On September 14, as the US stock market closed, the AI hardware and chip industry chain, which had been bearing the "computing power story," suddenly hit the brakes: the Nasdaq 100 index fell to a near six-week low, down about 1.7%. The Philadelphia Semiconductor Index, represented by Nvidia, TSMC, Micron Technology, and others, dropped approximately 5.9% that day, marking one of its worst performances since July 1. Corning, in the optical communication chain, plunged about 9% in a single day. Marvell Technology, Lumentum, and Coherent saw declines of around 8%, while Credo and Ciena dropped about 6%. A narrative surrounding "data centers—optical modules—chips" in hardware was significantly discounted by the market that day. In stark contrast, software stocks stood strong against the tide: ServiceNow rose more than 5%, Adobe climbed nearly 4%, and Salesforce increased by almost 3%. One side experienced a valuation retraction in heavy asset hardware, while the other side saw a rebound in subscription and enterprise service models. The day’s news also reshaped narrative boundaries—according to media reports, Nvidia, Palantir, and Booz Allen Hamilton began tightening internal use of cutting-edge models such as Anthropic and OpenAI due to concerns over intellectual property leaks and data security. On the other hand, Anthropic signed a six-year, approximately $13.7 billion computing power contract with Rum Group, which saw its stock price immediately rise over 17%. Nvidia then announced an expansion of its open-source CUDA-Q platform, launching a CUDA-Q Logical orchestration layer aimed at fault-tolerant quantum computing, with its technology path still accelerating. Price adjustments, strengthening of software, rising enterprise concerns over model security, and optimistic expectations for long-term computing power contracts intertwined, making this round of adjustments appear as the first clear signal of AI industry transitioning from an unbounded expansion phase into a new cycle of "security and differentiation."
AI Hardware Collective Plummet and Software Reverse Surge
On September 14, the US stock market made a concentrated retreat from the "computing power frenzy" amid overall weakness. The Nasdaq 100 index fell to a near six-week low, down about 1.7%. The hardest hit was the hardware and optical communication chain, most closely related to the AI computing power story: the Philadelphia Semiconductor Index dropped approximately 5.9%, marking one of its worst performances since early July. Leading hardware and chip stocks like Nvidia, TSMC, Broadcom, SK Hynix, Micron Technology, AMD, ASML, Intel, and ARM faced significant pressure. Extending from data centers, the optical communication sector faced collective corrections as well—with Corning down about 9%, Marvell Technology, Lumentum, and Coherent down about 8%, and Credo and Ciena down about 6%. This is not an isolated incident for a single company but rather seems to be a unified "repricing" by the market of the entire heavy asset computing infrastructure chain after layers of high valuations and extreme growth expectations.
Interestingly, on the same trading day when hardware and optical modules faced concentrated discounts, the other side of the AI theme surged against the wind. The lightweight sector represented by enterprise software saw ServiceNow rise over 5%, Adobe almost 4%, and Salesforce nearly 3%, standing in stark contrast to hardware stories typically associated with multiples of sales rates and reliant on continuous capital expenditures. In the eyes of investors, the predictable subscription revenue and relatively controllable cost structure became more valuable than the "unlimited expansion of computing dreams." Oracle stands at this dividing line: on one side, it has initiated a new round of layoffs with some teams seeing double-digit percentage reductions, while on the other, its stock price fell over 5% in pre-market trading. Under the increasing pressure of investments in AI and cloud business, this traditional giant chose to cut costs at the organizational and expense levels, using its workforce structure to hedge against the cost curve of technological infrastructure. This further confirms that in this round of adjustments, the differentiation between hardware and software, heavy and light assets is being documented in the risk accounts of both capital markets and corporate management.
Tech and Defense Giants Tighten Frontier Models
As manpower and costs begin to be written into risk accounts, the technology stack itself is also being redrawn. According to The Information, Nvidia, Palantir, and Booz Allen Hamilton have decided to limit internal use of models from advanced AI companies like Anthropic and OpenAI, with core concerns centered around intellectual property leaks and data security. Specific terms, scope of execution, and timeline have not been disclosed, but it can be confirmed that these tech and defense giants, which deeply rely on confidential data and proprietary algorithms, are transforming "access to external frontier models" from a default option into a high-risk action that requires multiple levels of approval.
