Beyond Fragmentation

The Main Argument

The system is not broken. It is working exactly as designed.

The term "governance" is multifaceted and lacks a direct Icelandic equivalent that captures its full scope. While often translated as "management" or "administration," this thesis employs the term governance architecture to encompass the broader framework of rules, ethics, accountability, and systems that shape AI development.

AI governance fails not from a lack of regulation, but through two fundamental mechanisms that systematically degrade human flourishing: Responsibility Fog — the engineered diffusion of accountability across fragmented structures — and Cognitive Debt — the compounding erosion of human judgment through algorithmic dependence.

These mechanisms create an accelerating feedback loop that leads to the Benevolent Cage — a system of total algorithmic care that offers safety and convenience at the price of human agency.

Life-Value Onto-Axiology

The thesis rejects traditional economic approaches, building instead on John McMurtry's Life-Value Onto-Axiology. This concept bridges ontology (what exists) and axiology (what is good). The core premise is that value is not a subjective preference, but an objective requirement for existence:

After McMurtry: value enables a more comprehensive range of thought, feeling, and action.

From this foundation, the thesis proposes the VALOR Framework as a governance alternative mapped onto the EU AI Act (2024).

Core Concepts

Responsibility Fog — My framing of systematic diffusion of accountability through fragmented authority and technical complexity. It builds on Dennis Thompson’s “problem of many hands” (1980), Mark Bovens’s work on accountability, and Andreas Matthias’s “responsibility gap” (2004).

Cognitive Debt — My extension of earlier software-engineering uses of the term to the compound costs of outsourcing judgment to algorithmic systems.

The Benevolent Cage — Totalizing algorithmic care that eliminates agency under the guise of protection.

The Investment-Sentiment Gap (ISG)

The Investment-Sentiment Gap is the thesis's interpretation of findings from Shao et al. (2025), Stanford SALT Lab's WORKBank study (844 tasks, 1,500 workers). They report that 41% of Y Combinator company-task mappings fall in the Low Priority and Automation Red Light zones, where worker desire for automation is low. Separately, the ten occupations with the highest automation desire account for 1.26% of observed Claude.ai usage. These percentages have different denominators and do not establish a dollar ratio.

The thesis discusses these findings alongside the Anthropic Economic Index, McKinsey's State of AI 2025, and EY/KPMG sentiment reports. Those reports measure different phenomena; they do not independently verify the same investment ratio.

Anthropic’s September 2025 Economic Index reports that directive Claude.ai conversations rose from 27% in its first sample, collected in late 2024 and early 2025, to 39% in August 2025. Its January 2026 report records a decline to 32% in November 2025. These are usage shares, not capital flows. The thesis reads the earlier change through McMurtry’s contrast between Money-Sequence logic (M→C→M′) and Life-Sequence logic (L→MofL→L′); that interpretation does not establish an accelerating investment gap.

"Lords do not invest in serf liberation."

The VALOR Framework

Five governance principles framed as a Declaration of Human Agency:

• V — Verification: The right to an unmediated view of reality.
• A — Alignment: The right to define your own goals.
• L — Legitimacy: The right to messy democratic self-governance.
• O — Oversight: The right to hold ultimate authority.
• R — Responsibility: The right to bear consequences.

Application and review

Three systems — Arctic Tracker, Gjöll, and BORG — illustrate applications of VALOR principles. Appendix D reports using a Neo4j knowledge graph and 39 analysis sweeps to examine the thesis’s structure. These applications and structural checks do not independently establish the framework’s effectiveness.

Interactive sample — 24 nodes. Hover nodes and edges to explore.

Acknowledgements

Special thanks to my supervisor, Prof. Giorgio Baruchello, for his invaluable guidance and introduction to McMurtry's Life-Value Onto-Axiology. I also extend my deepest gratitude to the external examiner, Dr. Dina Babushkina (University of Twente), and to the thesis committee — Dr. Chris DiCarlo and Dr. Stefán Frímann Jökulsson — for their professional review, encouragement, and honest feedback. Defended January 30, 2026.

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Post-Submission Experiments

The Meta-Experiment: Recursive Verification

Following the defense, I conducted a "Meta-Experiment" to test whether the thesis's own principles—specifically Verification and Oversight—could govern the AI generation of the thesis itself. I used GPT-5.2 to simplify the academic text into a "Fun Edition" while simultaneously orchestrating a 6-layer recursive verification system.

The experiment illustrated the use of verification and oversight in my own workflow:

• Layer 0 (Human): I wrote a Python script (flesch_kincaid.py) to measure the original thesis complexity (Grade 20.6).
• Layer 2 (AI): I used Claude to extend my own verification tools, ironically using AI to build the "police" for the other AI.
• Layer 3 (The Catch): The extended tools caught the AI hallucinating readability improvements (the initial experiment report records a claimed 71% fall in the readability score versus a measured 33.5%).

My project notes estimated $0.47 in model API costs for the initial experiment. This illustrates a low API cost for one workflow; it does not include human review time or establish the cost of making knowledge accessible more generally.