A Target-Audience Matrix for Positioning Our Work

1. Tech Executives / AI Architects

Pain Points: Model drift, hallucination, alignment failures, public backlash
Interests: Reliable reasoning, enterprise deployment, cost/performance tradeoffs
Use Language: Computability, truth constraints, operational logic, auditability, constrained generative models
Avoid Language: Philosophy, morality, ideology, ethics (unless formalized)
Value Proposition: “We give you the logic layer to make generative models reason with constraint, not just simulate coherence.”

2. Investors / Strategic Capital

Pain Points: Low moat in current LLMs, regulatory uncertainty, scaling risk
Interests: Competitive advantage, scalable safety, governance solutions
Use Language: Trust layer, decision engine, legal-grade outputs, B2B infrastructure, cost of error
Avoid Language: Theoretical, ontological, normative philosophy
Value Proposition: “This is the layer that makes AI outputs defensible, contractual, and compliant—opening new verticals.”

3. Academic Philosophers / Logicians / Formalists

Pain Points: Lack of grounding, hand-wavy ethics, language-vs-reason gap
Interests: Formal validity, computability, universalizable grammars
Use Language: Decidability, testifiability, operational semantics, grammars of cooperation, first principles
Avoid Language: Market, product, scaling, trust layer
Value Proposition: “A universal grammar of human cooperation, reducible to operational and testable logic, computable by machines.”

4. Skeptics / Journalists / Social Critics

Pain Points: Manipulation, bias, false neutrality, elite control
Interests: Transparency, accountability, fairness
Use Language: Reciprocity, deception detection, liability, non-manipulative outputs, evidence-based speech
Avoid Language: Optimization, compliance, abstract logic
Value Proposition: “This framework doesn’t hide values—it measures harm, cost, and deceit directly in the structure of speech.”

5. Policymakers / Regulatory Architects

Pain Points: Legal ambiguity, enforcement limits, black-box models
Interests: Liability frameworks, institutional stability, harm prevention
Use Language: Testifiable output, computable harm, audit trails, speech liability, contract-grade language
Avoid Language: Decentralization, anti-government, cognitive hierarchy
Value Proposition: “This provides a computable standard for regulation—outputs that can be judged for deception, negligence, or fraud.”

6. Alignment Researchers / Safety Labs

Pain Points: Reinforcement collapse, goal-misalignment, simulator incoherence
Interests: Interpretability, corrigibility, bounded optimization
Use Language: Adversarial truth testing, speech as a decision tree, moral logic without moralizing, constructive logic
Avoid Language: Human feedback, RLHF, alignment-by-preference
Value Proposition: “Instead of optimizing for human agreement, we test for cooperative truth—making models auditable, not just fine-tuned.”

7. Faith-Based or Morally-Conservative Communities

Pain Points: Moral relativism in AI, loss of community, cultural erosion
Interests: Moral stability, trustworthiness, intergenerational continuity
Use Language: Conscience, truthfulness, responsibility, non-manipulation, shared good
Avoid Language: Postmodernism, relativism, nihilism, social constructivism
Value Proposition: “This AI knows right from wrong—not because we programmed dogma, but because it tests for honesty, harm, and reciprocity.”

Source date (UTC): 2025-08-16 01:15:25 UTC

Original post: https://x.com/i/articles/1956525167297085858

A Target-Audience Matrix for Positioning Our Work A Target-Audience Matrix for P