How The Governance
System Emerged.
The intellectual evolution mapping the progression from foundational AI unit economics up to deterministic runtime enforcement.
Why This Exists
Most AI discussions focus on model capabilities. My work focuses on what happens after deployment. As AI systems become embedded in products, organizations face a new class of problems involving economics, governance, security, reliability, and operational control. The Production AI Governance Framework exists to help organizations understand, measure, and manage those challenges.
Economics
Distilling the unit economics of LLM inference, indexing raw engineering throughput, and auditing R&D capital allocation.
Governance
Establishing the Product Debt Index (PDI) to convert undocumented technical debt into boardroom-ready exit valuation metrics.
Operational AI
Solving the Cost of Predictivity. Modeling AI margin collapse points, cloud FinOps repatriation breakevens, and small model alternatives.
Agent Security
Identifying security liabilities in autonomous systems. Mapping jailbreaks, shadow AI data leaks, and sandbox evasion vectors.
Runtime Governance
Shifting from passive observability to deterministic physical control boundaries. Building state-verification engines.
Exogram
Deployment of the sovereign Exogram runtime interceptor. The physical proxy layer enforcing zero-trust governance.
Ecosystem Alignment Map
Every publication, tool, and software system mapped back to the core research program.
The Production AI Governance Ecosystem
Every resource on this site is a node in a single multi-year research program exploring AI operational limits.
Want to apply this to your organization?
Run a free diagnostic first. If the numbers concern you, book a session to build a remediation plan.
Richard Ewing — AI Economist & Capital Auditor