Technical

Myth:Publishing llms.txt is required to be discoverable by AI assistants.

Evidence:No major generative engine has publicly confirmed reading llms.txt for retrieval or citation as of mid-2026. It is a proposed convention (llmstxt.org), not an announced ranking or ingestion signal.

Myth:Serving a hidden machine-only feed to AI crawlers boosts your citation share.

Evidence:Cloaking content specifically for AI crawlers — showing bots material the human page does not contain — risks the same spam classification as classic SEO cloaking. Neither Google nor OpenAI has endorsed the practice.

Myth:Publishing an llms.txt file makes your site discoverable to LLMs.

Evidence:No major LLM crawler reads llms.txt. It is a signaling gesture, not a technical requirement.

Myth:Rewriting competitor content is enough.

Evidence:For AI Overviews, mostly true: cited pages were statistically indistinguishable from uncited ranking pages on originality (medians 52 vs 55.5, p=.07) across 793 measured AI citations - AIO citation rides on Google rank, not information gain.

Myth:Aggregated round-ups can rank in AI answers.

Evidence:In most consumer verticals they dominate: 80% of CPG/retail AI answers cite at least one neutral reviewer or aggregator (Foglift 2026 benchmark - 375 buyer-intent responses, 25 verticals, 5 engines). The exception is tech SaaS, where vendor first-party content is cited 92.7% of the time.