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Fable 5 – Median thinking declined in August

428 points · 293 comments · espeed

  1. talon8635 · · focus · HN ↗
    Could there be a benefit to releasing a new model, slowly dumbing it down over a couple months, then releasing a new model that’s marginally if at all better than the original to create a perceived improvement when in reality there isn’t really one?

    For an industry that’s stagnant in progress yet relies on new frequent releases to survive (non-progress being an existential risk), this could make sense.

    I have no idea if that’s what’s happened, I completely pulled it out of my butt. And I have no idea is the actual frontier is stagnating.

    1. jaredklewis · · focus · HN ↗
      This would only provides a benefit if we're approaching some sort of theoretical limit of how good LLMs can be with the current approaches and data.

      Otherwise, even if one company did something like this, everyone would notice because the other companies would be pulling ahead. Are all the AI developers coordinating a "dumbing down" of models? i.e. Are Open AI, Anthropic, Google, Meta, DeepSeek, Mistral, xAI, and so on, all working together?

      So we might be approaching some limit (the "there's only so round a sphere can get" argument). But I very much doubt there is some massive conspiracy between all the AI developers.

      1. ponkpanda · · focus · HN ↗
        There doesn't need to be an explicit conspiracy. It only needs all the US frontier labs to be facing the same economic pressure (logarithmic improvement / $). It's a pretty obvious strategy - it's not like the large labs can magic up huge volumes of extra compute as demand comes online; there are almost certainly tweaking model performance to occupy the compute available and margin/cash burn targets.

        That was one of the main points of the movie 'A Beautiful Mind' - that actors can coordinate without any explicit communication.

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