More Applications. More Automation. A Broken Hiring System.

Artificial Intelligence has broken hiring.

Not necessarily because AI is inherently bad, and not because candidates or employers are wrong for using it. The problem is that both sides are using increasingly powerful tools to optimize against a hiring system that wasn’t designed for any of this.

AI has made it incredibly easy to apply for jobs. Candidates can tailor résumés, write cover letters, answer application questions, research companies, and prepare for interviews in minutes. Naturally, people apply to more roles, including plenty where they may only be loosely qualified.

More Volume, More Automation

To handle the increase in applicant volume, employers are responding with more AI of their own. Screening tools rank candidates, identify skills, summarize experience, and filter hundreds or thousands of applications before a recruiter ever sees them. The recruiter’s workflow looks vastly different than it did just a few years ago. Today, we have candidates using AI to get through employer AI.

That’s where things start to get weird.

A company receiving 1,000 applications doesn’t suddenly have 1,000 viable candidates. It has 1,000 submissions. Strong candidates can get buried in the volume, while recruiters are increasingly forced to rely on automated systems to figure out who deserves attention.

The Signals Are Getting Weaker

While the resumes are piling up higher and higher, the signals and criteria we’ve historically used to evaluate candidates are becoming less reliable. A beautifully written résumé can no longer tell you someone is a great communicator. A thoughtful cover letter doesn’t necessarily mean someone spent an afternoon researching the company. A perfectly tailored application may simply mean someone knows how to use AI reasonably well.

This isn’t always a bad thing. Résumé writing was never a perfect measure of ability, and AI can help really talented people present their experience and skills more effectively. But it does mean the résumé increasingly becomes a starting point for evaluation rather than reliable proof that someone can actually perform the work.

Interviews Have to Improve

All of this AI-to-AI activity means interviews have to evolve too.

Predictable questions are becoming less useful when candidates can prepare polished answers to almost anything. Employers need to spend more time understanding how someone thinks. Give candidates realistic problems to solve. Ask how they would approach them, but keep digging. Why would they start there? Who would they involve? What resistance would they expect? What happened the last time they faced something similar?

Better yet, use behavioral-based questions to get past the polished answer and into what actually happened.

More often than not, real experience is imperfect. People remember the messy parts, the mistakes, the executives who pushed back, and the decisions they would make differently today. That kind of response is considerably harder to fake.

The wrong approach, however, is to keep adding more hurdles. Another assessment. Another phone screen. Another interview. Another presentation. Pretty soon candidates are going through seven rounds simply to prove they are who they say they are. Adding more steps doesn’t fix hiring. It makes an already broken process worse.

Relationships May Matter More

Somewhat ironically, I think AI may make relationships more important. When everyone can produce a polished application, a trusted person saying, “I worked with her for four years and I’d hire her again tomorrow,” suddenly carries much more weight. That recommendation offers something an optimized résumé doesn’t: credibility.

Hiring Needs Better Judgment

AI has made information incredibly cheap. Hiring doesn’t need more information. It needs better judgment. Until we rethink the process around that reality, adding more AI to both sides of hiring may simply give us more applications, more filtering, more noise, and less trust.