Product
Metrics and gating frameworks to prioritize feature deprecation, prevent product bloating, and ensure feature-level profitability.
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.
Product Debt Index
A quantitative metric representing the drag of backlog maintenance load on product exit valuation.
Product teams continually ship new features without auditing legacy carry, leading to a bloated system that stalls velocity and compresses Exit multiples.
Provides private equity partners and boards with a dollar-value discount index during due diligence.
Feature Deprecation
The systematic removal of low-value, high-maintenance features to recoup margin and velocity.
Product managers are rewarded for adding buttons, never for removing them, creating exponential codebase carrying costs.
Deprecating the bottom 20% of unused features restores up to 30% of engineering throughput.
AI Margin Collapse Point
The specific threshold where user volume and API usage cost exceeds subscription price.
Pricing is modeled on flat SaaS formulas, meaning high-frequency power users actively bleed capital on every query.
Predicts structural insolvency of specific product tiers, enabling value-based caps or credits.
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