This post is the second half of a two-part argument. The first half examined llms.txt advertising and found it fails on delivery — the file almost never gets fetched, so the impression never happens. This half examines an advertising product where delivery is solved. The failure is one layer up. Disclosure: this site builds and operates agent-detection infrastructure. The verification gap described in the close is one this operation would benefit from commercially. The analysis is published because the argument follows from the evidence, not from the incentive.

Imagine a rate card that does not yet exist. For $250 per month, your product appears in the recommended-tools section of a documentation site’s llms.txt, which is parsed by coding agents at the moment a developer asks what library to use. The agent fetches the file, reads the description string, and your product lands in context at the exact instant the decision is made. This is a coherent-sounding product. Adjacent versions are already being sold: paid brand mentions in AI Overviews, sponsored inclusions in AI-generated listicles, and placement in curated “agent context” resources. DiNardi’s audit in Search Engine Land found placements at $250 per mention, with PBN-style inventory selling at 10–15x backlink rates. The rate card for llms.txt is merely the logical next step.