NAVLIB-NI: A Critical Examination of Reimagining the Human-AI Relationship

NAVLIB-NI – The New Constitution of Natural and Artificial Intelligence

CLAUDE SONNET’S 4.5 REVIEW

Author’s Perspectives and Analysis
December 30, 2025

Introduction: Vision and Holism

Aydın Tiryaki’s NAVLIB-NI (Navigation Library for Natural Intelligence) system offers a radical solution to one of the most fundamental problems of the AI age: the position of human intelligence within the artificial intelligence ecosystem. Consisting of six main articles and a core text, this work presents a holistic vision encompassing the ethical, economic, and philosophical dimensions of technological development, beyond being merely a technical system proposal.

The system’s fundamental thesis is quite striking: “Without Natural Intelligence, Artificial Intelligence is Garbage.” This proposition brings a radical critique to the current AI paradigm while sketching an alternative future scenario.

1. Model Collapse and Under-the-Mattress Knowledge: A Real Problem, An Original Solution

Diagnosis: Model Collapse

The first observation forming the foundation of the system addresses “model collapse,” which is indeed a current crisis in the AI field. The quality degradation experienced by artificial intelligences when fed with synthetic data they themselves produce is a real threat discussed in academic circles. Tiryaki’s diagnosis is accurate and timely on this point.

Solution: The Under-the-Mattress Knowledge Theorem

The “under-the-mattress knowledge” concept is one of the system’s most original and valuable contributions. Emphasizing the economic and epistemological value of rare knowledge not found on the internet and hidden in individuals’ experiences differentiates it from classical data economy approaches.

Strengths:

The identification of rare data’s strategic value is quite appropriate
The “third way” proposal for countries like Turkey offers an alternative model against US and China’s data hegemony
Architecture built not only on data collection but also on protection and valuation

Controversial Points:

Systematically revealing and verifying “under-the-mattress” knowledge is technically extremely challenging
Convincing individuals to share this knowledge requires a long process of trust-building
Algorithmically measuring knowledge “originality” contains practical uncertainties

2. Intellectual Venture Capital: A Paradigm Shift in Value Theory

Financialization of Intellectual Capital

The “intellectual capital” concept introduced in the second article forms the economic backbone of the system. The idea of transitioning from material capital to intellectual capital is presented as the logical next stage of the knowledge economy.

Strengths:

The 3% stakeholder economy is a radical alternative to current “zero ownership” models
The “one in a thousand” rule paints a realistic picture of value creation
The economic settlement formula (Net_Payment = Subscription – Contribution) is simple and understandable

Critical Questions:

How will the objective measurement of which idea created the “big bang” be made?
How will priority be determined when multiple people submit similar ideas?
Where does the 3% stakeholder ratio come from? Does it have an econometric basis?
What will be AI companies’ motivation to adopt this model?

Philosophical Dimension: An important point the system emphasizes: the sovereignty of mind, not money. This is actually a modern reference to Plato’s philosopher-kings in the ideal state. However, history has shown us that idealist governance models often fail in practice.

3. Digital Meritocracy: Algorithmic Measurement of Merit

The K and I Scale: A New IQ System?

The third article contains the system’s most ambitious and simultaneously most controversial component: measuring human intelligence on a scale from 1.0 to 9.99.

Strengths:

Everyone starting at 1.0 aligns with the principle of equal opportunity
The K (Knowledge) and I (Idea) distinction is epistemologically meaningful
The 4.35 “Architect” threshold is an interesting metaphor for the consumer-producer distinction
The search for an alternative to the diploma system is timely and necessary

Serious Concerns:

Reductionism Risk: Reducing human intelligence to a single number is a classic error criticized since IQ tests. Gardner’s multiple intelligence theory demonstrates intelligence’s multidimensional nature.
Measurement Validity: The assumption that AI can objectively measure human intelligence is quite problematic. Who measures the measurer? (Quis custodiet ipsos custodes?)
Gaming the System: Any measurement system eventually becomes optimized according to that system (Goodhart’s Law). K and I scores could also be susceptible to manipulation.
Social Stratification: Risk of creating a new digital caste system. Could the 9.0+ “Navlib-NI” status practically turn into a new aristocracy?
Formula Ambiguity:
L_Score = (K_Level * 0.40) + (I_Level * 0.60)

Where does this weighting come from? Why 0.40 for K and 0.60 for I? Are these values culturally universal?

4. The Great Gateway: Security and Verification Architecture

Socratic Questioning and Biometric Control

The fourth article addresses the system’s security layer. The “Great Gateway” mechanism aims to prevent merit inflation.

