Unswappable! Why AI Can't Fake Being You on LinkedIn

Introduction

LinkedIn is changing quickly.

AI can now help us write posts, generate ideas, create images, build agents, and automate parts of our work. But as the technology becomes more capable, something else is happening: more and more people are starting to sound exactly the same.

In this episode, we sat down with Magali De Reu, a personal brand strategist from Belgium, to discuss what it takes to stand out in an AI-saturated world. We talked about LinkedIn, personal branding, authenticity, AI-assisted writing, human judgment, AI agents, and why your personality may be the one thing technology cannot commoditize.

Magali joined us for a lively conversation about her personality-first approach to branding and the importance of being known for something. Along the way, we also discussed viral LinkedIn posts, AI slop, vibe coding, the LinkedIn algorithm, and why the best content should sound unmistakably like the person who created it.

Meet the Guest

Magali De Reu is a Belgian personal brand strategist, ghostwriter, speaker, author, and content creator based in Antwerp.

She helps consultants, coaches, founders, speakers, and senior experts turn their personality and expertise into a brand that attracts the right clients. Her central belief is simple:

“Personality first. Expertise second.”

That idea became particularly important to Magali after leaving a startup in 2025. With nothing lined up, she started publishing consistently on LinkedIn. What began as an experiment developed into a successful business built around her own voice, perspective, and personality.

According to Magali's website, she has helped build more than 150 personal brands and has generated millions in client wins through personality-first positioning and inbound content. She is also a two-time TEDx speaker, former Forbes contributor, and the author of two books about neurodivergence: Aut of the Box and Allemaal Autcasts.

Magali is open about being autistic and having ADHD. For years, she was told to tone herself down and fit into a more conventional professional mould. Eventually, she decided to do the opposite: to make the parts of herself that others considered “too much” part of her brand.

That decision became both personally liberating and commercially successful.

Setting the Stage

The rise of generative AI has changed the value of expertise.

In the past, knowing something that other people did not know could be enough to make you stand out. Today, AI can explain almost any topic, produce reasonable first drafts, summarize complex information, and generate content in seconds.

That does not make expertise irrelevant. But it does make expertise easier to copy, reproduce, and commoditize.

This creates a difficult question for professionals, founders, creators, and consultants:

If AI can reproduce what you know, what makes people choose to work with you?

For Magali, the answer is personality, point of view, and judgment. Your knowledge may get you into the conversation, but the way you think, communicate, and show up is what makes people remember you.

In this blogpost, we explore the main themes from our conversation:

• Why generic AI-generated content is making LinkedIn feel increasingly repetitive.
• How Magali used personality to build a business after leaving a difficult startup situation.
• Why human judgment must remain part of any AI-assisted workflow.
• How to use AI without allowing it to erase your voice.
• Why your personal brand should attract the right audience and repel the wrong one.

Episode Highlights

The post that proved its own point

One of Magali's most famous LinkedIn posts showed a feed dominated by Claude-related content. The post captured the feeling that LinkedIn had become filled with people talking about the same AI tools in the same way.

The irony was that many people copied the visual without crediting Magali. Some even reposted the image while leaving her name visible.

As Magali pointed out during the episode, they had unintentionally proven the point she was making. People were copying an AI-related post without adding their own perspective or even taking the time to make the content their own.

The post became a turning point in Magali's growth on LinkedIn. It was not necessarily the most substantial piece of content she had ever created, but it was clear, timely, recognizable, and highly shareable.

It also demonstrated an important lesson: content does not have to be complicated to be effective. Sometimes the strongest posts are the ones that express an observation people already feel but have not yet articulated.

“I would rather get paid for who I am”

One of the strongest ideas in the conversation was Magali's distinction between showing people what you do and showing them who you are.

She explained that her biggest milestone was not simply earning money from her expertise. It was earning money while showing up as herself.

“I'd rather get paid for who I am than get paid for who I'm not.”Magali De Reu

That statement captures the shift from traditional professional branding to personality-led positioning. Instead of trying to appear universally acceptable, Magali argues that a strong brand should deliberately filter people.

