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 Kubernetes (K8s)?

Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. Originally developed by Google and now maintained by the CNCF, it has become the standard platform for running production workloads.

Key concepts: Pods (smallest deployable unit), Deployments (declarative updates), Services (network access to pods), Ingress (HTTP routing), ConfigMaps/Secrets (configuration), and Namespaces (resource isolation).

Kubernetes provides: automatic scaling (add/remove instances based on load), self-healing (restart failed containers), rolling updates (zero-downtime deployments), and service discovery (automatic DNS and load balancing).

The catch: Kubernetes is complex. Running a production Kubernetes cluster requires expertise in networking, security, monitoring, and resource management. For teams under 20 engineers, managed Kubernetes services (EKS, GKE, AKS) or simpler alternatives (Fly.io, Railway, Render) may be more appropriate.

Why It Matters

Kubernetes is the standard for production container orchestration but introduces significant operational complexity. The decision to adopt Kubernetes should be based on team size, scale requirements, and operational capability.

Frequently Asked Questions