# Pricing Access to Dynamic Information Services

arXiv: https://arxiv.org/abs/2510.09859 · HTML: https://mlsanity.com/paper/2510.09859/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 51 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Incomplete Contracting and AI Alignment](https://mlsanity.com/paper/1804.04268/) (2018)
- [Dynamically Aggregating Diverse Information](https://mlsanity.com/paper/1910.07015/) (2019)
- [Optimal Dynamic Allocation of Attention](https://mlsanity.com/paper/1812.06967/) (2018)

## Built on by (most cited first, 1 of 1)

- [Computational Challenges in Token Economics: Bridging Economic Theory and AI System Design](https://mlsanity.com/paper/2605.17410/) (2026)

## Abstract (arXiv)

A provider sells a dynamic information service---a real-time, capacity-constrained process that resolves a customer's uncertainty---to customers who differ privately in urgency. I characterize the revenue-optimal mechanism: deploy a single, undistorted information process---the one a customer with unlimited access would most prefer---and screen entirely through a one-dimensional menu of service caps. The provider leaves the product itself undistorted, unlike a Mussa--Rosen monopolist; all screening is absorbed into the cap. The mechanism rationalizes a recurring contractual form---a flat fee for capped access to a common process---spanning AI service tiers, expert-network consultations, and analyst-inquiry retainers. I identify the economic force behind the result, a convexity-preservation property of urgency screening, and give conditions under which the per-unit price declines in the cap.