Your brand should attract people who appreciate your perspective, values, and way of working. At the same time, it should repel people who are not a good fit.

That is not a failure. It is the point.

Deep Dive: Personality in the Age of AI

Expertise is becoming the baseline

AI is not making knowledge worthless. It is making knowledge less exclusive.

When everyone has access to tools that can generate competent explanations, summaries, posts, and strategies, simply demonstrating that you know something is no longer enough. The differentiator moves from information to interpretation.

Two people may understand the same subject, but they will not necessarily:

• Notice the same problem.
• Take the same position.
• Explain it in the same way.
• Use the same examples.
• Make the same decisions.
• Connect with the same audience.

That difference is personality expressed through expertise.

Magali's approach is not about being loud, exaggerated, or constantly vulnerable online. She made a clear distinction between personality-first branding and oversharing.

Personality-first does not mean publishing every private detail of your life. It does not mean turning LinkedIn into therapy. It does not mean forcing personal stories into every post.

Instead, it means communicating:

• What you believe.
• What you refuse to do.
• Which problems you care about.
• How you see the world.
• What kind of people you want to help.
• What makes your approach different.

The swappable test

One of Magali's most practical ideas was what we might call the swappable test.

Look at the first two lines of your LinkedIn post. Could somebody replace your name with somebody else's and publish it without changing anything?

If the answer is yes, the content is probably too generic.

For example:

“Most leadership problems are actually positioning problems.”

That may be a reasonable statement, but it could have been written by almost anybody. It sounds like a polished LinkedIn hook rather than something a specific person would naturally say.

Magali's challenge is to make your content unmistakably yours. That means using your own phrases, opinions, examples, humour, frustrations, and observations.

Your audience should be able to recognize you before they see your name.

AI should support the process, not replace the person

Magali does use AI. She uses it to explore angles, generate ideas, cross-reference notes, improve punctuation, and check whether a piece of writing makes sense.

However, she does not ask AI to create her voice from nothing.

Her process starts with her own thinking, experiences, diary entries, emails, meeting notes, and observations. AI can then help organize or challenge that material, but the core idea comes from her.

She also remains involved in the final editing process. AI may suggest a word such as “polished,” but Magali knows that she would never use that word in a natural conversation. That small difference matters.

AI can produce grammatically correct writing that is completely wrong for the person using it.

This is why AI-assisted content can still feel human, but only when the human remains present throughout the process. The technology can assist with the beginning and the end, but it should not automatically own the middle.

Human judgment is the non-negotiable layer

The one thing Magali does not want to outsource to AI is final decision-making.

She wants to remain able to distinguish between good and bad, useful and useless, appropriate and inappropriate. She wants to retain the ability to decide whether something is true, relevant, responsible, and aligned with her values.

That is especially important because AI can make poor ideas sound convincing.

As James put it during the conversation, AI is excellent at amplifying things—but it can also amplify stupidity.

The problem is not only that AI makes mistakes. The deeper problem is that it often makes mistakes fluently. A bad idea can arrive in a confident, well-structured paragraph, which makes it tempting to accept without thinking.

That is where critical thinking matters.

A useful AI workflow therefore needs a human at both ends:

• A human provides the context, intention, experience, and direction.
• AI helps with research, iteration, structure, or execution.
• A human reviews, challenges, edits, and approves the result.

James referred to this as keeping the first five percent and the final five percent: be involved at the start and at the end, while allowing AI to support the work in between.

The exact percentages are less important than the principle. Do not outsource the parts that require judgment, taste, responsibility, or personal meaning.

Real-Life Stories & Examples

From startup frustration to LinkedIn business

Magali's LinkedIn journey started after she left a startup she described as a difficult experience. She had no clear next step, so she began posting on LinkedIn.

At first, she viewed it partly as an experiment. Over time, she discovered that showing up consistently as herself attracted opportunities and clients.

The important part was not simply that she posted frequently. She treated LinkedIn as a place to connect with her ideal audience, test whether her offer was still relevant, and learn what resonated.

She did not treat the platform as a one-way broadcasting channel.

That distinction is important. Many people focus only on publishing content. Magali focused on the conversations around the content and the feedback those conversations provided.

