The Visibility Advantage Podcast

Every AI sees a different you: what AI rewards and how to close the gap

• Lynnaire Johnston • Season 1 • Episode 5

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Every AI tool composes its own version of you and this episode shows senior leaders what those systems reward, and how to close the gap between their expertise and their visibility. When one Link•Ability member gave the same prompt to ChatGPT, Claude, Gemini, and Perplexity — incognito — she received four noticeably different professional reputations. Lynnaire Johnston returns to Perception, the second foundation of the Link•Ability Blueprint, to unpack why AI rewards coherence over credentials. The 2026 research is blunt: around 75% of AI-cited LinkedIn content comes from individual profiles, just over half of cited voices have fewer than 10,000 followers, and content older than a year barely registers. You'll learn how to run the four-model test on yourself, audit the alignment between your profile and your activity, and publish content AI can actually cite – specific, structured, named, and fresh. Being found is only half the game; being trusted is the other half.

SHOW NOTES

A board chair, a conference organiser, and a prospective client each put your name into an AI tool and each one may receive a different version of you. When one senior professional gave the same prompt to ChatGPT, Claude, Gemini, and Perplexity, incognito, the four tools composed four noticeably different professional reputations from four different corners of her footprint. You don't control which version the decision-maker sees. But you can control whether every version tells the same story.

In this episode

Lynnaire Johnston returns to Perception, the second foundation of the Link•Ability Blueprint, because the evidence has caught up with the argument she made last episode. She explains why AI rewards coherence over credentials – parsing the alignment between your profile, your content, and your activity rather than weighing your CV – and why these systems give no second chances to presences that don't add up. Drawing on converging 2026 research from Meltwater, Semrush, and Profound, she breaks down what actually predicts whether AI surfaces you: individual voice, relevance, structure, and freshness – not reach. The episode closes with three practical actions any time-poor senior leader can take this fortnight, and an honest look at where trust fits once the machines have found you.

Key takeaways

  • You don't have one AI reputation – you have one per model. Test it yourself: open a private browser window (logged out, so results aren't personalised), and give ChatGPT, Claude, Gemini, and Perplexity the same prompt: ‘What can you find out about [your name], [your field], based in [your city]?’ Compare which sources each tool leans on, what's outdated, what's missing, and whether your expertise is described the way you'd describe it.
  • AI parses signal, not CVs. It reads whether your headline, About section, and activity tell one coherent story and when they contradict each other, it doesn't investigate; it moves to the next legible profile. Coherence has to be built in advance, because it cannot be explained afterwards.
  • Reach does not predict AI visibility. Just over half of the creators AI tools cite have fewer than 10,000 followers, and the 1,000–10,000 band contributes the single largest share of citations. Clarity and credibility beat audience size which means the field is more open to senior experts than most assume.
  • A dormant profile is an active negative signal. Nearly half of AI-cited content is under three months old; content older than a year barely registers. One clear, substantive post a fortnight, sustained, does more for discoverability than a viral moment followed by silence.
  • Publish for the citation layer using four words: specific, structured, named, fresh. Take a position on a real question, give your thinking a visible shape, name the actual tools and frameworks you mean, and keep the cadence going.

Links mentioned

Take the next step

If you ran the four-model test in your head while listening and didn't love the answer, the Link∙Ability Blueprint is designed for exactly this moment. It’s a strategic framework for LinkedIn visibility, credibility, and opportunity in the AI era. It explains how LinkedIn and AI systems actually work – and what professionals need to build before visibility, credibility, and opportunity can compound. At linkability.biz/services/the-linkability-blueprint there are editions for executives, coaches, consultants, job seekers, and business owners that are free to access.


Link•Ability Blueprint – the system Lynnaire uses with every client. linkability.biz/services/the-linkability-blueprint

Lynnaire on LinkedIn — Connect or follow her for regular AI visibility strategies and updates 

Lynnaire's book — Link•Ability: 4 Powerful Strategies to Maximise Your LinkedIn Success 

The Strategic Executive Visibility Review is designed to answer exactly that. It’s a one-off audit that reveals where your visibility stands right now. Find out more and book here.


