How Artificial Intelligence Really Works: An Understandable Primer Without Buzzwords

Everyone talks about AI. Few understand what actually happens when ChatGPT writes a text, Claude Code programs, or Midjourney generates an image. This article explains the fundamentals so that you can understand them without having studied computer science. Without oversimplification to the point of inaccuracy. Without buzzwords.

What "Artificial Intelligence" Means (and What It Does Not)

AI is an umbrella term. It describes computer systems that perform tasks for which humans normally need intelligence: understanding language, recognizing patterns, making decisions, combining things creatively.

What AI in 2026 is NOT: conscious, sentient, or "thinking" in the human sense. Even the most powerful language model has no understanding of what it says. It calculates the most probable continuation of a text. Extremely well, but fundamentally different from human thinking.

The AI that dominates everyday life in 2026 is called machine learning and specifically Deep Learning: programs that learn patterns from large datasets instead of being programmed rule by rule by humans.

Neural Networks: The Basic Principle

A neural network is a mathematical structure, inspired (very loosely) by the human brain. It consists of "neurons" (computing units) arranged in layers:

• Input layer: Receives data (text, image, sound)
• Hidden layers: Process the data through mathematical operations. The more layers, the "deeper" the network (hence "Deep Learning")
• Output layer: Delivers the result (next word, image classification, translation)

Each connection between neurons has a "weight": a number that determines how strongly the signal is passed on. A neural network with 100 billion such weights (like GPT-4) has 100 billion adjustable numbers that together constitute its "knowledge."

Training: How AI Learns

An untrained network produces nonsense. It becomes useful through training:

1. Collect data: Large amounts of text (books, websites, code, conversations), images, or whatever the model is supposed to be able to do.
2. Make a prediction: The network receives part of the data and is supposed to predict what comes next. For language: "The dog sits on the ___."
3. Measure the error: The network says "table," the correct answer was "sofa." The error is calculated as a number.
4. Adjust the weights: The 100 billion weights are shifted a tiny bit in the direction that reduces the error. This is called "backpropagation."
5. Repeat billions of times: This happens for billions of text excerpts. After enough repetitions, the network has learned patterns: grammar, facts, logic, style, code syntax.

This training costs millions. Not because of the software (which is often open source), but because of the hardware: thousands of high-performance GPUs (NVIDIA H100/Blackwell) computing for weeks or months. Training GPT-4 cost an estimated 100 million dollars. GPT-5.4 presumably more.

Transformers: The Architecture Behind ChatGPT, Claude and Co.

In 2017, Google published a paper titled "Attention Is All You Need." It described a new network architecture: the Transformer. This architecture is the foundation of all modern language models.

What makes the Transformer special: the Attention mechanism. Instead of processing text word by word (like older models), a Transformer can "look at" all other words in the text for each word and decide which are most relevant.

Example: In "The bank by the river was green," the model needs to know that "bank" here refers to a riverbank, not a financial institution. The Attention mechanism recognizes that "river" and "green" point to a riverbank. It weights these words more strongly than others.

This ability to recognize connections across long texts is the reason why language models in 2026 are so impressively coherent, even across thousands of words.

Tokens: The Language of AI

Language models do not read words. They read tokens. A token is a text fragment, typically 3-4 characters. "Artificial Intelligence" is, for example, 4-5 tokens. "AI" is 1 token.

Why tokens instead of words? Because it is more efficient. The model does not need to know every word in the world. It learns to assemble words from fragments. "Un" + "understand" + "able" yields "understandable," even if it has never seen the word as a whole.

When people say "GPT-5.4 has 1 million tokens of context," it means: the model can consider the last ~750,000 words of the conversation with each response. For comparison: "Lord of the Rings" (all three volumes) has approximately 576,000 words.

Inference: When AI Responds

Training is learning. Inference is application: the fully trained model receives a question and generates an answer.

The process, token by token:

1. User types: "What is the capital of France?"
2. The model calculates: Which token is most likely to come next? Answer: "The"
3. Next token: "capital" (probability 94%)
4. Next: "of" (99%)
5. Next: "France" (97%)
6. Next: "is" (98%)
7. Next: "Paris" (99.5%)
8. Next: "." (end token)

This happens in milliseconds. Each token is calculated individually, based on everything that came before. This is the reason why AI responses appear word by word, not as a complete block.

Why AI Hallucinates

The biggest problem with language models: they can invent things that sound plausible but are wrong. This is called hallucination.

Why? Because the model does not "know" what is true. It only knows what sounds probable. When you ask "Who won the Nobel Prize in Physics 2019?", the model calculates the most probable answer based on its training data. Most of the time, this is correct. But sometimes it combines facts incorrectly, invents sources, or generates plausible-sounding numbers that never existed.

This is not a bug that can simply be fixed. It is a property of the architecture. Language models are pattern completers, not knowledge databases. That is why fact-checking AI-generated content is essential.

GPU, VRAM, and Why AI Needs So Much Hardware

Neural networks consist of matrix multiplications: millions of numbers multiplied by millions of other numbers. GPUs (graphics processing units) can do this massively in parallel, CPUs cannot.

• VRAM: The GPU's memory. A model with 70 billion parameters needs approximately 35 GB of VRAM (at half precision). An NVIDIA RTX 4090 has 24 GB. Not enough. That is why large models run on professional GPUs (H100: 80 GB) or are distributed across multiple GPUs.
• Quantization: Models can be made "smaller" by reducing the precision of numbers (from 16-bit to 8-bit or 4-bit). Less memory, faster computation, but slightly lower quality.
• Training vs. Inference: Training requires 10-100x more computing power than inference. That is why OpenAI trains on clusters with 25,000+ GPUs, but inference runs on distributed, smaller setups.

RAG: How AI Accesses Custom Data

Retrieval-Augmented Generation (RAG) is the technique that makes AI systems useful for enterprises. The principle:

1. User asks a question
2. The system searches a database (SharePoint, file server, manuals) for relevant documents
3. The most relevant excerpts are provided to the language model as context
4. The model responds based on the found documents

Advantage: The model does not need to "know" everything. It receives the right information at runtime. This makes responses more current, more accurate, and verifiable (sources can be displayed).

Disadvantage: The quality depends on the search. If the retrieval finds the wrong documents, the model responds based on incorrect information. That is why maintaining data sources and search indexes is so important.

What Is Different in 2026 Compared to 2024

• Agentic AI: Models can not only respond but act: create files, call APIs, install software, navigate websites. This is the biggest leap.
• Reasoning: New models (GPT-5.4, Claude Opus 4.6) can "think," meaning they go through multiple steps internally before responding. This significantly improves logic and mathematics.
• Multimodality: Models process text, images, audio, and video simultaneously. A model can analyze a photo, describe the situation, and provide recommendations for action.
• Local AI: Thanks to quantization and better hardware, usable models run on regular PCs (Ollama, LM Studio). Not as good as cloud models, but good enough for many tasks. And: the data stays in-house.

What AI Cannot Do (as of 2026)

• Understand: AI recognizes patterns and generates text. It does not understand what it says.
• Reliably deliver facts: Hallucinations are not solved. Critical decisions always require human verification.
• Be creative in the human sense: AI recombines what it has learned. It has no intention, no vision, no opinion.
• Improve itself: AI models do not learn from their own mistakes unless they are explicitly retrained with feedback (RLHF).

AI in 2026 is the most powerful tool ever built. But it is a tool. Those who understand how it works can use it better, recognize its limits, and avoid errors. Those who treat it as magic will sooner or later be caught by reality.