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It’s been a topsy-turvy couple of months for the AI hype roller coaster, and the ride dipped this past week.
A mass selloff across the stock market overlapped with the aftermath of some earnings results in which AI remained a money sink, a report that Nvidia’s latest chip could be delayed, and other eyebrow-raising news.
The Magnificent Seven stocks—Apple, Alphabet, Amazon, Meta, Microsoft, Nvidia, and Tesla—had lost a collective $653 billion in value as of Tuesday, the Wall Street Journal reported, equivalent to more than Tesla’s market cap.
The uneasiness has been percolating for a while now. Some high-profile reports this summer questioned AI’s money-making potential relative to its enormous cost. Gartner Research has deemed cloud AI services tech to be plunging into the “trough of disillusionment,” the firm’s term for the phase of a hype cycle that follows an emerging technology’s failure to live up to initial expectations.
Big spenders: While most of their earnings results this quarter have largely exceeded analyst expectations on paper, Microsoft, Alphabet, and Meta didn’t do much to soothe investors seeking temperance in AI capital expenditures.
Alphabet CEO Sundar Pichai said during an earnings call that the risk of falling behind in the AI race outweighed the risk of overbuilding AI infrastructure.
CEO Satya Nadella said Microsoft is following demand cues when it comes to AI spending, which is mostly focused on long-term assets to be monetized over the next several years.
Meta CFO Susan Li had a matching refrain: “We don’t expect our GenAI products to be a meaningful driver of revenue in ’24,” she said. “But we do expect that they’re going to open up new revenue opportunities over time.”
Fears persist: Despite those assurances, those stocks dipped meaningfully this week.
Karthik Dinakar, a computer scientist at Harvard’s Berkman Klein Center and CTO at deep learning company Pienso, said he expects a “major course correction” in AI hype as revenues fail to keep pace with spending.
“All of the money that was pumped in, mostly funded by venture capital, happened with an idea that the moat was a really large language model and huge enterprise AI demand—that it’s going to be changing everything,” Dinakar said. “That hasn’t materialized.”
Now that the tech seems to be entering the aforementioned “trough,” businesses can get “back to the basics” and refocus on AI uses that actually add concrete value, Gartner analyst Arun Chandrasekaran told us.