Meta-style near zero quarter, the “one company problem” framing stops holding up.
  • OpenAI’s fundraising. Any stumble here has correlated downstream effects across Oracle, Nvidia, Microsoft, Broadcom, AMD and CoreWeave simultaneously.

Frequently Asked Questions

How much is Big Tech spending on AI in 2026?

Amazon, Microsoft, Alphabet and Meta together plan roughly $725 billion to $760 billion in 2026 capital expenditure, up about 77% to 84% from around $410 billion in 2025, driven mainly by AI data centers, GPUs and power infrastructure.

Why are investors worried about AI spending?

Capital expenditure is rising faster than free cash flow at some companies, Meta’s Q2 2026 free cash flow fell to $784 million, while enterprise customers report low measurable returns from AI pilots, raising doubts about whether spending will pay off as quickly as guided.

What percentage of AI projects fail to show ROI?

A 2025 to 2026 MIT study found 95% of enterprise generative AI pilots fail to deliver measurable profit and loss impact, despite $30 billion to $40 billion in enterprise investment. Only about 5% of deployments generate significant, measurable value.

What is circular AI financing?

It describes chip and cloud vendors like Nvidia and Oracle investing in AI labs like OpenAI, which then spend that money buying the vendors’ own products and services, inflating apparent demand and concentrating financial risk across a small group of interlinked companies.

Is AWS or Azure actually profiting from AI spending?

Yes. Amazon’s AWS segment reported roughly $42.2 billion in Q2 2026 revenue with $16.6 billion in operating income and an AI specific run rate above $25 billion growing at triple digit rates, among the clearest evidence that cloud infrastructure spending is monetizing.

What is Michael Burry’s argument against AI stocks?

Burry argues hyperscalers are understating depreciation by assuming AI chips and servers last 5 to 6 years when the real replacement cycle is closer to 2 to 3 years, which he estimates could overstate industry earnings by roughly $176 billion to $226 billion between 2026 and 2028.

The Bottom Line

This wasn’t the quarter that proved AI spending is a bubble, and it wasn’t the quarter that put the question to rest either. It was the quarter that stopped letting every hyperscaler hide behind the same story. Amazon and Microsoft’s cloud businesses are turning capex into revenue you can point to. Meta and SpaceX are still asking for patience while cash flow gets thinner. Burry’s depreciation math is a real number to track, not a settled verdict. And the enterprise side, where 95% of AI pilots still don’t move the P&L, is the ceiling that ultimately caps how far this entire cycle can run.

Over the next two quarters, watch for changes in stated useful life assumptions, watch whether any other hyperscaler posts a Meta-style cash flow quarter, and watch OpenAI’s fundraising, since its ripple effects reach further than any single stock.

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