June 5, 2026: The Philadelphia Semiconductor Index (SOX) plunged 10.3% in a single day, marking its largest one-day drop since 2020. Nvidia alone lost $279 billion in market value, Marvell plummeted 17% in a day, and Micron fell more than 13%. Across the entire AI chip sector, roughly $1.4 trillion evaporated in a single session. Judging by the day’s candlestick chart, the market seemed to be telling an "AI bubble burst" story—semiconductors faced the most intense selling pressure seen in years.
Yet, in the days that followed, the market narrative shifted dramatically. Marvell announced it would officially join the S&P 500 Index on June 22, sending its pre-market price soaring over 9%. The market expects passive funds to buy MRVL shares en masse before the rebalancing takes effect. Oracle’s earnings report revealed RPO backlog data that far exceeded expectations—from $138 billion at the end of FY2025, surging to $455 billion in Q1 FY2026, then $523.3 billion in Q2, and further to $553 billion in Q3. Nvidia’s FY2026 revenue hit $215.9 billion, with Q4 alone at $68.1 billion, up from just $39.3 billion a year earlier. AMD’s price has risen more than 130% year-to-date, and Oracle’s quarterly RPO backlog growth reached the $317 billion range.
These two sets of seemingly contradictory data coexist: a record-breaking sector crash alongside accelerating fundamentals. This forces every chip stock investor to make a crucial judgment—does this semiconductor correction mark a structural end to the AI industry’s logic, or is it a technical valuation reset after a crowded trade?
From an investment decision perspective, the question becomes more actionable: after $1.4 trillion vanished, are chip stocks at a "buy-the-dip" window, or still halfway up the mountain? To answer, we must look beyond the headlines and dissect the three underlying causes of the crash—macro interest rates, micro expectation gaps, and crowded trades—while examining whether the fundamental support for AI compute demand remains intact.
Macro Catalyst: Surging Interest Rates Hit High-Growth Stocks
On the surface, the trigger for this sell-off was Broadcom’s quarterly earnings report on June 3. The market expected Broadcom to raise its AI chip revenue guidance aggressively, but its Q3 outlook fell short of the most optimistic forecasts, sparking concentrated selling from investors at elevated prices. However, the actual deviation in Broadcom’s report was minor—AI segment revenue still grew 143% year-over-year, with no cliff-edge shift from growth to decline. The real question is: why did a non-catastrophic guidance miss trigger a 10%+ collective sell-off across the semiconductor sector?
The first layer of the answer lies in the dramatic shift in macro interest rates. As of early June 2026, the US 10-year Treasury yield had surged to 4.57%, and futures markets were pricing in at least two Fed rate hikes over the next 12 months. High-growth AI chip stocks are classic long-duration assets, whose valuations hinge on discounting future cash flows—when risk-free rates rise, discount rates climb, and high-valuation sectors face greater repricing pressure. This explains why high-rate environments first hit the stocks with the highest PE multiples.
This macro factor isn’t limited to semiconductors. Brent crude briefly topped $97 per barrel, and escalating geopolitical tensions in the Middle East further fueled uncertainty around inflation and rate trajectories. Macro changes don’t necessarily signal a fundamental deterioration in AI chip companies’ EPS forecasts, but their valuation anchor—the premium investors are willing to pay for each dollar of expected earnings—is systematically shifting downward. This means the current correction is not an isolated stock event, but a collective repricing of all high-growth assets.
Notably, even after SOX’s 10.3% single-day plunge, its year-to-date gain remains close to 80%. This fact provides a key analytical anchor: the drop is striking not because it erased all gains, but because it occurred at historically elevated valuation levels.
Micro Expectation Gaps and Crowded Trade Amplification
If rising macro rates set the "passive backdrop," Broadcom’s earnings miss acted as the "trigger." Together, they prompted a mass exodus of profit-takers who had accumulated huge gains in AI chip stocks. By late May, SOX had surged over 90% in just a few months, and doubling moves in stocks like Nvidia, AMD, and Marvell were common. When a stock doubles or triples in a short period, its shareholding structure becomes inherently less stable—any marginal change can spark large-scale profit-taking.
