Over the past two years, the first phase of the AI industry was defined by the “large model wars.” Parameter counts scaled from hundreds of billions to trillions, training costs rose into the hundreds of millions of dollars, and GPU clusters expanded from thousands of chips to tens of thousands. Everyone focused on which model was more powerful and who was closer to AGI.
By 2026, the driving logic has changed. Recent research from JPMorgan Chase suggests that the force behind continued AI infrastructure expansion will no longer be model training, but massive demand for inference. The largest consumer of compute will be AI agents operating across the world. Every request, interaction, and task execution consumes tokens. The industry is transitioning from the model era into the token industrial era.
What will drive the AI world is the system surrounding the production, distribution, scheduling, and consumption of tokens. As agents scale, the way tokens are generated in real time, distributed across regions, dynamically orchestrated, and efficiently consumed becomes a central challenge of the AI economy.
Jensen Huang has argued that AI is not simply a software industry, but an infrastructure system comparable to electricity and the internet. GoodVision AI views that economy as a seven-layer cake built around token flows: power infrastructure, AIDCs, GPUs, LLMs, token distribution, token optimization and intelligent scheduling, and AI agents.
The system remains immature. Some companies possess advanced GPUs but remain constrained by energy. Others have built massive AIDCs but lack orchestration. Some have powerful agents yet face high inference costs and latency. Others control edge nodes but cannot integrate them into a collaborative network. Only when the seven layers are connected and coordinated will AI move beyond the tool era into large-scale adoption.
Layer One: Power Infrastructure — The Energy of the AI Era
The Industrial Revolution was driven by coal and oil. The internet era competed over traffic and servers. In the AI era, the most fundamental battle returns to energy, because AI ultimately runs on electricity.
A large AI data center can consume as much power as a mid-sized city. GPUs can be purchased and facilities built, but power supply and grid capacity cannot keep up. This is why AI companies are turning back toward energy infrastructure. Upstream of the “token factories,” a new energy supercycle is emerging.
NextEra Energy, Dominion Energy, Duke Energy, Southern Company, and Exelon are benefiting from AI data center expansion. China Yangtze Power, China National Nuclear Power, CGN Power, China Three Gorges Renewables, China Longyuan Power, and CHD New Energy span hydropower, nuclear, wind, and solar. Competition is shifting from electricity price toward power lock-in rights. Whoever secures stable, low-cost energy controls the first layer of token production.
Layer Two: AIDC — The Token Factories
A single GPU has little meaning on its own. What matters is large-scale clustering. AIDCs concentrate thousands of GPUs into unified systems capable of stable token production. Yet traditional construction cycles take 18 to 36 months, while grid expansion may take even longer. Legacy infrastructure cannot keep pace with exponential token demand.
Equinix operates more than 240 data centers across over 30 countries and combines global interconnection with low-latency networks. Digital Realty is expanding through PlatformDIGITAL. Range Intelligent Computing Technology Group is evolving from traditional IDC services toward AI compute centers. CoreWeave, IREN, Applied Digital, and Cipher Mining represent another path: transforming mining infrastructure into high-performance AI compute.
GoodVision AI has chosen lighter, modular, rapidly replicable AI Factories. Rather than building one hyperscale facility, it deploys 2–4 MW inference-focused nodes in densely populated regions. These regional token factories are faster to deploy, closer to users, and better suited to globally distributed inference networks.
Layer Three: GPU — The Machines That Produce Tokens
If electricity is the energy source, GPUs are the production equipment. Training belongs to a small number of frontier companies; inference will extend into every application, device, and endpoint. Robots, autonomous vehicles, glasses, and collaboration among agents all consume tokens in real time.
NVIDIA remains the core of the global AI chip industry. H100, B200, and Blackwell define current standards, while CUDA, TensorRT, DGX, HGX, and the broader stack create an ecosystem moat. AMD challenges through MI300X and ROCm. Broadcom and Marvell pursue customized ASICs and high-speed interconnects, while Intel expands through server CPUs and Gaudi accelerators.
The surrounding infrastructure determines whether token production can operate reliably at scale. Vertiv leads in UPS and power management. Envicool supplies liquid cooling and thermal management. Zhongheng Electric, Kehua Data, and KSTAR contribute power infrastructure. Zhongji Innolight, Eoptolink, and TFC Communication supply optical connectivity. Dell, HPE, Supermicro, Lenovo, and Inspur assemble and deliver AI servers.
