AI Visibility Is Not Value
A series on AI visibility, source eligibility, platform-specific selection, attribution, measurement, value transfer, platform capture, and the citable archive that remains worth returning to.
Tag
search-and-discovery-systems
Research pipeline
A multi-stage constraint system that reconstructs, filters, and stress-tests a search-space to identify which semantic structures are stable enough to act on.
Semantic SEO Begins Before Optimisation
Many artists treat SEO as a checklist of optimisation tricks. In reality, the real advantage comes from designing a creative process that produces work algorithms can recognise without compromising the ideas behind it.
Semantic SEO Is Not Content Optimisation: It Is Graph Positioning
Most discussions of semantic SEO focus on improving content. Increasingly, the more useful question is where that content sits within the network of entities, relationships, and information that search systems use to retrieve and rank information.
How Search and Recommendation Systems Actually Work
Search engines, recommendation systems, and AI retrieval systems appear different on the surface. Underneath, they are solving the same problem: selecting a small amount of information from a much larger set of possibilities.
Why SEO Visibility Comes From Structural Legibility, Not Optimisation
Many artists treat SEO as a checklist of optimisation tricks. In reality, the real advantage comes from designing a creative process that produces work algorithms can recognise without compromising the ideas behind it.
Visibility Under Hostile Conditions
Online discovery systems reward recognisable formats rather than meaning. For artists, this creates an environment where visibility is possible, but only under conditions that often distort the work itself.