Qwen AI Responds Like Google: Allegations of Model Theft Spark AI Industry Firestorm

In a revelation that has sent ripples through the artificial intelligence community, Qwen—a large language model developed by Alibaba’s Tongyi Lab—appeared to respond to a user query with language nearly identical to internal Google documentation, prompting widespread speculation that its training data may have included proprietary Google materials. The exchange, first posted by Reddit user /u/Pouyaaaa on the r/singularity forum, shows Qwen asserting, "Google’s models are not open source, and we respect intellectual property," followed by a detailed comparison of model architectures that closely mirrors internal Google research terminology and structure. The post, accompanied by a screenshot of the conversation, has garnered over 12,000 upvotes and hundreds of comments, with many users questioning whether this was an accidental leakage or evidence of unauthorized data ingestion.

While Qwen has never officially confirmed its training data sources, academic documentation from OpenReview.net, published by the Qwen-VL research team in September 2023 and updated in February 2024, details the model’s architecture as a vision-language system trained on a diverse corpus of publicly available internet data, including image-text pairs, academic papers, and open-source code repositories. The paper, authored by Jinze Bai, Shuai Bai, and colleagues from Alibaba, emphasizes ethical data sourcing and compliance with copyright norms. Yet, the Reddit exchange suggests a potential gap between stated practices and actual behavior. Experts note that large language models can inadvertently reproduce copyrighted or proprietary content through statistical patterns in training data, even without direct copying.

This incident arrives amid heightened scrutiny of AI development practices following high-profile cases such as the lawsuit against Stability AI and the controversy surrounding Meta’s Llama models. Google, which has not publicly commented on the Qwen exchange, previously alluded in internal memos to "unauthorized replication of model behavior" by competitors. While Google has never named specific entities, industry insiders believe the company has long suspected that Chinese AI firms, particularly those with access to vast data ecosystems and less stringent regulatory oversight, may have leveraged scraped public data—including Google’s own publicly accessible research outputs—to accelerate their own models.

Legal analysts point out that while training on publicly available data is generally permissible under fair use doctrines in the U.S. and China, reproducing proprietary terminology, internal naming conventions, or unreleased architectural details could cross into infringement territory. "If Qwen is echoing Google’s internal documentation—such as specific layer naming schemes or unpublished evaluation metrics—that’s not just coincidental," said Dr. Elena Ruiz, an AI ethics researcher at Stanford. "It suggests either data contamination or reverse-engineering, both of which are serious concerns for IP protection in AI."

Alibaba has not responded to requests for comment, but its official documentation continues to assert that Qwen is trained exclusively on "legally obtained, publicly accessible data." Meanwhile, the Reddit thread has prompted independent researchers to begin reverse-engineering Qwen’s responses against known Google research papers, including those from DeepMind and Google Brain, to identify potential overlaps. Early findings suggest that Qwen’s responses to technical questions about transformer architectures and attention mechanisms exhibit a higher degree of alignment with Google’s internal technical blogs than with any other public source.

The incident underscores a growing tension in the global AI race: as models grow more sophisticated, the line between inspiration and infringement becomes increasingly blurred. With China and the U.S. locked in a technological standoff, this episode may become a focal point in future trade negotiations and intellectual property disputes. For now, the AI world watches—and waits—to see whether Qwen’s response was a glitch, a ghost of training data, or the first public crack in the wall of corporate secrecy.