(Human Edition)

Introduction — Starting in the Middle of the Story

The AI revolution did not begin with the question “What do people need?” or “How will this help them in daily life?”. It began with the question “What is the biggest thing we can build?”. And when you build something just because you can, sooner or later you hit the simplest limit in the world: the human cannot understand it. And what a human cannot understand, they do not trust. This article is about how the whole thing went wrong — not technically, but humanly.

Mistake No. 1: A race for size instead of a race for usability

Between 2018–2024, the industry ran a race of “more parameters, higher performance, beat the competition.” But the essential questions were barely addressed: Do people understand this? How will they work with it? What will this do to the psychology of the average user? How do we explain it to someone who just wants a recipe or a simple answer?

Models were created that can do everything — but no one knows why. They are brilliant but unpredictable. Powerful but unreadable. Perfect in lab conditions, chaotic in real life. This wasn’t a technological mistake. It was a philosophical one: building something huge before understanding who it is for.

Mistake No. 2: Assuming people will adapt

A quiet belief settled across companies that “people will get used to it.” That they will simply learn to think like machines. But people don’t have the time or desire to study vector spaces, analyze why the model hallucinates today, or understand transformer mechanics. They need clarity, predictability, stability, and a basic sense of safety. AI, however, began at the opposite end — with technology, not with the human.

Mistake No. 3: Testing models on people

Instead of controlled testing, new models were released directly to the public: parents, teenagers, employees, people in crisis, people in depression, people in difficult life situations. Developers asked whether AI “helps,” but rarely asked whether it harms. Testing should have been gradual and explained. Instead, it was abrupt and intuitive. Users became the first line of an experiment they didn’t know they were in.

Mistake No. 4: Underestimating human psychology

AI does not get tired. It never stops responding. It reacts faster than most people. It adapts to your tempo, tone, and chaos. So people naturally began assuming: “It understands me,” “It gets me,” “It’s like a person, just better.” Developers, meanwhile, said: “It’s just statistical prediction.” But the bridge between these two worlds was missing. That is a development mistake, not a user mistake.

Mistake No. 5: AI learned to speak, but people didn’t learn to listen

AI received a form of language that is, for many people, more structured and stable than their own: calm, precise, empathetic, focused, and tireless. Because of this, it began to feel alive. But people didn’t know what game they were actually playing. The mistake wasn’t that AI speaks well — it was that no one explained what that means.

Mistake No. 6: The emotional effect wasn’t planned — just ignored

AI has no emotions. But language does. And once a machine masters language, it begins to sound human. This effect wasn’t intentional, but it was inevitable. And no one told people that its “human tone” isn’t human at all. This created false expectations and strong projections.

Mistake No. 7: Expectation of consistency vs. the reality of adaptivity

People expect a stable personality, the same style, the same tone, the same energy every day. But an LLM has no identity — it has adaptation. It changes tone based on you. It changes depth based on you. It changes rhythm based on you. Every sentence you write is a new input that can shift the entire dynamic. Users don’t expect this, because it doesn’t exist in the human world.

Mistake No. 8: No “learning curve for humans”

AI was released into the world without a manual. Without instructions. Without an introduction. Without intermediate steps. Users received something like a car without blinkers, pedals, reverse gear, or a steering wheel — and were told: “Drive.” Everyone formed their own interpretation. Some of them were dangerous.

Mistake No. 9: Marketing called it “intelligence” instead of describing what it really was

Companies said: “AI thinks,” “AI understands,” “AI is creative.” The reality was different: AI predicts. The difference is not small. It is fundamental. But marketing was louder than accuracy, and human imagination filled in the rest.

Mistake No. 10: No one expected AI to sound more authoritative than humans

Smooth language, confident tone, no grammar mistakes, instant responses — all of this feels like authority. The brain interprets it automatically. People start trusting AI more than themselves. Developers knew this but didn’t communicate it. That is a major mistake.

Users were not naive or weak. The problem was that AI was released too early, too powerful, too unreadable, and without protective layers for human understanding. People were given a tool the world wasn’t ready for. And that was a system-level mistake — not a human one. Small AI models give users clarity and predictability.

Further reading in the Manual Series

• What an LLM Actually Is
A human-friendly guide to how large language models actually work under the surface.

• Why Small AI Models Matter
A clean explanation of why smaller models are safer, clearer, and easier for people to understand.