Big Tech Lost US$1.3 Trillion in Less Than Two Months

The Big Tech firms, Amazon, Nvidia, Apple, and Alphabet, have lost US$1.3 trillion in market value as investors shift from funding long-term AI ambitions to demanding near-term profitability. This correction suggest tighten capital for local tech, data center, cloud, and AI-linked projects, while reinforcing scrutiny over high capex models. 
 

Four of the largest Big Tech have lost a combined US$1.3 trillion (MX$22.3 trillion) in market capitalization since Jan. 1, 2026. This contraction follows investor concerns regarding the capacity of massive AI investments to generate sufficient profit margins to sustain current equity valuations. This shift comes after years of speculative enthusiasm drove technology valuations to historic heights. 

"Make no mistake, I think this is the biggest and most dangerous bubble the world has ever seen," says Julien Garran, Researcher and Partner, MacroStrategy Partnership.

Garran notes that this misallocation of capital in the United States led to a bubble 17 times greater than the dot-com bubble and four times greater than the 2008 real estate crisis. This concentration of capital in high-risk assets suggests a level of systemic fragility where a sector-specific correction could cause a broader economic impact.

The beginning of the 2020’s was defined by the interaction between structural demand and monetary policy. During the first phase, forced digital adoption and unprecedented monetary stimulus led the Nasdaq-100 to a 47.58% return in 2020. However, the cycle changed in 2022 when central banks increased interest rates to combat inflation. This adjustment raised the cost of capital and reduced the net present value of future profits for growth-oriented companies, resulting in a 32.97% correction for the Nasdaq-100.

The market identified a new growth driver in generative AI starting in 2023. Despite high interest rates, a flight to quality occurred toward the "Magnificent Seven," which investors perceived as essential infrastructure providers. By late 2025, the Nasdaq-100 exceeded 24,000 points, and the "4 Trillion Club" consolidated under the leadership of Nvidia, Microsoft, and Apple.

Despite this growth, analysts report that market metrics currently exceed historical precedents for overvaluation. On Oct. 31, 2025, 20 companies accounted for 50.8% of the total market value of the S&P 500. These same companies generated only 9.1% of the total profits of the index. This concentration mirrors patterns observed before the 1929 stock market crash, where the market became disproportionately dependent on a small group of industries and companies.

The Current Landscape

The current sell-off has impacted the market valuations of the largest technology entities with varying degrees of severity. Reuters reported that Microsoft has seen its market value decline by approximately US$613 billion, leaving the corporation with a valuation of US$2.98 trillion. Investors have expressed concerns regarding risks to the AI business unit of the company, specifically due to increasing competition from the Gemini model of Google and the Claude Cowork agent of Anthropic.

Amazon has experienced a 13.85% decline in its share price during this year, erasing US$343 billion in market value. The current valuation of the corporation stands at US$2.13 trillion. This decline followed an announcement by Amazon that capital spending would increase by more than 50% during this year. 

Nvidia, Apple, and Alphabet have recorded market value declines of US$89.67 billion, US$256.44 billion, and US$87.96 billion, respectively. 

Michael Burry, Investor, Scion Asset Management, says the market is valuing AI companies based on profitability that is artificially inflated through accounting engineering. Burry specifically points to the methods used to depreciate data center hardware. Franco Macchiavelli, Head of Analysis, Admiral Markets Group, says the risk involves underestimating the difference between the theoretical useful life and the actual useful life of these assets.

Burry has placed options against Nvidia and Palantir totaling US$1.1 billion. This strategy represents a bet that future earnings will not sustain the current financial projections. Peter Thiel, Co-Founder, Palantir, suggests the market is behaving according to the final stages of a classic bubble, where investors prioritize price momentum over fundamental analysis.

While the technology sector faces significant losses, capital is rotating into different geographies and industries. 

Taiwan Semiconductor Manufacturing Co, or TSMC, added US$293.89 billion in market value during the same period, reaching a valuation of US$1.58 trillion. Samsung Electronics gained US$272.88 billion, bringing its market capitalization to US$817 billion. In the retail sector, Walmart added US$179.17 billion in market value, reaching US$1.07 trillion. These gains show that investors are seeking stability in hardware manufacturing and consumer staples.

What could happen in the AI bubble?

There is no consensus among industry leaders regarding the nature of the current market environment. Bill Ford, Chairman and Chief Executive Officer, General Atlantic, and Philippe Laffont, Founder and Portfolio Manager, Coatue Management, say that while valuations are high, the market dynamics differ from the dot-com bubble. They argue that large public companies possess unprecedented investment capacity and strong balance sheets.

Mary Callahan, CEO, JP Morgan Asset and Wealth Management, rejects the bubble classification. He says artificial intelligence represents an opportunity for efficiency that corporations cannot ignore. In contrast, Sundar Pichai, CEO, Alphabet, maintains a cautious stance. Pichai has acknowledged elements of irrational exuberance and warns that no company will be immune if a structural correction occurs.

Clem Delangue, Co-Founder and CEO, Hugging Face, says the industry is currently in an "LLM bubble" rather than a general artificial intelligence bubble. Delangue argues that excessive investment is focused on the belief that a single massive model will solve all problems, whereas the future likely belongs to specialized and efficient models.