Thread

1. talon8635 · 1790012972 · 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. nxc18 · 1790013957 · focus · HN ↗

      There must be some benefit if all the providers are doing it independently.

      GPT5.6-Sol on Max thinking just became regarded as of a few days ago.

      The boosters will tell me it’s my fault for using such an old, cheap out-of-date low quality near useless wish.com model (that was SOTA and better than human coders one month ago).

      The cycle repeats.

      1. talon8635 · 1790019574 · focus · HN ↗

         Again, I’m out of my element here, but isn’t the entire industry dependent on “new better releases frequently”? If so, and if no one has made any meaningful breakthrough, might they all pursue this kind of deception just to stay afloat/“competitive”/relevant?

         Thanks for your insight

         1. onemoresoop · 1790033480 · focus · HN ↗

            See Shepard tone. Similarly model releases could be engineered to appear that they’re always getting better by slowly degrading and upgrading at the right time. That plus hitting some benchmarks and making a lot of noise around that.

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