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I think we have to be careful when assuming that model capabilities will continue to grow at the same rate they have grown in recent years. It is very well-docu

I think we have to be careful when assuming that model capabilities will continue to grow at the same rate they have grown in recent years. It is very well-documented their growth in recent years has been accompanied by an exponential increase in the cost of building these models, see for example (of many examples) [1]. These costs include not just the cost of GPUs but also the cost for reinforcement learning from human feedback (RLHF), which is not cheap either -- there is a reason that SurgeAI has over $1 billion in annual revenue (and ScaleAI was doing quite well before they were purchased by Meta) [2]. Maybe model capabilities WILL continue to improve rapidly for years to come, in which case, yes, at some point it will be possible to replace most or all white collar workers. In that case you are probably correct. The other possibility is that capabilities will plateau at or not far above current levels because squeezing out further performance improvements simply becomes too expensive. In that case Cory Doctorow's argument seems sound. Currently all of these tools need human oversight to work well, and if a human is being paid to review everything generated by the AI, as Doctorow points out, they are effectively functioning as an accountability sink (we blame you when the AI screws up, have fun.) I think it's worth bearing in mind that Geoffrey Hinton (infamously) predicted ten years ago that radiologists would all be out of a job in five years, when in fact demand for radiology has increased. He probably based this on some simple extrapolation from the rapid progress in image classification in the early 2010s. If image classification capabilities had continued to improve at that rate, he would probably have been correct. [1] https://arxiv.org/html/2405.21015v1 https://arxiv.org/html/2405.21015v1 [2] https://en.wikipedia.org/wiki/Surge_AI https://en.wikipedia.org/wiki/Surge_AI

lostmsu 9mo ago
No, models significantly improved at the same cost. Last year's Claude 3.7 has since been beaten by GPT-OSS 120B that you can run locally and is much cheaper to train.
- judahmeek 9mo ago
  And GPT-OSS's architecture improvements aren't already incorporated in SotA models?
  - lostmsu 9mo ago
    The point is, that contradicts the claim that lately the progress is only made by throwing more compute.
    - judahmeek 9mo ago
      That wasn't the claim made. The claim made was that improving SotA models has historically taken exponentially more compute. The claim implies that improving SotA models takes more compute even while integrating technological advancements to make models more efficient. Unless you think that such advancements have been historically ignored by the curators of SotA models?
      - lostmsu 9mo ago
        No, that was the claim made. They justified it with the paper that states what you say, but that's exactly the problem. The statement of paper is significantly weaker than the claim that there's no progress without exponential increase in compute. The statement of the the paper that SotA models require ever increasing compute, does not support "be careful when assuming that model capabilities will continue to grow" because it only speaks of ever growing models, but model capabilities of the models at the same compute cost continue growing too.

lostmsu 9mo ago

No, models significantly improved at the same cost. Last year's Claude 3.7 has since been beaten by GPT-OSS 120B that you can run locally and is much cheaper to train.

judahmeek 9mo ago
And GPT-OSS's architecture improvements aren't already incorporated in SotA models?
- lostmsu 9mo ago
  The point is, that contradicts the claim that lately the progress is only made by throwing more compute.
  - judahmeek 9mo ago
    That wasn't the claim made. The claim made was that improving SotA models has historically taken exponentially more compute. The claim implies that improving SotA models takes more compute even while integrating technological advancements to make models more efficient. Unless you think that such advancements have been historically ignored by the curators of SotA models?
    - lostmsu 9mo ago
      No, that was the claim made. They justified it with the paper that states what you say, but that's exactly the problem. The statement of paper is significantly weaker than the claim that there's no progress without exponential increase in compute. The statement of the the paper that SotA models require ever increasing compute, does not support "be careful when assuming that model capabilities will continue to grow" because it only speaks of ever growing models, but model capabilities of the models at the same compute cost continue growing too.