On July 1, 2026, a news report sent shockwaves through global capital markets. According to media sources, Meta is planning to launch cloud infrastructure services, offering its 'surplus' AI computing power and model access rights to external clients.

The announcement triggered extreme market divergence: Meta surged 8.8% on the day, adding approximately $127 billion to its market value; while AI cloud providers like CoreWeave and Nebius plummeted 13.92% and 17.01% respectively, and memory chip stocks such as SanDisk and Micron Technology fell over 10%. Panic subsequently spread to Asian markets—on July 2, Samsung Electronics dropped 9.06%, SK Hynix collapsed 14.57%, while China's A-share ChiNext and STAR Market indices declined 5.71% and 5.64% respectively.

The A-share tech sector also corrected, with AI supply chain segments like memory chips, optical modules, and PCBs broadly retreating. Popular stocks including GigaDevice, Eoptolink, Montage Technology, Dongshan Precision, and Jiangbo Long suffered sharp declines.

A full-blown debate over 'computing power overcapacity' erupted.

01. Why Did the Market Panic So Badly?

The market's panic logic is not hard to understand. Over the past two years, global tech giants have engaged in an unprecedented AI capital expenditure race. S&P data shows the five major cloud providers—Google, Amazon, Meta, Microsoft, and Oracle—had combined capital expenditures of approximately $412 billion in 2025, projected to climb to nearly $700 billion in 2026. Meta has been particularly aggressive—raising its 2026 capital expenditure guidance to $125-145 billion, up nearly 87% from $72.2 billion in 2025.

The valuations of AI hardware stocks are built on the core assumption of 'perpetual growth in tech giants' capital spending.' When one of the world's largest GPU buyers starts selling 'surplus' computing power, investors naturally worry: Did two years of advance purchasing create overcapacity? Is the narrative of 'computing power scarcity' collapsing?

These fears were amplified in an extremely crowded market. Global AI hardware, memory, and optical module sectors had seen staggering gains, making Meta's news a catalyst for profit-taking.

02. Is There Really an Overcapacity?

However, a closer examination of industry fundamentals suggests the 'overcapacity' claim lacks merit.

First, computing power leasing prices continue to rise. The cost of one-year NVIDIA H100 leasing contracts has rebounded from a low of approximately $1.70 per GPU/hour in October 2025. If overcapacity were real, leasing prices would decline rather than increase.

Second, tech giants' capital spending remains undiminished. Meta not only avoided budget cuts but raised its 2026 capital expenditure ceiling from $135 billion to $145 billion. The four major tech firms' combined 2026 capital spending will reach approximately $725 billion, up 77% from 2025.

Meta has also signed long-term computing power purchase contracts exceeding $100 billion with AMD, CoreWeave, Nebius, and others. If overcapacity were real, why continue massive 'stockpiling'?

More critically, structural mismatches exist. Industry insiders point out the issue isn't 'absolute overcapacity' but 'structural mismatches'—excess exists in low-end general-purpose computing and underutilized smart computing centers lacking application scenarios, while high-end AI computing power for large model training and inference remains severely insufficient. Data shows average effective utilization of smart computing clusters below 20%, with a 40% shortage of high-end capacity.

Zhongtai Securities bluntly states Meta's computing power leasing merely represents 'Meta Cloud' implementation, unrelated to overcapacity. Tracking recent slopes of Token generation and Annual Recurring Revenue (ARR) shows AI demand still in early steep-growth phase; North American cloud providers' backlogs equal approximately 14x quarterly revenue.

03. Meta's Real Intentions

For years, Meta has overly relied on advertising—accounting for over 90% of total revenue in Q4 2025. Unlike Google, Microsoft, and Amazon, Meta lacks cloud services to monetize AI investments. With AI costs escalating, transforming massive GPU clusters from 'pure cost burdens' into 'revenue-generating assets' represents a logical business decision.

Industrial Securities cites overseas investors noting Meta leases older non-core computing power to recycle cash flow while retaining premium resources like Rubin series for superintelligence development. Estimates suggest Meta could still monetize approximately 35% of idle computing capacity.

Bank of America points out Meta could generate $10-15 billion annually per exawatt through leasing 'surplus' capacity, addressing skepticism about its $145 billion capital spending.

Tianfeng Securities clarifies: 'The market shouldn't oversimplify this as 'peaking AI computing demand.' More accurately, Meta aims to transform AI infrastructure from pure cost centers into leasable, chargeable, platform-based assets.'

Zuckerberg himself foreshadowed this move. At May's shareholder meeting, he stated that entering cloud services would be 'absolutely under consideration' if computing power surplus existed, noting external companies nearly weekly offer premium prices for Meta's capacity. However, he emphasized Meta can still absorb most planned capacity.

04. Panic Outweighs Reality

In summary, this global tech stock shakeout reflects far more panic than actual computing power overcapacity.

No single event proves AI industry decline. Conversely, Meta's move—alongside NVIDIA's new AI infrastructure partnership model launched concurrently—signals AI infrastructure maturing from 'capital burning' to 'commercial operation.'

Golden Eagle Fund similarly attributes A-share corrections to profit-taking by early bulls rather than narrative shifts in AI computing demand. E Fund Management sees the adjustment as short-term shocks superposition (overlapping) with post-crowded AI trading risk release, not implying fundamental changes to tech growth themes.

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