Nasdaq Composite Hits Record High; SanDisk Tops $1,900 as Viral Muse Promises to Complete AI Commercialization Loop
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On September 22, Eastern Time, the Nasdaq Composite and Nasdaq 100 hit record highs, driven by falling oil prices, easing interest rate pressures, and strong tech earnings. Memory stocks and tech leaders like AMD and Meta surged, signaling that capital favors both hardware and cloud platforms. JPMorgan projects AI infrastructure investments will reach $1 trillion by 2027, supported by accelerating enterprise demand and robust cloud revenue growth. Meta’s AI agent Muse exemplifies this commercialization loop, driving rapid adoption. While valuation risks persist, the AI supply chain is transitioning from hardware scarcity to higher computing utilization and revenue generation.
TradingKey - On September 22, Eastern Time, the Nasdaq Composite Index hit an all-time high, reaching a peak of 27,272 points; the Nasdaq 100 Index crossed 30,000 points on September 21, serving as a major symbol of market sentiment; meanwhile, the Dow Jones Industrial Average was falling.
The direct driver behind the Nasdaq's rally stems from tech stocks regaining capital favor, alongside falling oil prices, a temporary easing of interest rate pressures, and the continued expansion of AI infrastructure demand. Sector-wise, memory stocks led the gains today, with SanDisk (SNDK) surging more than 8% intraday to cross the $1,900 threshold; Micron (MU) and SK Hynix ADR (SKHY) gained over 3%.
The previous AI rally was mainly driven by chips, memory, and optical communication equipment. Hardware companies enjoyed clear order growth and rapid profit realization, leading investors to award them higher valuations. However, as hardware supply expands, the market has begun to ask two key questions: can cloud providers sell their computing power, and can enterprise clients achieve quantifiable returns from AI?
The answer currently remains incomplete, yet it is more positive than a year ago. The upward logic of the AI supply chain is shifting from "hardware scarcity" to "increased utilization of computing power," and from "will capital expenditure stop" to "can capital expenditure generate a larger revenue pool."
Why the Nasdaq Hit Another Record High
First, the pullback in oil prices eased macroeconomic pressure.
Tech stocks are highly sensitive to interest rates and inflation expectations. When oil prices pull back, the market typically dials down concerns over energy-driven inflation. Even if this does not mean inflation risks have disappeared, it at least provides temporary breathing room for growth stock valuations.
Market performance on September 21 reflected this transmission mechanism. The Nasdaq surged while the energy sector lagged behind; the 10-year U.S. Treasury yield also pulled back. Macroeconomic data has not turned entirely friendly, but marginal shifts were enough for capital to refocus on high-growth assets.
Second, technology leaders regained their market leadership.
Yesterday, AMD (AMD) surged nearly 10%, with its intraday market capitalization reaching $1 trillion for the first time; Meta (META) gained over 11%, as market attention focused on user growth and commercialization prospects for its AI agent product Muse. The simultaneous strength in chipmakers and platform companies indicates that capital is no longer buying only "pick-and-shovel" hardware suppliers, but also enterprises commanding cloud platforms, user entry points, and distribution capabilities.
Finally, the Nasdaq 100 Index breaking through the 30,000 round-number mark reinforced market sentiment.
Although index levels themselves do not guarantee future returns, institutional capital, quantitative models, and media narratives tend to refocus attention around round numbers. When an index simultaneously meets three conditions—hitting new highs, active trading, and gains in leading stocks—the market easily enters a positive feedback loop: rising prices attract capital, capital pushes up heavyweight stocks, and heavyweight stocks further lift the index.
AI Capital Expenditure Continues to Accelerate as AI Commercialization Logic Closes the Loop
JPMorgan Chase Chairman and CEO Jamie Dimon stated during the JPMorgan India conference that AI investments centered on hyperscalers could grow from approximately $300 billion in 2025 to about $700 billion in 2026, reaching $1 trillion in 2027.
Capital is flowing into data centers, AI chips, networking equipment, cooling systems, power facilities, and industrial construction. The stronger the demand for AI computing power, the more infrastructure cloud providers need to invest in; the more developed the infrastructure, the lower the cost for enterprise customers to use models, which in turn further expands the scope of applications.
Although the market previously worried that hyperscalers would fall into the predicament of capital expenditures spiraling out of control before revenues materialize, recent discussions have shifted. JPMorgan data shows that top cloud providers' second-quarter cloud revenue grew by approximately 48% year-over-year, with remaining performance obligations and order backlogs approaching $1.7 trillion, indicating that demand may still exceed current supply capacity.
A distinction needs to be made here between two types of capital expenditures. The first type consists of upfront investments aimed at capturing future market share, featuring longer payback cycles and higher reliance on financing. The second type involves capacity expansion driven by signed orders and steadily growing cloud revenue, offering higher cash flow visibility. The market is gradually coming to believe that AI infrastructure spending is transitioning from the first model to the second.
When AI revenue growth is sufficient to cover incremental investments, a closed loop forms across the industry chain: cloud providers expand data centers, chip and networking equipment suppliers secure orders, enterprise customers obtain computing power, AI applications boost productivity, and ultimately, cloud service revenue growth is driven further.
The explosive popularity of Meta's personal AI agent app, Muse, launched on September 8, serves as evidence of this closed-loop logic in AI commercialization. Sensor Tower data shows that Muse reached 730,000 downloads within about five days of its launch and surpassed ChatGPT to claim the top spot last Friday. The firm noted that Muse's cumulative downloads within the same launch window were higher than those of ChatGPT, Claude, and Grok.
This content was translated using AI and reviewed for clarity. It is for informational purposes only.
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