Rohan Paul@rohanpaul_ai · XRohan Paul引用Charlotte Xia的博客，讨论Jim Fan的“Great Parallel”论点：具身AI将像LLM一样扩展。与语言不同，文本是压缩共享接口，物理行动分散于不同实体。尽管已有$5B+投资世界模型、$18B投入机器人，领域仍缺乏共享基准、架构收敛，且存在10万年的数据差距。世界模型能预测行动结果，但无法解决数据收集、评估、实时控制和部署可靠性。真正的创业机会在于数据循环、评估系统、记忆层、推理栈和垂直部署引擎等瓶颈。
Language had a strange advantage robotics does not：
Text is already a compressed， shared interface for human thought， while physical action is split across bodies， sensors， surfaces， speeds， and failure modes.
$5B + is already betting on world models， $18B has gone into robotics， and yet the field still has no widely trusted shared benchmark， no architecture convergence， and a 100，000-year data gap between robot experience and the data scale behind modern AI.
World models are promising because they try to predict what will happen before a robot acts， but prediction alone does not solve data collection， evaluation， real-time control， or deployment reliability.