Lynnaire Johnston

A few weeks ago, on one of our linkability member calls, one of our community ran a live experiment in front of the group. She opened four different AI tools, ChatGPT, Claude, Gemini, and Perplexity, and gave each one the same prompt. Surface whatever you can find out about me. Same wording for all four. And she did it incognito, no logins, no history, no personalization, a clean test. Four tools, one question, and four noticeably different answers. ChatGPT leaned heavily on her LinkedIn newsletters and articles. Claude and Gemini barely touched LinkedIn at all. They surfaced guest articles she'd written for the Content Marketing Institute. Years of work she had half forgotten about. Perplexity went broadest of all, YouTube and sources from right across the web. So here's the question I want you to sit with for the next little while. When someone puts your name into an AI tool, say a board chair, a conference organizer, or a potential client, which version of you are they getting? Because I promise you there's more than one. Hold that question. We'll come back to her for answers later in the episode, because what they reveal changes how you should think about everything else I'm about to tell you. Last episode, I introduced you to the second foundation of the linkability blueprint, the system I use with every client. That foundation is perception. And the core of it was this. There are now two systems deciding your professional reputation. The first is the one you've relied on your whole career: relationships, referrals, the colleague who mentions your name in a room you're not even in. That system still works and it still matters. But the second system is newer and it runs on completely different rules. It's data-based, it's algorithmic. It can't see your track record unless your track record is written down somewhere it can read. And it's increasingly making the first introduction on your behalf, deciding who gets surfaced, recommended, and trusted before a human ever sees your name. Since that episode went to air, two things have happened. The research is caught up. We now have hard numbers on exactly what the second system rewards, and I watched it play out live in that member demonstration you just heard about. So today we're going back into perception one level deeper. Three things I want to give you. First, the uncomfortable one, what AI actually rewards when it decides who to surface, and it's not what your career has trained you to expect. Second, the evidence. Real research as scale, and I'll tell you exactly where it comes from and why you can trust the direction of it. And third, where trust fits, because being found is only half the game, being chosen is the other half. Let's start with the uncomfortable one. If you've spent 20 or 25 years building a career, you carry an assumption so deep you probably don't know you hold it, that credentials, tenure, and track record are what get noticed. That work speaks, that seniority is legible, if you like. That assumption isn't wrong, but it's now incomplete, and the gap between wrong and incomplete is where opportunities are quietly going to other people. Here's what I mean. Not long ago I ran a version of the test our member ran. I asked an AI tool a simple question, who locally does the same work as me? And the result was not what 25 years in this field had trained me to expect. The person ranked first had a fraction of the connections, no published work, no LinkedIn specific content to speak of. By every traditional measure of authority, they should not have appeared anywhere near the top of that list, but they did. And once you understand how the second system reads, this makes complete sense. AI is not weighing your CV, it is passing your signal. What does that mean in practice? It's reading whether your profile is consistent with what you post, whether your content is structured clearly enough to be understood, whether the picture you present, your headline, your about section, your activity, your engagement, all adds up to one coherent story about what you know and who you serve. A leader with 25 years of genuine authority but a thin, inconsistent, rarely updated presence can be read by that system as having very little to offer. Meanwhile, someone with far less experience but a clear, consistent, well-structured presence reads as highly relevant. The system is not impressed by your history, it has no idea your history exists unless you've made it legible. And here's the part I most want you to hear. AI gives no second chances. If your profile is full of unrelated content, if your posts contradict your status focus, if there's a gap between what you say you do and what you actually publish, AI does not pause to investigate. It draws a conclusion and moves to the next more understandable profile. Coherence has to be built in advance. It cannot be explained afterwards. Now I know how that lands. It can feel unjust that a machine's reading of your consistency outweighs a quarter century of actual delivery. But sit with that for a moment because there's something genuinely liberating in it too. This is not a personality contest, is not a seniority contest. It's not about being louder, younger, or more online than the people currently outranking you. It's a legibility problem, a structural problem, and structural problems can be fixed. You