Chip stocks slide as fears mount over AI investment scale

Photo: Steve A Johnson / Pexels

Chip stocks slide as fears mount over AI investment scale

A softer-than-expected OpenAI revenue figure sent semiconductor shares lower as investors questioned how long the AI spending boom can last

Semiconductor stocks took a sharp hit in early October 2026. The trigger was a single number about OpenAI’s revenue that came in well below what the market had penciled in.

A $20 billion gap rattles the AI trade

On October 8, the Financial Times reported that OpenAI’s annualized revenue run rate was approaching $50 billion. Earlier market assumptions had pegged that figure at around $70 billion.

The reaction was swift. The Philadelphia Semiconductor Index, a widely watched basket of chip stocks, fell approximately 3.4% on October 8.

Intel took one of the heaviest blows, dropping 5.3%. Micron slid nearly 4.8%, while Broadcom lost 4.3%.

Nvidia declined by almost 3%.

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The logic behind the selling is fairly direct. OpenAI’s revenue helps fund the data centers that buy chips from these companies. If revenue grows slower than expected, the appetite for future hardware purchases could cool too.

Record results, muted applause

TSMC, the contract manufacturer that produces chips for much of the industry, reported record third-quarter revenue of $46.7 billion, a 50% increase year-on-year. The reaction was muted, with investors appearing more focused on whether current spending levels can be sustained.

On October 12, Broadcom CEO Hock Tan and Intel CEO Lip-Bu Tan both pointed to access to capital and rising financing costs as potential constraints on the AI infrastructure buildout. Both executives indicated that near-term demand remains strong, but flagged whether customers can keep affording hardware at current borrowing costs.

How we got here

AI-related stocks have driven most of the S&P 500’s returns, earnings growth, and capital expenditure growth since ChatGPT launched in November 2022.

Goldman Sachs projects that US hyperscalers will spend approximately $800 billion on capital expenditures in 2026, rising to $1.1 trillion in 2027.

In mid-September, influential figures in the AI field advocated for a more measured approach to AI development, adding to questions about the pace of the buildout.

What this means for investors

The October 8 reaction shows how fragile sentiment has become. A gap between expected and reported revenue at a single private company was enough to knock several percentage points off an entire sector index in one session.

The comments from Broadcom and Intel leadership suggest that the cost of capital, not just customer enthusiasm, could shape how fast the buildout proceeds.

Investors will likely compare upcoming hyperscaler capex guidance against the roughly $800 billion expected for 2026 and the $1.1 trillion forecast for 2027. Any sign of trimmed budgets would land directly on companies like Nvidia, Broadcom, Micron, and TSMC.

Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our Editorial Policy.
Chip stocks slide as fears mount over AI investment scale
Chip stocks slide as fears mount over AI investment scale

A softer-than-expected OpenAI revenue figure sent semiconductor shares lower as investors questioned how long the AI spending boom can last

Photo: Steve A Johnson / Pexels

Semiconductor stocks took a sharp hit in early October 2026. The trigger was a single number about OpenAI’s revenue that came in well below what the market had penciled in.

A $20 billion gap rattles the AI trade

On October 8, the Financial Times reported that OpenAI’s annualized revenue run rate was approaching $50 billion. Earlier market assumptions had pegged that figure at around $70 billion.

The reaction was swift. The Philadelphia Semiconductor Index, a widely watched basket of chip stocks, fell approximately 3.4% on October 8.

Intel took one of the heaviest blows, dropping 5.3%. Micron slid nearly 4.8%, while Broadcom lost 4.3%.

Nvidia declined by almost 3%.

Advertisement

The logic behind the selling is fairly direct. OpenAI’s revenue helps fund the data centers that buy chips from these companies. If revenue grows slower than expected, the appetite for future hardware purchases could cool too.

Record results, muted applause

TSMC, the contract manufacturer that produces chips for much of the industry, reported record third-quarter revenue of $46.7 billion, a 50% increase year-on-year. The reaction was muted, with investors appearing more focused on whether current spending levels can be sustained.

On October 12, Broadcom CEO Hock Tan and Intel CEO Lip-Bu Tan both pointed to access to capital and rising financing costs as potential constraints on the AI infrastructure buildout. Both executives indicated that near-term demand remains strong, but flagged whether customers can keep affording hardware at current borrowing costs.

How we got here

AI-related stocks have driven most of the S&P 500’s returns, earnings growth, and capital expenditure growth since ChatGPT launched in November 2022.

Goldman Sachs projects that US hyperscalers will spend approximately $800 billion on capital expenditures in 2026, rising to $1.1 trillion in 2027.

In mid-September, influential figures in the AI field advocated for a more measured approach to AI development, adding to questions about the pace of the buildout.

What this means for investors

The October 8 reaction shows how fragile sentiment has become. A gap between expected and reported revenue at a single private company was enough to knock several percentage points off an entire sector index in one session.

The comments from Broadcom and Intel leadership suggest that the cost of capital, not just customer enthusiasm, could shape how fast the buildout proceeds.

Investors will likely compare upcoming hyperscaler capex guidance against the roughly $800 billion expected for 2026 and the $1.1 trillion forecast for 2027. Any sign of trimmed budgets would land directly on companies like Nvidia, Broadcom, Micron, and TSMC.

Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our Editorial Policy.