If a textile factory only shows investors how many looms it has, but cannot explain the cost, qualification rate and orders per meter of fabric, it may be difficult for people to believe that it is operating well. Strangely, when it comes to the Token Factory, counting graphics cards, cabinets, and trillions of Tokens has become the most eye-catching achievement of some projects. As for who these tokens are sold to, how much they cost, and whether they can make money, the large screens in the exhibition hall often have no answer.
In 2026, Token factory is becoming a new hot word in the artificial intelligence industry. Jiangsu issued a word economic action plan to encourage computing centers to transform into word services; Beijing proposed the construction of a word factory and a hierarchical evaluation system. From competing for P computing power to showing how many Tokens are produced per day, artificial intelligence infrastructure has finally begun to focus on output. But output is not equal to sales volume, and call volume is not equal to operating income. A more acute question arises: Are we building a new type of intelligent computing infrastructure, or are we changing the name of the old computing power investment?
Token is not steel, more like a ticket
Token is the basic unit of measurement for information processing in large models. Token Factory organizes computing power, models, data and reasoning systems to continuously provide customers with measurable, billable and deliverable intelligent services. The word “factory” easily makes people think that Tokens can be produced first and then stored in warehouses, waiting for buyers, just like steel and cement. In fact, Tokens can be saved and reused, but today’s idle computing power will not become tomorrow’s additional capacity. Just like after a flight takes off, empty seats can no longer be sold. The aviation industry uses “available seat kilometers” to measure capacity and “revenue passenger kilometers” to measure real sales. The ratio of the two is the passenger load factor. Token factories also need their own passenger occupancy rate: theoretical peak, effective calls, and actual payment. The gap between the three can better reflect the operating level than the number of trillions of tokens produced per day.
Moreover, Tokens are not equivalent. Different types of Tokens have different generation costs; the number of Tokens consumed by different models to complete the same task may also vary greatly. One industrial intelligent agent used 100,000 Tokens to correctly handle an equipment failure, while another spent 1 million Tokens and still answered incorrectly. According to production statistics, the latter was actually ahead. This kind of ranking list is probably only liked by graphics card suppliers.
Therefore, when a factory claims to be able to produce trillions of Tokens every day, we at least have to ask: What model is used? What tasks can be accomplished? How fast is the response? What is the unit cost, and how many Tokens are actually sold to customers? Apart from quality, cost and customer demand, one trillion Tokens is just a beautiful number.
If output increases 1,000 times, output value may not increase 1,000 times.
The biggest fear when building a Token Factory is to follow the old path of investing in computing power. First build a computer room, purchase servers, and finally attract investment. The risk of computing power investment lies in the increasing misalignment between the financial depreciation cycle of equipment and the technology iteration cycle. Chips that have not yet recovered their costs on the books may lose their market competitiveness early as the performance and energy efficiency of new generation products improve.
More pressure comes from price. Research published by Epoch AI in September 2026 found that since 2023, in a set of capability tests, the call costs required to achieve equivalent performance have dropped by an average of about 47% per quarter, which is reduced to 1/13 of the original level in about one year. Even if the call volume of a Token factory increases 10 times, if the unit selling price drops to 1/10 of the original, the operating income will still remain unchanged. This is the so-called “Red Queen” dilemma: you have to run as fast as you can to stay where you are.
Therefore, a reasonable investment sequence should be to place orders first, expand production in stages, complete scenario verification, model adaptation and cost calculation first, and then allocate production capacity according to real demand. Batch tasks that are not sensitive to delays can reduce costs through peak-shift scheduling. OpenAI’s batch processing service allows tasks to be completed within 24 hours, and charges 50% less than regular calls. Businesses with high real-time and data security requirements can be deployed locally as needed. In this industry, winning customers in advance is more important than buying graphics cards in advance.
The more tokens you save, the harder it will be to do business?
The most direct business model of Token Factory is to charge according to the amount of Token calls. But here lies a paradox. Assume that an industrial intelligent agent originally required 100,000 Tokens to complete a task, but after optimization, it only requires 10,000 Tokens. The results are more accurate and the response is faster. If billed according to the call volume, the revenue of the same business may actually decrease. According to this logic, the model that wastes the most tokens may become the most active revenue-generating machine. Of course, the saved computing power can serve more customers, but this example reveals the limitations of pure pay-as-you-go billing. Token can measure resource consumption, but it is difficult to measure the value created by intelligent services.