Layer Four: LLMs — The Engines of Token Production
LLMs determine how tokens are understood, generated, and organized. OpenAI, Anthropic, Google, Meta, xAI, and DeepSeek have driven a global model race across text, multimodal reasoning, coding, agent collaboration, and long-term memory.
As the industry matures, the central question is no longer who owns the largest model, but who can operate models continuously at lower cost and higher efficiency. Competition is shifting toward token cost, inference efficiency, context capability, multi-agent collaboration, memory, and integration between models and infrastructure.
GoodVision AI is developing an optimization strategy by partnering with model providers and deploying models directly within AI Factories, evolving from compute rental toward Token-as-a-Service and a faster, more user-friendly inference experience.
Layer Five: Token Distribution — The Power Grid of the AI Era
Once AIDCs are built, compute must be made available to the world. Compute rental platforms break down centralized GPU resources, distribute them across networks, and rent them on demand to developers, enterprises, and applications.
AWS, Microsoft Azure, Google Cloud, Alibaba Cloud, and Tencent Cloud operate the largest networks. AI-native clouds such as CoreWeave, Nebius, and Nscale build platforms optimized specifically for training and inference. DigitalOcean and Vultr emphasize rapid deployment and lower-cost services for developers and startups. The challenge resembles an electrical grid: distributing fragmented compute resources efficiently at scale.
Layer Six: Token Optimization and Intelligent Scheduling — The Brain of the AI Era
Not every task is worth sending to the most expensive frontier model. Simple tasks can use local models, real-time workloads may be better suited to edge inference, and privacy-sensitive tasks may not be uploaded to the cloud. After asking whether enough compute exists, the industry must ask how compute can be used more intelligently.
The future architecture will route simple tasks to small models, complex workloads to cloud frontier models, privacy-sensitive workloads to the edge, and high-concurrency jobs across hybrid environments. GoodVision AI, QingCloud, Lambda, OpenRouter, and Fireworks AI are emerging in optimization and scheduling. UCloud, Capital Online, and Sugon are extending upward from GPU cloud resources into inference orchestration.
Layer Seven: Models and Agents — The Consumers of Tokens
This layer sits closest to users and is best positioned to capture adoption, but competition is intense. Jensen Huang has proposed that every company will become both a token producer and token consumer.
An agent may call multiple models, tools, and APIs while continuously performing inference, planning, and execution. Future token consumption will far exceed today’s human-AI conversations. Advanced users already build multi-agent systems with concurrent interactions and autonomous coordination, consuming billions of tokens per day.
It will not simply be one billion people using AI, but tens or hundreds of billions of agents operating simultaneously. The bottleneck will shift from model capability toward token scheduling efficiency. Microsoft, Google, Meta, and Amazon are embedding AI across software, search, social networks, and cloud services. Adobe, Salesforce, ServiceNow, and Palantir are advancing enterprise agents. Hugging Face is becoming global developer infrastructure, while iFLYTEK, Kunlun Tech, 360 Security Technology, Kingsoft Office, and SenseTime expand AI assistants and agent ecosystems in China.
Only When the Seven-Layer AI Cake Is Complete Will the AI World Truly Begin
Today’s AI industry still operates within an incomplete infrastructure system. Fragmentation, redundancy, and efficiency bottlenecks remain between power, AIDCs, GPUs, LLMs, token distribution, scheduling, and agents.
Only when this seven-layer cake is fully built and operates as a coordinated system will AI move beyond the tool era. Billions of agents will remain continuously online, collaborating and consuming compute and tokens. Every conversation, inference request, tool call, and automated task will rely on coordinated energy systems, GPUs, networks, scheduling platforms, and inference nodes.
The industry is evolving beyond software logic into an industrial system spanning energy, semiconductors, cloud computing, edge networks, and intelligent scheduling. Just as the Industrial Revolution required railways, power grids, and ports, and the internet required fiber, data centers, and cloud computing, AI maturity will be marked by a global intelligent infrastructure network capable of continuously producing, distributing, scheduling, and consuming tokens.
Once the seven layers are connected, competitive logic will be reshaped. The most important companies may no longer be those with the largest models, but those capable of connecting energy, compute, networks, models, and token flows into one integrated system.