After financing 1 billion dollars in half a year, the AI Token company of Tsinghua teachers and students was robbed

A round of financing came into our view — on July 13, Chujing Technology's Series A round of financing surfaced, led by Henan Investment Group's HuiRong Fund. Old shareholders such as True Knowledge Capital, Shangshi Capital, Starlink Capital, Shanghai Guofang Innovation, Honghui Fund, and Hangzhou Fucheng continued to increase their investment.

This is a team of Tsinghua teachers and students: founder and CEO Ai Zhiyuan and CTO Chen Xianglin are all from the Tsinghua University Computer Department High Performance Institute; Zheng Weimin, an Academician of the Chinese Academy of Engineering, also from the High Performance Institute; Professor Wu Yongwei of Tsinghua University is the chief scientist; Zhang Mingxing, an associate professor of computer science at Tsinghua University, as a co-sponsor, has led the company's technology strategy and key R&D research for a long time, and continues to drive breakthroughs in cutting-edge technology.

Three years ago, most domestic AI startups focused on big models. Even startups in the AI infrastructure field mostly focused on training, but Trendline Technology chose to start with a big model inference circuit to build a high-quality AI token factory. Now, with demand for AI tokens growing exponentially, this once hidden racetrack has finally caught fire. Similar to Smart Spectrum, Chujing Technology has completed the Tsinghua University technology transfer and shareholding, making it a typical project for the transformation of Tsinghua's scientific and technological achievements.

In just half a year, Chujing Technology has accumulated more than 1 billion dollars in financing.One by one, 100 billion and trillion-level high-quality AI token factories have been completed one after another, creating a new picture of the AI industry.

At the end of December of the same year, Chujing Technology was formally established. The founder and CEO of the company, Ai Zhiyuan, Ph.D., of the Institute of High Performance Computing at Tsinghua University, has worked as a R&D leader in various key departments such as big data, digitalization, and AI applications in listed companies, and has accumulated complete industry experience from technology research and development to large-scale implementation. Co-sponsor Zhang Mingming, an associate professor at Tsinghua University, has mainly carried out research work in the field of computer system architecture and has been deeply involved in infrastructure construction for leading model manufacturers. As the company enters the accelerated phase of marketization, in March of this year, Dr. Wu Wenjie became the president of Trend Technology. As a senior financial and strategy expert in the industry, he has a doctorate degree in finance from the University of Hong Kong, further strengthening the company's capabilities in strategy, investment and financing, internal control management, and global operations. As a result, a core team with technical background, commercial perspective and industry experience was formed.

Anchoring AI, the team made a choice that didn't seem mainstream at the time — when most AI entrepreneurs chose to invest in big model training, Trendland Technology focused on AI reasoning from the beginning. Simply understand, training is about creating a “smart brain,” and reasoning is how to use the brain efficiently.

“Training is a cost item; reasoning is a money-making item,” Ai Zhiyuan explained. At the time, their judgment was that only reasoning would actually produce economic benefits, and it would be a wider market. What Chujing Technology needs to do is to become the best partner for the construction and operation of token factories in the AI era, making the process of using the “brain” more efficient.

This is also the position of Trendland Technology — compared to other AI Token factories, Trendland aims to produce high-quality AI tokens. Ai Zhiyuan further explained that when the big model actually enters the production stage, the customer no longer needs a big model that “can chat”, but that can complete the actual business stably, efficiently, and inexpensively.

However, AI tokens that actually have enterprise-level implementation value need to continuously meet many important requirements such as low initial token latency, high concurrent load, stable output quality, structured result generation, and function calls on 100 billion or even trillion parameter models, while keeping the unit generation cost within an acceptable range for enterprises.

None of these capabilities are the hardest to achieve alone, and this is the choice of most AI infra companies. The real challenge, however, is that the customer's real demand is to establish these indicators simultaneously under actual production loads and remain stable over long periods of operation. According to data estimates, with different combinations of capabilities, there can be gaps of several times or even tens of times in production efficiency.

In order to achieve this goal, Chujing Technology has built full-link capabilities covering heterogeneous integration, intelligent coordination, and elastic expansion through world-pioneering technologies such as “full-system heterogeneous collaboration,” “storage-to-store conversion,” and “fictional and real isomorphism.” Instead of focusing on a single pain point, we optimized every aspect of AI token production, and ultimately achieved an order of magnitude of efficiency improvement.

Based on this, Trend Technology also proposed the Token as a Service (TaaS, Token as a Service) concept, and uses the self-developed ATAAS platform, a high-efficiency AI token production service platform, as the core. Through system architecture and engineering capabilities, it breaks the bottleneck of conversion between computing power hardware investment and AI token production capacity, and continuously and stably outputs high-quality AI tokens like a standardized production line.

Over the past two years, this team has rarely appeared in the public eye. But Trend Technology's choices are being validated by the industry — when tokens become the currency of the AI era, the best time has finally arrived.

In detail, in February of this year, Chujing Technology completed the Angel ++ round of financing with parallel technology investment; in May, it completed the Pre-A round of financing, and the capital camp was further expanded. It was co-led by Starlink Capital and China Holdings Fund, followed by institutions such as Honghui Fund, Tianhou Energy, Shangshi Capital, Tianjin Renai Hongsheng, Hangzhou Fucheng, etc., and the old shareholder GL Ventures (GL Ventures) continued to raise funds.

