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Problem is biggest models that use the biggest piles of hardware keep winning. The small ones are qualitatively always a generation behind. I suspect it will be public training of large models plus hybrid inference on augmented versions of large models. Already so in vision.
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I would imagine pricing will converge to some hardware requirement plus minor fees in image generation. Do these models have diminishing rates of return once they are good enough? So even 1-generation lag might not be enough value-add for you to pay higher price
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I think the thing that’s a bit strange to me is that each model has different styles - so it’s not a convergent market of accuracy and precision (like search, or vision) So personal preferences and enterprise needs become more visible and easier to compete on.