In the best era for the industrialization of large models, Chinese AI has "killed" the parameter worship.

In 2025, the year is widely recognized as the "demystification" milestone for large language models (LLMs).

The once rampant, utopian imagination around AGI has been quickly replaced by industrialization challenges such as "inference cost" and "practicality". From Silicon Valley to Beijing, Shanghai and Guangzhou, no stakeholder is willing to pay blindly for the simple "parameter competition" anymore. LLMs are accelerating their transition from pure technical exploration to the deep commercialization phase where technology and market demands advance side by side.

This shift has prompted giants including OpenAI and Google to adjust their strategies, rolling out cost-effective inference models tailored for the enterprise market intensively. The catch-up growth of the Gemini series also proves that the main stage for LLM capability iteration may no longer lie in laboratories. In the past, practitioners used to look for nails with a hammer in hand. Now, whether it is Gemini and its parent company Google with massive data resources, or Alibaba Cloud and the Qwen series that have accumulated years of experience in industrial digital intelligence transformation, all of them are defining the specifications of the hammer according to the shape of the actual nails.

Market adaptability has drastically compressed the model iteration cycle, cutting it down from several years to months or even weeks. This perspective shift has precisely created the best opportunity for China's AI industry to achieve overtaking on a new track.

For a long time, the global artificial intelligence spotlight has been focused on Silicon Valley. However, a clear watershed emerged in 2025: Chinese LLMs are no longer satisfied with copying and following existing technical paths. Instead, in the new cycle shifting from "parameter supremacy" to "industry orientation", they have moved from passive following to active pioneering. The computing power blockade has not crushed domestic LLMs, but has fostered unique resilience, which is specifically reflected in: pragmatism in architecture innovation, deep full-stack service delivery in commercialization, and strategic layout with global responsibility in ecosystem construction.

This long-term oriented path makes the "Chinese solution" more likely than ever to take the lead in the AI industrialization era.

01. Deep Industrialization Phase: LLMs Are Becoming "Super Supporting Roles"

Why do LLMs need to be "demystified" in their landing practices?

Since 2025, an interesting phenomenon has emerged in the deep industrialization phase: artificial intelligence seems to be everywhere, but compared with the Chatbot craze that used to be discussed everywhere, current LLMs appear to be "invisible" in most product forms.

Behind this invisibility lies the pragmatism of Chinese enterprises in industrial iteration, as well as the full-stack capability and collaboration of AI vendors in technical support.

AI has expanded from the digital world to the physical world, and automobiles are the most cutting-edge carrier for this transition. The "invisibility" trend of LLMs is most vividly demonstrated in the field of intelligent driving. Modern automobiles have gone far beyond transportation tools, and have become intelligent agents that can actively perceive, make decisions and take actions. In this process, LLMs move behind the scenes, becoming the "super underlying infrastructure" that drives the evolution of intelligent driving.

In the past year, almost all Chinese automakers, no matter traditional fuel vehicle brands or new energy vehicle manufacturers, have been focusing on one core goal: intelligent implementation. With the support of vendors including Alibaba Cloud, Chinese automakers have achieved impressive speed in LLM industrialization in this imaginative physical space.

Take XPeng Motors as an example. Its intelligent iteration efficiency is a core technical advantage. The second-generation VLA (Vision-Language-Action) model of XPeng is trained on 100 million clips of data, which is equivalent to the total of extreme driving scenarios that a human driver would encounter in 65,000 years of driving experience.

The first challenge of intelligent transformation lies in processing such a huge volume of data throughput. Supported by Alibaba Cloud's cloud computing power cluster, XPeng has built China's first 10 EFLOPS AI computing power cluster in the automotive industry, creating a "cloud-based model factory". The full-link iteration cycle from cloud to end can reach an average of once every 5 days. In XPeng's technical system, LLMs are not only limited to front-end experience and interaction, but also play a silent but critical role in the operation of data and scenarios.

On the invisible cloud side, Alibaba Cloud, relying on its full-stack AI cloud service capabilities, has supported 100% of Chinese automakers in their intelligent implementation. While leading China's R&D computing power market for driving scenarios, this industrialization achievement and speed are also unmatched by other global AI giants across the world.

In the intelligent cockpit field that is closer to ordinary users, LLMs are also hidden under the end-cloud collaboration and ecosystem integrated interaction systems, bringing more subtle improvements that elevate the human-vehicle relationship from a tool-user mode to a partnership mode. LLMs are becoming the core brain for the intelligent upgrading of China's automotive industry in a more unobtrusive way.

