AI hiring is producing more applications and more automation as LinkedIn tries to help recruiters find genuine skills inside a growing flood of AI-written resumes.
AI hiring is becoming harder to manage even as artificial intelligence makes it easier for job seekers and recruiters to move faster.
LinkedIn CEO Dan Shapero says the number of applications submitted per job seeker on the platform has risen about 30% since the pandemic. Many candidates now use AI to write resumes, tailor applications and apply to more roles in less time.
According to The Wall Street Journal, Shapero said the flood of AI-assisted applications is making it harder for employers to work out who can actually do the job.
That leaves AI hiring with an awkward problem. The technology helping candidates apply more efficiently can also make each application less useful as evidence of ability.
AI hiring is creating more applications and more noise
For job seekers, the attraction is easy to understand. A model can rewrite a resume for a specific vacancy, draft a cover letter and pull useful terms from a job description within seconds.
That saves time. It can also encourage people to apply for more jobs, including positions where their experience is only a loose match.
Recruiters then face hundreds or thousands of polished applications that may look increasingly similar. The problem is no longer simply finding candidates. Employers need to decide which applications reflect the person’s actual skills.
Shapero became LinkedIn CEO in April after 18 years at the company, most recently as chief operating officer. He told the Journal that employers are finding that judgment harder.
This changes what AI hiring tools need to accomplish. Faster resume screening has limited value if the system cannot separate useful evidence from generated wording.
LinkedIn is already trying to sell recruiters a solution.
In a LinkedIn analysis of its Hiring Assistant, the company said two-thirds of recruiters reported that finding qualified talent had become harder than a year earlier.
LinkedIn also said companies using Hiring Assistant made 11% more “quality hires” and hired 18% more high-demand talent on average than companies using traditional recruiting methods.
Those figures come from LinkedIn’s own analysis rather than an independent controlled study. The company examined more than 110 million member profiles and compared customers using Hiring Assistant with companies using LinkedIn Recruiter without the assistant.
Recruiters are using AI against an AI-generated flood
The response to the application surge is more automation.
LinkedIn’s Hiring Assistant can search profiles, evaluate skills and career signals, prepare candidate shortlists and help recruiters contact potential hires.
That creates a strange loop in AI hiring.
Candidates use AI to prepare and send applications. Employers use AI to filter them. Candidates then adjust their resumes to improve their chances of surviving automated screening.
The process risks becoming less about one person reading another person’s work and more about software interpreting material produced by other software.
There are legitimate benefits.
A recruiter handling thousands of applications cannot carefully read every document. AI can find candidates whose job titles or career paths might be missed by a simple keyword search.
AI hiring systems can also create new problems. Automated tools may give too much weight to familiar career patterns, especially if the underlying data reflects previous hiring decisions.
A candidate with the right skills but an unusual background could therefore be harder for a model to classify.
That is one reason verified skills may matter more as generated resumes become common.
The AI Decode previously covered HP’s plan to cut up to 6,000 jobs as it expands AI use. The story shows another side of the employment shift. Companies are using AI to recruit workers while also redesigning teams and automating internal work.
AI hiring could change entry-level recruitment first
AI hiring may create a particular problem for younger workers.
Entry-level candidates have less employment history to separate themselves from other applicants. If almost anyone can generate a clean resume and tailored cover letter, employers may put more weight on tests, portfolios, interviews and verified work.
Traditional application documents could become less important.
Candidates may also have to prove their skills earlier in the process.
For recruiters, AI can identify patterns across a large pool of applicants, but a high model score does not prove that somebody will perform well after being hired.
LinkedIn’s definition of a “quality hire” includes someone who stays at a company for at least 12 months and shows at least one sign of impact, such as being promoted, moving into another role or entering at manager level or above.
That is useful operating data. It is still LinkedIn’s own definition of hiring quality.
The debate around AI hiring therefore reaches beyond saving recruiters a few hours.
The AI Decode’s report on why some AI leaders want slower AI development also examined employment, including whether workers and institutions can adjust quickly enough as AI changes how jobs are structured.
Hiring is already showing that pressure.
LinkedIn wants AI hiring tools to restore lost signals
Shapero’s argument is that AI can help repair some of the hiring problems AI helped create.
LinkedIn has access to career histories, skills, recruiter behaviour and a large professional network. That gives its systems more information than a resume alone.
Still, employers have to decide how much judgment they want to hand to automated tools.
Job seekers face a similar choice. AI hiring tools can help candidates explain their experience more clearly, but mass-producing applications can make it harder for any single application to stand out.
The next phase of AI hiring may rely more heavily on evidence that is difficult to manufacture, including verified skills, previous work and direct assessments.
LinkedIn is betting that AI can identify those signals inside a noisier market.
Whether employers and candidates trust those systems to judge people fairly may become just as important as how quickly the systems process applications.
