Product
Metrics and gating frameworks to prioritize feature deprecation, prevent product bloating, and ensure feature-level profitability.
Observed Evidence
The Direct Experience
"During product portfolio audits, I watched teams continually build complex AI capabilities that customers never actually used. I noticed a consistent feedback loop: engineers shipped buttons to hit sprint targets, but the carrying cost of maintaining those features slowly strangled roadmap velocity. The mechanism is Feature Bloat - unused code paths requiring continuous testing, dependency updates, and compute power. This leads to the principle of the Product Debt Index (PDI): the carrying cost of legacy features decays long-term valuation. Ultimately, companies must systematically deprecate their bottom 20% of features to restore EBITDA and engineering throughput."
Core Analytical Axioms
Forensically proven concepts in this operational boundary.
Product Debt Index
Deep Dive Specification →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
Deep Dive Specification →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.
The Software Phase Transition
Deep Dive Specification →The structural breakdown of traditional product management as code creation costs approach zero, shifting teams from Solid (roadmaps and sprints) to Gas (autonomous AI creation and Product Economics).
Product leaders continue using developer capacity rationing and sprint velocity metrics when generative tools make code generation virtually free, creating organizational complexity and margin erosion.
Shifts product leadership from managing feature output to managing capital allocation, risk reduction, system architecture efficiency, and unit margins.
Want to apply this to your organization?
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Richard Ewing - AI Economist & Capital Auditor