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The more compute you need to get state of the art performance the closer to the plateau you are. If you didn't need the compute researchers would be getting better results with smarter training. Given that the gpt family of models need more energy to train than Nevada needs to keep the lights on they are very much on the flat part of the logistic growth curve.

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This isn't true. In fact, it's the reverse of true. If you think a bit more carefully about your argument, you'll realize that you've asserted that the single most revolutionary advance of modern deep networks (i.e. network architectures whose performance scales neatly with their parameter counts & training epochs) automatically portends "the plateau of forward progress."

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