“The precision of a word's meaning is inversely proportional to its commercial value as a marketing term.” Tony Seale's post reproduced verbatim with its full comment thread, then read against four independently authored documents — each showing a different AI-era term (context, ontology, System of Intelligence, trust boundary) undergoing the identical dilution, and each prescribing the same corrective: open-standards Linked Data.
KG curated by kg-generator + rdf-infographic-skill and Claude Sonnet 5 on behalf of Kingsley Uyi Idehen.
Reproduced verbatim, by Tony Seale.
All 7 comments visible without signing in, in original order.
Of the eight commenters on this post, Kingsley Uyi Idehen is the one who names the concrete mechanism Tony Seale's post leaves abstract: hyperlinks as identifiers, RDF, shared/homegrown ontologies — Linked Data principles manifesting a Semantic Web. He also observes that “silos are extremely stubborn and often well disguised by marketing communications that blur or appropriate terminology as a survival strategy” — Seale's Law, restated as a description of silo behavior itself.
Idehen is also the accountable person for this collection's curation, and for its mesh with four of his own previously published companion documents (see the Corroboration section below).
“The precision of a word’s meaning is inversely proportional to its commercial value as a marketing term.”
Seale's Law — Tony SealeConnect an organisation's data and organise that connected data through an explicit ontology — a model of the things the organisation cares about, what they mean, and how they relate.
No organisation operates alone — supply chains, regulators, partners, customers, research networks and financial systems all cross organisational boundaries. Meaning must travel too, via open standards.
Each of these four documents was authored independently of Tony Seale's post, about a different named term, yet each traces the exact pattern Seale's Law predicts.
Read: Alex Karp, Frontier Models and the Real Fight for Enterprise AI →
Interactive graph of every entity and relationship in the companion RDF — the post, its comment thread, the thesis framework, and the four corroboration cases. Click any node or edge label to resolve its full description via URIBurner.
Fifteen questions covering the post, the thread, and its mesh with four corroborating documents.
Tony Seale's formulation: 'The precision of a word's meaning is inversely proportional to its commercial value as a marketing term.' As a term becomes profitable to say in a sales deck, its precise meaning erodes even as the word itself keeps circulating.
Terms like 'Cloud', 'Platform', 'Transformation' and 'AI' each started by pointing at something real, then became useful in every sales deck; the word survived while the precise meaning did not.
It is about to become 'a beautiful container for vague promises' — attached to products, features, copilots, roadmaps and platforms, some real and much of it rebranding, right as organisations need clarity most.
Phase One is internal coherence: connect your data and organise it through an explicit ontology. Phase Two is interoperability: because no organisation operates alone, meaning must travel across organisational boundaries too, which requires open standards.
Connecting an organisation's data and organising that connected data through an ontology — an explicit model of the things the organisation cares about, what they mean, and how they relate.
Because supply chains, regulators, partners, customers, research networks and financial systems all cross organisational boundaries, AI working across those boundaries requires meaning to travel too — via open standards, not proprietary semantic theatre.
He restated the post's three-part imperative concretely: connect the data using hyperlinks as identifiers, model the meaning using shared/homegrown ontologies, and use open standards such as RDF — Linked Data principles manifesting a Semantic Web, a natural complement to LLMs' natural-language capabilities.
She asked how to 'protect the meaning' in practice, noting that 'ontologies' is already mentioned everywhere without people connecting it to the actual idea of connecting data — a live instance of the same dilution Seale's Law predicts, happening to 'ontology' itself.
Sierra's 'Context Engine' brands customer context as a proprietary, vendor-owned moat; the critical-perspective response shows the same compounding value is achievable via six loosely-coupled, standards-addressable layers (identity, authentication, authorization, data spaces, ontology) instead — the diluted term is 'context', the corrective is open infrastructure.
It documents AI engineers rediscovering ontologies as guardrails for agentic systems from four independent voices — the exact word ('ontology') Andrea Splendiani flags as already overused in this thread, being restored to substance via neurosymbolic AI and dereferenceable, queryable graphs.
