AI基础模型竞赛转向架构之争：后Transformer时代来临

AI’s foundation model race is shifting from who has the biggest model to which architecture can outgrow the transformer.

Architecture is becoming the real fault line in AI.

Mapping the Foundation Model Landscape:

The AI market is usually mapped by who is winning. The more consequential question is which research bet wins.

This is a discussion of the foundation model market based on what each lab is building and what architecture it is betting on, rather than who raised the most money or had the loudest launch.

Organized around the divide that will define the next 2 years.

The 2 real axes are scope and architecture: scope asks whether a lab is building a general model or a domain model, while architecture asks whether it is still scaling transformers or moving into the Post-Transformer camp.

The transformer still dominates because it turned attention into a scalable machine for prediction, and that 2017 design remains the backbone of modern foundation models.

The pressure now comes from a simple weakness: attention gets expensive as context grows, while real products increasingly demand long memory, low latency, and continuous interaction.

That is why the most interesting labs are no longer just asking who can train the largest model.

They are asking whether intelligence needs a different operating rhythm.

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