Why Are AI Semiconductors Pulling Back? Three Key Factors and How to Respond

Recently, AI-related stocks and the semiconductor sector have experienced a notable pullback, with Hong Kong-listed stocks $Hang Seng Index (800000.HK)$ 、 $Hang Seng TECH Index (800700.HK)$$HKEX Tech 100 Index (800666.HK)$ , as well as U.S.-listed stocks $S&P 500 Index (.SPX.US)$ 、 $NASDAQ 100 Index (.NDX.US)$ 、 $Dow Jones Industrial Average (.DJI.US)$ all undergoing varying degrees of adjustment, among which $Korea Index (.kospi.US)$ exhibited relatively high volatility. As part of investor education, this article focuses on three key factors to help investors rationally assess this market correction and avoid being swayed by isolated news items.

1. The core reason behind SK Hynix’s decline: profitability concerns, not merely deleveraging

$SK Hynix (000660.KR)$ has recently shown significant stock price volatility, with some market participants attributing it to details surrounding its U.S. listing and deleveraging in the Korean market. According to Bloomberg data, as of July 14, 2026, $SK hynix (SKHY.US)$ its American Depositary Receipts (ADRs) had traded for only three days, yet the premium over its local Korean share price rapidly widened to over 50%, indicating sustained strong market interest in the stock.

However, the biggest controversy surrounding SK Hynix in the market currently still lies inthe company's profitability. Of particular attention is a research report published on July 13 by Minsook Chae, a semiconductor analyst at Korea Investment & Securities (KIS). The report pointed out that SK Hynix’s core issue stems fromLong-Term Agreements (LTAs)which lock in prices, preventing HBM prices from rising in line with the broader DRAM price rally and resulting in profitability below market expectations.

This means that although the company’s revenue has grown, its gross margin has failed to expand as expected,shifting the market narrative from 'strong growth' to 'profitability falling short of expectations.'Coupled with heightened market concerns over regulatory risks related to leveraged products, this has further intensified selling pressure.

Understanding this logic reveals that these issues do not indicate weakening downstream demand.Demand for high-bandwidth memory (HBM) driven by AI compute remains robust in the medium to long term; it is only the near-term pace of profit realization that has fallen short of expectations, triggering capital rotation out of the hard-tech sector. This is a classic narrative adjustment, not a fundamental reversal.

2. Concerns over Hyperscaler CAPEX triggered by Meta’s sale of compute capacity

$Meta Platforms (META.US)$ Following the sale of part of its compute capacity, the market has grown concerned about capital expenditure (CAPEX) expectations for hyperscalers (large cloud providers), fearing a slowdown in AI infrastructure investment. However, public data suggests the situation is not so straightforward:

– According to OpenRouter platform data, Meta’s proprietary large language models have relatively low token usage (not ranked in the top ten), butoverall token usage across all models continues to rise.This indicates that AI demand is still expanding; Meta may simply be adjusting its compute allocation due to underperformance of its in-house models.

A more significant long-term risk lies inelevated hardware supply chain prices,：

– Key components such as printed circuit boards (PCBs) are experiencing price increases and tight supply.

– New chip capacity expansion is facing obstacles.

– End-market manufacturers such as $Apple (AAPL.US)$ have already passed on cost pressures through product price increases, indicating shrinking profit margins.

The market is now focused on hyperscalers’ execution efficiency and cost control regarding AI-related capital expenditures. Guidance from major tech companies during their Q2 earnings calls will be a key variable influencing the sector’s medium-term trajectory.

3. Geopolitical and macro liquidity risks intensify volatility

Beyond shifts in sector narratives, macroeconomic and geopolitical factors have amplified this recent downturn:

– The situation in Iran has become a market focal point, keeping energy prices elevated.

– The Federal Reserve maintains high interest rates, with FOMC meetings on July 29 and September 16 drawing significant attention as rate hike expectations continue to rise.

In a high-rate environment, growth-oriented tech stocks face heightened valuation pressure, reducing investors’ risk appetite and making emotional sell-offs more likely.

Summary: Narrative correction + sentiment shock, not a trend reversal

This decline is essentiallya phase-wise correction of sector and individual company narratives, compounded by liquidity and market sentiment shocks. In the short term, this reflects capital rotation and adjustment rather than a fundamental breakdown in the AI tech supply chain.

Medium- to long-term fundamentals remain worth watching：

– Demand for AI computing infrastructure continues to grow.

– Structural opportunities in key areas such as memory chips (especially HBM) remain intact.

– Overall token usage and developer activity continue to rise, with AI agent adoption and proliferation remaining critical drivers.

Amid current market volatility,diversificationfocusing on fundamentals is a key strategy to effectively reduce unsystematic risk. Investors should pay attention to real industry shifts, supply chain resilience, and management execution, avoiding blind chasing of rallies or panic selling.

In addition to diversifying holdings of key stocks through single-sector ETFs, investors can further allocatedifferent types of ETFs or assets with low correlation,and may also consider combining multipleinvestment targets with distinct risk-return profiles.For example, high-dividend sectors are typically concentrated in energy and financials, and have shown relatively resilient performance amid recent energy price increases and AI-related volatility—making this an effective risk management strategy in volatile markets.

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