Open weights vs. closed: An AI civil war's afoot, and the stakes are existential

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The open-weight Moonshot AI's Kimi K3 is faster than Anthropic Fable 5 in some ways, and is nearly as fast as Fable 5 and OpenAI's GPT-5.6 in others. That's fast. And that has some American AI companies and the Trump administration worried.

Michael Kratsios, Trump's director of the Office of Science and Technology Policy, has claimed that " Moonshot AI distilled Anthropic's Fable for the development of its K3. " In short, he's suggesting that China's Moonshot stole from Anthropic. That's not how everyone sees it. Many US AI companies see Moonshot's open-weight methods as perfectly legitimate.

You might think it would be simple -- good guys versus bad guys -- but it's not. Even Kratsios admitted, "Legitimate AI distillation used to create smaller, more efficient models plays a vital role in this open innovation ecosystem." But, he added, "large-scale, covert industrial distillation aimed at stealing proprietary US technology and undermining American research is unacceptable."

Are they really, though? The difference between illegitimate and legal uses of other models is paper-thin. It largely depends on where you sit. All AI models have been built on top of other models, and their data comes from essentially everything and anything their builders can grab.

So, immediately after the Kimi K3 kerfuffle surfaced, Microsoft released its new " Open Weights and American AI Leadership " policy statement. It defines open-weight models as systems anyone can download, inspect, modify, and run on their own infrastructure. As Microsoft and its allies, which include Amazon, Nvidia, Google, and a host of other tech giants, see it, the use of open weights is a net good for everyone, including US AI giants.

They aren't the only ones. Almost 200 Silicon Valley start-ups, the Little Tech Association , have urged the Trump administration not to limit access to Chinese open-source models.

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Microsoft's statement does a better job than most industry letters of tying open weights to actual economics. It argues that open models let startups, universities, hospitals, factories, and public institutions match the right model to the right job without paying frontier-model prices for every task. This makes AI economically sustainable for everyone. For now, we're still living at a time when frontier AI is comparatively cheap. That won't be the case much longer. AI pricing will explode by year's end. Open weights are the only way forward to affordable AI.