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AI glossary: the words of the AI economy, explained
The rulebook for the AI season.
AI (artificial intelligence)
Computer systems that do tasks we usually link to human thinking, like understanding language, spotting patterns or making predictions. On our scoreboard, "AI" is the whole league: chips, clouds, models and apps.
Source: NIST AI RMF 1.0 glossary and Britannica "artificial intelligence", checked 20 Oct 2026.
See it on the map: AI chips and Foundation models
Generative AI
AI that makes new content, such as text, images, code or audio, based on patterns it learned from lots of examples. ChatGPT, Claude and Gemini are examples.
Source: NIST AI 600-1 Generative AI Profile, checked 20 Oct 2026.
See it on the map: AI models and Foundation models
LLM (large language model)
A large language model is an AI system trained on huge amounts of text to predict the next word (token), which lets it write, summarize and answer questions. It's the engine inside most AI chatbots.
Source: NIST AI 600-1 and Google ML Glossary, checked 20 Oct 2026.
See it on the map: AI models and Foundation models
AI agent
An AI system that doesn't just answer but takes steps toward a goal: it plans, uses tools such as search or code, and checks its work, with less hand-holding. Think of a player who runs plays, not just calls them.
Source: Anthropic "Building effective agents", checked 20 Oct 2026.
See it on the map: AI applications
Token
The small chunk of text (often part of a word) that an AI model reads and writes. Usage limits and API prices are counted in tokens.
Source: OpenAI Help Center "What are tokens", checked 20 Oct 2026.
See it on the map: Foundation models
Hallucination
When an AI states something false or made-up as if it were fact. That's why we source every fact on our pages.
Source: NIST AI 600-1, checked 20 Oct 2026.
See it on the map: AI models
Machine learning
The way computers learn patterns from data instead of following only hand-written rules. Most modern AI is built this way.
Source: Google ML Glossary and NIST AI RMF glossary, checked 20 Oct 2026.
See it on the map: Foundation models
GPU
A graphics processing unit, a chip that does many small maths jobs at once. That makes it the workhorse for training and running AI models, and why NVIDIA and AMD sit in our AI Chips division.
Source: NVIDIA glossary, checked 20 Oct 2026.
Inference
Using a trained AI model to give an answer, as when you send a prompt and get a reply. Training is practice. Inference is game day.
Source: Google ML Glossary and NVIDIA glossary, checked 20 Oct 2026.
Data center (AI)
A building full of servers, chips, power and cooling where AI models are trained and run. AI data centers need far more electricity than most, which brings power companies into the league.
Source: IEA "Energy and AI" (2025), checked 20 Oct 2026.
See it on the map: Data centers and Power