Why Your AI Scripts Feel Cookie-Cutter: Tacit Knowledge and Neural Networks in 2026

Anyone who has tried to hand ChatGPT, Claude, Getmade or another language model (LLM) the job of writing a script for YouTube, Reels, a podcast or an article has felt emotional disappointment at least once.

The prompt seemed detailed, the role model was set and a tone-of-voice policy was written into the rules. But the result is still smooth, correct… sterile boredom.

The points are banal, the humor looks forced and the author’s style dissolves into averaged information noise. Why does this happen? It isn’t that neural networks are “not smart enough” or that you wrote the prompt badly.

The problem lies in a fundamental concept of cognitive science, tacit knowledge, and in how a machine’s thinking differs in principle from a human’s.

1. Two Types of Knowledge: Explicit vs Tacit

To understand why AI falls short when creating truly gripping content, let’s divide all the knowledge humanity works with into two categories:

Explicit Knowledge

This is everything that can easily be formalized, written as an instruction, and encoded in formulas, diagrams and textbooks.

• Examples: the rules of grammar, the structure of classic three-act dramaturgy, technical camera settings, video ranking algorithms.
• Accessibility to AI: 100%. Neural networks are trained precisely on explicit knowledge.

Tacit Knowledge

This is intuition, personal life experience, a sense of proportion, subtext, cultural context and that very “author’s instinct.” It is extremely hard (and often impossible) to formulate as rigid rules and algorithms.

• Examples: the ability to feel the exact timing for a joke, understanding which intonation will evoke empathy and which will cause rejection.
• Accessibility to AI: Unavailable. This knowledge isn’t in digital databases in explicit form.

An example with humor and “cringe”:

You can easily tell when a scene in a video feels “cringey” or unfunny, because you feel it in your gut. But try to write down an exact, 100% mathematical rule by which anyone on the planet could tell the boundary between funny and fake. It is impossible: perception depends on micro-context, cultural background and intuitive associations. That is tacit knowledge.

2. AI as a “Straight-A Student”: Why Perfection Is Boring

Artificial intelligence is a perfect “straight-A student.” It is a walking encyclopedia that never forgets anything and remembers all the rules of dramaturgy, the terms and the structure of successful videos.

But this “straight-A student” has one fatal problem: it has no personal life.

A neural network has no fails, no embarrassing memories, no specific sense of humor, no business screw-ups and no childhood impressions. It produces accurate, correct and… absolutely meaningless texts for emotional perception.

A person is valuable to an audience precisely for their subjectivity and imperfection:

• We are constantly wrong, and unique conclusions, insights and life experience that are in no textbook are born from exactly those mistakes.
• We are biased: we have a personal opinion the viewer can passionately agree with or argue against in the comments (and that is the main engine of engagement).
• We are imperfect: our voice slips, we use unexpected metaphors and notice strange details that the “straight-A student” counts as noise and deletes.

3. Why Do Neural Networks Create “Identical” Content?

Language models are trained on billions of pages of text. But this entire giant body consists exclusively of explicit knowledge.

AI is a master of averaging. It analyzes millions of patterns and outputs the arithmetic norm. When you ask a neural network to write a script “from scratch,” it inevitably turns to the most frequent and predictable patterns.

The result is content with no face. It lacks the tacit knowledge and the living human subjectivity that viewers come to media for.

4. The Right Pipeline: Human Subjectivity → AI Optimization

Does this mean neural networks are useless for creators? Absolutely not. It only means you need to fundamentally change the order of actions.

The main mistake of most content makers is to try to generate an AI skeleton and then “breathe life into it.” The exact opposite approach works:

The golden rule: first you build your tacit knowledge and subjective experience into the script, and only then bring in AI for structure, polish and optimization.

[ A person’s subjective experience ] ──► [ Tacit knowledge (draft) ] ──► [ AI optimization ] ──► [ A finished script ]

Step 1. The Initial Pour of “Tacit Knowledge” (the Human)

Write the first draft or the script skeleton yourself:

• Don’t ask AI to come up with ideas or arguments. Sketch them yourself, relying on intuition and experience.
• Write the way you feel. Use your own living metaphors, imperfect turns of phrase, personal observations, life fails and specific humor.
• Add subjectivity. Don’t be afraid of seeming wrong: your position is what evokes emotion.

Even if this draft seems chaotic, it carries your unique DNA, which is in no LLM’s training set.

Step 2. Switching on the AI Optimizer (the Neural Network)

When the living base with your tacit knowledge is already on paper, hand the text to the neural network. Use it not as an author but as a straight-A assistant:

1. Check logic and rhythm: “Point out the places in this draft where the thread of the narrative is lost or the transition between thoughts is weak.”
2. Find stylistic errors: “Fix typos and suggest clearer wording, but strictly keep my tone and characteristic expressions.”
3. Analyze counterarguments: “Imagine you are a skeptical viewer. Which of my subjective claims might raise questions?”
4. Format for the platform: “Break this passage into short points to display as text overlays on screen.”

The Takeaway for Creators

In the era of mass AI content, the value of “just correctly written text” has fallen to zero.

The viewer doesn’t come for an “encyclopedia,” because search and reference books exist for that. The viewer comes for a human view of things.

The uniqueness of content is determined not by how complex a prompt you wrote but by how much tacit knowledge and personal subjectivity you managed to put into the material before you pressed “Generate.”

Use AI as a perfect polishing assistant, but keep the author’s steering wheel in your own hands.