Semiconductor Sharp Correction $1.4 Trillion: Analysis of the Triple Drivers Behind the AI Chip Crash

June 5, 2026, the Philadelphia Semiconductor Index (SOX) plunged 10.3% in a single day, marking the largest one-day decline since 2020. Nvidia lost $279 billion in market value, Marvell dropped as much as 17% in one day, Micron fell over 13%, and the entire AI chip sector evaporated about $1.4 trillion within a single day. If you only look at the candlestick chart for that day, the market seems to be telling a story of an "AI bubble burst"—the semiconductor sector experienced the most intense selling pressure in recent years in the short term.

But in the following trading days, the market presented a completely different narrative. Marvell announced it would be officially included in the S&P 500 index on June 22, with pre-market shares jumping over 9%, as passive funds are expected to massively buy MRVL shares before the adjustment takes effect. Meanwhile, Oracle disclosed in its earnings report that its RPO backlog far exceeded market expectations—rising from $138 billion at the end of fiscal 2025 to $455 billion in Q1 2026, then reaching $523 billion in Q2, and further climbing to $553 billion in Q3. Nvidia’s full-year revenue for fiscal 2026 reached $215.9 billion, with $68.1 billion in Q4 alone, compared to only $39.3 billion a year earlier. AMD’s stock has surged over 130% this year, and Oracle’s RPO backlog growth in a quarter reached the scale of $317 billion.

Two seemingly contradictory sets of data coexist: a sector-wide crash that set a six-year record, and signals of fundamentals still accelerating. This puts all investors watching chip stocks at a critical juncture—whether this semiconductor correction is a structural end of the AI industry logic or a technical valuation reset after crowded trading.

From an investment decision perspective, this question can be directly translated into a more specific, actionable one: after $1.4 trillion evaporated, are chip stocks in a “buy-the-dip” window or still on the “mid-mountain” path? To answer this, one cannot stay at the headline level of a single-day decline but must dissect the three layers behind the plunge—macro rates, micro expectation gaps, and trading crowding—and examine whether the fundamental support for AI computing power demand has been truly broken.

Macro Catalyst: Rising Rates Slam High-Valuation Growth Stocks

On the surface, the trigger for this plunge was Broadcom’s June 3 quarterly earnings report. The market initially expected Broadcom to revise its AI chip revenue guidance upward more aggressively, but the actual guidance for Q3 did not meet the most optimistic expectations, triggering a concentrated sell-off of high-positioned funds. However, the deviation in Broadcom’s earnings was not large—its AI revenue still grew 143% year-over-year, with no cliff-like shift from growth to decline. The real question is: why did a performance guidance that was not critically off trigger a collective stampede of over 10% across the entire semiconductor sector?

The first layer of support for this explanation is the dramatic change in the macro interest rate environment. As of early June 2026, the U.S. 10-year Treasury yield soared to 4.57%, with futures markets almost fully pricing in at least two Fed rate hikes within the next 12 months. As a typical long-duration asset, high-growth AI chip stocks’ valuation hinges on discounting future cash flows over many years—when risk-free rates rise, the discount rate increases, and high-valuation sectors face greater revaluation pressure. This explains why the highest PE multiples are the first to be impacted in a high-rate environment.

This macro effect is not limited to semiconductors. Brent crude oil briefly broke above $97 per barrel, and escalating Middle East geopolitical tensions further heightened global inflation and interest rate uncertainty. These macro shifts do not necessarily mean that AI chip companies’ EPS forecasts have fundamentally worsened, but their valuation anchors—the premium investors are willing to pay for expected future earnings—are systematically shifting downward. This logic indicates that the current correction is not an isolated event at the stock level but a collective re-pricing of high-valuation growth assets.

It’s worth noting that even after a 10.3% single-day plunge, the Philadelphia Semiconductor Index’s year-to-date gain remains close to 80%. This fact provides an important analytical anchor: the decline is shocking not because it erased all gains but because it occurred when valuations were at historically high levels.