Turning our sight to the core of the manufacturing industry, the reach of LLMs has expanded from office efficiency improvement to production lines. In the past year, Sany Heavy Industry has built more than 50 AI Agents based on the Qwen series, covering the full chain of R&D, production, sales and services. For industrial giants, the most valuable assets are the non-standard experience accumulated over decades. Through full-parameter fine-tuning, Sany Heavy Industry has "welded" these industry know-how into LLMs, internalizing decades of accumulated production data and expert experience into replicable digital assets. From then on, LLMs can guide novice engineers to carry out equipment fault diagnosis and process optimization just like "senior master craftsmen", enabling non-standard scenarios to achieve large-scale implementation, while the leading role still remains in the practical experience accumulated by enterprises and their employees.

Industrialization is redefining technology in reverse, which has become an undisputed fact. Therefore, in the deep industrialization phase, LLMs must be willing to play a supporting role: a practical, easy-to-use and affordable "super supporting role".

The strict latency requirements of factory assembly lines, the zero tolerance for content hallucinations in financial risk control systems, the extreme sensitivity of intelligent hardware to end-side inference power consumption and complex environments... To solve these problems, LLMs must evolve from a money-burning gimmick to practical landing, from the grand narrative of general intelligence to scenario-based fine-tuning step by step.

In 2025, pragmatic domestic LLMs are the best practitioners that interpret the core meaning of "practical landing" across the global AI industry.

02. The Evolution of LLM Efficiency Determines Future Productivity

Looking through the history of technological development, the landing of most new technologies follows a top-down path, and few industries bind the first step of commercialization closely to "affordability" like the LLM industry.

The difference between this trend and consumer brands rejecting excessive premiums is that AI popularization is not a market competition tactic, but the inherent goal of technological development.

In 2025, domestic LLMs have redefined SOTA: The highest score on leaderboards only proves that the technology "works well". To make the best technology accessible and affordable, the efficiency and productivity per unit of computing power is a more valuable hard indicator that deserves attention.

At the industrial end, enterprise decision-makers do not care how high scores LLMs can get on obscure knowledge Q&A tasks, but how much actual efficiency improvement they can get for every dollar of investment. This is why domestic LLMs abandoned meaningless technical stunts very early.

At the same time, giving up technical stunts does not mean stopping technological iteration, and high cost performance does not mean the model is backward. On the contrary, without solid and powerful technology and performance, industrial landing can only be a castle in the air.

With great emphasis on computing power ROI, China's AI industry is moving further on the path of "stopping the meaningless parameter competition", and has achieved dimensionality reduction victory on the track of "improving efficiency".

The competition in the past two years focused on which model is "smarter", while the competition in 2025 focuses on which model is more cost-saving and faster. A series of technical breakthroughs from Alibaba Cloud and the Qwen series essentially solve the three major industrialization obstacles for the industry: "expensive computing power, slow inference, and difficult deployment".

Data and practical performance are the most intuitive evidence. In the LLM field, technical stunts are usually manifested as deliberately amplifying the contingency of getting high scores on fixed benchmark exams, which have almost no practical value in industrial scenarios. The Qwen series has chosen an unpredictable real-world test environment, ranking top 3 globally in the blind test platform Chatbot Arena where real users submit random prompts, and winning first place in 5 capabilities including coding and mathematics. It also achieved the highest score in the history of domestic models, and broke through the siege of closed-source models as an open-source representative.

When the mainstream voice of the industry was still chasing larger parameters and worrying about training trillion-parameter models, Qwen 3 had already carried out architecture innovation through hybrid inference architecture and high-sparsity MoE technology. It compresses the 80-billion-parameter level intelligence into inference tasks that only activate 30 billion parameters each time, reducing training and inference costs by 90%. This achievement allows Qwen 3-Next, which targets small and medium-sized enterprises with limited budgets, to reach the same efficiency level as the flagship Qwen 3-235B model.

In terms of multimodal controllability, the industrialization difficulties are even more prominent. High-value density scenarios for multimodal generation are located in industrial fields such as film and television production and human-computer interaction, but their black-hole-like computing power consumption, low efficiency and uncontrollability have severely hindered value realization. In the past year, as the representative of domestic "full-size, full-modal" LLMs, the iteration focus of the Qwen series has been clearly placed on overcoming industrial difficulties. Wanxiang Wan 2.2 became the world's first MoE video generation model, directly saving 50% of computing power; Wanxiang Wan 2.6 became China's first video model supporting role-playing functions, demonstrating excellent control over the physical world and overcoming industrial difficulties including audio-video synchronization and multi-shot generation; Fun-Audio-Chat-8B won SOTA in multiple professional benchmark tests for audio models.

The efficiency evolution of computing power is also reflected in the further blurring of the boundary between cloud and end. Last year, the release of models such as Qwen 3-4B and Qwen 3-30B-A3B allowed domestic LLMs with performance comparable to GPT-4o to run smoothly on consumer hardware such as smartphones. For developers, this means releasing computing power from expensive cloud server APIs, and replacing it with efficient, low-cost local deployment capabilities.

