Greenhouse CEO warns of AI doom loop as job seekers spend $20 to spam applications

Job Seekers and Recruiters Trapped in AI Doom Loop

The hiring market has reached a breaking point where both job seekers and employers are equally frustrated, according to Greenhouse CEO Daniel Chait. Job seekers are spending around $20 on AI tools to mass-apply to positions across the hiring platform, while recruiters deploy their own AI to filter through the resulting deluge. Chait calls this phenomenon an AI doom loop—a cycle where everyone uses automation to solve their individual problem, but collectively makes the entire system worse 1

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"This is the first time when really both sides have been unhappy," Chait told Fortune. "The market just isn't working for either side" 1

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. The problem stems from a fundamental imbalance: job seekers using AI can blast applications to thousands of positions overnight, while recruiters using AI to filter them respond by tightening their automated hiring systems, which then prompts even more mass applications.

Application Volume Reaches Crisis Levels

The numbers paint a stark picture of the current hiring ecosystem. Greenhouse currently hosts approximately 175,000 live jobs, with an average of 254 applicants per posting. More alarmingly, applications per recruiter have surged by 412% 1

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. Recruiters often receive hundreds or thousands of applications within just a day or two of posting a position, creating an impossible workload that forces them to rely on automation.

The scale of the problem extends beyond Greenhouse. More than 1.2 million applications were submitted last year in the U.K. alone for fewer than 17,000 graduate roles. One young man with a master's degree applied to thousands of positions over six months without receiving a single callback 1

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. This résumé ghosting has become so common that job seekers feel they have no choice but to turn to AI tools to mass-apply to every available position.

AI in Hiring Creates Identical Applications

The proliferation of AI-generated applications has created a new challenge for employers: homogeneity. Because so many candidates use similar AI tools to mass-apply, their applications begin to look remarkably alike, making it nearly impossible for recruiters to distinguish genuinely interested candidates from those who have auto-applied to a thousand jobs overnight 1

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. Even high-profile employers struggle with this issue. Arvind Jain, the ex-Google engineer behind the $7.2 billion AI startup Glean, explained that his company receives thousands of job applications every single day, yet he still can't find the people he needs because the best candidates get snapped up before he can review even a fifth of the applications 1

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Actress Anne Hathaway recently experienced this firsthand when she was spammed with ChatGPT-written thank you notes after a recent hire. "If you're out there thinking that you're getting away with something, there's a chance that you might be revealing yourself," Hathaway warned 1

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Greenhouse Launches Solutions to Break the Cycle

Recognizing that simply asking everyone to stop using AI isn't realistic, Daniel Chait and Greenhouse have launched two features designed to restore signal to the hiring process. The first is the My Dream Job feature, which allows job seekers to flag just one role per month across every company on Greenhouse's platform as their top priority. Because this designation is scarce—limited to one per month—it forces genuine intent and creates a signal that employers can actually trust. Nearly 500,000 "dream job" applications have been submitted since launch, and candidates using this feature get hired at roughly five times the rate of everyone else 1

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The second solution involves Greenhouse's recent acquisition of Ezra AI Labs, which enables AI voice interviews for every applicant—not just a shortlisted few. "Why would I send out automatic thousands of job applications if every single one of them I have to take an interview for?" Chait asked 1

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. The goal is to eliminate the incentive to spam applications in the first place while cutting out the résumé-screening stage entirely—a step Chait says is riddled with résumé-screening bias and adds roughly six days to the hiring process for little real benefit.

Human Networks Still Matter in AI's Role in Recruitment

As automated hiring systems become more prevalent, experts suggest that building human networks may be the most effective way to stand out. Netskope CEO Sanjay Beri has recommended that candidates focus on building relationships instead of relying entirely on online applications, noting that some of the best jobs are "never published." "Who you know, how you get to know them, and building your network will pay off over time," Beri said 2

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. This advice becomes particularly relevant as Goodwill's CEO Steve Preston has warned that job centers are bracing for an influx of jobless young people as AI eats into entry-level roles 1

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. The short-term implications are clear: candidates who continue to rely solely on AI tools to mass-apply will find themselves competing with millions of identical applications, while those who invest in personal connections and demonstrate genuine intent may find doors opening faster. Long-term, the hiring market may need to fundamentally restructure how it matches talent with opportunity, moving away from volume-based screening toward more meaningful signals of fit and interest.