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A Commodity Indexed Pricing Framework for Autonomous AI Agents

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  • Dalmia, Abha

Abstract

The success of autonomous AI agents is destroying the revenue models of the companies deploying them. Per seat licensing collapses when one agent replaces many users; cost plus pricing induces meter watching that limits adoption; pure outcome pricing transfers catastrophic inference cost risk to providers. We propose a three term linear pricing framework adapted from long term Liquefied Natural Gas (LNG) contracts: P = Σ(αᵢ·Cᵢ) + β·V + γ, decomposing price into a multi provider infrastructure floor, a value linked multiplier, and a platform constant. We introduce the Inference Capture Ratio (ICR) as a monetization health metric and use Salesforce's 2024 2026 Agentforce pricing evolution, alongside secondary evidence from Microsoft, Intercom, and Zendesk, to show how the market is converging on this structure.

Suggested Citation

  • Dalmia, Abha, 2026. "A Commodity Indexed Pricing Framework for Autonomous AI Agents," MPRA Paper 129348, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:129348
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    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J33 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Compensation Packages; Payment Methods
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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