What happens when we only look at the winners? 🙀
If you spend any time scrolling through LinkedIn, you might start to feel like you’re the only person on earth who has ever missed a deadline, lost a client, or been rejected from a job.
Your feed is a relentless stream of promotions, funding announcements, new certifications, and "thrilled to announce" posts. It looks like a highlight reel of humanity. And if you were to build a computer program to study this data and tell you what a "typical" career looks like, that program would come back with a very clear answer: Everyone is winning.
But here is the crucial twist: That answer is true, but it is not valid.
Let’s break down why that distinction matters—not just for algorithms, but for your own mental health and decision-making.
The "All Winners" Dataset. 🤦♂️
Imagine you are a detective trying to figure out how bank heists usually go. If you only interview the masterminds who got away with the money and are now living on a beach, you would conclude that robbing banks is a brilliant, foolproof career path.
You would be right in the sense that your data shows a 100% success rate. But your conclusion would be wildly invalid because you completely ignored the 99.9% of criminals who are sitting in prison.
This is exactly what happens when we train a model—or our own brains—on LinkedIn data.
LinkedIn is a voluntary platform. People are not forced to post their quarterly performance reviews. They choose to share their wins. No one logs on to write, “Excited to announce that I was passed over for the leadership role today,” or “Thrilled to share that my startup just burned through its cash reserve.”
Because of this, the "data" we consume from social media is fundamentally broken. It is a collection of victory laps, not the actual race.
The Danger of "True but Not Valid. ⚠
When a computer looks at this feed, it doesn't know about the 500 rejected applications that came before the "I got the job!" post. It only sees the result. So, it calculates that the average person on this network has a near-100% success rate.
Statistically, that number is true within that specific, filtered dataset. But it is not valid in the real world.
When we confuse what is "true" in a bubble with what is "valid" in reality, we start to believe that failure is rare. We start to think that if you just work hard enough, you will get the promotion. We start to believe that raising money for a business is easy. We start to feel like the odd one out when we face a setback.
This creates a dangerous cycle: We see success, we assume success is the norm, we feel inadequate when we fail, and so we hide our own failures. By hiding our failures, we add even more "success" data to the platform, making the illusion even stronger.
Why "No Failure" is the Biggest Red Flag.🚩
If you ever look at a profile or a company history and see a straight line from "Intern" to "CEO" with no detours, you aren't looking at a genius. You are looking at an edited biography.
In the real world, failure is not the exception; it is a requirement. Every scientific breakthrough, every successful business, and every major career pivot is built on a foundation of bad ideas, wrong turns, and embarrassing mistakes.
If your data shows zero failure, it doesn't mean the person is perfect. It means the data is incomplete. It means the storyteller has left out the chapters that teach the most valuable lessons.
How to Break the Illusion. 😤
So, how do we stop this skewed data from skewing our perspective?
1, Assume the "Invisible Year": When you see a massive success, mentally add a footnote: "Plus one year of struggle." Assume there is a hidden story of grit behind every "overnight" success.
2. Seek Out the "Post-Mortems": Look for people who talk about what went wrong. These are the most generous people on the internet because they are sharing the data that actually helps others learn.
3. Don't Compare Your B-Roll to Their Highlight Reel: This is an old saying, but it holds true. You are living your raw, unfiltered life. You are comparing it to the polished, filtered, 10-second clips that others choose to share.
The Takeaway. 🥡
Next time you feel like you are the only one struggling, remember the detective and the bank robber. The absence of failure in the data doesn't mean failure doesn't exist; it just means people aren't posting about it.
Success is not a straight line. It is a messy, zigzagging path full of potholes. And the people who are actually doing interesting things are the ones who have learned how to fix a flat tire—not the ones pretending the potholes aren't there.
Don't let the algorithm convince you otherwise. Your career is valid, even when it doesn't feel like a "win."
These are just my thoughts. What's your view?🤷♀️
Image credit: ChatGpt
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