正文 · AI 翻译
Shanghai Yidian and Shanghai Academy of Artificial Intelligence jointly unveiled the Yidian Intelligent Computing Data Chain ecosystem, “Flywheel Turns, Myriad Possibilities Emerge.”
According to the National Data Administration’s official website, China’s average daily token usage surged from about 100 billion in early 2024 to more than 500 trillion in June 2026. In just over 2 years, it grew more than 1,000-fold. Tokens are becoming a basic resource of the digital economy, like electricity and bandwidth.
Supply has surged in response, with cloud providers and intelligent computing centers setting up token factories. But the booming token business cannot avoid another fact: for customers seeking to use AI in their core operations, the sticking point is often not whether they can buy tokens, but whether they dare use them.
To answer whether customers dare use them, we must first look at how competition in the industry has evolved. The first generation competed on capacity: who had more available computing hours and larger clusters. The second competed on efficiency: who had lower inference costs and more refined scheduling. Now that AI is entering core business systems, customers repeatedly ask more fundamental questions: Is supply stable? Can a domestically produced system be delivered? Can processes be checked and responsibility traced?
All these questions ultimately point to the same issue: trust in token services.
Trust is the next dividing line in the token economy.
The token industry still has no common yardstick
To understand why trust is difficult to establish, start with a more basic layer. If you were buying token services for a company, the 3 most natural questions would be: How much does it cost? How well does it work? Can problems be traced?
The industry still has no uniform answers to the first 2 questions. Vendors use different billing definitions for “1 call”: with the same prompt and output length, token consumption can vary severalfold across platforms. Whether a service works well lacks comparable benchmarks even more: throughput, time to first token and accuracy are each defined and published differently. When buyers cannot make sense of the service they are purchasing, procurement decisions become a choice among specification sheets and depend heavily on experience and judgment.
The third hurdle is even harder to avoid than measurement and standards: traceability. Once token services are connected to core operations such as financial risk control, government document drafting and medical quality control, every call can affect business outcomes. Who ensures an uninterrupted supply? At what point did an anomaly occur, and can it be located and reconstructed? Whose domain did the data enter, and what can auditors use to check it? Most general-purpose token services are still inadequately prepared in these respects. When domestic sourcing becomes a mandatory requirement, another problem is added: without a common adaptation baseline across chips, models and toolchains, purchased computing power may not deliver usable capacity.
Insufficient capacity can be expanded, and high prices can be negotiated down. But missing standards, low compatibility rates and unclear boundaries of responsibility are difficult to solve simply by spending more money: they are problems of how the market is organized. Whoever first turns the solution into a deliverable, standardized product system is more likely to gain early entry to the market.
Where do faster, cheaper and more stable tokens come from?
The Yidian Intelligent Computing Cloud Token Factory is a response to this gap. Its capabilities can be viewed in 2 layers: an engine underneath that produces tokens quickly, economically and reliably, and a layer above it that gives customers the confidence to use the service over the long term.
The engine’s central goal is to be faster, cheaper and more stable. Faster means using a high-performance inference engine to optimize compilation and parallel scheduling for mainstream models, making high throughput and low latency production-grade inference capabilities promised at delivery, rather than merely metrics demonstrated in a test environment. These capabilities can support real-time responses in high-concurrency scenarios such as AI agents and multimodal applications. Cheaper means matching computing power to business demand: elastic scheduling smooths peaks and troughs in workloads, with resources allocated on demand and scaled dynamically. Customers are billed primarily for the tokens they actually consume, and an accessible unit cost turns “token freedom” from a slogan into a budget line item. More stable means more than a production-grade, highly available architecture. It also includes the ability to manage heterogeneous computing resources by pooling and scheduling different chips and clusters together. Fluctuations at a single point are absorbed by the pool, while businesses experience a continuously available supply.
