# AI基础模型竞赛转向架构创新：Transformer vs 后Transformer

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-07-02 06:14
- AIHOT 分数：46
- AIHOT 链接：https://aihot.virxact.com/items/cmr2nkj3809nesl8zrzduwf2q
- 原文链接：https://x.com/rohanpaul_ai/status/2072443845430898750

## AI 摘要

AI基础模型竞赛焦点从“谁有最大模型”转向“哪种架构能超越Transformer”。核心分界线是继续扩展Transformer还是转入后Transformer阵营。两大维度：范围（通用vs领域模型）和架构（Transformer vs后Transformer）。Transformer仍主导，但注意力机制随上下文增长成本激增，而实际产品需要长记忆、低延迟、持续交互。前沿实验室不再只问谁能训练最大模型，而是追问智能是否需要不同的运行节奏。这场架构之争将在未来2年定义行业格局。

## 正文

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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