‘AI Bubble’ Jitters Take Over Earnings Calls — Even As Tech Bosses Say It’s Different This Time

With input from the New York Times, CBC News, and Business Insider.

There’s officially a bubble in talking about an AI bubble.

The phrase “AI bubble” has exploded across corporate earnings calls and investor conferences, popping up in 42 transcripts between October and December — a 740% jump from the previous quarter, according to data firm AlphaSense.

For comparison:

• Q3 2025: 5 transcripts mentioned an AI bubble;
• Entire year 2024: 24;
• 2023: 9.

AlphaSense only counted cases where “AI” and “bubble” appeared within five words of each other, so these aren’t throwaway mentions — people are asking the question directly.

Now, executives from chipmakers to insurers are all getting some version of the same question: Is this AI boom about to blow up?

At UBS’s Global Technology and AI Conference on Dec. 2, AMD CEO Lisa Su was asked point blank if AI is in bubble territory.

Her answer: Nope.

“From the standpoint of, you know, do we see a bubble? We don’t see a bubble,” Su told investors.

Nvidia execs have been just as keen to swat the idea away.

• CFO Colette Kress referred to the “supposed AI bubble.”
• CEO Jensen Huang said on Nvidia’s November earnings call:

“There’s been a lot of talk about an AI bubble. From our vantage point, we see something very different.”

Others are taking the question more defensively than dismissively.

German chip-equipment maker Aixtron was asked whether customers might slow expansion plans if AI hype cools. French credit insurer Coface told investors it’s stress-testing big AI-related capital requests to see if they still make sense even if “bubble-type enthusiasm” fades.

In other words: everyone’s cashing the AI checks, but no one wants to be the last one holding the bag.

Short answer: the numbers are getting wild.

“We’ve been seeing these huge partnerships and people throwing out money amounts for infrastructure — like trillions of dollars — which I can’t remember ever hearing before,” said Sarah Hoffman, director of AI thought leadership at AlphaSense.

The problem, she said, is the gap between the trillions being spent and the billions in actual AI revenue. That mismatch is exactly the kind of thing investors fixate on.

Gil Luria, head of tech research at DA Davidson, said the spike in “AI bubble” chatter reflects what executives are already hearing from investors behind the scenes.

He pointed to what he calls “circular transactions” as a red flag:

“Nvidia investing a dollar in CoreWeave, CoreWeave borrows nine, and uses eight of them to buy Nvidia chips. That’s the echoes of bubbles past.”

Layer on top of that:

• Tech leaders like Sam Altman and Bill Gates saying AI spending might be getting frothy;
• Big Tech — Google, Meta, Microsoft, Amazon — all promising even higher data center spending;
• High-profile skeptics like Michael Burry (of “The Big Short” fame) shorting Nvidia.

…and you’ve got all the ingredients for bubble fear to dominate every Q&A session.

Executives can insist “this time is different” all they want, but Luria says only one thing will shut down the bubble narrative: cold, hard earnings.

“There’s nothing they can say that will change people’s perception of whether or not there’s a bubble,” he said. “The only thing they can do is show results, and those have to be financial results.”

So far, markets are seeing:

• Huge AI capex;
• Big promises about long-term productivity gains;
• But still relatively modest near-term AI revenue for most companies.

That’s not unusual for a transformative technology — but it’s exactly the pattern that sparks bubble comparisons.

Plenty of people in tech argue that focusing only on valuations misses the bigger picture.

The real value, they say, isn’t just in large language models themselves, but in what gets built on top of them — tools to write code, filter spam, generate video, automate workflows, and eventually power entire new industries.

Some compare AI to the early internet or smartphones: a general-purpose technology that takes years to fully show up in productivity numbers, but eventually rewires the economy.

And yes, a technology can be both:

• Overhyped now;
• Hugely important later.

The classic example is the dot-com boom. The internet really did change everything — just not exactly the way investors betting on Pets.com in 1999 imagined.

The honest answer: we don’t know yet — and that’s why everyone is talking about it.

What we do know:

• Corporate spending on AI infrastructure is massive and growing;
• Valuations for key AI players, especially chipmakers, are stretched by most historical standards;
• The “killer apps” that fully justify those valuations may still be years away;
• Investors, analysts and executives are all wrestling with the same tension between huge promises and still-developing profits.

For now, “AI bubble” has gone from fringe worry to mainstream earnings-call topic, and companies from Nvidia to small insurers are being forced to explain how their AI bets won’t blow up.

Whether that fear turns into a real bust — or just becomes another chapter in the story of a genuinely transformative technology — will depend less on what executives say on calls, and more on what shows up in their bottom lines over the next few years.