Why English Is Not the Language AI Uses (and What Vectors & Embeddings Actually Are)
One of the biggest misconceptions about AI is this:
“AI understands English.”
It doesn’t.
AI processes English, but it does not think in English, store knowledge in English, or reason in English.
To understand how AI actually works—and why it feels so intelligent—we need to talk about vectors, embeddings, and how meaning is represented mathematically.
This article walks from absolute beginner intuition to advanced technical understanding, step by step.
No hype. No mysticism. Just clarity.
The Core Truth (Start Here)
AI does not understand language the way humans do.
Instead:
- AI converts language into numbers
- Those numbers live in high-dimensional space
- Meaning comes from distance, direction, and relationships, not words
English is just the input format.
The real language of AI is math.
Step 1: If AI Doesn’t Use English, What Does It Use?
AI uses vectors.
A vector is simply:
A list of numbers that represents something.
Example:
[0.12, -0.88, 1.42, 0.03, ...]
That list might represent:
- A word
- A sentence
- A paragraph
- An image
- A sound
- A line of code
Humans see words.
AI sees numbers.
Step 2: What Is an Embedding?
An embedding is a way of turning something (like text) into a vector that captures meaning.
Think of it like this:
An embedding is a numerical fingerprint of meaning.
Example (Conceptual)
These sentences:
- “I love programming”
- “I enjoy coding”
Look different in English.
But their embeddings will be very close together in vector space because they mean similar things.
Meanwhile:
- “I love programming”
- “The banana is yellow”
Will be far apart.
Step 3: AI Thinks in Space, Not Sentences
Once text becomes vectors, AI works in space.
This space is called vector space or embedding space.
Each vector is a point in that space.
Key idea:
- Similar meanings → vectors close together
- Different meanings → vectors far apart
AI doesn’t ask:
“What does this sentence say?”
It asks:
“How close is this vector to other vectors?”
Step 4: What Is a Dimension?
You may hear phrases like:
- “768-dimensional embeddings”
- “1536-dimensional vectors”
This sounds intimidating, but it’s not.
A dimension is just a direction along which meaning can vary.
Human Analogy
Imagine describing food using numbers:
- Sweetness
- Saltiness
- Spiciness
That’s a 3-dimensional space.
Now imagine hundreds or thousands of such features:
- Tone
- Topic
- Intent
- Emotion
- Formality
- Technical depth
- Contextual usage
AI embeddings often use hundreds or thousands of dimensions to capture meaning precisely.
Step 5: Vertices, Vectors, and Geometry (Clearing Confusion)
You may hear the term vertices (plural of vertex).
Here’s how it fits:
- A vector describes a point
- A vertex is that point in space
So:
- Embedding = vector
- That vector corresponds to a vertex in high-dimensional space
AI reasoning is geometric:
- Measure distance
- Compare angles
- Find clusters
- Follow directions
This is why AI is incredibly good at:
- Similarity search
- Semantic matching
- Recommendations
- Retrieval-augmented generation (RAG)
Step 6: Why English Is Just a Skin, Not the Brain
When you type English into an AI:
- Text is tokenized (broken into pieces)
- Tokens are converted into embeddings
- AI operates purely on vectors
- Output vectors are converted back into text
English is just:
- An interface
- A serialization format
- A human-friendly layer
The intelligence lives in vector math, not grammar rules.
This is why:
- AI can translate languages easily
- Code, math, and prose coexist naturally
- Meaning survives paraphrasing
Step 7: A Simple Example (Beginner Friendly)
Imagine a tiny AI world with only 2 dimensions:
- X-axis = “Technology”
- Y-axis = “Emotion”
Now plot sentences:
- “I love JavaScript” → (High tech, positive emotion)
- “JavaScript is frustrating” → (High tech, negative emotion)
- “I enjoy cooking” → (Low tech, positive emotion)
AI doesn’t read these sentences.
It measures distances between their points.
Step 8: How This Enables Search, Chat, and RAG
This is why modern AI systems work so well.
Semantic Search
Instead of keyword matching:
- Convert query → embedding
- Compare to document embeddings
- Return closest matches
Chatbots
- User message → embedding
- Context embeddings retrieved
- Combined and fed to the model
RAG (Retrieval-Augmented Generation)
- Your documents → embeddings
- Stored in a vector database
- AI retrieves relevant chunks by distance
- Generates answers grounded in your data
This is not memorization.
It’s geometric reasoning over meaning.
Step 9: Why This Matters for Learners and Developers
Understanding embeddings changes how you:
- Prompt AI
- Design systems
- Debug “hallucinations”
- Build AI apps responsibly
You stop thinking:
“Why didn’t the AI understand my words?”
And start thinking:
“Why didn’t my meaning land close enough in vector space?”
That’s a huge mindset shift.
Step 10: Beginner → Advanced Summary
Beginner Takeaway
AI doesn’t understand English—it turns words into numbers and compares meaning using math.
Intermediate Takeaway
Embeddings represent meaning as vectors in high-dimensional space, where similarity is distance.
Advanced Takeaway
AI reasoning is fundamentally geometric, operating on vector relationships rather than symbolic language structures.
Final Thought
AI doesn’t “know” things.
It doesn’t “think” in words.
It doesn’t “understand” English.
It navigates meaning space.
Once you understand that:
- AI becomes less magical
- More predictable
- And far more powerful to work with intentionally
This is the foundation behind Vibe Learning:
Understand the system first—then build with it.