From Computing Power to Value: Restructuring Infrastructure in the AI Era and the New Engine for Industrial Growth | 2026 AI Partner · Beijing Yizhuang AI+ Industry Conference

1. The Rise of Token as a New Economic Unit

The central thesis of this article is compelling: Token is emerging as the "new quality productivity unit" of the AI era. This is not merely a technical observation but a profound economic reframing.

Traditionally, computing was measured in FLOPS or processing cycles. But as AI applications mature, the token — the basic unit of data that large language models process — has become the universal denominator across the entire value chain. This shift matters because it:

• Standardizes value measurement across heterogeneous layers (hardware, infrastructure, models, applications)
• Enables commercial viability by creating a common pricing language
• Reflects actual utility — tokens correlate directly with the work an AI system performs

Song Chen identifies three converging forces behind this transformation: application scenarios evolving toward Agent-based interactions, commercial models finally reaching closure, and national policy recognizing intelligent computing as critical infrastructure.

2. The Training-to-Inference Paradigm Shift

One of the article's most significant insights is the structural pivot from training to inference. By 2027, inference computing is projected to consume 65% of total AI compute, up from 40% in 2024.

This shift has massive implications:

• Training favors massive parallelism and throughput — it is batch-oriented, high-memory, and scheduled
• Inference demands low latency, high concurrency, elastic scaling, and real-time responsiveness — it must serve unpredictable, bursty user demand

This means intelligent computing centers must be fundamentally redesigned. The architecture that optimizes for training (large GPU clusters, high-bandwidth interconnects, batch processing) is not the same architecture that optimizes for inference (edge proximity, elastic resource pools, request-level load balancing). The article correctly identifies this as not a simple ratio change but a "fundamental paradigm shift in computing."

IDC's prediction that inference demand will be 5-10 times training demand reinforces the scale of this opportunity. Combined with multimodal AI expanding context windows from thousands to millions of tokens, single-request compute costs are growing exponentially — which simultaneously creates demand for more infrastructure and pressure to reduce per-token costs.

3. The "Token Factory" Model and Industry Restructuring

The article proposes a four-layer value chain reshaped by the token economy:

1. Chip Layer: GPU and ASIC specialization accelerates, with "token compute density" becoming the core metric
2. Intelligent Computing Center Layer: Transformation from "computing power warehouses" to "token production factories"
3. Model Layer: MaaS (Model as a Service) commoditizes AI technology with per-token pricing
4. Application Layer: Agents evolve from tools to productivity engines

Yingbo's strategic positioning is notably disciplined: they explicitly avoid building models or applications. Instead, they focus exclusively on being the infrastructure enabler — the company that designs, builds, and delivers turnkey intelligent computing centers. This is a classic "picks and shovels" strategy in a gold rush, which historically carries lower risk and more predictable revenue than betting on specific applications.

4. The Deeper Strategic Logic

Several deeper themes emerge from this presentation:

• The "single token cost" narrative suggests the industry is entering a phase where economics, not capability, becomes the primary competitive axis. When everyone can access capable models, the winner is whoever can serve tokens cheapest and fastest.
• China's 61% share of global token calls signals both massive domestic demand and a geopolitical dimension — China is building its own AI infrastructure stack, and companies like Yingbo are positioning within this national strategy.
• The trillion-dollar market projection (global intelligent computing center market reaching $550 billion by 2029) validates the infrastructure-first thesis — the foundation layer of AI will be one of the largest capital expenditure categories of the decade.

5. Broader Implications

This article reflects a maturation of the AI industry. The conversation has moved from "can we build capable models?" to "can we economically deploy them at scale?" This is analogous to the internet's evolution from proving connectivity works to building the data centers, CDNs, and cloud platforms that made global-scale deployment possible.

For stakeholders across the ecosystem — from policymakers to investors to enterprise adopters — the key takeaway is: the AI infrastructure race is no longer about raw computing power, but about efficient, scalable token production. The companies that master this will form the backbone of the AI economy for years to come.