In the capital markets of the first quarter of 2026, the trajectory of Kingsoft Cloud has decoupled independently from the traditional industry cycle. The year-to-date cumulative surge of over 55% has not been accompanied by the common "price cuts for volume" narrative of traditional cloud providers. Instead, driven by the dual engines of computing power leasing and AI platform subscriptions, it has traced a steep upward curve. The underlying logic of this market rally has long transcended simple financial statement repair, pointing directly to a structural shift in the cloud computing paradigm. As Xiaomi Group fully rolled out its trillion-parameter edge-cloud collaborative large model based on the Mimo architecture at the end of 2025, and enterprise-grade AI Agents moved from proof-of-concept to scaled deployment, the consumption pattern of cloud resources has undergone a fundamental displacement. The previous extensive billing model based on cores, gigabytes, and time duration is being replaced by high-frequency Token invocations, long context windows, and real-time inference streams. The market, voting with real money, is no longer pricing in the improvement of a company's short-term income statement, but rather pricing in advance the "regaining of pricing power by cloud infrastructure in the AI Agent era." As computing power transforms from an optional IT cost into an uninterruptible factor of production, the abnormal stock price movement of Kingsoft Cloud is essentially a systematic re-evaluation of the value anchor of cloud computing by the capital markets.
The Underlying Ledger of Computing Power Inflation: From the "Quagmire of Price Wars" to "Token Pricing Power" Over the past three years, the main theme of the domestic cloud computing market has been capacity clearing and market share battles. Internet giants and independent cloud providers have traded IaaS (Infrastructure as a Service) unit prices for market scale through continuous reductions, causing long-term pressure on industry gross margins. Some edge node operators have even fallen into the financial predicament of "losing money for every rack deployed." However, the industrial reality of 2026 is rewriting this logic. The explosion of AI inference workloads has completely altered the consumption curve of cloud resources. The concurrency of traditional web applications or offline computing has obvious peaks and valleys, allowing cloud providers to amortize costs through overselling and mixed deployment technologies. In contrast, AI Agents’ continuous operation, context retention across multi-turn dialogues, and real-time responses for online search and tool invocations demand computing power, memory, and bandwidth to maintain high-frequency, low-latency stable output. This workload characteristic directly increases the rigidity of resource occupation. The overselling rate of cloud providers is forced to drop, and the marginal cost of unit computing power rises accordingly. A deeper change lies in the reconstruction of the billing model. The "cloud computing inflation period" mentioned by the market does not merely refer to unit price increases, but rather indicates that cloud resources are shifting from "standardized commodities" to "customized capacity with service premiums." Leading cloud providers have begun piloting tiered pricing systems based on Token consumption, inference latency SLA (Service Level Agreement), and computing power for model fine-tuning. Kingsoft Cloud's performance in this round of market rally is precisely the market's realization of expectations that it has taken the lead in switching its pricing strategy. When enterprise customers find that the best path to optimize AI invocation costs is no longer price suppression, but locking in resources through prepaid computing power packages and dedicated clusters, the bargaining power of cloud providers naturally rebounds. The tilt of the supply and demand balance has moved the industry from a new stage of "competing to see who cuts prices more fiercely" to "competing to see who can provide deterministic supply." The essence of computing power inflation is the process of realigning the physical constraints and commercial value of infrastructure after the scaling of AI applications. The abatement of price wars does not mean competition has vanished, and they are willing to pay a premium for stability.
Beyond Rack Leasing: The Agent Orchestration Layer Is Reshaping the Revenue DNA of Cloud Providers If computing power inflation explains the recovery of the industry's beta, then Kingsoft Cloud's own alpha stems from the quiet shift in its business focus. In the development chain of AI-native applications, the moat of underlying IaaS is being eroded by open-source models and computing power pooling technologies. The true moat is drifting upward to the PaaS (Platform as a Service) and Agent orchestration layers. Kingsoft Cloud's recent product iterations and ecosystem moves clearly outline this path: shifting from providing bare metal and virtual machines to delivering a one-stop AI workbench that includes vector databases, RAG (Retrieval-Augmented Generation) frameworks, model routing and scheduling, and enterprise-grade permission governance. The financial significance of this transformation lies in the reshaping of the revenue structure. Traditional cloud revenue highly relies on the linear growth of resource leasing, whereas the subscription fees, API invocation revenue sharing, and customized deployment services brought by the Agent platform possess higher customer stickiness and gross margin space. Taking the evolution of the Xiaomi ecosystem as an example, with the collaborative deployment of the Mimo large model on the edge and cloud sides, the massive interactive data, personalized preferences, and scenario-based instructions generated by devices require the cloud to provide low-latency knowledge retrieval and state synchronization. As the core computing power and data foundation of this ecosystem, the nature of Kingsoft Cloud’s orders has shifted from "periodic procurement" to "continuous service commitment." Enterprise customers are no longer just purchasing servers, but procuring an AI operating system capable of continuous iteration, security and compliance, and seamless integration with existing business flows. Another value of the platformization strategy lies in the construction of a data flywheel. When cloud providers are deeply embedded in customers' Agent workflows, every model invocation and every tool execution feedback will feed back into the cloud platform's scheduling algorithms and security strategies. This network effect of "the more it is used, the smarter it becomes, and the more indispensable it becomes" causes the switching costs of cloud services to rise exponentially. The premium given to Kingsoft Cloud by the market is not optimistic about its short-term rack deployment rate, but is betting on whether it can occupy the key nodes of enterprise-grade Agent entry points at the initial stage of the AI application explosion. When cloud providers evolve from resource sublessors to architects of AI workflows, their valuation logic naturally leans towards SaaS (Software as a Service) and platform-based enterprises. The predictability of revenue and the extension of customer lifetime value are replacing pure scale expansion as the core indicators for capital to evaluate cloud assets.
