Global markets experienced significant turbulence overnight.

On the evening of June 5th, the three major U.S. stock indices all declined, with the Nasdaq falling nearly 2% and the S&P 500 dropping close to 1%. As of the latest update, the Philadelphia Semiconductor Index had extended its losses to 5.3%, with semiconductor stocks falling across the board. ARM dropped 9.09%, Intel (NASDAQ: INTC) shares fell 6.77%, NVIDIA declined 2.32%, TSMC was down 3.8%, Broadcom fell over 4%, Advanced Micro Devices dropped over 6%, and ASML declined over 4%.

U.S. memory-related stocks collectively retreated. Western Digital fell over 7%, Rambus and Silicon Motion Technology dropped over 6%, Micron Technology (NASDAQ: MU) shares declined over 5%, SanDisk fell over 5%, and Seagate Technology dropped over 4%.

On the news front, newly released data showed the U.S. added 172,000 non-farm jobs in May, significantly exceeding market expectations. This has further fueled market expectations for Federal Reserve interest rate hikes. Traders have now fully priced in a 25-basis-point rate hike by the Fed before December this year, assigning about a 60% probability to an October hike.

On June 5th, spot silver fell below $70 per ounce for the first time since April 7th, with a daily drop of 5.4%. Spot gold fell over $100 intraday, now trading at $4,350 per ounce, down 2.24%. Bitcoin once dropped over 5% to $60,461.88 per coin, hitting its lowest level since February 6th.

Additionally, Mark Zandi, Chief Economist at Moody's, warned in a recent report that if U.S. inflation expectations continue to rise, it could force the Federal Reserve to hike rates, even if it triggers a full economic recession.

Global Memory Giants Plunge in Tandem

It wasn't just U.S. memory giants; the world's top three memory chipmakers saw their shares plummet collectively. During Asian trading on June 5th, South Korean stocks triggered circuit breakers, with Samsung Electronics and SK Hynix both plunging. Samsung Electronics closed down over 6%, while SK Hynix tumbled nearly 10%, with its market capitalization falling below $1 trillion.

Even after the sharp decline, the year-to-date gains for the memory giants remain substantial. As of the close on the 4th, from the start of 2026 to that date, Micron Technology shares had surged 249.12%, Samsung Electronics was up 174.96%, and SK Hynix had risen 218.57%. Crowded trades represent the extreme manifestation of market consensus and also serve as a warning signal for accumulating risks.

Memory chips have become a critical bottleneck in AI infrastructure. Long-term supply agreements (LTAs) are transforming the traditionally strong-cyclical business into a long-term, high-margin cash flow stream with rigid guarantees. According to Counterpoint Research data, SK Hynix held a 58% share of the global High Bandwidth Memory (HBM) market in the first quarter of this year, with Samsung and Micron each accounting for 21%.

Micron Technology has indicated that the tight supply situation for HBM, DRAM, and NAND memory chips is expected to persist well beyond 2026. The core driver stems from robust demand for high-performance memory from AI applications, while supply is constrained by technological bottlenecks, making rapid capacity expansion difficult. SK Group Chairman Chey Tae-won has further predicted that the global memory chip supply gap could last until 2030, with the company increasing capital expenditure to bridge the supply-demand imbalance.

Behind the violent swings in memory giants' stock prices, "seeds of doubt" have been sown. At what stage is this memory super-cycle? How much more upside potential remains? How far is it from a bubble?

Reasons for the Rally's Pause

After a sustained and rapid price surge, the memory giants' rally has hit a "pause button." However, judging from various signs, the collective plunge of the top three memory makers appears more like a correction, with future demand still robust.

On June 5th, NVIDIA founder and CEO Jensen Huang stated that the company has certified HBM4 samples submitted by the world's three major memory chip manufacturers—Samsung Electronics, SK Hynix, and Micron Technology—which will become core components of its next-generation AI work platform, Vera Rubin.

