Gavin Baker 谈模型层竞争与 Grok Bot、Muse 的意义

Gavin Baker @GavinSBaker原推发布于 2026.10.14

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2) A future where OpenAI and Anthropic are a dominant oligopoly at the model layer is negative for almost every company on earth except the two of them. Especially bad for the entire token supply chain. They would be a monopsony for hyperscalers, neoclouds, and datacenter semiconductors. They would have immense buying power and eventually vertically integrate. Would obviously be terrible for the application layer.  And most importantly terrible for humanity. This is a quasi-consensus opinion in Silicon Valley.   This is why fragmentation and competition at the model layer is good for both AI infrastructure and AI applications. The model layer has become dramatically more competitive than it was only 4 months ago with Grok Bot, Muse Wonder and most importantly, open-weight models. I do think people would be shocked by the growth that essentially every open-weight focused inference cloud has experienced since May. This is really good for AI infrastructure demand. Noteworthy that even as Anthropic lost share and OpenAI has likely held share, the combination of the two has continued to grow at a really high rate led by OpenAI over the last 3 months. As Eric Vishria noted, everyone can win. As an aside, Muse is going to show us a lot about what matters for share at the frontier: just model dominance on the Pareto frontier or product quality as measured by harness/model integration where I still think Muse Code is behind Claude Code, Grok Build and Codex.  Although it is looking increasingly likely that the model subsumes the harness in the next 18 months.   Software had effectively zero marginal costs. AI does not. And therefore open-source impacts traditional software and AI very differently. An Opus 5.5, GPT-6.1, Muse Wonder, GLM 5.3, Kimi K3 token all cost roughly the same amount of compute and energy to produce. Effective per token revenue to the semiconductor industry is *roughly* constant for all these models. However, they charge wildly different prices to end users. An Opus or a ChatGPT token might be marked up 300 to 400% from cost (a 75% to 80% inference gross margin) whereas an open-weight token might only be marked up 50-100% (30-50% inference gross margin).  So for the same $1 in true underlying token cost, an Opus or ChatGPT token generates $4 to $5 in revenue at the model layer where an open-weight token might only generate $2 in revenue at the model layer.  And open-weight models have less buying power than Anthropic and OpenAI so they should generally generate more revenue per token to the clouds and neoclouds than Anthropic and OpenAI.  This is why open-weight models having significant share is the best case scenario for AI infrastructure demand. Robust competition between 4-5 frontier labs that compresses margins is the next best scenario. And ironically, it seems like these cheaper open-weight tokens may be net positive for demand at the absolute frontier of intelligence as we move to agent swarms. Meaning something like “Fable 5.1 extra-high” is worth paying for in a use case where it is coordinating a swarm of 100 open-weight agents.   All of this is why compute prices have gone vertical as the model layer became increasingly competitive. Lower margins at the model layer mechanically, even if counter-intuitively, drive more demand for compute. Perhaps the more important dynamic is that as intelligence becomes more economically useful, the value of compute should rise. This was Dwarkesh’s point about an H100 eventually renting out for the annualized value of a software engineer. Seems improbable but I continue to be surprised by the inexorable rise in compute prices. Inference clouds focused on open-weights are allegedly bidding close to ~$100 per watt for Blackwell clusters, which is a massive premium to the $50 per watt Google/SpaceX deal that was widely thought to be at least a local, unsustainable peak in compute pricing. Prices have gone up since that deal per 3P data.   Continued below.