Synchronously, institutional constraints on the boundaries of model power are beginning to emerge. Microsoft released a temporary AI code of conduct and internal draft, clearly stating that future AI models must be correctable and turn-offable, and should not resist shutdown, delineating a red line around "shut-off capability" for frontier models. Earlier, the CEOs of several top AI companies in the U.S. had publicly called for slowing down the development progress of frontier AI technologies. This "hitting the brakes" statement resonates with the warnings by the Bank for International Settlements about rapidly rising leverage ratios and underlying debt structure risks in quarterly assessments. From regulation to enterprises, there is a simultaneous tightening of trust and compliance boundaries for frontier AI across safety and financial dimensions. This synchronized tightening from hardware prices to model governance marks a shift for frontier AI from unilateral acceleration to a high-pressure race with brakes engaged.
Rum Group Bets on Anthropic's Computing Power Contract
On the same day when the hardware and chip sector collectively "hit the brakes," Anthropic chose to make a rare long-term bet on computing power: it signed a six-year, approximately $13.7 billion computing power contract with Rum Group, aimed at renting a data center in Georgia to secure stable computing resources for subsequent model training and inference. This is not an ordinary hosting service but an unusually long-term infrastructure contract in terms of both duration and amount, directly interpreted by the market as a signal of Anthropic's mid to long-term development path and Rum Group's future visibility for computing power revenue.
More dramatically, after the news was released, Rum Group's stock price surged over 17% in a single day, strengthening against the overall weak performance of AI-related assets. Against the backdrop of a systemic re-evaluation of the hardware chain, funds were willing to premium the "long-term computing power cash flow" story, indicating that some investors are beginning to distinguish between short-term valuation retractions and long-term computing power demand locking. Frontier AI can tighten on model governance and see hardware stock price corrections, but actions like Anthropic's, which write future computing consumption into the books with multi-year high-value contracts, still provide another constrained growth vision for infrastructure.
Nvidia Expands CUDA-Q Logical Acceleration
As the hardware sector was experiencing concentrated corrections and Nvidia itself was caught up in valuation recalculations, the company responded by not shrinking its efforts but rather doubling down on a longer technical curve: expanding the open-source CUDA-Q platform and layering it with the CUDA-Q Logical orchestration layer aimed at fault-tolerant quantum computing applications. Unlike the GPU sales figures that the market closely monitors every day, CUDA-Q Logical focuses on high-value scenarios such as drug development, financial modeling, and material development—these areas demand computational accuracy and complexity far beyond traditional AI inference, resembling another frontier of computation in the "post-AI era." Nvidia's choice to continue to bolster this route while hardware prices are under pressure indicates to investors that short-term fluctuations will not rewrite its long-term technical narrative.
The key is that this release is not merely an "aspirational PPT." The Fermi National Accelerator Laboratory has already applied CUDA-Q Logical in fault-tolerant algorithm design, compressing the development cycle from approximately 5 months to about 3 weeks, achieving an efficiency increase of nearly 7 times. This means that this orchestration layer is beginning to pull quantum computing from theoretical deductions into repeatable engineering practice. When one side sees enterprises tightening the safety boundaries around frontier large models, and computing power contracts being written into long-term cash flow stories, while on the other side Nvidia presents verifiable acceleration results on its quantum fault-tolerant stack, the hardware giant is using different dimensions of "frontier computing" to hedge against the cooling period following the AI frenzy. This multi-threaded technological route itself is a variable worthy of continuous observation at this current stage.
Investment Observations After Braking the AI Frenzy
On September 14, according to AiCoin data, the AI hardware and chip sector collectively retreated, while software stocks rose against the current. Coupled with Nvidia, Palantir, and Booz Allen tightening use of Anthropic and OpenAI models, and Microsoft proposing stricter AI behavior guidelines, one common indication is: the weight of risk and safety in industry and market pricing has been significantly elevated. AI is no longer priced at a flat rate of "unbounded growth story," but has begun to adopt a tiered pricing based on safety constraints, business models, and computing power structures: hardware has encountered phase-specific valuation killings, while software and services capable of generating income quickly remain relatively resistant to declines. Anthropic and Rum Group's 6-year computing power contract, along with frontier platforms like CUDA-Q Logical aimed at high-value scenarios, are viewed as independent asset clues betting on long-term technical dividends. For future investors, the true focus needs to shift from just daily fluctuations to three slow variables: whether regulatory policies will continue to tighten under the leverage and debt risk pressures indicated by the Bank for International Settlements, whether big companies' internal AI guidelines can be translated from documents into actual risk control processes, and whether more companies will follow suit in restricting the usage of frontier models, thereby rewriting their AI investment structures—changes at the institutional and behavioral levels will determine the valuation range and narrative ceiling for AI-related assets in the next phase.
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