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    The Model

    AI

    A moving label, not a technology. Points at whatever computers can newly, impressively do — right now, large language models.

    Matt Pocock
    Matt Pocock

    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:

    EraWhat "AI" meant
    1950sSymbolic reasoning — theorem provers, checkers programs.
    1960s–70sRule-based symbolic programs — ELIZA, SHRDLU.
    1980sExpert systems — thousands of hand-written if-then rules encoding human expertise.
    1990sGame-tree search — Deep Blue beating Kasparov (1997). Researchers avoided the word "AI" entirely
    2000sStatistical machine learning — spam filters, recommenders. Still sold as "machine learning", not "AI"
    2010sDeep learning — image recognition (AlexNet, 2012), AlphaGo (2016).
    2020sLarge 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.

    Avoid: "AI" in any technical claim — name the part you mean instead. "AI coding" as a label for the practice is fine; "the AI is hallucinating" is not.

    Usage:

    "The CTO wants to know whether AI could handle the triage queue."

    "Translate that before scoping it — she means an LLM in a harness with access to the ticket system. 'AI' on its own isn't a spec."

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