My friend Soren has an interesting piece on the price elasticity for models.

· Bits and Bobs 6/1/26
  • My friend Soren has an interesting piece on the price elasticity for models.
    • The complexity of tasks has a very fat tail.
    • Most tasks aren't very complex, and thus can be done with cheaper models.
    • But there is a long, fat tail of tasks that can use all of the model quality they can get.
    • For that tail, the quality of the frontier models is worth it, and the users would presumably be willing to pay significantly more.
    • But labs can't price discriminate with a self-serve model.
      • They have to set a price that makes them competitive for most uses, which requires leaving money on the table for the fat tails.
    • Two options for model providers:
    • 1) Move to a sales-gated API for all uses.
      • This would allow detecting the fat-tail use and bucketing them into the higher margin buckets based on use case.
      • This would be a significant headwind on demand and is much less customer-friendly; it can only be done if the provider has a significant proprietary edge, which none of them currently do.
      • Still, the duopoly pricing dynamics mean it's conceivable OpenAI and Anthropic could both switch around the same time, "independently," and that would be a stable equilibrium.
    • 2) Vertically integrate fat-tail use cases.
      • The lab itself doing, for example, drug discovery, and then keeping the profits.

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