Editor｜Yang Xuran

The exponential growth in Token usage is driving up the valuations of a number of companies.

Xunce Tech, known as the 'first Token stock,' saw its share price surge nearly sixfold from its offering price just 108 days after listing, reaching a total market capitalization of HK$105 billion.

Zhipu, which debuted on the Hong Kong Stock Exchange on January 8 this year, had a market value of approximately HK$58 billion on its first trading day. After releasing its first annual results, its stock price quickly broke through the HK$1,000 mark, and its market value surpassed HK$400 billion.

Zhang Peng, CEO of Zhipu, defines the keyword for 2026 as 'Token volume,' believing that breakthroughs in intelligence ceilings, coupled with exponential growth in Token consumption, jointly constitute the commercial value of the AGI era.

Among companies yet to go public, Yuezhian last month completed a new round of financing exceeding $1 billion, reaching a valuation of $18 billion, up from just $4.3 billion in a $500 million financing round at the end of last year.

This rapid valuation jump is directly linked to OpenClaw's announcement in February this year that it would set Kimi K2.5 as its official flagship model. Just one month after K2.5's release, Yuezhian's ARR (Annual Recurring Revenue) surpassed $100 million. This performance growth not only supported the valuation increase but also opened up the possibility of the company's IPO.

Both domestically and internationally, capital markets are pricing the rise of the Token economy with remarkable enthusiasm. In the coming months, we will have the opportunity to hear various wealth myths. This booming Token economy is completing cognitive popularization for all in a 'Great Leap Forward' manner while accumulating risks.

This is an in-depth value article from the content team of Business Focus WAVE. Follow us on multiple platforms.

Growth

When chatbots first emerged, few could have imagined that just two years later, the average daily Token usage of a single large model like Doubao could exceed 120 trillion. Even at a relatively low price of two yuan per million Tokens, this implies that 300 million yuan in real funds are being burned daily on Doubao.

In fact, similar scenarios are playing out among large model companies both domestically and internationally, with global average daily Token consumption growing exponentially.

Data released by the China Academy of Information and Communications Technology, under the Ministry of Industry and Information Technology, shows that as of March 2026, China's average daily Token usage had surpassed 140 trillion, a more than 1,000-fold increase from early 2024.

OpenRouter, the world's largest AI model API aggregation platform, reported that the number of Tokens processed weekly on its platform soared from 1.62 trillion in March 2025 to 16.90 trillion in March 2026, a more than tenfold increase in one year.

OpenRouter connects API interfaces from nearly all mainstream model providers, including Anthropic, OpenAI, Google, and Meta. Its weekly Token consumption curve essentially serves as a real-time monitoring chart for global AI application activity.

This Token consumption curve is nearly vertical, unlike the linear growth of GDP or the S-shaped curve of internet user penetration. It represents a unique development trend of the AI economy.

What is driving this explosion in Token demand? The answer lies in the technological evolution of artificial intelligence.

Early AI applications were primarily chatbots (Bots), where users input a sentence, and the model returned a response, consuming a few hundred to a few thousand Tokens per interaction.

However, since the second half of last year, new application paradigms represented by Agents and Claw have spread more rapidly and widely. Their common feature is transforming AI from a mere 'question-and-answer' dialogue tool into a digital employee capable of autonomous planning, tool invocation, and long-term task execution. This fundamental change in technological architecture has caused Token consumption to surge in ways unexpected by many users.

Industry estimates indicate that to achieve the same business objective, the Agent model consumes approximately 50 to 200 times more Tokens than the Bot model.

This is because Agents need to carry the entire historical conversation context when executing tasks, with complex tasks easily accumulating hundreds of thousands of Tokens in context windows.

Moreover, each Agent's thought process requires multiple rounds of reasoning, triggering API requests each time. They also need to continuously load system configuration files and memory banks to maintain task consistency and personalized experiences. This results in Token consumption under the Agent model resembling a black-box operation beyond subjective user control.

More alarmingly, the apparent Token consumption at this stage does not equate to genuine demand.

As AI adoption becomes corporate orthodoxy and Token consumption is increasingly included in employee performance metrics, a situation of 'pseudo-Token demand' has emerged.

Internally at Meta, some teams already use Token consumption as a metric for AI penetration. To 'appear AI-savvy,' some employees deliberately run large numbers of redundant model invocation tasks. Similar phenomena have been reported at Chinese tech giants like Tencent, where some business lines have even invented gray-area practices of 'Token inflation.'

This behavior—creating unnecessary consumption and exaggerating non-existent performance to avoid being left behind—is reminiscent of the absurdity of the Great Leap Forward.

Core

As Token consumption grows exponentially, a serious industrial question emerges: who will foot the bill, and who will benefit?

On April 15, the National Data Administration publicly solicited opinions on the 'Implementation Plan for Promoting the Construction of High-Quality Industry Datasets (Draft for Comments),' proposing for the first time in an official policy document to 'explore new trading models such as Token trading and establish a quantifiable, priceable dataset value system based on Tokens.'

From the moment 'Token trading' was written into national top-level design, Tokens ceased to be merely a technical concept and gradually became the legal pricing unit of the AI economy. In a sense, Token-based pricing is the core of the AI economy.

