Here’s a conversation I’ve had more times than I can count.
A hiring manager tells me they want an A-player. Someone exceptional. A difference-maker. A star. Not just someone who can do the job. Someone who will elevate the team.
Then they hand me the job description. And the must-haves include five to seven years of experience doing exactly this role, at exactly this type of company, in exactly this industry.
And I think: that’s often not what A-players want.
The Contradiction Nobody Talks About
Many top performers aren’t looking to repeat what they’ve already mastered. They’re often looking for larger problems, broader scope, greater impact, or new challenges. A chance to prove they can do something they haven’t fully done yet.
That’s often one of the qualities that makes them top performers in the first place.
When a hiring manager says they want someone who can “hit the ground running,” what they usually mean is: I want someone who has already done this exact job so I don’t have to invest in them. That’s a reasonable thing to want. It just may not be the same thing as wanting the candidate with the highest long-term upside – an A-player.
The more heavily you optimize for immediate productivity, the more likely you are to overlook candidates whose long-term upside exceeds their short-term readiness.
The person who has done the exact job before, at the exact same scale, in the exact same industry may be good. But they are likely not A-players choosing the role because it stretches them and challenges them.
Meanwhile, the candidate who is slightly outside the template — the one who has done 80% of the job at a different scale, or has the skills in a different context, or has the raw ability to learn the rest in 60 days. That candidate often gets filtered out.
That person may have been the A-player you were looking for.
AI Just Made This Structural
This was a human problem in 2016. It’s becoming a structural problem now.
When companies adopted AI resume screening, they fed it historical hiring data. Past job descriptions, past resumes of people who got hired, past patterns of who made it through the funnel. The systems learned to find more of the same.
That’s not a bug. That’s exactly what they were designed to do.
But here’s what it means in practice: AI resume screening is the most sophisticated “been there, done that” filter ever built. It can’t evaluate potential, ambition, or learning velocity. It can only match signals in your resume against signals in past hires.
A 2025 academic study modeling AI’s effect on hiring markets found that when everyone can submit polished AI-generated applications, workers in the top quintile of ability are hired 19% less often than before AI. Workers in the bottom quintile are hired 14% more often.
Read that again. The screening is working in reverse.
Why? The resume was already an imperfect signal before AI touched it. We were never directly measuring job ability. We were measuring resume-writing ability and used it as a proxy for performance. The candidate who knew how to present themselves on paper moved forward. The one who was actually better at the work but wrote a mediocre resume often did not.
AI made that worse in two directions at once. It trains screening tools to treat historical resume patterns as ground truth, while simultaneously giving everyone the ability to produce polished application materials. The result is that organizations end up placing more weight on job titles, company names, years of experience, and keywords — signals that were never the strongest predictors of future performance.
The candidate who is best at optimizing a resume is not necessarily the candidate who is best at the job.
To be fair, filters exist for a reason. Most organizations don’t have the capacity to deeply evaluate every applicant. The challenge isn’t having filters. The challenge is choosing filters that actually correlate with future performance.
Read the rest of the newsletter at You Want Top Talent But You’re Screening Them Out