谁会成为算力领域“宁德时代”？九章云极携“AI工厂”率先起跑

This article outlines key recent developments in the AI computing power sector and 9tcloud's differentiated development strategy, with core takeaways as follows:

1. The AI industry has moved beyond the initial technological breakthrough stage into industrialized production. Competing on large model parameter sizes is no longer the mainstream; the new competitive core is delivering intelligent capabilities at scale and low cost. Computing power has become a general-purpose fundamental production input, just like electricity and water.

2. Instead of following the crowd into large model end-user applications, 9tcloud has positioned itself in the underlying intelligent computing infrastructure track. Mirroring CATL's early path in power batteries, it launched its AI Factory strategy, and created the DCU unified computing power measurement standard and a full-link Token circulation system.

3. The computing power track is already primed for an industry leader to emerge; the market is in an explosive growth period with enormous room for expansion. 9tcloud has already built solid differentiated barriers, and is well-positioned to become the "CATL of the computing power sector" and a definer of the industry's infrastructure.

This article outlines industry trends amid the AI industrialization wave and actionable brand development strategies for brands, with core takeaways as follows:

1. In terms of industry and consumer trends: AI has fully penetrated all sectors, with over 40% of enterprises planning to embed AI agents into their core business systems. Demand for intelligent transformation across all industries is surging, and demand for computing power—the underlying infrastructure—is growing exponentially, creating new opportunities for product R&D and business upgrading for all brands.

2. Brands can learn from 9tcloud's differentiated positioning strategy: Like CATL, 9tcloud avoided the red ocean competition in the end market and positioned itself in the generic core infrastructure layer of the industrial chain, building competitive barriers by leading the development of industry standards to ultimately become a definer of the industry. This path offers valuable reference for new brands entering mature tracks.

3. Brands do not need to follow the traditional capital-heavy path for their own intelligent transformation. They can procure computing power and AI services via a pay-as-you-go model to lower entry barriers for transformation.

This article outlines market shifts, opportunities, risks and referenceable business models in the AI computing power sector for sellers, with core takeaways as follows:

1. In terms of growing markets and opportunities: AI industrialization has reached an inflection point, and the computing power track is in an explosive growth period. According to forecasts, China's computing power market will reach 4 trillion yuan by 2028, with a compound annual growth rate (CAGR) exceeding 40%. Global Token consumption has a CAGR as high as 3418%, meaning far greater growth headroom than traditional tracks.

2. In terms of shifting demand: Intelligent transformation across all sectors has generated new demand. Traditional heterogeneous computing lacks a unified measurement standard, making AI deployment costly and time-consuming for enterprises. The market has an urgent need for standardized, measurable, low-cost inclusive computing services.

3. Business model insights: Sellers can learn from 9tcloud's differentiated path: avoid homogeneous competition in large model end markets, position in the underlying computing infrastructure track, build lock-in effects by setting industry standards, innovate a consumption-based service model by shifting from capital-heavy construction to pay-as-you-go pricing, lower customer entry barriers, and build a positively reinforcing business flywheel.

This article outlines business opportunities brought by AI industrialization and insights on intelligent transformation for factories, with core takeaways as follows:

1. In terms of production and intelligent demand: AI is now deeply integrated into manufacturing, with AI quality inspection and smart production lines becoming core directions for manufacturing transformation. Factories have growing demand for low-cost, rapidly deployable AI capabilities and computing power, but the traditional capital-heavy transformation model carries too high a barrier for most factories to afford.

2. In terms of business collaboration opportunities: As AI industrialization enters an explosive growth phase, 9tcloud has announced plans to jointly incubate 1,000 high-value professional models and intelligent applications within three years, covering manufacturing and other physical industry sectors. Eligible factories can participate in cooperation to develop AI applications adapted to their own scenarios and capture dividends from industrial upgrading.

3. Insights for digital transformation: Factories do not need to blindly invest heavily to build their own self-owned computing clusters. They can adopt a consumption-based model of purchasing computing power and AI services on demand, leverage mature external computing infrastructure to quickly deploy intelligent applications, and lower the cost and barrier of transformation.

This article outlines development trends of the intelligent computing industry, core customer pain points and referenceable solutions for AI-related service providers, with core takeaways as follows:

1. In terms of industry development trends: AI has evolved from niche technical experimentation to industrialized production. The core of industry competition has shifted from whether a company can develop large-parameter models to whether it can deliver intelligent capabilities at scale, low cost and high stability. Underlying computing infrastructure has become a rigid demand across the industry, the track is in an explosive growth period, and market space is expanding exponentially.

2. Core current pain points for enterprise clients: Heterogeneous computing lacks a unified measurement standard and cannot be measured and traded like electricity or water. Traditional AI deployment requires heavy capital investment, long cycles and high costs, and traditional computing distribution models cannot meet the demand for large-scale intelligent delivery.

3. Reference for innovative solutions: 9tcloud launched a solution combining an AI dual-factory architecture, the DCU unified computing measurement standard, and a full-link Token circulation system. It splits operations into a Training Factory and a Token Factory to enable refined optimization at each stage, shifts AI deployment from a capital-heavy model to pay-as-you-go, and effectively reduces customer costs. This solution offers a useful reference for peer service providers.

This article outlines industry demand, innovative operation directions and risk mitigation strategies for AI platform players, with core takeaways as follows:

1. New industry demand for intelligent computing platforms: After AI entered industrialization, customer demand has shifted from simple computing hardware leasing to standardized, measurable, out-of-the-box full-link intelligent services. Customers want ready-to-use intelligent capabilities, not just raw computing resources.

2. Reference for platform operation innovation: 9tcloud pursues full-link in-house R&D spanning from underlying hardware clusters to upper-layer scheduling systems and service output, built a dual-factory system of Training Factory plus Token Factory, pioneered the DCU unified computing measurement standard, and developed a data-feedback-driven business flywheel. It transformed the traditional capital-heavy model into a pay-as-you-go consumption model that drastically lowers customer entry barriers, and will form extremely strong lock-in effects if the DCU standard becomes an industry consensus.

3. Risk mitigation: The current large model end track faces severe homogeneous competition and is a red ocean. The underlying infrastructure track offers far larger market space and higher barriers; positioning in this layer and setting industry standards makes it easier to build long-term competitive advantages, and effectively avoids the risk of homogeneous competition.

This article outlines new developments in the computing power sector during the AI industrialization stage, innovative business models and a typical case for industry researchers, with core takeaways as follows:

1. New industry developments: The AI industry has officially entered a new stage of industrialized production, with computing power emerging as a new general social productive force. The focus of industry competition has shifted from upstream large model R&D to the construction of underlying computing infrastructure. The lack of a unified computing measurement standard is the core bottleneck holding back current industry development. The computing power track is in an explosive growth period, with market size and growth far exceeding that of the power battery track in its early days, and the conditions are in place for a 100-billion-dollar-level industry leader to emerge.

2. Business model innovation: 9tcloud has innovated an AI dual-factory industrialization model, proposed the DCU unified computing power measurement standard, and built a business flywheel where DCU measures input, Token measures output, and data feedback drives model iteration. It transformed the traditional capital-heavy AI construction path into a pay-as-you-go consumption-based path, representing a major model innovation in the AI service industry.

3. Reference for industry research case: 9tcloud's development path replicates CATL's successful strategy of positioning in infrastructure and setting industry standards, and validates the pattern that players positioning in underlying general-purpose infrastructure are more likely to become industry definers during industrial upgrading. It offers a new, vivid case for studying the logic of AI industry upgrading.