The business model of Token Factory needs to be extended to smart services. At the basic layer, it provides efficient, stable, and measurable model inference services; at the platform layer, it delivers directly callable intelligent capabilities to developers through model adaptation, intelligent scheduling, performance optimization, and service guarantee; at the application layer, it can cooperate with industry software companies to explore charging based on tasks, service levels, and even actual business results. For manufacturing companies, what is really worth paying is how much equipment failures are reduced, how much the R&D cycle is shortened, and how much production efficiency is improved. Customers may not care whether 100 million or 1 billion Tokens are consumed to complete these tasks.
This does not mean that every Token factory must develop industry applications personally, but its business logic must shift from selling computing resources to delivering intelligent capabilities. When technological advancements help customers complete more tasks with fewer tokens, operators can also benefit from higher service quality, wider applications and more stable customer relationships. The competition in the Token Factory in the future will ultimately depend on who can deliver higher quality smart services at a lower cost.Token determines how to measure, and smart services determine why customers pay.
If you can buy a graphics card, you may not be able to run a good factory.
A common misjudgment in the Token Factory is to equate hardware configuration with production capacity. The chip provides the foundation, and the software determines how efficiently these computing power can be transformed into stable, low-cost intelligent services. The same GPU cluster uses different inference engines, scheduling systems and operation and maintenance strategies, and the actual production efficiency may be very different. There have been many practices in China. Baseflow Technology improves the operating efficiency of existing equipment through heterogeneous computing power scheduling, communication optimization and cluster management. In the Token Factory project in cooperation with Guizhou Mobile, Silicon Flow improved unit GPU inference performance by more than 30% under the same hardware conditions through operator fusion, memory optimization and dynamic batch processing. Software optimization has thus become an important path to expand effective production capacity and reduce unit costs.
This also places higher requirements on the investment and operation of Token factories. Local state-owned assets can provide infrastructure, long-term capital and public service supporting facilities, and can also introduce market entities through professional trusteeship, joint operations, etc., but they must ensure that technical capabilities, customer resources and operational responsibilities match. Who will be responsible for equipment depreciation, who will be responsible for technology updates, and how to share the losses of idle production capacity and customer loss? The burden of state-owned assets should be clearly calculated before investment. The criterion for measuring the effectiveness of state-owned capital participation should ultimately be how much real market demand has been leveraged and what kind of industrial ecology has been cultivated, rather than how many graphics cards are owned.
We must also be wary of a familiar industrial cycle: the government coordinates investment, state-owned assets purchase equipment, and then the government coordinates local units to purchase services. It seems that a closed business loop has been formed, but in fact it may just be financial funds circulating around the computer room. Token Factory ultimately still has to rely on independent and sustained market revenue to prove its value. After all, graphics cards can be bought with money, but customer orders will not automatically appear when the computer room is completed.
Tokens can be produced in other places, but the value should remain locally.
For local governments, there is another issue that deserves more attention: How many Token factories does a city need? What position should it occupy in the word economy industry chain? Areas with low energy costs and good conditions for large-scale supply can compete for large-scale inference production capacity. In areas where model and software talents are concentrated, inference engines and service platforms can be developed. Cities with intensive manufacturing industries are more worthy of laying out industry data, model evaluation, intelligent agent development and application integration. Cities do not need to produce every token locally, but they should try their best to keep application innovation and industrial benefits locally.
I even think that when evaluating the development level of a city’s word economy, more attention should be paid toEffective Token consumption and the industrial value it creates. Instead of ranking according to how many Token factories are built and how many Token coupons are issued, it is better to look at how many companies have actually used AI, how many production processes have been improved, and how much economic benefits can be created for every 1 yuan of AI cost invested. The growth in Token consumption is worth observing, but invalid calls can also increase the number. In particular, it is necessary to avoid subsidizing production and purchasing at the same time, and finally proving the success of the project by mutual settlement between the two parties.
The Token Factory is certainly worth building, but it must ultimately stand the test of the market. Otherwise, in a few years, we may see familiar scenes again: the computer room is brightly lit, the Token output in the exhibition hall is constantly beating, and the reception staff skillfully introduces the trillion-level production capacity. The visitors were very excited. Only the financial staff are still asking the slightly disappointing question: Who were these Tokens sold to?
Perhaps the most effective way to evaluate a Token factory is to turn off the big screen in the exhibition hall and open its customer orders and operating accounts.
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