Did Alibaba just kneecap its powerful Qwen AI team? Key figures depart in wake of latest open source release

Alibaba's Qwen team of AI researchers have been among the most prolific and well-regarded by international machine learning community — shipping dozens of powerful generalized and specialized generative models starting last summer, most of them entirely open source and free.

But now, just 24 hours after shipping the open source Qwen3.5 small model series—a release that drew public praise from Elon Musk for its "impressive intelligence density"—the project’s technical architect and several other Qwen team members have exited the company under unclear circumstances, raising questions and concerns from around the world about the future direction of the Qwen team and its focus on open source.

The departure of Junyang "Justin" Lin, the technical lead who steered Qwen from a nascent lab project to a global powerhouse with over 600 million downloads, alongside two fellow colleagues — staff research scientist Binyuan Hui and intern Kaixin Li — marks a volatile inflection point for Alibaba Cloud and its role as an international open source AI leader.

These three Qwen Team members announced their departures on X today, though they did not share the reasons or whether or not it they were voluntary. VentureBeat reached out to sources at Alibaba for more information and will update when we obtain it. Lin himself signed off with a simple post: "me stepping down. bye my beloved qwen."

While the company celebrates a technical triumph, the sudden exit of its core leadership suggests a deepening rift between the researchers who built the models and a corporate hierarchy now pivoting toward aggressive monetization.

The departing researchers' final gift: pocket-sized intelligence

The Qwen3.5 small model series (ranging from 0.8B to 9B parameters) represents a final masterstroke in "intelligence density" from the founding team.

The models employ a Gated DeltaNet hybrid architecture that allows a 9B-parameter model to rival the reasoning capabilities of much larger systems.

By utilizing a 3:1 ratio of linear attention to full attention, the models maintain a massive 262,000-token context window while remaining efficient enough to run natively on standard laptops and smartphones — even in web browsers.

Lin, a PKU humanities graduate and polyglot, has long advocated for this "algorithm-hardware co-design" to bypass compute constraints—a philosophy he detailed at the January 2026 Tsinghua AI Summit.

For the developer community, Qwen3.5 wasn't just another update; it was a blueprint for the "Agentic Inflection," where models shift from being chatbots to autonomous "all-in-one AI workers" capable of navigating UIs and executing complex code.

The enterprise dilemma

For the 90,000+ enterprises currently deploying Qwen via DingTalk or Alibaba Cloud, the leadership vacuum creates a crisis of confidence.

Many companies migrated to Qwen because it offered a "third way": the performance of a proprietary US model with the transparency of open weights.

Alibaba has recently consolidated its AI efforts into the "Qwen C-end Business Group," merging its model labs with consumer hardware teams. The goal is clear: transition Qwen from a research project into the operating system for a new era of AI-integrated glasses and rings.

However, the reported appointment of Hao Zhou, a veteran of Google DeepMind’s Gemini team, to lead the Qwen team indicates a shift from "research-first" to "metric-driven" leadership.

Industry analysts, including those cited by InfoWorld, warn that as Alibaba pushes to meet investor demands for revenue growth, the "open" in Qwen’s open-weight models may become a secondary priority — similar to what we saw with Meta after the disappointing release of its Llama 4 AI model last spring, and subsequent reorganization of its AI division, seeing the hiring of Scale AI co-founder and CEO Alexandr Wang and following departure of preeminent researcher Yann LeCun.

Enterprises relying on the Apache 2.0-licensed Qwen models now face the possibility that future flagships —such as the rumored Qwen3.5-Max—will be locked behind paid, proprietary APIs to drive Cloud DAU (Daily Active User) metrics.

The takeaway? If you value Qwen's open source efforts, download and preserve the models now, while you still can.

The "Gemini-fication" of Qwen?

The internal friction at Alibaba mirrors the tensions seen at OpenAI and Google: the "soul" of the machine is often at odds with the "scale" of the business. Xinyu Yang, a researcher at rival Chinese AI lab DeepSeek, captured this sentiment in a stark post on X: "Replace the excellent leader with a non-core people from Google Gemini, driven by DAU metrics. If you judge foundation model teams like consumer apps, don’t be surprised when the innovation curve flattens."

This "Gemini-fication"—the shift toward a highly regulated, product-centric culture—threatens the very agility that allowed Qwen to surpass Meta’s Llama in derivative model creation. For the global AI community, the loss of Junyang Lin is symbolic.

He was the primary bridge between China’s deep engineering talent and the Western open-source ecosystem. Without his advocacy, there are fears that the project will retreat into a "walled garden" strategy similar to its Western rivals.

'Leaving wasn't your choice'

The technical brilliance of the Qwen3.5 release has been overshadowed by the heartbreak of its creators. On social media, the sentiment among the team members who built the model is one of mourning rather than celebration:

Chen Cheng, a Qwen contributor, explicitly alluded to a forced departure, writing in a post on X: "I'm truly heartbroken. I know leaving wasn't your choice... I honestly can't imagine Qwen without you."

Li suggested the exit signaled the end of broader ambitions, such as a planned Singapore-based research hub: "Qwen could have had a Singapore base, all thanks to Junyang. But now that he's gone, there's no reason left to stay here."

What happens to Qwen's open source AI efforts from here on out?

The known facts are simple: Qwen has never been technically stronger, yet its founding core has been dismantled. As Alibaba prepares to face investors for its fiscal Q3 earnings report on March 5, the narrative will likely focus on "efficiency" and "commercial scale."

For the enterprises currently excited about the 60% cost reductions promised by Qwen3.5, the immediate future is bright.

But for the larger AI community, the cost of that efficiency may be the loss of the most vibrant open-source lab in the East.

As Hao Zhou takes the reins, the world is watching to see if Qwen remains a "model for the world" or becomes merely a component in Alibaba’s corporate bottom line.