The Algorithm Is a Mirror: How I Stopped Chasing Rankings and Started Building a Real Career
I used to put all my focus on where I stood in online rankings. I cared too much about being at the top. Then I saw that this way was not helping me grow or feel good about my work. I stopped running after the latest tips to beat the system. Now, I put my time into what really counts. I work at getting better and making things that matter. This helped me start a real job that I feel proud to call mine.
I learned that the algorithm only shows back what I do. If I try to please it too much, I lose what makes me, me. Now, I work to help people with what I know and what I make. These days, I feel happier with where I am.
I also realized this is a long game. There will be ups and downs. But I trust now that if I keep learning and if I help others, my work will speak for itself. That is how I built my own path in my job. The most important thing is to keep going, stay true, and remember there is more to a good career than just numbers on a screen.
By SEO Hobby Expert World
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I was 23 years old. I lived in a rented room. My desk was a door set on top of two filing cabinets. I made a quiet bet with myself. I wanted to see if I could build a website that meant something. This was not about making money, even though I did not have any. I wanted to show that I was able to do it. I needed to know if I could understand a system well enough to make it work for me. The system I worked on in 2014 was Google. At that time, Google was not answering me.
That night, I did something that felt like giving up. But it turned out to be the first real thinking I did in my work life. I stopped trying to put things in order. Instead, I started to think about what these rankings really showed. I opened a new text file. It was dated March 12, 2014, and the time was 3:04 a.m. I typed only one line: "What if the algorithm doesn't measure quality, but measures something that matches with quality in ways I have not thought about?"
I did not know at that time, but that question had everything I would learn in the next ten years. That was when I stopped being just someone who liked SEO and searched for ways to play with it. I started to look deeper at the things that shape how we pay attention on the internet. It was the night I saw that the algorithm is not something you just read and remember. It is more like a mirror. It shows back what you think and feel when you use it. If your ideas do not go deep, then your results will not go deep either. If you take your questions from other people, then your rankings will always stay with them.
The Correlation Project: What I Found When I Stopped Optimizing
The text file got bigger in the next weeks. It became a research file for me, and I named it "The Correlation Project." I did not share it with anyone. The goal was not to get visitors. I made it because I wanted to have clear thoughts. I found that I cannot think well unless I write things down.
The project was easy to set up but hard to do. I picked 50 search terms where my page was sitting on page two or three. Then for each one, I looked at every page that was ranking higher than mine. I did not use any SEO tools for this. I clicked and read each page right in my browser. I read everything. I also wrote down notes. I looked for how the content was set up, how strong their points were, how they used facts, and how the text read to me.
I paid extra attention to how the author talked to readers. Did the page make people feel dumb? Did it act like answering the search was just a hassle? Could I feel that the author had really thought about the topic, or did it feel like they just followed a set text pattern in their mind?
What I found broke the way I thought about this. The things I tested — like how often a word showed up, how the headings were set up, and how many times keywords were used as links — did not explain why some pages were in the top five results. What did make a page rank higher was not easy to measure and you could not fake it. The text needed to have new ideas in each paragraph, ideas that readers would not expect, and proof that the writer knew a lot about the topic before they wrote about it for Google.
The top pages were not just made for Google. They were made to help people understand things better. By 2014, Google was already smart enough to see what made one text better than another.
The Ghost in the Machine: When AI Changed Everything
In the summer of 2022, I got an email that I did not know would change the way I see myself at work. It was from a past client. I had helped him three years before with his SaaS company. We worked to build a system for getting new users in a natural way. That work helped the company raise money in Series A. We did not talk for a year and a half. The subject of the email was: "Have you tried this?"
The body of the email had just a link to a study called "Training language models to follow instructions with human feedback." There was also one line: "This is going to delete us."
I clicked on the link. I read the paper, or at least I tried to read it. A lot of it used technical terms that I could not fully understand then. I was working in SEO and liked to look at data, but I was not a machine learning engineer. Still, I got the main idea and felt something that I had not felt since I read that text file back in 2014 at three in the morning. It was that strange feeling when you see that your idea of how facts and stories travel in the world is about to change and not be useful anymore.
