Over the past two years, the narrative in the AI industry’s first half has mainly revolved around the “large model war” initiated by various tech giants. The number of parameters has moved from billions to trillions, training costs have gone up from tens of millions to hundreds of millions of dollars, and GPU clusters have expanded from thousands of cards to tens of thousands of cards. Everyone has been discussing whose model is stronger, who is closer to AGI, as if the end goal of AI competition is the performance of large models themselves.
However, as we reach the year 2026, the driving logic of the AI industry has changed. According to JPMorgan’s latest report, what will truly drive the continuous expansion of AI infrastructure in the future is no longer model training but the massive demand for AI inference. In the future, the most significant consumer of computing power will no longer be just training large models but the globally distributed AI Agents. Every call, every interaction, every task execution essentially consumes Tokens. The AI industry is transitioning from the “model era” to the “Token industrial era.”
Because what will truly drive the operation of the AI world in the future is not just the model itself but the production, distribution, scheduling, and consumption system built around Tokens. Especially as AI Agents begin to appear on a large scale, how Tokens are generated in real-time, distributed across regions, dynamically scheduled, and efficiently consumed will become the core new issues of the entire AI industry.
As recently proposed by Huang Renxun, AI is not a simple software industry but an infrastructure system similar to electricity and the internet. In his “Five-Layer Cake” architecture, the AI industry is divided into five layers: Energy, Chips, Infrastructure, Models, and Applications. And as the AI industry gradually transitions from the “training era” to the “inference era,” GoodVision AI tends to understand the entire AI economic chain as a “Seven-Layer Cake Structure” revolving around Tokens.
Layer One: Power – The Energy Foundation of the AI Era. Layer Two: AIDC – The Token Factory. Layer Three: GPU – The Production Equipment of Tokens. Layer Four: LLM – The Production Engine of Tokens. Layer Five: Token Distribution – The “Power Grid” of the AI Era. Layer Six: Token Optimization and Intelligent Scheduling – The Brain of the AI Era. Layer Seven: AI Agent – The Token Consumption Endpoint.
From energy and GPUs to AIDC, edge nodes, and model inference with intelligent scheduling, the AI industry is forming an unprecedented “Token industrial system.” However, at the current stage, this system is still far from mature. Some have the most advanced GPU but are constrained by power supply; some have built massive AIDCs but lack efficient scheduling; some have developed powerful AI Agents but face high inference costs and latency; some control edge nodes but cannot form a unified collaborative network. Although the entire industry chain is developing rapidly, there are still significant fragmentation, redundancy, and efficiency bottlenecks among the layers.
Only when these seven layers of infrastructure are truly connected, coordinated, and interconnected, will the AI industry transition from today’s “Tool Era” to the “Mass Adoption Era” in the intelligent world.
[BlockBeats]
The Token Industrial Era: How AI’s Seven-Layer Cake Will Reshape Crypto Markets
The AI industry is undergoing a fundamental shift from a model-centric “training era” to a token-driven “inference era,” with profound implications for crypto markets and investors. As articulated in JPMorgan’s latest report and expanded upon by GoodVision AI’s “Seven-Layer Cake” structure, we’re witnessing the emergence of a comprehensive token industrial system that will drive demand, value creation, and investment opportunities across both traditional and crypto markets.
From Training to Inference: The Economic Paradigm Shift
For the past two years, the AI narrative has been dominated by the “large model war” – a competition focused on parameter counts, training costs, and raw model performance. This era was characterized by massive GPU clusters and billion-dollar training budgets, essentially a race to build the most capable “brains” for AI systems. However, as we enter 2026, the economic logic is fundamentally shifting from training to inference.
The critical insight from JPMorgan’s analysis is that the true driver of future AI infrastructure expansion will be inference demand, not training. While training requires immense computational resources, it’s a finite process. In contrast, inference – the actual deployment and operation of AI models in real-world applications – is continuous, distributed, and potentially infinite in scale. This shift has profound implications for token economics, as every inference call, interaction, and task execution consumes tokens, creating a perpetual demand cycle.
The Seven-Layer Cake: A New Framework for AI-Token Integration
GoodVision AI’s “Seven-Layer Cake” structure provides a comprehensive framework for understanding how tokens will integrate with the AI stack:
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Power Layer: The energy foundation that enables all AI operations. As AI scales, so too will energy demands, creating investment opportunities in renewable energy, advanced cooling solutions, and efficient power distribution systems specifically designed for AI workloads.