Micro Expectation Gaps and Trading Crowding: Double Amplification

If macro rate hikes form the “passive background” of the correction, then Broadcom’s underwhelming guidance acts as the “trigger.” Both factors together triggered a concentrated retreat of profit-taking from the already highly accumulated positions within the AI chip sector. By the end of May, the Philadelphia Semiconductor Index had surged over 90% in just a few months, with Nvidia, AMD, Marvell, and others doubling or more. When a stock doubles or even triples in a short period, its chipholder structure’s stability diminishes significantly—any marginal change can trigger large-scale profit-taking.

An even more overlooked signal in this correction is the rapid reversal after the SOX hit its intra-year peak of 13,998 on June 3, falling about 16% over four trading days to around 11,900. This “touch-and-reverse” pattern is typically associated with technical corrections rather than a fundamental long-term trend reversal—longer-term declines tend to be slower and more gradual.

Meanwhile, Oracle’s data after the plunge sent a somewhat different signal. Its RPO backlog jumped from $138 billion at the start of FY2026 to $455 billion, then to $523 billion, and finally to $553 billion. The significance of this backlog is that it represents signed contracts not yet recognized as revenue—an immediate visible indicator of future cloud computing demand for AI power. If AI demand truly enters a trend turning point, such a massive backlog would be unlikely to appear. In other words, there is a structural disconnect between micro-level order visibility and macro-level price movements. This gap itself provides a necessary transition from “macro phenomena” to “individual asset analysis.”

Asset-Level Perspective: Marvell, Oracle, and Data Validation

After analyzing macro triggers and crowding mechanisms, the focus shifts to individual cases. Marvell’s trajectory is one of the most typical and dissectible examples in this correction. On June 5, the day of the sector plunge, Marvell fell 17%, one of the largest declines among AI chip stocks. Yet, just a week later, S&P Dow Jones announced that Marvell would be officially included in the S&P 500 on June 22, with pre-market shares jumping over 9%. Inclusion means passive index funds and ETFs will be forced to buy according to index weights—Marvell’s current market cap is about $230 billion, and even at the smallest weight, billions of dollars in passive buying will flow in. This structural buying, unrelated to fundamentals, triggered by index rules, can short-term offset valuation pressures. Inclusion in the S&P 500 is just one of the recent catalysts—Nvidia’s CEO previously called Marvell “the next trillion-dollar company,” and its ASIC chips for AI data centers further strengthen its position.

Looking at Oracle, during the plunge, its stock fell nearly 10% in a single day due to sector sentiment, but it quickly stabilized after its June 10 earnings report. Its RPO jumped from $138 billion to $553 billion, driven mainly by multi-year AI compute contracts with large cloud clients, including nearly 60% from a 5-year, $300 billion inference compute order with OpenAI. Such long-term, large-scale orders mean Oracle’s AI cloud infrastructure revenue visibility extends years ahead. The core significance of RPO data is that it directly addresses the market’s biggest concern during this correction: whether AI capital expenditure has peaked. Goldman Sachs estimates that S&P 500 companies’ overall capital spending in 2026 will still grow by 33%, with only a 3% increase in share buybacks—indicating that the AI investment cycle at the corporate level has not collectively decelerated.

Valuation Reset or Structural Shift: Data-Driven Judgment

Currently, the forward P/E of the Philadelphia Semiconductor Index has fallen from near the 99th percentile of its historical high to about the 75th percentile. Leading stocks’ valuations have compressed significantly: Nvidia’s expected PE has dropped from around 85x to about 67x, but based on 2027 earnings estimates, its forward PE is only about 17.5x, with a PEG ratio around 0.28. A common valuation metric for growth companies is PEG below 1, and 0.28 indicates that current valuation is relatively low compared to future growth potential. AMD’s expected PE is about 35x, with a 2026 EPS growth forecast of around 76%; Marvell’s expected PE is roughly 24–28x, with valuation gaps widening between top-tier and second-tier stocks.

This shift in valuation structure is noteworthy. Compared to the extreme valuations during the dot-com bubble, the current AI chip sector, while not cheap, has not yet entered a systemic collapse of valuations across the industry. The forward PE of broad tech indices is around 28–30x, still a clear distance from extreme historical lows.

