Why most LinkedIn profiles speak the wrong language to the wrong audience — and what to do about it.
Wayne Rainey · The Career Cantina
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A JobNet 2.0 session by Wayne Rainey of The Career Cantina. The premise in one line: most LinkedIn profiles are written for a human reader — but a machine reads them first. And the machine speaks a different language.
The resume has been through five distinct eras — from Leonardo da Vinci's handwritten pitch to the Duke of Milan in 1482, through the post-WWII formalization of the CV, the arrival of ATS scanning systems in the 1980s, Boolean keyword optimization in the 1990s-2000s, and now the shift to semantic search powered by AI and NLP.
Most HR and TA professionals have lived through the ATS and Boolean eras as practitioners. The shift now underway is different in kind, not just degree. The system is no longer looking for your words. It is trying to infer your meaning.
Keyword search reads your profile looking for words.
Semantic search reads your profile looking for a story.
Most LinkedIn profiles are written for one and not the other. The two readers have different questions, different languages, and different tolerances for ambiguity. Writing for only one means failing the other — and since the machine reads first, most profiles never make it to the human at all.
This logic applies equally to your resume. Same two readers. Same sequencing. Same stakes.
Uses semantic search to infer what kind of professional you are from the pattern of language across your entire profile. It reads nouns to categorize you, and verbs and proof to elevate you within that category once you're in it. All three elements work together.
Skims narrative text for evidence of action and outcome. Asking two questions: does this experience hold up under scrutiny? And would I want to have a conversation with this person?
We'll get to what you can and can't control — and where your energy is actually worth spending — shortly.
Semantic search reads the pattern of language across your entire profile to infer your professional identity. That means your nouns need to be in the right places — your headline, skills section, and job titles — not buried in paragraph text where the signal gets diluted.
Once a human opens your profile, they're looking for evidence of action — not just what roles you held, but what you actually did in them. Passive phrasing is common, understandable, and a problem:
Without proof, nouns are just labels anyone can type and verbs are just claims anyone can make. Proof means specificity — enough detail that the reader can visualize the scope of what you did. It doesn't require a number. Scope, scale, complexity, and context all qualify.
Proof isn't a third element — it's the load-bearing base. And it does double duty: it tells the machine where you belong and gives the human evidence to act on.
Not every HR role hands you a dashboard full of metrics. That's not a problem — it's a framing challenge. Here are four categories of proof that don't require a percentage:
One sentence. Three jobs simultaneously — it tells the machine where you belong and gives the human evidence to act on.
This is what happens before any human sees your name. The first three stages are machine-driven. The fourth is what the recruiter receives.
Binary — in the pool or not. The system decides whether your profile belongs in the results at all.
Weight and consistency of language. The system places you in a talent category.
Engagement, recency, profile history. Factors you cannot see.
A pre-sorted, pre-ranked pool. What happened between the recruiter's search and these results is a black box.
An ATS asks these when reading your resume. LinkedIn Recruiter asks them when reading your profile. Two systems. Same logic.
Recognizing your pattern is the first step to adjusting it.
Six LinkedIn phrases were read aloud. Attendees classified each one in real time — Noun, Verb, Proof, or None — and then answered the more important question: what's missing? The exercise is experiential. Its value lives in the moment of doing it, not in reading a summary after the fact.
If you want to run the exercise yourself, pull six phrases from your own LinkedIn profile and apply the same test: which element is it, and what's missing?
Run a diagnostic on your LinkedIn profile — checking your Discoverability, Categorization, and Ranking signals before a recruiter does: the DCR Tool at Happy Hour
Before the next session: Audit your LinkedIn About section through the Triangle. Find one sentence that has all three elements. Find one that's missing something. Come ready to share either one.
The session uses a few specific frameworks and named systems — DCR, The Triangle, 360Brew, Coherence. The glossary defines all ten, flags what's a Career Cantina original, and adds a living caveat on anything that may evolve.
Read the Glossary →