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Monetizing Intelligence: Advertising and Subscription Design on LLM Platforms

Paper recorded by Signals 4 on 2026-10-23 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-23 on arXiv · recorded by Signals 4 on 2026-10-26

Category: cs.AI · 人工智能 · first seen 2026-10-26

Abstract

Large language model (LLM) platforms introducing advertising must decide how much capability ad revenue should fund and what consumers should pay to avoid ads. We study a monopolistic platform that jointly chooses costly response quality, advertising intensity, and subscription prices when advertising reduces user value but better service raises advertising returns. Three findings follow. First, w

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#165 most recent of 960 cs.AI papers we have recorded · ↑ newer: Beyond Discrete Tokens: Preserving Numerical Continuity in Medical Vis · ↓ older: Yalda-Embedding: Open Development of a Competitive Persian Retrieval M
Cite this page: Monetizing Intelligence: Advertising and Subscription Design on LLM Platforms: the #165 most recent of 960 cs.AI papers we have recorded (as of 2026-10-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/monetizing-intelligence-advertising-and-subscription-design-on-llm-platforms.html
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