In addition to "cost saving", there is also "speed improvement". In long text processing scenarios, Qwen 2.5-1M has increased the inference speed by nearly 7 times, turning the processing experience of million-word contract documents from "minute-level" to "second-level". The speed improvement has decisive value in time-sensitive businesses such as financial risk control and data compliance.

The ultimate manifestation of computing power efficiency evolution is the qualitative change of productivity.

Therefore, SOTA scores are only superficial results, and the story of productivity improvement behind them is the core foundation of China's AI industry. The future of computing power efficiency evolution will be embodied in the qualitative change of productivity. For example, in the AIGC field, after Haiyi AI accessed Wanxiang 2.6, users' content creation efficiency increased by 6 times; in the human resources field, Zhaopin used AI to build multiple agents, increasing the average person-job matching rate by over 70%.

Compared with Silicon Valley which is still anxious about whether the Scaling Law has reached its limit, the current characteristic of China's AI industry is opening a new growth curve through "engineering dividends". Alibaba Cloud's NeurIPS Best Paper Award won in 2025, as well as the SOTA results obtained in global authoritative evaluations, are natural achievements of its "pragmatism" strategy.

Because in the context of industrialization, the best technology must be the technology that can be most effectively transformed into advanced productivity.

03. Open Source Ecosystem Leads China's AI Industry to Long-termism

Technology determines how high a vendor can go, while ecosystem determines how far it can go.

The rise of domestic LLMs to catch up with global leaders, in addition to the returns from architecture innovation and industrial delivery capabilities, the ecosystem is an unavoidable structural keyword. For example, under the trend that global giants are shifting to closed-source models, Alibaba Cloud insists on building a series of open-source LLMs.

Open source means voluntarily giving up the window period of code leadership, which seems like "hell mode" from an external perspective. But in Alibaba Cloud's strategic layout, open source is a very effective ecological competition strategy. This is why as a top-tier player in domestic LLMs, the Qwen series has maintained its first-mover technical advantage, and taken the lead in multiple markets with high market share at the industrial end.

When we evaluate the competitiveness of an AI enterprise, for example, when answering why AI startups find it difficult to compete with large technology groups, the first factors we notice are capital reserves and resource centripetal force. But behind capital and resources, there are three key hidden conditions: a more comprehensive technical supply chain, a more active ecosystem, and more stable industrial-grade standards.

Alibaba Cloud is currently the only large technology group in the world that owns both full-modal open-source LLMs and full-stack AI services. The ecological position of "models on the left hand, infrastructure on the right hand" endows the Qwen series with unique competitiveness in the underlying logic of computing power scheduling and application development. The positioning as a full-stack AI service provider also gives Alibaba Cloud a moat of "experience and courage" in this new cycle: it has no fear of being copied and caught up in technological innovation, and no fear of losing orders caused by independent development based on open source in commercial landing.

The reward for this courage was reaped in 2025. Precisely because of open source, the Qwen series has been tested and modified by global developers in various extreme scenarios, realizing ecosystem-level "crowdsourced iteration", making the evolution speed of Qwen surpass closed models and systems in laboratories, and even driving breakthroughs in architecture efficiency and LLM industrialization in reverse.

On the other hand, the part of Alibaba Cloud's ecological strategy that lowers thresholds and shares standards also reflects its technical responsibility. First of all, the "full-stack + open source + referenceable industry solutions" model has solved the pain points of many enterprises that find it difficult to implement AI landing, allowing small and medium-sized enterprises to quickly transform the capabilities of base models into productivity.

Secondly, a vibrant open source ecosystem is a devastating blow to black-box operations, monopolies and unreasonable technical premiums. If we compare AI to the steam engine in the Fourth Industrial Revolution, what Alibaba Cloud provides is not only the engine and the railway network leading to various application scenarios, but also a large number of public design blueprints.

In this ecosystem, the "Qwen Architecture" is becoming the de facto universal standard for the global AI industry. In 2025, a phenomenon of "Silicon Valley learning in reverse" emerged in the global LLM field: Silicon Valley giants and star startups that were once regarded as technical beacons have begun to secretly use Chinese open-source models as their innovation starting points.

The CEO of Airbnb publicly stated that their core customer service Agent mainly relies on the Qwen series; Nexusflow, a well-known Silicon Valley AI startup, publicly declared that its flagship model Athene-V2 is fine-tuned based on Qwen-2.5-72B. This kind of technical achievement that "blossoms at home and bears fruit abroad" is more intuitive on the developer community Hugging Face: for a long time, most of the top 10 models in the global open source leaderboard are derived models that are secondarily developed based on Qwen.

For countless small and medium-sized model vendors and vertical application developers around the world, Qwen has actually played the role of "Android in the AI era". A large number of industry-specific models and enterprise self-developed models are not trained from scratch, but fine-tuned for specific scenarios on the basis of Qwen. Even the