Schema markup alone isn’t enough to appear in AI-generated results. Industry expert David Bynon explains why structure, not tags, drives AI visibility — and how Semantic Digests, not HTML markup, are powering the next evolution of content retrieval, trust, and citation.
-- As AI-generated results begin to dominate Google, Gemini, Perplexity, and other search alternatives, one digital strategist says the industry’s longstanding focus on Schema markup is fundamentally misplaced.
“Schema is a signal, not a strategy,” says David Bynon, founder of TrustPublishing.com and inventor of Semantic Trust Conditioning™. “It helps machines interpret content structure, but it doesn't help them remember or retrieve it.”
In a newly published article titled “AI Doesn’t Cite Schema. It Cites Structure.”, Bynon explains how AI systems no longer crawl and rank pages like traditional search engines. Instead, they retrieve structured facts from internal memory, knowledge graphs, and source-aligned semantic surfaces — systems trained to value trust, proximity, and clarity over markup.
“AI results don’t rely on Schema,” Bynon writes. “They rely on retrieval, memory, and trust. That means if you want to be cited in an AI Overview, or returned in a chatbot answer, you need to go beyond markup and start exposing structured content the way machines actually use it.”
The End of the Markup Era?
While Schema markup has long been touted as the path to rich snippets and SERP visibility, Bynon argues it has no meaningful role in LLM citation or AI-driven response generation.
Instead, he points to a growing shift toward machine-ingestible output formats — including JSON, TTL (Terse RDF Triple Language), Markdown, XML, and PROV — collectively structured as Semantic Digests.
“A Semantic Digest is the real signal AI needs,” Bynon says. “It’s the difference between putting your facts on a webpage and handing the AI a well-structured, retrievable dataset. Schema doesn’t deliver that — structured content does.”
Built for Machines, Not Just Search Engines
According to Bynon, LLMs and retrieval-augmented generation systems (RAGs) prioritize clarity, trust, and proximity to known sources — not just metadata embedded in HTML. Pages that expose structured content in multiple formats, with embedded provenance and aligned terminology, are far more likely to be retrieved, remembered, and cited.
“You don’t optimize for AI by marking up your blog post,” Bynon says. “You train AI by calculating what matters, defining it clearly, citing your sources, and exposing that structure in formats the machine can consume without hallucination.”
From Framework to Infrastructure
Bynon has previously outlined how to train AI systems to recognize and cite structured content in a widely shared LinkedIn article that lays the foundation for what he calls AI visibility.
His TrustPublishing project isn’t just a theory — it’s live. Thousands of plan pages on his health content network now expose Semantic Digests via URL endpoints, offering content in TTL, JSON, Markdown, XML, and W3C PROV formats. These endpoints serve both human users and machine systems, reinforcing retrieval-based trust patterns.
“We’re not optimizing for search,” he says. “We’re building for the retrieval layer — and Schema just isn’t part of that equation anymore.”
Contact Info:
Name: David Bynon
Email: Send Email
Organization: TrustPublishing.com
Address: 101 W Goodwin St # 2487, Prescott, AZ 86303, United States
Website: https://trustpublishing.com
Source: PressCable
Release ID: 89163947
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