Job Seekers, The Hardships We've Been Experiencing are Real, and Finally Given a Name

New Stanford research raises troubling questions about automated hiring, overlooked talent, and who's being left behind.

If this job market feels unprecedentedly difficult, you may be right. And the problem may not be your resume, your experience, or your interviewing skills.

It may be the technology deciding whether you get an interview in the first place.

For years, job seekers have been told to optimize their resumes, tailor their cover letters, and learn how to beat applicant tracking systems (ATS). But new Stanford research raises a question we should have been asking all along: What if the problem isn't the applicant? What if it's the hiring process itself?

When I lost my job in January 2025, I entered the job market with decades of leadership experience, an extensive professional network, and a career I was proud of. I expected the search to be challenging. But after applying to 70 jobs and landing just one interview, I knew something wasn't right. I didn't expect to feel invisible.

That's the experience I described in Chapter 7 of my book, Redefined: Finding Purpose and Power After Job Loss Amid AI Disruption and Market Uncertainty. I called it the "bot loop." Applications went in. Automated responses came out. Meaningful conversations were increasingly difficult to secure.

What troubled me wasn't simply the rejection. It was the absence of any meaningful opportunity to demonstrate what I could contribute.

Recent research is helping explain why so many qualified professionals may be experiencing similar frustrations.

When Different Employers Make the Same Decision

In May 2026, researchers affiliated with Stanford University, Chapman University, and Northeastern University published Algorithmic Monocultures in Hiring. They examined approximately 4 million applications evaluated using algorithms from the same hiring technology vendor.

Among applicants who applied to 10 positions, 4% received negative algorithmic recommendations for every position, more frequently than researchers would expect if the decisions were independent. The researchers also found significant racial disparities at the individual-job level that were less apparent when they considered the results collectively.

Think about that. You apply to 10 different companies, believing you have 10 independent opportunities to demonstrate your qualifications. Yet employers using the same screening technology may reach similar conclusions about your candidacy.

Researchers call this phenomenon "algorithmic monoculture."

The study examined one assessment vendor, not every ATS, and doesn't prove that algorithms caused every rejection. Still, it raises serious questions about whether shared hiring technologies are limiting opportunities for qualified candidates.

We've Made Hiring More Efficient. But Have We Made It Better?

Technology allows employers to process thousands of applications in a fraction of the time it once took. That's valuable, especially when recruiters are overwhelmed by the volume of candidates.

But efficiency isn't necessarily effectiveness.

An algorithm can evaluate predefined criteria and identify patterns. It may miss the judgment developed through decades of leadership, the ability to navigate a crisis, or the potential of someone whose career doesn't follow a conventional path.

Companies routinely talk about innovation, adaptability, and the importance of diverse perspectives. Yet their hiring systems may favor conventional career paths and familiar patterns.

What happens when the person best equipped to transform an organization never makes it past the screening process?

That's not just a recruiting failure. It's a leadership failure.

In a recent interview with BambooHR News, I discussed another challenge facing employers: evaluating candidates in a workplace where AI is becoming part of everyday business. Organizations need people who know how to use these tools, but who also have the experience and judgment to question the results. That means looking beyond conventional qualifications to understand how candidates think, solve problems and apply technology responsibly.

Hiring practices need to keep pace with the changing workplace. The goal shouldn't be simply to process candidates faster, but to better understand what they can contribute.

You can automate a process. You cannot automate good judgment.

For Job Seekers, It's Time to Change the Strategy

One of the reasons I wrote Redefined was to help people regain a sense of control after job loss. The book's framework, Pause, Rebuild and Grow, offers a practical way forward. Several recommendations from Stanford's researchers reinforce those principles.

Pause. Stop letting an algorithm define your professional worth.

Repeated rejection can erode even the most accomplished professional's confidence. I've experienced it. But an automated recommendation is not a definitive measure of your abilities. Take stock of your accomplishments, reassess your strengths, and remember that your professional identity extends beyond your most recent job title.

Rebuild. Make relationships a bigger part of your job search.

Stanford's researchers encourage job seekers to pursue referrals and build connections beyond their immediate professional circles.

Reconnect with former colleagues. Attend industry events. Ask for introductions. Don't overlook acquaintances who may know of opportunities outside your existing network. And don't make submitting online applications the centerpiece of your search.

A resume tells an employer where you've worked. A conversation lets you show what you can contribute.

Grow. Demonstrate your capabilities beyond your resume.

Consulting, freelance work, volunteering, speaking engagements, and professional writing can showcase your expertise while opening new doors.

These aren't just activities to keep you busy while you look for employment. They let you build skills, expand your network, and explore possibilities you may not have considered before losing your job.

Your next chapter shouldn't be determined by an automated rejection.

Employers, This Is Your Wake-Up Call

Job seekers can change their approach, but they cannot fix the hiring systems employers choose.

Business leaders need to ask difficult questions. Do we understand how our algorithms evaluate candidates? Can we explain why qualified applicants are rejected? Are we examining outcomes for individual positions or relying on assurances from technology vendors?

Stanford researchers emphasize the importance of evaluating outcomes at the individual-job level, where disparities may otherwise remain hidden.

Organizations should demand transparency from vendors, independently assess their screening criteria, and establish meaningful human review. Recruiters should be able to challenge algorithmic recommendations, and candidates should have reasonable opportunities to correct inaccurate information.

Hiring success shouldn't be measured solely by how quickly a position is filled or how many applicants are eliminated. It should be measured by whether an organization is identifying and hiring the talent it needs.

There's also a reputational consideration.

An organization can spend millions communicating its commitment to employees, innovation, and inclusion. But if its hiring process leaves qualified candidates feeling invisible, those experiences become part of its reputation, too.

Every applicant is also a potential customer, referral source, business partner, or future employee. How organizations treat candidates says something about their values, whether leadership recognizes it or not.

AI has tremendous potential to improve hiring. It can reduce administrative work, support recruiters, and help organizations identify candidates they might otherwise overlook. But employers need to understand the technology they're using and be willing to question the results.

If you're caught in the bot loop, keep moving forward, but reconsider how you're approaching the search. Invest in relationships. Find ways to demonstrate your expertise. Explore opportunities beyond traditional employment. Most importantly, don't allow an automated rejection to become a judgment about your professional worth.

AI has tremendous potential to improve hiring, but technology is only as effective as the decisions behind it. Employers have a responsibility to ensure their systems aren't overlooking qualified candidates simply because their experience doesn't fit a predetermined mold. That requires more than trusting the technology. It requires oversight, accountability, and a willingness to recognize talent beyond an algorithm's recommendation.

Hiring isn't meant to eliminate applicants. It's to recognize talent.

Research: Algorithmic Monocultures in Hiring, 2026.

For more on navigating career disruption and finding purpose after job loss, explore my award-winning book, Redefined: Finding Purpose and Power After Job Loss Amid AI Disruption and Market Uncertainty.