AI

A moving label, not a technology. "AI" doesn't name a fixed thing the way model or token does — it points at whatever computers can newly, impressively do. Right now it points at large language models. It has pointed at very different things before:

Era	What "AI" meant
1950s	Symbolic reasoning — theorem provers, checkers programs.
1960s–70s	Rule-based symbolic programs — ELIZA, SHRDLU.
1980s	Expert systems — thousands of hand-written if-then rules encoding human expertise.
1990s	Game-tree search — Deep Blue beating Kasparov (1997). Researchers avoided the word "AI" entirely
2000s	Statistical machine learning — spam filters, recommenders. Still sold as "machine learning", not "AI"
2010s	Deep learning — image recognition (AlexNet, 2012), AlphaGo (2016).
2020s	Large language models — ChatGPT (2022) made "AI" mean chatbots

The pointer moves by a known mechanism, sometimes called the AI effect: once a technique works reliably, it gets renamed — it's "just" search, "just" statistics — and "AI" slides forward to the next unsolved thing. The observation is old. Bertram Raphael put it this way in 1971: "AI is a collective name for problems which we do not yet know how to solve properly by computer." Larry Tesler's version, from around 1979: "Intelligence is whatever machines haven't done yet."

This is why conversations about AI so often talk past each other. A claim like "AI can't reason" or "AI is overhyped" carries a hidden timestamp — it may be about expert systems, about 2010s image classifiers, or about last month's LLM, and each reference supports a different conclusion. When a discussion about AI stalls, the fix is usually to swap the word for whichever precise term is actually meant: the model, the harness, the agent, the context it was given.