The narrative of the AI industry has fundamentally shifted over the past 2 years, moving away from the 'large model war' characterized by parameter counts rising from billions to trillions and training costs escalating from tens of millions to hundreds of millions of dollars. While GPU clusters expanded from thousands to tens of thousands of cards, the focus is no longer solely on model performance or proximity to AGI. As the industry enters 2026, JPMorgan reports indicate that the primary driver for infrastructure expansion is no longer training but the massive, globally distributed demand for AI inference. The core consumer of computing power has transitioned to AI Agents, where every call, interaction, and task execution consumes Tokens, marking the evolution from a 'model era' to a 'Token industrial era.'

This structural transformation redefines the AI economy around the production, distribution, scheduling, and consumption of Tokens. As AI Agents proliferate, the critical challenges involve real-time Token generation, regional distribution, dynamic scheduling, and efficient consumption. Huang Renxun previously framed AI as an infrastructure system akin to electricity, proposing a 'Five-Layer Cake' of Energy, Chips, Infrastructure, Models, and Applications.

However, GoodVision AI observes that the transition to the inference era necessitates a more granular 'Seven-Layer Cake Structure' centered on Tokens, encompassing Power, AIDC, GPU, LLM, Token Distribution, Intelligent Scheduling, and AI Agents.

The first layer, Power, represents the foundational energy constraint, as AI data centers now consume electricity comparable to medium-sized cities. Global AIDCs face a paradox where GPUs and land are available, but power supply and grid scheduling lag. Consequently, the competitive landscape is shifting from electricity price competition to securing long-term, stable, low-cost energy procurement rights. In China, entities like Yangtze Power, China General Nuclear, and Three Gorges Corporation are pivotal, with nuclear and hydropower becoming critical for stability while wind and solar benefit from ESG demands. Similarly, US giants like NextEra Energy, Dominion Energy, and Exelon are upgrading from conventional utilities to core AI infrastructure resources, with Exelon's nuclear capacity addressing the need for all-weather high-stability electricity.

Layer Two, AIDC, functions as the Token Factory, consolidating thousands of GPUs into scaled clusters. Traditional construction cycles of 18 to 36 months are insufficient for exponential AI demand, prompting a shift toward modular and edge-deployable solutions. While Equinix and Digital Realty dominate global interconnection, Chinese operators like Runze Technologies are transitioning from traditional IDCs to AI computing centers. Simultaneously, former crypto mining firms such as CoreWeave, IREN, Applied Digital, and Cipher Mining are pivoting to AI infrastructure, with IREN emphasizing a 'green power + AI computing' model. GoodVision AI is differentiating itself by building lightweight, modular AI Factories with 2-4MW capacity, deploying regional nodes closer to users to align with the trend of edge inference.

Layer Three focuses on GPUs as the production equipment, where NVIDIA remains the absolute core with products like H100, B200, and Blackwell, supported by the CUDA ecosystem. AMD challenges this dominance with the MI300X and ROCm platform, while Broadcom and Marvell pursue ASIC and high-speed interconnect paths for efficiency. In China, Cambricon, Hygon, MorryThread, and Horizon Robotics are advancing domestic substitution, focusing on CUDA compatibility and cluster building. Beyond chips, the underlying infrastructure including liquid cooling from EIVIK, UPS systems from Vertiv and Zhongheng Electric, and optical modules from InnoLight and Xinyisheng, are becoming critical 'gold rush' businesses that determine the efficiency of Token generation per unit of time.

Layer Four, the LLM, acts as the Token Generation Engine, evolving from a demonstration of capability to a cost-efficient production tool. The competitive metric has shifted from parameter size to Token Cost, Inference Efficiency, Contextual Capability, and Multi-Agent Collaboration. While OpenAI, Anthropic, and Google compete for ecosystem entry, players like DeepSeek are reshaping the landscape through lower costs. GoodVision AI addresses this by deploying large models directly within its AI Factory data centers, transitioning from computing power leasing to direct Token service provision to enhance gross margins and user experience.

Layer Five involves Token Distribution, functioning as the AI era's 'Power Grid' through computing power rental platforms. Traditional cloud providers like AWS, Azure, and Alibaba Cloud are integrating AI GPU resources, while 'AI-native clouds' such as CoreWeave, Nebius, and Nscale offer specialized, flexible GPU clusters. In China, UCloud, Kingsoft Cloud, and Capital Online are major suppliers in this fragmented market. Layer Six, Token Optimization and Intelligent Scheduling, is the 'Brain' of the system, ensuring the right model processes the right task on the right computing power. As demand explodes, platforms like QingCloud, Lambda, OpenRouter, and Fireworks AI are critical for routing simple tasks to local models, complex tasks to cloud large models, and privacy-sensitive tasks to the edge. GoodVision AI notes that the future determinant of profit is not just GPU ownership but the ability to dynamically schedule models and Token traffic.

The final layer, AI Agents, represents the consumption endpoint, where the scale of usage will far exceed current human-AI interactions. Tech giants like Microsoft, Google, and Meta are embedding AI into all products, while enterprise software firms like Adobe and Salesforce are advancing automated workflows. The bottleneck is shifting from model capacity to Token scheduling efficiency, as future systems may involve 10 billion to 1 trillion Agents working simultaneously, consuming billions of Tokens daily. The industry remains fragmented, with advanced GPUs constrained by power, massive AIDCs lacking scheduling, and powerful models facing high latency. Only when these seven layers are fully interconnected will the AI industry transition from a 'Tool Era' to a 'Mass Adoption Era,' creating a smart infrastructure network that continuously produces, distributes, schedules, and consumes Tokens on a global scale.