Research
Working papers published open access on Zenodo and SSRN. The research agenda focuses on AI governance, access and agency, assessment integrity, human-AI knowledge work, higher education futures, labor markets, and strategic foresight.
A connected body of work on AI, agency, proof, and institutional design.
The papers below are grouped by theme rather than chronology. Together they form a practical research architecture: AI Matrix and AI Matrix Live address access and agency; FARABI addresses assessment credibility; Orchestrated Intelligence and IHACC address human-AI knowledge work; the Dynamic Research Continuum addresses research systems; the AI Passport addresses credentialing; Grey Swan addresses foresight and institutional risk.
Where multiple versions exist, the latest or most useful public version is listed.
The AI Matrix: Empowerment or Dependency? A Conceptual Framework
The original paper introducing the AI Matrix as a conceptual framework. Sets out the four-quadrant structure, dynamic transitions, and theoretical grounding in path dependency, socio-economic resilience, and governance theory.
The AI Matrix as Diagnostic: Access, Agency, and Adoption
The foundational framework separating access to AI tools from agency in their use. Broad access without agency produces passive dependency: high-looking outputs, weakened judgment, and hollow adoption.
Decomposing the Capability Overhang: Access, Agency, and the Geography of AI Adoption
Extends the AI Matrix into a geographic and organizational analysis of why AI capability accumulates unevenly. The capability overhang is explained by the access-agency distinction, not technology availability alone.
Managing AI Like It Matters: The Artificial Intelligence Operating System (AI-OS)
AI-OS treats AI adoption as an operating model question rather than a tool rollout. It works at the task level, making the human-AI boundary visible, auditable, and adjustable by evidence.
Beyond Detection: FARABI and the Assessment Credibility Shock in Higher Education
FARABI reframes assessment integrity as an evidence design problem. The primary issue is validity: whether the submitted work is defensible evidence of the claimed learning.
Orchestrated Intelligence: Rethinking Knowledge Work in the Age of AI
The defining capacity of an AI-era leader is the ability to design, sequence, and stage-manage complex human-AI workflows: decomposing problems, running accountable iteration loops, and making reasoning visible.
Flow Acceleration and the IHACC Model: Human-AI Co-Creation in Epistemology
IHACC argues that AI changes the structure of knowledge production, not just its speed. Acceleration without proof standards produces noise. Human judgment, verification, and epistemic standards must remain explicit.
The AI Passport: Towards a New Conceptual Framework for Global Skills Certification
Proposes a portable, renewable credential structure linked to program-level proof standards, making capability claims more legible to employers and helping people navigate AI-driven labor market transitions.
Too Slow for the AI Age? Building a Dynamic Research Continuum
The Dynamic Research Continuum proposes a versioned, continuously updated pipeline that maintains quality standards while closing the gap between frontier developments and peer-reviewed knowledge.
Business School 2030: A Manifesto for the AI Operating Environment
Business schools must redesign themselves as capability-and-proof institutions, integrating AI Matrix, FARABI, AI-OS, Orchestrated Intelligence, the AI Passport, and the Dynamic Research Continuum.
HEI 2030: A Manifesto for the Higher Education AI Operating Environment
Extends the business school manifesto to higher education institutions more broadly, addressing the structural challenges AI creates for degree credibility and proof of learning.
The Proof and Trust Shock: Generative AI and the Future of Mass Higher Education
Maps six futures for mass higher education and argues that the system faces four interacting walls: proof, jobs, cost, and legitimacy.
General-Purpose Versus Learning-Oriented AI: A Structured Cross-Source Synthesis
A systematic synthesis distinguishing general-purpose AI tools from tools specifically designed to support learning, identifying what the evidence supports and where inferential gaps remain.
A Generation at Risk? Creativity, PISA 2022, and the Demands of an AI Economy
Uses PISA 2022 creative thinking results as a warning about capability readiness for an AI-saturated world. Creativity must be treated as a measurable, teachable competence.
Is AI Part of the Recruitment Recession? A Research Agenda
The Jobs Wall captures the risk that AI tightens junior hiring pathways even while headline employment figures remain stable.
The Proof and Trust Shock: What the BLS 2024-34 Projections Suggest About the Jobs Wall
Uses US Bureau of Labor Statistics projections to ground the Jobs Wall concept and show how graduate employment routes are narrowing in ways standard labor analysis can understate.
Beyond Adoption: Intensity and Integration as the Missing Link in Firm-Level AI Impact
Argues that adoption rates are the wrong unit of analysis. What matters is intensity of use and depth of integration: the organizational version of the access-agency distinction.
Using AI in Scenario Planning: Letting It Rip or Doing the Right Thing?
Introduces the Grey Swan / Archimedes framework for governed, AI-assisted strategic foresight. A grey swan is visible in current data, consequential, and often ignored because it is uncomfortable.
The AI Triad: Power, Infrastructure, and Agency in US-EU-China Strategy
Applies the access-agency framework to the geopolitics of AI, examining infrastructure, governance, and the distribution of AI-derived agency across populations and institutions.
The Governance Gap: Public Priorities for the AI Economy
Assesses major government AI strategies against public priorities across jobs protection, redistribution of gains, safety, institutional accountability, and meaningful work.
The AI Labor Transition
Works through OpenAI’s April 2026 jobs exposure assumptions and argues that even optimistic readings imply large-scale transition pressures.
Speed Is Not the Whole Story: Anthropic’s Claude Study and Orchestrated Intelligence
A close reading of Anthropic’s research on Claude’s impact on knowledge work. Speed gains are real but secondary; the deeper issue is orchestration.
The LLM Usage Gap: Evidence from Anthropic, Microsoft, and OpenAI
Examines the gap between reported AI adoption and actual intensity of use. Widespread nominal adoption can coexist with shallow, unmanaged use.
Strategic Toolkits: Reading the Major AI Usage Reports
Practical frameworks for cutting through vendor framing in the Microsoft Copilot and OpenAI enterprise usage reports.