Talent Paradigm Revolution: From "Skill Accumulation" to "Model Calling"

The effectiveness of the traditional talent path is based on several long-standing conditions. First, knowledge and skills primarily attach to people. Second, skills have a sufficiently long validity period. Third, experience is usually positively correlated with output quality. Fourth, combining multiple capabilities requires organizations. Fifth, there is an acceptable correspondence between ability certification and actual ability. Generative AI changes these conditions one by one. Some knowledge capabilities can be obtained through interfaces; operational skills update faster; some work methods of excellent personnel can spread to more people through models; some tasks in cross-professional collaboration can be organized and completed by one person.

The most obvious change is that the distance between "knowing how to complete" and "being able to obtain completed results" has shortened. In the past, people who couldn't program could hardly build software prototypes; now, they can first describe requirements and then build a runnable version with models. However, the prototype running does not mean they already understand system security, maintenance costs, and user needs. Execution barriers have dropped, but judgment barriers still exist. A person can first conduct small-scale practice with models, identify knowledge gaps from customer feedback, then learn targeted knowledge, and subsequently improve the product. Learning becomes more embedded in the delivery process, and career growth is more like a continuous cycle.

But "calling models" does not automatically become a new wealth password. Capabilities that everyone can obtain can hardly support excess returns in the long run. The more widespread models become, the more specific the questions that truly need answering: who discovers problems worth solving, who provides the real-world information models lack, who judges whether output is reliable, who makes deliverables adopted by customers, who bears failure consequences, and who ultimately reaps the rewards? Knowledge still matters, but the connection between knowledge and income has changed; experience still matters, but proficiency from repeated execution, versus judgment that can identify anomalies, needs repricing.

Here, AI employees are execution roles with tasks, permissions, memory, and delivery standards. Retrieval, analysis, editing, and review can be parallelized, and the ultimately responsible person maintains consistency of goals and value prioritization. Organizational capability thus has the opportunity to exceed the total amount an individual can personally execute. But coordination costs do not automatically disappear. Context inconsistency, duplicate tasks, handoff omissions, and multiple roles jointly believing errors all require governance. The scale ceiling of a one-person company depends on how low a human burden it can maintain reliability.