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The constraint that defines competitive positioning in large language models is not compute at training time; it is who controls access to the weights after training completes.
Meta launched Muse Glimmer, described as the company's most powerful AI model to date, and announced plans to release Muse Spark 1.2 weights publicly, a move Zuckerberg framed as advancing U.S.
The announcement positions Meta directly against OpenAI and Anthropic. What weight release actually means for the stack Where this sits in the stack matters.
Proprietary labs, OpenAI and Anthropic among them, keep model weights behind API access controls. Downstream developers can call those models but cannot inspect, modify, or self-host the underlying weights.
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