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 从算力到价值：AI时代的基础设施重构与产业增长新引擎| 2026AI Partner·北京亦庄AI+产业大会

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.

宋琛的演讲为当前AI产业的发展提供了一个清晰且深刻的分析框架，其核心在于指出价值衡量尺度的转移。以下从几个层面进行解读：

1. 核心观点：Token作为新共识的崛起
演讲开宗明义地提出 “Token正在成为AI时代的新质生产力单位” 。这不仅仅是技术术语的变化，而是产业价值锚点的根本性转移。Token（在中文语境常译为“词元”，是AI模型处理的最小文本单位）从一个模型输入输出的计量符号，被提升到了贯穿芯片、算力、模型、应用全链条的通用计价单位和价值度量衡。理解这一点，是理解后续所有产业逻辑重构的前提。

2. 驱动因素：多维共振催生Token经济
演讲指出Token经济的爆发源于三个维度的共振：

• 应用端质变：Agent（智能体）作为新交互入口，其复杂任务导致单次请求的Token消耗量呈指数级增长，从千级跃升至百万级。这创造了海量且持续的需求。
• 商业闭环形成：AI应用（如文中提及的“龙虾”等Agent应用）已深度嵌入工作流，产生了可衡量的商业价值，使Token消耗从研发成本变成了可盈利的运营成本。
• 国家战略支撑：智能算力被纳入“新基建”，从政策层面为Token经济的底座——智算基础设施——提供了最高级别的保障和长期预期。

3. 产业链重构：从“仓库”到“工厂”的价值链升级
Token经济的崛起引发了一场自上而下的产业链重构。演讲描绘了一个清晰的四层价值链：

• 芯片层：核心指标从单纯的算力（FLOPS）转向Token计算密度，推动GPU和ASIC专用化。
• 智算中心层：角色发生根本转变，从提供通用算力的“仓库”，转变为高效、规模化生产Token的“工厂”。这是英博数科定位的主战场。
• 模型层：MaaS（模型即服务）模式普及，模型技术产品化、商品化，按Token计费成为标准。
• 应用层：Agent成为新入口，AI从辅助性“工具”进化为直接产出价值的“生产力”。
  贯穿这四个层级的，正是Token这一统一“货币”，它让价值流动变得可计量、可交易。

4. 基础设施范式转变：从“训练优先”到“推理优先”
这是演讲中揭示的一个关键结构性转变。数据预测，推理算力需求将大幅超越训练算力。这一转变的深层逻辑在于：

• 需求本质不同：训练追求一次性、大规模的并行吞吐；推理则追求常态化、低延迟、高并发的实时响应。
• 成本核心不同：训练成本是集中投入；推理成本则关乎每Token成本，是持续运营的关键。
• 设计逻辑不同：这要求智算中心的基础设施（如网络、存储、调度系统）必须为低延迟、弹性伸缩的推理负载重新设计，而不再是仅为训练的大批量任务服务。多模态和长上下文的发展进一步加剧了这种需求。

5. 产业增长新逻辑：聚焦“单Token成本”
演讲最后引出的增长路径新观点—— “单Token成本” ——是上述所有分析的必然归宿。当Token成为价值单位，产业竞争的核心就从“谁的模型参数大”转向了 “谁能以更低成本支撑海量Token流转” 。降低单Token成本意味着：

• 更高的智算中心运营效率。
• 更低的AI应用使用门槛和运营费用。
• 更广阔的商业应用空间和市场规模。
  因此，围绕Token生产全链条的效率优化，将成为未来AI产业增长的核心引擎。英博数科选择不做模型、不做应用，专注于成为“Token工厂”的全栈构建者，正是押注于这一未来