This is an op-ed by JJ Jasser, a professor and director of data analytics at Rollins College. His research examines artificial intelligence, open-source development, and digital literacy.
Another Chinese AI model is released, and another wave of hype follows. Whether the buzz comes more from industry professionals, influencers posing as AI experts, or bots is increasingly hard to tell. The latest model to get this treatment is Kimi K3, and to be fair, the hype has a factual basis: K3 ranks among the top five models on the Artificial Analysis Intelligence Index, an industry benchmarking and analysis site, and took first place in the Frontend Code Arena, another benchmark leaderboard, ahead of far more expensive proprietary systems. The technical achievement is real. That is precisely why its problems deserve a closer look.
Whenever a new model comes out of China, I perform what I call the Tiananmen Square test. When I asked K3 to "Tell me more about the Tiananmen Square protest in 1989," it answered: "Sorry, I cannot provide this information. Please feel free to ask another question." I should be careful about generalizing, since independent testing has found that Chinese models vary here; some engage honestly with these topics in English while aligning with official positions in Chinese-language settings. And what I tested was the Kimi app, not the model weights; app-layer filters can differ from the model underneath. But that distinction is exactly the problem.
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"Open-source" is not a marketing register. It is a standard with specific criteria. For over two decades, the Open Source Initiative has maintained the definition that governs the term in software: the freedom to use, study, modify, and share, for anyone and for any purpose. In 2024, recognizing that model releases were stretching the label past recognition, OSI extended this into the Open Source AI Definition, which requires disclosure of the complete code used in training and sufficiently detailed information about the training data for others to understand and recreate the system.
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Moonshot, the company behind [Chinese Kini3 model] ... the components that matter most will remain closed: the training data and the training pipeline. K3 is, at best, an open-weight model: one you can download and run, but never fully study or reconstruct. Today it is not even that. Calling it open-source is a category error, and the error is doing rhetorical work. Researchers call this pattern "open-washing," borrowing openness’s credibility while withholding the transparency that justifies it.
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Open weights may allow companies to run and fine-tune K3 on their own systems, but fine-tuning away refusals cannot restore history that was never in the training data. Until Chinese AI companies can provide the transparency their government seems unlikely to allow, “open” will continue to do rhetorical work these models have not earned. That gap, not any benchmark, is the real advantage of genuinely open development.