Foreign media commentary suggests that there is still a lack of sufficient evidence to support the claim that "_AI is destroying junior white-collar positions." The decline in recruitment for some junior positions in recent years may not be entirely attributed to AI; rising interest rates and a weakening recruitment cycle are also important factors contributing to this trend.
The article cites a study of 238 million job listings, which indicates that when the Federal Reserve began raising interest rates in March 2022, the visibility of job listings for positions with high AI exposure had peaked and then started to decline. These positions are mainly concentrated in the information, finance, and professional services industries, which are more susceptible to changes in interest rates.
However, the article does not deny the role of AI. The author believes that the more noteworthy change is not companies massively laying off junior employees, but rather reducing the recruitment of new staff. Moreover, the areas where this reduction is most evident are often those positions where AI can replace repetitive work.
What enterprises are reducing is the "investment in training".
The article argues that many junior positions did not exist solely for the purpose of producing immediate results in the past. Whether it's first-year lawyers in law firms or resident doctors in hospitals, the work they accomplish in the early stages often requires repeated review by their superiors, so efficiency alone is not a significant factor.
However, companies are still willing to bear this cost, as these positions essentially serve a training function, acting as a pathway for new employees to gradually develop into seasoned professionals. Once AI takes over some of the preliminary analysis, organization, and clerical tasks, it becomes easier for the company to reduce recruitment in these areas.
The problem lies not in layoffs, but in the contraction of the supply of experience.
The author believes that companies still need employees with judgment and experience, but fewer and fewer organizations are willing to continue to bear the cost of the process of developing that experience. In the past, junior employees produced work while receiving training, and part of that output could even offset the training costs; now, with this basic work being taken over by AI, the original balance has been disrupted.
The article argues that companies should not view junior recruitment merely as a cost that can be automated and reduced, but rather as an investment in future talent reserves. If mechanisms such as training, job rotation, and mentorship are not actively re-established, there may be a situation in the future where there is still a demand for senior talents, yet the supply will have decreased.
Colleges and universities also face the same pressures.
The article states that this issue does not only occur on the corporate side. Universities, especially their research and training systems, also rely on students accumulating skills through the process of completing specific tasks. If key training steps are replaced by machines, students may obtain answers more quickly, but it becomes more difficult for them to form independent judgment.
The author concluded that in the future, the recruitment cycle may pick up as the economic environment improves, but the previous model of bearing talent training costs incidental to daily production activities may not automatically return. For companies and universities, a more practical question is: who will be responsible for paying for talent training in the AI era?
