AI Application Layer Should Price Based on Recognizable Work Value

Sarah Wang, a partner at a16z, pointed out that AI application layer products should not mimic the model layer by pricing based on tokens, but rather should be priced based on recognizable work units. She believes that token pricing anchors the value of application products to units where costs are continuously decreasing, making it difficult for customers to predict context length, retrieval volume, or reasoning time, and improperly compares applications with raw computing power. She suggests a tiered pricing model based on value levels: model layer priced by tokens, and application layer priced by recognizable work units such as account research briefs, code modifications, and completed queries, which can be encapsulated through credits. Scenarios that are attributable and have clear value should be priced directly based on results, such as resolved customer service dialogues and qualified leads. The design of credits should map to different levels of work difficulty to protect gross margins and distinguish work value from delivery costs. Taking Clay as an example, its new pricing separates Data Credits (third-party data) from Actions (orchestrated work), applying cost pass-through without markup for reasoning models with significant cost fluctuations. Wang believes that pricing should anchor to value rather than computing cost, which helps customers understand the relationship between spending and value, and also benefits product providers in maintaining profit margins.

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