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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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Well at the moment even with billions of parameters requiring cloud provisioning they’re not really useful yet. Still in mix of toy/demo/lab stage. Copilot, protein structure, are only 2 I’d say are close to positive ROI now. Image generation is getting there. Text not so much