An AI agent named LM-Tree can out-calculate human editors by 40% when deciding exactly how much a news article is worth to a search engine.
Publishers struggle to figure out how to charge AI companies for the data used to train their models. The LM-Tree agent automatically discovers the value of different content types based on real-time buyer feedback. This automated mechanism outperforms traditional human-made taxonomies by a significant margin. Most people assume that data pricing is a static or arbitrary negotiation between giant corporations. This technology offers a concrete way for smaller creators to get paid based on the actual utility their work provides to an AI.
Pay-Per-Crawl Pricing for AI: The LM-Tree Agent
SSRN · 6507138
As AI systems shift from directing users to content toward consuming it directly, publishers need a new revenue model: charging AI crawlers for content access. This model, called pay-per-crawl, must solve a problem of mechanism selection at scale: content is too heterogeneous for a fixed pricing framework. Different sub-types warrant not only different price levels but different pricing rules based on different unstructured features, and there are too many to enumerate or design by hand. We prop