Alibaba Qianwen's Lead Creator Resigns: Reflections on the Technical Ecosystem Amidst the Variations of Open Source

On March 3, 2026, late at night, Lin Junyang, the head of large model technology at Alibaba Qwen, lightly typed a message on social media: “me stepping down. bye my beloved qwen.” Within just a few hours, this post quickly sparked a frenzy in the domestic and international AI circles — not only because Lin Junyang was Alibaba’s youngest P10 technical expert in history, but also because the Qwen team he led had just achieved a historic milestone in the global open-source community. The stark contrast between the technical highlight and the core personnel departure led outsiders to ask: What’s going on with Qwen? In just two months, three core members—Yubo Wen, head of Qwen Code, Hui Bin Yuan, and others—successively left, and a collective resignation at the peak moment cast a shadow over Alibaba Qwen’s future. Will the open-source strategy shift to closed source? Will the model’s capabilities decline? What will users do next?

From “Vertical Integration” to “Horizontal Division”: An Inevitable Clash of Ideas

According to multiple sources, the trigger for this personnel upheaval was Alibaba Cloud Tongyi Laboratory’s fundamental restructuring of the Qwen team’s organizational structure. In the past, the Qwen team adopted a “vertical integration” model: from pre-training, post-training, to specific model development, all handled by a single team throughout. This model’s short communication lines, unified goals, and rapid iteration were key to Qwen’s quick rise in the global open-source large model arena. As technical leader, Lin Junyang needed to oversee the entire chain—from underlying infrastructure to upper-layer model applications—end-to-end control, aligning with his pursuit of integrated technical efficiency.

However, Tongyi Laboratory’s new plan is to split the team into independent “horizontal division” groups: pre-training, post-training, text, multimodal, etc. This adjustment significantly shrinks Lin Junyang’s management scope and, more importantly, runs counter to his long-held technical philosophy.

Over the past year, Lin Junyang publicly or internally expressed views multiple times: in the current deep-water phase of large model development, closer integration and seamless communication between pre-training, post-training, and infrastructure teams are necessary, rather than fragmentation. In fact, since mid to late 2024, the Qwen team had begun trying to build its own dedicated infrastructure team to directly support model training needs—functions that were originally mainly provided by Alibaba Cloud’s AI platform PAI. This “decentralized” attempt itself reflected his persistent pursuit of “integrated” R&D efficiency.

When company decisions moved toward the opposite “horizontal division,” ideological conflict became the key reason for his departure. A team member’s comment on social media was poignant: “I am truly heartbroken. I know leaving was not your choice.” This “non-voluntary” departure exposes the harsh truth behind the technological halo—brutal organizational realities beneath the surface.

The KPI Strait: When DAU Becomes the Measure of All

Deeper conflicts stem from the fierce clash between open-source ideals and the KPIs of big tech companies’ commercialization.

Under Lin Junyang’s leadership, Qwen gained enormous reputation in the global developer community with its full-size open-source strategy: over 200k derivative models, over 1 billion downloads, long-term top rankings on Hugging Face, surpassing Meta’s Llama series, becoming one of the most active open-source large model ecosystems worldwide. Early 2026, its dominance in the global open-source community even triggered a “Qwen panic” in Silicon Valley.

However, these shining technological achievements seem to be no match for a colder metric inside Alibaba: DAU (Daily Active Users). According to insiders close to Alibaba, Lin Junyang’s evaluation focus has shifted entirely from model development capabilities to daily active user numbers. For a technical leader who believes “models are products,” this signifies a fundamental role redefinition. More brutally, internal assessments even labeled the Qwen-3.5 version, launched on New Year’s Eve, as a “semi-finished product”—despite the version receiving public praise from Elon Musk.

This misalignment in evaluation systems reveals a structural dilemma faced by all large companies doing open-source AI: open source requires long-term commitment and community trust, but corporate logic demands short-term, quantifiable returns. Questions like “You’re making such a fuss with open source, but why haven’t DAU caught up with Doubao?” once raised in senior management meetings, put open-source teams on the defensive.

