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How has the rise of ChatGPT and off-the-shelf LLMs disrupted AI recruiting tools?
The arrival of ChatGPT triggered a flood of cheap, off-the-shelf AI recruiting tools — while simultaneously giving job seekers the means to auto-generate and mass-submit polished resumes. The result is a broken loop: AI systems screening candidates are now drowning in AI-fabricated applications, a dynamic Josh Bersin bluntly calls "slop talking to slop." Recruiters report being so overwhelmed by fraudulent, AI-generated resumes that they can no longer sort through them effectively.
Hundreds of new tools, and a market that suddenly looked easy to disrupt
Before ChatGPT, building a credible AI-powered recruiting product required significant investment in proprietary models and proprietary data. Companies like Eightfold, HireVue, HiredScore, and SmartRecruiters had spent years developing systems trained on millions of candidate profiles and hiring outcomes. That barrier was largely what kept the market from being overrun.
After ChatGPT launched, that barrier collapsed almost overnight. Hundreds of startups began wrapping large language models into recruiting workflows — resume screening, candidate matching, interview scoring — and selling the result as enterprise-grade AI. As Josh Bersin covers in depth in The Josh Bersin Company podcast, what had been an expensive capability suddenly appeared to be available off the shelf to anyone willing to build a thin product layer on top of a general-purpose model.
Established players like Workday, SAP, LinkedIn, and Paradox had to contend with dozens of new entrants making similar-sounding promises. The market became genuinely hard to navigate — for buyers and candidates alike.
"Slop talking to slop" — when both sides of the hiring funnel automate at once
The disruption didn't only hit the employer side. Job seekers responded to the AI screening wave with their own automation. Tools proliferated to help candidates generate tailored, AI-polished resumes and submit them at scale — sometimes to hundreds of openings simultaneously with minimal human effort.
This created a deeply problematic symmetry. The same technology powering the screening tools was now powering the applications those tools were supposed to evaluate. One particularly striking finding: AI recruiting tools built on ChatGPT are 50 to 75% more likely to accept resumes also written with ChatGPT, compared to resumes written by hand or generated with Claude or Gemini. The system, in effect, rewards matching the model's own stylistic fingerprint — a finding explored further in this episode of The Josh Bersin Company.
The research behind this comes from computer scientists who examined hundreds of resumes across different AI-assisted and human-written conditions. It's not a marginal effect. It points to a fundamental validity problem: tools that are supposed to identify qualified candidates may instead be selecting for candidates who know which AI to use.
"We have AI creating resumes and producing and sending them out and AI consuming them — slop talking to slop — and the recruiters constantly tell us they're getting so flooded with fraudulent resumes that they really can't sort through it all."
Josh Bersin — Global Industry Analyst & CEO, The Josh Bersin Company. Bersin is one of the most widely cited analysts in the HR technology industry, tracking talent acquisition, HR systems, and workforce trends across large enterprises globally.
Recruiters at companies using tools from vendors like SeekOut, Maki People, SHL, or Paradox report the same on-the-ground reality: the volume of inbound applications has surged, but the signal-to-noise ratio has collapsed. Screening at scale was supposed to make hiring faster. Instead, it has made it harder for human recruiters to identify genuinely qualified candidates — a central tension unpacked in The Josh Bersin Company.
The economics of recruiting make this problem particularly high-stakes. Hiring a single highly-skilled employee already costs between $5,000 and $10,000 — and filling a senior executive or software engineering role can require paying an executive recruiter nearly a third to a half of that person's first-year salary. When the screening layer breaks down, those costs don't shrink. They compound. Every bad hire, every wasted screening cycle, every missed qualified candidate adds to a bill that was already significant — a point Bersin has long emphasized on his podcast.
See also
What legal and bias challenges have emerged around AI scoring tools in recruiting?
New York State, Illinois, and several other jurisdictions passed laws requiring companies to prove that their AI scoring or inference technology is not biased against protected classes of candidates. Employers using these tools now face real legal exposure if they cannot demonstrate fairness.
What was Eightfold's pioneering contribution to AI-powered recruiting?
Eightfold was a pioneering company that scraped profiles of individuals from across the internet, anonymized them, and used AI to study skills and technical capabilities — enabling a new kind of candidate matching that went far beyond keyword-based resume filtering.
How did AI first enter the recruiting space before large language models existed?
AI in recruiting started in the era of job boards like monster.com and CareerBuilder, which moved job advertising from print to online. Applicant tracking systems then brought basic automation to resume filtering and hiring workflows — long before LLMs existed.
Key takeaways
- ChatGPT's launch enabled hundreds of startups to release cheap AI recruiting tools, collapsing the barrier to entry that had previously protected established players like Eightfold, HireVue, and HiredScore.
- Job seekers responded in kind, building their own automation to mass-generate and mass-submit AI-polished resumes — creating a two-sided AI arms race inside the hiring funnel.
- AI screening tools built on ChatGPT are 50 to 75% more likely to favor resumes also written with ChatGPT, introducing a model-alignment bias that has nothing to do with candidate quality.
- Recruiters across the industry report being flooded with fraudulent, AI-generated applications they can no longer effectively filter — undermining the very efficiency gains these tools promised.