If the engine layer addresses whether the service can be used, the assurance layer’s 3 forms of trust address whether customers dare use it. The first is trust in service operations: multiple sources of supply and spare capacity keep supply uninterrupted, operating status can be observed in real time, and clearly defined SLAs and verifiable fulfillment provide a basis for entrusting core operations to the service over the long term. The second is trust in an independent domestic ecosystem: from the computing foundation to model adaptation, the system can be delivered with a fully domestic technology stack, supports mainstream domestic chips and open-source model ecosystems, and can evolve, reducing the risk of being locked into a single technical path. The third is trust in security and compliance: calls are logged and preserved as evidence throughout the entire process, data boundaries are clear and controllable, every call can be audited and every step traced. Processes can be examined, results explained and responsibility clearly assigned. Together, these 3 forms of trust underpin the system’s capabilities.
Trusted tokens in real business operations
Whether a token service is truly usable ultimately depends on whether it can be applied to specific business operations. Across 5 fields—smart devices, AI for science, the online new economy, autonomous driving and embodied intelligence—the Token Factory addresses different technical needs. Smart homes and wearable devices need coordination between devices and the cloud, unified access and consistent metering to deliver a stable, consistent AI experience on devices. AI4S applications such as drug development, materials computing and biological simulation need research computing power turned into a supply that can be called continuously, accelerating the production and application of results. AIGC creation and AI upgrades to online businesses test whether elastic supply can handle peaks and troughs in traffic. In-car and cloud-based agents for autonomous driving, along with robots’ multimodal needs for perception, decision-making and interaction, all call for low-latency inference and continuous availability. Real-time performance largely determines whether a robot responds promptly or slowly.
Across 6 industries, the value of trust becomes more concrete. Financial clients call on tokens for intelligent investment research, compliance reports, risk reviews and intelligent customer service. Logging and preserving evidence throughout the process is a strict regulatory requirement and a prerequisite for procurement. Manufacturing clients need specialized industrial models to support questions and answers about equipment operation and maintenance, quality inspection report generation and analysis of production anomalies. Model outputs directly inform production decisions, leaving no room for ambiguity. Education and cultural tourism applications seek safety, control and engaging experiences: the former serves AI study companions, teaching resource generation and hands-on programming training, while the latter supports AIGC content creation, intelligent guides, itinerary planning and multilingual explanations. Healthcare uses dedicated industry models and private deployments to carry out medical record quality control, clinical knowledge questions and answers, and literature analysis within data security boundaries. Urban governance focuses on drafting government documents, processing hotline requests, answering policy questions and analyzing public opinion; behind the efficiency gains is an overall improvement in public services. These scenarios may seem scattered, but they share a clear trait: all are already putting AI into their business processes and generally require a stable token supply service that can be traced and checked.
This deliverable, verifiable capability is becoming a model for industry practice. At the Shanghai Yidian Intelligent Computing Data Chain Innovation Forum, “Yidian Ignites Digital Intelligence, Cloud Chains Link All Things,” held during the Global Data Business Conference on October 31, Shanghai Yidian and Shanghai Academy of Artificial Intelligence jointly unveiled the Yidian Intelligent Computing Data Chain ecosystem, “Flywheel Turns, Myriad Possibilities Emerge.” Shanghai Academy of Artificial Intelligence provides the NEX-N2.5 large model to bring intelligence to applications; the Yidian Intelligent Computing Cloud Token Factory supports data processing and model training with city-scale computing capacity. Driven by application needs, data, models and computing power converge on the Dayan·Qidian platform. Data resources grow into data products, and the use of those products generates new data and new needs, setting the data flywheel in motion. Bringing the platform’s capabilities to business settings requires an accessible device. An all-in-one device released at the forum provides that endpoint. Its first release includes the “Find Data” function: once connected, it can quickly discover data and precisely match supply with demand, turning the search for data from a needle-in-a-haystack task into a guided search. More platform capabilities will continue to be added to the device.
Make “trust” a foundation for stability and security, rather than a selling point
Looking from this joint announcement to the industry as a whole, competition in tokens will not stop at capacity and price. When token services truly enter core business systems, stability, security and trust will all become entry requirements rather than added advantages. This is where the Yidian Intelligent Computing Cloud Token Factory has positioned itself in advance: it is building a deliverable, auditable and traceable system for operating token services, as well as selling tokens. From computing power scheduling to a trusted ecosystem, whoever first defines and delivers on this standard will be closer to defining the next generation of token services.
This article was provided by Shanghai Yidian and republished by QbitAI with permission. The views are those of the original author.