The Economics of Open-Weight Inference

Dark grey text on a black background: it's like they are trying not to let anyone read their article!

I think an interesting point is that hardware as of today still has no utility value after its reported lifetime has elapsed, which prevents neolabs and smaller labs from getting older HW clusters as the banks are not willing to give out loans against them. There is no agreed upon pricing for "expired" A100 clusters or similar.

This is clearly not true, and we are starting to see compute markets, but only for rental prices/H, not for the hardware itself. I feel like there is some artificial moat being built here to stimulate sales of new hardware, because an H100 at 1/16th the price will have comparable dollar/FLOP as Vera Rubin.

The problem, IMO, with open-weight models is that you accustom to the capabilities of frontier models too quickly; and downgrading to an open-weight "frontier minus 2" or "frontier minus 3" model is often painful, since they feel way less useful than their newer closed-weights counterpart. To be honest, I don't know any companies using OW models at a large scale for their operations (agents or chat assistants).

This makes the case that open weight models are not necessarily cheaper than closed weight models

But I'd still rather use them since it's inevitable the unsustainable margins of the closed weight providers lead to enshittification once they stop subsidizing demand

I'd rather spend more today with a workflow whose underlying unit economics are sustainable and don't force me to inevitably look for other solutions once the closed weight providers start optimizing for profit and extracting value

The style and font choice here is awful. Nearly impossible to read in the daytime.

Then why do people still believe that OpenAI any Anthropic have negative margins

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