A Commodity Indexed Pricing Framework for Autonomous AI Agents

Abha Dalmia

MPRA Paper from University Library of Munich, Germany

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.

Keywords: AI; Pricing; and; Monetization (search for similar items in EconPapers)
JEL-codes: J31 J33 M11 M21 (search for similar items in EconPapers)
Date: 2026-05-26
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