don't need to become someone you're not, you need the story you already own to be told consistently everywhere the second system looks. Now everything I've just said would be easy to dismiss as one strategist's opinion. So let's talk about the evidence, because in the past few months, the research has genuinely arrived. Depending on which research you look at, LinkedIn is now the first or second most cited source AI tools draw on when they answer questions. And for professional questions specifically, who's the expert, who should I talk to? One major study puts it at number one. Three separate research firms, three different models, all pointing the same way. And I'll be up front. All three have a commercial interest in AI visibility, and two partnered with LinkedIn itself. But that's exactly why the convergence matters, when competitors using different data land on the same conclusion, the direction is real. So that's the headline. The place where your professional story lives is now a primary source for the system's answering questions about people like you. But the detail underneath the headline is where it gets practical. Four findings, and every one of them should change how you think about your own visibility. First, around three-quarters of the LinkedIn Content AI Tools site comes from individual profiles, not company pages. Three to one, individuals over organizations. Your employer's marketing team cannot do this for you. When AI is deciding whose expertise to surface, it is reading people, which means your visibility is your job, not a task you can delegate to comms and consider it handled. Second, and if you take one number away from this episode, make it this one. Just over half of the people AI Tools cite have fewer than 10,000 followers. And the group contributing the single largest share of citations accounts with between 1 and 10,000 followers. Not the influencers, not the celebrities, people with modest, credible, focused audiences. Reach does not predict AI visibility. Relevance and credibility do. If you've been telling yourself you're too late to this, that the game belongs to people with huge followings, the data says the opposite. The game belongs to people who are clear. Third, freshness. Nearly half of everything AI cites was published within the previous three months. Content more than a year old barely registers. Think about what that means for a dormant profile. It isn't neutral. Silence to these systems reads is absence. No recent activity is interpreted as no current relevance. Your profile doesn't just sit there quietly holding your credentials, it ages out of the conversation. And fourth, structure. When researchers looked at what actually gets cited, the pattern was almost embarrassingly consistent. Clear headings, lists, named tools, named companies, named frameworks, hard numbers. Content built to help someone make a decision, not vague thought leadership, not motivational prose. AI cites content it can pass and extract. Clarity, it turns out, now does double duty. It serves a human reader who values insight delivered well, and the system that needs structure to interpret it. Put those four together and notice something. Individual voice, modest audience, recent activity, clear structure. Not one of those is out of reach for a time poor senior leader. Nothing on that list requires celebrity, volume, or a personality transplant. The second system's rules are demanding, but they are learnable, and they favor exactly the kind of substance you already have. Which brings me back to where we started. Our member, four AI tools, and four different answers. Because here's what that demonstration actually revealed, and it's something almost nobody in this conversation is saying yet. You don't have one AI reputation, you have several. Claude and Gemini largely bypassed LinkedIn and reached for guest articles she'd written elsewhere, work from years ago on someone else's platform. Perplexity cast the widest net of all, pulling in YouTube in sources from across the open web. And remember, this was incognito. No login, no chat history, no personalization. What she saw wasn't the tools adapting to her, it was the tools genuinely waiting different corners of her professional footprint differently. Each model has its own reading habits. Each one composes its own version of who you are from whichever sources it favors. Now play that forward. You do not control which AI a decision maker asks. The board chair, shortlisting non-executive directors, might use co-pilot because that's what her organization rolled out. The conference organizer might use perplexity. The prospective client might ask ChatGPT. Each of them gets a different version of you, and you'll never know which one made the impression. So what's the strategy? You cannot optimize for one model. There's no single algorithm to please. The only rational response is coherence across your entire footprint. One consistent story, the same expertise, the same focus, the same through line, everywhere any of these systems might look. Your LinkedIn profile, your activity, your guest articles, your interviews, your videos, your bylines. When the story is the same, wherever the model reads, it stops mattering which model gets asked. And this is why perception in the linkability blueprint was never just polish your profile. The profile is the