On the first trading day of the second half of 2026, the US stock market experienced a dramatic divergence. Social media giant Meta soared nearly 10% in a single day on news of "selling computing power," while AI hardware sectors such as memory chips, semiconductor equipment, and optical communications suffered a broad selloff. The Philadelphia Semiconductor Index (SOX) plunged 6.27% in one day, closing at 13,353.28. Micron Technology (MU) dropped 10.57%, SanDisk (SNDK) fell 10.62%, Intel (INTC) lost 9.03%, and Applied Materials (AMAT) declined 9.97%.
Is this crash a sign that the AI hardware bull market has ended, or is it simply an emotionally driven overreaction?
How a Single Report Triggered a Meltdown in the Semiconductor Sector
Before the US market opened on July 1 (ET), Bloomberg reported that Meta is developing a cloud infrastructure business plan to sell AI computing power and model access to external clients. The project, internally codenamed "Meta Compute," is led by Meta’s head of infrastructure Santosh Janardhan, Superintelligence Lab AI executive Daniel Gross, and Meta President Dina Powell McCormick.
The core takeaway: Meta is considering commercializing its vast AI infrastructure—including data centers and AI chips—following a model similar to Amazon AWS’s Bedrock service. Upon release, the market’s interpretation quickly shifted from "Meta is opening up a new revenue stream" to a more pressing question: If Meta has enough computing power to sell externally, has its demand for upstream chips, storage, and optical modules reached a temporary saturation point?
This narrative shift directly triggered a wave of selling across the semiconductor sector. Of the 30 constituents in the SOX index, only 2 stocks closed higher. The storage segment took the brunt, with the Roundhill Storage ETF plunging 10.82% in a single day. Semiconductor equipment stocks collapsed in tandem: KLA (KLAC) fell 11.77%, Teradyne (TER) dropped 11.68%. In optical communications, Corning (GLW) tumbled 13.62%, and Astera Labs slid 10.80%.
Why Micron and SanDisk Were Hit the Hardest
Memory chip stocks suffered the most severe losses in this rout. Micron Technology dropped 10.57%, SanDisk fell 10.62%, Western Digital (WDC) lost over 6%, and Seagate Technology (STX) declined more than 5%.
This structural difference is not coincidental. Memory chips are a core beneficiary of AI infrastructure—large model training and inference require massive amounts of HBM (high-bandwidth memory) and SSDs (solid-state drives). Micron is one of the biggest beneficiaries of this trend. Just a week earlier (June 24), Micron reported its fiscal Q3 2026 results: revenue reached $41.46 billion, up 345.7% year-over-year; GAAP net income was $28.24 billion, surging 1,398.3% year-over-year. Gross margin hit 84.9%, setting a new record for the fifth consecutive quarter. The company’s revenue guidance for the next quarter is about $50 billion, well above analysts’ consensus of $43.58 billion.
Such extraordinary performance growth has naturally set the stage for profit-taking. Micron’s stock surged more than 260% in the first half of 2026. When the narrative shifts from "computing power is always scarce" to "computing power may be in surplus," the substantial unrealized gains accumulated earlier become a direct driver for concentrated selling. As one of the most aggressively re-rated segments in the AI hardware supply chain, memory chips naturally became the prime target for profit-taking.
Why Meta’s Sale of Computing Power Was Seen as a "Surplus Signal"
The market’s interpretation of Meta’s cloud business plan as a "computing power surplus" signal is underpinned by a logical chain.
First: Expectations that capital expenditure has peaked. Meta’s 2026 capex guidance is already at $125–$145 billion. In Q1 2026, the four major North American cloud providers (Amazon, Microsoft, Google, Meta) together spent $130.6 billion in capex, up about 70% year-over-year. Until now, the market has tolerated "unrestrained" AI capex from tech giants, based on the premise that "computing power is absolutely scarce"—as long as demand outstrips supply, any capex can be absorbed. Meta’s move to sell surplus computing power directly challenges this belief.
Second: Fears of a supply-demand reversal. Rich Privorotsky, head of Goldman Sachs’ 1-Delta trading desk, previously predicted: "The market’s core premise has always been computing power scarcity. Once supply increases and rental prices fall, the shortage narrative will be overturned, and hardware will feel the pain first." Meta’s move validates this logic: when a leading cloud provider starts "selling out" its computing power, it signals internal overcapacity, or at least structural idleness.