The paper talked about InstructGPT. This was an early system related to what would soon be ChatGPT. The big idea was not the language model itself, as it was just a bigger model that used things people had made before. The new thing was a way to use feedback from people, called RLHF. This helped the model sound more helpful and less like it was only guessing the next word. The model could do what you asked. It could say when it made a mistake. It could say no to wrong requests. So, it looked and acted a bit like it could understand and work with you.
I closed the paper at 11 p.m. I did not sleep that night. I sat at the same door-desk I had used for eight years. I had changed the filing cabinets for real legs a long time ago. I started to think, with all the focus I had, as my job depended on knowing a trend before everyone else. If a machine could make clear text that follows instructions and can be used as much as needed, what was I selling?
## The Consultant's Reckoning
I want to talk about what I sold back then in my career. A lot of people who do knowledge work sell the same thing. Even now, many of them have not really stopped to look at what they do and ask those hard questions.
My consulting practice was based on three things. First, I had strong understanding of the market. I could look at the company's market, what content they had, and where their competitors stood. This helped me find chances that other marketers did not see. Second, I had good eye for reviewing text. I could read a draft and say where it did not feel strong, where proof was missing, where readers would get bored, and how to fix these issues. Third, I had the ability to produce text. I could write, or guide others to write, content that followed the plan and was to a level that search engines liked.
The first pillar, which is seeing patterns and making smart moves, felt safe to me. I had spent a lot of time learning from practice, and no machine had that kind of skill yet. The third pillar, which is how much work a person or machine can do, seemed open to change. If a machine can make a first draft in a few seconds, what takes people hours, then the way things are done will turn around. But the second pillar, making good choices about text, kept me from sleep. I came to see with worry that making good choices in writing is really about seeing patterns in text. I can spot weak points in a text because I have seen many bad arguments before and learned to notice them. A large language model is set up in almost the same way. It is trained on so many books and articles, and it can spot text patterns in much more detail than people can.
I was not scared that the machine could write better than me. I was scared that the machine could edit better than me. And I started to feel that the gap between editing and planning was not as big as I thought.
The Notebook Nobody Sees: How I Saved My Mind and Thinking
In January 2023, about four months after I first tried ChatGPT, I started something new that I feel saved my thinking and creative life. I began keeping what I called a "Perception Log." I gave it that name on purpose and as a joke to myself. I knew "Perception Log" sounded like a term some Silicon Valley work coach would use and sell as a Notion template for $47. Still, I kept the name, because it fit. That is what the document was—a record of things I noticed and felt.
The rules were simple. Each day, I had to write one entry. Every entry had to include something new that I saw but did not see before. It could be a pattern in search results that went against what most people think. It could be a side of client relationships that showed why people did not follow some advice. It could be something in how language models give answers that told me more about the training data. The most important thing was that it had to be new to me. I could not write down things I knew before. I could only write about things that I just learned to see.
The practice was hard in ways I did not see coming. In the first week, I looked at blank pages for a long time. I saw that most of my work was about taking and using what others said instead of coming up with my own ideas. I could make an expert’s plan clear and interesting. I could use what worked before in a different place. But when I was asked to "notice something new," I saw I was not good at seeing things myself. I used the skills of breaking things down and talking about them. I forgot to build the skill of simply looking at what is there.
This made me feel small. I now think it is the most important thing I found in my job. I made a group of people who read my work and a consulting practice because I could use, and share, what is already known. But then machines came along. These machines can use and share what is already known much faster and better than us. I saw that what I had to offer was not on solid ground. The Perception Log was my chance to get something that is really mine.
The Anatomy of a Perception
Let me show you what a Perception Log entry looks like. The practice is not helpful if you do not see an example. Here is Entry #23. It is from February 14, 2023.
I watched a client's content team go over some AI-made drafts today. I saw something new. Their problem was not about facts being wrong or changes in tone. It was hard for them to say what was missing. One editor said, "This feels like it was written by someone who has never wanted anything." That hit me. That is what's missing. The AI writes like it does not know what it wants. It has never tried to get a skeptical person to do something. It has not felt worried that people will get it wrong. It does not need someone to act.