A management practice professor from a French business school expressed that this episode resembles a "forced cooling of crowded trades," not the end of the memory super-cycle. The decline of memory giants is a normal correction during an "uptrend," a collective repricing by the market of previous gains, valuations, and positioning, rather than a sudden deterioration in company fundamentals. Furthermore, the giants have seen very large gains over the past year. The market has already repriced them from traditional cyclical stocks to AI infrastructure bottleneck assets. This shift itself is not incorrect, but the short-term rise was too rapid, and any minor disturbance can trigger profit-taking.

Regarding this memory market cycle, the professor believes it can no longer be analyzed using the old cyclical framework of "DRAM price increase—capacity expansion—price drop." In the past, memory was tied to the consumer electronics cycle; now, memory is the throughput bottleneck within AI computing systems. No matter how powerful GPUs are, if HBM, server DRAM, enterprise SSDs, and network bandwidth cannot keep up, computing utilization will not increase. The first-quarter results of memory giants showed significant sequential improvement, indicating a typical reallocation of industry profits.

Currently, the stock prices of memory giants have already priced in high earnings elasticity for 2026 and have partially front-loaded some of the positive expectations for 2027. If viewed solely through the lens of traditional cyclical stock valuations, a forward PE of around 10x is no longer cheap. However, the professor reminds us that if they are placed within the "AI bottleneck asset" framework, the market is willing to assign higher valuations because they are not selling ordinary memory but the core constraint variable for AI training and inference efficiency.

Therefore, the professor views this decline as a healthy pullback. What truly warrants caution is not the magnitude of a single-day drop, but rather monitoring three types of signals: HBM long-term agreement loosening, DRAM contract prices falling for two consecutive quarters, and cloud providers revising down their AI capital expenditures. Currently, these signals are not appearing simultaneously. In other words, stock prices are correcting, but the industry trend has not reversed.

Where Does the Super-Cycle Stand?

The expansion of artificial intelligence data centers is exacerbating the global imbalance between supply and demand for memory chips. Its impact has spread from the technology sector to areas such as automobiles, medical devices, and retail. The pattern of memory chip supply falling short of demand shows no signs of ending; the super-cycle remains unfinished.

The professor believes the current memory super-cycle is roughly in its middle-to-late stages but has not yet reached its final peak. 2024 to 2025 is the stage for inventory recovery and price rebound, while 2026 is the stage for profit realization and valuation reassessment. The window that truly requires high vigilance will likely be in the second half of 2027 to the first half of 2028. This is because the peak of the memory industry is typically not the moment when prices are at their highest, but when the market begins to see three signals appear simultaneously: new supply emerging, customer bargaining power recovering, and returns on capital expenditure declining.

Currently, the industry still has two supports. One is that HBM capacity remains tight. AI and server demand are driving continuous upward revisions to memory contract prices, supplier inventories have bottomed out, high-end applications have become the main source of profits, and next-generation HBM will gradually become a revenue driver. The other is that cloud provider capital expenditure has not yet hit the brakes. Market data shows that cloud platforms and AI application companies' capital expenditure plans for 2026 are 77% higher than last year, indicating that AI infrastructure expansion remains at a high level. However, signs of a peak have also been planted. SK Hynix plans to double its wafer capacity over the next five years, and Samsung and Micron are also ramping up efforts in HBM and advanced DRAM. Any super-profit will attract super-supply; this is the iron law of the semiconductor cycle. The difference this time is that supply release won't be that fast because HBM is not ordinary DRAM. It involves advanced packaging, TSV, yield rates, customer certification, and GPU platform integration; it's not something that can be scaled up immediately by simply buying more equipment.

Looking ahead, the professor's judgment for the top three memory giants is: Before the third quarter of 2026, the market still has a basis for repeated upward moves; from the fourth quarter of 2026 to the first half of 2027, it will be a stage of parallel profit realization and valuation digestion; after the second half of 2027, if new capacity begins to be released while traditional DRAM and NAND prices decline, the market will start trading the cycle peak.

