# seale-philosophy-eats-ai-ontological-core-2025-05-30

## Veille

Philosophy Eats AI: enterprise ontological core, business semantics, knowledge graph, semantic data products

## Titre Article

Philosophy Eats AI: Why Your Business Needs an Ontological Core

## Date

2025-05-30

## URL

https://www.knowledge-graph-guys.com/blog/philosophy-eats-ai

## Keywords

ontology, ontological core, philosophy, business semantics, knowledge graph, semantic data products, DPROD, enterprise AI, value creation, AI reasoning, competency questions, business vocabulary, MIT Sloan, Michael Schrage, David Kiron

## Authors

Tony Seale

## Ton

**Profile**: Knowledge graph consultant perspective, strategic and conceptual register, intermediate level

**Description**: Tony Seale adopts a tone of intellectual manifesto, starting from a provocative MIT Sloan quote ("if software eats the world and AI eats software, what eats AI?") to unfold an argument structured in layers. The style is that of a semantic evangelist who translates philosophical concepts into concrete business imperatives. The author uses striking phrasing ("this is not a matter of mission statements, it is a matter of semantics") and builds a step-by-step reasoning: problem → vocabulary → ontological core → data products → virtuous cycle. The target audience consists of technical decision-makers and data strategists seeking to understand why their AI projects fail to deliver expected value.

## Pense-betes

- **Founding quote**: Michael Schrage and David Kiron in MIT Sloan: "If software eats the world and AI eats software, what eats AI?" Answer: philosophy. Not academic, but practical and operational.
- **Central problem**: Most organizations treat AI as a technical improvement (models, prompts) without asking the fundamental question: what does AI actually learn? What's missing is a structured philosophy defining the company's operational logic.
- **Value-creation vocabulary**: Every company creates value in its own unique way (loyalty, optimization, trust, efficiency). But most have not formalized this semantics into an ontology — a machine-readable structure on which AI systems can reason.
- **Ontological core**: Fundamental concepts defining the company's identity. They emerge through the use cases that generate real value and the "competency questions" that test their relevance.
- **Without an ontology, AI learns from noise**: If the fundamental logical structures are not made explicit, models learn from noise rather than meaningful patterns.
- **Semantic Data Products**: Based on the open DPROD specification. Data is treated as a valued asset with clear governance, not as raw material. Each dataset "knows what it is, why it matters, and how it fits into the bigger picture."
- **Virtuous cycle**: AI helps define the organizational ontology, and that ontology guides how AI thinks and the value it generates. This is the real opportunity.
- **Link with knowledge graph**: The approach creates a "distributed, AI-ready knowledge graph, where each dataset knows its identity and role."
- **Parallel with the watch corpus**: Connects to the fiche ia-monopsychisme-serres-averroes-aquin on philosophy/AI links, but with an operational rather than metaphysical angle. Also connects to the fiches on context graphs and knowledge graphs (Netflix UDA, Foundation Capital).

## RésuméDe400mots

Tony Seale starts from a provocative question posed by Michael Schrage and David Kiron in MIT Sloan: "If software eats the world and AI eats software, what eats AI?" The answer is **philosophy** — not in its academic sense, but as a practical discipline indispensable for extracting real value from AI systems.

The central problem is that most organizations treat AI as a purely technical improvement. They build models, experiment with prompts, but never ask the fundamental question: **what does AI actually learn?** Behind every model lies a deeper issue: the absence of a structured philosophy defining the company's operational logic.

Every company creates value in its own unique way — through loyalty, optimization, trust, or efficiency. Yet most have not formalized this semantics into an **ontology**, that is, a machine-readable structure on which AI systems can reason. The author insists: "This is not a matter of mission statements. It is a matter of semantics formalized into an ontology." Without making these fundamental logical structures explicit, models learn from noise rather than meaningful patterns.

The article introduces the concept of the **ontological core**: the fundamental concepts that define a company's identity. This core emerges through the use cases that generate real value and the "competency questions" that test the relevance of each concept. It becomes a lens that focuses AI reasoning on what actually matters.

Seale then connects this philosophy to data architecture via **Semantic Data Products**, based on the open DPROD specification. This approach treats data as a valued asset with clear governance, rather than as raw material. The result is a "distributed, AI-ready knowledge graph, where each dataset knows what it is, why it matters, and how it fits into the bigger picture."

The conclusion identifies the real opportunity: creating a **virtuous cycle** where AI helps define the organizational ontology, while that ontology guides how AI thinks and the value it generates. The article positions ontology not as an academic modeling exercise, but as the missing link between AI investment and business value creation.

## GrapheDeConnaissance

- Tony Seale —affirme_que→ la philosophie mange l'IA (AFFIRMATION, 0.97)
- philosophie —s_applique_à→ raisonnement IA (CONCEPT, 0.95)
- identité entreprise —est_basé_sur→ noyau ontologique (CONCEPT, 0.96)
- ontologie —permet→ raisonnement machine sur sémantique métier (CONCEPT, 0.95)
- Semantic Data Products —est_basé_sur→ DPROD (TECHNOLOGIE, 0.94)
- Semantic Data Products —permet→ knowledge graph distribué prêt pour IA (CONCEPT, 0.93)
- Michael Schrage —publie→ question philosophie mange IA (CONCEPT, 0.96)
- David Kiron —publie→ question philosophie mange IA (CONCEPT, 0.96)
- MIT Sloan —publie→ réflexion philosophie et IA (CONCEPT, 0.95)
- noyau ontologique —améliore→ pertinence modèles IA (CONCEPT, 0.92)
- IA —permet→ définition de l'ontologie organisationnelle (CONCEPT, 0.9)
- absence ontologie —réduit→ qualité apprentissage IA (CONCEPT, 0.91)

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Canonical: https://www.thekb.eu/en/fiches/seale-philosophy-eats-ai-ontological-core-2025-05-30/