The Blackwell-Hart Technical Bulletins
The Blackwell-Hart Technical Bulletins serve as the research and commentary archive for the Blackwell-Hart Methodology™ (BHM™). The archive documents applied observations, case studies, and structured analysis related to AI-assisted discovery systems, authority infrastructure, and entity interpretation.
Blackwell-Hart Methodology™ Technical Bulletin 26-31: Relevance Is Not Recommendation — When an Entity Fits the Question but Is Not Selected
An entity can be relevant to a question without being selected for recommendation. Blackwell-Hart Methodology™ (BHM™) distinguishes retrieval, recognition, interpretation, relevance, and recommendation as separate observable stages, providing a more precise basis for evaluating how AI-assisted discovery systems select entities under defined query conditions.
Building BHM™: From Evaluation to Validation
Discover how BHM™ approaches methodology validation through repeatability, reproducibility, independent scrutiny, and evidence-based conclusions in AI-assisted discovery.
When AI Knows Who You Are but Doesn't Recommend You
AI systems may recognize an entity when named but omit it from answers to relevant, non-branded questions. This experiment examines entity resolution, source relationships, category association, and recommendation across ChatGPT and Gemini.
When AI Stops Searching and Starts Acting
AI agents can do more than find information. The Medicare incident shows why AI systems that can act on objectives create new questions about boundaries, access, and control.
Building BHM™: Why Observation Wasn't Enough
BHM™ developed from observing AI-assisted discovery outcomes to establishing evidence, measurement, baselines, and repeatable conditions for evaluating change.
Building BHM™: The Creation of a Methodology™
Discover how BHM™ developed from observation and recurring patterns into a methodology through testing, documentation, refinement, and measurement.
Building BHM™: The Creation of a Methodology™
m methodology is not created from one idea. It is built through observation, experimentation, documentation, and validation. This is how the Blackwell-Hart Methodology™ developed.
When Identity Becomes Uncertain: Why Authority Infrastructure Matters in the AI Era
As AI makes digital imitation easier, organisations face a new challenge: being correctly identified and trusted. Explore why authority infrastructure is becoming essential in AI-assisted discovery.
The AI Visibility Conversation Is Evolving. Here's What's Still Missing
As AI visibility becomes a mainstream marketing topic, organisations need more than dashboards. Discover why methodology is the missing piece behind AI interpretation, confidence, and recommendation.
Last Week, Squarespace Validated the AI Visibility Market
Squarespace's AI Visibility feature signals a major shift in digital discovery. Learn why measuring AI visibility is only the first step toward building AI authority.