In the latest round, investors' enthusiasm continued unabated: led by Henan Investment Group's Foreign Exchange Fund, old shareholders such as Zhenzhi Capital, Shangshi Capital, Starlink Capital, Shanghai Guofang Innovation, Honghui Fund, China Holdings Fund, and Hangzhou Fucheng continued to increase their investments. So far, in half a year, Trend Technology has raised more than 1 billion dollars. The investment community has learned that the company's next round of financing is already on its way.

As far as can be seen, more and more mainstream institutions are choosing to bet on trends and cast early votes relating to the future. The continuous support from old shareholders is also the strongest endorsement of trending industry judgments, technical strength, and phased achievements.

Behind this, the initial judgment of the trend eventually became a reality: with the rapid spread of AI Coding, OpenClaw, etc., demand for large-scale inference was being released at an accelerated pace, and AI business implementation was fully exploding. However, in the past, the team's world-leading technological innovation centered around storage-based conversion, system-wide heterogeneous collaboration, and fictional and real homogeneity, etc., and is finally reflected in high-quality AI token production efficiency — the stronger the technical ability, the higher the reasoning efficiency, the lower the cost per token, and the greater the profit margin for the enterprise.

As a result, Chujing Technology was contested by the venture capital community. Investors increasingly recognize the line the team has been adhering to since its inception — “fewer models, deeper optimization”. The focus of the trend is not to expand the number of models, but to select key large models for in-depth refinement in response to actual production scenarios, and continuously improve their performance, stability, cost efficiency, coordination capabilities, and cluster operation level.

Ai Zhiyuan used an imaginary analogy: compared to building a “big store” that sells everything, trend would prefer to become a “boutique specialty store.” Compared to continuously expanding the number of models, the company hopes to concentrate resources on a few high-productivity models and high-value scenarios, so that the same computing power can continue to output more high-quality AI tokens.

The business judgment behind it is that enterprise-level customers end up paying for business results rather than for the number of model compatible models. In fact, now the competitive pattern of big models has gradually subsided. “Currently, less than 10% of domestic leading models occupy the vast majority of the AI token market.” Based on this judgment, Tendency concentrates resources on a small number of leading models and core scenarios to achieve the compound interest effect of continuous optimization.

The investment community has obtained a set of data: since the 2026 Spring Festival, the average AI token production efficiency of a single computer has increased by more than 3 times, and the total production of high-quality AI tokens has increased more than 30 times. Among them, a model with large trillion-level parameters has achieved an average daily production capacity of trillion-level high-quality AI tokens. Meanwhile, the monthly revenue for the month of 2026 alone has surpassed the full year of 2025, and the revenue scale continues to grow rapidly.

According to Trend Technology, the final competition for AI infrastructure is not only who has more GPUs, let alone how many models it supports, but who can continue to produce more, more stable, and higher quality AI tokens. These are exactly the abilities that investors are interested in.

AI reasoning is exploding.

According to data from the National Data Bureau, as of March 2026, the average number of daily token calls in China has exceeded 140 trillion dollars, an increase of more than a thousand times over two years ago. The core factor driving the AI token explosion is the overall expansion of inference requirements. This also confirms investors' judgment that AI reasoning will become one of the largest markets in the world.

When tokens become the “water, electricity, and coal” of the AI era, a hidden and high-growth low-level business is surfacing — the “AI Token Factory.” The reason behind it is simple: Whoever can supply high-quality AI tokens in a lower cost, more stable, and more controlled manner in the future can seize the opportunity in the new token economy.

As a result, an AI Token factory “battle for position” was staged across the country. As racetracks become more crowded, commercialization becomes a question AI infrastructure companies must answer.

To a certain extent, Trend has greater ambitions: not only to become an AI token factory, but also an AI token factory designer, builder, producer, and operator, and an indispensable part of the AI Token ecosystem.

This is also reflected in two trending business models: one is the direct management model, which directly produces high-quality AI tokens and supplies them to leading model makers, internet platforms, AI application companies, and large enterprise customers after leasing or obtaining computing power resources to obtain higher profits by improving AI Token production capacity and operational efficiency; the other type is a co-operation model that undertakes the overall planning, system integration, construction, delivery, and subsequent co-operation of AI Token factories for customers who have plans or already have computing power resources.

Ai Zhiyuan further explained that more and more listed companies, central enterprises, and local intelligent computing centers are hoping to shift from traditional computing power leasing to AI token production with higher added value. But there aren't many teams that actually have the ability to design inference systems, heterogeneous computation, and operate. Trend provides a complete set of AI Token factory design and construction solutions to help partners complete the transformation from “selling computing power” to “selling high-quality AI tokens.”

The real AI token economy should not be one company doing all the steps alone, but rather involving more industry partners to jointly establish an ecosystem. Obviously, trends have become a key part of the ecosystem, connecting industry partners such as models, computing power, and applications, and driving the AI industry from single-point innovation to ecological symbiosis.

This is also an inevitable direction for the evolution of the industry. Every wave of technology truly changes the world, often not when the new technology is born, but because the infrastructure that supports its operation is mature only qualitatively. Just as the steam engine era requires railroads; the Internet age requires fiber and data centers. In the AI era, a new infrastructure is also needed to enable intelligence to flow stably, efficiently, and inexpensively to thousands of industries.

Behind this, huge business opportunities lie in the new AI infrastructure circuit. Only by deepening innovation at the bottom and collaborating with the industrial ecosystem can we cross the technology cycle and continue to release long-term industrial commercial value.