Xunce Tech is called the 'first Token stock' because it pioneered adjustments to its business model, fully transitioning to a new model of Token-based consumption pricing and revenue sharing. It established a growth model where 'revenue = Token price × invocation count × number of module applications.'

Currently, Token-based revenue accounts for about 5% of Xunce Tech's total revenue, with the company projecting this proportion to rapidly increase to 20-30% by year-end. Consequently, the market's valuation logic for Xunce has undergone significant changes, breaking free from traditional price-to-sales ratio constraints and allowing for greater imagination.

Xunce Tech's model also demonstrates that in the AI economy, large model providers will likely play the role of 'refiners,' processing underlying computing power and data into directly consumable 'finished products' priced in Tokens, while controlling key ecological niches in the value distribution system.

Meanwhile, cloud service providers will likely play roles akin to 'power plants' and 'power grids.' They do not directly price Tokens but determine their underlying costs.

Take Alibaba Cloud as an example: by the end of February 2026, its cumulative external commercial revenue for the 2026 fiscal year had surpassed 100 billion yuan, with AI-related product revenue maintaining high growth momentum and achieving double-digit percentage growth for the tenth consecutive quarter.

Rising demand has led to price increases. Last week, Alibaba Cloud issued three product price increase notices in four days, adjusting service prices for certain model units in its BaiLian platform and free API quotas for DataWorks—highlighting cloud providers' influence on Token costs.

In the traditional architecture, cloud service providers' main revenue came from infrastructure billing for virtual machines, storage, and networking. Entering the Agent era, they can fully promote resource (Token)-based usage billing and secure long-term contract revenue through Agent platform subscriptions, developer ecosystem suites, and industry-specific solutions.

Computing power providers resemble even more upstream raw material and fuel suppliers. NVIDIA's high-end GPUs remain at the core of the industrial chain, while high-bandwidth memory (HBM) is now in short supply. The three major memory manufacturers—Samsung, SK Hynix, and Micron—face capacity constraints while raising profit margins.

Some may worry about potential overcapacity in computing power or inflated stock prices for NVIDIA and memory manufacturers, but these factors do not affect the historical trajectory of Token-based pricing.

Token-based pricing provides a clear calculation basis for value distribution across the entire industrial chain.

Just as the establishment of kilowatt-hours enabled the formation of electricity markets and views/impressions became the pricing unit for Douyin and WeChat Channels, Tokens are enabling the AI economy to transition from 'feeling useful' to 'being accountable and taxable,' becoming a tangible economic component.

Carnival

Token-based pricing clarifies the operational logic of the AI economy. However, as a transformative technological revolution, does the economic logic of AI align with traditional economic principles?

At the very least, the traditional economic theory of supply-demand equilibrium does not fully apply to the AI economy.

In traditional models, supply and demand are two independent curves that intersect. In the AI economy, however, supply itself improves supply quality through data feedback loops. The rightward shift of the demand curve occurs not due to external income changes but because the supply curve itself has shifted downward and to the right.

At this stage, we are more likely to witness a replay of the 'Jevons Paradox' from the steam era in the AI age. The Jevons Paradox states that when steam engine efficiency improves and coal consumption per horsepower decreases, total coal consumption actually surges because cheaper steam power spurs the creation of more factories, trains, and ships.

Similarly, the lower the unit production cost of Tokens, the more groups are willing to consume them, and the more scenarios prefer Tokens over human labor, ultimately driving up the total cost of Tokens—or rather, the total value of the AI economy.

According to domestic media statistics, Token production costs have dropped by over 99% in the past two years. The cost per million Tokens for GPT-4 fell from $37.5 to $0.14 in 2025. However, according to Silicon Valley venture capital firm Menlo Ventures, global corporate AI spending in 2025 increased 3.2 times compared to 2024.

If this trend continues, even if the unit price of Tokens approaches zero, the total value of Tokens consumed globally (volume × unit price) and its contribution to GDP could grow by hundreds or thousands of times.

This explains why companies like Zhipu and MiniMax, despite massive losses, are still valued by capital markets at levels exceeding many traditional internet firms—markets are pricing not today's profits but the future total value of the Token economy.

Moreover, what is produced with Tokens is becoming increasingly valuable.

The value created by the same one million Tokens can vary ten thousandfold across scenarios, depending entirely on the tasks performed. Tokens used for casual chat are worth a few cents, those for coding hundreds or thousands of yuan, and those for quantitative trading or mergers and acquisitions tens of thousands of yuan.

Stanford University's 2026 AI Index Report estimates that generative AI alone created approximately $172 billion in consumer surplus for U.S. consumers in 2024, with users deriving far more value than they actually paid.

It is worth noting that after AI replaces a significant amount of human mental labor, traditional labor supply theories will also face challenges. The traditional demand curve will collapse as a whole due to shrinking purchasing power, while the supply side remains robust due to automation. This is what Keynes referred to as 'insufficient effective demand caused by technological unemployment.'

It's just that at this stage, everything surrounding the exponential growth of Token consumption is still cloaked in prosperity, leading the entire industrial chain, both upstream and downstream, as well as the capital market, into a frenzy.

However, history has repeatedly proven that whenever a new technology is endowed with infinite imaginative potential by the capital market, the bubble always reaches its end before the value does.