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This article argues that the current trend of building AI agents, often relying on RAG pipelines and tool chaining, is largely a distraction that doesn't meaningfully advance intelligence or create new economic value, and is unlikely to survive future base model advancements.
AI 生成摘要
本文認為，目前建構AI代理人的趨勢，常依賴檢索增強生成（RAG）管道和工具鏈，很大程度上是一種分散注意力的行為，並未真正提升智能或創造新的經濟價值，且不太可能在未來基礎模型發展中存續。
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Over the past two years, a peculiar belief has quietly taken hold in the AI community: that the future of intelligence lies in building agents. Everywhere you look, engineers are creating RAG pipelines, chaining tools together, wrapping large language models with orchestration frameworks, and calling the result “AI systems.”
It looks impressive. It feels productive. It satisfies the engineering instinct to build.
But here is the uncomfortable truth: most AI agents are not progress. They are decoration.
They do not meaningfully extend intelligence. They do not create new economic value. And they rarely survive the next generation of base models. In most cases, building AI agents is not a step toward the future of intelligence — it is a temporary distraction from understanding what intelligence actually is.
This is not a technical argument. It is a structural one.
At its core, a large language model is a function approximator:
Everything an LLM produces is a transformation of input signals and its learned internal representation…
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