Gen Z Job Seekers Rewrite Resumes to Avoid Sounding Like Machines

According to TechRadar, a growing number of Gen Z job seekers are rewriting their CVs because they worry their applications will read as if a machine wrote them. The concern is that automated tools used by employers may flag polished or generic language, so applicants are trying to strike a balance between sounding human and sounding suitable for an automated screen.

TechRadar reports that the pattern was highlighted by a new survey from hiring platform Greenhouse, which polled more than 1,100 job seekers in the UK and Ireland. The research suggests younger applicants are more alert to the difficulties of AI-driven job screening than people over 60 are. The article frames the trend as part of a wider problem: applicants feel they must present themselves in ways that satisfy software as well as the people who will read their applications.

The outlet describes the situation as a kind of stalemate. Applicants are adjusting their writing to get past filters, while the tools that screen them are themselves becoming more common across hiring. TechRadar notes that AI can speed up the application process but can also make it feel less meaningful, both for the people applying and for those reviewing large volumes of submissions.

The available reporting does not provide a full picture of how widely employers use AI screening, how often applications are actually rejected by these tools, or how the survey's findings break down beyond the age comparison it highlights. Those details are not covered in the material, so they should be treated as open questions rather than established findings.

Background

Automated applicant screening has been part of corporate hiring for years. Large employers receive far more applications for each opening than recruiters can read in full, so many rely on applicant tracking systems that sort résumés by keywords, job titles, and required qualifications. Over time, these systems have been joined by newer tools that use machine learning and language models to rank candidates, summarize experience, or draft responses to recruiters.

Job seekers have adapted to earlier forms of screening before. Advice columns and career coaches have long recommended mirroring the wording of a job description, using standard section headings, and avoiding complex formatting that some parsing software struggles to read. Many of these tips were built around keyword matching rather than around judgment of tone or style.

The arrival of generative AI has changed the picture. Candidates now use chatbots to write cover letters and tailor résumés, which in turn has led some employers to look for signs of machine-written text. Detection of AI-generated writing is imperfect, and the tools used to check for it can produce false positives. This creates a difficult position for applicants who did their own writing but worry it may read as generic, polished, or formulaic.

The result is a feedback loop in which applicants and employers each respond to the other's tools. Applicants adjust their style to satisfy screening software, while employers search for better ways to filter the volume they receive. Whether this loop improves hiring outcomes for either side is a question that remains open.

Why it matters

For job seekers, the trend means that writing a résumé is no longer only about describing one's experience clearly. It has become a question of how the text will be interpreted by software that the applicant cannot see and may not understand. Young people entering the labor market, who often have less work history to lean on, may feel this pressure most sharply. Being told to sound more personal, or to avoid sounding automated, asks them to perform authenticity for a machine.

For employers, the situation raises questions about the purpose of automated screening. If applicants are rewriting their materials to avoid sounding like AI, the signal that screening tools are meant to capture may become distorted. Recruiters could end up filtering for stylistic habits rather than for skills, and qualified candidates could be missed for reasons unrelated to their ability to do the job.

The story also touches on fairness and trust. When a hiring process is opaque, people who are less familiar with its rules may be disadvantaged, and those who feel uncertain about how they are being judged may withdraw or lose confidence in the process. Even if the tools are not intended to discriminate, a lack of transparency can make the experience feel arbitrary.

More broadly, the episode illustrates how quickly generative AI is changing ordinary professional tasks. Writing a CV, once a relatively private exercise, is now shaped by assumptions about what machines will reward and what human readers will tolerate.

What to watch

Watch for data that goes beyond surveys of how applicants feel. Useful evidence would include how often automated tools reject candidates, whether those rejections correlate with writing style rather than qualifications, and how employers audit their systems. Regulators in some regions have begun examining automated hiring tools, and any new rules on transparency or appeal rights could change how these systems are built and used.

Also watch whether employers begin disclosing when AI is involved in screening, and whether job seekers gain a way to request human review. Changes in recruiter practice, such as shifting toward skills assessments or structured interviews, could reduce the pressure to write for software. Finally, watch whether the Greenhouse findings are repeated in other surveys and markets, which would show whether this is a broad shift or a localized pattern.