Recruiters face an unprecedented flood of applications. Since 2022, the average recruiter has seen a 412% jump in applications handled, while each open role attracts 111% more candidates. Many teams operate with roughly half the staff they once had after successive layoffs. The result is a system under severe strain.
Layered on top is an escalating arms race: candidates lean on AI to generate polished resumes and cover letters, while employers deploy AI filters to detect and discard low-quality or fabricated submissions. This cycle frequently filters out strong candidates—people who excel in conversation and real work but struggle to compress their value into two pages of formal credentials. Traditional applicant tracking systems and AI detectors can also amplify existing biases rather than eliminate them.
Daniel Chait, CEO of the applicant tracking platform Greenhouse, argues that the fix is not better resume screening but a fundamental shift away from it. In a recent interview, he described resumes as tools built around credentialism: the bold name at the top, education, job titles, and tenure. Those signals have long shaped who advances, often before anyone evaluates actual capability.
A well-known 2003 study illustrated the problem starkly. Researchers sent identical resumes that differed only in whether the name at the top sounded stereotypically African American or white. Applicants with Black-sounding names received roughly 50% fewer interview invitations. Resume review, Chait noted, has carried these distortions for decades.
AI interviews, he contends, offer a practical way around the gatekeeping. Structured, skills-focused conversations conducted by AI can be delivered at scale, any time of day, at low cost. Candidates get a fair chance to demonstrate what they can do rather than what their past titles or alma maters signal. Companies that adopt this approach can stop screening people out early and instead invite anyone who passes a standardized interview to speak with a human. Bias is reduced because the first filter no longer rests on names, zip codes, or pedigree.
Chait is careful to distinguish good AI from bad. The technology itself is not the problem; poorly designed systems that simply automate flawed human processes are. Properly built AI interviews, grounded in structured hiring practices, can open doors that credential-focused screening keeps closed.
Greenhouse is not alone in this direction. Platforms such as Culture Test (linked to investor and podcaster Steven Bartlett) and Crossover already emphasize values alignment, psychometric insight, and AI-led interviews that candidates can take on their own schedule. More employers are experimenting with culture-fit assessments and skills demonstrations as primary filters.
AI interview technology remains imperfect and is still evolving rapidly. Yet the trajectory is clear. The resume—once the indispensable first document of the job search—is increasingly viewed as a relic of an earlier era. The emerging model prioritizes demonstrated ability, reduces reliance on potentially biased signals, and gives a wider range of people a genuine opportunity to show what they can contribute.
In this shift, the goal is not to remove human judgment but to apply it later and more fairly—after candidates have had a chance to present themselves on equal footing.