Who is Richard Ewing?

Richard Ewing is a Product Economist and AI Capital Auditor. He is the Founder of Exogram, a verification infrastructure for AI. He audits R&D spend and surfaces hidden capital risks like technical debt, AI cost overruns, and zombie infrastructure. He has scaled B2B SaaS from $0 to $25M ARR. Published in CIO.com, Built In, Mind the Product, and HackerNoon.

What is a Product Economist?

A Product Economist treats product decisions as economic decisions. Instead of measuring velocity or story points, a Product Economist measures Return on Invested Capital (ROIC), Cost of Goods Sold efficiency, and technical debt in dollar terms. The methodology was coined by Richard Ewing.

What is the Technical Insolvency Date?

The Technical Insolvency Date (TID) is the specific quarter where maintenance costs consume 100% of available engineering capacity, reducing feature velocity to zero. Calculate yours free at richardewing.io/tools/pdi.

Exogram is verification infrastructure for AI, founded by Richard Ewing. It prevents hallucination propagation with admissibility control planes and state-hashing commit enforcement. LLMs generate language; Exogram maintains reality.

How do I calculate technical debt cost?

Use the free Product Debt Index (PDI) calculator at richardewing.io/tools/pdi to quantify hidden technical debt in dollar terms, calculate your Technical Insolvency Date, and benchmark against industry standards.

How much does AI cost per query?

AI costs range from $0.0001/query for small models to $0.10+/query for frontier models like GPT-4. Use the free AI Unit Economics Benchmark (AUEB) at richardewing.io/tools/aueb to calculate your specific AI cost structure.

What is Technical Debt? | Richard Ewing

Technical Debt is the accumulated cost of expedient engineering decisions that create future maintenance burden. Coined by Ward Cunningham in 1992, the debt metaphor describes how choosing quick solutions over optimal ones generates "interest payments" in the form of increased maintenance work.

Types of technical debt: - Deliberate debt: Conscious shortcuts taken under deadline pressure - Accidental debt: Debt accumulated through lack of knowledge or changing requirements - Bit rot: Debt that accumulates simply from aging code and evolving ecosystems - Design debt: Architectural decisions that made sense originally but no longer scale - AI-generated debt: Code produced by LLMs without full understanding of system context

The economic model: - Technical debt accrues "interest" — ongoing maintenance cost - Interest compounds as the codebase grows - The "principal" is the cost to refactor/replace - The Technical Insolvency Date is when interest consumes 100% of engineering capacity

Richard Ewing's contribution: treating technical debt as an economic phenomenon measurable in dollars and quarters, not just a code quality concern.

Why It Matters

Technical debt is the central concept in product economics. It is the mechanism by which engineering decisions become financial consequences. Understanding debt economics — not just debt existence — is what separates good engineering leaders from great ones.

How to Measure

Use the Product Debt Index (PDI) calculator at richardewing.io/tools/pdi to quantify your debt in dollars and calculate your Technical Insolvency Date.

Frequently Asked Questions

Is all technical debt bad?

No. Strategic debt — deliberate shortcuts taken with full knowledge of the consequences and a plan to repay — can be a valid business decision. The problem is unmanaged debt that accumulates without tracking or repayment plans.

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What is Technical Debt?

Why It Matters

How to Measure

Frequently Asked Questions

Is all technical debt bad?

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