After AI emerged, content production became easy. Writing a piece of copy, creating an image, editing a video—the costs all dropped sharply. But easy does not equal effective. When every company can rapidly produce content, the amount of information users face surges accordingly, and whether content can be seen and remembered becomes an even harder problem.
From "Can It Be Produced" to "Is It Worth Producing"
In an era of content scarcity, producing more meant more opportunities; in an era of content abundance, producing more makes it easier to be drowned out. The focus therefore needs to shift from "production" to "selection": among all the content that can be made, choose the part that is truly useful to users. This requires companies to know clearly who their users are, what they care about, and in what scenarios they will use this content. Judgment should come from understanding users, not from the efficiency of production tools. The stronger the tools, the more important selection becomes.
Content Must Have a Place to Land, Not Just Be Published
After content is published, if there is no clear landing path, users leave after viewing it, and the value is very limited. A reasonable approach is to give content somewhere to go: after users become interested in a piece of content, they can be guided to a place where they can learn more, leave information, and establish contact. This "place" is usually the official website or a company-owned channel. Content is responsible for bringing in attention, while owned channels are responsible for receiving and accumulating it. Only when the two work together is content more than exposure—it becomes part of the conversion chain.
Don't Use Tools to Replace Judgment
AI can imitate tone and quickly produce a draft, but it does not understand your users, nor is it responsible for the results of the content. Over-relying on generated text can easily make content look correct but lack warmth, missing details that only real experience can provide. A more stable division of labor puts tools at the execution layer: let them handle first drafts, organization, and multi-version testing; leave judging content direction, injecting real information, and reviewing publication to people responsible for the business and users. Maintain this division of labor, and content can remain credible while efficiency improves.
The challenge of content work has never been insufficient production, but whether, after production, it can be seen by the people who need it and whether they are willing to stay. AI changes the speed of production; it has not changed this goal.