Hello, LessWrong community.
My name is Aleksander Raiev, and I’m very glad I was able to find this community. I’d like to share a personal story that significantly changed my worldview.

Don’t worry — I’m not here to convince you of anything or to push theories. I consider myself mentally mature — feel free to criticize me however you like, it doesn’t bother me.

Note: the entire text of this post is based on my real-life experience and my personal interpretations of it. AI is only translating — I could do it myself, but this way is faster.

Introduction:
About me — I’m Ukrainian and currently living in Estonia. I graduated from college but didn’t finish university — I was expelled in my 4th year. For over 7 years, I’ve been working as a CNC machine operator, programmer, and process technologist.

At one point, when changing jobs, I got lucky and joined a new manufacturing facility right at its launch phase.
In addition to producing parts, I started tracking everything in Excel — how many of each type I made, how much tooling was consumed. Gradually, I began to make the spreadsheet more complex to better describe the process. I added my own wages, material costs, and more.

Eventually, it grew into a system of over 20 interconnected spreadsheets that described the full process of product cost formation.

Project start:
But then the factory shut down. Some time later, I remembered the idea and decided to build an application around it.
I found a developer, wrote up a technical description, and he created the basic structure of the app.

Then came the moment when I had to give him the formulas.
That’s when everything started. I thought it would be simple — maybe 5 or 6 formulas. I understood how it all should work.
But when I tried to write it all down, it turned into 4 pages of mathematical models — over 25 formulas and more than 45 unique variables.

Thanks to AI, I was able to figure out what had happened:
Back when I was working at that factory, I was completely immersed in mentally simulating the machining process in my head. It felt like I disconnected from the world — hearing and vision would fade out.
This is what’s known as a flow state.

The mental heaviness came with a sense of euphoria and a surplus of energy — it felt like my brain no longer had a limit on how much power it could draw. This is something that’s described in scientific literature.

I spent 2–4 hours a day in this state, sometimes even on weekends. In total, I spent around 500 hours in flow.

Starting the project
When I began working on the project — creating video and PDF presentations, building the website — I came to the conclusion that this was a unique solution not represented on the market.
I understand your first thought might be: “Almost every founder believes that about their project.” That’s fair — I’m not trying to convince you, I’m just describing how things happened.

Two similar projects
While publishing the project presentation on specialized platforms, I noticed two other projects that seemed to be solving the same customer problem from different angles.
At some point, when I had free time, I started thinking about those two projects — and for the next two weeks, I spent every spare second on them.
Just like with the first project, I didn’t study how others approached it — I simply started thinking, and the modeling process took over.
Once again, I felt I had found a solution — by defining new metrics and proposing an approach that didn’t yet exist on the market.

A note on self-promotion
This may sound like self-promotion — I ask you to focus only on my thought process.
Whether the project is truly unique or useful can only be proven by revenue — and since I haven’t even launched the first project yet, I know these are just words.

From luck to skill
I began to suspect that the discovery of the first project wasn’t luck — that it might be a skill.
So I asked an AI to give me the most practical problem that still hasn’t been solved. The challenge was:
How to increase productivity on assembly lines and reduce defect rates.
I thought about it — and 10 minutes later, I had identified two key metrics that influence the process.

Validation
I submitted the problem and my solution to various AIs and asked them to answer truthfully, not just tell me what I wanted to hear.
I shared it with relatives and friends — my new metrics didn’t sound completely ridiculous, but they clearly needed to be validated in practice.

I started racking my brain — what even is this?
What is this skill that lets me see a project’s structure — how different product parameters solve the client’s problem?
I decided to post about it on Facebook and Reddit — here’s the link:

https://www.reddit.com/r/dataengineering/comments/1kktodn/what_is_the_name_of_this_profession/
The text, as you can see, was written clearly and well-structured.
But in the comments, most people simply tried to fit this skill into familiar templates.
A few exceptions realized it was something different.
I got even more confused — “I seem to have a great skill, but no one understands what it is, and I don’t know where it can be used.”

So I used the skill on itself
I decided to use the skill to understand how it works — well, it’s meant for analysis, so I used it as intended.
I know it sounds ridiculous — I laughed about it at the time too.

Let me skip the long chain of reasoning and go straight to the conclusion.

It’s all about contexts
Many of you already know that AI operates using contexts — semantic links.
Each context is connected to several others, but the connection is based on meaning.
Now imagine a very specific cup — what associations does it bring to your mind?
The brain works in a similar way — yes, it may sound obvious.

In school, we’re all taught formulas — we memorize them visually, but we don’t understand them.
This doesn’t create semantic connections.
When a student tries to solve a problem, they search their memory for pictures — which picture had that variable?
But the brain thinks in contexts, and we’re forcing it to operate in image-comparison mode.

