The training cost for a model is constant. The more individual use that model gets the lower the training-cost-per-inference-query gets, since that one-time training cost is shared across every inference prompt.

It is true that there are always more training runs going, and I don't think we'll ever find out how much energy was spent on experimental or failed training runs.

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dietr1ch 6 hours ago | parent [-]

> The training cost for a model is constant

Constant until the next release? The battle for the benchmark-winning model is driving cadence up, and this competition probably puts a higher cost on training and evaluation too.

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simonw 6 hours ago | parent [-]

Sure. By "constant" there I meant it doesn't change depending on the number of people who use the model.

	▲	dietr1ch 3 hours ago \| parent [-]
		I got that part, it's just that it overlooks the power consumption of the AI race. I wish we reach the everything is equally bad phase so we can start enjoying the more constant cost of the entire craze to build your own model with more data than the rest.