Artificial intelligence (AI)
"Artificial intelligence" is a moving label. Unlike "model" or "token", the term doesn’t name a fixed thing — it points at whatever computers can newly, impressively do. Each time a technique becomes reliable it stops being called "AI" and gets a more specific name, and the label slides forward to the next thing still unsolved.
This is the AI effect: once a technique works, it gets renamed — "just" search, "just" statistics — and "AI" moves on. Bertram Raphael put it in 1971: "AI is a collective name for problems which we do not yet know how to solve properly by computer." Larry Tesler’s variant, from around 1979: "Intelligence is whatever machines haven’t done yet."
The label’s drift is visible across eras:
Era | What "AI" meant |
|---|---|
1950s | Symbolic reasoning — theorem provers, checkers |
1960s–70s | Rule-based symbolic programs — ELIZA, SHRDLU |
1980s | Expert systems — hand-written if-then rules |
1990s | Game-tree search — Deep Blue beats Kasparov (1997); researchers avoided the word "AI" |
2000s | Statistical machine learning — spam filters, recommenders; sold as "ML", not "AI" |
2010s | Deep learning — AlexNet (2012), AlphaGo (2016) |
2020s | Large language models — ChatGPT (2022) made "AI" mean chatbots |
This is why conversations about AI talk past each other. A claim like "AI can’t reason" carries a hidden timestamp — it depends entirely on which era’s technique the speaker has in mind. When a discussion stalls, swap "AI" for the precise term: the model, the harness, the agent, the context.
At its core, though, the term is about agency — the capacity of a machine to act, to make decisions, and to pursue goals with some degree of autonomy.
Related topics include:
- Computer science
- Agent
- Agent loop
- Agent skills
- Agent2Agent (A2A)
- Agent harness
- Agent orchestration
- AI assistant
- AI coding assistant
- AI constitutionalism
- Context engineering
- Generative AI
- Large language model (LLM)
- Loop engineering
- Machine learning
- Model Context Protocol
- Open-weight model
- Recursive self-improvement (RSI)
- Transformer architecture
References
- Dictionary of AI Coding, Matt Pocock: Plain-English glossary of the vocabulary of AI coding: models, context windows, tokens, harnesses, tools, failure modes, handoffs, and patterns of work.
- What Is ChatGPT Doing … and Why Does It Work?, Stephen Wolfram: Accessible first-principles explanation of next-token prediction, embeddings, and the transformer’s attention mechanism, plus the concept of computational irreducibility.