A subtle signal emerged during this correction: after SOX hit its yearly peak of 13,998 on June 3, it dropped about 16% in four trading days to around 11,900. This "quick reversal after hitting a target" price pattern is highly characteristic of technical pullbacks, not a fundamental-driven, long-term trend reversal—which typically shows slower, more sustained declines.
Meanwhile, Oracle’s post-crash data sent a somewhat contradictory signal. Its RPO backlog jumped from $138 billion to $455 billion, $523.3 billion, and $553 billion over the first three quarters of FY2026. This backlog matters because it represents future cloud revenue from signed contracts not yet recognized, making it the most direct indicator of visible AI compute demand in the market. If AI compute demand were truly at a turning point, such backlog growth would be nearly impossible. In other words, there’s a clear structural difference between micro-level order visibility and market-level price volatility. This difference provides a necessary bridge from "macro phenomena" to "individual asset analysis."
Individual Asset Perspective: Marvell, Oracle, and Data Validation
After analyzing macro catalysts and crowded trade mechanics, it’s time to focus on individual cases. Marvell’s price action is among the most illustrative in this correction. On June 5, Marvell fell 17%, the steepest drop among AI chip stocks that day. Yet, just a weekend later, S&P Dow Jones Indices announced Marvell’s official inclusion in the S&P 500 on June 22, sending its pre-market price up more than 9%. Index inclusion means passive index funds and ETFs must buy shares based on index weight. With Marvell’s current market cap around $230 billion, even at minimum weighting, tens of billions in passive buying will enter the market. This structural buying is unrelated to fundamentals, triggered purely by index rules, but can offset downward valuation pressure in the short term. S&P 500 inclusion is just one catalyst for Marvell—Nvidia’s CEO has publicly called Marvell "the next trillion-dollar company," and its custom ASIC chips are increasingly central to AI data center networks.
Turning to Oracle: during the crash, Oracle’s stock dropped nearly 10% in a day due to sector sentiment, but rebounded quickly after its June 10 earnings release. Its RPO backlog soared from $138 billion to $553 billion, driven by multi-year AI compute contracts with major cloud customers, nearly 60% of which came from a five-year, $300 billion inference compute order with OpenAI. This long-term, large-scale order structure means Oracle’s AI cloud infrastructure revenue visibility now stretches years into the future. The significance of RPO data is that it directly addresses the market’s biggest concern during this crash: whether AI capital expenditure has peaked. Goldman Sachs data shows S&P 500 companies are expected to grow capital expenditures by 33% in 2026, while stock buybacks will only rise 3%. There’s no sign of a collective pause in corporate AI investment cycles.
Valuation Reset or Structural Shift? Data-Driven Judgment
Currently, SOX’s forward PE ratio has fallen from near the historical 99th percentile to about the 75th percentile. Leading stocks’ valuations have compressed: Nvidia’s expected PE dropped from around 85x to about 67x, but based on 2027 earnings, its forward PE is only about 17.5x, with a PEG of roughly 0.28. Growth companies are typically considered attractively valued when PEG is below 1, and a 0.28 reading suggests current valuations are low relative to future growth. AMD’s expected PE is about 35x, with 2026 EPS growth forecast at 76%; Marvell’s expected PE is 24–28x, with a widening gap between top-tier and second-tier stocks.
This change in valuation structure is notable. Compared to the extreme valuations during the dot-com bubble, the current AI chip sector isn’t cheap, but hasn’t reached systemic collapse levels. Broad tech indices have a forward PE of about 28–30x, still well below historical extremes.
