From ‘Showcasing Skills’ to ‘Generating Revenue’: A Content Creator’s Path to Refining AI Tools
Recently, a content creator known as ‘Uncle Huang’ shared his practical experience in the development and application of Artificial Intelligence (AI) tools. He spent over 100 hours building multiple AI applications, which he calls ‘Skills,’ but eventually found that he frequently uses only a few that directly create economic value.
Core Tool: A Writing Skill Oriented Towards “High Conversion”
One of his core tools is an ‘Interview Writing Skill.’ This tool has solidified his Standard Operating Procedure (SOP), automating the process from active interviews to content generation, which he claims has increased his writing efficiency fivefold. However, its greatest value is not efficiency, but its data-driven guidance. By analyzing the readership and conversion data of over ten articles, the AI tool revealed a key pattern: high readership does not equate to high business conversion. For example, an article with 7,056 reads had a Revenue Per Mille (RPM) as high as 1,526 yuan, while another article with 9,738 reads had an RPM of only 328 yuan—a nearly five-fold difference. Based on this insight, the tool now helps the creator choose between strategies for ‘high readership’ and ‘high conversion,’ allowing for more precise achievement of business goals.
AI Value Philosophy: Optimizing SOPs, Not Showcasing Technology
’Uncle Huang’s’ practice reflects a pragmatic AI application philosophy: the value of an AI tool lies not in its technical complexity or quantity, but in the depth of its optimization of existing workflows (SOPs). He calls tools that only achieve basic functions ‘Demos,’ while he considers those that have been refined through data feedback, continuous iteration, and can solve real business problems as ‘premium’ tools. The process from ‘Demo’ to ‘premium’ requires developers to pay close attention to user feedback and conversion data, locate problems, and make fine-tuned adjustments. This process is similar to product development, with the goal of making the tool genuinely serve the business.
Conclusion: Forging AI Tools into Personal Digital Assets
According to the Pareto principle (the 80/20 rule), a few core tools contribute the vast majority of the value. ’Uncle Huang’s’ case shows that for creators and knowledge workers, the real challenge is not developing a large number of AI applications, but identifying and polishing those that can optimize core processes and directly or indirectly generate income. When an AI tool can stably improve work efficiency and business returns, it transforms from a technical demonstration into a valuable personal digital asset.