Third: The collapse of momentum trading strategies. The hardware sector’s plunge triggered a breakdown in momentum strategies. Goldman’s high-beta momentum basket (mainly chip and storage stocks) fell 9% in a single day after historic gains. This is a classic "crowded trade" unwind—when the narrative reverses, mass exits by similarly positioned investors amplify the selloff.
Why Wall Street Is Deeply Divided on the "Computing Power Surplus" Narrative
Although the market voted with its feet by dumping hardware stocks, Wall Street institutions are far from unanimous in their interpretations.
The bears, led by Goldman Sachs, warn of the risk that the scarcity narrative is being upended. UBS trader Christina Dwyer said Meta’s news "shifts the narrative toward greater financial discipline, easing concerns about endlessly rising capex," but the talk of "excess capacity" also raises doubts about underlying AI demand.
The bulls, represented by Nomura Securities, have tracked new global data center projects since Q4 2025 as a leading indicator for Asia’s semiconductor and hardware supply chains. The latest data shows that the number of new global data center projects tracked by Nomura rose from about 240 at the end of March to around 280, with GW-scale projects increasing from over 40 to about 50. Based on these estimates, new global data center deployment capacity will rise from 26GW in 2026 to 32GW in 2027, with 23GW still expected in 2028. Nomura concludes that peak AI infrastructure demand is still ahead, not behind.
Nomura further warns that the AI semiconductor cycle is far from peaking; the second half of 2026 could see an "epic" supply chain mismatch—as Nvidia’s Rubin architecture and AWS Trainium 3 enter mass production, shortages of advanced packaging, PCBs, CCLs, and other components will drive another round of price hikes and earnings upgrades.
The core dispute: Is Meta’s "surplus" a systemic overcapacity, or just structural idleness? The bulls argue that Meta is still buying new GPUs at scale, and external companies are still paying premiums for computing power. This looks more like a company optimizing resource allocation at a particular moment, not a systemic industry inflection point.
How Overvalued Was the SOX Index Before the Crash?
To understand this crash, we need to return to a fundamental dimension: valuation.
In the first half of 2026, the SOX index surged more than 80%. Before the crash, its price-to-earnings ratio was about 26 times forward earnings—well above the 10-year average of 19x and close to the 30x recent high set in 2024. By comparison, the Nasdaq 100 traded at 23x, and the S&P 500 at 20x.
The semiconductor sector’s valuation premium had reached historic extremes. Against this backdrop, any marginal narrative shift—regardless of fundamental impact—could trigger a major valuation correction.
Michael Burry, the real-life inspiration for "The Big Short," revealed his latest positions the day before the crash. In addition to continuing to short Nvidia, Applied Materials, Tesla, and the SOXX semiconductor ETF, he added Caterpillar to his short list for the first time. Burry noted that the SOX index was about 65% above its 200-day moving average—a situation seen only once before, during the 2000 dot-com bubble. He publicly warned: "What’s happening now looks more like the final stage of a bubble, not the start of a new bull market."
Whether Burry’s call is correct remains to be seen, but the comparison is telling: even without Meta’s news, the semiconductor sector’s valuations were already at a level needing "an explanation."
How Macro Policy and Sector Rotation Intensified Semiconductor Selling
The semiconductor selloff was not an isolated event, but the result of multiple macro factors converging.
Federal Reserve policy: On July 1, Fed Chair Kevin Walsh said at the ECB’s annual central bank forum that upside risks to inflation had eased in recent weeks, but he remained committed to bringing inflation down to the 2% target. Walsh avoided commenting on whether rates would be raised at the July FOMC meeting, emphasizing no forward guidance on future rate policy. LSEG data shows the market still prices in at least one Fed rate hike this year. A high-rate environment continues to pressure richly valued tech hardware.
Sector rotation: US stocks saw significant gains in the first half: the Dow rose 8.9%, its best first-half performance since 2021; the S&P 500 climbed 9.6%; the Nasdaq gained 12.8%. Jeff Kilburg, founder of KKM Financial, noted, "The big rotation continues into Q3, with funds flowing out of recently profitable tech stocks and into traditional Dow blue chips." As one of the best-performing sectors in H1, semiconductors naturally became a key source of outflows.
