AI Résumé Tools Are Quietly Locking Out First-Time Job Seekers

A new paper asks whether labor markets can function in the age of AI. AI-assisted job-search tools have become increasingly popular by making it easier to find and apply to jobs — but by making it easier for applicants to generate and tailor application materials, they can also reduce how informative those materials are about applicant fit. The researchers study this tradeoff in a hiring market where applicants differ in experience and latent match quality and firms use noisy application materials to decide whom to screen. As application materials become less informative, a Bayesian firm rationally relies more heavily on coarse observables such as prior experience, according to the article. Among the four applicant types defined by experience and compatibility for the job, inexperienced-compatible applicants are the most exposed: they lack observable experience and lose the individualized information that could distinguish them from other inexperienced candidates. When screening is costly, these changes can also generate inefficient screening failures in which firms screen no applicants or screen only experienced applicants. The researchers then show that multistage hiring can arise as an endogenous firm response — a relatively inexpensive intermediate assessment allows firms to acquire new evidence of fit before costly full screening, potentially restoring screening opportunities that disappear under one-stage hiring and giving inexperienced-compatible applicants a path to screening. Their results show how AI can shift the central friction in hiring from submitting applications to obtaining credible evaluation, creating entry barriers for high-fit workers without prior experience.