Articles by the creator of GENOME™
Ideas and methods for turning AI features into learning systems and durable advantage.
From dormant data to signals, then to living memory
Your company has data. That does not mean your AI can learn.
Many companies believe they are ready for AI because they have data. But stored data is not necessarily a usable signal.
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AI in your product: grafted on or embedded in its DNA?
Why I created GENOME™
Why GENOME™ formalises the difference between AI added as a feature and constitutive intelligence embedded in the product itself.
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Why most AI projects create no lasting advantage
The AI illusion
Most AI projects produce copyable features. GENOME™ distinguishes additive AI from constitutive AI that accumulates advantage over time.
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The three paths to enterprise AI — and how to choose yours
Graft, integrate or rethink
Between AI layered on top and AI embedded in the DNA lies a gradient. GENOME™ defines three paths: graft, integrate or rethink.
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What truly changes under the hood
The principles of AI-native systems
AI-native systems are defined not by visible AI features, but by how intelligence organises the product, captures signals, learns and compounds advantage.
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When usage becomes your most valuable asset
Compound advantage in AI
The real power of an AI-native system lies not only in its technology, but in what it learns, retains and reinjects with every interaction.
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It reveals whether your system knows how to use it
AI does not replace expertise
The Ford case shows that AI cannot compensate for lost expertise. It becomes valuable when human knowledge is captured, structured and reinjected into a learning system.
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