How Far is the Memory Chip Bubble?

Amid the surge in memory giants this year, giants like NVIDIA, Apple, and Microsoft have performed modestly.

The professor does not believe NVIDIA, Microsoft, or Apple have been truly neglected. More accurately, the market is shifting from "buying established leaders" to "buying segments with greater marginal change." Over the past two years, NVIDIA has been the biggest beneficiary of AI capital expenditure, Microsoft the clearest giant in AI commercialization, and Apple the representative for consumer electronics and on-device AI expectations. But at this point in 2026, the market naturally asks: Whose earnings can continue to be revised upward? Whose valuation still has an expectation gap? Therefore, it's not surprising that funds have temporarily shifted towards memory, PCBs, CPO, optical modules, liquid cooling, and power equipment. These segments were previously undervalued and have suddenly become bottlenecks in AI computing infrastructure. Capital markets favor bottlenecks because they imply pricing power, the ability to raise prices, and earnings elasticity.

For currently systemically underweighted tech leaders and software sectors, the professor believes they warrant renewed attention. Companies like Microsoft, Amazon, and Google appear on the surface to be the payers for AI capital expenditure, but they are also the distribution gateways for AI applications and cloud platforms. Enterprise clients are unlikely to piece together models, buy computing power, and handle deployment themselves; they will most likely end up using AI through platforms like Azure, AWS, Google Cloud, and various enterprise software platforms. In other words, hardware rises first because orders materialize first; software rises later because revenue recognition and profit improvement take time.

The next phase of the market may spread from "selling shovels" to "those who make money using the shovels." NVIDIA remains the core, but its valuation is no longer cheap; memory and optical communication are still in a prosperous period, but volatility will be greater. What might truly be repriced are the application layers: cloud platforms, enterprise software, data management, security, and Agent workflows.

Some alarm bells have also sounded. Ray Dalio, founder of Bridgewater Associates, has warned that the current artificial intelligence frenzy sweeping global capital markets exhibits typical bubble characteristics. Historical experience shows that such bubbles often eventually burst, typically when investors begin to cash in their profits.

In the early stages of technological change, it is difficult for companies to accurately judge whether the scale of investment is reasonable, often facing a dilemma: either invest heavily to compete for market dominance, even at the risk of overinvestment; or remain cautious but potentially miss market opportunities and fall behind competitors. Dalio emphasized that whether AI companies can achieve profitability matching their current high valuations in the future is one of the key factors determining the ultimate direction of this frenzy.

In the professor's view, AI does have bubble components, especially among some companies with no revenue, no cash flow, and relying solely on stories for financing, where the bubble is already evident. However, labeling the entire AI industry as a repeat of the 2000 internet bubble is inaccurate. In 2000, many companies had no revenue. Today's companies like NVIDIA, Microsoft, Amazon, Google, and Micron have real revenue, real orders, and real cash flows, which is a significant difference.

What needs vigilance is that if enterprise profits at the AI application end cannot grow rapidly, some investors fear this could trigger a significant market decline.

The professor stated that from 2023 to 2025, the market bought into model capabilities, GPU shortages, and capital expenditure expectations. After 2026, the market will begin demanding report cards from the application end. In other words, AI cannot forever remain in the narrative of "future productivity will improve"; it must translate into enterprise revenue growth, cost reduction, margin improvement, and cash flow enhancement.

If application-end profits cannot grow rapidly, the market may experience a significant decline, but it is more likely to be a structural decline rather than a complete collapse of the entire AI industry. The most dangerous assets are three types: AI concept stocks with no revenue, private AI companies maintaining valuations through financing, and hardware companies whose valuations already reflect perfect growth but whose profits are beginning to slow. Conversely, companies with cash flow, customer gateways, and cloud platform and software distribution capabilities, after adjustments, will become the direction for capital reallocation.