The text is clear, but it has no drive. There is nothing pushing it. A person, even one who is just okay at writing, goes toward a goal. The AI just tries to finish the text in the way it should, with no real purpose. These are not the same. You don't notice the gap when you read just to get facts. But you feel it when you read to be moved or to do something. I have written a note to myself about "going toward a goal" versus "finishing text with no purpose." I want to think more on this.
I wrote that text in about fifteen minutes at the end of a client meeting. It soon became the start of a framework I spent the next year working on. Later, it would become the main idea of how I do consulting.
I saw how there is a gap between where you want to go and how likely you are to finish something. This showed something many talks about AI do not cover. It explained why AI content can get facts right but feel flat. The machine does not try to move the reader or make them feel anything. It only tries to fill in a pattern. That changes everything.
But here is what matters for us: I did not get that idea by thinking a lot about AI. I got it by watching a group of people react to AI output. I saw what words they used when the words they already knew were not enough. This feeling was not from thinking deep. It was from paying attention. Thinking came after. It did matter, but it was like working on the raw stuff that was found before. The finding part was paying attention.
The Three Perceptual Habits
Over the next few months, I started to see some patterns in what I noticed. The things I saw that helped me most later turned into ways to work, client projects, and later things I shared with people. All of these things fit into three main types of paying attention. Right now, I teach these as the Three Perceptual Habits. I think these are skills that people can learn, but most people do not practice them on purpose.
Habit One: Pay attention to what does not fit in. The best chance to see things clearly is when something does not follow the way you think it should. This happens when a client's site traffic drops and your plan cannot explain why. Or when a post you thought would do well does not, and another post you did not think much of does better. Or when an AI result looks strong but also feels empty, and you cannot explain that right away. Most people see these things and feel ready to ignore them or fix them fast so they can feel safe in what they already believe. Someone trying to improve how they see things will notice these signs as hints that their plan is not finished.
Habit Two: Pay Attention to the Words People Use When They Struggle. When people—including your clients, team, readers, or even you—try to say something but can't find the right words, they are close to a new idea. A phrase that feels awkward, a comparison that is almost right, or a thought that ends with "you know what I mean?"—these show the start of a new thought that is not clear yet. My note about "directional intent" was because I listened to an editor who said the AI wrote "like someone who has never wanted anything." That was not an easy thing to understand. It was someone's way to find meaning. Most people would just agree and keep going. But the person who helps find new ideas stops and says: What is this phrase truly trying to show?
Habit Three: Pay attention to areas next to your own. The best ways to see things often happen when you link your field to another field that looks like it has nothing in common. I found this out by chance. At the time, I was starting my consulting business again. I began to read things outside SEO and AI because I felt bored and believed talk in my area had become tired. I read books on how labs work. I read about old craft groups. I looked at how jazz players play together. I read about big mistakes in military spying. In each area next to mine, I saw people tackling problems much like the ones in my field. But, they used different words and methods to find answers.
From Perception to Framework
A perception is not a contribution yet. It is just raw stuff. To change how others think, a perception has to be made into a clear plan. This step is where many people stop. They have ideas but cannot set them up. They see something that matters but cannot build a way for others to get to their spot.
The process has four stages:
Stage One: Name It. You have to turn what you notice into a label that people can remember. For example, "Directional intent versus probabilistic completion" is a name. A good name should have clear meaning to people and be short enough for them to remember. A name lets people take your idea with them. They can use it later, even when you are not there. If you do not give your thought a name, it will not spread. If it does not spread, it cannot make an impact.
Stage Two: Diagram It. The idea needs to be shown in a way that puts it in space or gives it structure. This is to show how it connects to other things. You don't have to make an exact picture, but making one is often helpful. You can also use a set of ideas in groups, show the range, show patterns, or show a step-by-step path. The main goal is to go from “I see this thing” to “I see how this thing fits with the others.”
