Thread

1. amelius · 1789928450 · focus · HN ↗

   Will that be enough for AI's hunger?

   1. GoToRO · 1789929801 · focus · HN ↗

      It will be just in time for when AI will run very well on consumer hardware and the need for data centers will collapse.

      1. IshKebab · 1789930756 · focus · HN ↗

         That's never going to happen. By the time you can run current frontier models on your $10k desktop the frontier will have massively advanced and people will want those models instead.

         1. hypfer · 1789931551 · focus · HN ↗

            I'm not sure if this prediction will hold true.

            We're not seeing the progress in those "frontier models" that we have previously seen. There's certainly still gas left in tank tank, but we're way into the diminishing returns by now.

            Cloud inference still beats hardware investments by orders of magnitude of course, but that's only if your data doesn't really matter to you.

            1. airspresso · 1789931811 · focus · HN ↗

               We are certainly not in the diminishing returns phase for LLM progress. No sign of that yet.

               1. bunderbunder · 1789932800 · focus · HN ↗

                  I’ll grant that for specialized applications like coding agents and mathematics, but even there I suspect that most the real gains are actually taking place in the harness.

                  But I suspect returns may have already diminished into negative territory for at least some other use cases. One of my least favorite job responsibilities in this brave new era is figuring out how to avoid performance and behavior regressions when an older model were using for some application reaches end of life. It’s getting uncommon for me to look at our benchmark results and say, “Oh, good, it does better on one of the newer models!”

                  1. 3eb7988a1663 · 1789935682 · focus · HN ↗

                     One thing that I really want to know - the better models from today vs a year ago - what has changed. They have already pre-trained on all available public data. Scooping up the last percentage of archaic texts which were never digitized is not going to move the needle.

                     Is it just that the providers are generating tons of synthetic datasets on coding tasks so that the models get more exposure to the right thing to do? Every time someone points out an LLM stupidity they add some training data to patch over the weakness (trivial to generate "there are two 'l's in llama")?

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