There’s no amount of intelligence that can get packed into AI models that replaces the need for context. For any sufficiently general purpose AI, you will always have to guide it in the direction you want as it has an infinite range of directions it can go in. As long as the same model is used by a lawyer, an engineer, a financial analyst, or a healthcare professional, and as long as you’re trying to do anything uniquely differentiated or specific, then instructions, domain context, and proprietary data will always need to get into the context window for the model to be useful. This is partly why AI automation doesn’t come for free, and why there’s still a wide spectrum of who’s getting the largest gains from AI and who’s not. You have to put in real work, and you get real value on the other end. This is one of the advantages that applied AI will also have in the market. Any layer of abstraction above just the raw intelligence that can meaningfully get you off to the races faster will likely continue to be valuable.
无论往 AI model 里塞进多少智能，都无法取代对 context（上下文）的需求。对于任何足够通用的 AI，你始终都必须把它引导到你想要的方向，因为它可能前往的方向实际上是无限的。只要同一个 model 被 lawyer、engineer、financial analyst 或 healthcare professional 共同使用，并且你想做的事情具有任何独特差异化或具体性，那么 instruction（指令）、domain context（领域上下文）和 proprietary data（专有数据）就始终需要进入它的 context window（上下文窗口），这个 model 才会真正有用。这也是为什么 AI automation（自动化）并不是免费获得的部分原因之一，也说明了为什么在“谁从 AI 中获得最大收益、谁没有”这件事上，至今仍存在很大的光谱差异。你必须投入真正的工作，而另一端你也会获得真正的价值。这也是 applied AI（应用型 AI）在市场中的优势之一。任何建立在纯粹原始智能之上的 abstraction layer（抽象层），只要能切实帮助你更快起步并进入状态，就很可能会持续具备价值。