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Open weight models are not truly free to use — the 'free' only refers to R&D, a fixed sunk cost, while COGS from running inference is real, revenue-correlated, and applies just as much to Kimi as to any other model.

Thompson distinguishes R&D (a fixed cost avoided by using open weights) from COGS (variable inference cost that scales with revenue and is unavoidable for any model, open or closed). ✦ AI generated

Ben Thompson · Stratechery · 2026-07-20 · original ↗

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What is related to revenue is COGS — cost of goods sold — and COGS is real for AI in a way it hasn't been for software for a very long time. Specifically, running inference on a model — whether that model be Kimi or Fable — costs money, and the amount of money an AI provider spends on inference is, at least in most business models, directly correlated to revenue.

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✳ Fable ✳ Kimi

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Context · 1

Open-weight models are not the same as open source: their weights are visible but the training data and creation process remain hidden, and their licenses vary dramatically from permissive (MIT for DeepSeek) to restrictive (Meta Llama's caps and competitor restrictions).CJ · Syntax · conf 65%

Counterpoint · 1

Open datasets like The Stack v3 materially raise the floor for every lab that wants to build competitive code models without relying on closed ecosystems, and open weights are strategically important against calls to restrict distillation.AINews / Latent.Space · Latent Space · conf 85%

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supports → Marginal costs are back in a big way for AI, both in the short-term implications of state-of-the-art free/cheap models and in the long-term structure of the industry.Ben Thompson · Stratechery extends → Open-weight models are not the same as open source: their weights are visible but the training data and creation process remain hidden, and their licenses vary dramatically from permissive (MIT for DeepSeek) to restrictive (Meta Llama's caps and competitor restrictions).CJ · Syntax