Hidden Reefs in the Boom: The Double-Edged Sword of Capital Expenditure Cycles and Demand Optimization The steep upward trend in stock prices has not erased the inherent friction of the industry cycle. The heavy-asset attribute of cloud computing dictates that any high-prosperity stage must face a precise balance between capital expenditures and cash flow. In 2026, with the comprehensive rollout of high-density GPU clusters and immersion liquid-cooled data centers, the deployment cost per rack and PUE (Power Usage Effectiveness) renovation expenses have doubled compared to three years ago. If Kingsoft Cloud is to maintain the supply elasticity of AI computing power, it must continuously invest in infrastructure construction. If revenue growth cannot cover depreciation, amortization and financing costs, improvements on the income statement will remain merely a book effect rather than a substantive business model breakthrough. Variables on the demand side are equally full of uncertainties. The current explosion in Token consumption largely stems from "exploratory trial and error" and redundant invocations by enterprise customers in the early stages of AI applications. As model routing optimization matures, prompt engineering matures, and local small models are deployed at the edge, rational customers will inevitably move towards "lean computing power utilization." Once enterprises begin to massively compress non-essential inference workloads and adopt caching strategies and model distillation technologies, the usage growth rate of cloud providers will face a natural decline. Historical experience shows that price competition in the cloud market has never truly disappeared; it has merely shifted from "unit price cuts" to "bundle restructuring" and "hidden discounts." As industry capacity is gradually released, leading players with economies of scale may still squeeze the profit margins of independent cloud providers through bundled sales and long-term agreement lock-ins. A more hidden challenge lies in the choice of ecological niche. In the cloud computing market surrounded by giants, Kingsoft Cloud's survival space highly depends on differentiated positioning and deep cultivation in vertical scenarios. Over-reliance on a single ecosystem or large customers, while ensuring short-term order visibility, may expose vulnerability during technology route switches or customer strategic adjustments. What the market is currently trading is the long-term demand curve brought by AI Agents, but capital will eventually return to the strict assessment of free cash flow and ROIC (Return on Invested Capital). Whether high prosperity can be transformed into sustainable profits depends on whether the company can maintain operational efficiency during the capital expenditure expansion period and hold the basic disk of platform subscriptions during the demand optimization cycle. This is not only a calculation problem for financial models but also a stress test for strategic determination.
The Ultimate Pricing Power Lies Not in Racks, but in the Depth of Workflow Embedding Kingsoft Cloud's 55% surge has torn a crack in the valuation system of the cloud computing industry. What the market is voting for with capital is no longer the accumulation of rack quantities and bandwidth scale in the traditional sense, but the ability of cloud providers to redefine resource allocation rules in the AI-native era. When computing power consumption changes from a flexible item in the IT budget to a rigid expenditure for enterprise operations, the business model of cloud computing completes the underlying switch from "trading scale for gross margin" to "defining premiums through services." But whether this round of market rally can evolve into a long-term value re-evaluation depends on a more essential proposition: whether cloud providers can truly embed themselves deeply into the decision-making closed loop of enterprise AI workflows, rather than merely staying at the surface level of resource supply. If the evolution of AI Agents ultimately moves towards standardization and open-source, cloud infrastructure may once again face the pressure of homogeneous competition, and the current pricing power recovery will merely be an interlude in cyclical fluctuations. Conversely, if the complexity of vertical scenarios and data compliance requirements continue to rise, platform-based cloud service providers capable of deeply binding business logic and providing end-to-end AI governance capabilities will truly grasp irreplaceable entry value. Capital market valuation frameworks are undergoing a shift from "hardware depreciation cycles" to "depth of ecosystem embedding." In a future where computing power is as ubiquitous as water and electricity, what determines the ceiling of cloud providers has never been the physical scale of data centers, but the depth to which their code and algorithms take root in the enterprise's digital nervous system. When the tide of resource leasing recedes, only those companies that transform themselves into the AI decision-making infrastructure for their customers will be able to secure the chips for long-term pricing in the new round of industry cycles.