Sam spent three evenings on the application. Not on the covering letter — on the research. The company’s recent work, where it had been, where it was going, the names of people who might read what Sam wrote. The application itself took one more evening. By Friday afternoon it was in.
Then nothing. Not a receipt. Not a holding message. Eventually not even a rejection. Just the particular silence that follows something you cared about and cannot un-send.
Three weeks later, Sam checked the job board. The listing was gone. The role had been filled.
What the filter decided
Sam’s application was read. Just not by a person.
An applicant tracking system — software designed to process high volumes of applications before any recruiter makes contact — compared Sam’s CV against a pattern. The pattern was built from historical data: previous successful hires, their job titles, their prior employers, the particular density of certain keywords in certain fields. Sam’s CV did not match. The system moved on.
There was no decision Sam could appeal. No criterion to review, reconsider, or present counter-evidence against. No one in the organisation knew the application had been received, let alone assessed. The system did not tell Sam a judgement had been made. It simply stopped.
The silence was the answer.
The recruiter who left the room
This is not a new failure. It is the endpoint of a process that has been under way since the mid-1990s.
When specialist recruitment agencies dominated the market, the relationship between candidate and employer was mediated by a person with knowledge of both sides. That person knew what a candidate’s career trajectory meant — why they had moved, what they had built, what they were capable of next. The relationship was imperfect, and expensive, and slow. But it was a relationship.
Mass-market job boards made applying easier and made that relationship rarer. Then platforms introduced Quick Apply — a feature that lets a candidate submit to a role in two clicks using a profile already on file. The frictionlessness was sold as convenience. What it produced was volume. Hundreds of applications per role became the norm, and the only rational response to hundreds of applications per role, with no additional resource, is to filter automatically.
AI-powered filtering is the logical outcome of that chain. And it has now reached the point where AI-generated CVs — assembled by tools that know what ATS systems favour — are gaming AI-powered filters in turn. The signal is indistinguishable from the noise.
No human being in the system made a wrong decision. No individual is responsible for what Sam experienced. Which is precisely what makes it so difficult to fix.
I have sat on the other side of this. As a hiring manager I have opened a shortlist and known — not suspected, known — that the candidates in front of me were not the best people who applied. The best candidates were somewhere in the stack the system never surfaced. There was no mechanism to go back and look.
The scale of the silence
Sam’s experience is not unusual. A 2025 analysis by Jobscan found an ATS system detectable at 97.8% of Fortune 500 companies. A Harvard Business School survey of more than 2,000 executives found that 94% of employers with middle-skills roles and 88% of those with high-skills roles believed that automated filtering was removing qualified candidates using criteria such as job title, credentials, and years of experience rather than actual ability to do the work. They continue to use the systems.
The most visible legal consequence is currently making its way through the American courts. In Mobley v. Workday, Inc., five people over the age of forty applied for hundreds of jobs using Workday’s platform and received almost no interviews. In May 2025, a federal court in California certified a collective action — a case that may now cover hundreds of millions of job seekers over forty. The claim is that an automated system made systematic decisions about people based on protected characteristics, without any individual in the process being aware of or accountable for what was happening.
In November 2024, the UK Information Commissioner’s Office published a report raising concerns about AI recruitment software operating in Britain — software found to be filtering candidates on the basis of gender, race, and sexual orientation, invisibly, with no individual in the chain accountable for what it decided.
For Sam, none of that is the point. The point is that Sam spent four evenings preparing something that was never read. And will never know why.
A wall with a letterbox
Sam could not use the system to present themselves clearly. There was no mechanism to ask a question, add context, or correct a misreading. The system did not help Sam do what they were trying to do — it removed them from the process without their knowledge.
A competent recruiter who knew the role and spent twenty minutes with Sam’s application would have surfaced things no pattern-match can — the reasoning behind a career move, the range of a role that looks narrow on paper, the capability that does not translate cleanly into keywords. The automated system did not supplement that judgement. It replaced it, at lower quality.
The system applied the same pattern-match that it applied to everyone. That pattern was trained on past hires which means it was designed to reproduce the past. If the past encoded bias — by employer, by educational institution, by the names and formatting conventions of particular social groups — the system reproduced that bias at scale, invisibly, with no one accountable.
A system that cannot help the candidate, cannot add to the process, and cannot distinguish one person from another is not a recruitment tool. It is a wall with a letterbox. Applications go in. Nothing comes back out. And no one inside knows what the wall contains.
If you own any part of this
Most people reading this have sat somewhere in this system. Has this happened to you, or to someone you know — the care, the preparation, the submission, the silence? Not a rejection. Just nothing.
If you work in technology or HR, what would you change tomorrow — not after the next procurement cycle, not after the regulatory guidance arrives, but tomorrow?
If you are currently using these systems to hire, do you know what they are filtering out on your behalf? Not what they are selecting. What they are removing.
Is optimising for volume the right goal — or have we confused efficiency with quality at precisely the moment quality matters most?
My opinion
The harm in recruitment is not a technical malfunction. It is a design choice made upstream, long before the system was deployed, invisible to everyone downstream — including the organisation that bought it and the candidate it rejected.
Recruitment is a human activity. We hire people, not machines — so why are decisions about who gets considered being made by software with no understanding of context, trajectory, or circumstance? The outcome is already visible: candidates submitting fictitious CVs engineered to pass an automated filter, because they know that what they have actually done matters less than whether they can predict the pattern.
Until someone in the process is accountable for what the system decided, Sam’s experience will keep happening, at scale, in silence.
Authors note:
Sam is a fictional character. Their story is drawn from a combination of professional observation and personal proximity to real events. The experiences described are real. The person is not.
You’re reading The Next Evolution by Neil Catton, articles that explore the human world and the intersection of technology, they try and ask difficult questions - not to scare - but to inform. If someone forwarded this to you, you can subscribe free at neilcatton.substack.com.
Neil Catton is the author of The Next Evolution, The Cognitive Crucible and The Shadow System - available on Amazon, and writes at the intersection of technology, ethics, and human purpose.
A further consequence of automation is that it can quietly exclude those who do not know how to optimize for the algorithm. Not everyone knows how to use AI to craft a CV that passes automated filters. In many domains now—from recruitment to writing platforms—success depends increasingly not only on the quality of one's work, but on understanding the logic of the algorithm itself.
This resonates, Neil. The story of Sam captures something that’s become a systemic failure dressed up as efficiency. The original intent - democratising access, removing gatekeepers, giving every candidate a fair shot, was genuinely good. But somewhere between that vision and today’s reality, we traded human judgment for pattern-matching at scale, and called it progress.
What strikes me is that AI is now compounding the problem from both ends. Candidates are using it to engineer CVs that satisfy the algorithm. Companies are using it to screen those CVs. So we’ve essentially created an arms race between two machines, with actual human potential trapped somewhere in the middle. The signal-to-noise problem you describe isn’t just getting worse - it’s accelerating, and neither side can easily step off the treadmill unilaterally.
I agree it needs to be human-centred, but I share your uncertainty about how we get there in a digital, highly connected and high volume world. Perhaps the honest starting point is that we stop pretending these systems are neutral. They’re not filtering for the best candidates; they’re filtering proxies who meet predefined tick boxes and resemble past hires. Naming it as a choice might at least open the door to making a different one.