Can Labor Markets Function in the Age of AI? The Evaluation Bottleneck in Hiring

2026-09-24

AI Summary

This study addresses how AI-generated content diminishes the informational value of application materials, compelling recruiters to rely on coarse-grained screening metrics that impede inexperienced yet highly matched candidates from entering the labor market. To investigate this issue, we construct a Bayesian game-theoretic model integrated with a microstructural analysis framework of the labor market, conceptualizing multi-stage hiring mechanisms as endogenous response strategies to AI-induced information distortion and evaluation bottlenecks. We demonstrate that multi-stage recruitment can reconstruct credible assessment pathways, effectively mitigating employment barriers caused by AI technologies while preserving market access for inexperienced but well-suited applicants. This work provides a novel theoretical perspective for understanding and optimizing screening efficiency in labor markets disrupted by generative artificial intelligence.

Abstract

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. We 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. We ask how AI affects downstream screening and hiring, and which applicants are most adversely affected. As application materials become less informative, a Bayesian firm rationally relies more heavily on coarse observables such as prior experience. 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. We 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. This can restore screening opportunities that disappear under one-stage hiring and give inexperienced-compatible applicants a path to screening. Our 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. Multistage hiring can endogenously arise in response, restoring evaluation opportunities that would otherwise disappear and helping preserve market functioning.

Problem

• AI-assisted hiring
• evaluation bottleneck
• labor markets
• information asymmetry
• screening

Innovation

• AI-assisted hiring
• Bayesian screening model
• multistage hiring
• evaluation bottleneck
• information asymmetry