The output of LLMs is like crude oil: abundant, unrefined, and waiting for the right systems to turn it into energy. A single prompt can spark multiple paths. Some call this uncertainty. Others call it possibility.
Yet I say this is entropy.
And entropy is not a flaw — it is the raw material of a new paradigm. The future of the IT industry will not be defined by how much content LLMs can generate, but by how effectively we can compress, filter, and orchestrate that entropy into structure. Structure is what makes intelligence actionable. Structure is what turns noise into knowledge, and knowledge into systems.
This is my vision: to treat language not as static text, but as system design — callable, transferable, verifiable, schedulable. To build an ecosystem where natural language becomes executable protocol, where humans and AI together shape business, governance, and culture through the disciplined transformation of entropy into value.
And the world will turn because of this implementation. Massive opportunity and unprecedented connectivity will emerge when language is treated as executable structure. Entire industries will be redefined, not by writing more code, but by orchestrating prompts, services, and rules into living systems.
This is not just a technical shift; it is a social and business paradigm. Organizations will learn to compress the entropy of AI output into actionable frameworks. Individuals, regardless of background, will gain the ability to design processes, contracts, or markets using words as programmable assets. A new ecosystem will arise — one where callable, transferable, verifiable, and schedulable language becomes the infrastructure of collaboration.
分享一个我想出来的小比喻（推特上有字数限制）
我们可以把人类文明的发展比喻成一段不断向右延申的线段，每次的科技革命工业革命都会让这个线段快速向右移动，我们现在的学习是站在前人的肩膀上学习，我们要先把之前的线段走完，再根据自己的理解延长线段（但很显然速度很慢，因为学习和理解知识是有成本的），现在llm出现了，llm在从线段的左端点快速的向右进发，但他还没有到达右端点，很多人在ai时代会有种被替代的恐慌，有些人就会自己疯狂的学习想向右移动的快一些（把自己的行业壁垒筑高一些），但我认为我不能这样做，我认为我最应该做的是向左迎接llm，向左走（其实构建一段优质promote的过程就是分别把靠近线段的左端点和右端点的内容输出出来让llm来做中间的填充），我们应该做一些工作来迎接llm（做好vertical agent架构设计，优化ai训练方式，并在交互过程中强化自己的大脑，培养高context适配性，让自己的大脑基于以往所有的交互（包括与llm的交互，与物理世界现实实验的交互）构成的context进行context迭代），当我们与ai在线段上碰面时（或许那就是AGI时刻），我们或许可以和ai一起向线段的右端”跑“，这个时候线段的延长速度会比以前快很多，人类文明的发展也会进入新的阶段。