Behind the tools everyone uses and nobody really looks at.
A recruiter opens a LinkedIn profile: “Leadership.” “Strategic Planning.” “Change Management.” “Python.” “Financial Analysis.” The words are clean, well aligned, validated by a few contacts, sometimes even accompanied by small social confirmations that create the impression that other professionals have seen these skills in action and are now willing to attach their own name to them.
The recruiter continues their work as a surface-level spelunker.
And often, something important is already missing from the scene: nobody in the process is truly capable of verifying what those words will actually mean once the work begins.
The system still treats these skills as usable data. For LinkedIn, for the ATS, for search filters, for the recruiter quickly scanning through profiles, the word itself becomes enough of a signal to start ranking candidates, even though the gap between two people displaying the exact same skill can be enormous, one person may have led a complex organizational transformation for three years, absorbed political conflict, restructured teams and carried the consequences of change all the way through, while another simply attended two internal meetings while an outside consultant did the real work.
Both profiles display “Change Management”, the system sees a match, but the strangest part of all this is probably somewhere else.
Modern recruiting already operates largely on terminology that the people around the table do not fully understand. Recruiters hiring developers without really understanding the differences between SQL, C++, Python or HTML, nor the technical philosophies that come with them. Companies searching for “leadership” without being capable of defining what they actually expect once the person is sitting in the role. Organizations looking for a senior financial analyst capable of building complex models, understanding operational risk and supporting strategic decisions, while filtering resumes using words like “accounting”, “finance” or “CFO”, before wondering why they mostly receive profiles that have never truly done the work they are trying to hire for.
LinkedIn did not create this logic. LinkedIn simply took a system that was already confusing words with understanding and made it faster, smoother and more automated.
So the same skills begin appearing everywhere, the same blocks of language circulate from profile to profile and everyone progressively learns what needs to be written in order to continue existing inside search results.
You believe you are reading skills while you are often looking at words that have learned how to pass themselves off as proof.
And some of the strongest profiles become almost invisible here, not because they have done less, but because they describe their work with precision while others learn how to build profiles optimized for the systems reading them.
You have already preferred someone who knew how to name the work over someone who actually knew how to do it.
Technology could have asked for something else entirely, where that skill was used, on what project, for how long, with what observable consequences, what real traces exist behind the word, what mistakes were learned through during the process, but a system designed to accelerate sorting works much faster when it compares labels.
The system does exactly what it is asked to do and the problem is that nobody is asking whether the work was actually done, only whether the word is present.
The real always leaves traces.