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AIDC Layer: The “token factory” where raw computational power is transformed into usable AI capacity. AI Data Centers are evolving from simple hosting facilities to sophisticated token production facilities, with monetization models increasingly centered around token-based pricing.
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GPU Layer: The “production equipment” for tokens. GPU scarcity has become a critical bottleneck, driving innovation in alternative computing architectures, virtualization technologies, and secondary markets for GPU access – all of which will have tokenized representations.
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LLM Layer: The “production engine” that generates tokens through model inference. As language models become more specialized and efficient, we’ll see the emergence of model-specific tokens and micro-payment systems for accessing different model capabilities.
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Token Distribution Layer: The “power grid” of the AI era. This layer focuses on how tokens are moved, exchanged, and valued across the AI ecosystem. We anticipate significant innovation in decentralized exchanges, cross-chain protocols, and specialized AMMs designed for AI token flows.
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Token Optimization Layer: The “brain” that coordinates resource allocation and scheduling. This represents a convergence of AI and blockchain, with autonomous systems optimizing token usage across the entire stack – a natural fit for decentralized autonomous organizations (DAOs) and algorithmic market makers.
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AI Agent Layer: The “consumption endpoint” where tokens are ultimately utilized. As AI agents become more autonomous and capable, they will develop their own economic behaviors, creating secondary markets for agent-produced tokens and services.
Market Implications and Investment Opportunities
The transition to a token-based AI economy creates several key investment themes:
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Infrastructural Tokens: Projects that provide physical infrastructure (power, cooling, GPU access) with token-based economic models will capture significant value. We’re particularly interested in tokens that represent ownership stakes in AI data centers or provide revenue shares from computational services.
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AI Agent Economies: The rise of autonomous AI agents will create entirely new economic systems. Tokens that facilitate agent-to-agent transactions, resource sharing, and collaboration will likely see substantial growth. Look for protocols that enable AI agents to own wallets, manage finances, and participate in decentralized markets.
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Cross-Layer Optimization: The most valuable opportunities may lie in protocols that coordinate across multiple layers of the AI stack – particularly those that can solve the fragmentation and inefficiencies mentioned in the article. Tokenized marketplaces that match computational resources with specific inference needs could capture significant value.
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Inference-Centric Tokens: Unlike current AI tokens that primarily represent model ownership or access, future tokens will increasingly be tied to actual inference consumption. This creates a more predictable revenue model and stronger fundamental use case.
Risks and Challenges
Despite the promising outlook, several risks warrant consideration:
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Energy Constraints: The power layer represents a fundamental bottleneck. Projects that fail to adequately address energy sustainability and availability may face regulatory challenges or operational limitations.
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Centralization Risks: Much of the AI infrastructure is currently dominated by centralized players. The token economy must genuinely decentralize value creation to avoid simply creating new forms of centralized control.
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Valuation Complexity: Valuing AI-related tokens will be significantly more complex than traditional crypto assets, requiring sophisticated models that account for computational utility, energy costs, network effects, and agent adoption.
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Regulatory Uncertainty: As AI systems become more autonomous and economically significant, regulatory frameworks will likely emerge that impact token usage, data privacy, and computational sovereignty.
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Technological Convergence: The success of this vision depends on the seamless integration of blockchain, AI, and physical infrastructure – a complex technical challenge that many projects may struggle to overcome.
The Path Forward: From Tool Era to Mass Adoption
The article correctly identifies that the AI industry must overcome significant fragmentation and inefficiencies before reaching the “Mass Adoption Era.” For crypto investors, this means focusing on projects that solve real coordination problems across the AI stack rather than those merely riding the AI hype wave.
The most promising opportunities lie at the intersection of layers – particularly in protocols that can efficiently distribute tokens across the AI infrastructure, optimize resource allocation, and enable seamless agent interactions. These projects will likely capture the most value as the industry transitions from today’s fragmented state to a cohesive token industrial system.
As we look toward 2026 and beyond, the convergence of AI and token economies represents one of the most significant technological and economic shifts of our time. Investors who understand this seven-layer framework and identify projects that genuinely advance the coordination and efficiency of the AI stack will be well-positioned to capture substantial value in the emerging token industrial era.