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For the last year and a half, AI hasn’t just been a technology — it’s been a worldview. Nvidia $NVDA’s stock tore through Wall Street expectations and crowned the company more valuable than Microsoft $MSFT and Apple $AAPL. Microsoft pledged to spend like a sovereign wealth fund to bulk up Azure. Google $GOOGL rewired its entire roadmap around Gemini. Meta $META, never shy about a grand narrative, promised that “superintelligence” was within reach — and CEO Mark Zuckerberg spent like it. The numbers matched the rhetoric: a trillion of market cap here, a trillion there, tens of billions in quarterly capital expenditures.
The refrain was simple and contagious: inevitability.
But inevitability can have a short shelf life in the world of technology. Over the past month, three jolts in particular have rattled the story. OpenAI CEO Sam Altman, who has made a career out of selling a version of the future, said the quiet part out loud: Asked if he thinks we’re in an AI bubble, he said, “Yes.” Meta, after months of splashy AI hiring and rhetoric, has reportedly frozen recruitment and chopped up its “super” lab. And MIT published research that went viral on LinkedIn, estimating that 95% of enterprise AI pilots return no business value. That trifecta — a prophet hedging, a zealot pausing, and academics bringing receipts — turned inevitability into a question.
Markets noticed.
Nvidia, the totem of the AI boom, will be treated less like a stock and more like a stress test for the entire economy when it reports its quarterly earnings on Wednesday. Its earnings call isn’t just another quarterly update — it’s the hinge on which the hype rests. Wall Street expects another record, roughly $46 billion in revenue. But at a $4 trillion valuation, “better than expected” may not be good enough. If the golden child stumbles, even slightly, the talk on whether the tide is turning on AI gets louder. Already, cracks have shown. After a mid-August surge, tech stocks, including Nvidia and other AI-heavy firms, pulled back by about 1.6%, even as energy and real estate rose. Analysts warn that the Nasdaq $NDAQ’s 41% gain since April may have inflated valuations — a frothiness that makes every earnings print feel like a cliffhanger.
And that’s why Nvidia’s earnings call has been elevated into a kind of secular rite.
AI’s signs of strain aren’t confined to trading screens. They’re everywhere.
Take Humane’s AI Pin. The $700 wearable was hyped as the next iPhone — an AI-native device to liberate us from screens. It lasted less than a year before its assets were offloaded to HP $HPQ in what amounted to a mercy sale. Or, take Microsoft’s Recall, the feature billed as a photographic memory for your PC. Privacy watchdogs called it a surveillance nightmare, and the company had to walk back its rollout plans. For an industry that loves to declare “this changes everything,” the first wave of consumer products has changed very little, except investor patience.
The corporate numbers don’t look much better. MIT’s study put hard math on what many CIOs already suspected: Nearly all of those shiny AI pilots amount to little more than slideware. That finding rippled through boardrooms and social media feeds because it finally gave executives cover to say what they’d been whispering: AI demos are impressive, but they’re not showing up in the P&L.
Even among developers, the ground feels shaky. Stack Overflow’s 2025 survey found that while 84% of coders now use AI tools, only 3% say they “highly trust” the outputs. Adoption is skyrocketing, but confidence is collapsing. The result is a paradox: AI is everywhere, and yet no one quite depends on it.
Meta’s reported recent pivot has only added to the sense of recalibration. After a year of Zuckerberg touting “superintelligence” and stuffing its payroll with AI hires for mind-boggling sums, the company suddenly froze recruitment, restricted transfers, and broke its mega-lab into four groups. The official line was focus. Some analysts called it discipline. But to most people watching, it looked like fatigue. For an industry that treats “more” as a strategy, a pause from one of the biggest spenders was its own kind of confession.
Wall Street hasn’t pulled the plug. Wedbush analyst (and raging tech bull) Dan Ives insists this is still “the second inning” of an AI bull market.
But the market’s edginess shows up in the tape: Palantir $PLTR shares plunged more than 9% in a single session amid bubble chatter, while Nvidia dropped about 3.5% the same week. And Erik Gordon, a University of Michigan professor known for his bubble calls, warned Business Insider that the AI bust could prove even uglier than the dot-com collapse — pointing to CoreWeave’s stunning 33% valuation plunge, a $24 billion wipeout in just 48 hours, as a canary in the coal mine.
