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.

Glossary/Microservices Architecture

Architecture & Design

1 min read

What is Microservices Architecture?

TL;DR

Microservices architecture is an approach to software design where an application is composed of small, independent services that communicate over well-defined APIs.

Microservices architecture is an approach to software design where an application is composed of small, independent services that communicate over well-defined APIs. Each service owns its own data, can be deployed independently, and is typically maintained by a small team.

Benefits: Independent scaling, technology diversity, fault isolation, faster deployment cycles.

Costs: Network complexity, distributed data management, operational overhead, debugging difficulty.

Why It Matters

Microservices introduce significant operational complexity that directly impacts engineering economics. Richard Ewing's diagnostic evaluates whether a team's microservices architecture is providing proportional value or just adding complexity — many teams adopt microservices prematurely, increasing costs without corresponding benefits.

How to Measure

Track: services per engineer ratio, inter-service latency, deployment independence (can you deploy one service without affecting others?), and operational cost per service.

Frequently Asked Questions

When should I move from monolith to microservices?

When your team size exceeds what can effectively work on a single codebase (typically 20-30 engineers), when different parts of the system need to scale independently, or when deployment coordination becomes the bottleneck.