How ChatGPT knows everything (almost)

ChatGPT can write poems, fix broken code, explain quantum theory, and debate moral philosophy—all in the same conversation. It’s quick, polished, and often surprisingly persuasive. For many, it feels like talking to a superhuman librarian who’s read the entire internet. But behind the confidence lies a stranger truth: ChatGPT doesn’t know anything in the way we understand it.

There’s no brain. No memory of past events. No understanding of meaning. Instead, what powers this tool is something much simpler—and far more mechanical. ChatGPT works by predicting the next word in a sentence, using patterns it learns. It mimics reasoning, empathy, even humour. But what’s really happening behind the scenes? Where does it get its “knowledge”? Why does it sometimes make things up? And perhaps most importantly—how should we actually use it?

Built on a mountain of words

ChatGPT didn’t read the internet—it absorbed the patterns of language that exist on the internet.

Its training involved scanning through hundreds of billions of words from books, websites, articles, social media, and public datasets like Common Crawl and Wikipedia. That includes everything from 19th-century novels to user reviews on Amazon, from medical research abstracts to casual Reddit threads. Not to understand them, but to spot the structures they share.

By feeding it massive volumes of text, engineers helped the model learn how humans typically write. It picked up the rhythm of English sentences, how arguments are usually formed, how jokes land (sometimes), and which facts often appear together. It doesn’t store specific documents. Instead, it builds a kind of probability map—a statistical sense of what usually follows what.

For example, if someone types:

“The capital of France is…”

The model has seen millions of variations of this phrase and “knows” that “Paris” is the most likely next word. Not because it fact-checked it, but because that pattern appears consistently in the data it trained on.

It’s pattern recognition, not knowledge—imitation, not memory. But at scale—and with the right prompts—it begins to look a lot like intelligence.

Why ChatGPT still makes things up (and always will, in some way)

One of the most striking aspects of ChatGPT is how confidently it can be incorrect. It might give you a citation that doesn’t exist, invent a historical event, or tell you a made-up law with perfect formatting. These aren’t glitches—they’re baked into how the model works.

ChatGPT is designed to be fluent, not factual. Its goal is to generate plausible-sounding responses based on patterns in language, not to verify whether what it’s saying is true.

If a prompt asks for a list of sources, it will try to generate something that looks like a list of sources. If it sees that scientific questions often include references to Harvard or Nature, it may insert those names even if no such article exists.

This tendency is known as hallucination—not because the AI is imagining things in the human sense, but because it produces information that appears real but isn’t. And since it doesn’t know what’s real, it can’t catch itself in the act.

Even newer versions, equipped with better data and guardrails, still occasionally fabricate. Not out of malice or error—but because making things sound right is the entire point.

What ChatGPT doesn’t know (and never can)

For all its fluency, ChatGPT has no idea what it’s saying. It doesn’t understand joy, context, consequences, or emotion—not in the way humans do. It doesn’t know what year it is, unless you tell it. It doesn’t remember past conversations unless a session is active. And it doesn’t have a belief, a memory, or a goal. Everything it generates is based on pattern and probability, not awareness.

You can ask it for a joke about cats, and it might respond with something clever. But it doesn’t know what’s funny. It’s pulling from examples of humour it has seen before, blending phrasing that looks like a joke. The laugh is yours, not the machine’s.

This is also why it can’t really form opinions. When it offers one, it’s usually echoing what people tend to say, packaged politely. It’s not choosing a side. It’s picking what sounds like the correct next sentence in a conversation about choosing sides.

In that sense, ChatGPT is not a mind—it’s a mirror. One trained on our words, reflecting them back with eerie accuracy, but none of the understanding that gave those words meaning in the first place.

What it’s actually good for (and where it still fails)

For all its limits, ChatGPT is remarkably useful—if you know how to use it well. It’s a powerful writing assistant, a code companion, a research explainer, and a brainstorming partner. Need to draft a tricky email? Outline a proposal? Understand the basics of a niche scientific concept? It’ll get you 80% there, fast. It shines in creative and iterative tasks:

• Generating ideas for social content
• Summarising long articles
• Translating tone (from casual to formal, or vice versa)
• Turning vague thoughts into clear sentences
• Writing code snippets or debugging errors (though not always bug-free) 

But it still stumbles in areas where accuracy, nuance, or ethics matter.

• It can’t reliably provide real-time data or personal advice.
• It may present outdated or biased information as fact.
• And it can’t make moral judgments—only repeat patterns of how people talk about them. 

In high-stakes contexts—healthcare, law, finance, relationships—it should never be the sole decision-maker. It doesn’t know your life. It can’t assess consequences. And it won’t correct itself unless you prompt it to.

Why understanding AI helps us stay human.

We live in a moment where AI tools like ChatGPT can write resumes, translate ancient poetry, or simulate a conversation with your younger self. And while that feels revolutionary, it’s also deeply misunderstood.

 Used wisely, ChatGPT can be a powerful extension of our creativity, our curiosity, and even our compassion. But only if we remember one thing: it doesn’t replace human thinking. It just makes the work of thinking a little more collaborative.