anchor, but perception is alignment. Profile, posting, and presence working as one connected system, telling one story to two audiences at once. The humans who read you and the machines that read you first. Now everything so far has been about being found, but I want to be honest with you about the limits of that because being found is only half of what visibility is for. AI gets you into the room, humans still decide whether you're invited to stay. The board chair whose short list you make still picks up the phone. The conference organizer still watches your talks, the client still meets you, no algorithm signs the engagement letter. And here's the deeper shift running underneath all of this. As AI makes information abundant, as research, analysis, and expertise on demand become things anyone can summon in seconds, information itself stops being the differentiator. Your knowledge, the thing you have spent decades accumulating, is more accessible to more people than it has ever been. What stays scarce is everything AI cannot manufacture. Trust, judgment, relationships, a reputation that real people will vouch for. I think we're watching senior professionals divide into two groups. Experts nobody knows, and experts people trust. And the second group wins, not because they're smarter, because they're visible, they're credible, and they built that credibility before they needed it. Here's the elegant part, and it's why this whole episode holds together as one idea rather than two. Coherence, the exact quality the machine's reward, is also what earns trust from humans. A story that adds up is what an algorithm can pass, and is also what a person believes. When your profile says one thing, your content demonstrates it, and your track record confirms it, both audiences reach the same conclusion. This person is exactly who they appear to be. That is what trust is made of. You're not doing two jobs, you're doing one job witnessed by two audiences. So what do you do with all of this? Three things in order of effort. The first takes 15 minutes. The third is a standing habit. All three are things I work through with clients inside the blueprint. Consider this the self-service version. First, run the four model test yourself right away. Open a window or incognito browser window so you're not logged into anything. Now that matters because you want the answer a stranger would get, not the answer personalised for you. Then put the same prompt into Claude, ChatGPT, Gemini, and Perplexity, asking it what it can find out about you, your role or field, and based in your location. Then read the four answers side by side and ask four questions. Which sources is each tool leaning on? What's outdated? What's missing entirely? And the big one, is your expertise described the way you would describe it? Most senior leaders who run this test are genuinely surprised. Whatever you find, you now know what the second system currently believes about you. That's your baseline. Second, run the alignment audit. Open your own LinkedIn profile and look at three things as a stranger would. Then ask one question. Do these tell one story or three? Could a stranger, human or machine, name your expertise in a single sentence from these alone? If your headline says one thing, your about section meanders through your whole career, and your activity is a scatter of unrelated topics. You now know exactly why this system can't categorize you. And remember the finding from earlier? The words in your profile are the primary data used to categorize your expertise. Vague words, vague category, invisible you. This is a writing job, not a reinvention. Most of my clients fix it in a focused afternoon. Third, when you do publish, publish for the citation layer. Four words to write on a sticky note. Specific, structured, named, fresh. Specific, take a position on a real question in your field. Don't muse about it. Structured. Have a clear opening line, a numbered list or clear sections so both audiences can follow the shape of your thinking. Named. Mention the actual tools, companies, and frameworks you're talking about. Vague expertise doesn't get cited. And fresh. Remember half of what AI cites is less than three months old, so cadence beats intensity. One clear, substantive post a fortnight sustained, will do more for your discoverability than a viral moment followed by six months of silence. Consistency compounds. Virality evaporates. Let me pull this together because today was really one idea seen from three angles. The second system, AI, is now reading you, with or without your participation. What it rewards is not credentials, but coherence. A presence where profile, content and activity tell one legible story. The research confirms it at scale. Individual voices over company pages. Clarity over reach. Recency over history and structure over polish. And because different AI tools read different corners of your footprint, for models, for reputations, remember, coherence everywhere is the only strategy that doesn't depend on guessing which tools get asked. Do that work, and you're not just optimizing for machines. You're building the things humans have always needed before they choose someone. A story that adds up. In other words, a trust.

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Lynnaire Johnston

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