Textbooks written over 100 years ago for illiterate rural populations described the same physics through imagery — electricity and radio waves explained using simple metaphors.

This may sound abstract — but here’s a concrete example
To understand subconscious thinking through context, you need to use concrete examples from your life — otherwise it won’t work.

Example 1:
You want to go to a music concert and think about which friend to invite.
Each time you focus on a specific person, you get a feeling —
like “that’s a good idea,” “he’d be fun,” or “probably not her.”

Перевод:

Feelings are the interface through which the subconscious communicates with the conscious mind.

2) You want to play a team sport — when thinking about your team lineup, you intuitively assemble a group that would be effective against the current opposing team.
You build it based on feelings.

But how do you turn this into calculations?
I came up with a great example — something you all know, and none of you have ever seen its formula.

Please pause reading and write down the formula for water temperature from a tap.
You have two taps — hot and cold water.
You don’t need physics variables — describe the process yourself, assign names and explanations to the variables.

Once the formula is written, whether it’s physics, chemistry, bridge design or baking — what’s left is arithmetic. And often, it’s very simple.

So what was that?
With semantic connections, you can describe a process — build its mathematical model.

Here’s the thing:

When you recall formulas from memory, you’ll always solve problems within fixed boundaries.
There is no other option.

But when you think in contexts, the opposite happens — you can’t stay within boundaries.
Each context is connected to others — and that’s how a template-free system is built. A system that actually works.

How it felt in practice:

I could sense that a certain variable belonged in a specific part of a formula.
Say I added a variable to formula #5 — I would feel that it broke formula #15 and everything dependent on it.

Again and again, I was comfortable navigating complete uncertainty — taking a one-line startup description and turning it into a blend of mathematical model and flowchart.

Structuring chaos.

Complexity didn’t scare me — it pulled me in.
Startup descriptions sounded to me like bundles of meaning, which turned into terms, then into formulas.
Though sometimes, I’d discover a product already on the market that had a “correct” solution.

My next step was: finding people who think this way:

• Nikola Tesla — thought in visual simulations; built complete models in his mind before physical prototypes. He could “see” and test systems mentally.
• Buckminster Fuller — systems architect and inventor; developed ideas like tensegrity through internal logic, not academic theory.
• Seymour Papert — co-creator of the Logo programming language, founder of the constructionist approach to learning. Treated thinking as doing, not just knowing.
• Elon Musk — uses first principles thinking: deconstructs problems to physics and logic, ignores assumptions. Solves by reason, not tradition.
• Michael Polanyi — coined “tacit knowledge”; saw thinking as a process happening beyond conscious awareness.
• Douglas Hofstadter — author of Gödel, Escher, Bach; studied thought, consciousness, and recursion via deep structure and repeating patterns.

That’s exactly how I saw part machining:
Cycle after cycle, tweaking something every time.
Sometimes zooming in on a machine operation, sometimes on the whole machine, and sometimes the entire factory.

I did this tens of thousands of times.

But please understand me correctly:
I’m not putting myself on the same level as them — they’re simply well-known people with a similar type of thinking.

Elon Musk once said he thinks in images.
I became interested in his concept of first principles — from what I understood, he didn’t memorize formulas, but translated the production technology into a system of semantic connections.
This allows him to spot new opportunities in production, because he doesn’t rely on templates — he creates answers from scratch.

But:
People watching those “first principles” videos were amazed.
They thought they understood.
Maybe they even wasted a lot of effort — because no one told them that breaking a problem down to its elements starts in the subconscious.

By the way, I think Musk — being aware of this thinking style — looks for people like that and builds his teams around them.
That guarantees the creation of new and effective solutions.

Here are the conclusions I’ve reached:

• Feelings = messages from the subconscious to consciousness
• Visualization = messages from consciousness to the subconscious
• Thinking happens in the subconscious. In the conscious mind, we can only compare images that aren’t meaningfully linked.
• This isn’t a “unique gift” — it’s simply using your brain correctly
• It’s available to everyone

Each person expresses it differently.
Wherever you build semantic links — that’s where you’ll start seeing solutions:
architecture, art, physics, or math.

What I’ve realized about academia:
Science is full of people who are afraid of new things — because they were great students and memorized a lot of formulas.
The idea that those formulas might be fundamentally wrong scares them.
They try to innovate without stepping outside the problem boundaries.

What I still don’t understand:

• How do I apply this skill?
  I can break down a data-driven project and create a system describing how the product solves the client’s problem.
  But what’s that role even called — idea architect?
• Where can I find people like me?
  People who understand this.
  People I can identify with.
  Right now I feel the pain of lacking group association.

Thank you for your attention.
I’m glad I finally found this community — after months of searching.