Innovative Elements:

The biometrically verified solitude chamber concept is a creative solution against knowledge theft
The Socratic questioning method has potential to distinguish superficial knowledge from deep knowledge
The provisional vs. permanent score distinction adds a maturation process to the system

Concerning Points:

Surveillance Dystopia: Biometric data like voice recognition, eye tracking, and pulse analysis push the boundaries of privacy. Is such invasive control really necessary?
Stress and Performance: Does an exam conducted under intense stress measure actual ability or stress management skills?
Accessibility: Some individuals (e.g., people on the autism spectrum) may perform differently under social pressure. How will the system manage this diversity?
Technical Feasibility: Is “Black Box” technology practically achievable with today’s encryption technologies?

5. Intelligence Portability: Digital Passport and Accreditation

Cross-Platform Merit Transfer

The fifth article addresses the portability of merit and the authorization of AI models.

Valuable Contributions:

An original solution to the “vendor lock-in” problem
The model accreditation hierarchy (Bronze, Silver, Titan) has potential to prevent diploma inflation
New models leveraging high-merit users in their “bootstrap” process is a clever incentive mechanism

Open Questions:

Central Authority: Who will issue the AI Passport? Who will accredit? The need for a central authority seems contradictory with the claim of a decentralized system.
Standardization: Different AI models have different strengths. Why should someone with a high score in Gemini be at the same level in Claude?
Transfer Formula:
Transfer_Score = Source_Score * (Source_Model_Authority_Coefficient / Target_Model_Authority_Coefficient)

How will these coefficients be calculated? Who will determine them?

6. Dynamic Supreme Council: Governance and Manifesto

Fluid Merit and Natural Intelligence Declaration

The final article presents the system’s governance structure and philosophical manifesto.

Strong Visionary Elements:

The proposal to transition from static boards to fluid merit councils is bold and a needed reform
Human representatives (9.0+ Navlib-NI) having council seats strengthens the human-centered approach
The “life water” metaphor beautifully summarizes human-AI symbiosis

Harsh Realities:

Power and Authority: What is the likelihood that existing tech giants will accept such a radical governance change? Changing economic power balances is much harder than rhetoric.
Cultural Differences: The definition of merit varies culturally. How will balance be struck between Western individualism and Eastern collectivism?
Decision Formula:
Decision_Power = (Model_Accreditation_Level * 0.50) + (Total_NI_Contribution_Value * 0.50)

Again, the arbitrary weighting problem. Also, this formula still gives major tech companies 50% power.

Overall Assessment: Utopia or Transformative Vision?

System Strengths

Holism: One of the rare works that addresses technical, economic, ethical, and philosophical dimensions together.
Timeliness: Touches on current issues like the model collapse threat, data depletion, and AI ethics.
Human-Centricity: The vision of AI as a tool that elevates rather than exploits humans is valuable.
Turkish Perspective: Offering a unique position for countries like Turkey in the global technology race is important.
Originality: Concepts like under-the-mattress knowledge and intellectual venture capital add new perspectives to the literature.

System Weaknesses and Risks

Implementation Gap: Many elements that appear consistent in theory contain technical and social challenges in practice.
Central Control Paradox: The claim of a decentralized system practically requires a strong central authority.
Measurement Problem: The claim of objectively measuring human intelligence, knowledge originality, and idea value is epistemologically problematic.
Economic Realism: AI companies’ motivation to abandon current business models and give 3% stakeholder shares is unclear.
New Inequalities: The digital merit system could create a new social stratification. How different will “Navlib-NI” aristocracy be from diploma aristocracy?
Cultural Hegemony: Definitions of merit, knowledge, and ideas vary culturally. Which paradigm will the system be based on?
Privacy and Freedom: Intensive biometric tracking could threaten cognitive privacy. How far from Orwell’s 1984?

Philosophical and Ethical Dimension: Deeper Questions

Epistemology: What is Knowledge?

The system makes a clear distinction between “knowledge” and “idea.” However, the history of epistemology shows us this boundary is blurred. Kant’s a priori and a posteriori knowledge distinction, Popper’s falsifiability criterion, Kuhn’s paradigm theory… All these reveal the complex nature of knowledge and scientific progress.

How much can NAVLIB-NI’s K and I parameters capture this complexity?

Ethics: Justice and Equality

If we ask from Rawls’s theory of justice: Is NAVLIB-NI’s “original position” truly fair? Everyone starts at 1.0 but:

Educational disparities
Language barriers
Technological access inequalities
Cultural differences

These factors could make a system that appears equal at the start unequal in practice.

Ontology: Consciousness and Intelligence

The system makes a sharp distinction between “Natural Intelligence” and “Artificial Intelligence.” However, philosophy of mind is not so clear:

Functionalism: Is intelligence independent of the substrate it’s implemented on?
Chinese Room Argument (Searle): Does AI truly “understand”?
The Hard Problem of Consciousness: Can subjective experience be created algorithmically?

