AI Bubble Fears Intensify: The Logic Behind Chip Stocks Selling Off Across US and Asian Markets

The AI Boom Faces Market Skepticism

Recently, chip-related stocks have declined simultaneously in both US and Asian markets, reflecting growing investor anxiety about the prospects of the artificial intelligence industry. This round of price correction is not an isolated event, but rather a collective reassessment of the sustainability of the AI narrative.

Over the past two years, the AI wave has almost single-handedly propped up the valuation highs of the semiconductor industry and the broader tech sector. From NVIDIA to TSMC, from Micron to Samsung and SK Hynix in South Korea, AI-driven demand for computing power has propelled these companies' stock prices ever higher. The core driving force behind this wave comes from the explosive demand for training and inference of large language models (LLMs)—frontier model training, exemplified by GPT-4, requires tens of thousands of high-end GPUs (such as NVIDIA H100/H200) working in concert, with a single training run costing tens of millions of dollars. This unprecedented demand for computing power directly drove NVIDIA's data center revenue to grow several-fold between 2023 and 2024, while also propelling the entire supply chain—from TSMC's advanced process foundry services and CoWoS packaging capacity to SK Hynix's HBM (High Bandwidth Memory)—into a super-cycle boom. However, when market sentiment shifts from euphoria to caution, chip stocks are often the first to bear the brunt—they are both the most direct beneficiaries of the AI boom and the most sensitive barometers of bubble concerns.

Why Chip Stocks Are the Focal Point of AI Bubble Concerns

There is an inherent logic to why the chip industry reacts most dramatically in this round of AI volatility.

High Valuations Bring High Volatility Risk

AI-related chip companies have already "priced in" years of optimistic expectations during their past valuation expansion. When the market begins to question whether AI capital expenditures can be sustained or whether downstream applications can truly monetize, these inflated valuations face correction pressure. Any signal about slowing demand or lengthening return-on-investment timelines can trigger concentrated selling.

Chain Reactions Across the Global Semiconductor Supply Chain

Semiconductors operate within a highly globalized supply chain. The US handles design and some advanced manufacturing, while Asia (particularly Taiwan and South Korea) undertakes critical manufacturing and memory production. Specifically, the global semiconductor industry exhibits highly specialized division of labor: US companies (NVIDIA, AMD, Qualcomm) dominate chip architecture design, the Netherlands' ASML provides irreplaceable EUV lithography equipment, TSMC handles approximately 90% of the world's advanced process (sub-7nm) foundry work, and South Korea's Samsung and SK Hynix hold over 95% of the global HBM memory market share. This deeply intertwined structure means that demand fluctuations at any node are quickly transmitted across the entire chain. Additionally, geopolitical factors (such as US chip export controls on China and cross-strait tensions) add extra uncertainty premiums to this supply chain. Therefore, when US chip stocks decline, Asian markets typically follow in subsequent trading sessions, creating cross-market, cross-timezone chain reactions. The synchronized decline across US and Asian markets this time is a textbook example of this supply chain linkage.

The Sustainability Question of AI Capital Expenditure

At the core of investor concerns lies a question that still lacks a definitive answer: Can the current massive AI infrastructure investments ultimately translate into sustainable profits?

Major tech giants are pouring astronomical amounts of capital expenditure into data centers and GPU clusters. According to public financial reports, the combined capital expenditure of Microsoft, Google, Meta, and Amazon in 2024 is projected to exceed $200 billion, with a significant portion directed toward AI infrastructure. These investments primarily flow in three directions: first, procurement of GPUs/AI accelerators; second, construction and expansion of data centers (including supporting facilities like power and cooling); and third, network interconnect equipment (such as InfiniBand switches). However, in contrast to these astronomical investments, the direct revenue from AI applications (such as ChatGPT subscriptions, enterprise API calls, and AI feature surcharges) cannot yet fully cover the depreciation costs of these capital expenditures, creating what is known as the "investment-return gap."

The pace of AI application commercialization, actual enterprise customer willingness to pay, and the balance between model training costs and returns all carry considerable uncertainty. Once the market perceives that the payback period for these investments is too long, or that demand growth falls short of expectations, the valuation support for upstream chip suppliers begins to erode.

These concerns are not unfounded. Historically, every technology boom has been accompanied by excessive capital optimism, typically followed by a bubble-squeezing adjustment period. Investors drawing parallels between the current AI boom and the 2000 dot-com bubble have valid reasons—back then, Cisco, Lucent, and other networking equipment companies saw their valuations soar on the "internet infrastructure buildout" narrative, but when telecom carrier capital expenditure cycles peaked, these companies' stock prices crashed by over 80%. However, there are key differences: during the internet bubble, massive investment flowed toward startups lacking business models, whereas current AI capital spending is primarily led by cash-flow-rich tech giants, and AI has already demonstrated quantifiable productivity improvements in areas like code generation, customer service, and drug discovery. This makes the current cycle more likely to experience a "soft landing" style valuation correction rather than a catastrophic collapse. Whether AI will replay the bubble-burst script or chart a different path remains the central point of market disagreement.

The Dialectic Between Short-Term Volatility and Long-Term Trends

You may not have noticed, but short-term stock price fluctuations and long-term technology trends are not always in sync. Even with periodic valuation corrections, AI's structural demand for computing power remains genuine.

From a longer time horizon, AI technology penetration is still in its early stages, and as the core of computing infrastructure, the long-term demand logic for chips has not been disproven. The structural factors supporting this assessment span multiple dimensions: first, the continued scaling of model sizes—Scaling Laws indicate that model performance improves systematically with increases in parameter count and training data, driving sustained demand for larger compute clusters; second, the exponential growth in inference demand—the daily operation of every AI application requires continuous inference computing power, and total inference compute consumption has begun to exceed that of the training phase; third, the rise of AI Agents and multimodal models, which impose higher requirements for real-time computation and memory bandwidth. Furthermore, "sovereign AI" strategies promoted by governments worldwide are also driving the construction of localized AI computing infrastructure in various regions, providing geopolitically-driven incremental demand for chips.

The current stock price adjustment is more of a correction at the market sentiment and valuation level, rather than a fundamental reversal of industry fundamentals.

For investors, the key lies in distinguishing between two things:

• Short-term market sentiment fluctuations—which drive price swings
• The long-term evolution of the AI industry itself—which determines the direction of value

Rational observers should guard against emotionally-driven chasing of rallies and panic selling, while also not dismissing the deep momentum of technological transformation because of short-term pullbacks.

Conclusion: The AI Boom Enters a Period of Rational Scrutiny

This round of collective pressure on chip stocks across US and Asian markets is a microcosm of the AI boom entering a "period of rational scrutiny." The market is shifting from unconditional optimism toward cautious questioning of commercialization pathways and investment returns.

For practitioners and investors following the AI and semiconductor industries, this serves as both a risk warning and an opportunity to reassess industry fundamentals. The long-term value of AI is certain, but the road to value realization is destined to be anything but smooth. Every market fluctuation is a test of this narrative's resilience.