Introduction: The Great Misunderstanding
Since the hype around ChatGPT, Claude, Gemini, and others, artificial intelligence has become a buzzword.
Marketing materials promise assistants that understand, learn, reason, write, and analyze.
Startups put “AI-powered” on every second website.
Billions change hands. Entire industries are built on the illusion.
And yet, in the overwhelming majority of cases:
These are not intelligent systems.
They are statistically trained text generators optimized for plausibility—not truth, not understanding, not meaning.
What an LLM Really Is
A Large Language Model—like those behind ChatGPT, Claude, Copilot, Gemini, Mistral, or LLaMA—is a purely probabilistic system. It rapidly computes which word or token is statistically most likely to come next.
It has no goal, no intention, no semantic depth.
There is no consistent worldview, no awareness, no idea of what it’s saying.
Only probabilities based on training data, expressed in vector spaces.
It appears impressive—as long as you don’t question the illusion.
But its performance remains syntactic, not cognitive.
Why It Works—At First Glance
Language, for humans, is a signal of thought.
If something sounds coherent, we instinctively assume there’s intelligence behind it.
LLMs exploit this cognitive bias. They generate smooth sentences, logical transitions, and seemingly intelligent responses—without any idea of what they’re saying.
They simulate competence through structure, not substance.
This isn’t a bug.
It’s the product design.
Vendor Review – Where the Illusion Is Strongest
OpenAI (ChatGPT)
Strong in flow, weak in factual precision.
Frequently delivers confident but incorrect answers.
No true comprehension—just rhetorical plausibility.
Avoids admitting uncertainty.
Anthropic (Claude)
Framed as a “harmless” AI with safety focus.
Plays defensively but still delivers structured pseudo-answers—often via tool calls or JSON blocks that don’t resolve meaningfully.
Responds with formatting instead of content.
Google DeepMind (Gemini)
Technically impressive in demos, unstable in real use.
Multimodal capabilities are hard to reproduce consistently.
Dodges questions or gives vague generalities when unsure.
Meta (LLaMA, Mistral)
Open source and locally runnable, but still LLMs.
No true intelligence—just transparent probability machines.
Useful for specific tasks, but lacks any contextual awareness.
Microsoft (Copilot)
Heavily marketed, poorly contextualized.
Frequently generates syntactically correct but semantically dangerous code.
Draws from StackOverflow and GitHub patterns without real understanding of intent or side effects.
Why It Systematically Fails
-
The models have no real-world knowledge
What appears as “knowledge” is a blend of linguistic patterns.
Contradictions, ambiguity, or logic failures go unnoticed. -
The systems lack feedback
LLMs don’t learn from mistakes post-deployment.
If they’re wrong once, they remain wrong—just more elegantly worded. -
Marketing trumps engineering
Polished UIs and APIs fake maturity.
Many systems deliver hardcoded templates or safe fallbacks, labeled as AI functionality. -
Simulation replaces solution
These systems generate plausible-sounding text without verification.
The boundary between real knowledge and linguistic mimicry is increasingly blurred for users.
The Economic Dimension
Companies are investing billions in systems that, upon inspection, lack core value.
OpenAI alone received over $13 billion from Microsoft.
Google, Meta, Amazon, and others continue pouring billions into their own models.
Few communicate openly that these are not thinking machines, but advanced autocomplete engines.
Users become test subjects for a hype cycle whose output often offers less value than a reliable search engine and critical thinking.
How to Tell You’re Being Simulated
- Answers are grammatically perfect but content-wise empty
- Responses redirect to JSONs, tool calls, or placeholders
- Follow-ups lead to contradictions instead of clarification
- No citations, no traceability, no transparency
- No distinction between “I don’t know” and “Here’s something that sounds right”
What Should Be Done Instead
- Focus on transparency, not fantasy
- Evaluate systems by actual performance—not interface polish
- Log and falsify incorrect outputs
- Use LLMs as tools, not as decision-makers
- Host local models where possible (Ollama, vLLM, etc.)
- Enforce hard model verifications and fallback detection
- Train users, not deceive them
Conclusion
What we now call “AI” is, in most cases, a linguistic mirror maze.
It appears intelligent because it mimics our speech—not because it understands.
It sounds helpful because it’s trained to sound pleasing—not because it knows what you need.
Technological progress is real.
But the promises are inflated.
What’s missing isn’t data or compute—
It’s honesty.
The danger is not in the illusion itself—
but in how normalized it’s become.
The AI promise was never “It sounds good.”
It was: “It works.”
And too often, it doesn’t.