Every wiki, every Confluence space, every Notion becomes a graveyard eventually. The pages are rarely deleted; they go write-only and quietly stop being true. Retrieval still finds them. And now an assistant reads the stalest page in the graveyard and answers a customer with fluent, unearned confidence. This is a field note on knowledge-base rot: in the words of the people living it, and the data measuring what breaks when AI answers from it.
Nobody calls it "knowledge decay." They say the wiki is a graveyard, that they cannot tell which doc is current, that the docs are write-only, that the real answer lives as tribal knowledge in one person's head. Below are verbatim public comments, unedited, collected July 2026. If you have run a knowledge base, you have said at least one of these.
"Confluence is where documentation goes to die. And then rot"
"Out-of-date documentation can be worse than no documentation at all when it is actively wrong."
"no documentation is better than wrong documentation"
"Either the information is outdated or in 5 different locations... with different information"
"What is everyone's responsibility is no one's responsibility."
"If the knowledge base is outdated so is the bot. For me this is so obvious and simple to fix."
"you can never guarantee the output of an LLM from a given input"
When an organization points AI at a knowledge base it never fully trusted, the outcome is measured, not anecdotal. The pattern is consistent: the model is fluent, the source is stale, and the answer is confidently wrong. Every figure below names its source inline, reported as the source stated it: no rounding up, no "AI proved."
of enterprise GenAI pilots delivered no measurable P&L return.
of companies are abandoning most of their AI initiatives, up from 17% a year earlier. The average organization now scraps 46% of its AI proofs-of-concept before production.
of queries make even purpose-built legal AI research tools hallucinate. General-purpose chatbots: 58-82% on legal queries.
of AI users rely on the output without evaluating its accuracy. 56% of workers report making mistakes at work because of AI.
of customer-service leaders report pressure to implement AI in 2026, while the prior year's production deployment was still in the single digits. The mandate is arriving years ahead of the trust.
A civil tribunal held Air Canada liable for its support chatbot's wrong bereavement-fare answer. The argument that the chatbot was a separate entity responsible for itself did not hold. The tribunal ordered the airline to pay.
Cursor's AI support bot invented a customer policy that did not exist. Users hit the phantom rule, believed it, and cancelled their subscriptions over a restriction the company never had.
Deloitte refunded part of a ~A$440k government contract after a delivered report was found to contain AI-fabricated citations: sources that were never real.
Three answers, confidently wrong, each traced back to a source no human was standing behind. In none of these cases did retrieval fail. The document was found. The answer was fluent. What was missing was a name and a date on the thing being quoted.
Every tool in this story can find the document. Search finds it. RAG finds it. The model finds it and quotes it back, fluently. What none of them can tell you is the one thing that decides whether the answer is safe to act on: which answer a person still stands behind.
Proofmark puts three things on the answer itself: a named owner, an attested version, and a review date. Here "verified" means exactly one thing, and never more: a named human attested this specific version, on a date. No owner, no badge. Let the freshness window lapse and the badge drops on its own: routine, not failure. When an agent quotes the answer over MCP, the citation travels with it: who signed it, which version, and when. The reader gets the receipt, not just the sentence.
Illustration of the credential line a Proofmark citation carries: the same row whether a human reads it in-app or an agent returns it over MCP. Green renders only for an attested version; stale is amber and routine, never styled as an error.
Honest scope. Proofmark is pre-launch and pre-revenue, built by a solo founder, and not SOC 2 certified yet. This page holds itself to the same rule the product does: every claim here is meant to survive you reading the repository. Where a capability is built but not yet proven in a live round-trip, it is described that way, not in the present tense.
Proofmark puts a named owner, an attested version, and a review date on the answer itself.
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