The AI-generated review that was wrong

Magali described using an AI-based submission checklist to review LinkedIn posts before sending them to her.

Sometimes the AI marked a weak post as acceptable. At other times, it rejected content that was perfectly good.

This illustrates a common problem with AI evaluation systems: they can apply rules consistently without understanding context.

A checklist may be able to verify whether a post includes a hook or follows a particular structure. It cannot always determine whether the post sounds like the person who wrote it, whether the central idea is worth sharing, or whether the tone is appropriate for the intended audience.

A post can meet every formal requirement and still be lifeless.

Vibe coding and the impressive demo

We also discussed the difference between building something that works once and building something that is reliable, secure, and scalable.

James shared an example of creating something quickly while waiting to board a plane. It worked well enough for a presentation, which made it useful for that specific situation.

But would it have been secure? Would it have scaled? Would it have worked for thousands of users?

Probably not.

That is the difference between a successful demo and a production-ready solution.

The same principle applies to AI-generated content and AI agents. It is possible to create something impressive very quickly, but speed does not remove the need for expertise. Someone still needs to understand the underlying problem, assess the risks, test the output, and decide whether the result is fit for purpose.

Personal branding is not one-size-fits-all

Magali also made an important point about personality.

Personality-first branding does not mean that everyone should communicate like Magali. Her style is energetic, direct, humorous, and deliberately distinctive. That works because it is genuinely hers.

Someone else may have a much drier or quieter style, and that can work just as well.

The goal is not to copy somebody else's personality. The goal is to stop hiding your own.

A strong personal brand can be expressive and energetic, but it can also be calm, analytical, reserved, or understated. What matters is consistency and recognizability.

LinkedIn is more than a social platform

Magali does not see LinkedIn only as a platform for distributing content.

She uses it to:

• Understand what her audience is struggling with.
• Test whether her offer remains relevant.
• Have conversations with potential clients.
• Identify which ideas create meaningful engagement.
• Build trust over time.
• Make it easier for people to discover her work.

This is becoming even more important as people increasingly use AI tools for research and recommendations. Your LinkedIn content may not only be read by people scrolling through a feed. It may also be used as part of the information that helps others evaluate you.

That means being visible is useful, but being recognizable and credible is even more important.

Key Takeaways

• AI is commoditizing expertise, which makes personality and point of view more valuable.
• Your content should sound like you—not like a generic professional, a writing template, or an AI model.
• A strong personal brand should attract the right people and repel the wrong ones.
• Personality-first does not mean oversharing or turning LinkedIn into therapy.
• Use AI to support research, ideation, editing, and iteration, but do not outsource your judgment.
• Start with your own experiences, notes, ideas, and observations before asking AI to help.
• Always review AI output for accuracy, relevance, tone, and personal fit.
• If your name can be swapped for somebody else's without changing the post, your content is probably too generic.
• A good-looking demo is not the same as a secure, scalable, production-ready solution.
• LinkedIn is not just a broadcast channel; it is a place to listen, test, connect, and learn.
• The most valuable part of your brand may be the part you were once told to tone down.
• In a world of AI fatigue and AI-generated sameness, genuine connection is likely to become more valuable—not less.

Closing Thoughts

Our conversation with Magali left us with a clear impression: AI is not necessarily making people less important. It is making the difference between generic and distinctive much easier to see.

When everyone has access to the same tools, the people who stand out will not necessarily be the ones with the most sophisticated prompts or the largest collection of AI agents. They will be the people with something real to say—and the courage to say it in their own way.

AI can help us move faster. It can help us explore more ideas, automate repetitive work, and turn rough thoughts into usable outputs. But it cannot replace the responsibility of deciding what is worth saying, what is true, and what represents us.

As Magali put it during the episode, the human part is not a weakness in the system. It is the moat.

You can find Magali on LinkedIn, where she shares her views on personal branding, AI, content, and building a business around who you really are. She also offers a newsletter, community, and coaching programmes through her website.

And as always, we would love to hear your perspective: where do you draw the line between using AI to amplify your work and allowing it to replace too much of your own thinking?