Engineering
System-level validation structures to address vibe-coding debt, calculate velocity-insolvency horizons, and enforce testing standards.
Contextual Boundary
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.
Core Analytical Axioms
Forensically proven concepts in this operational boundary.
Technical Insolvency Date
The projected quarter when codebase maintenance load consumes 100% of engineering capacity, reducing feature velocity to zero.
Organizations ignore technical debt growth until feature shipping halts completely, rendering them uncompetitive.
Establishes a concrete deadline for boards to fund core modernization and refactoring.
Vibe Coding Debt
The rapid accumulation of unverified, AI-copilot-generated code that lacks architectural coherence.
Engineers generate thousands of lines of syntax using LLMs without understanding the architectural blast radius, leading to system failure.
Vibe coding codebases deteriorate 4x faster than human-written codebases, accelerating the Technical Insolvency Date.
SLM vs API Arbitrage
The decision framework for replacing expensive commercial APIs with fine-tuned Small Language Models.
Hosting commercial model APIs at scale burns excessive margins when a 7B local parameter model can perform the task at 90% lower cost.
Preserves long-term SaaS gross margin profile by localizing standard workflows.
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