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Mastermind recap

AIMM Session — December 19, 2025: GEO Authority Architecture

· AIMM 2025 · 90 min

Facilitators: Lou D'Alo

The Last Call From Taiwan (And The One That Might Change How AI Finds You)

“AI doesn’t return links. It returns voices. The question is no longer ‘can you be found?’ — it’s ‘are you recognized as an authority worth citing?’” — Don Back

30-Second Summary

Final session of 2025 broadcast live from Lou’s last days in Taiwan (full ceremonial dress). Don Back led tight, structured presentation on Generative Engine Optimization (GEO) — specifically how LinkedIn has become primary source LLMs actively index. Group dove into ontologies, business canons, how Claude Skills replace weeks of prototyping. Part year-end reflection, part operating manual for 2026.

Who Was In The Room

Lou D’Alo (host, Taiwan), Don Back, Kasimir Lehto-Gado, Sandra, Kenan, Taryn, Helen, Lisa, Luann

GEO Is the New SEO — Rules Completely Different

Don: game shifted. SEO era: prize was traffic. GEO era: prize is becoming the voice that answers the question.

Tactical difference stark:

  • SEO optimizes for keywords, backlinks, page structure
  • GEO optimizes for concepts, frameworks, consistent causal logic, named ideas

Most LinkedIn content fails GEO world: every post different. To LLM: “random person, no worldview, skip.” GEO rewards: same idea from different angles, consistent language, named frameworks, clear POV, published repeatedly. Not one viral post. Ten posts reinforcing same belief from different entry points.

The 3-Layer GEO Architecture

Don’s cascading framework for building AI-legible authority:

Layer 1 — Canon: Core beliefs about why problem exists. First principles, not opinions. Don’t advertise canon, teach through it. (“Clarity precedes confidence.” “Tools fail when identity unclear.” “Most problems structural, not personal.”) Ten posts explaining same idea builds authority.

Layer 2 — Frameworks: How canon becomes usable. Step-by-step models, checklists, stages, processes. Without canon, frameworks drift and contradict. Framework: where readers engage. Canon: where authority built.

Layer 3 — Diagnostics: Where reader is now. Self-assessments, readiness indicators, FAQ structures, failure patterns. Builds trust fast, reduces defensiveness. AI values structured diagnostic content for inherent scaffolding.

GEO article template:

  1. Open with compelling problem
  2. Explain why problem exists
  3. Introduce named idea/model
  4. Show consequences of wrong approach
  5. Offer clear reframe
  6. Repeat core language across articles

LinkedIn Is Now an LLM Source

Lou didn’t know LinkedIn opened backend to LLMs. Assumed gated, login-required. Turns out LinkedIn quietly became richest source of professional reasoning AI trains on and indexes. LLMs actively reading to determine who gets cited.

Strategic implication: LinkedIn not just networking tool. Live, continuously updated body of professional authority signals.

Practical implication flagged by Don: Most people’s LinkedIn reads like a resume. The opportunity is to rewrite it as an authority document — consistent terminology, named frameworks, clear intellectual positioning.

Kenan asked about the timeline for LinkedIn indexing changes — Don’s read: it’s happening now, not coming. The models being deployed in late 2025 and early 2026 are already pulling from LinkedIn at scale.

Kasimir’s Ontology Filter

Kasimir ran entire body of work through ontology-building. Came out with 5 pillars as editorial filter:

  1. Sovereign Identity & Inner Command
  2. Strategic Clarity & Decision Architecture
  3. Time, Energy & Attention Command
  4. AI-Augmented Leverage Systems
  5. Doctrine, Narrative & Ecosystem Infrastructure

Every content connects to one or doesn’t get published. Virtual board AI parses incoming questions through pillars first.

Don: pillars excellent, push higher. What are canons they derive from? Things always true, from which pillars flow? Make implicit explicit, consistency compounds with AI signals.

His 5-pillar test for whether something earns a place in your ontology:

  1. Is it distinctively yours? (not just restated conventional wisdom)
  2. Is it durable? (will it still be true in 3 years?)
  3. Is it retrievable? (can an LLM pattern-match to it by name or concept?)
  4. Does it connect to adjacent concepts? (isolated ideas are dead ends in a knowledge graph)
  5. Have you demonstrated it, not just stated it? (case studies, frameworks, diagnostics — not just claims)

“Your ontology is your editorial filter. If an idea can’t pass all five, it’s content. If it passes all five, it’s canon.” — Kasimir Lehto-Gado

Claude Skills as Functionality-First Prototyping

Lou full reversal on Claude Skills (was skeptical earlier). After building ICH/FAQ system for Ken using Skill Creator: now convert.

Workflow unlocked it:

  1. Ken wrote ideas as documents
  2. Lou fed each to Claude: “understand, write me spec”
  3. All specs combined to Skill Creator: “turn into skill”
  4. Result: fully functional prototype, shareable zip, no hosting, no frontend, no VPS

Why matters: Skills prototype functionality before building app. Test thinking first (what system does, not how looks), then consider UI. When scaling: take skills (prompts + Python + templates) to Claude Code: “Turn into application.”

Member Threads

Helen flagged a specific use case: using LLM-indexed LinkedIn content as a bridge between her consulting positioning and her research work. Discussion about how ontologies can span professional domains when the underlying intellectual frame is consistent.

Sandra raised the question of voice consistency across platforms — if LLMs are indexing LinkedIn and your website separately, do they need to use the same vocabulary? Short answer from Don: yes, because LLMs recognize authority partly through lexical consistency across sources. Different words = different authority signals.

Luann connected the ontology framework to her brand positioning work — the 5-pillar filter maps directly onto how she evaluates whether a client’s brand concept is durable or trend-dependent.

The Closing From Taiwan

Lou closed from his rooftop in Taiwan, in the final days of a two-month stay, with what he called a “ceremony-appropriate” closing thought:

“We’ve spent two months asking how AI finds you. The answer keeps coming back to the same thing: it finds what’s coherent, consistent, and genuinely yours. Build that. The rest is noise.”

What To Take Into 2026

  1. Audit your LinkedIn as an authority document, not a resume. Apply Kasimir’s 5-pillar filter to every claim you make there.
  2. Name your frameworks. Unnamed insights are invisible to LLMs. Named frameworks are citable.
  3. Build a Canon layer — three to five foundational positions that won’t change. These are your GEO anchor content.
  4. Consider a Claude Skill for one expertise-based process. You’ll learn more from building it than from planning it.
  5. Watch the LinkedIn indexing trend. It will be a significant AI discoverability vector in 2026.