So, is this correction a structural shift or a valuation reset? If we systematically review data across multiple layers:
First, demand-side signals are consistent. Nvidia’s annual revenue surpassed $215.9 billion, Oracle’s RPO backlog jumped from $138 billion to $553 billion in three quarters, and Goldman Sachs expects S&P 500 capital expenditures to grow 33% in 2026—three different sources and dimensions all point in the same direction: AI compute demand and capital spending remain robust.
Second, supply-side capacity constraints are evident. Nvidia’s CEO has said the "memory shortage" will persist for years and has secured capacity in advance to manage supply chain bottlenecks. With expanding demand and limited supply, AI chip prices and margins are likely to stay strong.
However, the other side of the coin must be considered. The market’s pricing for AI chips is already ahead of actual performance. As pricing shifts from "AI future potential" to "quarterly performance consistently beating expectations," any quarterly slowdown can trigger valuation compression. SOX’s forward PE remains at the 75th percentile historically, meaning even after the crash, the sector isn’t priced as undervalued, but rather as "high, but no longer extremely high."
Cross-verifying multiple data points, this correction is best understood as a "valuation reset after crowded trading." The long-term growth foundation for AI chips is intact, but short-term valuations are reverting to more sustainable levels, and the market’s tolerance for expectation gaps has systematically decreased. A structural directional shift would require at least one of three conditions: sustained decline in forward-looking demand indicators, large-scale capital expenditure cuts or delays by major customers, or a trend decline in leading companies’ margins—none of which are currently supported by the data.
Cross-Market Allocation Feasibility and Trading Tools
For investors focused on medium- and long-term chip stock opportunities, this correction offers a clearer analytical framework: amid lingering macro uncertainty, chip stocks’ demand fundamentals and valuation levels have entered a more analyzable zone. Cross-market asset allocation strategies are particularly valuable now.
Recently, Gate has expanded its trading infrastructure through deep technical collaboration with compliant broker Alpaca. Users can trade over 10,000 NYSE and NASDAQ-listed stocks directly in a single account using USDT, covering the entire industry chain from AI chip giants to cloud computing and data center infrastructure. The unified USDT settlement eliminates the need to transfer funds between platforms, allowing all allocation operations within one interface.
This integrated platform model is especially relevant in today’s market: as macro variables shift rapidly, cross-market capital mobility directly impacts investment execution quality. Increased volatility in semiconductors has boosted demand for dynamic rebalancing between US equities and crypto assets. Gate’s fractional share trading (starting at just $1) lets investors with varying capital levels participate in high-priced tech stocks like Nvidia, offering flexible investment options. The process for trading US stocks on Gate is straightforward: log in, select the desired US stock in the TradFi section, use USDT to complete the transaction, and enjoy an experience consistent with traditional crypto trading.
Valuation adjustments in the semiconductor industry rarely end immediately after a single-day crash, but this correction creates a window where price and fundamentals diverge again. NVDA dipping below $200, Oracle’s RPO soaring to $553 billion, and Marvell’s S&P 500 inclusion countdown—these are not simple "buy signals" or the end of the AI cycle, but key facts investors must independently assess within a multi-variable decision framework.
Conclusion
Starting from SOX’s 10.3% single-day plunge and $1.4 trillion evaporated, through layered analysis of macro rates, micro expectation gaps, and crowded trades, and onto individual data points like Marvell’s index inclusion, Oracle’s RPO backlog, and Nvidia’s performance, the analytical path leads to this conclusion: this semiconductor correction is not a structural turning point for AI compute demand, but a valuation reset and release after rapid gains. The AI infrastructure build-out continues, but market expectations each quarter have reached extreme levels, with tolerance far lower than in 2024–2025. For investors, this means the "long narrative" still holds, but valuation requires stricter safety margins.
Markets are pricing mechanisms, and every crash creates new information asymmetries. The fundamental signals supported by data—Oracle’s backlog orders, Marvell’s index inclusion, Nvidia’s revenue structure—are the true anchors through volatility. Ultimately, facts decide the outcome, and investment direction depends on each investor’s independent assessment of the probabilities and odds those facts represent.