Because the value of open-source ecosystems is indirect, long-term, and hard to attribute precisely, while DAU is direct, short-term, and visible daily.

Will the Open-Source Strategy Change? A Signal to Watch

Lin Junyang’s departure, along with the exit of core members like Yubo Wen, Hui Bin Yuan, and Li Kaixin, raises a critical question: Will Qwen’s open-source strategy change? Based on current signals, this concern is not unfounded.

Qwen’s open-source strategy over the past three years has been textbook-level: from 7B to trillion-parameter models, full-size models open-sourced; over 200k derivative models on Hugging Face, with over 1 billion downloads; maintaining the top spot globally in open-source rankings, even surpassing Meta’s Llama series. This approach not only attracted numerous API customers for Alibaba Cloud but also established a “trustworthy Chinese technology” image among global developers.

But now, with the collective departure of the “open-source ambassadors,” community confidence is beginning to waver. Foreign media have issued warnings: enterprises relying on Qwen’s 90k users should beware of a possible shift toward closed source. After all, open source is not just a technical choice but a trust contract—trust often hinges on key individuals.

Although Alibaba has not explicitly stated whether it will change its open-source approach, strategic focus has quietly shifted. Models are no longer the goal but serve as infrastructure for super apps and hardware ecosystems. When “user adoption” replaces “model strength” as the top priority, open-source strategies that require long-term investment and yield little short-term return risk being marginalized.

For developers and enterprises relying on the Qwen ecosystem, it may be time to consider alternatives. Domestic models like Kimi, Yuanbao, Doubao, DeepSeek, and others perform well in Chinese language capabilities and specific scenarios, backed by stable strategic support.

Will Technical Capabilities Decline? Short-term Fluctuations and Long-term Potential

The departure of core personnel inevitably raises doubts about whether Qwen’s model capabilities will decline. In the short term, personnel changes may impact R&D progress, reduce collaboration efficiency, and delay or alter ongoing projects. New team members need time to familiarize themselves with projects and technologies, which could affect the speed and quality of model iteration.

Long-term, Qwen’s technological evolution will not halt. The past success relied heavily on Tongyi Laboratory’s long-term accumulation. The lab still has academic heavyweights like Zhong Hong and Zhou Hao, capable of supporting Qwen’s development. However, three core issues are now evident: the continuity of the technical organization—can it operate independently without key figures? How to maintain trust within the global open-source community? And how to balance rapid application growth with long-term foundational research investment? Although aligning overall goals and unified routes may clarify future directions and concentrate resources, making execution more decisive, the ultimate effect remains to be seen. This landmark personnel shake-up in China’s AI industry not only influences Alibaba’s AI future but also profoundly impacts the long-term trajectory of China’s open-source large model race.

The Industry Shift and the Beginning of New User Choices

The collective departure of Alibaba Qwen’s core team is fundamentally a collision between big tech’s commercialization logic and technological idealism, an inevitable phase in AI industry development. From OpenAI to Google, Meta to Alibaba, instability among core researchers has become routine. When capital pursues immediate returns and organizations emphasize business goals, technological ideals often take a backseat.

For Qwen, this personnel upheaval is both a challenge and an opportunity for transformation—if they can balance commercialization with open-source strategies, retain core talent, they may still maintain industry standing; but if they pursue short-term commercial metrics at the expense of R&D and community building, they risk losing competitive edge.

For ordinary users and developers, this upheaval may not be entirely negative. China’s AI market is not dominated by a single player but features a vibrant landscape. Models like Doubao, Kimi, Yuanbao, DeepSeek offer strong performance in Chinese language and specific scenarios, providing more choices and making AI tools more diverse and personalized.

The core value of AI lies in providing users with efficient, convenient services. Only by staying true to technological初心 and respecting user needs can companies establish a firm foothold in the industry. Looking ahead, we hope Qwen can navigate through the turbulence and find a clear development path. Meanwhile, exploring other high-quality large models and embracing diverse options will help users find the most suitable AI partners. After all, in the AI era, the freedom to choose is the greatest convenience.