Vendor-owned 'System of Intelligence' ontologies (Palantir Ontology, Databricks Unity Catalog) recreate the lock-in they claim to solve — the diluted term is 'ontology' rebranded as a proprietary control point; the corrective is an enterprise-owned Knowledge Graph on Linked Data principles, queryable via SPARQL by any model.
Its 'trust boundary' is framed as needing a new legal instrument, patents' functional equivalent; the critical perspective shows the same boundary is already achievable today via open standards — HTTP loose coupling, an enterprise knowledge graph, and attribute-based access control — no new word or law required.
Because a closed, proprietary graph 'printed on the brochure' as an ontology is exactly the 'proprietary semantic theatre' the post warns against — the word 'ontology' retains commercial value as a feature name while losing the openness that gave the underlying concept its original precision.
RDF and RDFS/OWL for machine-computable statements and ontology construction, HTTP-addressable hyperlinks as identifiers, and SPARQL for querying — Linked Data principles manifesting a Semantic Web, per Kingsley Idehen's comment and all four meshed companion documents.
Connect the data, model the meaning, use open standards, prepare for interoperability — and do it before the market hollows out 'semantics' the way it already hollowed out 'Cloud', 'Platform', 'Transformation' and 'AI'.
Terms from the post alongside terms reused from the four meshed companion documents.
Tony Seale's coined principle: a word's meaning-precision is inversely proportional to its commercial value as a marketing term.
The pattern Seale's Law formalizes: a technology term points at something real, becomes useful in a sales deck, then survives as a word while its meaning does not.
The state in which an organisation's concepts are explicit, identities are consistent, and business meaning is not trapped inside departmental dialects.
Connecting an organisation's data and organising it through an explicit ontology.
The requirement that meaning travel across organisational boundaries via open standards, since no organisation operates alone.
An explicit model of the things an organisation cares about, what they mean, and how they relate.
Using hyperlinks as identifiers to connect data across organisational and system boundaries — Kingsley Idehen's named mechanism for Phase One.
The open, standards-based vision Linked Data principles manifest; named directly in Kingsley Idehen's comment on this post.
The open standard for machine-computable statements and ontology construction that Kingsley Idehen's comment names as the concrete Phase Two mechanism.
From the Context Engine vs. Context Infrastructure companion document: the layer enabling deterministic reasoning via RDFS/OWL, one of six loosely-coupled infrastructure layers proposed as the antidote to a proprietary 'Context Engine'.
From the Karp/theCUBE companion document: the governed layer of business rules, policies, processes and tacit knowledge that vendor-branded ontologies claim, and often fail, to hold openly.
From the Reverse Information Paradox companion document: a hard boundary across which nothing crosses without consent — argued achievable today via the same open standards Seale's post prescribes.
From the Ontologies Are So Back mesh companion document: the fusion of probabilistic LLM agents with symbolic ontology systems that restores substance to the word 'ontology'.
The post's own four imperative steps.
Use hyperlinks as identifiers to connect data across systems and departments, per Kingsley Idehen's comment — the concrete first move of Phase One.
Organise the connected data through an explicit ontology — a model of the things the organisation cares about, what they mean, and how they relate — using shared and/or homegrown ontologies expressed as machine-computable sentences.
Adopt open standards such as RDF for machine-computable statements and ontology construction, instead of proprietary semantic theatre or another silo with 'ontology' printed on the brochure.
Because supply chains, regulators, partners, customers, research networks and financial systems all cross organisational boundaries, ensure meaning can travel across those boundaries too — Phase Two, arriving fast.
This knowledge graph collection reproduces Tony Seale's LinkedIn post and its comment thread verbatim, then meshes the thesis against four companion documents already published in this corpus. The original document was transformed into RDF (schema.org + a lightweight local ontology) using kg-generator, then rendered as this HTML infographic using rdf-infographic-skill, powered by Claude Sonnet 5.
Technology Stack:
The interesting question is not whether an LLM can be attacked, because it can. The question is whether the surrounding system can detect drift, constrain authority, prove provenance, and fail closed when the operating conditions change.
That’s the problem I’m working on with governed agentic systems.