Stage Three: Pressure-Test It. You need to see if the framework works in real life. Let it break where it isn’t strong. Most thought leaders skip this step because it can feel tough. You take your nice framework and look for spots where it does not hold up. Show it to smart people and let them try to pull it apart. Try the framework on edge cases to see if it can still work. Most frameworks don’t make it through the first test without some change. Yours might be improved, changed, or dropped. That is not failing. It is part of the process.
Stage Four: Teach It. The last step is to share the framework so someone can use it without you. You can do this by speaking, writing, or recording it. Try to think about what could confuse people. Handle questions. Give examples for all situations where the framework fits. Teaching does not mean you just pass along the idea. It is a test to see if your explanation is clear. If you cannot explain it to a stranger who really wants to learn, then you need to know it better yourself.
The Economics of Perception
I want to finish by making the economic point clear. Some people reading this may say: "This sounds smart, but I need to run my business. I want more traffic, leads, and money. How does the way people see things change my results?"
The answer is: everything, in the long run.
Before AI, it was possible to run a good content business by focusing on how well you did the work. If you wrote articles that were better than most of the top results for your target keywords, you could get real traffic. Better articles were those that were easier to read, gave more detail, and were well organized. You really had a chance to win at this. It was not too hard to reach that level.
In the AI time we are in now, it is harder than ever for people to stand out just by doing well. Now, anyone who wants to can make a "good enough" article in a minute. Because of this, being "better than 80%" does not help you much. You start at a higher point now. The only way to stand out is to be in the top 1% when it comes to making really good work. Even so, the gap gets smaller every few months as the tools get better.
The other way is to try to win by how you are seen. You can be the person or brand that spots things most people miss. You can build ways of understanding that others do not have. You can see where the market is heading before most see it. This is not a separate game. It is the only game left that can still grow.
The economic idea here is clear. Doing the work is becoming common. Making choices is also getting a bit more common. How you see things is staying special and does not look like it will change. So, this means that how much you get for seeing things in a new way is going up. Every bit of real new thinking you give now is worth more than before, because more people are doing good work, but there are not enough fresh ideas out there for all who want them.
The Perception Log was not just something I did for fun. It was my way to put time into the one thing I am sure lasts. That is my own skill to notice things that machines miss.
The Practice, Distilled
You do not need me to say yes before you begin a Perception Log. You just need a file to write in and the will to start. I want to share some tips that could have stopped me from struggling for months.
First, focus on writing a lot, not on making each entry perfect. You do not need to write great entries now. What matters is to practice paying attention. Many entries you make will be small, unfinished, or mistakes. This is normal and fine. You are not making a book right now. You are building a skill. A book can happen after you build that skill.
Second, choose clear details instead of broad ideas. Do not write, "I noticed that AI content lacks soul." That is a common thing to say, not what you saw. Write exactly what you saw. Say the exact line that made you feel this, the way a client acted that showed the gap, the search that did not match what you thought. What people feel comes from these details. You build broad ideas from many of these feelings, after you get enough to find patterns.
Third, keep it steady instead of waiting for ideas. It is easier to keep a daily practice than one where you do it only when you feel that spark. A daily practice takes away most choices. You do not ask yourself if you should write today. You ask yourself when you will do it. The question is not "did I feel something today?" but "what did I feel today, and can I say it clearly?" The idea here is that you feel something every day. Putting that into words is what you need to do.
I have now kept a log of what I see and feel for more than three years. It holds over a thousand entries. Most of these entries are private and I will not share them with anyone. The entries that did become public — the frameworks, the essays, the talks, and this book — all started in this log. The notebook that no one sees is the base for everything that people do see.
That is not a figure of speech. This is a way to show how strong true knowledge can be. It grows in private and over time. There is no big group watching. You do it to understand something better than you knew before.
This article is taken from the SEO Hobby Expert World book series. If you want to read the whole system, including all steps of the "Directional Intent" way and the Three Perceptual Habits practice book, you can check the blog at https://www.blogger.com/profile/09527871889297907769.
Related resources:
- The Two-Line Cliffhanger: How AI Influencers Bring Content Types Together
AI Influencer Storytelling: The Complete SEO Audit (Free 17-slide deck)
Topic Cluster Strategy: Build authority with one post at a time
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