Macro data: US ADP private payrolls rose by 98,000 in June, below expectations. June manufacturing PMI fell 0.7 points from the previous month to 53.3, missing the expected 53.9. Softer economic data further fueled the shift from risk assets to defensive sectors.
Has the AI Hardware Bull Market Really Ended?
Returning to the original question: is this crash the end of the bull market, or a sharp mid-cycle correction?
From a fundamentals perspective, the long-term growth logic for AI hardware demand remains intact. Global data center projects continue to expand, AI chip supply and demand remain imbalanced, and memory chip supply is tight—these structural drivers have not disappeared because of a single Meta report. Just a week before the crash, Micron posted revenue growth of 346% and net profit up nearly 14x, with guidance well above expectations. On the supply side, semiconductors face multiple hard constraints, with new capacity typically lagging by 2–3 years; through 2026–2027, chips, memory, and equipment related to computing power will likely remain in short supply.
But from a market perspective, digesting the valuation bubble may take time. With the SOX index at 26x forward earnings, the sector up over 80% in H1, and Micron up more than 260% YTD, these numbers suggest that even with a solid long-term outlook, a meaningful correction is needed to absorb current valuations.
Goldman Sachs Delta One head Rich Privorotsky may have provided the most accurate framework: "The first hyperscaler to signal spending cuts will see its stock rise, while its upstream supply chain will come under pressure." This was borne out precisely in the July 1 session—Meta surged nearly 10%, while chip stocks collapsed across the board.
From this perspective, the July 1 crash looks more like a structural repricing within the AI investment cycle: capital is shifting focus from pure hardware infrastructure buildout to free cash flow stability and computing power utilization. This doesn’t mean the end of AI hardware investment, but rather that the market now demands hardware suppliers prove they can maintain growth and profitability as computing power shifts from "scarce" to "abundant."
Conclusion
On July 1, 2026, the Philadelphia Semiconductor Index plunged 6.27% in a single day, with Micron and SanDisk both tumbling over 10%—one of the sharpest single-day corrections in the AI hardware bull market to date. News that Meta plans to sell surplus AI computing power shattered the market’s core belief in "absolute computing power scarcity." Combined with extreme sector valuations, crowded momentum trades, and macro-driven sector rotation, this triggered a broad selloff.
However, Wall Street remains deeply divided. Institutions like Nomura believe the peak for AI infrastructure demand is still ahead, with global data center projects accelerating. Others, like Goldman Sachs, warn that the end of the scarcity narrative will keep pressure on hardware. This debate will be tested further in the upcoming US earnings season.
For investors, the July 1 crash offers an important window: the long-term logic for AI hardware investment depends on whether computing power demand can continue to outpace supply growth. This is not a question that can be answered overnight; it requires several quarters of capex data, computing power rental prices, and corporate earnings to validate.
Frequently Asked Questions (FAQ)
Q1: How much did the Philadelphia Semiconductor Index drop on July 1, 2026?
A: The Philadelphia Semiconductor Index (SOX) fell 893.68 points, down 6.27%, closing at 13,353.28.
Q2: What were the specific declines for Micron Technology and SanDisk?
A: Micron Technology (MU) dropped 10.57%, SanDisk (SNDK) fell 10.62%. Intel dropped 9.03%, and Applied Materials fell 9.97%.
Q3: What exactly did Meta plan to do that triggered this selloff?
A: Meta is developing a cloud infrastructure business plan to sell AI computing power and model access to external clients, codenamed "Meta Compute." The market interpreted this as a signal that Meta has surplus computing power internally, sparking concerns that AI hardware demand may have peaked.
Q4: How are Wall Street institutions divided on this event?
A: Institutions like Goldman Sachs warn that the end of the computing power scarcity narrative will pressure hardware, while Nomura and others believe the peak for AI infrastructure demand is still ahead, with global data center projects increasing. The second half of 2026 may see an "epic" supply chain mismatch.
Q5: What was the semiconductor sector’s valuation level before the crash?
A: Before the crash, the SOX index traded at about 26 times forward earnings, well above its 10-year average of 19x. The index rose over 80% in the first half of 2026.
Q6: Does this crash mean the AI hardware bull market is over?
A: There’s no definitive answer yet. In the long run, structural drivers for AI hardware demand remain, and global data center construction is still accelerating. But digesting the short-term valuation bubble may take time, as the market shifts from a "computing power scarcity" narrative to repricing capital efficiency and utilization.
