随著人工智慧在求职过程中的普及，传统的履历和求职信充斥著毫无实质内容的「廉价对话」，使招募人员难以辨识优秀的候选人。面对海量且看似完美的申请文件，企业越来越难以从中筛选出真正具备实力的人才，导致面试的通过率大幅下降。

为了应对这项挑战，招募市场正重新转向依赖人脉网络和前主管的推荐信来寻找合适的员工。这些推荐信具有极高的真实价值，因为推荐人必须投入额外时间并承担自身专业声誉的风险，这种高成本的背书在人工智慧生成内容泛滥的时代显得更加珍贵与可靠。

然而，这种高度依赖内部推荐的招募趋势也引发了新的隐忧，因为人们倾向于信任与自己相似的人，这可能会强化现有的职场不平等并严重削弱团队的多样性。企业在追求高效率招聘的同时，必须寻求创新的评估方法，让候选人能在流程早期展现实力，以维持公平与包容的招募环境。

With the proliferation of artificial intelligence in the job application process, traditional resumes and cover letters have become filled with hollow "cheap talk," making it difficult for recruiters to identify outstanding candidates. Faced with a massive volume of seemingly perfect applications, companies are finding it increasingly hard to filter out genuinely talented individuals, resulting in a significant drop in interview progression rates.

To counter this challenge, the recruitment market is reverting to a reliance on personal networks and letters of recommendation from former supervisors to find suitable employees. These letters carry exceptional authentic value because the recommender must invest extra time and risk their own professional reputation, making this high-cost endorsement much more precious and reliable in an era flooded with AI-generated content.

However, this growing trend of relying heavily on internal referrals also raises new concerns, as people naturally tend to trust those similar to themselves, which could reinforce existing workplace inequalities and severely undermine team diversity. While pursuing highly efficient recruitment, companies must seek innovative assessment methods that allow candidates to demonstrate their actual abilities early in the process, ensuring a fair and inclusive hiring environment.