AI in Hiring Spirals Out of Control: Fake Job Postings and Filters Destroy the Labor Market

The problem of AI in hiring has already moved beyond isolated fraud stories: now technologies simultaneously help create fake job postings and become part of real selection processes, in which a candidate may never meet a real person. This hits both sides of the market at once. A job seeker loses time, hope, and sense of support, while an employer risks missing a strong specialist because they failed to pass an automated filter or couldn't please a templated system.

In letters published by Guardian in response to Victoria Turk's article about the rise of AI fraud in recruiting, readers describe a broader problem. According to them, AI today is used not only to promise non-existent roles and extract data from people looking for work. It increasingly appears in real hiring: in automated responses, initial screening, resume sorting, and communication with candidates.

As a result, good specialists may never reach an interview, and companies lose exactly the people they were trying to find. The more opaque the chain of decisions, the harder it is to understand why a response was ignored and where exactly the screening took place. One of the letter authors, a specialist in researching potential donors for organizations, writes that he knows AI's strengths from his own work, but tries to use it only when other tools have been exhausted.

His argument is simple: after twenty years of experience, he can prepare a report with the nuances and conclusions that only human understanding provides. This is, he says, what employers pay for. This thought is equally important for recruiting: when a candidate writes text with AI's help, and a company evaluates it also through AI, the substantive signal disappears from the process—the signal that normally allows distinguishing a formally correct profile from a truly valuable one.

Automation promises recruiters speed and savings, especially when a single job posting receives a large flow of responses. But along with speed comes a new surface for abuse. Fake recruiters can create convincing job postings at scale, send plausible messages, and copy the language of real companies.

For a person who urgently needs work, such signals are hard to verify because they act under pressure from time and money. In the worst case, a person manages to send their resume, personal data, and even complete a test task for a non-existent company. The more the real hiring market becomes accustomed to impersonal letters, bots, and templated stages, the easier it becomes for scammers to dissolve into this background and appear plausible.

There is also less visible damage. Even where there is no fraud, excessive reliance on AI changes the logic of selection. The system begins to reward not necessarily the best experience, but the ability to correctly format a resume for the algorithm, choose the right keywords, or generate a screening-friendly response.

Job candidates with real expertise but without perfect digital packaging lose out. Employers also lose out: instead of a strong employee, they get a person who better adapted to the mechanics of the filter. This is especially dangerous in professions where context, judgment, empathy, and unconventional thinking are valued.

The main conclusion from this discussion is that AI in hiring should remain an auxiliary tool, not a replacement for trust and human evaluation. Companies need verifiable communication channels, transparent selection stages, and human involvement in key decisions. Job seekers need healthy skepticism toward overly smooth offers and automated messages that cannot be verified.

Otherwise, the labor market will gradually turn into an exchange of templates between machines, where both the job seeker's hope and the employer's chance to meet a suitable person are equally easily lost.