If sentiment is wobbling, the spending machine hasn’t slowed. In fact, it’s accelerating. Microsoft just guided to roughly $30 billion in capex for the current quarter — the largest quarterly spending in corporate U.S. history. Google parent company Alphabet raised its 2025 budget to $85 billion. Meta, freeze notwithstanding, bumped its capex range to between $66 and $72 billion. These aren’t cautious checks. And the trend extends far beyond those companies. The Financial Times estimates that global AI infrastructure spending could hit $3 trillion by 2029, with nearly $750 billion pouring into data centers in just the next two years. It’s an arms race built on concrete and servers, not just rhetoric.
Why? Because the infrastructure race is the only part of the AI boom that still feels like a sure thing. Nvidia’s GPUs are the scarcest resource in tech. CoreWeave, a cloud startup that barely existed three years ago, is now buying up data centers as if they’re beachfront property. Analysts may debate the future of copilots and chatbots, but no one questions the future of compute.
The line from Big Tech is consistent: The returns are there in cloud, ads, and developer services; the spending is the bottleneck. That’s why the market can wobble on sentiment and still finance another data center. But there’s also concentration risk baked in. Tech giants now make up roughly 40% of the S&P 500. If AI sentiment turns, it’s not just a few stocks at stake — the entire market could feel it.
There’s a macro-version of this paradox, too. A John Thornhill column in the Financial Times argues that we’re in Carlota Perez’s “installation” phase — manic investment, messy results — before a “golden age” can materialize. Deutsche Bank analysts have echoed the concern, warning that the AI buildout mirrors past bubbles from 18th-century canals to the 1990s dot-com and telecom frenzies: vast overbuilding justified by the promise of transformation, only to pop when belief thinned. The New Yorker made a similar case this week, saying we’re in an AI profit drought: vast spending, thin P&L evidence, long J-curve. All three narratives map cleanly onto what operators are saying privately.
But the physical costs are coming due. AI workloads demand so much power that Google signed a deal with the Tennessee Valley Authority and a nuclear startup just to keep its Southeast data centers running. Researchers have quantified the water consumption of model training, showing that every breakthrough comes with an invisible utility bill. The “cloud,” it turns out, is built of concrete, copper, and cooling towers.
Regulators have woken up, too. On Aug. 2, the EU’s AI Act began applying obligations to general-purpose models: transparency about training data, mandatory risk assessments, and new safety disclosures. The stricter rules will come in 2026, but the first bite is already here. In the U.S., agencies are circling corporate filings, probing whether companies are “AI-washing” their earnings calls. Copyright fights rage in the courts — The New York Times is suing OpenAI — even as other media groups cut licensing deals.
And then there’s China. After Washington’s on-again-off-again export bans, Beijing has reportedly discouraged domestic firms from buying Nvidia’s China-compliant H20 chip. That’s not trivial; Nvidia makes roughly a quarter of its revenue in China. That’s the kind of geopolitical tremor that could ripple through Wednesday’s earnings call.
So has the tide turned?
In sentiment, maybe. Altman’s bubble line broke the spell of inevitability. MIT’s study turned hype into numbers, and the numbers were ugly. Meta’s reported reorganization suggests that even the loudest boosters know when to pause. Developers are adopting but not trusting. Artists are suing. Regulators are writing (and rewriting) the rules. The “AI will change everything” pitch no longer lands as gospel.
But in capital, not yet. The hyperscalers are still writing historic checks. NVDA is still the most important ticker in the market. If Wednesday’s earnings blow past expectations again, the spending spree will look vindicated. But if they disappoint — even slightly — the bubble chorus will likely grow louder.
Silicon Valley has always run on myth as much as math. The myth of inevitability allowed companies to raise obscene sums, to spend like nation-states, to paper over the fact that 95% of AI pilots go nowhere. The myth was strong enough to make a $4 trillion company out of Nvidia, to have Microsoft reimagine itself as an infrastructure empire, to send Zuckerberg chasing “superintelligence” like it’s a Marvel subplot. But myths don’t last forever. Eventually, someone reads the balance sheet.
A year ago, AI was destiny. Chatbots were oracles, GPUs were holy relics, and anyone questioning the frenzy was accused of missing the future. Now? Its prophet mutters “bubble,” and its supposed killer apps are retreating under the weight of their own demos. The question isn’t whether or not AI is important — it obviously is. The question is whether or not importance is enough to sustain trillion-dollar valuations and multitrillion-dollar infrastructure bets. If inevitability is gone, then AI, like every industry before it, will have to survive the harder test: proving it.