So, is this correction a structural shift or a valuation reset? When systematically considering multiple layers of data:

First, demand-side signals remain strong. Nvidia’s full-year revenue exceeded $2.159 trillion, Oracle’s RPO backlog surged from $138 billion to $553 billion over three quarters, and Goldman Sachs expects S&P 500 capital expenditure to grow 33% in 2026—three different sources, three dimensions pointing to the same conclusion: AI compute demand orders and capital spending have not contracted.

Second, supply-side capacity constraints are evident. Nvidia’s CEO explicitly states that “memory shortages” will persist for years, and capacity has been pre-locked to address supply chain bottlenecks. In an environment of expanding demand and limited supply, AI chip prices and gross margins tend to remain relatively strong.

But the other side must also be viewed cautiously. Market pricing for AI chips has already significantly outpaced actual earnings realization. As the market shifts from “AI future potential” pricing to “quarterly earnings beats” pricing, any quarterly marginal slowdown could trigger valuation compression. The forward P/E of the SOX remains at about the 75th percentile historically, meaning even after the plunge, the market is not pricing the sector as undervalued but rather at a “high but no longer extremely overvalued” level.

Integrating multiple data points, this correction leans more toward a “crowded trade” valuation reset. The long-term growth fundamentals of AI infrastructure remain intact, but short-term valuation normalization to more sustainable levels and reduced tolerance for expectation gaps are more aligned with current market conditions. A true structural shift would require at least one of three conditions: persistent decline in forward-looking demand indicators, major clients reducing or delaying capex plans, or leading companies’ gross margins trending downward—none of which are sufficiently supported by current data.

Cross-Market Allocation Feasibility and Trading Tools

For investors focused on medium- to long-term opportunities in chip stocks, this correction offers a clearer framework: in an environment where macro uncertainties are not fully resolved, the demand fundamentals and valuation levels of chip stocks have entered a relatively more analyzable range. Cross-market asset allocation strategies are especially valuable now.

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This integrated platform’s practical significance in the current environment is that when macro variables change rapidly, cross-market capital mobility efficiency directly impacts decision execution quality. The increased volatility in the semiconductor sector amplifies the need for dynamic rebalancing between US equities and crypto assets. Gate’s fractional share trading feature (starting from $1) allows investors with smaller capital to participate in high-priced stocks like Nvidia at lower thresholds, providing more flexible investment options. The process of trading US stocks via Gate is designed to be straightforward: after logging into Gate, select the desired US stock in the TradFi section, complete the trade with USDT, and enjoy an experience consistent with traditional crypto trading.

The valuation adjustment in the semiconductor industry typically does not end with a single-day plunge, but this correction offers an opportunity to revisit the gap between market prices and fundamental data. Signals like Nvidia falling below $200, Oracle’s RPO soaring to $553 billion, and Marvell’s inclusion countdown are not “buy signals” in isolation nor the end of the AI cycle, but key facts that investors need to analyze independently within a multi-variable decision framework.

Conclusion

Starting from SOX’s 10.3% single-day plunge and $1.4 trillion evaporation, through the three layers of macro rates, micro expectation gaps, and crowding, to individual data points like Marvell’s index inclusion, Oracle’s backlog, and Nvidia’s revenue structure, this analysis indicates that: this semiconductor correction is not a structural turning point for AI compute demand but rather a valuation reset and crowding release after rapid gains. The long-term infrastructure of AI remains in progress, but the market’s expectations for each quarter have been pushed to very high levels, with tolerance for expectation gaps significantly narrowed compared to 2024–2025. This suggests that while the “long-term logic” of the narrative remains valid, valuation considerations require more rigorous safety margins.

Markets are pricing mechanisms; every big drop creates new information asymmetries. Fundamental signals supported by data—Oracle’s backlog, Marvell’s index inclusion, Nvidia’s revenue structure—are the real anchors that can help investors navigate volatility. Ultimately, judgments are always based on facts, and the direction of decisions depends on each investor’s independent assessment of the probabilities and odds associated with these facts.