How MSP-1 Helps Language Models Work Better
MSP-1 reduces inference cost and ambiguity by giving language models clear, early signals about a page’s intent and structure.
MSP-1 — AI-friendly Semantics for Trusted Information.
Articles
MSP-1 Is Not SEO (And Why SEO Still Matters)
MSP-1 isn’t about ranking in search; it’s about what AI agents do after they find your site..
The Move from Search Discovery to Citation Discovery
Traditional search is still the web’s primary entry point, including for AI agents. MSP-1 doesn’t compete with that. It starts where SEO stops: the moment an agent decides what to trust, reuse, summarize, or ignore.
The “Inference Wall”: Why AI’s Future Depends on a Structured Web
The golden age of “cheap” AI is officially over. We’ve enjoyed a subsidized ride, with flat-rate subscriptions masking the true cost of compute.But as 2025 drew to a close, the industry hit what engineers are calling the “Inference Wall.”
There Is No Content Restructuring Tactic That Is Large Language Model-Agnostic
A factual analysis of why content restructuring methods are inherently model-dependent, and how a declaration layer like MSP-1 differs by separating meaning from layout
Citation Consistency as a Prerequisite for Trust in Answer Engines
Stable AI citations require explicit semantic grounding at the source, not increasingly sophisticated inference.