Big Tech AI Bubble: Earnings Under Scrutiny

Intermediate | November 9, 2025

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Big Tech AI Bubble: Fear Grows Ahead of Earnings Reports

This week, major U.S. tech companies are reporting their July–September results, and one question is gaining traction: is the Big Tech AI bubble real? (reuters.com) Firms like Microsoft, Alphabet (Google’s parent), Amazon.com, and Meta Platforms are expected to show strong revenue growth—double digits in many cases. However, despite the impressive top‐line numbers, investor caution is growing because underlying profits and returns from AI investments remain uncertain.

AI Investment Soars—but Returns Are Still in Question

Together, big tech firms and major cloud providers are projected to spend around $400 billion on AI infrastructure this year. (marketscreener.com) At the same time, studies suggest only a small portion of AI projects deliver measurable business results—a widely‑cited MIT study found only about 5% of projects achieved measurable gains. (investing.com) This gap between investment and outcome is fueling worry that the Big Tech AI bubble may be driven more by hype than by real performance.

Investor Behaviour Reflects Dot‑Com Echoes

With valuations soaring, some investors are drawing parallels to the late 1990s internet boom. A number of asset managers are revisiting “dot‑com era playbooks” in an effort to avoid speculative risk in the AI space. (reuters.com) They are asking: Are we paying for real value today, or future promise that may not be realized?

What This Means for Business and English Learners

For business professionals and English learners alike, this story highlights the importance of understanding terms such as “valuation”, “capital expenditure”, “pilot project”, “monetisation” and “hype vs fundamentals”. In business English, phrases like “AI capex”, “growth expectations”, and “bubble risk” are now part of everyday discussions. Professionals must also think critically about how to communicate these financial concepts clearly in meetings and reports.

Vocabulary

Specter (noun) – a ghost or haunting concern; here, a looming risk.
Example: “The specter of an AI bubble looms over Big Tech earnings.”
Valuation (noun) – the assigned worth of a company.
Example: “High valuations raise concerns about sustainability.”
Capital expenditure (capex) (noun) – money spent on acquiring or upgrading physical assets.
Example: “AI capex for cloud data centres is pushing into the hundreds of billions.”
Pilot project (noun) – a small-scale, preliminary activity conducted to evaluate viability.
Example: “Most AI pilot projects fail to scale into full deployment.”
Monetisation (noun) – the process of turning something into money or revenue.
Example: “Many companies are still struggling with AI monetisation.”
Hype (noun) – excessive publicity or promotion.
Example: “Investor hype around AI may be outpacing actual returns.”
Fundamental (adj) – central or foundational.
Example: “Analysts say fundamentals have not yet caught up with the hype.”
Infrastructure (noun) – the basic physical and organisational systems needed for the operation of a society or organisation.
Example: “AI infrastructure spending includes data centres, chips and networks.”
Bubble (noun) – a market situation where asset prices exceed their intrinsic value often followed by a crash.
Example: “Concerns are growing that the AI boom may be a bubble.”
Exuberance (noun) – a state of being very enthusiastic or energetic.
Example: “Investor exuberance has some professionals comparing today’s market to 1999.”

Discussion Questions (About the Article)

What signals from Big Tech earnings suggest the AI boom could be a bubble?
Why is there a gap between large investment in AI infrastructure and actual business returns?
How are investors using lessons from the internet boom of the late 1990s in today’s market?
In your business or industry, do you see more hype or fundamentals when it comes to AI?
How could you explain the difference between “investment in AI” and “monetisation of AI” in business English?

Discussion Questions (About the Topic)

What defines a technology bubble, and does the current AI investment have those features?
Should companies slow down AI spending until clearer returns are visible? Why or why not?
How should professionals adapt their English and communication strategy in an era of rapid AI investment?
What role do you think regulators should play in preventing speculative bubbles in tech?
From a free‑market perspective, how should investors approach high‑valuation companies betting on long‑term trends?

Related Idiom

“Don’t put all your eggs in one basket” – don’t risk everything on one plan or investment.
Example: “As tech firms pour billions into AI, investors might remember not to put all their eggs in one basket.”

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This article was inspired by: Reuters