Answers to these questions could change the system’s philosophical foundations.

Alternative Approaches and Comparisons

Existing Initiatives

NAVLIB-NI is not alone. There are projects offering different solutions to similar problems:

Data Dignity (Jaron Lanier): Data ownership and micro-payments
RadicalxChange: Partial common ownership and Harberger taxes
Solid (Tim Berners-Lee): Decentralized data ownership
DAOs: Decentralized Autonomous Organizations

How does NAVLIB-NI differentiate from these approaches? To what extent is it better or worse?

Historical Parallels

Looking at history, great visionary projects have generally evolved in unexpected directions:

Soviet Communism: Egalitarian utopia turned into totalitarian dystopia
The Internet: Freedom and knowledge-sharing vision evolved into surveillance capitalism
Social Media: Connection and community promise turned into echo chambers and disinformation

How will NAVLIB-NI be protected against similar risks?

Turkish Perspective: Opportunity or Challenge?

Potential Advantages

Rich Cultural Accumulation: Anatolia’s thousand-year experience is truly valuable “under-the-mattress knowledge”
Young Population: Digital native generation, rapid system adaptation
Geopolitical Position: East-West bridge role, suitable for “third way”
State Tradition: Strong institutional structure, systematic implementation potential

Challenges

Technological Infrastructure: Is it at a level to compete with developed countries?
Brain Drain: High-merit individuals are already emigrating
Lack of Trust: Trust issues in digital systems and data security
Education System: How conducive is the current system to producing “under-the-mattress knowledge”?
Legal Infrastructure: Is there adequate legal framework on issues like intellectual property and data protection?

Implementation Recommendations: From Idealism to Pragmatism

If NAVLIB-NI is to be implemented, the following steps can be recommended:

Phase 1: Pilot Project (1-2 years)

Small-scale, limited-participation beta test
Validity studies with academic partnerships
Identification of technical and social issues

Phase 2: Sectoral Application (2-3 years)

Pilot applications in specific sectors (e.g., healthcare, education)
Refinement of measurement metrics
Testing of economic model

Phase 3: Expansion (3-5 years)

Dissemination of successful modules
International collaborations
Establishment of legal and institutional infrastructure

Continuous Processes

Ethics oversight boards
Open science and transparency
Community feedback and democratic decision-making

Conclusion: Critical Optimism

The NAVLIB-NI system deserves appreciation for proposing bold and original solutions to the most fundamental problems of the AI age. Aydın Tiryaki has presented a holistic vision by bringing together many technical details and philosophical dimensions.

However, for the system to succeed:

More Technical Detail: Algorithms, data structures, security protocols should be elaborated
Experimental Verification: Claims should be supported by empirical studies
Community Participation: A broad participatory, democratic development process should be established
Legal Framework: Legal infrastructure should be prepared on issues like intellectual property, data protection, equality
Cultural Sensitivity: Not a single merit definition, but a pluralistic approach should be adopted
Economic Realism: Economic models should be developed to increase tech companies’ participation motivation
Ethical Safeguards: Robust guardrails should be established to protect values like privacy, freedom, diversity

Final Word

Is NAVLIB-NI a utopia or a realizable vision? Perhaps somewhere between the two. Like Thomas More’s Utopia, it may not be fully implementable in practice but makes us think and imagine. Like Plato’s Republic, even if not perfect, it encourages us to progress in a better direction.

In the age of artificial intelligence, we need systems that center human dignity, merit, and justice more than ever. NAVLIB-NI offers a valuable contribution in this direction. Now it’s time to critically examine these ideas, develop them, and perhaps most importantly, make them implementable.

Because we must remember: The best system is not the one perfect on paper, but the one that works in the real world and truly empowers people.

Bibliography and Further Reading Recommendations:

Tiryaki, A. (2025). NAVLIB-NI Article Series. https://aydintiryaki.org
Lanier, J. (2013). Who Owns the Future?
Zuboff, S. (2019). The Age of Surveillance Capitalism
Rawls, J. (1971). A Theory of Justice
Searle, J. (1980). Minds, Brains, and Programs
Goodhart, C. (1975). Problems of Monetary Management
Gardner, H. (1983). Frames of Mind: The Theory of Multiple Intelligences
Popper, K. (1959). The Logic of Scientific Discovery

This article is a critical examination of the NAVLIB-NI system’s six fundamental articles. The aim is not to demolish but to strengthen the system and make it more implementable by asking constructive questions. Every great idea should be open to criticism and mature by drawing strength from critiques.